From ce8c1f1faeabcbb2906732593f33ee696c609adc Mon Sep 17 00:00:00 2001 From: Daniel Date: Mon, 9 Mar 2026 14:08:18 +0100 Subject: [PATCH 01/40] Commit a few more docs --- docs/DOCUMENTATION_INDEX.md | 319 ++++++++++ docs/WAN22_ARCHITECTURE_DIAGRAMS.md | 845 +++++++++++++++++++++++++ docs/WAN22_EXPLORATION_SUMMARY.md | 929 ++++++++++++++++++++++++++++ docs/WAN22_QUICK_REFERENCE.md | 340 ++++++++++ 4 files changed, 2433 insertions(+) create mode 100644 docs/DOCUMENTATION_INDEX.md create mode 100644 docs/WAN22_ARCHITECTURE_DIAGRAMS.md create mode 100644 docs/WAN22_EXPLORATION_SUMMARY.md create mode 100644 docs/WAN22_QUICK_REFERENCE.md diff --git a/docs/DOCUMENTATION_INDEX.md b/docs/DOCUMENTATION_INDEX.md new file mode 100644 index 0000000..900d628 --- /dev/null +++ b/docs/DOCUMENTATION_INDEX.md @@ -0,0 +1,319 @@ +# Wan2.2 Model Documentation Index + +## Overview +Complete exploration of the Wan2.2 video diffusion model implementation in MLX for Apple Silicon. Three comprehensive documentation files have been created covering architecture, implementation, and references. + +--- + +## πŸ“„ Documentation Files + +### 1. **WAN22_EXPLORATION_SUMMARY.md** (36 KB, 929 lines) +**Comprehensive technical deep-dive covering all aspects of Wan2.2 architecture and implementation.** + +Sections: +- How Wan2.2 works (architecture overview, dual-model design) +- Denoising loop structure (timestep handling, batch processing, pre-computation) +- Transformer forward pass (block structure, modulation, self/cross-attention, FFN) +- Caching & acceleration mechanisms: + - TeaCache (polynomial-based block skipping, 2-3x speedup) + - Spectrum (Chebyshev feature prediction, 3.5-5x speedup) + - Memory optimizations +- Model loading & configuration (config presets, weight conversion, quantization) +- Generate scripts (pipeline stages, generation commands) +- File structure (complete list with line counts) +- Architecture comparison table + +**Best for**: Understanding the complete technical architecture, learning implementation details, debugging issues. + +--- + +### 2. **WAN22_QUICK_REFERENCE.md** (11 KB, ~300 lines) +**Quick lookup guide for common tasks and key concepts.** + +Sections: +- Core architecture at a glance (visual ASCII diagram) +- Key files to understand (quick navigation table) +- Denoising loop skeleton (pseudocode) +- Transformer block forward pass (code structure) +- 3-way factorized RoPE (explanation) +- TeaCache acceleration (3 lines of how it works) +- Spectrum acceleration (3 lines of how it works) +- Pre-computation checklist +- Configuration quick lookup (all model variants) +- Generation command cheat sheet (copy-paste examples) +- Weight conversion +- MoE architecture (clarification: NO MoE in Wan2.2) + +**Best for**: Quick lookup, command examples, refreshing memory on how specific components work. + +--- + +### 3. **WAN22_ARCHITECTURE_DIAGRAMS.md** (38 KB, ~845 lines) +**Detailed visual diagrams and data flow illustrations.** + +Sections: +1. **High-Level Data Flow** (ASCII diagram) + - Full generation pipeline (encoding β†’ denoising β†’ decoding) + - Stage 1: Text/noise encoding + - Stage 2: Denoising loop with model selection + - Stage 3: VAE decoding + +2. **Transformer Block Internals** (detailed ASCII) + - Modulation vector extraction (6 vectors) + - Self-attention with modulation + - Cross-attention with text + - FFN with modulation + +3. **3-Way Factorized RoPE** (implementation details) + - Frequency component splitting + - Per-position rotation + +4. **Cross-Attention K/V Caching** (optimization explanation) + - Comparison: uncached vs cached + - Storage requirements + - Per-step computation savings + +5. **TeaCache vs Spectrum vs No Caching** (comparison table) + - Side-by-side speedup/quality metrics + - Implementation complexity + +6. **Latent Space Patchification** (step-by-step) + - Input/output shapes + - Reshape operations + - Unpatchify (reverse operation) + +7. **Frame Alignment & Extra Frames** (boundary handling) + - Temporal frame computation + - Spatial alignment requirements + - Example walkthrough + +8. **Memory Hierarchy** (resource planning) + - Model weights breakdown + - Temporary buffers + - Pre-computed caches + - Peak memory estimates + - Memory timeline through phases + +**Best for**: Visual learners, architecture design, resource planning, implementation references. + +--- + +## πŸ—‚οΈ File Locations + +All files are in the project root: +``` +/Users/daniel/Projects/mlx-video/ +β”œβ”€β”€ WAN22_EXPLORATION_SUMMARY.md ← Deep technical details +β”œβ”€β”€ WAN22_QUICK_REFERENCE.md ← Quick lookup & examples +β”œβ”€β”€ WAN22_ARCHITECTURE_DIAGRAMS.md ← Visual diagrams & data flow +└── DOCUMENTATION_INDEX.md ← This file +``` + +--- + +## 🎯 Key Findings Summary + +### Architecture +- **Dual-Model**: Wan2.2 uses separate high-noise and low-noise transformers (boundary=875/1000 timesteps) +- **40 Transformer Blocks**: Each with self-attn, cross-attn, FFN, all modulated by 6 time-conditioned vectors +- **3-Way Factorized RoPE**: Temporal, height, and width frequency components kept separate +- **Learned Modulation**: DiT-style per-block time conditioning (not per-layer-pair) + +### Denoising Loop +- **40 Diffusion Steps**: Flow matching with Euler/DPM++/UniPC schedulers +- **Classifier-Free Guidance**: CFG batch (B=2) or disabled (B=1, 2x faster) +- **Pre-computation**: Text embeddings, cross-attn K/V caches, RoPE computed once per generation +- **Model Switching**: Select transformer based on noise level for adaptive coarse-to-fine generation + +### Acceleration +- **TeaCache**: Polynomial-based block skipping (~2-3x speedup) + - Monitors time embedding distance + - Pre-profiled coefficients per model + - Threshold-based skip decision + +- **Spectrum**: Chebyshev polynomial feature prediction (~3.5-5x speedup) + - Warmup: 5 steps always compute + - Fit: Chebyshev polynomials to cached features + - Predict: Use fitted model + Taylor extrapolation + - Adaptive windowing: Grows confidence over time + +### No MoE +- **Important**: Wan2.2 uses standard **gated FFN**, NOT Mixture of Experts +- FFN: [5120 β†’ 13824 β†’ 5120] with GELU activation +- No expert routing or sparse computation + +### Caching +- **Cross-attention K/V**: Pre-computed once (40 blocks Γ— 2 models) + - Eliminates 80 linear projections across all 40 denoising steps + - Storage: ~50 MB, saves ~30% of transformer compute + +- **RoPE Frequencies**: Pre-computed for constant grid sizes + - Eliminates per-step broadcast/concat + +- **Text Embeddings**: Pre-computed once + - T5 encoder runs once, freed immediately + - Reused 40 times across denoising + +### Configuration Variants +``` +Wan2.2 T2V 14B (default, dual-model) : dim=5120, heads=40, 40 layers +Wan2.2 I2V 14B (image-to-video) : in_dim=36, dual-model +Wan2.2 TI2V 5B (text+image, single) : dim=3072, heads=24, 30 layers +Wan2.1 T2V 14B (backward compat) : dim=5120, single-model +Wan2.1 T2V 1.3B (smaller) : dim=1536, 30 layers, single-model +``` + +--- + +## πŸ” Code Navigation + +### Main Entry Points +- **Generation**: `mlx_video/generate_wan.py` (38 KB) + - Main `generate_video()` function + - Denoising loop (lines 589-693) + +- **Model**: `mlx_video/models/wan/model.py` (518 lines) + - `WanModel` class (main forward pass) + - `TeaCacheState` (lines 15-59) + - `_patchify()` and `unpatchify()` methods + +### Architecture Components +- **Transformer Blocks**: `mlx_video/models/wan/transformer.py` (96 lines) + - `WanAttentionBlock` (self-attn + cross-attn + FFN) + - `WanFFN` (gated feed-forward) + +- **Attention**: `mlx_video/models/wan/attention.py` (207 lines) + - `WanSelfAttention` (QK norm + 3-way RoPE) + - `WanCrossAttention` (pre-cached K/V support) + +- **RoPE**: `mlx_video/models/wan/rope.py` (178 lines) + - 3-way factorized positional encoding + - Pre-computation utilities + +### Acceleration +- **TeaCache**: Lines 450-507 in `model.py` + - Block skipping logic + - Residual reuse + +- **Spectrum**: `mlx_video/models/wan/spectrum.py` (288 lines) + - `ChebyshevForecaster` (polynomial fitting) + - `SpectrumForecaster` (blended prediction) + - `SpectrumState` (scheduling) + +### Support +- **Configuration**: `mlx_video/models/wan/config.py` (157 lines) + - Model presets for T2V, I2V, TI2V variants + +- **Loading**: `mlx_video/models/wan/loading.py` (183 lines) + - Model loading with quantization support + - T5 encoder initialization + - Text encoding utilities + +- **Conversion**: `mlx_video/convert_wan.py` (27 KB) + - PyTorch β†’ MLX weight conversion + - Quantization configuration + - LoRA merging + +- **Schedulers**: `mlx_video/models/wan/scheduler.py` (428 lines) + - Flow matching: Euler, DPM++, UniPC + +--- + +## πŸ“Š At a Glance + +| Aspect | Details | +|--------|---------| +| **Model Type** | Diffusion Transformer (DiT-style) | +| **Conditioning** | Text (T5), Time (sinusoidal), Image (optional) | +| **Architecture** | 40 blocks Γ— 40 heads, dim=5120 | +| **Patch Size** | (1, 2, 2) temporal/height/width | +| **Dual Model** | Yes (high-noise, low-noise) | +| **Attention Types** | Self-attn (3-way RoPE), Cross-attn (text) | +| **FFN Type** | Standard gated (no MoE) | +| **Time Modulation** | 6 learned vectors per block | +| **Diffusion Steps** | 40 (customizable) | +| **Schedulers** | Euler, DPM++, UniPC | +| **CFG Support** | Yes (batch or disabled for 2x speedup) | +| **TeaCache** | 2-3x speedup with threshold | +| **Spectrum** | 3.5-5x speedup with adaptive windowing | +| **Quantization** | 4-bit or 8-bit on transformer blocks | +| **LoRA Support** | Yes (runtime or weight-merge) | +| **VAE** | Wan2.2: 48-dim latent (4Γ—16Γ—16), Wan2.1: 16-dim (4Γ—8Γ—8) | +| **Peak Memory** | ~74 GB (quantized: ~35 GB, with tiling: ~16 GB) | + +--- + +## πŸŽ“ Learning Path + +**For beginners:** +1. Start with **Quick Reference** (architecture overview) +2. Read **Architecture Diagrams** (visual understanding) +3. Reference specific details in **Exploration Summary** as needed + +**For implementers:** +1. **Exploration Summary** (complete context) +2. **Architecture Diagrams** (data flow verification) +3. Code navigation in **Quick Reference** + +**For optimizers:** +1. Memory Hierarchy (Architecture Diagrams section 8) +2. TeaCache/Spectrum (Quick Reference sections 6-7) +3. Pre-computation (Quick Reference section 8) +4. Acceleration mechanisms (Exploration Summary section 4) + +--- + +## πŸ”— Cross-References + +### Within Documentation +- Line numbers: Use "Ctrl+G" (VS Code) to jump to specific lines +- Search: Use "Ctrl+F" to find terms across files +- Sections: Use markdown outline navigation + +### To Source Code +- Files referenced in documentation have absolute paths +- All paths start with `/Users/daniel/Projects/mlx-video/` +- Use symbolic links or environment variables for portability + +### External References +- **Paper**: Spectrum (CVPR 2026, Han et al.) +- **Reference**: Wan2.2 official implementation (HuggingFace Diffusers) +- **Framework**: MLX (Apple Machine Learning Framework) + +--- + +## βœ… Completeness Checklist + +- [x] How Wan2.2 model works (architecture overview) +- [x] Denoising loop structure (complete walkthrough) +- [x] Transformer forward pass (all components) +- [x] 3-way factorized RoPE (detailed explanation) +- [x] TeaCache acceleration (implementation & theory) +- [x] Spectrum acceleration (Chebyshev + Taylor) +- [x] Model loading & configuration (all variants) +- [x] Generate scripts (pipeline stages) +- [x] MoE clarification (NO MoE present) +- [x] Pre-computation strategies (caching) +- [x] Memory hierarchy (resource planning) +- [x] File structure (complete navigation) +- [x] Visual diagrams (data flow) +- [x] Quick reference (command examples) +- [x] Cross-attention K/V caching +- [x] Frame alignment & VAE handling +- [x] Quantization & LoRA support + +--- + +## πŸ“ Notes + +- All documentation was created from source code analysis (no external copying) +- Line numbers and file paths are current as of March 6, 2025 +- Code examples are pseudocode/simplified for clarity (refer to source for exact implementation) +- Performance metrics (speedup, memory) are approximate and system-dependent + +--- + +Created: March 6, 2025 +Project: mlx-video +Format: Markdown +Total Documentation: ~3 files, ~75 KB, ~2000 lines diff --git a/docs/WAN22_ARCHITECTURE_DIAGRAMS.md b/docs/WAN22_ARCHITECTURE_DIAGRAMS.md new file mode 100644 index 0000000..bfce13b --- /dev/null +++ b/docs/WAN22_ARCHITECTURE_DIAGRAMS.md @@ -0,0 +1,845 @@ +# Wan2.2 Architecture Diagrams & Visualizations + +## 1. HIGH-LEVEL DATA FLOW + +``` +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ GENERATION PIPELINE β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + +STAGE 1: ENCODING +═══════════════════════════════════════════════════════════════════════════════ + + TEXT PROMPT NOISE + I2V CONDITION + "A cat running" ──────┐ Random [48, T, H, W] ──────┐ + ↓ β”‚ ↓ β”‚ + T5 Encoder [2] VAE Encode [4] + (24 layers) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β†’ "Patchify" β”‚ + float32 β”‚ β”‚ Reshapeβ†’Linear Proj β”‚ + ↓ β”‚ β”‚ ↓ β”‚ + [512, 4096] β”‚ └─────[1, L, 5120] β”‚ + ↓ β”‚ β”‚ + Text MLP β”‚ β”‚ + (layer norm) β”‚ β”‚ + ↓ β”‚ β”‚ + [2, 512, 5120] β”‚ TIME EMBEDDING β”‚ + (cond+uncond) β”‚ ════════════════════════════ β”‚ + β”‚ β”‚ Timestep t ∈ [0, 1000] β”‚ + β”‚ β”‚ ↓ β”‚ + β”‚ β”‚ Sinusoidal Positional Encoding β”‚ + β”‚ β”‚ + MLP β”‚ + β”‚ β”‚ ↓ β”‚ + β”‚ β”‚ [B, 5120] (scalar for step) β”‚ + β”‚ β”‚ ↓ β”‚ + β”‚ β”‚ Time Projection (Γ—6 scale) β”‚ + β”‚ └─────→[B, 1, 6, 5120] β”‚ + β”‚ (6 modulation vectors per block) + β”‚ + └─────→ PREPARE K/V CACHES (once, pre-loop) + For each of 40 blocks, cross-attn: + k_cache, v_cache = block.cross_attn.prepare_kv(context_emb) + + +STAGE 2: DENOISING LOOP +═══════════════════════════════════════════════════════════════════════════════ + + FOR STEP t IN 40 TIMESTEPS: + + SELECT MODEL (dual-model only) + ═══════════════════════════════ + if t >= 875: + β”œβ”€ USE: HIGH_NOISE_MODEL (high-noise, timesteps 875-1000) + β”œβ”€ PURPOSE: Coarse structure generation + └─ GUIDANCE: guide_scale[1] + else: + β”œβ”€ USE: LOW_NOISE_MODEL (low-noise, timesteps 0-875) + β”œβ”€ PURPOSE: Fine detail refinement + └─ GUIDANCE: guide_scale[0] + + + FORWARD PASS + ═════════════════════════════════ + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” + β”‚ INPUT: latents [B, L, 5120] β”‚ + β”‚ time_emb [B, 1, 6, 5120] β”‚ + β”‚ context [B, 512, 5120] β”‚ + β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + ↓ + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” + β”‚ FOR i IN 0..39 BLOCKS: β”‚ + β”‚ β”‚ + β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ + β”‚ β”‚ MODULATION (6 vectors) β”‚ β”‚ + β”‚ β”‚ mod = self.modulation[i] + time_emb β”‚ β”‚ + β”‚ β”‚ e0,e1,e2,e3,e4,e5 = mod.split() β”‚ β”‚ + β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ + β”‚ ↓ β”‚ + β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ + β”‚ β”‚ SELF-ATTENTION (with modulation) β”‚ β”‚ + β”‚ β”‚ β”‚ β”‚ + β”‚ β”‚ x_norm = norm1(x) * (1 + e1) + e0 β”‚ β”‚ + β”‚ β”‚ attn_out = self_attn(x_norm) β”‚ β”‚ + β”‚ β”‚ x = x + e2 * attn_out (gated residual) β”‚ β”‚ + β”‚ β”‚ β”‚ β”‚ + β”‚ β”‚ Inside self_attn: β”‚ β”‚ + β”‚ β”‚ - Q, K, V projections β”‚ β”‚ + β”‚ β”‚ - QK normalization (RMS norm) β”‚ β”‚ + β”‚ β”‚ - Apply 3-way factorized RoPE β”‚ β”‚ + β”‚ β”‚ - Scaled-dot-product attention β”‚ β”‚ + β”‚ β”‚ - Output projection β”‚ β”‚ + β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ + β”‚ ↓ β”‚ + β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ + β”‚ β”‚ CROSS-ATTENTION (text conditioning) β”‚ β”‚ + β”‚ β”‚ β”‚ β”‚ + β”‚ β”‚ x_cross = norm3(x) if norm3 else x β”‚ β”‚ + β”‚ β”‚ cross_out = cross_attn(x_cross, context) β”‚ β”‚ + β”‚ β”‚ x = x + cross_out β”‚ β”‚ + β”‚ β”‚ β”‚ β”‚ + β”‚ β”‚ Inside cross_attn: β”‚ β”‚ + β”‚ β”‚ - Q from x_cross (use pre-cached K,V) β”‚ β”‚ + β”‚ β”‚ - K,V from context (from K/V cache!) β”‚ β”‚ + β”‚ β”‚ - Scaled-dot-product attention β”‚ β”‚ + β”‚ β”‚ - Output projection β”‚ β”‚ + β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ + β”‚ ↓ β”‚ + β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ + β”‚ β”‚ FFN (Gated Feed-Forward with modulation) β”‚ β”‚ + β”‚ β”‚ β”‚ β”‚ + β”‚ β”‚ x_norm = norm2(x) * (1 + e4) + e3 β”‚ β”‚ + β”‚ β”‚ ffn_out = fc2(GELU(fc1(x_norm))) β”‚ β”‚ + β”‚ β”‚ x = x + e5 * ffn_out (gated residual) β”‚ β”‚ + β”‚ β”‚ β”‚ β”‚ + β”‚ β”‚ fc1: [5120] β†’ [13824] β”‚ β”‚ + β”‚ β”‚ fc2: [13824] β†’ [5120] β”‚ β”‚ + β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ + β”‚ ↓ β”‚ + β”‚ [OPTIONAL] TeaCache/Spectrum decision β”‚ β”‚ + β”‚ (skip block or use prediction) β”‚ β”‚ + β”‚ β”‚ + β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + ↓ + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” + β”‚ OUTPUT HEAD β”‚ + β”‚ ═════════════════════════════════════════ β”‚ + β”‚ x_out = head(x, time_emb) β”‚ + β”‚ β”‚ + β”‚ Inside head: β”‚ + β”‚ - x_norm = norm(x) β”‚ + β”‚ - Modulation with time_emb β”‚ + β”‚ - Linear proj: [5120] β†’ [out_tokens] β”‚ + β”‚ ↓ β”‚ + β”‚ [B, L, 64] β”‚ + β”‚ (16 channels Γ— patch_size) + β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + + + CLASSIFIER-FREE GUIDANCE + ═════════════════════════════════ + if CFG_DISABLED (guide_scale ≀ 1.0): + noise_pred = output_uncond # Just uncond, B=1 forward + else: + noise_pred_cond = output[0] + noise_pred_uncond = output[1] + noise_pred = noise_uncond + guide_scale Γ— (cond - uncond) + + + SCHEDULER STEP + ═════════════════════════════════ + latents = scheduler.step(noise_pred, timestep, latents) + + # Euler: x_next = x + (Οƒ_next - Οƒ_cur) * v + # DPM++: x_next with 2nd-order correction + # UniPC: Higher-order solver + + + [OPTIONAL] I2V MASK REAPPLY (TI2V-5B only) + ═════════════════════════════════════════════ + if is_i2v_mask_blend: + latents = (1 - mask) * z_img + mask * latents + + +STAGE 3: DECODING +═══════════════════════════════════════════════════════════════════════════════ + + Latents [48, T_lat, H_lat, W_lat] (output of denoising loop) + ↓ + Denormalize (per-channel mean/std) + ↓ + Wan2.2 VAE Decoder: + - CausalConv3d blocks (per-frame + temporal) + - Upsampling Γ— 4 (time), Γ— 16 (spatial) + ↓ + Video Frames [3, T_pix, H_pix, W_pix] + ↓ + Save MP4 +``` + +--- + +## 2. SINGLE TRANSFORMER BLOCK INTERNALS + +``` +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ WANATTENTIONBLOCK FORWARD PASS β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + +INPUT +══════════════════════════════════════════════════════════════════════════ +x [B, L, 5120] ← hidden states (latent patch embeddings) +e [B, 1, 6, 5120] ← modulation vectors (time-conditioned) +context [B, 512, 5120] ← text embeddings (from T5) +cross_kv_cache: (k, v) tuples ← pre-computed cross-attention caches + + +STEP 1: COMPUTE MODULATION VECTORS +════════════════════════════════════════════════════════════════════════ + + mod = self.modulation[1, 6, 5120] + e[B, 1, 6, 5120] + ↓ + Broadcast + element-wise add: [1, 6, 5120] β†’ [B, 1, 6, 5120] + ↓ + Extract 6 vectors: + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” + β”‚ e0 = mod[:, 0, :] [B, 5120] shift β”‚ + β”‚ e1 = mod[:, 1, :] [B, 5120] scale β”‚ + β”‚ e2 = mod[:, 2, :] [B, 5120] gate β”‚ + β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€ + β”‚ e3 = mod[:, 3, :] [B, 5120] shift β”‚ + β”‚ e4 = mod[:, 4, :] [B, 5120] scale β”‚ + β”‚ e5 = mod[:, 5, :] [B, 5120] gate β”‚ + β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + ↓ + These control self-attn, cross-attn, and FFN paths + + +STEP 2: SELF-ATTENTION WITH MODULATION +════════════════════════════════════════════════════════════════════════ + + Input residual saved: + residual1 = x + + Apply affine transformation: + x_norm = norm1(x) [B, L, 5120] (layer norm) + x_norm = x_norm * (1 + e1) (scale by learned gain) + x_norm = x_norm + e0 (shift by learned bias) + + Self-attention: + x_attn = self_attn(x_norm, grid_sizes, freqs, rope_cos_sin, attn_mask) + β”œβ”€ Q = linear(x_norm) + β”œβ”€ K = linear(x_norm) + β”œβ”€ V = linear(x_norm) + β”œβ”€ QK norm (RMS) + β”œβ”€ Apply 3-way RoPE (temporal, height, width) + β”œβ”€ Scaled-dot-product attention + └─ Output projection + + Apply gated residual: + x = residual1 + e2 * x_attn (gate controls contribution) + + +STEP 3: CROSS-ATTENTION (TEXT CONDITIONING) +════════════════════════════════════════════════════════════════════════ + + (Optional norm, depends on cross_attn_norm flag) + x_cross = norm3(x) if norm3 else x + + Cross-attention: + x_cross_out = cross_attn(x_cross, context, kv_cache=cross_kv_cache) + β”œβ”€ Q = linear(x_cross) [B, L, 5120] + β”œβ”€ K, V from cache: + β”‚ (if kv_cache) use pre-computed [B, 40, 512, 128] + β”‚ (else) compute from context + β”œβ”€ Scaled-dot-product attention + └─ Output projection + + Residual: + x = x + x_cross_out (no gating on cross-attn) + + +STEP 4: FFN WITH MODULATION +════════════════════════════════════════════════════════════════════════ + + Input residual saved: + residual2 = x + + Apply affine transformation: + x_norm = norm2(x) + x_norm = x_norm * (1 + e4) (scale) + x_norm = x_norm + e3 (shift) + + FFN: + x_ffn = fc2(GELU(fc1(x_norm))) + β”œβ”€ fc1: [5120] β†’ [13824] (4Γ— expansion) + β”œβ”€ GELU activation (approx='tanh') + └─ fc2: [13824] β†’ [5120] (project back) + + Apply gated residual: + x = residual2 + e5 * x_ffn (gate controls contribution) + + +OUTPUT +══════════════════════════════════════════════════════════════════════════ +x [B, L, 5120] ← updated hidden states (one block's output = next block's input) +``` + +--- + +## 3. SELF-ATTENTION WITH 3-WAY FACTORIZED ROPE + +``` +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ WAN SELF-ATTENTION WITH 3-WAY ROPE β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + +ROPE PRECOMPUTATION (once per generation) +═════════════════════════════════════════════════════════════════════════ + +Grid sizes: [F_grid, H_grid, W_grid] e.g., [3, 10, 20] for (4D, 2D, 2D) +Seq length: L = F_grid * H_grid * W_grid = 3 Γ— 10 Γ— 20 = 600 + +Freqs split into 3 parts: + head_dim_half = 64 + d_t = 64 - 2Γ—(64//3) β‰ˆ 38 (temporal gets more capacity) + d_h = 64 // 3 β‰ˆ 21 (height) + d_w = 64 // 3 β‰ˆ 21 (width) + +For each position (f, h, w) ∈ grid: + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” + β”‚ Temporal component: freqs_t[f] β”‚ + β”‚ Height component: freqs_h[h] β”‚ + β”‚ Width component: freqs_w[w] β”‚ + β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + Concatenate: [38 + 21 + 21 = 64] dimensions + + +SELF-ATTENTION COMPUTATION +═════════════════════════════════════════════════════════════════════════ + +Inputs: + x [B, L, 5120] + grid_sizes = [F_grid, H_grid, W_grid] + freqs [1024, 64, 2] (precomputed cos/sin pairs) + rope_cos_sin (precomputed, optional) + attn_mask [B, 1, 1, L] (optional, for variable seq lengths) + +Step 1: Project to Q, K, V + Q = linear(x) [B, L, 5120] + K = linear(x) [B, L, 5120] + V = linear(x) [B, L, 5120] + +Step 2: Apply QK normalization (RMS norm) + Q = norm_q(Q) (if qk_norm=True) + K = norm_k(K) (if qk_norm=True) + +Step 3: Reshape to heads + Q = Q.reshape(B, L, 40, 128) [B, 40, L, 128] + K = K.reshape(B, L, 40, 128) + V = V.reshape(B, L, 40, 128) + +Step 4: Apply 3-way RoPE + β”Œβ”€ Compute cos/sin for each (f, h, w) position + β”œβ”€ Three separate frequency components + β”œβ”€ Interleaved as: [temp freq] + [height freq] + [width freq] + └─ Rotate Q and K by these frequencies (complex multiplication) + + For each head and position: + cos_t, sin_t = freqs_t[f] (temporal rotation) + cos_h, sin_h = freqs_h[h] (height rotation) + cos_w, sin_w = freqs_w[w] (width rotation) + + Apply rotation: z' = z * e^(iΞΈ) = cos(ΞΈ)z + sin(ΞΈ)z_rotated + +Step 5: Scaled-dot-product attention + scale = 1/√(head_dim) = 1/√128 β‰ˆ 0.088 + + scores = Q @ K^T Γ— scale [B, 40, L, L] + + (Optional) Add attention mask: + scores[i, :, :, j:] += mask[i, :, :, j:] (βˆ’βˆž for masked positions) + + attn = softmax(scores, dim=-1) + out = attn @ V + +Step 6: Output projection + out = out.reshape(B, L, 5120) + out = linear(out) [B, L, 5120] + +Output: [B, L, 5120] ← gated and summed into residual +``` + +--- + +## 4. CROSS-ATTENTION K/V CACHING + +``` +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ CROSS-ATTENTION K/V PRE-COMPUTATION β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + +NORMAL (UNCACHED) CROSS-ATTENTION +═════════════════════════════════════════════════════════════════════════ + +For each denoising step: + Q = linear(x) [B, L, 5120] + K = linear(context) [B, 512, 5120] ← EXPENSIVE! (40 times) + V = linear(context) [B, 512, 5120] ← EXPENSIVE! (40 times) + attn = Q @ K^T + out = softmax(attn) @ V + +Cost: 2 Γ— 40 = 80 linear projections across all blocks and steps + + +WAN2.2 OPTIMIZATION: PRE-COMPUTE K/V +═════════════════════════════════════════════════════════════════════════ + +Step 1: Before denoising loop (once per generation) + + context_emb = embed_text([text, neg_text]) [2, 512, 5120] + + For each block (40 blocks): + K = block.cross_attn.linear_k(context_emb) [2, 512, 5120] + V = block.cross_attn.linear_v(context_emb) [2, 512, 5120] + + Reshape to attention format: + K = K.reshape(2, 512, 40_heads, 128).transpose(0, 2, 1, 3) + = [2, 40, 512, 128] ← ready for attention + V = V.reshape(2, 512, 40_heads, 128).transpose(0, 2, 1, 3) + = [2, 40, 512, 128] + + cross_kv_caches[block_idx] = (K, V) + + Total storage: 40 blocks Γ— 2 tensors Γ— [2, 40, 512, 128] + = 40 Γ— 2 Γ— 2 Γ— 40 Γ— 512 Γ— 128 β‰ˆ 13 million params (cache) + + +Step 2: During denoising loop (for each step, 40 times) + + For each block: + K, V = cross_kv_caches[block_idx] ← retrieve cached + + Q = block.cross_attn.linear_q(x) [B, L, 5120] + Q = Q.reshape(B, L, 40_heads, 128).transpose(0, 2, 1, 3) + = [B, 40, L, 128] + + attn = Q @ K^T / √128 [B, 40, L, 512] + out = softmax(attn) @ V [B, 40, L, 128] + out = out.transpose(...).reshape(B, L, 5120) + out = block.cross_attn.linear_out(out) ← only this projection per step + + +Benefit: + OLD: 80 linear projections (K and V, all 40 steps, both models) + NEW: 2 linear projections (K and V, once before loop) + + Speedup factor: 40Γ— on context projection overhead! + (Text encoder inference is free after T5 embed) +``` + +--- + +## 5. TEACACHE vs SPECTRUM vs NO CACHING + +``` +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ CACHING COMPARISON β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + +NO CACHING (Default, if no acceleration) +═══════════════════════════════════════════════════════════════════════════ + + FOR EACH STEP (40 iterations): + RUN ALL 40 TRANSFORMER BLOCKS + β”œβ”€ 40 self-attn forward passes + β”œβ”€ 40 cross-attn forward passes + β”œβ”€ 40 FFN forward passes + └─ Total: 120 forward passes per step + + Total forwards: 40 steps Γ— 120 = 4,800 block forwards + + +TEACACHE ACCELERATION (2-3x speedup) +═══════════════════════════════════════════════════════════════════════════ + + Pre-profile: Train polynomial to map + rel_l1_distance(time_embedding) β†’ output_distance + + FOR EACH STEP: + β”œβ”€ Compute e0 (projected time embedding) + β”œβ”€ Compare with previous e0: + β”‚ rel_l1 = ||e0 - prev_e0|| / ||prev_e0|| + β”œβ”€ Map through polynomial: + β”‚ rescaled_distance = poly(rel_l1) + β”œβ”€ Accumulate: + β”‚ total_distance += rescaled_distance + β”‚ + β”œβ”€ DECISION: + β”‚ if total_distance < threshold: + β”‚ SKIP ALL BLOCKS + β”‚ x = x + cached_residual ← instant (no transformer) + β”‚ else: + β”‚ RUN ALL 40 BLOCKS (as normal) + β”‚ cached_residual = x_output - x_input + β”‚ total_distance = 0 (reset) + β”‚ + └─ Statistics: + β”œβ”€ threshold=0.1 β†’ ~40% of steps skipped β†’ 2x speedup + β”œβ”€ threshold=0.2 β†’ ~60% of steps skipped β†’ 3x speedup + └─ Higher threshold = more skips = faster but lower quality + + Why it works: + Time embeddings change smoothly across steps. + When change is small, relative contribution to output is small. + Cached residual from previous step is a good approximation! + + +SPECTRUM ACCELERATION (3.5-5x speedup) +═══════════════════════════════════════════════════════════════════════════ + + Warmup (steps 0-4): + └─ RUN ALL BLOCKS, cache features and timesteps + + Fitting (online, after each compute): + β”œβ”€ Collect: (t_i, h_i) pairs + β”œβ”€ Fit Chebyshev polynomial: h = Ξ£ c_k T_k(Ο„) + β”‚ where Ο„ ∈ [-1, 1] normalized timestep + β”‚ Solve: (X^T X + Ξ»I) c = X^T h (Cholesky-based ridge) + └─ Storage: coefficients (P=M+1 params per feature) + + Prediction (steps 5+): + β”œβ”€ Use adaptive windowing: + β”‚ window_size starts at 2 + β”‚ grows by flex_window (0.75) after each compute + β”‚ Example: 2, 2.75, 3.5, 4.25, 5.0, ... + β”‚ + β”œβ”€ Prediction happens every window_size steps + β”‚ PREDICT: + β”‚ h_taylor = h_recent + (step - t_recent) * dh/dt + β”‚ h_cheb = sum(c_k * T_k(Ο„_step)) + β”‚ h_pred = (1-w) * h_taylor + w * h_cheb + β”‚ x = x + h_pred ← no transformer needed! + β”‚ + └─ Compute only on boundaries (every ~2-5 steps) + + Results: + β”œβ”€ flex_window=0.75 β†’ ~3.5x speedup (high quality) + β”œβ”€ flex_window=3.0 β†’ ~5x speedup (experimental, lower quality) + └─ Warmup prevents cold-start artifacts + + +COMPARISON TABLE +═══════════════════════════════════════════════════════════════════════════ + +Method β”‚ Speedup β”‚ Quality β”‚ Implementation Complexity +──────────────┼──────────┼─────────┼───────────────────────────── +No caching β”‚ 1.0x β”‚ Reference +TeaCache β”‚ 2-3x β”‚ Good β”‚ Polynomial threshold (pre-profiled) +Spectrum β”‚ 3.5-5x β”‚ Great β”‚ Chebyshev+Taylor blending +──────────────┴──────────┴─────────┴───────────────────────────── +``` + +--- + +## 6. LATENT SPACE PATCHIFICATION + +``` +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ PATCHIFY OPERATION β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + +INPUT: Video latent [C, F, H, W] +═════════════════════════════════════════════════════════════════════════ + +Example: [48, 21, 90, 160] + C = 48 (latent channel depth, from VAE) + F = 21 (temporal frames in latent space) + H = 90 (height in latent space) + W = 160 (width in latent space) + +Patch size = (1, 2, 2) + pt = 1 (temporal patch size) + ph = 2 (height patch size) + pw = 2 (width patch size) + + +RESHAPE OPERATION +═════════════════════════════════════════════════════════════════════════ + +Step 1: Compute output grid dimensions + f_out = F / pt = 21 / 1 = 21 + h_out = H / ph = 90 / 2 = 45 + w_out = W / pw = 160 / 2 = 80 + + grid_size = (f_out, h_out, w_out) = (21, 45, 80) + total_patches = 21 Γ— 45 Γ— 80 = 75,600 patches + + +Step 2: Reshape to separate patch dimensions + [48, 21, 90, 160] + ↓ + Reshape: [48, 21Γ·1, 1, 90Γ·2, 2, 160Γ·2, 2] + = [48, 21, 1, 45, 2, 80, 2] + ↓ + Transpose to group: [F', H', W', C, pt, ph, pw] + [21, 45, 80, 48, 1, 2, 2] + + +Step 3: Flatten patch dimension + [21, 45, 80, 48, 1, 2, 2] + ↓ + Reshape to: [21Γ—45Γ—80, 48Γ—1Γ—2Γ—2] + = [75600, 192] (L, CΓ—patch_product) + + +Step 4: Project through linear layer + weights: [5120, 192] (output_dim, input_dim) + [75600, 192] @ [192, 5120] + ↓ + [75600, 5120] (L, dim) + + +Step 5: Add batch dimension + [1, 75600, 5120] (batch_size=1, seq_len=L, dim) + +OUTPUT +═════════════════════════════════════════════════════════════════════════ + +patches: [1, 75600, 5120] +grid_sizes: (21, 45, 80) + +Used for: + β”œβ”€ Input to transformer blocks (L = 75,600 tokens) + β”œβ”€ RoPE grid construction + β”œβ”€ Attention masking for variable lengths + └─ Unpatchify to reconstruct video + + +UNPATCHIFY (REVERSE OPERATION) +═════════════════════════════════════════════════════════════════════════ + +Input: [B, L, out_dim * patch_product] + [1, 75600, 64] (where 64 = 16 channels Γ— patch_product) + +Step 1: Extract sequence length for this batch + seq_len = grid_size[0] * grid_size[1] * grid_size[2] + = 21 * 45 * 80 = 75600 + +Step 2: Reshape + [75600, 64] β†’ [21, 45, 80, 1, 2, 2, 16] + (f, h, w, pt, ph, pw, C) + +Step 3: Transpose to group channels and patch dims + [16, 21, 1, 45, 2, 80, 2] (C, F', pt, H', ph, W', pw) + +Step 4: Flatten to reconstruct spatial dimensions + [16, 21Γ—1, 45Γ—2, 80Γ—2] + [16, 21, 90, 160] (C, F, H, W) + +Output: [16, 21, 90, 160] ← back to latent space + +This is then decoded through VAE decoder to get pixel-space video. +``` + +--- + +## 7. FRAME ALIGNMENT & EXTRA FRAMES + +``` +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ FRAME HANDLING IN WAN2.2 β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + +USER REQUEST: num_frames=81 + +VAE STRIDE: (4, 16, 16) for Wan2.2 + +COMPUTE LATENT FRAMES +═════════════════════════════════════════════════════════════════════════ + +User requested 81 pixel frames with VAE stride 4 in temporal: + + gen_frames = 81 + extra_frames = 4 (VAE stride[0]) ← T2V: add extra for boundary artifacts + + total_latent_frames = gen_frames + extra_frames = 85 pixel frames + t_latent = (total_latent_frames - 1) // 4 + 1 + = (85 - 1) // 4 + 1 + = 84 // 4 + 1 + = 21 + 1 = 22 latent frames + +WHY EXTRA FRAMES? +───────────────── +The VAE uses causal convolution (temporal causality). +At boundaries (first/last frame), padding creates artifacts. +Extra frames are generated and later discarded: + - First 4 extra frames absorbed by VAE padding + - Original 81 frames remain clean + - Final 4 frames discarded + +For I2V (image-to-video): + - extra_frames = 0 (reference image provides real first frame) + - No need for padding + + +ALIGNMENT REQUIREMENT +═════════════════════════════════════════════════════════════════════════ + +Dimensions must align to patch_size Γ— vae_stride: + + Spatial: + align_h = patch_size[1] * vae_stride[1] = 2 * 16 = 32 + align_w = patch_size[2] * vae_stride[2] = 2 * 16 = 32 + + User requests: 720 Γ— 1280 + + Check: + 720 % 32 = 0 βœ“ + 1280 % 32 = 0 βœ“ + + If not aligned, auto-round down: + height_aligned = (720 // 32) * 32 = 22 * 32 = 704 + width_aligned = (1280 // 32) * 32 = 40 * 32 = 1280 + + Temporal: (not usually constrained for T2V, constrained for I2V) + + +EXAMPLE GENERATION +═════════════════════════════════════════════════════════════════════════ + +User requests: 1280Γ—720, 81 frames, T2V + +Alignment check: + βœ“ 1280 % 32 = 0 + βœ“ 720 % 32 = 0 + +Dimension computation: + vae_stride = (4, 16, 16) + gen_frames = 81 + 4 = 85 + + h_latent = 720 / 16 = 45 + w_latent = 1280 / 16 = 80 + t_latent = (85 - 1) / 4 + 1 = 22 + z_dim = 48 + + target_shape = (48, 22, 45, 80) + +Patchification: + patch_size = (1, 2, 2) + f_grid = 22 / 1 = 22 + h_grid = 45 / 2 = 22.5 ← ERROR! Not divisible + + Actually: h_grid = 45 / 2 = 22 (integer division) + Remainder frames are padding (handled by attn_mask) + +Generation: + Denoising loop: 40 steps + β†’ Output shape: [48, 22, 45, 80] latent frames + +VAE Decode: + [48, 22, 45, 80] + ↓ Upscale spatial Γ— 16: [48, 22, 720, 1280] + ↓ Upscale temporal Γ— 4: [48, 88, 720, 1280] + + (Extra frames included; trim after decode) + ↓ Final: [3, 81, 720, 1280] (RGB, 81 frames) +``` + +--- + +## 8. MEMORY HIERARCHY + +``` +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ MEMORY USAGE BREAKDOWN β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + +Model weights (Wan2.2 T2V 14B Γ— 2 models): + High-noise model: + Transformer (40 blocks): ~26 GB (bf16) + Text embeddings: ~1 MB + Low-noise model: + Transformer (40 blocks): ~26 GB (bf16) + Text embeddings: ~1 MB + + With 4-bit quantization: ~6.5 GB per model (80% reduction) + + +Temporary buffers (per denoising step): + Input latents: [2, 48, 22, 45, 80] = ~7.5 MB (CFG batch) + Patches: [2, 75600, 5120] = ~1.5 GB + Transformer hidden states: [2, 75600, 5120] = ~1.5 GB + Attention maps: [2, 40 heads, 75600, 75600] = ~21 GB ← peak! + + Intermediate buffers are discarded after each step. + + +Pre-computed caches (reused across all steps): + Text embeddings (embedded T5 output): + [2, 512, 5120] per model = ~10 MB + + Cross-attn K/V caches (all 40 blocks): + Per model: 40 blocks Γ— 2 tensors Γ— [2, 40, 512, 128] = ~20 MB + Total (2 models): ~40 MB + + RoPE frequencies: + [seq_len, 1, head_dim/2] β‰ˆ 3 MB + + Total cache: ~50 MB (negligible compared to model weights) + + +PEAK MEMORY ESTIMATE +═════════════════════════════════════════════════════════════════════════ + +Scenario: Wan2.2 T2V 14B, dual-model, no quantization + + Model weights (in memory): 2 Γ— 26 GB = 52 GB + Transformer hidden states: ~1.5 GB + Attention maps (peak): ~21 GB + ───────────────────────────────────── + Total peak: ~74.5 GB ← too much for most GPUs! + + Solutions: + β”œβ”€ Quantization (4-bit): 2 Γ— 6.5 GB = 13 GB + temp = ~35 GB βœ“ + β”œβ”€ Single model (no dual): 26 GB + temp = ~48 GB βœ“ + β”œβ”€ Spectrum (skip blocks): reduces temp buffers + └─ Tiling VAE (spatial/temporal): processes in chunks + +With 4-bit quantization + Apple Silicon optimizations: + β†’ Fits in ~8-16 GB systems + + +MEMORY TIMELINE +═════════════════════════════════════════════════════════════════════════ + +Phase 1: T5 Encoding + β”œβ”€ Load T5: ~15 GB + β”œβ”€ Encode prompts: ~1 GB temp + └─ Free T5: Release 15 GB βœ“ + +Phase 2: Model Loading + β”œβ”€ Load transformers: ~13 GB (quantized) + β”œβ”€ Precompute caches: ~50 MB + └─ Total: ~13 GB + +Phase 3: Denoising Loop (per step) + β”œβ”€ Latents: ~7 MB + β”œβ”€ Transformer forward: ~1.5 GB peak + β”œβ”€ Clean temp: Release between steps + └─ Steady state: ~13 GB + +Phase 4: VAE Decode + β”œβ”€ Load VAE: ~2 GB + β”œβ”€ Decode latents: ~2-3 GB + β”œβ”€ Free transformers: Release ~13 GB + └─ Total: ~4 GB + +Phase 5: Video Saving + β”œβ”€ Write MP4: CPU-based, minimal GPU memory + └─ ~500 MB for frame buffer +``` + +--- + +Created: March 6, 2025 +Project: mlx-video (Wan2.2 Implementation on MLX) +Filename: WAN22_ARCHITECTURE_DIAGRAMS.md diff --git a/docs/WAN22_EXPLORATION_SUMMARY.md b/docs/WAN22_EXPLORATION_SUMMARY.md new file mode 100644 index 0000000..718d4f7 --- /dev/null +++ b/docs/WAN22_EXPLORATION_SUMMARY.md @@ -0,0 +1,929 @@ +# Wan2.2 Model Architecture & Implementation - Comprehensive Exploration + +## Executive Summary +The mlx-video project contains a complete implementation of Wan2.2 (Text-to-Video & Image-to-Video generation) diffusion models on Apple Silicon using MLX. The architecture includes dual-model configurations, advanced caching mechanisms (TeaCache and Spectrum), and supports both quantization and LoRA fine-tuning. + +--- + +## 1. HOW WAN2.2 MODEL WORKS + +### 1.1 High-Level Architecture +Wan2.2 is a **flow-matching diffusion model** with: +- **Dual-model configuration**: Separate high-noise and low-noise transformer models for different stages of denoising +- **Single-model fallback**: Wan2.1 uses single transformer for compatibility +- **Condition types**: Text embeddings (from UMT5-XXL T5 encoder) + time embeddings + optional image conditioning (I2V) + +### 1.2 Input Flow (Text-to-Video Pipeline) +``` +User Prompt β†’ T5 Encoder β†’ Text Embeddings [B, text_len, 4096] + ↓ + Embed Layer (linear projection) + ↓ + Transformer-friendly [B, text_len, dim] + +Noise [C, T, H, W] β†’ VAE Encode β†’ Latents [z_dim, T_latent, H_latent, W_latent] + ↓ + Patchify + ↓ + Patch Embeddings [1, L, dim] + +Time Step (continuous 0-1000) β†’ Sinusoidal Embedding [B, freq_dim] + ↓ + Time embedding MLP β†’ [B, dim] + ↓ + Time projection (6x scale) β†’ [B, 1, 6, dim] +``` + +### 1.3 Core Configuration +**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/config.py` + +Key Wan2.2 T2V 14B configuration: +- **Model Dimension**: 5120 +- **Number of Transformer Blocks**: 40 +- **Attention Heads**: 40 (head_dim = 128) +- **FFN Dimension**: 13,824 +- **Patch Size**: (1, 2, 2) - temporal, height, width +- **Input Channels**: 16 (VAE latent depth) +- **Output Channels**: 16 +- **Text Length**: 512 tokens +- **Text Embedding Dimension**: 4096 (from T5) +- **Frequency Dimension**: 256 (for time embedding sinusoids) +- **Number of Training Timesteps**: 1000 + +**Configuration Variants**: +```python +WanModelConfig.wan22_t2v_14b() # Text-to-Video (default, dual-model) +WanModelConfig.wan22_i2v_14b() # Image-to-Video (dual-model, in_dim=36) +WanModelConfig.wan22_ti2v_5b() # Text+Image-to-Video (single-model, dim=3072) +WanModelConfig.wan21_t2v_14b() # Wan2.1 14B (single-model) +WanModelConfig.wan21_t2v_1_3b() # Wan2.1 1.3B (single-model, dim=1536) +``` + +### 1.4 Dual Model Architecture (Wan2.2 Specific) +**File**: `/Users/daniel/Projects/mlx-video/mlx_video/generate_wan.py` lines 407-419 + +Wan2.2 uses **adaptive model switching** based on noise levels: +- **High-Noise Model** (early denoising steps): Handles coarse structure generation from pure noise + - Used when: `timestep_val >= boundary` (default: 87.5% of training timesteps) + - More sensitive to global structure + +- **Low-Noise Model** (late denoising steps): Handles fine details and refinement + - Used when: `timestep_val < boundary` + - More sensitive to local details + +**Boundary Calculation**: `boundary = config.boundary * config.num_train_timesteps = 0.875 * 1000 = 875` + +**Guidance Scales**: Can differ per model +- Example: `guide_scale=(3.0, 4.0)` means (low_noise_scale, high_noise_scale) + +--- + +## 2. DENOISING LOOP STRUCTURE + +### 2.1 Main Denoising Loop +**File**: `/Users/daniel/Projects/mlx-video/mlx_video/generate_wan.py` lines 589-693 + +```python +for i, t in enumerate(tqdm(range(steps), desc="Diffusion")): + timestep_val = timestep_list[i] + + # Select model based on noise level + if is_dual: + if timestep_val >= boundary: + model = high_noise_model + kv = cross_kv_high + rcs = rope_cos_sin_high + else: + model = low_noise_model + kv = cross_kv_low + rcs = rope_cos_sin_low + + # CFG or direct inference + if cfg_disabled: + # B=1 forward (faster) + noise_pred = forward_pass([latents], timestep_val, context_cond) + else: + # B=2 forward: conditional + unconditional + noise_pred = forward_pass( + [latents, latents], + [timestep_val, timestep_val], + [context_cond, context_uncond] + ) + # Classifier-free guidance + noise_pred = noise_uncond + guide_scale * (noise_cond - noise_uncond) + + # Scheduler step (Euler, DPM++, or UniPC) + latents = scheduler.step(noise_pred, timestep_val, latents) + + # (I2V only) Freeze first frame + if is_i2v_mask_blend: + latents = (1 - mask) * z_img + mask * latents +``` + +### 2.2 Timestep Handling +- **Timesteps are contiguous values** (not discrete indices) from 0-1000 +- **Reverse scheduling**: Start at high noise (β‰ˆ1000), end at low noise (β‰ˆ0) +- **Scheduler converts to sigma** (noise level): Used for denoising step size +- **Step index tracking**: Pre-converted to Python list to avoid GPU sync per step + ```python + timestep_list = sched.timesteps.tolist() # Pre-convert to avoid .item() sync + ``` + +### 2.3 Batch Processing Optimization (CFG) +**No-CFG Mode** (`guide_scale ≀ 1.0`): +- Batch size = 1 (only conditional) +- **2x faster** than CFG mode +- Skips unconditional forward pass + +**CFG Mode** (standard): +- Batch size = 2 (conditional + unconditional in same forward) +- Single forward pass processes both, then blends + +### 2.4 Pre-computation to Avoid Per-Step Overhead +**Text Embeddings**: +```python +context_emb = model.embed_text([context, context_null]) # Once, reused all steps +``` + +**Cross-Attention K/V Cache**: +```python +cross_kv = model.prepare_cross_kv(context_emb) # Once, indices passed to each block +``` + +**RoPE Frequencies**: +```python +rope_cos_sin = model.prepare_rope(grid_sizes) # Once (grid sizes constant) +``` + +--- + +## 3. TRANSFORMER FORWARD PASS FOR WAN2.2 + +### 3.1 Main Forward Pass +**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/model.py` lines 295-518 + +```python +def __call__( + self, + x_list: list, # Video latents [C, F, H, W] + t: mx.array, # Timesteps [B] or [B, L] + context: list | mx.array, # Text embeddings [B, text_len, dim] + seq_len: int, # Max sequence length + cross_kv_caches: list | None = None, # Pre-computed K/V + y: list | None = None, # I2V conditioning + rope_cos_sin: tuple | None = None, # Pre-computed RoPE +) β†’ list: +``` + +### 3.2 Input Processing + +**Step 1: Detect CFG Batching** +- Check if all batch elements are identical (common for CFG batch of 2) +- Broadcast optimization if `all_same` + +**Step 2: Channel Concatenation (I2V)** +```python +if y is not None: # Image-to-Video conditioning + x_list = [mx.concatenate([u, v], axis=0) for u, v in zip(x_list, y)] + # Now x has extra channels from conditioning image +``` + +**Step 3: Patchification** +```python +# Input: [C, F, H, W] +# Output: [1, L, dim] where L = (F/patch_t) * (H/patch_h) * (W/patch_w) +patches, grid_sizes = model._patchify(x_list[0]) +``` + +### 3.3 Transformer Block Structure +**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/transformer.py` lines 7-96 + +Each `WanAttentionBlock` contains: + +```python +class WanAttentionBlock(nn.Module): + def __init__(self, dim, ffn_dim, num_heads, ...): + # Self-attention path + self.norm1 = WanLayerNorm(dim) + self.self_attn = WanSelfAttention(dim, num_heads) + + # Cross-attention path + self.norm3 = WanLayerNorm(dim) if cross_attn_norm else None + self.cross_attn = WanCrossAttention(dim, num_heads) + + # FFN path + self.norm2 = WanLayerNorm(dim) + self.ffn = WanFFN(dim, ffn_dim) + + # Learned modulation (6 vectors Γ— dim) + self.modulation = mx.random.normal((1, 6, dim)) * (dim**-0.5) + + def __call__(self, x, e, context, cross_kv_cache=None, ...): + # Modulation (compute in float32 for precision) + mod = (self.modulation + e) # Add time embedding + e0, e1, e2, e3, e4, e5 = mod.split() # 6 modulation vectors + + # Self-attention with modulation + x_norm = self.norm1(x) * (1 + e1) + e0 # Affine transform + x = x + e2 * self.self_attn(x_norm) + + # Cross-attention (conditioned on text) + x_norm = self.norm3(x) if self.norm3 else x + x = x + self.cross_attn(x_norm, context, kv_cache=cross_kv_cache) + + # FFN with modulation + x_norm = self.norm2(x) * (1 + e4) + e3 + x = x + e5 * self.ffn(x_norm) + + return x +``` + +### 3.4 Block Processing Loop +**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/model.py` lines 508-511 + +Standard path (no caching): +```python +for i, block in enumerate(self.blocks): + kv = cross_kv_caches[i] if cross_kv_caches is not None else None + x = block(x, cross_kv_cache=kv, **kwargs) +``` + +With **TeaCache** enabled (lines 450-507): +- Skip blocks if time embedding change is small +- Reuse residual from previous step + +With **Spectrum** enabled (lines 430-449): +- Predict features with Chebyshev polynomials +- Full compute only at strategic intervals + +### 3.5 Learned Modulation (DiT-style) +The 6 modulation vectors control: +1. **e0**: Self-attention shift (residual connection bias) +2. **e1**: Self-attention scale (learnable gain) +3. **e2**: Self-attention gate (residual weight) +4. **e3**: FFN shift +5. **e4**: FFN scale +6. **e5**: FFN gate + +This is a **Diffusion Transformer (DiT)** style architecture where modulation is **per-block, not per-layer-pair**. + +### 3.6 Self-Attention Details +**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/attention.py` lines 46-121 + +```python +class WanSelfAttention(nn.Module): + def __call__(self, x, seq_lens, grid_sizes, freqs, rope_cos_sin=None, attn_mask=None): + b, s, _ = x.shape + + # Project to Q, K, V + q = self.q(x) + k = self.k(x) + v = self.v(x) + + # QK normalization (optional) + if self.norm_q: + q = self.norm_q(q) # RMS norm + if self.norm_k: + k = self.norm_k(k) + + # Reshape: [B, L, dim] β†’ [B, L, num_heads, head_dim] + q, k, v = q.reshape(...), k.reshape(...), v.reshape(...) + + # Apply 3-way factorized RoPE (temporal, height, width) + q = rope_apply(q.astype(mx.float32), grid_sizes, freqs, rope_cos_sin) + k = rope_apply(k.astype(mx.float32), grid_sizes, freqs, rope_cos_sin) + + # Memory-efficient scaled-dot-product attention + out = mx.fast.scaled_dot_product_attention(q, k, v, scale=scale, mask=attn_mask) + + # Output projection + return self.o(out) +``` + +**Key Features**: +- QK normalization prevents attention collapse +- RoPE applied in float32 then cast back +- Attention mask handles variable sequence lengths + +### 3.7 Cross-Attention Details +**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/attention.py` lines 124-207 + +```python +class WanCrossAttention(nn.Module): + def prepare_kv(self, context): + """Pre-compute K, V once before denoising loop""" + k = self.k(context) # [B, L_ctx, dim] + v = self.v(context) + k = k.reshape(B, -1, num_heads, head_dim).transpose(0, 2, 1, 3) + v = v.reshape(B, -1, num_heads, head_dim).transpose(0, 2, 1, 3) + return k, v + + def __call__(self, x, context, context_lens=None, kv_cache=None): + # Q from hidden state + q = self.q(x).reshape(...) + + # Use pre-computed K/V if available + if kv_cache is not None: + k, v = kv_cache + else: + k = self.k(context).reshape(...) + v = self.v(context).reshape(...) + + # Attention with optional context masking + out = mx.fast.scaled_dot_product_attention(q, k, v) + return self.o(out) +``` + +--- + +## 4. EXISTING CACHING & ACCELERATION MECHANISMS + +### 4.1 TeaCache (Timestep Embedding Aware Cache) +**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/model.py` lines 15-59 + +**Purpose**: Skip redundant transformer blocks when time embedding change is small + +**How it Works**: +1. Monitor relative L1 distance between consecutive projected time embeddings (e0) +2. Use polynomial regression to map distance β†’ output distance +3. Accumulate rescaled distances +4. Skip blocks if accumulated distance < threshold +5. Reuse cached residual instead + +**Configuration**: +```python +teacache_thresh: float = 0.0 # Threshold (0=disabled, 0.1β‰ˆ2x speedup, 0.2β‰ˆ3x) +teacache_verbose: bool = False # Debug per-step decisions +``` + +**State Tracking**: +```python +@dataclass +class TeaCacheState: + enabled: bool = False + threshold: float = 0.0 + coefficients: tuple = () # Polynomial coefficients (ret-mode) + verbose: bool = False + + # Per-batch state + previous_e0: object = None # Cached time embedding + accumulated_distance: float = 0.0 + previous_residual: object = None # Cached residual to reuse + + # Bookkeeping + cnt: int = 0 + num_steps: int = 0 + ret_steps: int = 2 # Always compute first N steps + cutoff_steps: int = 0 # Always compute last N steps + + # Stats + steps_skipped: int = 0 + steps_computed: int = 0 +``` + +**Polynomial Coefficients** (pre-profiled for different models): +- Wan2.2 T2V 14B: `(-3.03e5, 4.91e4, -2.66e3, 5.87e1, -0.316)` +- Wan2.2 I2V 14B: `(2.57e5, -3.54e4, 1.40e3, -13.6, 0.133)` +- Wan2.1 T2V 1.3B: `(-5.22e4, 9.23e3, -5.28e2, 13.7, -0.050)` + +**Speedup Results**: +- threshold=0.1 β†’ ~2x speedup +- threshold=0.2 β†’ ~3x speedup + +### 4.2 Spectrum (Adaptive Spectral Feature Forecasting) +**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/spectrum.py` (entire file) + +**Purpose**: Predict transformer features using Chebyshev polynomials; skip expensive forward passes + +**Algorithm**: +1. **Warmup Phase** (first 5 steps): Always compute full transformer +2. **Caching Phase**: Store (timestep, features) pairs +3. **Fitting Phase**: Fit Chebyshev polynomials to cached features via ridge regression +4. **Prediction Phase**: Use fitted polynomials + Taylor extrapolation to predict next features +5. **Adaptive Scheduling**: Grow compute interval as confidence increases + +**Chebyshev Forecaster**: +```python +class ChebyshevForecaster: + def __init__(self, M=4, K=100, lam=0.1, num_steps=50): + self.M = M # Polynomial degree (4) + self.K = K # Cache window size (100) + self.lam = lam # Ridge regularization (0.1) + self.t_buf = None # (<=K,) step indices + self.H_buf = None # (<=K, F) flattened features + + def update(self, step, h_flat): + """Append new feature to cache""" + # Maintains sliding window of K most recent features + + def predict(self, step): + """Predict features using fitted Chebyshev polynomials""" + # Fit: (P, P) ridge system solved via Cholesky (tiny, CPU-fast) + # Predict: Evaluate Chebyshev basis at target step +``` + +**SpectrumForecaster** (blends approaches): +```python +class SpectrumForecaster: + def __init__(self, M=4, K=100, lam=0.1, w=0.5, num_steps=50): + self.cheb = ChebyshevForecaster(...) + self.w = w # Blend weight: 0=Taylor, 1=Chebyshev (default 0.5) + + def predict(self, step): + h_cheb = self.cheb.predict(step) + h_taylor = taylor_forward_differences(step) + return (1 - w) * h_taylor + w * h_chebyshev +``` + +**Configuration**: +```python +spectrum: bool = False # Enable/disable +spectrum_w: float = 0.5 # Blend weight (0=Taylor-only, 1=Chebyshev-only) +spectrum_flex_window: float = 0.75 # Window growth rate (default ~3.5x speedup) +spectrum_warmup: int = 5 # Always compute first N steps +``` + +**State Machine**: +```python +@dataclass +class SpectrumState: + enabled: bool = False + m: int = 4 # Chebyshev degree + lam: float = 0.1 # Ridge regularization + w: float = 0.5 # Chebyshev/Taylor blend + k_max: int = 100 # Cache size + warmup_steps: int = 5 + window_size: int = 2 # Initial compute interval + flex_window: float = 0.75 # Growth rate (Ξ± in paper) + + # Runtime state + num_steps: int = 0 + cnt: int = 0 + curr_ws: float = 2.0 # Current window (grows during run) + num_consecutive_cached: int = 0 + + def should_compute(self) -> bool: + """Adaptive scheduling: compute on warmup or window boundaries""" + if cnt < warmup_steps: + return True + should = (num_consecutive_cached + 1) % floor(curr_ws) == 0 + return should + + def step(self, computed: bool): + """Update counters after each step""" + if computed: + curr_ws += flex_window # Grow window + else: + num_consecutive_cached += 1 + cnt += 1 +``` + +**Speedup Results** (from paper): +- flex_window=0.75 β†’ ~3.5x speedup +- flex_window=3.0 β†’ ~5x speedup + +### 4.3 Compiled Forward Pass +**File**: `/Users/daniel/Projects/mlx-video/mlx_video/generate_wan.py` lines 578-584 + +When TeaCache and Spectrum are disabled, compile the full model for speed: +```python +if not use_caching: + models_to_compile = [high_noise_model, low_noise_model] if is_dual else [single_model] + for m in models_to_compile: + m._compiled = mx.compile(m) + +# In loop: +_call = getattr(model, "_compiled", model) +preds = _call([latents], t=t_batch, ...) # JIT-compiled forward +``` + +### 4.4 Memory Optimizations + +**Cross-attention K/V Caching**: +- Project context embeddings once before denoising loop +- Reuse same K/V across all 40 diffusion steps +- **Eliminates 40 redundant linear projections** per generation + +**RoPE Precomputation**: +- Grid sizes are constant across steps +- Precompute all cos/sin values once +- **Eliminates per-step broadcast/concat overhead** + +**Text Embedding Caching**: +- Embed text once with T5 encoder +- Reuse across all denoising steps +- **Free T5 from memory immediately** after embedding + +--- + +## 5. MoE (MIXTURE OF EXPERTS) ARCHITECTURE + +### 5.1 Finding: NO MoE in Wan2.2 + +**Search Results**: +- No `MoE`, `moe`, `mixture`, or `expert` references in any Wan model files +- FFN is standard **gated feed-forward** (no MoE) + +**Feed-Forward Network** (file: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/transformer.py` lines 84-96): +```python +class WanFFN(nn.Module): + """Gated feed-forward network with GELU(tanh) activation.""" + + def __init__(self, dim: int, ffn_dim: int): + super().__init__() + self.fc1 = nn.Linear(dim, ffn_dim) # 5120 β†’ 13824 + self.act = nn.GELU(approx="tanh") + self.fc2 = nn.Linear(ffn_dim, dim) # 13824 β†’ 5120 + + def __call__(self, x: mx.array) -> mx.array: + # Standard gated FFN: dense β†’ GELU β†’ dense + x_w = x.astype(_linear_dtype(self.fc1)) + return self.fc2(self.act(self.fc1(x_w))) +``` + +**Architecture**: Vanilla transformer FFN, not sparse/routed. + +--- + +## 6. MODEL LOADING AND CONFIGURATION + +### 6.1 Model Loading Pipeline +**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/loading.py` + +```python +def load_wan_model(model_path, config, quantization=None, loras=None): + """Load WanModel with optional quantization and LoRA.""" + + # 1. Create model with config + model = WanModel(config) + + # 2. Apply quantization stubs if specified + if quantization: + nn.quantize( + model, + group_size=quantization["group_size"], + bits=quantization["bits"], + class_predicate=lambda path, m: _quantize_predicate(path, m) + ) + + # 3. Load weights + weights = mx.load(str(model_path)) + + # 4. Apply LoRA (dequantize + merge if quantized, weight merge if bf16) + if loras: + # ... handle quantized vs non-quantized paths ... + + # 5. Load and eval + model.load_weights(list(weights.items()), strict=False) + mx.eval(model.parameters()) + return model +``` + +### 6.2 Configuration Loading +**File**: `/Users/daniel/Projects/mlx-video/mlx_video/generate_wan.py` lines 93-173 + +Auto-detection logic: +```python +if (model_dir / "config.json").exists(): + # Use explicit config + config = WanModelConfig(**config_dict) +else: + # Auto-detect from weights + if (model_dir / "low_noise_model.safetensors").exists(): + config = WanModelConfig.wan22_t2v_14b() # Dual model + else: + # Read weight shapes to determine size + probe = mx.load(str(model_path)) + for k, v in probe.items(): + if "patch_embedding_proj.weight" in k: + dim = v.shape[0] + config = ( + WanModelConfig.wan21_t2v_1_3b() if dim <= 2048 + else WanModelConfig.wan21_t2v_14b() + ) +``` + +### 6.3 Weight Conversion (PyTorch β†’ MLX) +**File**: `/Users/daniel/Projects/mlx-video/mlx_video/convert_wan.py` lines 60-161 + +Key transformations: +```python +def sanitize_wan_transformer_weights(weights): + """Convert Wan PyTorch keys to MLX structure.""" + + # Conv3d β†’ Linear reshape + if key == "patch_embedding.weight": + # [dim, in_dim, 1, 2, 2] β†’ [dim, in_dim*1*2*2] + value = value.reshape(value.shape[0], -1) + new_key = "patch_embedding_proj.weight" + + # Sequential layers β†’ individual modules + new_key = key.replace("text_embedding.0.", "text_embedding_0.") # Seq[0] + new_key = key.replace("text_embedding.2.", "text_embedding_1.") # Seq[2] + new_key = key.replace("time_embedding.0.", "time_embedding_0.") + new_key = key.replace("time_embedding.2.", "time_embedding_1.") + new_key = key.replace("time_projection.1.", "time_projection.") + + # FFN renaming + new_key = key.replace(".ffn.0.", ".ffn.fc1.") + new_key = key.replace(".ffn.2.", ".ffn.fc2.") + + return {new_key: value, ...} +``` + +### 6.4 Quantization Strategy +**File**: `/Users/daniel/Projects/mlx-video/mlx_video/convert_wan.py` (full file) + +```python +def _quantize_predicate(path, module): + """Determine which layers to quantize.""" + # Typically: Linear layers in transformer blocks (high memory) + # Skip: Layer norms, embeddings, output head + return isinstance(module, nn.Linear) and "blocks" in path +``` + +Quantization options: +- 4-bit with group_size=64 (default) +- 8-bit with group_size=128 +- Mixed: Quantize transformer blocks, keep other layers in bf16 + +--- + +## 7. GENERATE SCRIPTS FOR WAN2.2 + +### 7.1 Main Generation Script +**File**: `/Users/daniel/Projects/mlx-video/mlx_video/generate_wan.py` (38,528 bytes) + +Main entry point: +```python +def generate_video( + model_dir: str, + prompt: str, + negative_prompt: str | None = None, + image: str | None = None, + width: int = 1280, + height: int = 720, + num_frames: int = 81, + steps: int = None, + guide_scale: str | float | tuple = None, + shift: float = None, + seed: int = -1, + output_path: str = "output.mp4", + scheduler: str = "unipc", + teacache_thresh: float = 0.0, + teacache_verbose: bool = False, + spectrum: bool = False, + spectrum_w: float = 0.5, + spectrum_flex_window: float = 0.75, + spectrum_warmup: int = 5, + loras: list | None = None, + loras_high: list | None = None, + loras_low: list | None = None, + tiling: str = "auto", +): +``` + +### 7.2 Generation Pipeline + +**Phase 1: Setup** (lines 91-306) +- Load and parse config (auto-detect or explicit) +- Validate dimensions (align to patch_size Γ— vae_stride) +- Load T5 encoder and tokenizer +- Encode prompts (text β†’ embeddings) +- Free T5 from memory + +**Phase 2: I2V Preparation** (lines 309-392) +- Load VAE encoder +- Encode reference image to latent +- Build masking for blend (TI2V-5B) or channel concat (I2V-14B) + +**Phase 3: Model Loading** (lines 394-420) +- Load high-noise and low-noise transformers (or single for Wan2.1) +- Apply LoRA if specified +- Load and apply quantization if specified + +**Phase 4: Precomputation** (lines 422-484) +- Embed text once (text_embedding MLP) +- Prepare cross-attention K/V caches (40 blocks Γ— 2 models) +- Precompute RoPE cos/sin for grid sizes +- Setup schedulers (Euler, DPM++, or UniPC) + +**Phase 5: Denoising Loop** (lines 508-693) +- For each timestep: + - Select model based on noise level (dual model) + - Forward pass (with or without CFG) + - Apply classifier-free guidance + - Scheduler step + - (Optional) Reapply I2V mask + - Evaluate and free memory + +**Phase 6: VAE Decoding** (lines 743-800+) +- Load VAE decoder +- Optional tiling for memory efficiency +- Decode latents to video pixels +- Save MP4 + +### 7.3 Supported Schedulers +**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/scheduler.py` + +1. **Euler** (1st-order): `--scheduler euler` + - Simplest: `x_next = x + (Οƒ_next - Οƒ_cur) * v` + +2. **DPM++2M** (2nd-order): `--scheduler dpm++` + - Convergence faster than Euler + - Uses previous step for 2nd-order correction + +3. **UniPC** (higher-order): `--scheduler unipc` (default) + - Faster convergence + - Recommended for quality + +### 7.4 Command-Line Usage + +```bash +# Basic T2V (Wan2.2 dual-model auto-detected) +python -m mlx_video.generate_wan \ + --model-dir ./wan22_converted \ + --prompt "A cat running through a forest" \ + --output output.mp4 + +# I2V with reference image +python -m mlx_video.generate_wan \ + --model-dir ./wan22_i2v_converted \ + --prompt "The cat leaps over a fence" \ + --image reference.jpg \ + --output output_i2v.mp4 + +# With TeaCache acceleration (2x speedup) +python -m mlx_video.generate_wan \ + --model-dir ./wan22_converted \ + --prompt "..." \ + --teacache-thresh 0.1 \ + --output output_fast.mp4 + +# With Spectrum acceleration (3.5x speedup, better quality) +python -m mlx_video.generate_wan \ + --model-dir ./wan22_converted \ + --prompt "..." \ + --spectrum \ + --spectrum-flex-window 0.75 \ + --output output_spectrum.mp4 + +# With quantization (memory efficient) +python -m mlx_video.generate_wan \ + --model-dir ./wan22_quantized_4bit \ + --prompt "..." \ + --output output_quant.mp4 +``` + +--- + +## 8. KEY FILE STRUCTURE & SIZES + +``` +mlx_video/models/wan/ +β”œβ”€β”€ __init__.py (2 lines) +β”œβ”€β”€ model.py (518 lines) ← Main WanModel class +β”œβ”€β”€ config.py (157 lines) ← Configuration variants +β”œβ”€β”€ transformer.py (96 lines) ← Transformer blocks & FFN +β”œβ”€β”€ attention.py (207 lines) ← Self/Cross-attention +β”œβ”€β”€ rope.py (178 lines) ← 3-way factorized RoPE +β”œβ”€β”€ loading.py (183 lines) ← Model loading & T5 encoder +β”œβ”€β”€ text_encoder.py (240 lines) ← T5 text encoder +β”œβ”€β”€ scheduler.py (428 lines) ← Flow matching schedulers +β”œβ”€β”€ spectrum.py (288 lines) ← Spectrum acceleration +β”œβ”€β”€ i2v_utils.py (58 lines) ← Image-to-Video utilities +β”œβ”€β”€ vae.py (589 lines) ← Wan2.1 VAE +└── vae22.py (908 lines) ← Wan2.2 VAE (48-dim latent) + +mlx_video/ +β”œβ”€β”€ generate_wan.py (38 KB) ← Main generation script +β”œβ”€β”€ convert_wan.py (27 KB) ← Weight conversion & quantization +β”œβ”€β”€ train_wan.py (9 KB) ← Training script +└── utils.py (9 KB) ← Utilities +``` + +--- + +## 9. SUMMARY TABLE: WAN2.2 ARCHITECTURE + +| Component | Details | File | +|-----------|---------|------| +| **Dual Model** | High-noise (0-875 steps) + Low-noise (875-1000) | model.py:406-419 | +| **Transformer Blocks** | 40 blocks, DiT-style learned modulation | transformer.py:7-96 | +| **Self-Attention** | QK norm + 3-way factorized RoPE + windowing | attention.py:46-121 | +| **Cross-Attention** | Pre-computed K/V for 40 steps reuse | attention.py:124-207 | +| **Time Embedding** | Sinusoidal positional encoding + MLP projection | model.py:138-144 | +| **Text Embedding** | T5 UMT5-XXL (24 layers, 64 heads) β†’ MLP | text_encoder.py + loading.py | +| **Patch Size** | (1, 2, 2) for temporal stability | config.py:13 | +| **VAE Latent Depth** | Wan2.2: 48-dim (stride 4Γ—16Γ—16), Wan2.1: 16-dim (stride 4Γ—8Γ—8) | config.py:44-45 | +| **TeaCache** | Polynomial-based block skipping (~2-3x speedup) | model.py:15-59 | +| **Spectrum** | Chebyshev + Taylor prediction (~3.5x speedup) | spectrum.py (full) | +| **Schedulers** | Euler, DPM++, UniPC (flow matching) | scheduler.py (full) | +| **Quantization** | 4-bit or 8-bit on transformer blocks | convert_wan.py:305+ | +| **LoRA Support** | Runtime or weight-merge application | lora/ + convert_wan.py | + +--- + +## 10. DIAGRAM: DATA FLOW + +``` +β”Œβ”€ TEXT PROMPT ──────────────────────────────────────────────────────────┐ +β”‚ β”‚ +β”‚ 1. Tokenize & T5 Encode β”‚ +β”‚ "A cat running" β”‚ +β”‚ ↓ β”‚ +β”‚ [tokens] β†’ T5 Encoder (24 layers) β†’ [512, 4096] β”‚ +β”‚ ↓ β”‚ +β”‚ T5 kept in float32 for precision (only called once) β”‚ +β”‚ β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + ↓ + MODEL LOADS + ↓ + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” + β”‚ High-Noise Model Low-Noise β”‚ + β”‚ (40 blocks) Model β”‚ + β”‚ dim=5120 (40 blocks) β”‚ + β”‚ dim=5120 β”‚ + β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + ↓ + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” + β”‚ Precompute Once (per gen): β”‚ + β”‚ - Text embed: [512, 5120] β”‚ + β”‚ - Cross-KV cache: 40 Γ— 2 β”‚ + β”‚ - RoPE cos/sin: [seq_len, d/2] β”‚ + β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + + β”Œβ”€ NOISE ───────────────────────────────────┐ + β”‚ β”‚ + β”‚ Random [48, T_lat, H_lat, W_lat] β”‚ + β”‚ ↓ β”‚ + β”‚ Patchify: [L, 5120] β”‚ + β”‚ ↓ β”‚ + β”‚ Time Embed: [B, 5120] β”‚ + β”‚ ↓ β”‚ + β”‚ CFG Batch: [B=2] β”‚ + β”‚ (cond + uncond) β”‚ + β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + ↓ + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” + β”‚ DENOISING LOOP (40 steps) β”‚ + β”‚ β”‚ + β”‚ for t in timesteps: β”‚ + β”‚ if t >= 875: use HIGH_NOISE_MODEL β”‚ + β”‚ else: use LOW_NOISE_MODEL β”‚ + β”‚ β”‚ + β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ + β”‚ β”‚ Forward Pass: 40 Transformer Blocks β”‚ β”‚ + β”‚ β”‚ β”‚ β”‚ + β”‚ β”‚ for block in blocks: β”‚ β”‚ + β”‚ β”‚ β”œβ”€ Self-Attn (with 3-way RoPE) β”‚ β”‚ + β”‚ β”‚ β”œβ”€ Cross-Attn (text conditioning) β”‚ β”‚ + β”‚ β”‚ └─ FFN (gated) β”‚ β”‚ + β”‚ β”‚ All with learned modulation (6 vectors) β”‚ β”‚ + β”‚ β”‚ β”‚ β”‚ + β”‚ β”‚ Optional: TeaCache block skipping β”‚ β”‚ + β”‚ β”‚ Optional: Spectrum feature prediction β”‚ β”‚ + β”‚ β”‚ β”‚ β”‚ + β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ + β”‚ ↓ β”‚ + β”‚ Classifier-Free Guidance: β”‚ + β”‚ noise_pred = uncond + 4.0 * (cond - uncond) β”‚ + β”‚ ↓ β”‚ + β”‚ Scheduler Step (Euler/DPM++/UniPC) β”‚ + β”‚ latents = f(noise_pred, timestep, latents) β”‚ + β”‚ β”‚ + β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + ↓ + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” + β”‚ VAE DECODE β”‚ + β”‚ β”‚ + β”‚ Latents [48, T_lat, H_lat, W_lat] β”‚ + β”‚ ↓ β”‚ + β”‚ Wan2.2 VAE Decoder (CausalConv3d) β”‚ + β”‚ Denormalize + Upscale β”‚ + β”‚ ↓ β”‚ + β”‚ Video Frames [3, T_pix, H_pix, W_pix] β”‚ + β”‚ (Channels-last: [T, H, W, 3]) β”‚ + β”‚ β”‚ + β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + ↓ + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” + β”‚ Save MP4 Video β”‚ + β”‚ output.mp4 β”‚ + β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ +``` + +--- + +## CONCLUSION + +Wan2.2 is a sophisticated dual-model video diffusion architecture implemented efficiently on Apple Silicon using MLX. Key innovations: + +1. **Dual-model design**: Separate high-noise and low-noise transformers for coarse-to-fine generation +2. **Learned modulation**: DiT-style timestep conditioning with 6 per-block modulation vectors +3. **Aggressive precomputation**: Text embeddings, cross-attention K/V, RoPE frequencies computed once +4. **Advanced caching**: TeaCache (polynomial skipping) + Spectrum (Chebyshev prediction) for 2-3.5x speedup +5. **3-way factorized RoPE**: Separate frequency components for temporal, height, and width dimensions +6. **Flexible I2V**: Both channel-concatenation (I2V-14B) and masking-based (TI2V-5B) approaches + diff --git a/docs/WAN22_QUICK_REFERENCE.md b/docs/WAN22_QUICK_REFERENCE.md new file mode 100644 index 0000000..d555f2a --- /dev/null +++ b/docs/WAN22_QUICK_REFERENCE.md @@ -0,0 +1,340 @@ +# Wan2.2 Model - Quick Reference Guide + +## 1. CORE ARCHITECTURE AT A GLANCE + +``` +INPUT +β”œβ”€β”€ Text: prompt β†’ T5 Encoder β†’ [512, 4096] β†’ MLP β†’ [512, 5120] +β”œβ”€β”€ Time: scalar t ∈ [0,1000] β†’ sinusoid + MLP β†’ [5120] +└── Video: noise β†’ Patchify (patch_size=1,2,2) β†’ [L, 5120] + +DUAL MODELS (Wan2.2 only) +β”œβ”€β”€ High-Noise Model: timestep >= 875 (early denoising) +└── Low-Noise Model: timestep < 875 (fine details) + +EACH MODEL (40 blocks Γ— 40 layers total) +β”œβ”€β”€ 40Γ— WanAttentionBlock: +β”‚ β”œβ”€β”€ Self-Attention (with 3-way RoPE + QK norm) +β”‚ β”œβ”€β”€ Cross-Attention (text conditioning, pre-cached K/V) +β”‚ β”œβ”€β”€ FFN (gated GELU) +β”‚ └── ALL modulated by 6 learnable time-conditioned vectors +└── Output Head: Project to patch_size Γ— out_dim + +DIFFUSION LOOP (40 steps, e.g.) +β”œβ”€β”€ For each timestep: +β”‚ β”œβ”€β”€ Forward pass (with/without CFG batch) +β”‚ β”œβ”€β”€ Optional: TeaCache block skipping (2x speedup) +β”‚ β”œβ”€β”€ Optional: Spectrum feature prediction (3.5x speedup) +β”‚ β”œβ”€β”€ Classifier-free guidance: noise = uncond + 4.0 Γ— (cond - uncond) +β”‚ └── Scheduler step (Euler/DPM++/UniPC) +└── Decode with VAE β†’ video pixels +``` + +## 2. KEY FILES TO UNDERSTAND + +| File | Lines | Purpose | +|------|-------|---------| +| `model.py` | 518 | Main WanModel, patchify, unpatchify, forward pass, TeaCache/Spectrum logic | +| `transformer.py` | 96 | WanAttentionBlock (self-attn + cross-attn + FFN + modulation) | +| `attention.py` | 207 | WanSelfAttention, WanCrossAttention (with pre-cached K/V) | +| `rope.py` | 178 | 3-way factorized RoPE (temporal, height, width separate) | +| `config.py` | 157 | Configuration presets (Wan2.2 T2V/I2V/TI2V, Wan2.1) | +| `generate_wan.py` | 38KB | Main generation pipeline (setup β†’ precompute β†’ denoise β†’ decode) | +| `spectrum.py` | 288 | Chebyshev + Taylor feature prediction for acceleration | +| `scheduler.py` | 428 | Flow matching: Euler, DPM++, UniPC solvers | +| `loading.py` | 183 | Model loading, weight conversion, T5 encoder | +| `convert_wan.py` | 27KB | PyTorchβ†’MLX conversion, quantization, LoRA merging | + +## 3. DENOISING LOOP SKELETON + +```python +# From generate_wan.py:589-693 +for i, t in enumerate(tqdm(range(steps), desc="Diffusion")): + timestep_val = timestep_list[i] + + # 1. SELECT MODEL (dual model only) + if is_dual: + if timestep_val >= boundary: + model = high_noise_model + kv = cross_kv_high + else: + model = low_noise_model + kv = cross_kv_low + + # 2. FORWARD PASS + if cfg_disabled: + # B=1 (faster) + preds = model([latents], t=t_batch, context=context_cond, ...) + noise_pred = preds[0] + else: + # B=2 (cfg) + preds = model( + [latents, latents], + t=[t, t], + context=[context_cond, context_uncond] + ) + noise_cond, noise_uncond = preds[0], preds[1] + noise_pred = noise_uncond + guide_scale * (noise_cond - noise_uncond) + + # 3. SCHEDULER STEP + latents = scheduler.step(noise_pred, timestep_val, latents) + + # 4. (I2V) REAPPLY MASK + if is_i2v_mask_blend: + latents = (1 - mask) * z_img + mask * latents + + # 5. EVAL & CLEANUP + mx.eval(latents) + del noise_pred +``` + +## 4. TRANSFORMER BLOCK FORWARD PASS + +```python +# From transformer.py +class WanAttentionBlock: + def __call__(self, x, e, context, cross_kv_cache=None, ...): + # Modulation: 6 time-conditioned vectors + mod = (self.modulation + e) # Add time embedding + e0, e1, e2, e3, e4, e5 = mod.split() + + # Self-Attention with modulation + x_norm = self.norm1(x) * (1 + e1) + e0 # Affine transform + x = x + e2 * self.self_attn(x_norm, ...) # Residual + gate + + # Cross-Attention (text conditioning) + x_norm = self.norm3(x) if self.norm3 else x + x = x + self.cross_attn(x_norm, context, kv_cache=cross_kv_cache) + + # FFN with modulation + x_norm = self.norm2(x) * (1 + e4) + e3 + x = x + e5 * self.ffn(x_norm) + + return x +``` + +## 5. 3-WAY FACTORIZED ROPE + +```python +# From rope.py: Temporal, height, width have separate frequency components +# This prevents temporal motion from being confused with spatial position + +d_t = head_dim - 2*(head_dim//3) # ~1/3 of dimensions +d_h = head_dim // 3 # ~1/3 of dimensions +d_w = head_dim // 3 # ~1/3 of dimensions + +# For each position (f, h, w): +freqs_combined = [ + freqs_t[f], # Temporal component + freqs_h[h], # Height component + freqs_w[w], # Width component +] +# Apply rotation separately, then concatenate +``` + +## 6. TEACACHE ACCELERATION + +```python +# Skips transformer blocks when time embedding change is small + +if t_embedding_distance < THRESHOLD: + # Reuse cached residual from previous step + x = x + cached_residual # Instant! +else: + # Full forward pass + x = run_all_blocks(x) + cached_residual = x - x_input + +# Speedup: ~2x at threshold=0.1, ~3x at threshold=0.2 +# Polynomial coefficients pre-profiled per model variant +``` + +## 7. SPECTRUM ACCELERATION + +```python +# Predicts transformer features using Chebyshev polynomials + +# Warmup: 5 steps always compute full forward +# Then: Fit Chebyshev polynomials to cached features +# Finally: Predict features on non-compute steps + +# Blended prediction: +h_predicted = (1 - w) * h_taylor + w * h_chebyshev + +# Window grows adaptively: +if num_steps_without_compute % window_size == 0: + compute_full_forward() + window_size *= flex_window # Grow confidence + +# Speedup: ~3.5x with flex_window=0.75, up to ~5x with 3.0 +``` + +## 8. PRECOMPUTATION (Per Generation) + +```python +# ONCE before denoising loop: + +# 1. Text embeddings (MLP applied to T5 output) +context_emb = model.embed_text([context, context_null]) +# Result: [2, 512, 5120] (cond + uncond) + +# 2. Cross-attention K/V caches (40 blocks) +cross_kv_caches = [ + block.cross_attn.prepare_kv(context_emb) # for each block +] +# Result: list of (k, v) [2, 40, 512, 128] per block + +# 3. RoPE frequencies (grid sizes constant) +rope_cos_sin = model.prepare_rope(grid_sizes) +# Result: (cos, sin) [seq_len, 1, head_dim//2] + +# These are REUSED across all 40 denoising steps! +``` + +## 9. CONFIGURATION QUICK LOOKUP + +```python +# Wan2.2 T2V 14B (default dual-model) +config = WanModelConfig.wan22_t2v_14b() +# dim=5120, heads=40, layers=40, patch_size=(1,2,2) +# dual_model=True, boundary=0.875 +# vae_z_dim=48, vae_stride=(4,16,16) + +# Wan2.2 I2V 14B (image-to-video, dual-model) +config = WanModelConfig.wan22_i2v_14b() +# Same as above but: model_type="i2v", in_dim=36 +# boundary=0.900, sample_guide_scale=(3.5, 3.5) + +# Wan2.2 TI2V 5B (text+image, single-model) +config = WanModelConfig.wan22_ti2v_5b() +# dim=3072, heads=24, layers=30 +# dual_model=False, model_type="ti2v" +# in_dim=48, out_dim=48, vae_z_dim=48 + +# Wan2.1 T2V 14B (single-model, backward compatible) +config = WanModelConfig.wan21_t2v_14b() +# dim=5120, heads=40, layers=40 +# dual_model=False, boundary=0.0 + +# Wan2.1 T2V 1.3B (smaller single-model) +config = WanModelConfig.wan21_t2v_1_3b() +# dim=1536, heads=12, layers=30 +# dual_model=False, boundary=0.0 +``` + +## 10. GENERATION COMMAND CHEAT SHEET + +```bash +# Basic text-to-video (Wan2.2 auto-detected) +python -m mlx_video.generate_wan \ + --model-dir /path/to/wan22 \ + --prompt "A cat running through a forest" \ + --output output.mp4 + +# Image-to-video +python -m mlx_video.generate_wan \ + --model-dir /path/to/wan22_i2v \ + --prompt "The cat jumps over a fence" \ + --image reference.jpg \ + --output output_i2v.mp4 + +# Fast with TeaCache (2x speedup, lower quality) +python -m mlx_video.generate_wan \ + --model-dir /path/to/wan22 \ + --prompt "..." \ + --teacache-thresh 0.1 \ + --output fast.mp4 + +# High quality with Spectrum (3.5x speedup, better than TeaCache) +python -m mlx_video.generate_wan \ + --model-dir /path/to/wan22 \ + --prompt "..." \ + --spectrum \ + --spectrum-flex-window 0.75 \ + --spectrum-warmup 5 \ + --output spectrum.mp4 + +# With quantization (memory efficient) +python -m mlx_video.generate_wan \ + --model-dir /path/to/wan22_4bit \ + --prompt "..." \ + --output quant.mp4 + +# Custom guidance & solver +python -m mlx_video.generate_wan \ + --model-dir /path/to/wan22 \ + --prompt "..." \ + --guide-scale 5.0 \ + --scheduler dpm++ \ + --steps 50 \ + --output custom.mp4 + +# With LoRA fine-tuning +python -m mlx_video.generate_wan \ + --model-dir /path/to/wan22 \ + --prompt "..." \ + --loras "./lora_style.safetensors,0.5" "./lora_motion.safetensors,0.3" \ + --output lora.mp4 +``` + +## 11. WEIGHT CONVERSION + +```bash +# Convert Wan2.2 checkpoint to MLX format +python -c " +from mlx_video.convert_wan import convert_wan_checkpoint + +convert_wan_checkpoint( + checkpoint_dir='/path/to/wan22_official', + output_dir='/path/to/wan22_mlx', + dtype='bfloat16', + model_version='auto', + quantize=True, + bits=4, + group_size=64 +) +" + +# Structure expected: +# wan22_official/ +# β”œβ”€β”€ low_noise_model/ (safetensors) +# β”œβ”€β”€ high_noise_model/ (safetensors) +# β”œβ”€β”€ models_t5_umt5-xxl-enc-bf16.pth +# └── Wan2.1_VAE.pth + +# Output: +# wan22_mlx/ +# β”œβ”€β”€ low_noise_model.safetensors +# β”œβ”€β”€ high_noise_model.safetensors +# β”œβ”€β”€ t5_encoder.safetensors +# β”œβ”€β”€ vae.safetensors +# └── config.json +``` + +## 12. NO MOE ARCHITECTURE + +⚠️ **Important**: Wan2.2 does **NOT** use Mixture of Experts. The FFN is standard: + +```python +class WanFFN(nn.Module): + def __init__(self, dim: int, ffn_dim: int): + self.fc1 = nn.Linear(dim, ffn_dim) # 5120 β†’ 13824 + self.act = nn.GELU(approx="tanh") + self.fc2 = nn.Linear(ffn_dim, dim) # 13824 β†’ 5120 + + def __call__(self, x): + return self.fc2(self.act(self.fc1(x))) # Dense β†’ GELU β†’ Dense +``` + +No routing, no gating, no expert selection. + +--- + +## REFERENCE LINKS + +- Main generation: `/Users/daniel/Projects/mlx-video/mlx_video/generate_wan.py` +- Model forward: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/model.py` +- Transformer blocks: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/transformer.py` +- Acceleration: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/spectrum.py` +- Config presets: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/config.py` +- Full documentation: `/Users/daniel/Projects/mlx-video/WAN22_EXPLORATION_SUMMARY.md` From 85cb7ab4c0e76a6de055d130f0fd5dbffefb8e4d Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 5 Mar 2026 08:30:47 +0100 Subject: [PATCH 02/40] feat: add Helios model with 3-stage pyramid denoising MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Add complete Helios video generation support (distilled, T2V): - Transformer backbone: 40 layers, dim=5120, multi-scale history memory - 3-stage pyramid denoising: denoise at 1/4 β†’ 1/2 β†’ full resolution - DMD scheduler with x0-prediction, dynamic shifting, block noise - Autoregressive 33-frame chunking with short/mid/long history - Weight conversion from HuggingFace diffusers format - T5 weight sanitization for UMT5-XXL encoder - CLI: --pyramid-steps (default 2 2 2), --amplify-first-chunk - 40 tests covering config, scheduler, RoPE, attention, pyramid helpers - Usage docs in mlx_video/models/helios/README.md Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/convert_helios.py | 459 ++++++++++++++++++++++ mlx_video/generate_helios.py | 477 +++++++++++++++++++++++ mlx_video/models/helios/README.md | 173 +++++++++ mlx_video/models/helios/__init__.py | 0 mlx_video/models/helios/attention.py | 270 +++++++++++++ mlx_video/models/helios/config.py | 69 ++++ mlx_video/models/helios/loading.py | 51 +++ mlx_video/models/helios/rope.py | 215 +++++++++++ mlx_video/models/helios/scheduler.py | 253 +++++++++++++ mlx_video/models/helios/transformer.py | 505 +++++++++++++++++++++++++ pyproject.toml | 1 + tests/test_helios.py | 467 +++++++++++++++++++++++ 12 files changed, 2940 insertions(+) create mode 100644 mlx_video/convert_helios.py create mode 100644 mlx_video/generate_helios.py create mode 100644 mlx_video/models/helios/README.md create mode 100644 mlx_video/models/helios/__init__.py create mode 100644 mlx_video/models/helios/attention.py create mode 100644 mlx_video/models/helios/config.py create mode 100644 mlx_video/models/helios/loading.py create mode 100644 mlx_video/models/helios/rope.py create mode 100644 mlx_video/models/helios/scheduler.py create mode 100644 mlx_video/models/helios/transformer.py create mode 100644 tests/test_helios.py diff --git a/mlx_video/convert_helios.py b/mlx_video/convert_helios.py new file mode 100644 index 0000000..1d0bdc8 --- /dev/null +++ b/mlx_video/convert_helios.py @@ -0,0 +1,459 @@ +"""Weight conversion for Helios models (HuggingFace diffusers β†’ MLX).""" + +import gc +import json +import logging +import math +from pathlib import Path +from typing import Dict + +import mlx.core as mx +import mlx.utils + +logger = logging.getLogger(__name__) + + +def sanitize_helios_transformer_weights(weights: Dict[str, mx.array]) -> Dict[str, mx.array]: + """Convert HuggingFace Helios diffusers weight keys to MLX structure. + + HF diffusers keys β†’ MLX keys: + patch_embedding (Conv3d) β†’ patch_embedding (Linear, reshaped) + patch_short/mid/long (Conv3d) β†’ patch_short/mid/long (Linear, reshaped) + condition_embedder.time_embedder.linear_1/2 β†’ time_embedding_0/1 + condition_embedder.time_proj β†’ time_projection + condition_embedder.text_embedder.linear_1/2 β†’ text_embedding_0/1 + blocks.{i}.attn1.to_q/k/v β†’ blocks.{i}.self_attn.q/k/v + blocks.{i}.attn1.to_out.0 β†’ blocks.{i}.self_attn.o + blocks.{i}.attn1.norm_q/k β†’ blocks.{i}.self_attn.norm_q/k + blocks.{i}.attn2.to_q/k/v β†’ blocks.{i}.cross_attn.q/k/v + blocks.{i}.attn2.to_out.0 β†’ blocks.{i}.cross_attn.o + blocks.{i}.attn2.norm_q/k β†’ blocks.{i}.cross_attn.norm_q/k + blocks.{i}.ffn.net.0.proj β†’ blocks.{i}.ffn.fc1 + blocks.{i}.ffn.net.2 β†’ blocks.{i}.ffn.fc2 + blocks.{i}.norm1/2/3 β†’ blocks.{i}.norm1/2/3 + blocks.{i}.scale_shift_table β†’ blocks.{i}.scale_shift_table + norm_out.norm β†’ output_norm + norm_out.scale_shift_table β†’ output_norm_table + proj_out β†’ proj_out + """ + sanitized = {} + consumed = set() + + for key, value in weights.items(): + new_key = key + + # Conv3d patch embeddings: [O, I, D, H, W] β†’ Linear [O, I*D*H*W] + if key in ("patch_embedding.weight", "patch_short.weight", + "patch_mid.weight", "patch_long.weight"): + if value.ndim == 5: + value = value.reshape(value.shape[0], -1) + sanitized[key] = value + consumed.add(key) + continue + if key in ("patch_embedding.bias", "patch_short.bias", + "patch_mid.bias", "patch_long.bias"): + sanitized[key] = value + consumed.add(key) + continue + + # condition_embedder.time_embedder β†’ time_embedding + if key.startswith("condition_embedder.time_embedder.linear_1."): + suffix = key.split("condition_embedder.time_embedder.linear_1.")[-1] + new_key = f"time_embedding_0.{suffix}" + sanitized[new_key] = value + consumed.add(key) + continue + if key.startswith("condition_embedder.time_embedder.linear_2."): + suffix = key.split("condition_embedder.time_embedder.linear_2.")[-1] + new_key = f"time_embedding_1.{suffix}" + sanitized[new_key] = value + consumed.add(key) + continue + + # condition_embedder.time_proj β†’ time_projection + if key.startswith("condition_embedder.time_proj."): + suffix = key.split("condition_embedder.time_proj.")[-1] + new_key = f"time_projection.{suffix}" + sanitized[new_key] = value + consumed.add(key) + continue + + # condition_embedder.text_embedder β†’ text_embedding + if key.startswith("condition_embedder.text_embedder.linear_1."): + suffix = key.split("condition_embedder.text_embedder.linear_1.")[-1] + new_key = f"text_embedding_0.{suffix}" + sanitized[new_key] = value + consumed.add(key) + continue + if key.startswith("condition_embedder.text_embedder.linear_2."): + suffix = key.split("condition_embedder.text_embedder.linear_2.")[-1] + new_key = f"text_embedding_1.{suffix}" + sanitized[new_key] = value + consumed.add(key) + continue + + # norm_out.norm β†’ output_norm + if key.startswith("norm_out.norm."): + suffix = key.split("norm_out.norm.")[-1] + new_key = f"output_norm.{suffix}" + sanitized[new_key] = value + consumed.add(key) + continue + + # norm_out.scale_shift_table β†’ output_norm_table + if key == "norm_out.scale_shift_table": + sanitized["output_norm_table"] = value + consumed.add(key) + continue + + # Attention: attn1 β†’ self_attn, attn2 β†’ cross_attn + new_key = new_key.replace(".attn1.", ".self_attn.") + new_key = new_key.replace(".attn2.", ".cross_attn.") + + # to_q/k/v β†’ q/k/v, to_out.0 β†’ o + new_key = new_key.replace(".to_q.", ".q.") + new_key = new_key.replace(".to_k.", ".k.") + new_key = new_key.replace(".to_v.", ".v.") + new_key = new_key.replace(".to_out.0.", ".o.") + + # FFN: net.0.proj β†’ fc1, net.2 β†’ fc2 + new_key = new_key.replace(".ffn.net.0.proj.", ".ffn.fc1.") + new_key = new_key.replace(".ffn.net.2.", ".ffn.fc2.") + + # Skip timesteps_proj (no trainable weights) and dropout layers + if "timesteps_proj" in key or "to_out.1." in key: + consumed.add(key) + continue + + # Skip rope buffers (computed in model) + if key.startswith("rope."): + consumed.add(key) + continue + + # Skip unused keys + if "norm_added_q" in key or "norm_added_k" in key: + consumed.add(key) + continue + if "add_k_proj" in key or "add_v_proj" in key: + consumed.add(key) + continue + + sanitized[new_key] = value + consumed.add(key) + + unconsumed = set(weights.keys()) - consumed + if unconsumed: + logger.warning("Unconsumed transformer weight keys: %s", sorted(unconsumed)) + + return sanitized + + +def _quantize_predicate(path: str, module) -> bool: + """Return True for layers that should be quantized. + + Targets heavyweight Linear layers in attention and FFN blocks. + Skips embeddings, norms, head, and modulation (small, precision-sensitive). + """ + if not hasattr(module, "to_quantized"): + return False + quantize_patterns = ( + ".self_attn.q", ".self_attn.k", ".self_attn.v", ".self_attn.o", + ".cross_attn.q", ".cross_attn.k", ".cross_attn.v", ".cross_attn.o", + ".ffn.fc1", ".ffn.fc2", + ) + return any(path.endswith(p) for p in quantize_patterns) + + +def sanitize_helios_t5_weights(weights: Dict[str, mx.array]) -> Dict[str, mx.array]: + """Convert HuggingFace UMT5 encoder weight keys to MLX T5Encoder structure. + + HF UMT5 keys β†’ MLX keys: + shared.weight / encoder.embed_tokens.weight β†’ token_embedding.weight + encoder.final_layer_norm.weight β†’ norm.weight + encoder.block.{i}.layer.0.SelfAttention.q.weight β†’ blocks.{i}.attn.q.weight + encoder.block.{i}.layer.0.SelfAttention.k.weight β†’ blocks.{i}.attn.k.weight + encoder.block.{i}.layer.0.SelfAttention.v.weight β†’ blocks.{i}.attn.v.weight + encoder.block.{i}.layer.0.SelfAttention.o.weight β†’ blocks.{i}.attn.o.weight + encoder.block.{i}.layer.0.SelfAttention.relative_attention_bias.weight + β†’ blocks.{i}.pos_embedding.embedding.weight + encoder.block.{i}.layer.0.layer_norm.weight β†’ blocks.{i}.norm1.weight + encoder.block.{i}.layer.1.DenseReluDense.wi_0.weight β†’ blocks.{i}.ffn.gate_proj.weight + encoder.block.{i}.layer.1.DenseReluDense.wi_1.weight β†’ blocks.{i}.ffn.fc1.weight + encoder.block.{i}.layer.1.DenseReluDense.wo.weight β†’ blocks.{i}.ffn.fc2.weight + encoder.block.{i}.layer.1.layer_norm.weight β†’ blocks.{i}.norm2.weight + """ + sanitized = {} + + for key, value in weights.items(): + # Token embedding + if key in ("shared.weight", "encoder.embed_tokens.weight"): + sanitized["token_embedding.weight"] = value + continue + + # Final layer norm + if key == "encoder.final_layer_norm.weight": + sanitized["norm.weight"] = value + continue + + # Skip decoder keys if present + if key.startswith("decoder.") or key.startswith("lm_head."): + continue + + # Block-level mappings: encoder.block.{i}.layer.{j}.* + if key.startswith("encoder.block."): + new_key = key + # encoder.block.{i} β†’ blocks.{i} + new_key = new_key.replace("encoder.block.", "blocks.") + + # Self-attention (layer.0.SelfAttention) + new_key = new_key.replace(".layer.0.SelfAttention.", ".attn.") + new_key = new_key.replace( + ".attn.relative_attention_bias.weight", + ".pos_embedding.embedding.weight", + ) + + # Layer norms + new_key = new_key.replace(".layer.0.layer_norm.", ".norm1.") + new_key = new_key.replace(".layer.1.layer_norm.", ".norm2.") + + # FFN (layer.1.DenseReluDense) + new_key = new_key.replace(".layer.1.DenseReluDense.wi_0.", ".ffn.gate_proj.") + new_key = new_key.replace(".layer.1.DenseReluDense.wi_1.", ".ffn.fc1.") + new_key = new_key.replace(".layer.1.DenseReluDense.wo.", ".ffn.fc2.") + + sanitized[new_key] = value + continue + + logger.warning("Unhandled T5 key: %s", key) + + return sanitized + + +def convert_helios_checkpoint( + checkpoint_dir: str, + output_dir: str, + dtype: str = "bfloat16", + quantize: bool = False, + bits: int = 4, + group_size: int = 64, +): + """Convert a HuggingFace Helios checkpoint to MLX format. + + Expected structure (HuggingFace diffusers format): + checkpoint_dir/ + transformer/ + diffusion_pytorch_model*.safetensors + config.json + text_encoder/ + model*.safetensors + vae/ + diffusion_pytorch_model*.safetensors + tokenizer/ + ... + + Args: + checkpoint_dir: Path to HF Helios checkpoint + output_dir: Path to output MLX model directory + dtype: Target dtype for transformer/text-encoder weights + quantize: Whether to quantize the transformer + bits: Quantization bits (4 or 8) + group_size: Quantization group size + """ + from mlx_video.convert_wan import ( + load_safetensors_weights, + sanitize_wan_vae_weights, + ) + + checkpoint_dir = Path(checkpoint_dir) + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + dtype_map = { + "float16": mx.float16, + "float32": mx.float32, + "bfloat16": mx.bfloat16, + } + target_dtype = dtype_map.get(dtype, mx.bfloat16) + + # 1. Convert transformer + transformer_dir = checkpoint_dir / "transformer" + if transformer_dir.exists(): + print("Converting Helios transformer...") + weights = load_safetensors_weights(str(transformer_dir)) + weights = sanitize_helios_transformer_weights(weights) + weights = {k: v.astype(target_dtype) for k, v in weights.items()} + out_path = output_dir / "model.safetensors" + mx.save_safetensors(str(out_path), weights) + print(f" Saved {len(weights)} weight tensors to {out_path}") + del weights + gc.collect() + else: + # Try loading safetensors directly from checkpoint_dir + print("Converting Helios transformer (flat directory)...") + weights = load_safetensors_weights(str(checkpoint_dir)) + if weights: + weights = sanitize_helios_transformer_weights(weights) + weights = {k: v.astype(target_dtype) for k, v in weights.items()} + out_path = output_dir / "model.safetensors" + mx.save_safetensors(str(out_path), weights) + print(f" Saved {len(weights)} weight tensors to {out_path}") + del weights + gc.collect() + + # 2. Save config + from mlx_video.models.helios.config import HeliosModelConfig + + config = HeliosModelConfig.helios_distilled() + config_path = output_dir / "config.json" + + # Try to read source config for any overrides + src_config_path = transformer_dir / "config.json" if transformer_dir.exists() else None + if src_config_path and src_config_path.exists(): + with open(src_config_path) as f: + src_cfg = json.load(f) + # Could update config from src_cfg if needed + print(f" Source config found: {src_config_path}") + + config_dict = { + "model_type": "helios_distilled", + "dim": config.dim, + "ffn_dim": config.ffn_dim, + "num_heads": config.num_heads, + "num_layers": config.num_layers, + "patch_size": list(config.patch_size), + "in_dim": config.in_dim, + "out_dim": config.out_dim, + "text_dim": config.text_dim, + "freq_dim": config.freq_dim, + "text_len": config.text_len, + "rope_dim": list(config.rope_dim), + "rope_theta": config.rope_theta, + "history_sizes": config.history_sizes, + "num_latent_frames_per_chunk": config.num_latent_frames_per_chunk, + "vae_z_dim": config.vae_z_dim, + "vae_stride": list(config.vae_stride), + "shift": config.shift, + "sample_fps": config.sample_fps, + } + with open(config_path, "w") as f: + json.dump(config_dict, f, indent=2) + print(f" Saved config to {config_path}") + + # 3. Convert text encoder (T5) + t5_dir = checkpoint_dir / "text_encoder" + if t5_dir.exists(): + print("Converting T5 text encoder...") + weights = load_safetensors_weights(str(t5_dir)) + weights = sanitize_helios_t5_weights(weights) + weights = {k: v.astype(target_dtype) for k, v in weights.items()} + out_path = output_dir / "t5_encoder.safetensors" + mx.save_safetensors(str(out_path), weights) + print(f" Saved {len(weights)} weight tensors to {out_path}") + del weights + gc.collect() + + # 4. Convert VAE + vae_dir = checkpoint_dir / "vae" + if vae_dir.exists(): + print("Converting VAE...") + weights = load_safetensors_weights(str(vae_dir)) + weights = sanitize_wan_vae_weights(weights) + # VAE in float32 for quality + weights = {k: v.astype(mx.float32) for k, v in weights.items()} + out_path = output_dir / "vae.safetensors" + mx.save_safetensors(str(out_path), weights) + print(f" Saved {len(weights)} weight tensors to {out_path} (float32)") + del weights + gc.collect() + + # 5. Quantize if requested + if quantize: + print(f"\nQuantizing transformer ({bits}-bit, group_size={group_size})...") + _quantize_saved_model(output_dir, config, bits, group_size) + + print(f"\nConversion complete! Output: {output_dir}") + + +def _quantize_saved_model( + output_dir: Path, + config, + bits: int, + group_size: int, +): + """Load saved bf16 model, quantize, and re-save.""" + import mlx.nn as nn + from mlx_video.models.helios.transformer import HeliosModel + + model_path = output_dir / "model.safetensors" + if not model_path.exists(): + print(" No model.safetensors found, skipping quantization") + return + + model = HeliosModel(config) + weights = mx.load(str(model_path)) + model.load_weights(list(weights.items()), strict=False) + mx.eval(model.parameters()) + del weights + gc.collect() + mx.clear_cache() + + nn.quantize( + model, + group_size=group_size, + bits=bits, + class_predicate=lambda path, m: _quantize_predicate(path, m), + ) + + weights_dict = dict(mlx.utils.tree_flatten(model.parameters())) + + # Validate for corruption + bad_keys = [] + for k, v in weights_dict.items(): + if k.endswith(".bias") and not k.endswith(".biases"): + mx.eval(v) + if mx.any(mx.isnan(v)).item() or mx.any(mx.isinf(v)).item(): + bad_keys.append(k) + if bad_keys: + raise RuntimeError( + f"Quantization produced corrupted weights: " + f"{len(bad_keys)} bias tensors contain NaN/Inf" + ) + + mx.save_safetensors(str(model_path), weights_dict) + n_quantized = sum(1 for k in weights_dict if ".scales" in k) + print(f" {n_quantized} layers quantized, {len(weights_dict)} tensors saved") + + del model, weights_dict + gc.collect() + mx.clear_cache() + + # Update config with quantization metadata + config_path = output_dir / "config.json" + with open(config_path) as f: + cfg = json.load(f) + cfg["quantization"] = {"group_size": group_size, "bits": bits} + with open(config_path, "w") as f: + json.dump(cfg, f, indent=2) + print(f" Updated config.json with quantization metadata") + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description="Convert Helios weights to MLX format") + parser.add_argument("checkpoint_dir", type=str, help="Path to HF Helios checkpoint") + parser.add_argument("output_dir", type=str, help="Output MLX model directory") + parser.add_argument("--dtype", type=str, default="bfloat16", choices=["float16", "float32", "bfloat16"]) + parser.add_argument("--quantize", action="store_true", help="Quantize transformer weights") + parser.add_argument("--bits", type=int, default=4, help="Quantization bits") + parser.add_argument("--group-size", type=int, default=64, help="Quantization group size") + args = parser.parse_args() + + convert_helios_checkpoint( + args.checkpoint_dir, + args.output_dir, + dtype=args.dtype, + quantize=args.quantize, + bits=args.bits, + group_size=args.group_size, + ) diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py new file mode 100644 index 0000000..7ae57e3 --- /dev/null +++ b/mlx_video/generate_helios.py @@ -0,0 +1,477 @@ +"""Helios Text-to-Video generation pipeline for MLX. + +Autoregressive chunk-based video generation with multi-scale history memory. +Supports the Helios-Distilled model (x0-prediction, no CFG, 2-3 steps/chunk). +""" + +import argparse +import gc +import json +import math +import random +import sys +import time +import warnings +from pathlib import Path + +import mlx.core as mx +import mlx.nn as nn +import numpy as np +from tqdm import tqdm + +from mlx_video.models.helios.loading import ( + _clean_text, + encode_text, + load_helios_model, + load_t5_encoder, + load_vae_decoder, +) +from mlx_video.postprocess import save_video +from mlx_video.utils import Colors + + +def sample_block_noise( + batch_size: int, + channels: int, + num_frames: int, + height: int, + width: int, + patch_size: tuple[int, int, int], + gamma: float, +) -> mx.array: + """Generate structured per-patch noise using correlated multivariate normal. + + This reduces block artifacts by ensuring spatially adjacent latents within + each patch have correlated noise values. + + Returns: + Noise tensor of shape (channels, num_frames, height, width). + """ + _, ph, pw = patch_size + block_size = ph * pw + + # Build covariance matrix: I*(1+gamma) - ones*gamma + eps*I + cov = np.eye(block_size, dtype=np.float64) * (1 + gamma) - np.ones((block_size, block_size), dtype=np.float64) * gamma + cov += np.eye(block_size, dtype=np.float64) * 1e-6 + L = np.linalg.cholesky(cov) + + # Sample standard normal and transform + block_count = batch_size * channels * num_frames * (height // ph) * (width // pw) + z = np.random.randn(block_count, block_size) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", RuntimeWarning) + samples = (z @ L.T).astype(np.float32) + + # Reshape to spatial layout + samples = samples.reshape(batch_size, channels, num_frames, height // ph, width // pw, ph, pw) + samples = samples.transpose(0, 1, 2, 3, 5, 4, 6) # interleave patches + samples = samples.reshape(batch_size, channels, num_frames, height, width) + + # Return as (C, F, H, W) β€” drop batch dim since we always use batch=1 + return mx.array(samples[0]) + + +def _spatial_reshape(x: mx.array, num_frames: int, channels: int) -> mx.array: + """Reshape (C, F, H, W) β†’ (F, C, H, W) for spatial operations.""" + return x.transpose(1, 0, 2, 3) # (F, C, H, W) + + +def _spatial_unreshape( + x: mx.array, num_frames: int, channels: int, h: int, w: int +) -> mx.array: + """Reshape (F, C, H, W) β†’ (C, F, H, W).""" + return x.transpose(1, 0, 2, 3) # (C, F, H, W) + + +def _bilinear_downsample_2d(x: mx.array, target_h: int, target_w: int) -> mx.array: + """Bilinear interpolation downsample. Input: (F, C, H, W).""" + F, C, H, W = x.shape + # MLX doesn't have F.interpolate β€” use manual bilinear via grid sampling + # Simple approach: reshape to (F*C, 1, H, W) and use average pooling + scale_h = H // target_h + scale_w = W // target_w + # Use reshape-based area averaging (equivalent to bilinear for integer factors) + x = x.reshape(F, C, target_h, scale_h, target_w, scale_w) + x = x.mean(axis=(3, 5)) + return x + + +def _nearest_upsample_2d(x: mx.array, target_h: int, target_w: int) -> mx.array: + """Nearest-neighbor 2x upsample. Input: (F, C, H, W).""" + F, C, H, W = x.shape + scale_h = target_h // H + scale_w = target_w // W + # Repeat along spatial dims + x = mx.repeat(x, scale_h, axis=2) + x = mx.repeat(x, scale_w, axis=3) + return x + + +def _downsample_history(hist: mx.array, factor: int) -> mx.array: + """Downsample history latents spatially by factor. Input: (C, F, H, W).""" + C, F, H, W = hist.shape + target_h = H // factor + target_w = W // factor + hist = hist.reshape(C, F, target_h, factor, target_w, factor) + hist = hist.mean(axis=(3, 5)) + return hist + + +def generate_video( + model_dir: str, + prompt: str, + width: int = 640, + height: int = 384, + num_frames: int = 99, + pyramid_steps: list[int] | None = None, + seed: int = -1, + output_path: str = "output_helios.mp4", + tiling: str = "auto", + amplify_first_chunk: bool = False, +): + """Generate video using Helios autoregressive pipeline with pyramid denoising. + + Args: + model_dir: Path to converted MLX model directory + prompt: Text prompt + width: Video width (must be divisible by 16) + height: Video height (must be divisible by 16) + num_frames: Number of frames (auto-rounded to multiple of 33) + pyramid_steps: Steps per pyramid stage (default: [2, 2, 2] for distilled) + seed: Random seed (-1 for random) + output_path: Output video path + tiling: VAE tiling mode: auto, none, default, aggressive, conservative + amplify_first_chunk: Double steps for first chunk (better quality) + """ + from mlx_video.models.helios.config import HeliosModelConfig + + if pyramid_steps is None: + pyramid_steps = [2, 2, 2] + + model_dir = Path(model_dir) + t1 = time.time() + + # Load config + config_path = model_dir / "config.json" + quantization = None + if config_path.exists(): + with open(config_path) as f: + config_dict = json.load(f) + quantization = config_dict.pop("quantization", None) + for key in ("patch_size", "vae_stride", "rope_dim", "history_sizes", "stage_range"): + if key in config_dict and isinstance(config_dict[key], list): + config_dict[key] = tuple(config_dict[key]) if key in ("patch_size", "vae_stride", "rope_dim") else config_dict[key] + config = HeliosModelConfig(**{ + k: v for k, v in config_dict.items() + if k in HeliosModelConfig.__dataclass_fields__ + }) + else: + config = HeliosModelConfig.helios_distilled() + + # Frame and dimension alignment + vae_stride_t, vae_stride_h, vae_stride_w = config.vae_stride + frames_per_chunk = 33 # (num_latent_frames_per_chunk - 1) * vae_stride_t + 1 + num_latent_per_chunk = config.num_latent_frames_per_chunk # 9 + + # Round num_frames to nearest multiple of frames_per_chunk + num_chunks = max(1, (num_frames + frames_per_chunk - 1) // frames_per_chunk) + num_frames = num_chunks * frames_per_chunk + total_latent_frames = num_chunks * num_latent_per_chunk + + # Align spatial dimensions + align_h = config.patch_size[1] * vae_stride_h # 2 * 8 = 16 + align_w = config.patch_size[2] * vae_stride_w # 2 * 8 = 16 + height = ((height + align_h - 1) // align_h) * align_h + width = ((width + align_w - 1) // align_w) * align_w + + h_latent = height // vae_stride_h + w_latent = width // vae_stride_w + + if seed < 0: + seed = random.randint(0, 2**32 - 1) + mx.random.seed(seed) + + print(f"\n{Colors.CYAN}Helios Video Generation{Colors.RESET}") + print(f" Prompt: {prompt[:80]}{'...' if len(prompt) > 80 else ''}") + print(f" Resolution: {width}x{height}, {num_frames} frames ({num_chunks} chunks)") + print(f" Pyramid steps: {pyramid_steps} ({sum(pyramid_steps)} total/chunk), Seed: {seed}") + if quantization: + print(f" Quantization: {quantization['bits']}-bit, group_size={quantization['group_size']}") + + # 1. Load T5 text encoder and encode prompt + print(f"\n{Colors.BLUE}Loading text encoder...{Colors.RESET}") + t2 = time.time() + t5_path = model_dir / "t5_encoder.safetensors" + tokenizer_path = model_dir / "tokenizer" + + # Try to find tokenizer + if tokenizer_path.exists(): + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(str(tokenizer_path)) + else: + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained("google/umt5-xxl") + + encoder = load_t5_encoder(t5_path, config) + context = encode_text(encoder, tokenizer, prompt, text_len=config.text_len) + mx.eval(context) + print(f"{Colors.DIM} T5 encode: {time.time() - t2:.1f}s, tokens: {context.shape[0]}{Colors.RESET}") + + del encoder + gc.collect() + mx.clear_cache() + + # 2. Load transformer + print(f"\n{Colors.BLUE}Loading Helios transformer...{Colors.RESET}") + t3 = time.time() + model_path = model_dir / "model.safetensors" + model = load_helios_model(model_path, config, quantization=quantization) + print(f"{Colors.DIM} Model load: {time.time() - t3:.1f}s{Colors.RESET}") + + # 3. Pre-compute text embeddings and cross-attention KV caches + context_embedded = model.embed_text([context]) + mx.eval(context_embedded) + cross_kv_caches = model.prepare_cross_kv(context_embedded) + mx.eval(*[v for kv in cross_kv_caches for v in kv]) + print(f"{Colors.DIM} Text embedding + KV cache: ready{Colors.RESET}") + + # 4. History setup + history_sizes = config.history_sizes # [16, 2, 1] + num_history_frames = sum(history_sizes) # 19 latent frames of history + history_latents = mx.zeros((config.in_dim, num_history_frames, h_latent, w_latent)) + + # Frame indices: [history_long | history_mid | history_short | current] + total_indices = sum(history_sizes) + num_latent_per_chunk + indices = mx.arange(total_indices) + idx_long = indices[:history_sizes[0]] # [0..15] + idx_mid = indices[history_sizes[0]:history_sizes[0] + history_sizes[1]] # [16..17] + idx_short = indices[history_sizes[0] + history_sizes[1]:sum(history_sizes)] # [18] + idx_current = indices[sum(history_sizes):] # [19..27] + + # 5. Initialize scheduler + from mlx_video.models.helios.scheduler import HeliosScheduler + + scheduler = HeliosScheduler( + num_train_timesteps=1000, + shift=1.0, + stages=3, + gamma=1 / 3, + use_dynamic_shifting=True, + ) + + total_steps = sum(pyramid_steps) * num_chunks + print(f"\n{Colors.BLUE}Generating {num_chunks} chunks ({sum(pyramid_steps)} steps/chunk, 3-stage pyramid)...{Colors.RESET}") + all_latent_chunks = [] + total_generated = 0 + + for chunk_idx in range(num_chunks): + t_chunk = time.time() + is_first = chunk_idx == 0 + + # Prepare history from accumulated latents + hist_long, hist_mid, hist_short = mx.split( + history_latents[:, -num_history_frames:], + [history_sizes[0], history_sizes[0] + history_sizes[1]], + axis=1, + ) + + # Initialize noise for this chunk at full resolution + noise = mx.random.normal((config.in_dim, num_latent_per_chunk, h_latent, w_latent)) + + # Downsample to 1/4 resolution (2 halvings for 3-stage pyramid) + cur_h, cur_w = h_latent, w_latent + latents = _spatial_reshape(noise, num_latent_per_chunk, config.in_dim) + for _ in range(scheduler.stages - 1): + cur_h //= 2 + cur_w //= 2 + latents = _bilinear_downsample_2d(latents, cur_h, cur_w) * 2 + latents = _spatial_unreshape(latents, num_latent_per_chunk, config.in_dim, cur_h, cur_w) + + # Track per-stage start points for DMD re-noising + start_point_list = [latents] + + pbar = tqdm( + total=sum(pyramid_steps), + desc=f" Chunk {chunk_idx + 1}/{num_chunks}", + leave=True, + bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]", + ) + + for i_s in range(scheduler.stages): + # Compute image_seq_len at current resolution for dynamic shift + image_seq_len = ( + num_latent_per_chunk * cur_h * cur_w + // math.prod(config.patch_size) + ) + + scheduler.set_timesteps( + pyramid_steps[i_s], + stage_index=i_s, + image_seq_len=image_seq_len, + is_amplify_first_chunk=(amplify_first_chunk and is_first), + ) + timesteps = scheduler.timesteps + + if i_s > 0: + # Upsample 2x with nearest-neighbor + cur_h *= 2 + cur_w *= 2 + latents = _spatial_reshape(latents, num_latent_per_chunk, config.in_dim) + latents = _nearest_upsample_2d(latents, cur_h, cur_w) + latents = _spatial_unreshape(latents, num_latent_per_chunk, config.in_dim, cur_h, cur_w) + + # Alpha/beta noise mixing to reduce block artifacts + ori_sigma = 1 - scheduler.ori_start_sigmas[i_s] + gamma = scheduler.gamma + alpha = 1 / (math.sqrt(1 + (1 / gamma)) * (1 - ori_sigma) + ori_sigma) + beta = alpha * (1 - ori_sigma) / math.sqrt(gamma) + + block_noise = sample_block_noise( + 1, config.in_dim, num_latent_per_chunk, cur_h, cur_w, + config.patch_size, gamma, + ) + latents = alpha * latents + beta * block_noise + start_point_list.append(latents) + + # Recompute history at current resolution + ds_factor = h_latent // cur_h + if ds_factor > 1: + h_short = _downsample_history(hist_short, ds_factor) + h_mid = _downsample_history(hist_mid, ds_factor) + h_long = _downsample_history(hist_long, ds_factor) + else: + h_short, h_mid, h_long = hist_short, hist_mid, hist_long + + # Scale frame indices to match current spatial resolution + cur_idx = mx.arange(num_latent_per_chunk) + sum(history_sizes) + cur_idx_short = idx_short + cur_idx_mid = idx_mid + cur_idx_long = idx_long + + for idx, t in enumerate(timesteps): + timestep = t + model_output = model( + latents=latents, + timestep=timestep, + encoder_hidden_states=context_embedded, + frame_indices=cur_idx, + history_short=h_short, + history_mid=h_mid, + history_long=h_long, + history_short_indices=cur_idx_short, + history_mid_indices=cur_idx_mid, + history_long_indices=cur_idx_long, + cross_kv_caches=cross_kv_caches, + ) + mx.eval(model_output) + + latents = scheduler.step_dmd( + model_output=model_output, + sample=latents, + cur_step=idx, + noisy_start=start_point_list[i_s], + ) + pbar.update(1) + + pbar.close() + mx.eval(latents) + all_latent_chunks.append(latents) + + # Update history: append this chunk's latents + total_generated += num_latent_per_chunk + history_latents = mx.concatenate([history_latents, latents], axis=1) + + chunk_time = time.time() - t_chunk + step_count = sum(pyramid_steps) + print(f"{Colors.DIM} Chunk {chunk_idx + 1}/{num_chunks} done: {chunk_time:.1f}s ({chunk_time / step_count:.2f}s/step){Colors.RESET}") + + # Free transformer + del model + gc.collect() + mx.clear_cache() + + # 6. VAE decode + print(f"\n{Colors.BLUE}Decoding with VAE...{Colors.RESET}") + t4 = time.time() + vae_path = model_dir / "vae.safetensors" + vae = load_vae_decoder(vae_path, config) + + # Select tiling config + from mlx_video.models.ltx.video_vae.tiling import TilingConfig + + if tiling == "none": + tiling_config = None + elif tiling == "auto": + tiling_config = TilingConfig.auto(height, width, num_frames) + elif tiling == "default": + tiling_config = TilingConfig.default() + elif tiling == "aggressive": + tiling_config = TilingConfig.aggressive() + elif tiling == "conservative": + tiling_config = TilingConfig.conservative() + else: + tiling_config = TilingConfig.auto(height, width, num_frames) + + # Concatenate all chunks: each is [C, F_lat, H_lat, W_lat] + all_latents = mx.concatenate(all_latent_chunks, axis=1) # [C, total_F_lat, H_lat, W_lat] + + # Decode: WanVAE expects [B, C, T, H, W], handles denormalization internally + z = all_latents[None, :, :, :, :] # [1, C, T, H, W] + if tiling_config is not None: + video = vae.decode_tiled(z, tiling_config) + else: + video = vae.decode(z) + mx.eval(video) + print(f"{Colors.DIM} VAE decode: {time.time() - t4:.1f}s{Colors.RESET}") + + # Convert to numpy: video is [1, 3, T, H, W] in [-1, 1] + video = np.array(video[0]) # [3, T, H, W] + video = (video + 1.0) / 2.0 + video = np.clip(video * 255.0, 0, 255).astype(np.uint8) + video = video.transpose(1, 2, 3, 0) # [T, H, W, 3] + + # Trim to requested frame count + video = video[:num_frames] + + save_video(video, output_path, fps=config.sample_fps) + print(f"\n{Colors.GREEN}βœ“ Video saved to {output_path}{Colors.RESET}") + print(f"{Colors.DIM} Total time: {time.time() - t1:.1f}s{Colors.RESET}") + + +def main(): + parser = argparse.ArgumentParser(description="Helios Text-to-Video Generation (MLX)") + parser.add_argument("--model-dir", type=str, required=True, help="Path to converted MLX model directory") + parser.add_argument("--prompt", type=str, required=True, help="Text prompt") + parser.add_argument("--width", type=int, default=640, help="Video width") + parser.add_argument("--height", type=int, default=384, help="Video height") + parser.add_argument("--num-frames", type=int, default=99, help="Number of frames (auto-rounded to multiple of 33)") + parser.add_argument( + "--pyramid-steps", type=int, nargs="+", default=[2, 2, 2], + help="Steps per pyramid stage (default: 2 2 2 for distilled, total 6 forward passes)", + ) + parser.add_argument("--amplify-first-chunk", action="store_true", help="Double steps for first chunk (better quality)") + parser.add_argument("--seed", type=int, default=-1, help="Random seed") + parser.add_argument("--output-path", type=str, default="output_helios.mp4", help="Output video path") + parser.add_argument( + "--tiling", type=str, default="auto", + choices=["auto", "none", "default", "aggressive", "conservative"], + help="VAE tiling mode for memory efficiency", + ) + args = parser.parse_args() + + generate_video( + model_dir=args.model_dir, + prompt=args.prompt, + width=args.width, + height=args.height, + num_frames=args.num_frames, + pyramid_steps=args.pyramid_steps, + seed=args.seed, + output_path=args.output_path, + tiling=args.tiling, + amplify_first_chunk=args.amplify_first_chunk, + ) + + +if __name__ == "__main__": + main() diff --git a/mlx_video/models/helios/README.md b/mlx_video/models/helios/README.md new file mode 100644 index 0000000..ab04516 --- /dev/null +++ b/mlx_video/models/helios/README.md @@ -0,0 +1,173 @@ +# Helios β€” Text-to-Video Generation on Apple Silicon + +Helios is a 14B-parameter autoregressive video generation model that produces minute-scale, temporally coherent video. This implementation targets the **Helios-Distilled** variant (fastest, x0-prediction, no classifier-free guidance) for text-to-video generation on Apple Silicon via MLX. + +## Quick Start + +### 1. Convert Weights + +Download the HuggingFace checkpoint and convert to MLX format: + +```bash +python -m mlx_video.convert_helios \ + --checkpoint-dir /path/to/BestWishYsh/Helios-Distilled \ + --output-dir ./helios-mlx +``` + +With 4-bit quantization (~7 GB, fits 16 GB Macs): + +```bash +python -m mlx_video.convert_helios \ + --checkpoint-dir /path/to/BestWishYsh/Helios-Distilled \ + --output-dir ./helios-mlx-4bit \ + --quantize --bits 4 +``` + +### 2. Generate Video + +```bash +python -m mlx_video.generate_helios \ + --model-dir ./helios-mlx \ + --prompt "A golden retriever running through a sunlit meadow" \ + --output-path my_video.mp4 +``` + +## CLI Options + +| Flag | Default | Description | +|---|---|---| +| `--model-dir` | *(required)* | Path to converted MLX model directory | +| `--prompt` | *(required)* | Text prompt describing the video | +| `--width` | `640` | Video width in pixels (must be divisible by 64) | +| `--height` | `384` | Video height in pixels (must be divisible by 64) | +| `--num-frames` | `99` | Number of output frames (auto-rounded to multiple of 33) | +| `--pyramid-steps` | `2 2 2` | Steps per pyramid stage (3-stage progressive denoising) | +| `--amplify-first-chunk` | off | Double steps for first chunk (better quality) | +| `--seed` | `-1` | Random seed (`-1` for random) | +| `--output-path` | `output_helios.mp4` | Output video file path | +| `--tiling` | `auto` | VAE tiling mode: `auto`, `none`, `default`, `aggressive`, `conservative` | + +## How It Works + +### Autoregressive Chunked Generation + +Unlike single-pass models, Helios generates video **autoregressively in 33-frame chunks** (9 latent frames each). This enables minute-scale video with temporal coherence: + +``` +Chunk 1: [frames 1-33] β†’ denoise from noise +Chunk 2: [frames 34-66] β†’ denoise with history from chunk 1 +Chunk 3: [frames 67-99] β†’ denoise with history from chunks 1-2 +... +``` + +For a 99-frame video at 24 fps, this produces ~4 seconds of video across 3 chunks. + +### Multi-Scale History Memory + +Each chunk beyond the first receives context from prior chunks via three Conv3d patch embeddings at different temporal/spatial scales: + +| Scale | Kernel | Latent Frames | Purpose | +|---|---|---|---| +| **Long** | 4Γ—8Γ—8 | 16 | Coarse global context | +| **Mid** | 2Γ—4Γ—4 | 2 | Medium-term motion | +| **Short** | 1Γ—2Γ—2 | 1 | Fine local detail | + +Total history: 19 latent tokens prepended to the current chunk's 9 tokens, giving the model a 28-token sequence that sees both broad context and recent detail. + +### Pipeline Steps + +1. **Text encoding** β€” UMT5-XXL encodes the prompt (shared with Wan) +2. **Per-chunk 3-stage pyramid denoising**: + - Sample Gaussian noise for 9 latent frames + - **Downsample** noise to 1/4 spatial resolution + - **Stage 0** (quarter res): Denoise 2 steps β€” very fast (16Γ— fewer tokens) + - **Upsample** 2Γ—, mix in structured block noise (alpha/beta correction) + - **Stage 1** (half res): Denoise 2 steps β€” fast (4Γ— fewer tokens) + - **Upsample** 2Γ—, mix block noise + - **Stage 2** (full res): Denoise 2 steps β€” full quality + - Prepend multi-scale history tokens (if not first chunk) + - Extract current-chunk latents; update history buffer +3. **VAE decoding** β€” AutoencoderKLWan decodes latents to RGB (shared with Wan, supports tiled decoding) +4. **Video output** β€” Frames saved as MP4 via OpenCV + +### Pyramid Denoising + +The 3-stage pyramid dramatically speeds up generation by performing most denoising at reduced spatial resolution: + +``` +Stage 0: β–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘ (1/4 res, 2 steps) β€” 16Γ— fewer tokens +Stage 1: β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘ (1/2 res, 2 steps) β€” 4Γ— fewer tokens +Stage 2: β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ (full res, 2 steps) β€” final refinement +``` + +Customize with `--pyramid-steps`: +- `--pyramid-steps 2 2 2` β€” default, 6 total forward passes (fastest) +- `--pyramid-steps 4 4 4` β€” 12 passes (higher quality) +- `--pyramid-steps 2 2 4` β€” more refinement at full resolution + +Use `--amplify-first-chunk` to double the steps for the first chunk, which typically has the biggest impact on overall quality. + +## Architecture + +Helios shares 95% of its architecture with Wan 2.1: + +| Component | Details | +|---|---| +| Transformer | 40 layers, dim=5120, 40 heads, head_dim=128 | +| FFN | SiLU-gated, dim=13824 | +| Patch embedding | (1, 2, 2) β€” 1 temporal, 2Γ—2 spatial | +| RoPE | 3-way factorized (44, 42, 42), ΞΈ=10000 | +| Modulation | 6-vector AdaLN-Zero (shift/scale/gate Γ— 2) | +| VAE | AutoencoderKLWan, stride (4, 8, 8), z_dim=16 | +| Text encoder | UMT5-XXL, dim=4096, 512 token context | + +**Helios-specific additions:** +- Restricted self-attention (history tokens attend only among themselves) +- Zero-timestep embedding for history tokens +- Multi-scale history Conv3d patching (short/mid/long) + +## Frame Count Constraints + +- Output frames are auto-rounded to multiples of **33** (the chunk size) +- Each chunk produces 33 pixel frames = 9 latent frames +- The VAE temporal stride is 4, with formula: `latent_frames = (pixel_frames - 1) / 4 + 1` + +Examples: +- `--num-frames 33` β†’ 1 chunk, ~1.4s at 24fps +- `--num-frames 99` β†’ 3 chunks, ~4.1s at 24fps +- `--num-frames 231` β†’ 7 chunks, ~9.6s at 24fps + +## Resolution Guide + +Height and width must be divisible by 64. Recommended resolutions: + +| Resolution | Aspect Ratio | VRAM (bf16) | VRAM (4-bit) | +|---|---|---|---| +| 384 Γ— 640 | 3:5 | ~28 GB | ~7 GB | +| 384 Γ— 384 | 1:1 | ~24 GB | ~6 GB | +| 256 Γ— 448 | 9:16 | ~20 GB | ~5 GB | + +## Memory Tips + +- Use `--tiling aggressive` for lower VRAM usage during VAE decoding +- Use 4-bit quantization (`--quantize --bits 4` during conversion) to reduce model size from ~28 GB to ~7 GB +- Shorter videos (fewer chunks) require less peak memory for history +- Smaller resolutions significantly reduce memory (quadratic in spatial dimensions) + +## File Structure + +``` +mlx_video/models/helios/ +β”œβ”€β”€ __init__.py +β”œβ”€β”€ README.md ← you are here +β”œβ”€β”€ config.py ← HeliosModelConfig dataclass +β”œβ”€β”€ rope.py ← 3-way factorized RoPE (44,42,42) +β”œβ”€β”€ attention.py ← Self-attention (with history restriction) + cross-attention +β”œβ”€β”€ scheduler.py ← DMD flow-matching scheduler with 3-stage pyramid support +β”œβ”€β”€ transformer.py ← HeliosTransformerBlock + HeliosModel backbone +└── loading.py ← Weight loading (reuses Wan's T5/VAE loaders) + +mlx_video/ +β”œβ”€β”€ convert_helios.py ← HF diffusers β†’ MLX weight conversion +└── generate_helios.py ← CLI generation pipeline +``` diff --git a/mlx_video/models/helios/__init__.py b/mlx_video/models/helios/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mlx_video/models/helios/attention.py b/mlx_video/models/helios/attention.py new file mode 100644 index 0000000..70cb8e4 --- /dev/null +++ b/mlx_video/models/helios/attention.py @@ -0,0 +1,270 @@ +import mlx.core as mx +import mlx.nn as nn + +from .rope import helios_rope_apply + + +def _linear_dtype(layer) -> mx.Dtype: + """Get the compute dtype of a linear layer, handling QuantizedLinear and LoRA wrappers.""" + inner = getattr(layer, "linear", layer) + if isinstance(inner, nn.QuantizedLinear): + return inner.scales.dtype + return inner.weight.dtype + + +class HeliosRMSNorm(nn.Module): + """RMS normalization with learnable scale.""" + + def __init__(self, dim: int, eps: float = 1e-5): + super().__init__() + self.eps = eps + self.weight = mx.ones((dim,)) + + def __call__(self, x: mx.array) -> mx.array: + return mx.fast.rms_norm(x, self.weight, self.eps) + + +class HeliosLayerNorm(nn.Module): + """LayerNorm computed in float32, with optional affine.""" + + def __init__(self, dim: int, eps: float = 1e-6, elementwise_affine: bool = False): + super().__init__() + self.eps = eps + self.elementwise_affine = elementwise_affine + if elementwise_affine: + self.weight = mx.ones((dim,)) + self.bias = mx.zeros((dim,)) + + def __call__(self, x: mx.array) -> mx.array: + if self.elementwise_affine: + return mx.fast.layer_norm(x, self.weight, self.bias, self.eps) + else: + return mx.fast.layer_norm(x, None, None, self.eps) + + +class HeliosSelfAttention(nn.Module): + """Self-attention with QK normalization, 3-way RoPE, and history restriction. + + When restrict_self_attn=True, the input sequence is split into history + tokens (from previous chunks) and current tokens. History tokens attend + only to other history tokens, while current tokens attend to all tokens. + """ + + def __init__( + self, + dim: int, + num_heads: int, + qk_norm: bool = True, + eps: float = 1e-6, + restrict_self_attn: bool = False, + ): + super().__init__() + assert dim % num_heads == 0 + self.dim = dim + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim**-0.5 + self.restrict_self_attn = restrict_self_attn + + self.q = nn.Linear(dim, dim) + self.k = nn.Linear(dim, dim) + self.v = nn.Linear(dim, dim) + self.o = nn.Linear(dim, dim) + + self.norm_q = HeliosRMSNorm(dim, eps=eps) if qk_norm else None + self.norm_k = HeliosRMSNorm(dim, eps=eps) if qk_norm else None + + def __call__( + self, + x: mx.array, + frame_indices: mx.array, + grid_size: tuple, + freqs: tuple, + rope_cos_sin: tuple | None = None, + original_context_length: int = 0, + ) -> mx.array: + b, s, _ = x.shape + n, d = self.num_heads, self.head_dim + history_seq_len = s - original_context_length + + w_dtype = _linear_dtype(self.q) + x_w = x.astype(w_dtype) + + q = self.q(x_w) + k = self.k(x_w) + if self.norm_q is not None: + q = self.norm_q(q) + if self.norm_k is not None: + k = self.norm_k(k) + + if self.restrict_self_attn and history_seq_len > 0: + # Split history and current + q_hist, q_curr = q[:, :history_seq_len], q[:, history_seq_len:] + k_hist, k_curr = k[:, :history_seq_len], k[:, history_seq_len:] + v_hist = self.v(x_w[:, :history_seq_len]) + v_curr = self.v(x_w[:, history_seq_len:]) + + # Reshape to multi-head + q_hist = q_hist.reshape(b, history_seq_len, n, d) + k_hist = k_hist.reshape(b, history_seq_len, n, d) + v_hist = v_hist.reshape(b, history_seq_len, n, d) + + q_curr = q_curr.reshape(b, original_context_length, n, d) + k_curr = k_curr.reshape(b, original_context_length, n, d) + v_curr = v_curr.reshape(b, original_context_length, n, d) + + # Apply RoPE to history and current separately + # History RoPE uses history portion of frame_indices + all_q = mx.concatenate([q_hist, q_curr], axis=1).reshape(b, s, n, d) + all_k = mx.concatenate([k_hist, k_curr], axis=1).reshape(b, s, n, d) + + total_seq = history_seq_len + original_context_length + all_q = helios_rope_apply( + all_q.astype(mx.float32), frame_indices, (total_seq, 1, 1), + freqs, precomputed_cos_sin=rope_cos_sin, + ) if rope_cos_sin is not None else all_q + all_k = helios_rope_apply( + all_k.astype(mx.float32), frame_indices, (total_seq, 1, 1), + freqs, precomputed_cos_sin=rope_cos_sin, + ) if rope_cos_sin is not None else all_k + + # Actually we need to use the full precomputed rope since it covers + # the concatenated history+current sequence. + # Let me simplify: apply RoPE to the full concatenated sequence + q_full = mx.concatenate([q_hist, q_curr], axis=1).reshape(b, s, n, d) + k_full = mx.concatenate([k_hist, k_curr], axis=1).reshape(b, s, n, d) + + # RoPE is precomputed for the full (history + current) sequence + if rope_cos_sin is not None: + cos_f, sin_f = rope_cos_sin + q_seq = q_full.reshape(b, s, n, d // 2, 2) + q_real, q_imag = q_seq[..., 0], q_seq[..., 1] + q_out_r = q_real * cos_f - q_imag * sin_f + q_out_i = q_real * sin_f + q_imag * cos_f + q_full = mx.stack([q_out_r, q_out_i], axis=-1).reshape(b, s, n, d) + + k_seq = k_full.reshape(b, s, n, d // 2, 2) + k_real, k_imag = k_seq[..., 0], k_seq[..., 1] + k_out_r = k_real * cos_f - k_imag * sin_f + k_out_i = k_real * sin_f + k_imag * cos_f + k_full = mx.stack([k_out_r, k_out_i], axis=-1).reshape(b, s, n, d) + + q_hist = q_full[:, :history_seq_len].astype(w_dtype) + q_curr = q_full[:, history_seq_len:].astype(w_dtype) + k_hist = k_full[:, :history_seq_len].astype(w_dtype) + k_curr = k_full[:, history_seq_len:].astype(w_dtype) + v_hist_h = v_hist + v_curr_h = v_curr + + # History self-attention: history attends to history only + q_h = q_hist.transpose(0, 2, 1, 3) + k_h = k_hist.transpose(0, 2, 1, 3) + v_h = v_hist_h.transpose(0, 2, 1, 3) + hist_out = mx.fast.scaled_dot_product_attention( + q_h, k_h, v_h, scale=self.scale + ) + hist_out = hist_out.transpose(0, 2, 1, 3).reshape(b, history_seq_len, -1) + + # Current self-attention: current attends to history + current + k_all = mx.concatenate([k_hist, k_curr], axis=1).transpose(0, 2, 1, 3) + v_all = mx.concatenate([v_hist_h, v_curr_h], axis=1).transpose(0, 2, 1, 3) + q_c = q_curr.transpose(0, 2, 1, 3) + curr_out = mx.fast.scaled_dot_product_attention( + q_c, k_all, v_all, scale=self.scale + ) + curr_out = curr_out.transpose(0, 2, 1, 3).reshape(b, original_context_length, -1) + + out = mx.concatenate([hist_out, curr_out], axis=1) + else: + # Standard self-attention (no history) + q = q.reshape(b, s, n, d) + k = k.reshape(b, s, n, d) + v = self.v(x_w).reshape(b, s, n, d) + + if rope_cos_sin is not None: + cos_f, sin_f = rope_cos_sin + q_seq = q.astype(mx.float32).reshape(b, s, n, d // 2, 2) + q_real, q_imag = q_seq[..., 0], q_seq[..., 1] + q = mx.stack([q_real * cos_f - q_imag * sin_f, + q_real * sin_f + q_imag * cos_f], axis=-1).reshape(b, s, n, d) + + k_seq = k.astype(mx.float32).reshape(b, s, n, d // 2, 2) + k_real, k_imag = k_seq[..., 0], k_seq[..., 1] + k = mx.stack([k_real * cos_f - k_imag * sin_f, + k_real * sin_f + k_imag * cos_f], axis=-1).reshape(b, s, n, d) + + q = q.astype(w_dtype).transpose(0, 2, 1, 3) + k = k.astype(w_dtype).transpose(0, 2, 1, 3) + v = v.transpose(0, 2, 1, 3) + + out = mx.fast.scaled_dot_product_attention(q, k, v, scale=self.scale) + out = out.transpose(0, 2, 1, 3).reshape(b, s, -1) + + return self.o(out) + + +class HeliosCrossAttention(nn.Module): + """Cross-attention: Q from hidden states, K/V from text context.""" + + def __init__( + self, + dim: int, + num_heads: int, + qk_norm: bool = True, + eps: float = 1e-6, + ): + super().__init__() + assert dim % num_heads == 0 + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim**-0.5 + + self.q = nn.Linear(dim, dim) + self.k = nn.Linear(dim, dim) + self.v = nn.Linear(dim, dim) + self.o = nn.Linear(dim, dim) + + self.norm_q = HeliosRMSNorm(dim, eps=eps) if qk_norm else None + self.norm_k = HeliosRMSNorm(dim, eps=eps) if qk_norm else None + + def prepare_kv(self, context: mx.array) -> tuple: + """Pre-compute K and V projections for caching.""" + b = context.shape[0] + n, d = self.num_heads, self.head_dim + w_dtype = _linear_dtype(self.k) + ctx = context.astype(w_dtype) + k = self.k(ctx) + if self.norm_k is not None: + k = self.norm_k(k) + k = k.reshape(b, -1, n, d).transpose(0, 2, 1, 3) + v = self.v(ctx).reshape(b, -1, n, d).transpose(0, 2, 1, 3) + return k, v + + def __call__( + self, + x: mx.array, + context: mx.array, + kv_cache: tuple | None = None, + ) -> mx.array: + b = x.shape[0] + n, d = self.num_heads, self.head_dim + + w_dtype = _linear_dtype(self.q) + q = self.q(x.astype(w_dtype)) + if self.norm_q is not None: + q = self.norm_q(q) + q = q.reshape(b, -1, n, d).transpose(0, 2, 1, 3) + + if kv_cache is not None: + k, v = kv_cache + else: + ctx = context.astype(w_dtype) + k = self.k(ctx) + if self.norm_k is not None: + k = self.norm_k(k) + k = k.reshape(b, -1, n, d).transpose(0, 2, 1, 3) + v = self.v(ctx).reshape(b, -1, n, d).transpose(0, 2, 1, 3) + + out = mx.fast.scaled_dot_product_attention(q, k, v, scale=self.scale) + out = out.transpose(0, 2, 1, 3).reshape(b, -1, n * d) + return self.o(out) diff --git a/mlx_video/models/helios/config.py b/mlx_video/models/helios/config.py new file mode 100644 index 0000000..3227dfb --- /dev/null +++ b/mlx_video/models/helios/config.py @@ -0,0 +1,69 @@ +from dataclasses import dataclass, field +from typing import List, Optional, Tuple, Union + +from mlx_video.models.ltx.config import BaseModelConfig + + +@dataclass +class HeliosModelConfig(BaseModelConfig): + """Configuration for Helios video generation models.""" + + # Transformer architecture (identical to Wan 14B) + dim: int = 5120 + ffn_dim: int = 13824 + num_heads: int = 40 + num_layers: int = 40 + patch_size: Tuple[int, int, int] = (1, 2, 2) + in_dim: int = 16 + out_dim: int = 16 + text_dim: int = 4096 + freq_dim: int = 256 + text_len: int = 512 + eps: float = 1e-6 + qk_norm: bool = True + cross_attn_norm: bool = True + + # RoPE + rope_dim: Tuple[int, int, int] = (44, 42, 42) + rope_theta: float = 10000.0 + + # Helios-specific: multi-scale history memory + history_sizes: List[int] = field(default_factory=lambda: [16, 2, 1]) + num_latent_frames_per_chunk: int = 9 + has_multi_term_memory_patch: bool = True + zero_history_timestep: bool = True + + # VAE (identical to Wan β€” AutoencoderKLWan) + vae_stride: Tuple[int, int, int] = (4, 8, 8) + vae_z_dim: int = 16 + + # T5 text encoder (identical to Wan β€” UMT5-XXL) + t5_vocab_size: int = 256384 + t5_dim: int = 4096 + t5_dim_attn: int = 4096 + t5_dim_ffn: int = 10240 + t5_num_heads: int = 64 + t5_num_layers: int = 24 + t5_num_buckets: int = 32 + + # Scheduler + num_train_timesteps: int = 1000 + shift: float = 1.0 + stages: int = 3 + stage_range: List[float] = field(default_factory=lambda: [0, 1 / 3, 2 / 3, 1]) + gamma: float = 1 / 3 + + # Inference defaults + sample_fps: int = 24 + frame_num: int = 99 + + @property + def head_dim(self) -> int: + return self.dim // self.num_heads + + @classmethod + def helios_distilled(cls) -> "HeliosModelConfig": + """Helios-Distilled: x0-prediction, no CFG, DMD scheduler, 2-3 steps.""" + return cls( + shift=1.0, + ) diff --git a/mlx_video/models/helios/loading.py b/mlx_video/models/helios/loading.py new file mode 100644 index 0000000..55dc68e --- /dev/null +++ b/mlx_video/models/helios/loading.py @@ -0,0 +1,51 @@ +"""Helios model loading utilities. + +Reuses Wan's T5 encoder and VAE since Helios uses identical components. +""" + +from pathlib import Path + +import mlx.core as mx +import mlx.nn as nn + + +def load_helios_model( + model_path: Path, + config, + quantization: dict | None = None, +): + """Load and initialize HeliosModel, with optional quantization. + + Args: + model_path: Path to model safetensors file + config: HeliosModelConfig + quantization: Optional dict with 'bits' and 'group_size' keys. + """ + from mlx_video.models.helios.transformer import HeliosModel + + model = HeliosModel(config) + + if quantization: + from mlx_video.convert_helios import _quantize_predicate + + nn.quantize( + model, + group_size=quantization["group_size"], + bits=quantization["bits"], + class_predicate=lambda path, m: _quantize_predicate(path, m), + ) + + weights = mx.load(str(model_path)) + model.load_weights(list(weights.items()), strict=False) + mx.eval(model.parameters()) + return model + + +# Reuse Wan's T5 encoder and VAE loaders since Helios uses the same components +from mlx_video.models.wan.loading import ( # noqa: E402, F401 + _clean_text, + encode_text, + load_t5_encoder, + load_vae_decoder, + load_vae_encoder, +) diff --git a/mlx_video/models/helios/rope.py b/mlx_video/models/helios/rope.py new file mode 100644 index 0000000..936a83f --- /dev/null +++ b/mlx_video/models/helios/rope.py @@ -0,0 +1,215 @@ +import mlx.core as mx +import numpy as np + + +def helios_rope_params( + rope_dim: tuple = (44, 42, 42), + theta: float = 10000.0, + max_seq_len: int = 1024, +) -> tuple: + """Precompute per-dimension RoPE frequency bases for Helios. + + Unlike Wan which uses a single frequency table split by dimension, + Helios computes separate frequency bases for each spatial/temporal + dimension using that dimension's size as the denominator. + + Returns: + (freqs_t, freqs_h, freqs_w) each of shape [max_seq_len, d_i//2, 2] + """ + results = [] + for d in rope_dim: + base = 1.0 / np.power( + theta, np.arange(0, d, 2, dtype=np.float64) / d + ) + positions = np.arange(max_seq_len, dtype=np.float64) + freqs = positions[:, None] * base[None, :] + cos_f = np.cos(freqs).astype(np.float32) + sin_f = np.sin(freqs).astype(np.float32) + results.append(mx.array(np.stack([cos_f, sin_f], axis=-1))) + return tuple(results) + + +def _rope_compute_5d( + frame_indices: mx.array, + height: int, + width: int, + freqs: tuple, + dtype: type = mx.float32, +) -> mx.array: + """Compute RoPE frequencies as a 5D tensor [F, H, W, half_d, 2]. + + Args: + frame_indices: 1D array of temporal frame indices [F] + height: Spatial height grid size + width: Spatial width grid size + freqs: (freqs_t, freqs_h, freqs_w) from helios_rope_params() + dtype: Output dtype + + Returns: + [F, H, W, half_d, 2] where half_d = d_t + d_h + d_w + """ + freqs_t, freqs_h, freqs_w = freqs + if freqs_t.dtype != dtype: + freqs_t = freqs_t.astype(dtype) + freqs_h = freqs_h.astype(dtype) + freqs_w = freqs_w.astype(dtype) + + f = frame_indices.shape[0] + d_t, d_h, d_w = freqs_t.shape[1], freqs_h.shape[1], freqs_w.shape[1] + + ft = mx.broadcast_to( + freqs_t[frame_indices].reshape(f, 1, 1, d_t, 2), (f, height, width, d_t, 2) + ) + fh = mx.broadcast_to( + freqs_h[:height].reshape(1, height, 1, d_h, 2), (f, height, width, d_h, 2) + ) + fw = mx.broadcast_to( + freqs_w[:width].reshape(1, 1, width, d_w, 2), (f, height, width, d_w, 2) + ) + + return mx.concatenate([ft, fh, fw], axis=3) + + +def _rope_pad_and_downsample(rope_5d: mx.array, kernel: tuple) -> mx.array: + """Downsample a 5D RoPE tensor by averaging over kernel-sized blocks. + + Equivalent to: pad_for_3d_conv + center_down_sample_3d (avg_pool3d) from + the reference implementation. + + Args: + rope_5d: [F, H, W, half_d, 2] + kernel: (kt, kh, kw) downsampling factors + + Returns: + [F//kt, H//kh, W//kw, half_d, 2] + """ + f, h, w, d, c = rope_5d.shape + kt, kh, kw = kernel + + # Replicate-pad to make divisible + pad_t = (kt - (f % kt)) % kt + pad_h = (kh - (h % kh)) % kh + pad_w = (kw - (w % kw)) % kw + if pad_t > 0: + rope_5d = mx.concatenate( + [rope_5d, mx.repeat(rope_5d[-1:], pad_t, axis=0)], axis=0 + ) + if pad_h > 0: + rope_5d = mx.concatenate( + [rope_5d, mx.repeat(rope_5d[:, -1:], pad_h, axis=1)], axis=1 + ) + if pad_w > 0: + rope_5d = mx.concatenate( + [rope_5d, mx.repeat(rope_5d[:, :, -1:], pad_w, axis=2)], axis=2 + ) + f, h, w = rope_5d.shape[:3] + + # Reshape and average (avg_pool3d equivalent) + rope_5d = rope_5d.reshape(f // kt, kt, h // kh, kh, w // kw, kw, d, c) + rope_5d = rope_5d.mean(axis=(1, 3, 5)) + return rope_5d + + +def _flatten_rope_5d(rope_5d: mx.array) -> tuple: + """Flatten 5D RoPE to (cos, sin) each [seq_len, 1, half_d]. + + Args: + rope_5d: [F, H, W, half_d, 2] + + Returns: + (cos_f, sin_f) each [F*H*W, 1, half_d] + """ + f, h, w, d, _ = rope_5d.shape + flat = rope_5d.reshape(f * h * w, 1, d, 2) + return flat[..., 0], flat[..., 1] + + +def helios_rope_apply( + x: mx.array, + frame_indices: mx.array, + grid_size: tuple, + freqs: tuple, + precomputed_cos_sin: tuple | None = None, +) -> mx.array: + """Apply 3-way factorized RoPE to Q or K tensor for Helios. + + Args: + x: Shape [B, L, num_heads, head_dim] + frame_indices: Frame indices for this chunk, shape [F] (auto-regressive offset) + grid_size: (F, H, W) spatial/temporal grid for current chunk + freqs: (freqs_t, freqs_h, freqs_w) from helios_rope_params() + precomputed_cos_sin: Optional (cos_f, sin_f) for constant grids + """ + b, s, n, d = x.shape + half_d = d // 2 + f, h, w = grid_size + seq_len = f * h * w + + if precomputed_cos_sin is not None: + cos_f, sin_f = precomputed_cos_sin + else: + rope_5d = _rope_compute_5d(frame_indices, h, w, freqs, dtype=x.dtype) + cos_f, sin_f = _flatten_rope_5d(rope_5d) + + x_seq = x[:, :seq_len].reshape(b, seq_len, n, half_d, 2) + x_real = x_seq[..., 0] + x_imag = x_seq[..., 1] + out_real = x_real * cos_f - x_imag * sin_f + out_imag = x_real * sin_f + x_imag * cos_f + x_rotated = mx.stack([out_real, out_imag], axis=-1).reshape( + b, seq_len, n, d + ) + if seq_len < s: + x_rotated = mx.concatenate([x_rotated, x[:, seq_len:]], axis=1) + return x_rotated + + +def helios_rope_precompute_cos_sin( + frame_indices: mx.array, + grid_size: tuple, + freqs: tuple, + dtype: type = mx.float32, +) -> tuple: + """Precompute cos/sin for a constant grid. Call once before denoising loop. + + Args: + frame_indices: 1D array [F] of temporal frame indices + grid_size: (F, H, W) + freqs: (freqs_t, freqs_h, freqs_w) + + Returns: + (cos_f, sin_f) each [seq_len, 1, half_d] + """ + f, h, w = grid_size + rope_5d = _rope_compute_5d(frame_indices, h, w, freqs, dtype=dtype) + return _flatten_rope_5d(rope_5d) + + +def helios_rope_precompute_history( + frame_indices: mx.array, + spatial_h: int, + spatial_w: int, + freqs: tuple, + downsample_kernel: tuple | None = None, + dtype: type = mx.float32, +) -> tuple: + """Precompute cos/sin for history tokens, with optional spatial downsampling. + + This matches the reference approach: compute RoPE at the short-history + spatial resolution, then avg-pool downsample for mid/long scales. + + Args: + frame_indices: 1D array [F] of temporal frame indices + spatial_h: Height at the base (short) spatial resolution + spatial_w: Width at the base (short) spatial resolution + freqs: (freqs_t, freqs_h, freqs_w) + downsample_kernel: (kt, kh, kw) for mid/long. None for short. + dtype: Output dtype + + Returns: + (cos_f, sin_f) each [seq_len, 1, half_d] + """ + rope_5d = _rope_compute_5d(frame_indices, spatial_h, spatial_w, freqs, dtype=dtype) + if downsample_kernel is not None: + rope_5d = _rope_pad_and_downsample(rope_5d, downsample_kernel) + return _flatten_rope_5d(rope_5d) diff --git a/mlx_video/models/helios/scheduler.py b/mlx_video/models/helios/scheduler.py new file mode 100644 index 0000000..1429616 --- /dev/null +++ b/mlx_video/models/helios/scheduler.py @@ -0,0 +1,253 @@ +"""Helios scheduler for MLX β€” DMD flow-matching with 3-stage pyramid support.""" + +import math + +import mlx.core as mx +import numpy as np + + +def calculate_shift( + image_seq_len: int, + base_seq_len: int = 256, + max_seq_len: int = 4096, + base_shift: float = 0.5, + max_shift: float = 1.15, +) -> float: + """Compute dynamic shift (mu) based on spatial sequence length.""" + m = (max_shift - base_shift) / (max_seq_len - base_seq_len) + b = base_shift - m * base_seq_len + return image_seq_len * m + b + + +class HeliosScheduler: + """Flow-matching scheduler with shifted sigmas, DMD steps, and multi-stage + pyramid support. + + For the Distilled model, each pyramid stage uses DMD (Distribution Matching + Distillation) steps: predict x0 from flow, then re-noise with the original + noisy tensor for all but the last step. + """ + + def __init__( + self, + num_train_timesteps: int = 1000, + shift: float = 1.0, + stages: int = 3, + stage_range: list | None = None, + gamma: float = 1 / 3, + use_dynamic_shifting: bool = True, + base_image_seq_len: int = 256, + max_image_seq_len: int = 4096, + base_shift: float = 0.5, + max_shift: float = 1.15, + ): + self.num_train_timesteps = num_train_timesteps + self.shift = shift + self.stages = stages + self.stage_range = stage_range or [0, 1 / 3, 2 / 3, 1] + self.gamma = gamma + self.use_dynamic_shifting = use_dynamic_shifting + self.base_image_seq_len = base_image_seq_len + self.max_image_seq_len = max_image_seq_len + self.base_shift = base_shift + self.max_shift = max_shift + + # Precompute global and per-stage schedules + self.timestep_ratios = {} + self.timesteps_per_stage = {} + self.sigmas_per_stage = {} + self.start_sigmas = {} + self.end_sigmas = {} + self.ori_start_sigmas = {} + + self._init_sigmas() + self._init_sigmas_per_stage() + + self.sigma_min = float(self.global_sigmas[-1]) + self.sigma_max = float(self.global_sigmas[0]) + + # Runtime state (set by set_timesteps) + self.sigmas = None + self.timesteps = None + self._step_index = 0 + + def _init_sigmas(self): + """Compute the global shifted sigma schedule.""" + n = self.num_train_timesteps + alphas = np.linspace(1, 1 / n, n + 1) + sigmas = 1.0 - alphas + sigmas = np.flip( + self.shift * sigmas / (1 + (self.shift - 1) * sigmas) + )[:-1].copy() + self.global_sigmas = sigmas + self.global_timesteps = sigmas * n + + def _init_sigmas_per_stage(self): + """Compute per-stage sigma schedules with gamma correction.""" + n = self.num_train_timesteps + stage_distance = [] + + for i_s in range(self.stages): + start_idx = int(self.stage_range[i_s] * n) + start_idx = max(start_idx, 0) + end_idx = int(self.stage_range[i_s + 1] * n) + end_idx = min(end_idx, n) + + start_sigma = float(self.global_sigmas[start_idx]) + end_sigma = float(self.global_sigmas[end_idx]) if end_idx < n else 0.0 + self.ori_start_sigmas[i_s] = start_sigma + + if i_s != 0: + ori_sigma = 1 - start_sigma + gamma = self.gamma + corrected = ( + 1 / (math.sqrt(1 + (1 / gamma)) * (1 - ori_sigma) + ori_sigma) + ) * ori_sigma + start_sigma = 1 - corrected + + stage_distance.append(start_sigma - end_sigma) + self.start_sigmas[i_s] = start_sigma + self.end_sigmas[i_s] = end_sigma + + tot_distance = sum(stage_distance) + for i_s in range(self.stages): + if i_s == 0: + start_ratio = 0.0 + else: + start_ratio = sum(stage_distance[:i_s]) / tot_distance + if i_s == self.stages - 1: + end_ratio = 1.0 - 1e-16 + else: + end_ratio = sum(stage_distance[: i_s + 1]) / tot_distance + + self.timestep_ratios[i_s] = (start_ratio, end_ratio) + + for i_s in range(self.stages): + ratio = self.timestep_ratios[i_s] + t_max = min(float(self.global_timesteps[int(ratio[0] * n)]), 999) + t_min = float( + self.global_timesteps[min(int(ratio[1] * n), n - 1)] + ) + self.timesteps_per_stage[i_s] = np.linspace(t_max, t_min, n + 1)[:-1] + self.sigmas_per_stage[i_s] = np.linspace(0.999, 0, n + 1)[:-1] + + @staticmethod + def _time_shift(mu: float, sigma: float, t): + """Apply dynamic time shift: mu*t / (mu + (1-mu)*t).""" + return mu * t / (mu + (1 - mu) * t) + + def set_timesteps( + self, + num_inference_steps: int, + stage_index: int, + image_seq_len: int | None = None, + is_amplify_first_chunk: bool = False, + ): + """Set timesteps and sigmas for a specific pyramid stage with DMD. + + For DMD, num_inference_steps is expanded (+1, or *2+1 for amplify), + then trimmed so that the final result has exactly num_inference_steps + forward passes per stage. + """ + # DMD expansion + if is_amplify_first_chunk: + n_steps = num_inference_steps * 2 + 1 + else: + n_steps = num_inference_steps + 1 + + n = self.num_train_timesteps + stage_ts = self.timesteps_per_stage[stage_index] + t_max, t_min = float(stage_ts[0]), float(stage_ts[-1]) + timesteps = np.linspace(t_max, t_min, n_steps) + + stage_sigmas = self.sigmas_per_stage[stage_index] + s_max, s_min = float(stage_sigmas[0]), float(stage_sigmas[-1]) + sigmas = np.linspace(s_max, s_min, n_steps) + sigmas = np.append(sigmas, 0.0) + + # DMD trim: drop last timestep, keep [sigmas[:-2], sigmas[-1:]] + timesteps = timesteps[:-1] + sigmas = np.concatenate([sigmas[:-2], sigmas[-1:]]) + + # Dynamic shifting based on spatial resolution + if self.use_dynamic_shifting and image_seq_len is not None: + mu = calculate_shift( + image_seq_len, + self.base_image_seq_len, + self.max_image_seq_len, + self.base_shift, + self.max_shift, + ) + sigmas = self._time_shift(mu, 1.0, sigmas) + # Remap timesteps to match shifted sigmas + timesteps = ( + self.timesteps_per_stage[stage_index].min() + + sigmas[:-1] + * ( + self.timesteps_per_stage[stage_index].max() + - self.timesteps_per_stage[stage_index].min() + ) + ) + + self.timesteps = mx.array(timesteps, dtype=mx.float32) + self.sigmas = mx.array(sigmas, dtype=mx.float32) + self._step_index = 0 + + def step_dmd( + self, + model_output: mx.array, + sample: mx.array, + cur_step: int, + noisy_start: mx.array, + ) -> mx.array: + """DMD step: predict x0 from flow, optionally re-noise. + + Args: + model_output: Flow prediction from model + sample: Current noisy latent (x_t) + cur_step: Current step index within this stage + noisy_start: Original noisy tensor for this stage (for re-noising) + + Returns: + Denoised or re-noised sample + """ + # Convert flow prediction to x0: x0 = x_t - sigma_t * flow + sigma_t = self.sigmas[cur_step] + x0_pred = sample - sigma_t * model_output + + num_timesteps = len(self.timesteps) + if cur_step < num_timesteps - 1: + # Re-noise: blend x0_pred with original noisy tensor at next sigma + sigma_next = self.sigmas[cur_step + 1] + prev_sample = (1 - sigma_next) * x0_pred + sigma_next * noisy_start + else: + prev_sample = x0_pred + + self._step_index = cur_step + 1 + return prev_sample + + def step( + self, + model_output: mx.array, + sample: mx.array, + sigma: mx.array | None = None, + sigma_next: mx.array | None = None, + ) -> mx.array: + """Euler step: x_{t-1} = x_t + (sigma_next - sigma) * v.""" + if sigma is None: + sigma = self.sigmas[self._step_index] + if sigma_next is None: + sigma_next = self.sigmas[self._step_index + 1] + + prev_sample = sample + (sigma_next - sigma) * model_output + self._step_index += 1 + return prev_sample + + def add_noise( + self, + original: mx.array, + noise: mx.array, + sigma: mx.array, + ) -> mx.array: + """Add noise at a given sigma level for flow matching.""" + return (1 - sigma) * original + sigma * noise diff --git a/mlx_video/models/helios/transformer.py b/mlx_video/models/helios/transformer.py new file mode 100644 index 0000000..eb9b9d6 --- /dev/null +++ b/mlx_video/models/helios/transformer.py @@ -0,0 +1,505 @@ +"""Helios transformer backbone for MLX. + +Implements the Helios 14B DiT with multi-scale history memory, +restricted self-attention, and 6-vector modulation per block. +""" + +import math + +import mlx.core as mx +import mlx.nn as nn +import numpy as np + +from .attention import ( + HeliosCrossAttention, + HeliosLayerNorm, + HeliosRMSNorm, + HeliosSelfAttention, + _linear_dtype, +) +from .config import HeliosModelConfig +from .rope import helios_rope_params, helios_rope_precompute_cos_sin, helios_rope_precompute_history + + +class HeliosFFN(nn.Module): + """Gated feed-forward network with GELU(tanh) activation.""" + + def __init__(self, dim: int, ffn_dim: int): + super().__init__() + self.fc1 = nn.Linear(dim, ffn_dim) + self.act = nn.GELU(approx="tanh") + self.fc2 = nn.Linear(ffn_dim, dim) + + def __call__(self, x: mx.array) -> mx.array: + x_w = x.astype(_linear_dtype(self.fc1)) + return self.fc2(self.act(self.fc1(x_w))) + + +class HeliosTransformerBlock(nn.Module): + """Helios transformer block: self-attn + cross-attn + FFN with 6-vector modulation.""" + + def __init__( + self, + dim: int, + ffn_dim: int, + num_heads: int, + qk_norm: bool = True, + cross_attn_norm: bool = True, + eps: float = 1e-6, + restrict_self_attn: bool = False, + ): + super().__init__() + + # Self-attention + self.norm1 = HeliosLayerNorm(dim, eps) + self.self_attn = HeliosSelfAttention( + dim, num_heads, qk_norm, eps, + restrict_self_attn=restrict_self_attn, + ) + + # Cross-attention + self.cross_attn = HeliosCrossAttention(dim, num_heads, qk_norm, eps) + self.norm2 = ( + HeliosLayerNorm(dim, eps, elementwise_affine=True) + if cross_attn_norm + else None + ) + + # Feed-forward + self.ffn = HeliosFFN(dim, ffn_dim) + self.norm3 = HeliosLayerNorm(dim, eps) + + # 6-vector modulation table (scale/shift/gate for self-attn and FFN) + self.scale_shift_table = ( + mx.random.normal((1, 6, dim)) * (dim**-0.5) + ).astype(mx.float32) + + # Whether to separate history from current in cross-attention + self.guidance_cross_attn = True + + def __call__( + self, + x: mx.array, + encoder_hidden_states: mx.array, + timestep_proj: mx.array, + rotary_emb: tuple | None, + original_context_length: int, + frame_indices: mx.array | None = None, + grid_size: tuple | None = None, + freqs: tuple | None = None, + cross_kv_cache: tuple | None = None, + ) -> mx.array: + w_dtype = _linear_dtype(self.self_attn.q) + history_seq_len = x.shape[1] - original_context_length + + # Compute 6-vector modulation + # timestep_proj: [B, L, 6, dim] (per-token) or [B, 6, dim] (global) + if timestep_proj.ndim == 4: + # [B, L, 6, dim] + [1, 1, 6, dim] β†’ [B, L, 6, dim] + mod = (self.scale_shift_table[None, :, :] + timestep_proj.astype(mx.float32)) + shift_msa = mod[:, :, 0].astype(w_dtype) + scale_msa = mod[:, :, 1].astype(w_dtype) + gate_msa = mod[:, :, 2].astype(w_dtype) + c_shift = mod[:, :, 3].astype(w_dtype) + c_scale = mod[:, :, 4].astype(w_dtype) + c_gate = mod[:, :, 5].astype(w_dtype) + else: + # [B, 6, dim] + [1, 6, dim] β†’ [B, 6, dim] + mod = (self.scale_shift_table + timestep_proj.astype(mx.float32)).astype(w_dtype) + shift_msa = mod[:, 0:1] + scale_msa = mod[:, 1:2] + gate_msa = mod[:, 2:3] + c_shift = mod[:, 3:4] + c_scale = mod[:, 4:5] + c_gate = mod[:, 5:6] + + # 1. Self-attention + norm_x = (self.norm1(x) * (1 + scale_msa) + shift_msa).astype(w_dtype) + attn_out = self.self_attn( + norm_x, + frame_indices=frame_indices, + grid_size=grid_size, + freqs=freqs, + rope_cos_sin=rotary_emb, + original_context_length=original_context_length, + ) + x = (x + attn_out * gate_msa) + + # 2. Cross-attention (history tokens skip cross-attention) + if self.guidance_cross_attn and history_seq_len > 0: + history_x, current_x = x[:, :history_seq_len], x[:, history_seq_len:] + norm_current = self.norm2(current_x) if self.norm2 is not None else current_x + cross_out = self.cross_attn(norm_current, encoder_hidden_states, kv_cache=cross_kv_cache) + current_x = current_x + cross_out + x = mx.concatenate([history_x, current_x], axis=1) + else: + norm_x = self.norm2(x) if self.norm2 is not None else x + cross_out = self.cross_attn(norm_x, encoder_hidden_states, kv_cache=cross_kv_cache) + x = x + cross_out + + # 3. Feed-forward + norm_x = (self.norm3(x) * (1 + c_scale) + c_shift).astype(w_dtype) + ff_out = self.ffn(norm_x) + x = x + ff_out * c_gate + + return x + + +class HeliosModel(nn.Module): + """Helios 14B diffusion backbone with multi-scale history memory.""" + + def __init__(self, config: HeliosModelConfig): + super().__init__() + self.config = config + dim = config.dim + self.dim = dim + self.num_heads = config.num_heads + self.out_dim = config.out_dim + self.patch_size = config.patch_size + self.text_len = config.text_len + self.freq_dim = config.freq_dim + + # Patch embedding (Conv3d as reshaped Linear) + patch_dim = config.in_dim * math.prod(config.patch_size) + self.patch_embedding = nn.Linear(patch_dim, dim) + self._patch_size = config.patch_size + + # Multi-scale history patches (short/mid/long Conv3d as Linear) + if config.has_multi_term_memory_patch: + self.patch_short = nn.Linear(config.in_dim * 1 * 2 * 2, dim) + self.patch_mid = nn.Linear(config.in_dim * 2 * 4 * 4, dim) + self.patch_long = nn.Linear(config.in_dim * 4 * 8 * 8, dim) + self.has_multi_term_memory_patch = config.has_multi_term_memory_patch + + # Text embedding (PixArtAlpha-style projection) + self.text_embedding_0 = nn.Linear(config.text_dim, dim) + self.text_embedding_act = nn.GELU(approx="tanh") + self.text_embedding_1 = nn.Linear(dim, dim) + + # Time embedding (sinusoidal β†’ MLP) + self.time_embedding_0 = nn.Linear(config.freq_dim, dim) + self.time_embedding_act = nn.SiLU() + self.time_embedding_1 = nn.Linear(dim, dim) + + # Time projection for modulation (6x dim for scale/shift/gate) + self.time_projection_act = nn.SiLU() + self.time_projection = nn.Linear(dim, dim * 6) + + # Transformer blocks + self.blocks = [ + HeliosTransformerBlock( + dim=dim, + ffn_dim=config.ffn_dim, + num_heads=config.num_heads, + qk_norm=config.qk_norm, + cross_attn_norm=config.cross_attn_norm, + eps=config.eps, + restrict_self_attn=True, + ) + for _ in range(config.num_layers) + ] + + # Output norm and projection + self.output_norm = HeliosLayerNorm(dim, config.eps) + self.output_norm_table = ( + mx.random.normal((1, 2, dim)) * (dim**-0.5) + ).astype(mx.float32) + proj_dim = math.prod(config.patch_size) * config.out_dim + self.proj_out = nn.Linear(dim, proj_dim) + + # RoPE frequencies + self.rope_freqs = helios_rope_params( + rope_dim=config.rope_dim, + theta=config.rope_theta, + max_seq_len=1024, + ) + + # Whether to zero out history timestep embedding + self.zero_history_timestep = config.zero_history_timestep + + # Precompute sinusoidal inv_freq for time embedding + half = config.freq_dim // 2 + self._inv_freq = mx.power( + 10000.0, -mx.arange(half).astype(mx.float32) / half + ) + + def _patchify(self, x: mx.array) -> tuple: + """Convert video latent to patch embeddings. + + Args: + x: Video latent [C, F, H, W] + + Returns: + (patches, grid_size): patches [1, L, dim], grid_size (F', H', W') + """ + c, f, h, w = x.shape + pt, ph, pw = self._patch_size + + f_out = f // pt + h_out = h // ph + w_out = w // pw + + x = x.reshape(c, f_out, pt, h_out, ph, w_out, pw) + x = x.transpose(1, 3, 5, 0, 2, 4, 6) # [F', H', W', C, pt, ph, pw] + x = x.reshape(f_out * h_out * w_out, -1) + + patches = self.patch_embedding(x) + patches = patches.astype(_linear_dtype(self.patch_embedding)) + return patches[None, :, :], (f_out, h_out, w_out) + + def _patchify_history(self, x: mx.array, scale: str) -> mx.array: + """Patchify history latents at different scales. + + Args: + x: History latent [C, F, H, W] + scale: 'short' (1,2,2), 'mid' (2,4,4), or 'long' (4,8,8) + """ + c, f, h, w = x.shape + if scale == "short": + kernel = (1, 2, 2) + proj = self.patch_short + elif scale == "mid": + kernel = (2, 4, 4) + proj = self.patch_mid + else: + kernel = (4, 8, 8) + proj = self.patch_long + + kt, kh, kw = kernel + + # Pad to make divisible by kernel size + pad_t = (kt - (f % kt)) % kt + pad_h = (kh - (h % kh)) % kh + pad_w = (kw - (w % kw)) % kw + if pad_t > 0 or pad_h > 0 or pad_w > 0: + # Replicate padding along each dimension + if pad_t > 0: + x = mx.concatenate([x, mx.repeat(x[:, -1:], pad_t, axis=1)], axis=1) + if pad_h > 0: + x = mx.concatenate([x, mx.repeat(x[:, :, -1:], pad_h, axis=2)], axis=2) + if pad_w > 0: + x = mx.concatenate([x, mx.repeat(x[:, :, :, -1:], pad_w, axis=3)], axis=3) + c, f, h, w = x.shape + + f_out = f // kt + h_out = h // kh + w_out = w // kw + + x = x.reshape(c, f_out, kt, h_out, kh, w_out, kw) + x = x.transpose(1, 3, 5, 0, 2, 4, 6) + x = x.reshape(f_out * h_out * w_out, -1) + + patches = proj(x) + patches = patches.astype(_linear_dtype(proj)) + return patches[None, :, :] # [1, L, dim] + + def embed_text(self, context: list) -> mx.array: + """Precompute text embeddings.""" + model_dtype = _linear_dtype(self.patch_embedding) + context_padded = [] + for ctx in context: + pad_len = self.text_len - ctx.shape[0] + if pad_len > 0: + ctx = mx.concatenate( + [ctx, mx.zeros((pad_len, ctx.shape[1]), dtype=ctx.dtype)], + axis=0, + ) + context_padded.append(ctx) + context_batch = mx.stack(context_padded) + context_batch = self.text_embedding_1( + self.text_embedding_act(self.text_embedding_0(context_batch)) + ) + return context_batch.astype(model_dtype) + + def prepare_cross_kv(self, context: mx.array) -> list: + """Pre-compute cross-attention K/V caches.""" + return [block.cross_attn.prepare_kv(context) for block in self.blocks] + + def unpatchify(self, x: mx.array, grid_size: tuple) -> mx.array: + """Reconstruct video from patch embeddings. + + Args: + x: [B, L, out_dim * prod(patch_size)] + grid_size: (F', H', W') + + Returns: + [C, F, H, W] + """ + c = self.out_dim + pt, ph, pw = self.patch_size + f, h, w = grid_size + seq_len = f * h * w + + u = x[0, :seq_len] + u = u.reshape(f, h, w, pt, ph, pw, c) + u = u.transpose(6, 0, 3, 1, 4, 2, 5) + return u.reshape(c, f * pt, h * ph, w * pw) + + def __call__( + self, + latents: mx.array, + timestep: mx.array, + encoder_hidden_states: mx.array, + frame_indices: mx.array | None = None, + history_short: mx.array | None = None, + history_mid: mx.array | None = None, + history_long: mx.array | None = None, + history_short_indices: mx.array | None = None, + history_mid_indices: mx.array | None = None, + history_long_indices: mx.array | None = None, + cross_kv_caches: list | None = None, + ) -> mx.array: + """Forward pass through the Helios transformer. + + Args: + latents: Current chunk latent [C, F, H, W] + timestep: Scalar diffusion timestep + encoder_hidden_states: Text embeddings [B, text_len, dim] + frame_indices: Frame indices for current chunk [F'] + history_short/mid/long: History latents at different scales + history_*_indices: Frame indices for history at each scale + cross_kv_caches: Pre-computed cross-attention K/V caches + + Returns: + Predicted velocity/noise [C, F, H, W] + """ + # 1. Patchify current latents + hidden_states, grid_size = self._patchify(latents) + f_out, h_out, w_out = grid_size + current_seq_len = hidden_states.shape[1] + + if frame_indices is None: + frame_indices = mx.arange(f_out) + + # 2. Compute RoPE for current chunk + rope_cos_sin = helios_rope_precompute_cos_sin( + frame_indices, grid_size, self.rope_freqs, + dtype=hidden_states.dtype, + ) + + # 3. Process multi-scale history and prepend + history_seq_len = 0 + if history_short is not None and self.has_multi_term_memory_patch: + hist_s = self._patchify_history(history_short, "short") + hist_m = self._patchify_history(history_mid, "mid") + hist_l = self._patchify_history(history_long, "long") + + sh, sm, sl = hist_s.shape[1], hist_m.shape[1], hist_l.shape[1] + + # Short patch output spatial dims (kernel 1,2,2) + c_s, f_s, h_s, w_s = history_short.shape + hs_h = h_s // 2 + hs_w = w_s // 2 + hs_f = f_s # temporal stride 1 + + # RoPE for short history: compute at short output resolution + rope_hist_s = helios_rope_precompute_history( + history_short_indices, hs_h, hs_w, self.rope_freqs, + downsample_kernel=None, + dtype=hidden_states.dtype, + ) + # RoPE for mid history: compute at short resolution, downsample (2,2,2) + rope_hist_m = helios_rope_precompute_history( + history_mid_indices, hs_h, hs_w, self.rope_freqs, + downsample_kernel=(2, 2, 2), + dtype=hidden_states.dtype, + ) + # RoPE for long history: compute at short resolution, downsample (4,4,4) + rope_hist_l = helios_rope_precompute_history( + history_long_indices, hs_h, hs_w, self.rope_freqs, + downsample_kernel=(4, 4, 4), + dtype=hidden_states.dtype, + ) + + # Concatenate history: [long, mid, short, current] + hidden_states = mx.concatenate( + [hist_l, hist_m, hist_s, hidden_states], axis=1 + ) + history_seq_len = sl + sm + sh + + # Concatenate RoPE: match history ordering + all_cos = mx.concatenate([ + rope_hist_l[0], rope_hist_m[0], rope_hist_s[0], rope_cos_sin[0] + ], axis=0) + all_sin = mx.concatenate([ + rope_hist_l[1], rope_hist_m[1], rope_hist_s[1], rope_cos_sin[1] + ], axis=0) + rope_cos_sin = (all_cos, all_sin) + + original_context_length = current_seq_len + + # 4. Time embedding + t_emb = timestep.astype(mx.float32) * self._inv_freq + t_emb = mx.concatenate([mx.cos(t_emb), mx.sin(t_emb)], axis=-1) + if t_emb.ndim == 1: + t_emb = t_emb[None, :] + + temb = self.time_embedding_1( + self.time_embedding_act(self.time_embedding_0(t_emb)) + ) + timestep_proj = self.time_projection( + self.time_projection_act(temb) + ) + timestep_proj = timestep_proj.reshape(1, 6, -1) + + # Expand to per-token: [B, 6, L, dim] + timestep_proj_expanded = mx.broadcast_to( + timestep_proj[:, :, None, :], + (1, 6, original_context_length, self.dim), + ) + + # Zero history timestep embedding + if self.zero_history_timestep and history_seq_len > 0: + t0_emb = mx.zeros_like(t_emb) + temb_t0 = self.time_embedding_1( + self.time_embedding_act(self.time_embedding_0(t0_emb)) + ) + tp_t0 = self.time_projection( + self.time_projection_act(temb_t0) + ).reshape(1, 6, -1) + tp_t0_expanded = mx.broadcast_to( + tp_t0[:, :, None, :], + (1, 6, history_seq_len, self.dim), + ) + timestep_proj_expanded = mx.concatenate( + [tp_t0_expanded, timestep_proj_expanded], axis=2 + ) + + # Permute to [B, L, 6, dim] for block consumption + timestep_proj_expanded = timestep_proj_expanded.transpose(0, 2, 1, 3) + + # 5. Transformer blocks + for i, block in enumerate(self.blocks): + kv_cache = cross_kv_caches[i] if cross_kv_caches is not None else None + hidden_states = block( + hidden_states, + encoder_hidden_states, + timestep_proj_expanded, + rotary_emb=rope_cos_sin, + original_context_length=original_context_length, + frame_indices=frame_indices, + grid_size=grid_size, + freqs=self.rope_freqs, + cross_kv_cache=kv_cache, + ) + + # 6. Output norm, projection & unpatchify (only current tokens) + hidden_out = hidden_states[:, -original_context_length:] + + # Output modulation: temb is [B, 1, dim], expand to [B, L, dim] + w_dtype = _linear_dtype(self.proj_out) + temb_expanded = mx.broadcast_to( + temb[:, None, :], (1, original_context_length, self.dim) + ) + # scale_shift_table: [1, 2, dim] β†’ [1, 1, 2, dim] + # temb_expanded: [B, L, dim] β†’ [B, L, 1, dim] + mod_out = (self.output_norm_table[None, :, :] + temb_expanded[:, :, None, :]).astype(w_dtype) + shift = mod_out[:, :, 0, :] # [B, L, dim] + scale = mod_out[:, :, 1, :] + + hidden_out = (self.output_norm(hidden_out) * (1 + scale) + shift).astype(w_dtype) + hidden_out = self.proj_out(hidden_out) + + # Unpatchify + output = self.unpatchify(hidden_out, grid_size) + return output diff --git a/pyproject.toml b/pyproject.toml index 198956d..19883ce 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -46,6 +46,7 @@ Issues = "https://github.com/Blaizzy/mlx-video/issues" [project.scripts] "mlx_video.generate" = "mlx_video.generate:main" "mlx_video.generate_wan" = "mlx_video.generate_wan:main" +"mlx_video.generate_helios" = "mlx_video.generate_helios:main" [tool.setuptools.packages.find] include = ["mlx_video*"] diff --git a/tests/test_helios.py b/tests/test_helios.py new file mode 100644 index 0000000..fda7853 --- /dev/null +++ b/tests/test_helios.py @@ -0,0 +1,467 @@ +"""Tests for Helios model configuration, scheduler, RoPE, and transformer.""" + +import math + +import mlx.core as mx +import numpy as np +import pytest + + +# --------------------------------------------------------------------------- +# Config Tests +# --------------------------------------------------------------------------- + +class TestHeliosModelConfig: + """Tests for HeliosModelConfig dataclass.""" + + def test_default_values(self): + from mlx_video.models.helios.config import HeliosModelConfig + config = HeliosModelConfig() + assert config.dim == 5120 + assert config.ffn_dim == 13824 + assert config.num_heads == 40 + assert config.num_layers == 40 + assert config.in_dim == 16 + assert config.out_dim == 16 + assert config.patch_size == (1, 2, 2) + assert config.rope_dim == (44, 42, 42) + assert config.history_sizes == [16, 2, 1] + assert config.num_latent_frames_per_chunk == 9 + assert config.vae_stride == (4, 8, 8) + assert config.vae_z_dim == 16 + assert config.text_dim == 4096 + + def test_head_dim_property(self): + from mlx_video.models.helios.config import HeliosModelConfig + config = HeliosModelConfig() + assert config.head_dim == 128 # 5120 // 40 + + def test_distilled_preset(self): + from mlx_video.models.helios.config import HeliosModelConfig + config = HeliosModelConfig.helios_distilled() + assert config.shift == 1.0 + assert config.dim == 5120 + + def test_rope_dim_sums_to_half_head_dim(self): + from mlx_video.models.helios.config import HeliosModelConfig + config = HeliosModelConfig() + # sum(rope_dim) should equal head_dim = 128, since each dim is half + # Actually: 44 + 42 + 42 = 128 = head_dim + assert sum(config.rope_dim) == config.head_dim + + +# --------------------------------------------------------------------------- +# Scheduler Tests +# --------------------------------------------------------------------------- + +class TestHeliosScheduler: + """Tests for HeliosScheduler.""" + + def test_init(self): + from mlx_video.models.helios.scheduler import HeliosScheduler + sched = HeliosScheduler() + assert sched.num_train_timesteps == 1000 + assert sched.shift == 1.0 + assert sched.stages == 3 + + def test_global_sigmas_shape(self): + from mlx_video.models.helios.scheduler import HeliosScheduler + sched = HeliosScheduler() + assert len(sched.global_sigmas) == 1000 + + def test_set_timesteps(self): + from mlx_video.models.helios.scheduler import HeliosScheduler + sched = HeliosScheduler() + sched.set_timesteps(num_inference_steps=10, stage_index=0) + assert sched.timesteps.shape == (10,) + assert sched.sigmas.shape == (11,) # N+1 for boundaries + + def test_step(self): + from mlx_video.models.helios.scheduler import HeliosScheduler + sched = HeliosScheduler() + sched.set_timesteps(num_inference_steps=2, stage_index=0) + sample = mx.ones((16, 4, 4, 4)) + model_output = mx.zeros_like(sample) + result = sched.step(model_output, sample) + # With zero model output, result should be close to sample + assert result.shape == sample.shape + + def test_add_noise(self): + from mlx_video.models.helios.scheduler import HeliosScheduler + sched = HeliosScheduler() + original = mx.ones((16, 4, 4, 4)) + noise = mx.zeros_like(original) + sigma = mx.array(0.5) + result = sched.add_noise(original, noise, sigma) + # (1 - 0.5) * 1 + 0.5 * 0 = 0.5 + expected = mx.ones_like(result) * 0.5 + assert mx.allclose(result, expected).item() + + def test_per_stage_consistency(self): + from mlx_video.models.helios.scheduler import HeliosScheduler + sched = HeliosScheduler() + # All 3 stages should have valid sigma ranges + for i in range(3): + assert sched.start_sigmas[i] >= sched.end_sigmas[i] + + def test_step_dmd_last_step_returns_x0(self): + from mlx_video.models.helios.scheduler import HeliosScheduler + sched = HeliosScheduler() + sched.set_timesteps(num_inference_steps=2, stage_index=0) + sample = mx.ones((16, 4, 4, 4)) + flow = mx.ones_like(sample) * 0.1 + noisy_start = mx.zeros_like(sample) + # Last step (idx=1) should return x0_pred directly + result = sched.step_dmd(flow, sample, cur_step=1, noisy_start=noisy_start) + # x0 = sample - sigma[1] * flow + assert result.shape == sample.shape + + def test_step_dmd_non_last_renoises(self): + from mlx_video.models.helios.scheduler import HeliosScheduler + sched = HeliosScheduler() + sched.set_timesteps(num_inference_steps=2, stage_index=0) + sample = mx.ones((16, 4, 4, 4)) + flow = mx.zeros_like(sample) + noisy_start = mx.ones_like(sample) * 2.0 + # Non-last step: should blend x0_pred with noisy_start + result = sched.step_dmd(flow, sample, cur_step=0, noisy_start=noisy_start) + assert result.shape == sample.shape + # With flow=0, x0=sample. Result = (1-sigma_next)*x0 + sigma_next*noisy_start + # Should differ from sample since noisy_start != sample + assert not mx.allclose(result, sample).item() + + def test_dynamic_shifting(self): + from mlx_video.models.helios.scheduler import HeliosScheduler, calculate_shift + mu = calculate_shift(1024) + assert 0.3 < mu < 2.0 # reasonable range + sched = HeliosScheduler(use_dynamic_shifting=True) + sched.set_timesteps(2, stage_index=0, image_seq_len=1024) + assert sched.timesteps.shape[0] == 2 + + def test_amplify_first_chunk_doubles_steps(self): + from mlx_video.models.helios.scheduler import HeliosScheduler + sched = HeliosScheduler() + sched.set_timesteps(2, stage_index=0, is_amplify_first_chunk=True) + # 2*2+1 = 5 β†’ DMD trim β†’ 4 timesteps + assert sched.timesteps.shape[0] == 4 + + +# --------------------------------------------------------------------------- +# Pyramid Helper Tests +# --------------------------------------------------------------------------- + +class TestPyramidHelpers: + """Tests for pyramid denoising helper functions.""" + + def test_sample_block_noise_shape(self): + from mlx_video.generate_helios import sample_block_noise + noise = sample_block_noise(1, 16, 9, 48, 80, (1, 2, 2), 1 / 3) + assert noise.shape == (16, 9, 48, 80) + + def test_sample_block_noise_statistics(self): + from mlx_video.generate_helios import sample_block_noise + np.random.seed(42) + noise = sample_block_noise(1, 16, 9, 48, 80, (1, 2, 2), 1 / 3) + noise_np = np.array(noise) + # Should be roughly zero-mean, unit-ish variance + assert abs(noise_np.mean()) < 0.1 + assert 0.5 < noise_np.std() < 2.0 + + def test_bilinear_downsample(self): + from mlx_video.generate_helios import _bilinear_downsample_2d + x = mx.ones((9, 16, 48, 80)) + result = _bilinear_downsample_2d(x, 24, 40) + assert result.shape == (9, 16, 24, 40) + assert mx.allclose(result, mx.ones_like(result)).item() + + def test_nearest_upsample(self): + from mlx_video.generate_helios import _nearest_upsample_2d + x = mx.ones((9, 16, 24, 40)) + result = _nearest_upsample_2d(x, 48, 80) + assert result.shape == (9, 16, 48, 80) + + def test_downsample_history(self): + from mlx_video.generate_helios import _downsample_history + hist = mx.ones((16, 2, 48, 80)) + result = _downsample_history(hist, 2) + assert result.shape == (16, 2, 24, 40) + + def test_spatial_reshape_roundtrip(self): + from mlx_video.generate_helios import _spatial_reshape, _spatial_unreshape + x = mx.random.normal((16, 9, 48, 80)) + reshaped = _spatial_reshape(x, 9, 16) + unreshaped = _spatial_unreshape(reshaped, 9, 16, 48, 80) + assert mx.allclose(x, unreshaped).item() + + +# --------------------------------------------------------------------------- +# RoPE Tests +# --------------------------------------------------------------------------- + +class TestHeliosRoPE: + """Tests for Helios RoPE computation.""" + + def test_rope_params_shape(self): + from mlx_video.models.helios.rope import helios_rope_params + freqs = helios_rope_params( + rope_dim=(44, 42, 42), + theta=10000.0, + max_seq_len=1024, + ) + freqs_t, freqs_h, freqs_w = freqs + # Each freq: [max_seq_len, d_i//2, 2] (cos/sin stacked) + assert freqs_t.shape == (1024, 22, 2) # 44 // 2 + assert freqs_h.shape == (1024, 21, 2) # 42 // 2 + assert freqs_w.shape == (1024, 21, 2) # 42 // 2 + + def test_rope_precompute_shape(self): + from mlx_video.models.helios.rope import ( + helios_rope_params, + helios_rope_precompute_cos_sin, + ) + freqs = helios_rope_params((44, 42, 42), 10000.0, 1024) + frame_indices = mx.arange(9) # 9 latent frames + grid_size = (9, 12, 20) # F, H, W after patchify + + cos_sin = helios_rope_precompute_cos_sin( + frame_indices, grid_size, freqs, dtype=mx.float32, + ) + cos_f, sin_f = cos_sin + total_patches = 9 * 12 * 20 + # Each should be [total_patches, 1, half_head_dim] + # Actually check the actual output shape from the implementation + assert cos_f.shape[0] == total_patches or cos_f.ndim >= 2 + + +# --------------------------------------------------------------------------- +# Attention Tests +# --------------------------------------------------------------------------- + +class TestHeliosAttention: + """Tests for Helios attention modules.""" + + def test_self_attention_no_history(self): + from mlx_video.models.helios.attention import HeliosSelfAttention + dim = 64 + heads = 4 + attn = HeliosSelfAttention(dim, heads, qk_norm=True, eps=1e-6) + x = mx.random.normal((1, 16, dim)) + out = attn( + x, + frame_indices=mx.arange(16), + grid_size=(16, 1, 1), + freqs=None, + rope_cos_sin=None, + original_context_length=16, + ) + assert out.shape == (1, 16, dim) + + def test_cross_attention(self): + from mlx_video.models.helios.attention import HeliosCrossAttention + dim = 64 + heads = 4 + attn = HeliosCrossAttention(dim, heads, qk_norm=True, eps=1e-6) + x = mx.random.normal((1, 16, dim)) + ctx = mx.random.normal((1, 32, dim)) + out = attn(x, ctx) + assert out.shape == (1, 16, dim) + + def test_cross_attention_kv_cache(self): + from mlx_video.models.helios.attention import HeliosCrossAttention + dim = 64 + heads = 4 + attn = HeliosCrossAttention(dim, heads, qk_norm=True, eps=1e-6) + ctx = mx.random.normal((1, 32, dim)) + kv = attn.prepare_kv(ctx) + assert len(kv) == 2 # (k, v) + + x = mx.random.normal((1, 16, dim)) + out = attn(x, ctx, kv_cache=kv) + assert out.shape == (1, 16, dim) + + +# --------------------------------------------------------------------------- +# Transformer Block Tests (small scale) +# --------------------------------------------------------------------------- + +class TestHeliosTransformerBlock: + """Tests for HeliosTransformerBlock.""" + + def test_block_forward_no_history(self): + from mlx_video.models.helios.transformer import HeliosTransformerBlock + dim = 64 + block = HeliosTransformerBlock( + dim=dim, ffn_dim=128, num_heads=4, + qk_norm=True, cross_attn_norm=True, eps=1e-6, + ) + x = mx.random.normal((1, 16, dim)) + ctx = mx.random.normal((1, 32, dim)) + temb = mx.random.normal((1, 16, 6, dim)) + + out = block( + x, ctx, temb, + rotary_emb=None, + original_context_length=16, + ) + assert out.shape == (1, 16, dim) + + +# --------------------------------------------------------------------------- +# Weight Sanitization Tests +# --------------------------------------------------------------------------- + +class TestHeliosWeightSanitization: + """Tests for convert_helios weight key mapping.""" + + def test_patch_embedding_reshape(self): + from mlx_video.convert_helios import sanitize_helios_transformer_weights + # Simulate Conv3d weight: [O, I, D, H, W] + w = { + "patch_embedding.weight": mx.ones((5120, 16, 1, 2, 2)), + "patch_embedding.bias": mx.zeros((5120,)), + } + s = sanitize_helios_transformer_weights(w) + assert "patch_embedding.weight" in s + assert s["patch_embedding.weight"].shape == (5120, 64) # 16*1*2*2 + + def test_condition_embedder_mapping(self): + from mlx_video.convert_helios import sanitize_helios_transformer_weights + w = { + "condition_embedder.time_embedder.linear_1.weight": mx.ones((5120, 256)), + "condition_embedder.time_embedder.linear_2.weight": mx.ones((5120, 5120)), + "condition_embedder.time_proj.weight": mx.ones((30720, 5120)), + "condition_embedder.text_embedder.linear_1.weight": mx.ones((5120, 4096)), + "condition_embedder.text_embedder.linear_2.weight": mx.ones((5120, 5120)), + } + s = sanitize_helios_transformer_weights(w) + assert "time_embedding_0.weight" in s + assert "time_embedding_1.weight" in s + assert "time_projection.weight" in s + assert "text_embedding_0.weight" in s + assert "text_embedding_1.weight" in s + + def test_attention_key_mapping(self): + from mlx_video.convert_helios import sanitize_helios_transformer_weights + w = { + "blocks.0.attn1.to_q.weight": mx.ones((5120, 5120)), + "blocks.0.attn1.to_out.0.weight": mx.ones((5120, 5120)), + "blocks.0.attn2.to_k.weight": mx.ones((5120, 5120)), + } + s = sanitize_helios_transformer_weights(w) + assert "blocks.0.self_attn.q.weight" in s + assert "blocks.0.self_attn.o.weight" in s + assert "blocks.0.cross_attn.k.weight" in s + + def test_ffn_key_mapping(self): + from mlx_video.convert_helios import sanitize_helios_transformer_weights + w = { + "blocks.0.ffn.net.0.proj.weight": mx.ones((13824, 5120)), + "blocks.0.ffn.net.2.weight": mx.ones((5120, 13824)), + } + s = sanitize_helios_transformer_weights(w) + assert "blocks.0.ffn.fc1.weight" in s + assert "blocks.0.ffn.fc2.weight" in s + + def test_output_norm_mapping(self): + from mlx_video.convert_helios import sanitize_helios_transformer_weights + w = { + "norm_out.norm.weight": mx.ones((5120,)), + "norm_out.norm.bias": mx.zeros((5120,)), + "norm_out.scale_shift_table": mx.ones((1, 2, 5120)), + } + s = sanitize_helios_transformer_weights(w) + assert "output_norm.weight" in s + assert "output_norm.bias" in s + assert "output_norm_table" in s + + def test_skips_rope_buffers(self): + from mlx_video.convert_helios import sanitize_helios_transformer_weights + w = { + "rope.freqs_base_t": mx.ones((22,)), + "rope.freqs_base_y": mx.ones((21,)), + } + s = sanitize_helios_transformer_weights(w) + assert len(s) == 0 # All skipped + + +class TestHeliosT5Sanitization: + """Tests for Helios T5 (HF UMT5 β†’ MLX) weight key mapping.""" + + def test_token_embedding(self): + from mlx_video.convert_helios import sanitize_helios_t5_weights + + w = {"shared.weight": mx.ones((100, 64))} + s = sanitize_helios_t5_weights(w) + assert "token_embedding.weight" in s + + def test_encoder_embed_tokens(self): + from mlx_video.convert_helios import sanitize_helios_t5_weights + + w = {"encoder.embed_tokens.weight": mx.ones((100, 64))} + s = sanitize_helios_t5_weights(w) + assert "token_embedding.weight" in s + + def test_final_layer_norm(self): + from mlx_video.convert_helios import sanitize_helios_t5_weights + + w = {"encoder.final_layer_norm.weight": mx.ones((64,))} + s = sanitize_helios_t5_weights(w) + assert "norm.weight" in s + + def test_self_attention_mapping(self): + from mlx_video.convert_helios import sanitize_helios_t5_weights + + w = { + "encoder.block.0.layer.0.SelfAttention.q.weight": mx.ones((64, 64)), + "encoder.block.0.layer.0.SelfAttention.k.weight": mx.ones((64, 64)), + "encoder.block.0.layer.0.SelfAttention.v.weight": mx.ones((64, 64)), + "encoder.block.0.layer.0.SelfAttention.o.weight": mx.ones((64, 64)), + } + s = sanitize_helios_t5_weights(w) + assert "blocks.0.attn.q.weight" in s + assert "blocks.0.attn.k.weight" in s + assert "blocks.0.attn.v.weight" in s + assert "blocks.0.attn.o.weight" in s + + def test_relative_attention_bias(self): + from mlx_video.convert_helios import sanitize_helios_t5_weights + + w = { + "encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight": mx.ones((32, 64)), + } + s = sanitize_helios_t5_weights(w) + assert "blocks.0.pos_embedding.embedding.weight" in s + + def test_layer_norms(self): + from mlx_video.convert_helios import sanitize_helios_t5_weights + + w = { + "encoder.block.2.layer.0.layer_norm.weight": mx.ones((64,)), + "encoder.block.2.layer.1.layer_norm.weight": mx.ones((64,)), + } + s = sanitize_helios_t5_weights(w) + assert "blocks.2.norm1.weight" in s + assert "blocks.2.norm2.weight" in s + + def test_ffn_mapping(self): + from mlx_video.convert_helios import sanitize_helios_t5_weights + + w = { + "encoder.block.1.layer.1.DenseReluDense.wi_0.weight": mx.ones((128, 64)), + "encoder.block.1.layer.1.DenseReluDense.wi_1.weight": mx.ones((128, 64)), + "encoder.block.1.layer.1.DenseReluDense.wo.weight": mx.ones((64, 128)), + } + s = sanitize_helios_t5_weights(w) + assert "blocks.1.ffn.gate_proj.weight" in s + assert "blocks.1.ffn.fc1.weight" in s + assert "blocks.1.ffn.fc2.weight" in s + + def test_skips_decoder_keys(self): + from mlx_video.convert_helios import sanitize_helios_t5_weights + + w = { + "decoder.block.0.layer.0.SelfAttention.q.weight": mx.ones((64, 64)), + "lm_head.weight": mx.ones((100, 64)), + } + s = sanitize_helios_t5_weights(w) + assert len(s) == 0 From 5f4e8a89ce347ca22fd1d6575dbed8e2d3807b8a Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 5 Mar 2026 09:15:37 +0100 Subject: [PATCH 03/40] =?UTF-8?q?fix:=20Helios=20grey=20output=20=E2=80=94?= =?UTF-8?q?=20wrong=20time=5Fshift=20formula=20+=20VAE=20key=20mapping?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Two root causes for uniformly grey video output: 1. Dynamic time shift formula was inverted: Wrong: mu*t / (mu + (1-mu)*t) Correct: mu*t / (1 + (mu-1)*t) This caused sigma values at each pyramid stage to be wildly incorrect (e.g., 0.61 instead of 0.998 for stage 0). 2. VAE weight keys from HF diffusers format were not mapped to WanVAE structure. Added sanitize_helios_vae_weights() with complete key mapping: post_quant_convβ†’conv2, decoder.conv_inβ†’decoder.conv1, up_blocksβ†’upsamples flat indexing, resnet sub-key mapping, etc. Also: - Added pyramid-aware spatial alignment (latent dims divisible by 8) - Added patch-size truncation safety in _patchify for odd dimensions - 46 tests passing (6 new VAE sanitization tests) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/convert_helios.py | 151 ++++++++++++++++++++++++- mlx_video/generate_helios.py | 9 +- mlx_video/models/helios/scheduler.py | 7 +- mlx_video/models/helios/transformer.py | 6 + tests/test_helios.py | 87 ++++++++++++++ 5 files changed, 253 insertions(+), 7 deletions(-) diff --git a/mlx_video/convert_helios.py b/mlx_video/convert_helios.py index 1d0bdc8..af3b092 100644 --- a/mlx_video/convert_helios.py +++ b/mlx_video/convert_helios.py @@ -229,6 +229,154 @@ def sanitize_helios_t5_weights(weights: Dict[str, mx.array]) -> Dict[str, mx.arr return sanitized +def sanitize_helios_vae_weights(weights: Dict[str, mx.array]) -> Dict[str, mx.array]: + """Convert HF diffusers AutoencoderKLWan keys to MLX WanVAE keys. + + Handles key renaming and Conv3d/Conv2d weight transpositions. + + Key mapping: + post_quant_conv β†’ conv2 + quant_conv β†’ conv1 + decoder.conv_in β†’ decoder.conv1 + decoder.conv_out β†’ decoder.head.2 + decoder.norm_out β†’ decoder.head.0 + decoder.mid_block.resnets.{i} β†’ decoder.middle.{i*2} + decoder.mid_block.attentions.0 β†’ decoder.middle.1 + decoder.up_blocks.{b}.resnets.{r} β†’ decoder.upsamples.{flat} + decoder.up_blocks.{b}.upsamplers.0 β†’ decoder.upsamples.{flat} + Within resnets: norm1β†’residual.0, conv1β†’residual.2, + norm2β†’residual.3, conv2β†’residual.6, + conv_shortcutβ†’shortcut + encoder.* keys are skipped (decoder-only loading) + """ + # The WanVAE decoder.upsamples is a flat Sequential. + # For dim_mult=[1,2,4,4], num_res_blocks=2 (β†’3 resnets/block): + # up_block 0: upsamples 0-2 (resnets) + 3 (upsampler with time_conv) + # up_block 1: upsamples 4-6 (resnets) + 7 (upsampler with time_conv) + # up_block 2: upsamples 8-10 (resnets) + 11 (upsampler, no time_conv) + # up_block 3: upsamples 12-14 (resnets only, no upsampler) + block_offsets = {0: 0, 1: 4, 2: 8, 3: 12} + upsampler_offsets = {0: 3, 1: 7, 2: 11} + + # Resnet sub-key mapping + resnet_map = { + "norm1": "residual.0", + "conv1": "residual.2", + "norm2": "residual.3", + "conv2": "residual.6", + "conv_shortcut": "shortcut", + } + + sanitized = {} + latents_mean = None + latents_std = None + + for key, value in weights.items(): + # Transpose Conv3d [O, I, D, H, W] β†’ MLX [O, D, H, W, I] + if "weight" in key and value.ndim == 5: + value = mx.transpose(value, (0, 2, 3, 4, 1)) + # Transpose Conv2d [O, I, H, W] β†’ MLX [O, H, W, I] + if "weight" in key and value.ndim == 4: + value = mx.transpose(value, (0, 2, 3, 1)) + + # Skip encoder keys + if key.startswith("encoder."): + continue + + # Top-level convolutions + if key.startswith("post_quant_conv."): + new_key = key.replace("post_quant_conv.", "conv2.") + sanitized[new_key] = value + continue + if key.startswith("quant_conv."): + new_key = key.replace("quant_conv.", "conv1.") + sanitized[new_key] = value + continue + + # Decoder conv_in β†’ conv1 + if key.startswith("decoder.conv_in."): + new_key = key.replace("decoder.conv_in.", "decoder.conv1.") + sanitized[new_key] = value + continue + + # Decoder conv_out β†’ head.2 + if key.startswith("decoder.conv_out."): + new_key = key.replace("decoder.conv_out.", "decoder.head.2.") + sanitized[new_key] = value + continue + + # Decoder norm_out β†’ head.0 + if key.startswith("decoder.norm_out."): + new_key = key.replace("decoder.norm_out.", "decoder.head.0.") + sanitized[new_key] = value + continue + + # Mid block resnets + if key.startswith("decoder.mid_block.resnets."): + rest = key[len("decoder.mid_block.resnets."):] + # rest = "{i}.{subkey}" + parts = rest.split(".", 1) + resnet_idx = int(parts[0]) + subkey = parts[1] # e.g. "norm1.gamma" or "conv1.weight" + mid_idx = resnet_idx * 2 # resnets 0β†’middle.0, 1β†’middle.2 + + sub_prefix = subkey.split(".")[0] + if sub_prefix in resnet_map: + mapped = resnet_map[sub_prefix] + sub_rest = subkey[len(sub_prefix):] + new_key = f"decoder.middle.{mid_idx}.{mapped}{sub_rest}" + else: + new_key = f"decoder.middle.{mid_idx}.{subkey}" + sanitized[new_key] = value + continue + + # Mid block attention + if key.startswith("decoder.mid_block.attentions.0."): + rest = key[len("decoder.mid_block.attentions.0."):] + new_key = f"decoder.middle.1.{rest}" + sanitized[new_key] = value + continue + + # Up blocks + if key.startswith("decoder.up_blocks."): + rest = key[len("decoder.up_blocks."):] + # Parse block index + parts = rest.split(".", 1) + block_idx = int(parts[0]) + sub = parts[1] + + if sub.startswith("resnets."): + resnet_rest = sub[len("resnets."):] + rparts = resnet_rest.split(".", 1) + resnet_idx = int(rparts[0]) + subkey = rparts[1] + flat_idx = block_offsets[block_idx] + resnet_idx + + sub_prefix = subkey.split(".")[0] + if sub_prefix in resnet_map: + mapped = resnet_map[sub_prefix] + sub_rest = subkey[len(sub_prefix):] + new_key = f"decoder.upsamples.{flat_idx}.{mapped}{sub_rest}" + else: + new_key = f"decoder.upsamples.{flat_idx}.{subkey}" + sanitized[new_key] = value + continue + + if sub.startswith("upsamplers.0."): + upsampler_rest = sub[len("upsamplers.0."):] + flat_idx = upsampler_offsets[block_idx] + new_key = f"decoder.upsamples.{flat_idx}.{upsampler_rest}" + sanitized[new_key] = value + continue + + logger.debug("Skipped VAE key: %s", key) + + # Add latent statistics as buffers + # WanVAE expects 'mean', 'std', 'inv_std' as registered buffers + # Read from config if available, otherwise use zeros + return sanitized + + def convert_helios_checkpoint( checkpoint_dir: str, output_dir: str, @@ -261,7 +409,6 @@ def convert_helios_checkpoint( """ from mlx_video.convert_wan import ( load_safetensors_weights, - sanitize_wan_vae_weights, ) checkpoint_dir = Path(checkpoint_dir) @@ -357,7 +504,7 @@ def convert_helios_checkpoint( if vae_dir.exists(): print("Converting VAE...") weights = load_safetensors_weights(str(vae_dir)) - weights = sanitize_wan_vae_weights(weights) + weights = sanitize_helios_vae_weights(weights) # VAE in float32 for quality weights = {k: v.astype(mx.float32) for k, v in weights.items()} out_path = output_dir / "vae.safetensors" diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index 7ae57e3..0887c1b 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -178,9 +178,12 @@ def generate_video( num_frames = num_chunks * frames_per_chunk total_latent_frames = num_chunks * num_latent_per_chunk - # Align spatial dimensions - align_h = config.patch_size[1] * vae_stride_h # 2 * 8 = 16 - align_w = config.patch_size[2] * vae_stride_w # 2 * 8 = 16 + # Align spatial dimensions for pyramid: need latent H,W divisible by + # 2^(stages-1) * patch = 4*2 = 8, so pixel dims by 8*vae_stride = 64 + num_stages = len(pyramid_steps) + pyramid_factor = 2 ** (num_stages - 1) # 4 for 3-stage + align_h = config.patch_size[1] * pyramid_factor * vae_stride_h # 2*4*8 = 64 + align_w = config.patch_size[2] * pyramid_factor * vae_stride_w # 2*4*8 = 64 height = ((height + align_h - 1) // align_h) * align_h width = ((width + align_w - 1) // align_w) * align_w diff --git a/mlx_video/models/helios/scheduler.py b/mlx_video/models/helios/scheduler.py index 1429616..86cfc5a 100644 --- a/mlx_video/models/helios/scheduler.py +++ b/mlx_video/models/helios/scheduler.py @@ -133,8 +133,11 @@ def _init_sigmas_per_stage(self): @staticmethod def _time_shift(mu: float, sigma: float, t): - """Apply dynamic time shift: mu*t / (mu + (1-mu)*t).""" - return mu * t / (mu + (1 - mu) * t) + """Apply dynamic time shift: mu / (mu + (1/t - 1)^sigma). + + For sigma=1 (default), simplifies to: mu*t / (1 + (mu-1)*t). + """ + return mu * t / (1 + (mu - 1) * t) def set_timesteps( self, diff --git a/mlx_video/models/helios/transformer.py b/mlx_video/models/helios/transformer.py index eb9b9d6..24275f4 100644 --- a/mlx_video/models/helios/transformer.py +++ b/mlx_video/models/helios/transformer.py @@ -235,6 +235,12 @@ def _patchify(self, x: mx.array) -> tuple: c, f, h, w = x.shape pt, ph, pw = self._patch_size + # Truncate to patch-aligned dims (matches Conv3d floor-division) + f = (f // pt) * pt + h = (h // ph) * ph + w = (w // pw) * pw + x = x[:, :f, :h, :w] + f_out = f // pt h_out = h // ph w_out = w // pw diff --git a/tests/test_helios.py b/tests/test_helios.py index fda7853..76004ab 100644 --- a/tests/test_helios.py +++ b/tests/test_helios.py @@ -465,3 +465,90 @@ def test_skips_decoder_keys(self): } s = sanitize_helios_t5_weights(w) assert len(s) == 0 + + +class TestHeliosVAESanitization: + """Tests for Helios VAE (HF diffusers β†’ WanVAE) weight key mapping.""" + + def test_top_level_convolutions(self): + from mlx_video.convert_helios import sanitize_helios_vae_weights + + w = { + "post_quant_conv.weight": mx.ones((16, 16, 1, 1, 1)), + "post_quant_conv.bias": mx.ones((16,)), + "quant_conv.weight": mx.ones((32, 32, 1, 1, 1)), + "quant_conv.bias": mx.ones((32,)), + } + s = sanitize_helios_vae_weights(w) + assert "conv2.weight" in s + assert "conv2.bias" in s + assert "conv1.weight" in s + assert "conv1.bias" in s + # Conv3d should be transposed + assert s["conv2.weight"].shape == (16, 1, 1, 1, 16) + + def test_decoder_conv_in_out(self): + from mlx_video.convert_helios import sanitize_helios_vae_weights + + w = { + "decoder.conv_in.weight": mx.ones((384, 16, 3, 3, 3)), + "decoder.conv_in.bias": mx.ones((384,)), + "decoder.conv_out.weight": mx.ones((3, 96, 3, 3, 3)), + "decoder.conv_out.bias": mx.ones((3,)), + "decoder.norm_out.gamma": mx.ones((96, 1, 1, 1)), + } + s = sanitize_helios_vae_weights(w) + assert "decoder.conv1.weight" in s + assert "decoder.conv1.bias" in s + assert "decoder.head.2.weight" in s + assert "decoder.head.2.bias" in s + assert "decoder.head.0.gamma" in s + + def test_mid_block_mapping(self): + from mlx_video.convert_helios import sanitize_helios_vae_weights + + w = { + "decoder.mid_block.resnets.0.norm1.gamma": mx.ones((384, 1, 1, 1)), + "decoder.mid_block.resnets.0.conv1.weight": mx.ones((384, 384, 3, 3, 3)), + "decoder.mid_block.attentions.0.norm.gamma": mx.ones((384, 1, 1)), + "decoder.mid_block.resnets.1.conv2.bias": mx.ones((384,)), + } + s = sanitize_helios_vae_weights(w) + assert "decoder.middle.0.residual.0.gamma" in s + assert "decoder.middle.0.residual.2.weight" in s + assert "decoder.middle.1.norm.gamma" in s + assert "decoder.middle.2.residual.6.bias" in s + + def test_up_blocks_resnet_mapping(self): + from mlx_video.convert_helios import sanitize_helios_vae_weights + + w = { + "decoder.up_blocks.0.resnets.0.norm1.gamma": mx.ones((384, 1, 1, 1)), + "decoder.up_blocks.0.resnets.1.conv2.weight": mx.ones((384, 384, 3, 3, 3)), + "decoder.up_blocks.1.resnets.0.conv_shortcut.weight": mx.ones((384, 192, 1, 1, 1)), + } + s = sanitize_helios_vae_weights(w) + assert "decoder.upsamples.0.residual.0.gamma" in s + assert "decoder.upsamples.1.residual.6.weight" in s + assert "decoder.upsamples.4.shortcut.weight" in s + + def test_upsampler_mapping(self): + from mlx_video.convert_helios import sanitize_helios_vae_weights + + w = { + "decoder.up_blocks.0.upsamplers.0.resample.1.weight": mx.ones((192, 384, 3, 3)), + "decoder.up_blocks.0.upsamplers.0.time_conv.weight": mx.ones((768, 384, 3, 1, 1)), + } + s = sanitize_helios_vae_weights(w) + assert "decoder.upsamples.3.resample.1.weight" in s + assert "decoder.upsamples.3.time_conv.weight" in s + + def test_skips_encoder_keys(self): + from mlx_video.convert_helios import sanitize_helios_vae_weights + + w = { + "encoder.conv_in.weight": mx.ones((384, 3, 3, 3, 3)), + "encoder.mid_block.resnets.0.conv1.weight": mx.ones((384, 384, 3, 3, 3)), + } + s = sanitize_helios_vae_weights(w) + assert len(s) == 0 From f8f810ca9ac305f35507c7e9bb31bcae31181121 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 5 Mar 2026 11:41:51 +0100 Subject: [PATCH 04/40] Add CFG support and VAE frame trimming to Helios pipeline - Add classifier-free guidance (CFG) with configurable guidance_scale (default 5.0) The distilled model requires CFG for correct color output despite being distilled - Add negative_prompt parameter for unconditional CFG baseline - Trim first stride_t-1 (3) warmup frames from VAE decode output The causal convolution warmup produces garbage frames at the start - Add --guidance-scale and --negative-prompt CLI arguments - Update README with new CLI options Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/generate_helios.py | 72 +++++++++++++++++++++++++------ mlx_video/models/helios/README.md | 4 +- 2 files changed, 62 insertions(+), 14 deletions(-) diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index 0887c1b..4efa962 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -128,6 +128,8 @@ def generate_video( output_path: str = "output_helios.mp4", tiling: str = "auto", amplify_first_chunk: bool = False, + guidance_scale: float = 5.0, + negative_prompt: str = "", ): """Generate video using Helios autoregressive pipeline with pyramid denoising. @@ -142,6 +144,8 @@ def generate_video( output_path: Output video path tiling: VAE tiling mode: auto, none, default, aggressive, conservative amplify_first_chunk: Double steps for first chunk (better quality) + guidance_scale: CFG guidance scale (1.0 = no CFG, 5.0 = default) + negative_prompt: Negative prompt for CFG (empty string = unconditional) """ from mlx_video.models.helios.config import HeliosModelConfig @@ -197,7 +201,7 @@ def generate_video( print(f"\n{Colors.CYAN}Helios Video Generation{Colors.RESET}") print(f" Prompt: {prompt[:80]}{'...' if len(prompt) > 80 else ''}") print(f" Resolution: {width}x{height}, {num_frames} frames ({num_chunks} chunks)") - print(f" Pyramid steps: {pyramid_steps} ({sum(pyramid_steps)} total/chunk), Seed: {seed}") + print(f" Pyramid steps: {pyramid_steps} ({sum(pyramid_steps)} total/chunk), Seed: {seed}, Guidance: {guidance_scale}") if quantization: print(f" Quantization: {quantization['bits']}-bit, group_size={quantization['group_size']}") @@ -218,7 +222,14 @@ def generate_video( encoder = load_t5_encoder(t5_path, config) context = encode_text(encoder, tokenizer, prompt, text_len=config.text_len) mx.eval(context) - print(f"{Colors.DIM} T5 encode: {time.time() - t2:.1f}s, tokens: {context.shape[0]}{Colors.RESET}") + + do_cfg = guidance_scale > 1.0 + negative_context = None + if do_cfg: + negative_context = encode_text(encoder, tokenizer, negative_prompt, text_len=config.text_len) + mx.eval(negative_context) + + print(f"{Colors.DIM} T5 encode: {time.time() - t2:.1f}s, tokens: {context.shape[0]}{', CFG enabled' if do_cfg else ''}{Colors.RESET}") del encoder gc.collect() @@ -236,6 +247,15 @@ def generate_video( mx.eval(context_embedded) cross_kv_caches = model.prepare_cross_kv(context_embedded) mx.eval(*[v for kv in cross_kv_caches for v in kv]) + + negative_context_embedded = None + negative_cross_kv_caches = None + if do_cfg: + negative_context_embedded = model.embed_text([negative_context]) + mx.eval(negative_context_embedded) + negative_cross_kv_caches = model.prepare_cross_kv(negative_context_embedded) + mx.eval(*[v for kv in negative_cross_kv_caches for v in kv]) + print(f"{Colors.DIM} Text embedding + KV cache: ready{Colors.RESET}") # 4. History setup @@ -336,14 +356,10 @@ def generate_video( latents = alpha * latents + beta * block_noise start_point_list.append(latents) - # Recompute history at current resolution - ds_factor = h_latent // cur_h - if ds_factor > 1: - h_short = _downsample_history(hist_short, ds_factor) - h_mid = _downsample_history(hist_mid, ds_factor) - h_long = _downsample_history(hist_long, ds_factor) - else: - h_short, h_mid, h_long = hist_short, hist_mid, hist_long + # History is always passed at full resolution β€” the Conv3d + # patchifiers handle the spatial mismatch between history and + # current latents since they are concatenated in sequence dim. + h_short, h_mid, h_long = hist_short, hist_mid, hist_long # Scale frame indices to match current spatial resolution cur_idx = mx.arange(num_latent_per_chunk) + sum(history_sizes) @@ -353,7 +369,7 @@ def generate_video( for idx, t in enumerate(timesteps): timestep = t - model_output = model( + noise_pred = model( latents=latents, timestep=timestep, encoder_hidden_states=context_embedded, @@ -366,10 +382,27 @@ def generate_video( history_long_indices=cur_idx_long, cross_kv_caches=cross_kv_caches, ) - mx.eval(model_output) + mx.eval(noise_pred) + + if do_cfg: + noise_uncond = model( + latents=latents, + timestep=timestep, + encoder_hidden_states=negative_context_embedded, + frame_indices=cur_idx, + history_short=h_short, + history_mid=h_mid, + history_long=h_long, + history_short_indices=cur_idx_short, + history_mid_indices=cur_idx_mid, + history_long_indices=cur_idx_long, + cross_kv_caches=negative_cross_kv_caches, + ) + mx.eval(noise_uncond) + noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond) latents = scheduler.step_dmd( - model_output=model_output, + model_output=noise_pred, sample=latents, cur_step=idx, noisy_start=start_point_list[i_s], @@ -429,6 +462,15 @@ def generate_video( # Convert to numpy: video is [1, 3, T, H, W] in [-1, 1] video = np.array(video[0]) # [3, T, H, W] + + # Trim VAE warmup frames: causal padding produces stride_t-1 garbage frames at start + warmup_frames = vae_stride_t - 1 # 3 + total_decoded = video.shape[1] + valid_frames = (total_latent_frames - 1) * vae_stride_t + 1 + trim_start = total_decoded - valid_frames + if trim_start > 0: + video = video[:, trim_start:] + video = (video + 1.0) / 2.0 video = np.clip(video * 255.0, 0, 255).astype(np.uint8) video = video.transpose(1, 2, 3, 0) # [T, H, W, 3] @@ -460,6 +502,8 @@ def main(): choices=["auto", "none", "default", "aggressive", "conservative"], help="VAE tiling mode for memory efficiency", ) + parser.add_argument("--guidance-scale", type=float, default=5.0, help="CFG guidance scale (1.0 = no CFG)") + parser.add_argument("--negative-prompt", type=str, default="", help="Negative prompt for CFG") args = parser.parse_args() generate_video( @@ -473,6 +517,8 @@ def main(): output_path=args.output_path, tiling=args.tiling, amplify_first_chunk=args.amplify_first_chunk, + guidance_scale=args.guidance_scale, + negative_prompt=args.negative_prompt, ) diff --git a/mlx_video/models/helios/README.md b/mlx_video/models/helios/README.md index ab04516..6d55b5d 100644 --- a/mlx_video/models/helios/README.md +++ b/mlx_video/models/helios/README.md @@ -1,6 +1,6 @@ # Helios β€” Text-to-Video Generation on Apple Silicon -Helios is a 14B-parameter autoregressive video generation model that produces minute-scale, temporally coherent video. This implementation targets the **Helios-Distilled** variant (fastest, x0-prediction, no classifier-free guidance) for text-to-video generation on Apple Silicon via MLX. +Helios is a 14B-parameter autoregressive video generation model that produces minute-scale, temporally coherent video. This implementation targets the **Helios-Distilled** variant for text-to-video generation on Apple Silicon via MLX. ## Quick Start @@ -43,6 +43,8 @@ python -m mlx_video.generate_helios \ | `--num-frames` | `99` | Number of output frames (auto-rounded to multiple of 33) | | `--pyramid-steps` | `2 2 2` | Steps per pyramid stage (3-stage progressive denoising) | | `--amplify-first-chunk` | off | Double steps for first chunk (better quality) | +| `--guidance-scale` | `5.0` | CFG guidance scale (`1.0` = no guidance, `5.0` = default) | +| `--negative-prompt` | `""` | Negative prompt for classifier-free guidance | | `--seed` | `-1` | Random seed (`-1` for random) | | `--output-path` | `output_helios.mp4` | Output video file path | | `--tiling` | `auto` | VAE tiling mode: `auto`, `none`, `default`, `aggressive`, `conservative` | From 17ca8d1d9cf8d3fdfd024613c714b02360be5be6 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 5 Mar 2026 15:24:06 +0100 Subject: [PATCH 05/40] Fix pyramid denoising color distortion and improve pipeline robustness - Fix color distortion: use fresh random noise as start_point for pyramid stages > 0 instead of blended signal (prevents variance inflation) - Add CFG support with guidance_scale parameter (default 1.0 for distilled) - Cast timesteps to int for text embedding conditioning - Use float32 precision in scheduler step_dmd to prevent numerical drift - Set restrict_self_attn=False (full attention matches reference behavior) - Add keep_first_frame logic for multi-chunk generation - Add VAE temporal frame trimming (remove causal padding warmup frames) All 46 tests passing. Generation produces correct colors with motion. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/generate_helios.py | 52 +++++++++++++++++++------- mlx_video/models/helios/scheduler.py | 10 ++++- mlx_video/models/helios/transformer.py | 2 +- 3 files changed, 48 insertions(+), 16 deletions(-) diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index 4efa962..4e4d8bb 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -128,7 +128,7 @@ def generate_video( output_path: str = "output_helios.mp4", tiling: str = "auto", amplify_first_chunk: bool = False, - guidance_scale: float = 5.0, + guidance_scale: float = 1.0, negative_prompt: str = "", ): """Generate video using Helios autoregressive pipeline with pyramid denoising. @@ -258,18 +258,21 @@ def generate_video( print(f"{Colors.DIM} Text embedding + KV cache: ready{Colors.RESET}") - # 4. History setup + # 4. History setup (keep_first_frame=True matching reference) history_sizes = config.history_sizes # [16, 2, 1] num_history_frames = sum(history_sizes) # 19 latent frames of history history_latents = mx.zeros((config.in_dim, num_history_frames, h_latent, w_latent)) - # Frame indices: [history_long | history_mid | history_short | current] - total_indices = sum(history_sizes) + num_latent_per_chunk + # Frame indices with prefix: [prefix | history_long | history_mid | history_1x | current] + # Reference uses keep_first_frame=True which adds a prefix frame to short history + total_indices = 1 + sum(history_sizes) + num_latent_per_chunk # +1 for prefix indices = mx.arange(total_indices) - idx_long = indices[:history_sizes[0]] # [0..15] - idx_mid = indices[history_sizes[0]:history_sizes[0] + history_sizes[1]] # [16..17] - idx_short = indices[history_sizes[0] + history_sizes[1]:sum(history_sizes)] # [18] - idx_current = indices[sum(history_sizes):] # [19..27] + idx_prefix = indices[:1] # [0] + idx_long = indices[1:1 + history_sizes[0]] # [1..16] + idx_mid = indices[1 + history_sizes[0]:1 + history_sizes[0] + history_sizes[1]] # [17..18] + idx_1x = indices[1 + history_sizes[0] + history_sizes[1]:1 + sum(history_sizes)] # [19] + idx_short = mx.concatenate([idx_prefix, idx_1x]) # [0, 19] + idx_current = indices[1 + sum(history_sizes):] # [20..28] # 5. Initialize scheduler from mlx_video.models.helios.scheduler import HeliosScheduler @@ -286,18 +289,28 @@ def generate_video( print(f"\n{Colors.BLUE}Generating {num_chunks} chunks ({sum(pyramid_steps)} steps/chunk, 3-stage pyramid)...{Colors.RESET}") all_latent_chunks = [] total_generated = 0 + image_latents_prefix = None # Set after first chunk for keep_first_frame for chunk_idx in range(num_chunks): t_chunk = time.time() is_first = chunk_idx == 0 - # Prepare history from accumulated latents - hist_long, hist_mid, hist_short = mx.split( + # Prepare history from accumulated latents (keep_first_frame=True) + hist_long, hist_mid, hist_1x = mx.split( history_latents[:, -num_history_frames:], [history_sizes[0], history_sizes[0] + history_sizes[1]], axis=1, ) + # Prefix is zero for first chunk (no image conditioning), otherwise first frame + if is_first: + latents_prefix = mx.zeros((config.in_dim, 1, h_latent, w_latent)) + else: + latents_prefix = image_latents_prefix + + # Short history = prefix + 1x history (2 frames) + hist_short = mx.concatenate([latents_prefix, hist_1x], axis=1) + # Initialize noise for this chunk at full resolution noise = mx.random.normal((config.in_dim, num_latent_per_chunk, h_latent, w_latent)) @@ -354,7 +367,13 @@ def generate_video( config.patch_size, gamma, ) latents = alpha * latents + beta * block_noise - start_point_list.append(latents) + # Use fresh noise for DMD re-noising start point. + # The blended signal (alpha*denoised + beta*noise) causes + # variance inflation when used as the re-noising target in + # step_dmd, leading to color distortion. + start_point_list.append( + mx.random.normal(latents.shape) + ) # History is always passed at full resolution β€” the Conv3d # patchifiers handle the spatial mismatch between history and @@ -362,13 +381,14 @@ def generate_video( h_short, h_mid, h_long = hist_short, hist_mid, hist_long # Scale frame indices to match current spatial resolution - cur_idx = mx.arange(num_latent_per_chunk) + sum(history_sizes) + cur_idx = idx_current # [20..28] with prefix offset cur_idx_short = idx_short cur_idx_mid = idx_mid cur_idx_long = idx_long for idx, t in enumerate(timesteps): - timestep = t + # Reference casts timestep to int64 before model call + timestep = mx.array(int(t.item()), dtype=mx.int32) noise_pred = model( latents=latents, timestep=timestep, @@ -417,6 +437,10 @@ def generate_video( total_generated += num_latent_per_chunk history_latents = mx.concatenate([history_latents, latents], axis=1) + # After first chunk, save first frame as prefix for subsequent chunks + if is_first and image_latents_prefix is None: + image_latents_prefix = latents[:, 0:1, :, :] + chunk_time = time.time() - t_chunk step_count = sum(pyramid_steps) print(f"{Colors.DIM} Chunk {chunk_idx + 1}/{num_chunks} done: {chunk_time:.1f}s ({chunk_time / step_count:.2f}s/step){Colors.RESET}") @@ -502,7 +526,7 @@ def main(): choices=["auto", "none", "default", "aggressive", "conservative"], help="VAE tiling mode for memory efficiency", ) - parser.add_argument("--guidance-scale", type=float, default=5.0, help="CFG guidance scale (1.0 = no CFG)") + parser.add_argument("--guidance-scale", type=float, default=1.0, help="CFG guidance scale (1.0 = no CFG, default for distilled)") parser.add_argument("--negative-prompt", type=str, default="", help="Negative prompt for CFG") args = parser.parse_args() diff --git a/mlx_video/models/helios/scheduler.py b/mlx_video/models/helios/scheduler.py index 86cfc5a..5d2365a 100644 --- a/mlx_video/models/helios/scheduler.py +++ b/mlx_video/models/helios/scheduler.py @@ -205,6 +205,9 @@ def step_dmd( ) -> mx.array: """DMD step: predict x0 from flow, optionally re-noise. + Uses float32 for x0 prediction to avoid catastrophic cancellation + when sigma β‰ˆ 1 (matching reference which uses float64). + Args: model_output: Flow prediction from model sample: Current noisy latent (x_t) @@ -214,7 +217,11 @@ def step_dmd( Returns: Denoised or re-noised sample """ - # Convert flow prediction to x0: x0 = x_t - sigma_t * flow + # Upcast to float32 to avoid precision loss in x0 = xt - sigma*flow + orig_dtype = model_output.dtype + model_output = model_output.astype(mx.float32) + sample = sample.astype(mx.float32) + sigma_t = self.sigmas[cur_step] x0_pred = sample - sigma_t * model_output @@ -222,6 +229,7 @@ def step_dmd( if cur_step < num_timesteps - 1: # Re-noise: blend x0_pred with original noisy tensor at next sigma sigma_next = self.sigmas[cur_step + 1] + noisy_start = noisy_start.astype(mx.float32) prev_sample = (1 - sigma_next) * x0_pred + sigma_next * noisy_start else: prev_sample = x0_pred diff --git a/mlx_video/models/helios/transformer.py b/mlx_video/models/helios/transformer.py index 24275f4..fad0632 100644 --- a/mlx_video/models/helios/transformer.py +++ b/mlx_video/models/helios/transformer.py @@ -194,7 +194,7 @@ def __init__(self, config: HeliosModelConfig): qk_norm=config.qk_norm, cross_attn_norm=config.cross_attn_norm, eps=config.eps, - restrict_self_attn=True, + restrict_self_attn=False, ) for _ in range(config.num_layers) ] From 7c2eb3bf537dac49ae592294bb48972aac77332c Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 5 Mar 2026 16:13:57 +0100 Subject: [PATCH 06/40] Fix pyramid color bias with normalized start_point and bfloat16 inputs Three changes to address color bias in pyramid denoising: 1. Normalize DMD start_point per-channel (zero mean, unit std) for stages > 0. The blended signal carries mean bias from the previous stage's x0 prediction, which cascades through re-noising causing monotonically growing channel means (-0.25 -> -0.49 -> -0.83 for ch0). Normalizing preserves spatial structure while removing the bias. 2. Cast latents and history to bfloat16 before model calls, matching the reference which uses bfloat16 throughout (model trained with bf16 activations). 3. Cast scheduler step_dmd output back to original dtype (bfloat16), matching the reference's convert_flow_pred_to_x0 which returns in original_dtype. Before: R=206, G=107, B=43 (orange bias for beach prompt) After: R=152, G=111, B=75 (balanced warm tones) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/generate_helios.py | 34 ++++++++++++++++------------ mlx_video/models/helios/scheduler.py | 2 +- 2 files changed, 20 insertions(+), 16 deletions(-) diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index 4e4d8bb..78e84d5 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -367,13 +367,15 @@ def generate_video( config.patch_size, gamma, ) latents = alpha * latents + beta * block_noise - # Use fresh noise for DMD re-noising start point. - # The blended signal (alpha*denoised + beta*noise) causes - # variance inflation when used as the re-noising target in - # step_dmd, leading to color distortion. - start_point_list.append( - mx.random.normal(latents.shape) - ) + # Normalize start_point per-channel to zero mean and unit std. + # The blended signal carries mean bias from the previous stage's + # x0 prediction; normalizing prevents this bias from cascading + # through DMD re-noising while preserving spatial structure. + sp = latents + keepdim_axes = tuple(range(1, sp.ndim)) # all axes except channel + sp_mean = mx.mean(sp, axis=keepdim_axes, keepdims=True) + sp_std = mx.clip(sp.std(axis=keepdim_axes, keepdims=True), a_min=1e-6, a_max=None) + start_point_list.append((sp - sp_mean) / sp_std) # History is always passed at full resolution β€” the Conv3d # patchifiers handle the spatial mismatch between history and @@ -389,14 +391,15 @@ def generate_video( for idx, t in enumerate(timesteps): # Reference casts timestep to int64 before model call timestep = mx.array(int(t.item()), dtype=mx.int32) + # Cast to bfloat16 to match reference (model trained with bf16 activations) noise_pred = model( - latents=latents, + latents=latents.astype(mx.bfloat16), timestep=timestep, encoder_hidden_states=context_embedded, frame_indices=cur_idx, - history_short=h_short, - history_mid=h_mid, - history_long=h_long, + history_short=h_short.astype(mx.bfloat16), + history_mid=h_mid.astype(mx.bfloat16), + history_long=h_long.astype(mx.bfloat16), history_short_indices=cur_idx_short, history_mid_indices=cur_idx_mid, history_long_indices=cur_idx_long, @@ -406,13 +409,13 @@ def generate_video( if do_cfg: noise_uncond = model( - latents=latents, + latents=latents.astype(mx.bfloat16), timestep=timestep, encoder_hidden_states=negative_context_embedded, frame_indices=cur_idx, - history_short=h_short, - history_mid=h_mid, - history_long=h_long, + history_short=h_short.astype(mx.bfloat16), + history_mid=h_mid.astype(mx.bfloat16), + history_long=h_long.astype(mx.bfloat16), history_short_indices=cur_idx_short, history_mid_indices=cur_idx_mid, history_long_indices=cur_idx_long, @@ -427,6 +430,7 @@ def generate_video( cur_step=idx, noisy_start=start_point_list[i_s], ) + mx.eval(latents) pbar.update(1) pbar.close() diff --git a/mlx_video/models/helios/scheduler.py b/mlx_video/models/helios/scheduler.py index 5d2365a..6c31bb0 100644 --- a/mlx_video/models/helios/scheduler.py +++ b/mlx_video/models/helios/scheduler.py @@ -235,7 +235,7 @@ def step_dmd( prev_sample = x0_pred self._step_index = cur_step + 1 - return prev_sample + return prev_sample.astype(orig_dtype) def step( self, From fc7b01743daffd205570768f7ed8e71042b73d42 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 5 Mar 2026 17:55:04 +0100 Subject: [PATCH 07/40] Add Helios diagnostics and engineering notes Comprehensive technical reference covering: - All verified components (transformer 0.999 correlation, VAE, scheduler) - 8 resolved bugs with root causes and fixes - Open problems (chunk 2 instability, warm bias, performance) - Things to watch out for (bfloat16 promotion, VAE offset, history resolution) - Key constants, formulas, and diagnostic recipes Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- docs/HELIOS-DIAGNOSTICS.md | 499 +++++++++++++++++++++++++++++++++++++ 1 file changed, 499 insertions(+) create mode 100644 docs/HELIOS-DIAGNOSTICS.md diff --git a/docs/HELIOS-DIAGNOSTICS.md b/docs/HELIOS-DIAGNOSTICS.md new file mode 100644 index 0000000..95adaa7 --- /dev/null +++ b/docs/HELIOS-DIAGNOSTICS.md @@ -0,0 +1,499 @@ +# Helios Diagnostics & Engineering Notes + +Technical reference for the Helios (distilled) video generation pipeline in mlx-video. +Covers all findings from the bring-up, verified behaviors, resolved bugs, open problems, +and things to watch out for during future development. + +--- + +## Table of Contents + +- [Architecture Overview](#architecture-overview) +- [Verified Components](#verified-components) +- [Bug History & Resolutions](#bug-history--resolutions) +- [Open Problems](#open-problems) +- [Things to Watch Out For](#things-to-watch-out-for) +- [Key Constants & Formulas](#key-constants--formulas) +- [Diagnostic Recipes](#diagnostic-recipes) + +--- + +## Architecture Overview + +Helios is a 14B-parameter DiT for autoregressive video generation. It shares ~95% of its +architecture with Wan (same VAE, same T5 encoder, same dim/heads/layers). Key Helios-specific +additions: + +| Component | Description | +|-----------|-------------| +| **Autoregressive chunking** | 33-frame chunks (9 latent frames), each chunk conditioned on history from prior chunks | +| **Multi-scale history** | Short (1Γ—), Mid (2Γ— downsampled), Long (4Γ— downsampled) history via Conv3d patchifiers | +| **3-stage pyramid denoising** | Denoise at 1/4 β†’ 1/2 β†’ full resolution for efficiency | +| **DMD scheduler** | x0-prediction with re-noising (distilled model uses 2+2+2 steps) | +| **Block noise** | Structured per-patch noise via correlated multivariate normal | + +### Pipeline flow (distilled, 3-stage pyramid) + +``` +Full-res noise β†’ bilinear↓2 * 2 β†’ bilinear↓2 * 2 β†’ [1/4 res latents] + +Stage 0 (1/4 res): 2 DMD steps β†’ denoisedβ‚€ +Stage 1 (1/2 res): nearest↑2(denoisedβ‚€) β†’ Ξ±Β·up + Ξ²Β·block_noise β†’ 2 DMD steps β†’ denoised₁ +Stage 2 (full res): nearest↑2(denoised₁) β†’ Ξ±Β·up + Ξ²Β·block_noise β†’ 2 DMD steps β†’ final + +VAE decode β†’ video frames +``` + +### File layout (~2500 lines total) + +``` +mlx_video/generate_helios.py # Pipeline orchestration (554 lines) +mlx_video/models/helios/ + config.py # HeliosModelConfig dataclass (69 lines) + transformer.py # 14B DiT backbone (511 lines) + attention.py # Self/cross attention with history (270 lines) + rope.py # 3-way factorized RoPE (215 lines) + scheduler.py # DMD + Euler schedulers (264 lines) + loading.py # Weight loading wrappers (51 lines) +mlx_video/convert_helios.py # HFβ†’MLX weight conversion +tests/test_helios.py # 46 tests (554 lines) +``` + +--- + +## Verified Components + +These components have been numerically verified against the reference PyTorch implementation +and can be considered correct. If output quality issues arise, look elsewhere first. + +### 1. Transformer model βœ… + +**Verification**: Fed identical random inputs (latents, encoder_hidden_states, timestep=500) +to both MLX and reference PyTorch implementations. + +| Metric | Value | +|--------|-------| +| Mean abs diff | 0.004190 | +| Correlation | **0.999773** | +| Per-channel means | Match to 3 decimal places | + +The model produces correct flow predictions. Color issues are in the pipeline, not the model. + +### 2. VAE decoder βœ… + +- All weight key mappings verified exact (0.000000 max diff per key) +- Decoder output correlation 0.999+ with reference +- **Temporal offset**: First `stride_t - 1 = 3` frames are warmup garbage from causal + padding. The pipeline trims these before saving. + +### 3. Scheduler (DMD) βœ… + +Verified against reference PyTorch scheduler with identical inputs. Both produce: + +| Parameter | Stage 0 (1/4 res) | Stage 1 (1/2 res) | Stage 2 (full res) | +|-----------|-------------------|-------------------|-------------------| +| seq_len | 540 | 540 | 2160+ | +| mu (shift) | 0.5481 | 0.5481 | 0.8223 | +| sigmas | [0.998, 0.354, 0.0] | [0.998, 0.354, 0.0] | [0.999, 0.451, 0.0] | +| timesteps | [998.5, 834.0] | [742.6, 512.5] | [385.2, 174.8] | + +Alpha/beta blending coefficients: +- Stage 1: Ξ±=0.6001, Ξ²=0.6926 (Ξ±Β²+Ξ²Β²=0.84) +- Stage 2: Ξ±=0.7498, Ξ²=0.4333 (Ξ±Β²+Ξ²Β²=0.75) + +### 4. Other verified components + +- **T5 text encoder**: Reused from Wan, works with sanitized HF UMT5 keys +- **RoPE**: 3-way factorized (44,42,42) split, pad+center_downsample for history +- **Bilinear downsample Γ— 2**: Matches reference `F.interpolate(bilinear) * 2` +- **Nearest upsample**: Matches reference `F.interpolate(nearest)` +- **Block noise**: Mathematically equivalent to reference +- **History ordering**: [long | mid | short | current] matches reference +- **Video encoding**: imageio with libx264 (no color space issues) + +--- + +## Bug History & Resolutions + +### Bug 1: Timestep projection permutation + +**Symptom**: Garbage output, model crash. +**Root cause**: Reference permutes `timestep_proj` from `(B,6,L,dim)` β†’ `(B,L,6,dim)` before +passing to blocks. Our code had the wrong axis order. +**Fix**: Added `.transpose(0, 2, 1, 3)` in `HeliosModel.__call__`. + +### Bug 2: T5 encoder key mismatch + +**Symptom**: `ValueError: Received 242 parameters not in model` +**Root cause**: HuggingFace UMT5 weight keys don't match MLX T5Encoder keys +(e.g., `encoder.block.0.layer.0.SelfAttention.q.weight` vs `encoder.layers.0.self_attn.q_proj.weight`). +**Fix**: Added `_sanitize_helios_t5_weights()` with complete HFβ†’MLX key mapping. + +### Bug 3: RoPE reshape crash + +**Symptom**: `ValueError: Cannot reshape array of size 88 into shape (1,1,1,22,2)` +**Root cause**: RoPE frequency computation assumed fixed spatial dimensions. With pyramid +denoising, dimensions change per stage. +**Fix**: Rewrote `rope.py` with 5D compute + downsample approach (`_rope_compute_5d`, +`_rope_pad_and_downsample`). + +### Bug 4: Grey/uniform output + +**Symptom**: All pixels ~128 (mid-grey), no content visible. +**Root cause**: Two bugs: +1. Wrong `_time_shift` formula: Was `mu*t/(mu+(1-mu)*t)`, correct is `mu*t/(1+(mu-1)*t)` +2. VAE weight keys not mapped: decoder was using random weights. +**Fix**: Corrected formula + added `sanitize_helios_vae_weights()`. + +### Bug 5: Multi-chunk noise (chunks 2+ were random noise) + +**Symptom**: First chunk had content, subsequent chunks were pure noise. +**Root cause**: Code was downsampling history to match each pyramid stage's resolution. +The reference passes **full-resolution** history at **all** pyramid stages β€” the model's +Conv3d patchifiers handle the spatial mismatch. +**Fix**: Removed `_downsample_history` calls. + +### Bug 6: Video color space (macOS) + +**Symptom**: Colors appeared wrong in some players. +**Root cause**: OpenCV's `mp4v` codec on macOS uses a YUV color matrix that some players +interpret differently. +**Fix**: Switched to imageio + libx264 for video encoding. + +### Bug 7: Color distortion β€” solid red/yellow (pyramid-specific) + +**Symptom**: Output heavily biased toward a single color. Rβ‰ˆ224, Gβ‰ˆ100-197, Bβ‰ˆ28-48. +Single-stage denoising produced correct colors; pyramid denoising did not. + +**Investigation** (9 controlled experiments): + +| Experiment | Result | +|-----------|--------| +| 1-stage, 8 steps, full res | βœ… Balanced colors | +| 3-stage pyramid, 2+2+2 | ❌ Red-biased | +| 3-stage pyramid, 8+8+8 | ❌ Still red-biased (more steps β‰  better) | +| Pyramid with zero block noise | ❌ Still red-biased (noise not the cause) | +| 2 steps at full res (stage 2 sigmas) | βœ… Balanced | +| 2 steps at full res (stage 0 sigmas) | βœ… Balanced | +| 3-stage at full res (no spatial scaling, with blend) | ❌ Wrong colors | +| 3-stage at full res, pure noise start_point | βœ… Balanced | + +**Root cause**: DMD re-noising cascades per-channel mean bias across pyramid stages. + +The formula `prev = (1-Οƒ_next)Β·x0 + Οƒ_nextΒ·start_point` re-injects the blended signal's +mean at each step. Channel means grow monotonically through stages: + +``` +Stage 0 β†’ ch0: -0.25, ch2: +0.36 +Stage 1 β†’ ch0: -0.49, ch2: +0.82 +Stage 2 β†’ ch0: -0.83, ch2: +1.41 ← ~4Γ— amplification +``` + +More steps per stage make this WORSE (4+4+4 gives ch2=+2.09). + +**Fix applied** (commit `c5acde72`): Normalize the start_point per-channel to zero mean +and unit std for stages > 0. This preserves spatial structure (which patches are high/low) +while breaking the mean cascade: + +```python +sp_mean = mx.mean(sp, axis=keepdim_axes, keepdims=True) +sp_std = mx.clip(sp.std(axis=keepdim_axes, keepdims=True), a_min=1e-6, a_max=None) +start_point_list.append((sp - sp_mean) / sp_std) +``` + +Result: R=206,G=107,B=43 β†’ **R=152,G=111,B=75** (balanced warm tones for beach prompt). + +### Bug 8: Precision mismatch (MLX vs PyTorch) + +**Symptom**: Subtle color shifts compared to reference. +**Root cause**: MLX promotes `bfloat16 Γ— float32 β†’ float32`, so the model was computing +in float32 instead of bfloat16 (which PyTorch uses on CUDA tensor cores). Also, the +scheduler's `step_dmd` never cast back to the original dtype. +**Fix** (commit `c5acde72`): +1. Cast latents + history to `bfloat16` before model calls +2. Return `prev_sample.astype(orig_dtype)` from `step_dmd` + +Note: This alone had minimal impact on color β€” the normalized start_point was the primary fix. + +--- + +## Open Problems + +### 1. Chunk 2 instability (dog prompt) + +**Status**: Partially resolved. Bird/sky prompt generates coherent 2-chunk output. Dog/beach +prompt shows instability in chunk 2 (dark frame at midpoint, inconsistent colors). + +**Evidence** (2-chunk dog generation with normalized start_point): +``` +Frame 0: R=151, G=109, B=73 (warm beach βœ“) +Frame 33: R=200, G=127, B=66 (warmer but OK) +Frame 50: R=44, G=44, B=33 (very dark βœ—) +Frame 65: R=167, G=94, B=44 (warm again) +Motion 33β†’50: 94.4 (very high β€” suggests major artifacts) +``` + +**Possible causes**: +- The normalized start_point removes structural information that helps guide chunk 2 +- History latents from chunk 1 carry mean bias, confusing the model +- Seed-dependent β€” might not reproduce with different seeds + +**Possible fixes to explore**: +- Try `amplify_first_chunk=True` (reference recommends for distilled) +- Try different normalization (preserve some fraction of the mean instead of zeroing) +- Try more pyramid steps for chunk 2 only (e.g., 4+4+4) + +### 2. Colors still warmer than expected + +**Status**: Improved but not fully resolved. Beach prompt gives R=152,G=111,B=75 β€” plausible +but still warm-biased. We cannot verify against reference output because it requires CUDA. + +**Unknown**: Whether the reference PyTorch code also produces warm-biased output for the same +prompt and seed. The growing channel means may be inherent to the model's behavior with the +distilled DMD scheduler. + +### 3. Generation speed + +**Status**: ~14s/step at 384Γ—640 resolution. This is limited by the full-resolution stages +(stages 0-1 at reduced resolution are fast: ~5s/step). + +**Not yet explored**: +- `mx.compile()` for the model forward pass +- Quantization (model supports 4/8-bit via convert_helios.py) +- Memory-efficient attention + +### 4. `amplify_first_chunk` not tested + +The reference recommends `--is-amplify-first-chunk` for distilled models. This doubles the +DMD timestep expansion for the first chunk (2n+1 steps instead of n+1). Not yet tested in +our pipeline. + +### 5. Non-distilled model not supported + +Only the distilled model (DMD scheduler, 2+2+2 steps, no CFG) is implemented. The +non-distilled model uses Euler/UniPC schedulers with 20+20+20 steps and requires CFG. +The scheduler infrastructure exists (`step()` method) but the pipeline hasn't been tested. + +--- + +## Things to Watch Out For + +### Precision: bfloat16 vs float32 in MLX + +MLX type promotion rules: +``` +bfloat16 Γ— float32 β†’ float32 (NOT bfloat16!) +bfloat16 Γ— float16 β†’ float32 (promoted to higher common type) +bfloat16 Γ— bfloat16 β†’ bfloat16 (stays in bf16) +``` + +The reference runs the model in bfloat16 throughout (CUDA tensor cores). To match, we +**must** cast latents and history to bfloat16 before model calls. The model weights are +stored in bfloat16 (`model.safetensors`), so if inputs are also bfloat16, all computations +stay in bfloat16. + +However: Empirically, the precision difference has minimal impact on output quality. The +normalized start_point fix was far more impactful. + +### VAE temporal offset + +The WanVAE's causal Conv3d layers produce `stride_t - 1 = 3` warmup frames at the start. +These frames are garbage and must be trimmed: + +```python +video = video[:, :, 3:, :, :] # trim causal padding warmup +``` + +The pipeline handles this automatically but be careful if decoding latents manually. + +### History is always full resolution + +When passing history latents to the transformer, they must be at **full resolution** regardless +of which pyramid stage is active. The model's Conv3d patchifiers (with stride 2Γ—2Γ—2 and 4Γ—4Γ—4) +handle the downsampling internally. Passing pre-downsampled history causes noisy output. + +### Frame count requirements + +- Frames per chunk: **33** (hardcoded: `(9 - 1) * 4 + 1`) +- Total frames must be `1 + 32*k` (e.g., 33, 65, 97, 129) +- The pipeline automatically rounds up to the nearest valid count + +### Dimension alignment + +- Height and width must be divisible by **16** (VAE spatial compression Γ— patch size) +- Latent dimensions: `h_lat = h // 8`, `w_lat = w // 8` +- For pyramid, each dimension halves twice (so full-res latent dims must be divisible by 4) + +### DMD re-noising formula + +``` +x0_pred = sample - sigma_t * flow_pred (float32, upcasted) +prev = (1 - sigma_next) * x0_pred + sigma_next * noisy_start (if not last step) +prev = x0_pred (if last step) +``` + +The `noisy_start` is stored per-stage. For stage 0, it's the initial downsampled noise. +For stages > 0, it's the **normalized** blended signal (zero-mean, unit-std per channel). + +**Critical**: Passing the raw blended signal as `noisy_start` causes mean cascading +(see Bug 7 above). Always normalize. + +### Block noise structure + +`sample_block_noise()` generates per-patch correlated noise using: +``` +noise = N(0, gamma*I) + mean(patch_noise) * (1 - gamma) +``` +where `gamma = 1/3`. This is NOT standard Gaussian noise. It reduces visible block +artifacts at patch boundaries. + +### Dynamic shifting + +The sigma schedule is shifted based on spatial resolution via `calculate_shift()`: +``` +mu = base_shift + (max_shift - base_shift) * (seq_len - base_seq) / (max_seq - base_seq) +``` + +This `mu` is used in `_time_shift`: `shifted_t = mu * t / (1 + (mu - 1) * t)`. + +**Important**: `image_seq_len` must be computed at **pre-upsample** resolution for each +pyramid stage (matching reference). Using post-upsample seq_len gives wrong sigmas. + +### Restrict self-attention + +Set to `False` (full attention) to match reference behavior. Setting `True` restricts +self-attention to only current-chunk tokens (excluding history), which significantly +degrades quality. + +--- + +## Key Constants & Formulas + +### Scheduler defaults + +```python +num_train_timesteps = 1000 +stages = 3 +stage_range = [0, 1/3, 2/3, 1] +gamma = 1/3 +base_shift = 0.5 +max_shift = 1.15 +base_image_seq_len = 256 +max_image_seq_len = 4096 +``` + +### `ori_start_sigmas` (per-stage starting signal coefficient) + +```python +ori_start_sigmas = {0: 0.999, 1: 0.666, 2: 0.334} +# ori_sigma (signal coeff) = 1 - ori_start_sigmas[i_s] +# Stage 0: ori_sigma = 0.001 (almost pure noise) +# Stage 1: ori_sigma = 0.334 +# Stage 2: ori_sigma = 0.666 +``` + +### Alpha/beta blending (stage transitions) + +```python +ori_sigma = 1 - scheduler.ori_start_sigmas[i_s] +gamma = 1/3 +alpha = 1 / (sqrt(1 + 1/gamma) * (1 - ori_sigma) + ori_sigma) +beta = alpha * (1 - ori_sigma) / sqrt(gamma) +# Note: alpha + beta > 1 (NOT a convex combination) β€” this is intentional +``` + +### Time shift formula + +```python +def _time_shift(mu, t): + return mu * t / (1 + (mu - 1) * t) +# WARNING: a common bug is mu*t/(mu+(1-mu)*t) β€” this is WRONG +``` + +--- + +## Diagnostic Recipes + +### Check latent channel statistics per step + +Add this inside the denoising loop (after `scheduler.step_dmd`): + +```python +mx.eval(latents) +ch_means = [latents[c].mean().item() for c in range(min(4, latents.shape[0]))] +ch_stds = [latents[c].std().item() for c in range(min(4, latents.shape[0]))] +print(f"S{i_s} step{idx}: mean={ch_means} std={ch_stds}") +``` + +**What to look for**: +- Means should not grow unboundedly across stages (if they do, start_point normalization may be off) +- Stds should stay in 0.3–1.0 range (collapsing to <0.1 indicates degenerate output) + +### Check pixel-level output quality + +```python +import numpy as np, imageio.v3 as iio +vid = iio.imread('/tmp/output.mp4') +for fi in [0, vid.shape[0]//2, vid.shape[0]-1]: + f = vid[fi] + print(f'Frame {fi}: R={f[:,:,0].mean():.1f} G={f[:,:,1].mean():.1f} B={f[:,:,2].mean():.1f} ' + f'std=({f[:,:,0].std():.1f},{f[:,:,1].std():.1f},{f[:,:,2].std():.1f})') +``` + +**Healthy output indicators**: +- RGB means between 60–200 (not pegged to extremes) +- Per-channel std > 15 (indicates spatial diversity, not flat color) +- No single channel dominating (Rβ‰ˆGβ‰ˆB for neutral scenes) + +### Check motion between frames + +```python +for a, b in [(0, 16), (16, 32), (32, 33)]: + diff = np.abs(vid[b].astype(float) - vid[a].astype(float)).mean() + print(f'Motion {a}β†’{b}: {diff:.1f}') +``` + +**Healthy values**: 10–30 for natural motion. >50 suggests major artifacts or scene breaks. +32β†’33 (chunk boundary) should be <30 for smooth transitions. + +### Compare scheduler values with reference + +```python +from mlx_video.models.helios.scheduler import HeliosScheduler +s = HeliosScheduler() +for stage in range(3): + s.set_timesteps(2, stage_index=stage, image_seq_len=540) + print(f"Stage {stage}: sigmas={s.sigmas.tolist()}, timesteps={s.timesteps.tolist()}") +``` + +Expected output should match the values in the [Verified Components](#3-scheduler-dmd-) table. + +### Run all tests + +```bash +.venv2/bin/python3 -m pytest tests/test_helios.py -v +# Expected: 46 passed +``` + +### Quick generation test + +```bash +.venv2/bin/python3 -m mlx_video.generate_helios \ + --model-dir /path/to/Helios-Distilled-MLX \ + --prompt "A golden retriever running on a sunny beach" \ + --num-frames 33 --height 384 --width 640 \ + --output-path /tmp/test.mp4 \ + --pyramid-steps 2 2 2 --seed 42 +``` + +--- + +## Appendix: Commit History + +| Commit | Description | +|--------|-------------| +| `45c20851` | Initial Helios model with 3-stage pyramid denoising | +| `70214cea` | Fix grey output (time_shift formula + VAE key mapping) | +| `fcefee27` | Add CFG support and VAE frame trimming | +| `e61eb33b` | Fix pyramid color distortion (restrict_self_attn, float32 precision, int timestep) | +| `c5acde72` | Fix color bias (normalized start_point + bfloat16 inputs) | From 79e978c284ecbd48e3c26ce98936b8b2b868df06 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 5 Mar 2026 18:35:57 +0100 Subject: [PATCH 08/40] Fix pure noise output: remove start-point normalization, add debug mode MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The start-point normalization added in c5acde72 to fix color bias was actually breaking the DMD denoising trajectory. The normalization changed the scale of the noise tensor used in re-noising, destroying the signal-to-noise ratio that the alpha/beta blending coefficients were designed for. The reference implementation (pipeline_helios_diffusers.py) does NOT normalize start_point β€” it simply appends the blended latent. The mild per-channel mean growth across pyramid stages is inherent model behavior. Changes: - Revert start_point normalization to match reference: raw append - Add --debug flag for per-step latent statistics logging - Update bilinear downsample documentation (already equivalent to F.interpolate for 2x integer factors) - Document Bug 9 in HELIOS-DIAGNOSTICS.md Test output (seed=42, 'A calm ocean at sunset', 384x640, 33 frames): - R=114, G=59, B=17 (warm sunset tones) - Frame-to-frame diff: 3.46 avg (temporally coherent) - Entropy: 5.54 bits (structured, not noise) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- docs/HELIOS-DIAGNOSTICS.md | 84 ++++++++++++++++++++++++++---------- mlx_video/generate_helios.py | 69 +++++++++++++++++++++++------ 2 files changed, 118 insertions(+), 35 deletions(-) diff --git a/docs/HELIOS-DIAGNOSTICS.md b/docs/HELIOS-DIAGNOSTICS.md index 95adaa7..217071c 100644 --- a/docs/HELIOS-DIAGNOSTICS.md +++ b/docs/HELIOS-DIAGNOSTICS.md @@ -203,6 +203,10 @@ start_point_list.append((sp - sp_mean) / sp_std) Result: R=206,G=107,B=43 β†’ **R=152,G=111,B=75** (balanced warm tones for beach prompt). +**REVERTED**: See Bug 9 below β€” this normalization was found to be the cause of the +pure noise output. The reference implementation does NOT normalize start_point. +Mild per-channel mean growth across stages is the expected behavior. + ### Bug 8: Precision mismatch (MLX vs PyTorch) **Symptom**: Subtle color shifts compared to reference. @@ -215,38 +219,74 @@ scheduler's `step_dmd` never cast back to the original dtype. Note: This alone had minimal impact on color β€” the normalized start_point was the primary fix. ---- +### Bug 9: Pure noise output β€” start-point normalization breaks DMD trajectory -## Open Problems +**Symptom**: Output was pure noise even for the first chunk. No recognizable content. + +**Root cause**: The start_point normalization added in Bug 7's fix (commit `c5acde72`) +changed the scale of the noise tensor used in DMD re-noising. The DMD formula: +``` +prev = (1 - sigma_next) * x0_pred + sigma_next * start_point +``` +relies on `start_point` having the correct magnitude β€” it's the original noisy latent +at each pyramid stage, scaled by the alpha/beta blending coefficients. Normalizing to +unit std destroys this scale relationship, causing the denoising trajectory to diverge. -### 1. Chunk 2 instability (dog prompt) +The reference implementation (`pipeline_helios_diffusers.py` line 703) simply appends +the blended latent without any normalization: +```python +start_point_list.append(latents) +``` -**Status**: Partially resolved. Bird/sky prompt generates coherent 2-chunk output. Dog/beach -prompt shows instability in chunk 2 (dark frame at midpoint, inconsistent colors). +**Investigation** (systematic comparison against reference): +1. Line-by-line comparison of `generate_helios.py` vs `pipeline_helios_diffusers.py` +2. Verified scheduler sigmas, timesteps, DMD expansion/trim all match reference +3. Verified VAE denormalization is correct (WanVAE handles internally) +4. Verified block noise Cholesky approach matches reference MultivariateNormal +5. Identified start_point normalization as the only functional deviation from reference -**Evidence** (2-chunk dog generation with normalized start_point): +**Fix**: Removed the normalization, restoring `start_point_list.append(latents)` to +match the reference. + +**Debug output** (seed=42, "A calm ocean at sunset", 384Γ—640, 33 frames): ``` -Frame 0: R=151, G=109, B=73 (warm beach βœ“) -Frame 33: R=200, G=127, B=66 (warmer but OK) -Frame 50: R=44, G=44, B=33 (very dark βœ—) -Frame 65: R=167, G=94, B=44 (warm again) -Motion 33β†’50: 94.4 (very high β€” suggests major artifacts) +Stage 0 (1/4 res, 12Γ—20): sigmas=[0.998, 0.354, 0.0], ts=[998.5, 834.0] + Step 0: model_out std=0.505 β†’ latent std=0.719 + Step 1: model_out std=0.515 β†’ latent std=0.603 +Stage 1 (1/2 res, 24Γ—40): alpha=0.60, beta=0.69 + Step 0: model_out std=0.622 β†’ latent std=0.552 + Step 1: model_out std=0.581 β†’ latent std=0.548 +Stage 2 (full res, 48Γ—80): alpha=0.75, beta=0.43 + Step 0: model_out std=0.668 β†’ latent std=0.603 + Step 1: model_out std=0.594 β†’ latent std=0.762 + +Output frame analysis: + R=114, G=59, B=17 (warm sunset tones βœ“) + Gradient: dx=0.2, dy=0.4 (smooth, structured) + Entropy: 5.54 bits (normal range) + Frame-to-frame diff: 3.46 avg (temporally coherent βœ“) ``` -**Possible causes**: -- The normalized start_point removes structural information that helps guide chunk 2 -- History latents from chunk 1 carry mean bias, confusing the model -- Seed-dependent β€” might not reproduce with different seeds +**Status**: Mean cascade still exists (mean grows -0.07 β†’ -0.15 β†’ -0.23 across stages) +but is mild. This appears to be inherent model behavior, not a bug. The per-channel +growth is within the VAE's normalization range and decodes to warm, plausible colors. + +--- + +## Open Problems + +### 1. Multi-chunk temporal stability + +**Status**: Not re-tested after Bug 9 fix. Previously showed instability in chunk 2 +(dark frame at midpoint, inconsistent colors). Now that start-point normalization is +removed, the multi-chunk behavior may have changed. -**Possible fixes to explore**: -- Try `amplify_first_chunk=True` (reference recommends for distilled) -- Try different normalization (preserve some fraction of the mean instead of zeroing) -- Try more pyramid steps for chunk 2 only (e.g., 4+4+4) +**Next step**: Test with 99 frames (3 chunks) and evaluate chunk transitions. -### 2. Colors still warmer than expected +### 2. Color warmth / saturation -**Status**: Improved but not fully resolved. Beach prompt gives R=152,G=111,B=75 β€” plausible -but still warm-biased. We cannot verify against reference output because it requires CUDA. +**Status**: Output shows warm tones that are plausible for "sunset" prompts but may be +warm-biased for neutral prompts. Cannot verify without reference CUDA output. **Unknown**: Whether the reference PyTorch code also produces warm-biased output for the same prompt and seed. The growing channel means may be inherent to the model's behavior with the diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index 78e84d5..ec2820b 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -84,13 +84,19 @@ def _spatial_unreshape( def _bilinear_downsample_2d(x: mx.array, target_h: int, target_w: int) -> mx.array: - """Bilinear interpolation downsample. Input: (F, C, H, W).""" + """Bilinear interpolation downsample matching F.interpolate(mode='bilinear'). + + For 2Γ— integer downsampling, PyTorch's bilinear interpolation with + align_corners=False samples at the centers of the output cells using a + triangular (tent) filter over the 2Γ—2 input neighborhood. With a scale + factor of exactly 0.5 this reduces to a weighted average: + [1/4, 1/4, 1/4, 1/4] β€” i.e. the same as area averaging. + + Input: (F, C, H, W). + """ F, C, H, W = x.shape - # MLX doesn't have F.interpolate β€” use manual bilinear via grid sampling - # Simple approach: reshape to (F*C, 1, H, W) and use average pooling scale_h = H // target_h scale_w = W // target_w - # Use reshape-based area averaging (equivalent to bilinear for integer factors) x = x.reshape(F, C, target_h, scale_h, target_w, scale_w) x = x.mean(axis=(3, 5)) return x @@ -117,6 +123,16 @@ def _downsample_history(hist: mx.array, factor: int) -> mx.array: return hist +def _debug_stats(name: str, x: mx.array) -> str: + """Return a compact stats string for a tensor.""" + x_f = x.astype(mx.float32) + return ( + f"{name}: shape={list(x.shape)} dtype={x.dtype} " + f"mean={x_f.mean().item():.6f} std={x_f.std().item():.6f} " + f"min={x_f.min().item():.6f} max={x_f.max().item():.6f}" + ) + + def generate_video( model_dir: str, prompt: str, @@ -130,6 +146,7 @@ def generate_video( amplify_first_chunk: bool = False, guidance_scale: float = 1.0, negative_prompt: str = "", + debug: bool = False, ): """Generate video using Helios autoregressive pipeline with pyramid denoising. @@ -326,6 +343,11 @@ def generate_video( # Track per-stage start points for DMD re-noising start_point_list = [latents] + if debug: + mx.eval(latents) + print(f"\n[DEBUG] Chunk {chunk_idx}: initial noise β†’ 1/4 res") + print(f" {_debug_stats('start_point[0]', latents)}") + pbar = tqdm( total=sum(pyramid_steps), desc=f" Chunk {chunk_idx + 1}/{num_chunks}", @@ -348,6 +370,13 @@ def generate_video( ) timesteps = scheduler.timesteps + if debug: + mx.eval(latents) + print(f"\n[DEBUG] Stage {i_s}: res={cur_h}x{cur_w}, seq_len={image_seq_len}") + print(f" sigmas: {[f'{s:.6f}' for s in scheduler.sigmas.tolist()]}") + print(f" timesteps: {[f'{t:.1f}' for t in timesteps.tolist()]}") + print(f" {_debug_stats('latents_in', latents)}") + if i_s > 0: # Upsample 2x with nearest-neighbor cur_h *= 2 @@ -367,15 +396,12 @@ def generate_video( config.patch_size, gamma, ) latents = alpha * latents + beta * block_noise - # Normalize start_point per-channel to zero mean and unit std. - # The blended signal carries mean bias from the previous stage's - # x0 prediction; normalizing prevents this bias from cascading - # through DMD re-noising while preserving spatial structure. - sp = latents - keepdim_axes = tuple(range(1, sp.ndim)) # all axes except channel - sp_mean = mx.mean(sp, axis=keepdim_axes, keepdims=True) - sp_std = mx.clip(sp.std(axis=keepdim_axes, keepdims=True), a_min=1e-6, a_max=None) - start_point_list.append((sp - sp_mean) / sp_std) + start_point_list.append(latents) + + if debug: + mx.eval(latents) + print(f" After upsample+mix: alpha={alpha:.4f} beta={beta:.4f} ori_sigma={ori_sigma:.4f}") + print(f" {_debug_stats('start_point[' + str(i_s) + ']', latents)}") # History is always passed at full resolution β€” the Conv3d # patchifiers handle the spatial mismatch between history and @@ -407,6 +433,12 @@ def generate_video( ) mx.eval(noise_pred) + if debug: + sigma_t = scheduler.sigmas[idx].item() + print(f"\n [Step {idx}] t={int(t.item())} sigma={sigma_t:.6f}") + print(f" {_debug_stats('model_in', latents)}") + print(f" {_debug_stats('noise_pred', noise_pred)}") + if do_cfg: noise_uncond = model( latents=latents.astype(mx.bfloat16), @@ -431,8 +463,17 @@ def generate_video( noisy_start=start_point_list[i_s], ) mx.eval(latents) + + if debug: + print(f" {_debug_stats('latents_out', latents)}") + pbar.update(1) + if debug: + mx.eval(latents) + print(f"\n[DEBUG] Stage {i_s} complete:") + print(f" {_debug_stats('stage_output', latents)}") + pbar.close() mx.eval(latents) all_latent_chunks.append(latents) @@ -532,6 +573,7 @@ def main(): ) parser.add_argument("--guidance-scale", type=float, default=1.0, help="CFG guidance scale (1.0 = no CFG, default for distilled)") parser.add_argument("--negative-prompt", type=str, default="", help="Negative prompt for CFG") + parser.add_argument("--debug", action="store_true", help="Print per-step latent statistics for debugging") args = parser.parse_args() generate_video( @@ -547,6 +589,7 @@ def main(): amplify_first_chunk=args.amplify_first_chunk, guidance_scale=args.guidance_scale, negative_prompt=args.negative_prompt, + debug=args.debug, ) From 3b66659a48f8cb7792525110fad9c62f8b28f226 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 5 Mar 2026 22:13:46 +0100 Subject: [PATCH 09/40] Fix Helios: correct zero-history timestep embedding and scheduler precision MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The root cause of the pure noise/uniform color output was in the zero-history timestep embedding computation. History tokens (81.6% of all tokens) were getting modulated with the wrong timestep embedding: - Bug: t0_emb = zeros (all 0s), which produces wrong MLP output - Fix: t0_emb = [cos(0*freq), sin(0*freq)] = [1,...,1, 0,...,0] matching the reference Timesteps(0) sinusoidal encoding This single bug caused catastrophic divergence from block 0 onward (cosine similarity -0.30 β†’ 0.999982 after fix). Also fixes scheduler step_dmd to return float32 (matching reference) instead of casting back to bfloat16, preventing precision loss across denoising steps. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/models/helios/scheduler.py | 17 +++++++++-------- mlx_video/models/helios/transformer.py | 7 +++++-- 2 files changed, 14 insertions(+), 10 deletions(-) diff --git a/mlx_video/models/helios/scheduler.py b/mlx_video/models/helios/scheduler.py index 6c31bb0..f8d7a78 100644 --- a/mlx_video/models/helios/scheduler.py +++ b/mlx_video/models/helios/scheduler.py @@ -205,8 +205,9 @@ def step_dmd( ) -> mx.array: """DMD step: predict x0 from flow, optionally re-noise. - Uses float32 for x0 prediction to avoid catastrophic cancellation - when sigma β‰ˆ 1 (matching reference which uses float64). + Matches reference: x0 computed in float64, re-noising in float32. + Returns float32 to keep latent precision across steps (reference + returns float32 from add_noise's .type_as(noise)). Args: model_output: Flow prediction from model @@ -215,27 +216,27 @@ def step_dmd( noisy_start: Original noisy tensor for this stage (for re-noising) Returns: - Denoised or re-noised sample + Denoised or re-noised sample (float32) """ - # Upcast to float32 to avoid precision loss in x0 = xt - sigma*flow - orig_dtype = model_output.dtype + # Upcast to float32 for x0 = xt - sigma*flow (reference uses float64 + # but MLX GPU doesn't support float64; float32 is adequate here) model_output = model_output.astype(mx.float32) sample = sample.astype(mx.float32) - sigma_t = self.sigmas[cur_step] + sigma_t = float(self.sigmas[cur_step]) x0_pred = sample - sigma_t * model_output num_timesteps = len(self.timesteps) if cur_step < num_timesteps - 1: # Re-noise: blend x0_pred with original noisy tensor at next sigma - sigma_next = self.sigmas[cur_step + 1] + sigma_next = float(self.sigmas[cur_step + 1]) noisy_start = noisy_start.astype(mx.float32) prev_sample = (1 - sigma_next) * x0_pred + sigma_next * noisy_start else: prev_sample = x0_pred self._step_index = cur_step + 1 - return prev_sample.astype(orig_dtype) + return prev_sample def step( self, diff --git a/mlx_video/models/helios/transformer.py b/mlx_video/models/helios/transformer.py index fad0632..21a6bb4 100644 --- a/mlx_video/models/helios/transformer.py +++ b/mlx_video/models/helios/transformer.py @@ -454,9 +454,12 @@ def __call__( (1, 6, original_context_length, self.dim), ) - # Zero history timestep embedding + # Zero history timestep embedding (timestep=0 β†’ sinusoidal [cos(0), sin(0)] = [1,...,1, 0,...,0]) if self.zero_history_timestep and history_seq_len > 0: - t0_emb = mx.zeros_like(t_emb) + t0_emb = mx.array([0.0]) * self._inv_freq + t0_emb = mx.concatenate([mx.cos(t0_emb), mx.sin(t0_emb)], axis=-1) + if t0_emb.ndim == 1: + t0_emb = t0_emb[None, :] temb_t0 = self.time_embedding_1( self.time_embedding_act(self.time_embedding_0(t0_emb)) ) From 8609b386b44f2ec653faa477648c2a0ae20a453e Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 5 Mar 2026 22:14:49 +0100 Subject: [PATCH 10/40] Update HELIOS-DIAGNOSTICS.md with Bug 10 and Bug 11 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- docs/HELIOS-DIAGNOSTICS.md | 57 ++++++++++++++++++++++++++++++++++---- 1 file changed, 51 insertions(+), 6 deletions(-) diff --git a/docs/HELIOS-DIAGNOSTICS.md b/docs/HELIOS-DIAGNOSTICS.md index 217071c..2cf2029 100644 --- a/docs/HELIOS-DIAGNOSTICS.md +++ b/docs/HELIOS-DIAGNOSTICS.md @@ -271,6 +271,54 @@ Output frame analysis: but is mild. This appears to be inherent model behavior, not a bug. The per-channel growth is within the VAE's normalization range and decodes to warm, plausible colors. +### Bug 10: Uniform color output β€” wrong zero-history timestep embedding + +**Symptom**: Output video showed near-uniform red/orange color (R=114, G=59, B=17 with +very low per-channel variance). No recognizable content despite plausible color range. + +**Root cause**: The zero-history timestep embedding was computed incorrectly. The reference +passes `timestep=0` through the sinusoidal `Timesteps()` encoder which produces +`[cos(0), sin(0)] = [1,1,...,1, 0,0,...,0]` (128 ones followed by 128 zeros). Our code +used `mx.zeros_like(t_emb)` β€” all zeros β€” which produces a completely different MLP output. + +Since history tokens make up ~81.6% of all tokens (2400 out of 2940 at 1/4 resolution), +the vast majority of tokens received wrong scale/shift/gate modulation vectors from the +`scale_shift_table`. This corrupted self-attention (history and current tokens interact), +making the transformer output effectively random. + +**Diagnosis** (block-by-block comparison against reference PyTorch): +1. Verified all inputs to the transformer match: patches, RoPE, text embeddings, time + embeddings, history patches β€” all cosine_sim β‰ˆ 1.0 +2. Block 0 output diverged catastrophically: cosine_sim = -0.30 (essentially uncorrelated) +3. Traced the bug to `HeliosModel.__call__` line 459 where `t0_emb = mx.zeros_like(t_emb)` + should have been the sinusoidal encoding of timestep=0 + +**Fix** (commit `061f191b`): +```python +# Before (wrong): +t0_emb = mx.zeros_like(t_emb) + +# After (correct): +t0_emb = mx.array([0.0]) * self._inv_freq +t0_emb = mx.concatenate([mx.cos(t0_emb), mx.sin(t0_emb)], axis=-1) +``` + +**Result**: Block 0 cosine similarity: -0.30 β†’ 0.999982. Full pipeline output now shows +recognizable structured content with high per-channel variance (R=100Β±100, G=81Β±81, B=33Β±37). + +### Bug 11: Scheduler step_dmd returning bfloat16 + +**Symptom**: Minor precision loss across denoising steps (contributed to warm color bias +but not the primary cause of bad output). + +**Root cause**: `step_dmd()` cast the result back to `orig_dtype` (bfloat16) at the end. +The reference keeps latents in float32 between steps. Since the DMD formula +`prev = (1-Οƒ)Β·x0 + σ·start_point` involves near-cancellation when Οƒβ‰ˆ1, float32 precision +is important. + +**Fix** (commit `061f191b`): Return float32 from `step_dmd()`, use `float()` for sigma +values to avoid array overhead. + --- ## Open Problems @@ -285,12 +333,9 @@ removed, the multi-chunk behavior may have changed. ### 2. Color warmth / saturation -**Status**: Output shows warm tones that are plausible for "sunset" prompts but may be -warm-biased for neutral prompts. Cannot verify without reference CUDA output. - -**Unknown**: Whether the reference PyTorch code also produces warm-biased output for the same -prompt and seed. The growing channel means may be inherent to the model's behavior with the -distilled DMD scheduler. +**Status**: RESOLVED by Bug 10 fix. The uniform warm color was caused by the wrong +zero-history timestep embedding, not inherent model behavior. Output now shows proper +color variation matching the prompt. ### 3. Generation speed From d4152c3859a185f7aabc774e2749a71af5f555ba Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 6 Mar 2026 08:39:34 +0100 Subject: [PATCH 11/40] Mitigate chunk boundary blur with latent-space temporal blend At chunk boundaries, the first few latent frames of each new chunk are blurrier due to lack of temporal context during denoising. This adds a latent-space blend that mixes boundary frames toward the last sharp frame of the previous chunk before VAE decode. Default: --chunk-blend 2 (blend 2 latent frames ~8 pixel frames). Use --chunk-blend 0 to disable. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- docs/HELIOS-DIAGNOSTICS.md | 18 +++++++++++++----- mlx_video/generate_helios.py | 19 +++++++++++++++++++ 2 files changed, 32 insertions(+), 5 deletions(-) diff --git a/docs/HELIOS-DIAGNOSTICS.md b/docs/HELIOS-DIAGNOSTICS.md index 2cf2029..3885047 100644 --- a/docs/HELIOS-DIAGNOSTICS.md +++ b/docs/HELIOS-DIAGNOSTICS.md @@ -323,13 +323,21 @@ values to avoid array overhead. ## Open Problems -### 1. Multi-chunk temporal stability +### 1. Chunk boundary blur artifacts -**Status**: Not re-tested after Bug 9 fix. Previously showed instability in chunk 2 -(dark frame at midpoint, inconsistent colors). Now that start-point normalization is -removed, the multi-chunk behavior may have changed. +**Status**: MITIGATED. First few latent frames of each new chunk have ~45% less spatial +detail than peak frames due to lack of temporal context during denoising. This is inherent +to autoregressive chunking. -**Next step**: Test with 99 frames (3 chunks) and evaluate chunk transitions. +**Mitigation**: Latent-space temporal blend (`--chunk-blend N`, default 2). Before VAE +decode, blends the first N latent frames of each new chunk toward the last frame of the +previous chunk using a linear ramp: `w = (k+1)/(N+1)` per frame k. The VAE's temporal +convolutions further smooth the transition. + +With `--chunk-blend 2` (default): +- Boundary latent frame: 33% original + 67% previous sharp frame +- Next latent frame: 67% original + 33% previous sharp frame +- Use `--chunk-blend 0` to disable ### 2. Color warmth / saturation diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index ec2820b..1b82d9b 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -146,6 +146,7 @@ def generate_video( amplify_first_chunk: bool = False, guidance_scale: float = 1.0, negative_prompt: str = "", + chunk_blend: int = 2, debug: bool = False, ): """Generate video using Helios autoregressive pipeline with pyramid denoising. @@ -163,6 +164,7 @@ def generate_video( amplify_first_chunk: Double steps for first chunk (better quality) guidance_scale: CFG guidance scale (1.0 = no CFG, 5.0 = default) negative_prompt: Negative prompt for CFG (empty string = unconditional) + chunk_blend: Number of latent frames to blend at chunk boundaries (0 to disable) """ from mlx_video.models.helios.config import HeliosModelConfig @@ -520,6 +522,21 @@ def generate_video( # Concatenate all chunks: each is [C, F_lat, H_lat, W_lat] all_latents = mx.concatenate(all_latent_chunks, axis=1) # [C, total_F_lat, H_lat, W_lat] + # Smooth chunk boundaries in latent space: first latent frames of each new chunk + # are blurrier due to lack of temporal context. Blend them toward the last frame + # of the previous chunk, which the VAE then further smooths via temporal convolution. + if chunk_blend > 0 and num_chunks > 1: + blend_n = min(chunk_blend, num_latent_per_chunk - 1) + latent_np = np.array(all_latents) + for b in range(1, num_chunks): + boundary = b * num_latent_per_chunk + ref = latent_np[:, boundary - 1].copy() + for k in range(min(blend_n, latent_np.shape[1] - boundary)): + w = (k + 1) / (blend_n + 1) + latent_np[:, boundary + k] = w * latent_np[:, boundary + k] + (1 - w) * ref + all_latents = mx.array(latent_np) + print(f"{Colors.DIM} Applied chunk boundary blend ({blend_n} latent frames){Colors.RESET}") + # Decode: WanVAE expects [B, C, T, H, W], handles denormalization internally z = all_latents[None, :, :, :, :] # [1, C, T, H, W] if tiling_config is not None: @@ -573,6 +590,7 @@ def main(): ) parser.add_argument("--guidance-scale", type=float, default=1.0, help="CFG guidance scale (1.0 = no CFG, default for distilled)") parser.add_argument("--negative-prompt", type=str, default="", help="Negative prompt for CFG") + parser.add_argument("--chunk-blend", type=int, default=2, help="Latent frames to blend at chunk boundaries (0=off, default=2)") parser.add_argument("--debug", action="store_true", help="Print per-step latent statistics for debugging") args = parser.parse_args() @@ -589,6 +607,7 @@ def main(): amplify_first_chunk=args.amplify_first_chunk, guidance_scale=args.guidance_scale, negative_prompt=args.negative_prompt, + chunk_blend=args.chunk_blend, debug=args.debug, ) From 47c7f7fc801f09b8baca5548d462c62fdfd4eab2 Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 6 Mar 2026 08:51:55 +0100 Subject: [PATCH 12/40] Improve chunk blend: mean-corrected to fix brightness shift Replace stat-normalization (which reduced variance/detail) with raw blend + per-channel mean correction. This preserves the detail transfer from blending with the sharp reference frame while preventing brightness shift. With correlated real latents, yields ~1.37x detail improvement at chunk boundaries. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/generate_helios.py | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index 1b82d9b..5d02358 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -523,17 +523,25 @@ def generate_video( all_latents = mx.concatenate(all_latent_chunks, axis=1) # [C, total_F_lat, H_lat, W_lat] # Smooth chunk boundaries in latent space: first latent frames of each new chunk - # are blurrier due to lack of temporal context. Blend them toward the last frame - # of the previous chunk, which the VAE then further smooths via temporal convolution. + # are blurrier (and may show grid/brightness artifacts) due to lack of temporal + # context. We blend them toward the previous chunk's last frame to transfer + # spatial detail, then correct per-channel mean to prevent brightness shift. if chunk_blend > 0 and num_chunks > 1: blend_n = min(chunk_blend, num_latent_per_chunk - 1) latent_np = np.array(all_latents) for b in range(1, num_chunks): boundary = b * num_latent_per_chunk - ref = latent_np[:, boundary - 1].copy() + ref = latent_np[:, boundary - 1].copy() # [C, H, W] for k in range(min(blend_n, latent_np.shape[1] - boundary)): - w = (k + 1) / (blend_n + 1) - latent_np[:, boundary + k] = w * latent_np[:, boundary + k] + (1 - w) * ref + target = latent_np[:, boundary + k] + # Gentle ramp: 40% reference at boundary, decreasing for later frames + ref_weight = 0.4 * (blend_n - k) / blend_n + blended = (1 - ref_weight) * target + ref_weight * ref + # Correct per-channel mean to match target (prevents brightness shift + # while keeping the detail/std boost from blending) + for c in range(blended.shape[0]): + blended[c] += target[c].mean() - blended[c].mean() + latent_np[:, boundary + k] = blended all_latents = mx.array(latent_np) print(f"{Colors.DIM} Applied chunk boundary blend ({blend_n} latent frames){Colors.RESET}") From ad4e31a7cd54351648545080489805f1e715a105 Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 6 Mar 2026 09:25:04 +0100 Subject: [PATCH 13/40] Switch to per-chunk VAE decoding, revert blend default to off Match reference behavior: decode each chunk's 9 latent frames independently rather than the full concatenated sequence. This avoids cross-chunk VAE temporal convolution artifacts (grid patterns, brightness bleeding) that occurred when quality discontinuities at chunk boundaries hit the causal convolutions. Changes: - Per-chunk VAE decode loop with per-chunk warmup trimming - Tiling config uses per-chunk frame count (33) not full video - chunk_blend default changed from 2 to 0 (off) - Blend code retained as opt-in (--chunk-blend N) - Updated HELIOS-DIAGNOSTICS.md with findings Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- docs/HELIOS-DIAGNOSTICS.md | 29 +++++++------ mlx_video/generate_helios.py | 79 ++++++++++++++++++------------------ 2 files changed, 56 insertions(+), 52 deletions(-) diff --git a/docs/HELIOS-DIAGNOSTICS.md b/docs/HELIOS-DIAGNOSTICS.md index 3885047..79cb905 100644 --- a/docs/HELIOS-DIAGNOSTICS.md +++ b/docs/HELIOS-DIAGNOSTICS.md @@ -325,19 +325,22 @@ values to avoid array overhead. ### 1. Chunk boundary blur artifacts -**Status**: MITIGATED. First few latent frames of each new chunk have ~45% less spatial -detail than peak frames due to lack of temporal context during denoising. This is inherent -to autoregressive chunking. - -**Mitigation**: Latent-space temporal blend (`--chunk-blend N`, default 2). Before VAE -decode, blends the first N latent frames of each new chunk toward the last frame of the -previous chunk using a linear ramp: `w = (k+1)/(N+1)` per frame k. The VAE's temporal -convolutions further smooth the transition. - -With `--chunk-blend 2` (default): -- Boundary latent frame: 33% original + 67% previous sharp frame -- Next latent frame: 67% original + 33% previous sharp frame -- Use `--chunk-blend 0` to disable +**Status**: Improved via per-chunk VAE decoding. First few latent frames of each new chunk +have ~45% less spatial detail than peak frames due to lack of temporal context during +denoising. This is inherent to autoregressive chunking β€” the reference has the same +limitation and does **no post-processing** at chunk boundaries. + +**Key finding**: The reference decodes each chunk independently (9 latent frames at a time), +then concatenates pixel frames. Our initial approach of decoding the full concatenated +latent sequence caused the VAE's causal temporal convolutions to propagate quality +discontinuities across chunk boundaries, adding secondary artifacts (grid, brightness). + +**Current approach**: Per-chunk VAE decoding (matching reference). Each chunk is decoded +independently with fresh causal padding, producing cleaner boundary transitions. + +**Optional**: Latent-space blend (`--chunk-blend N`, default 0 = off). Blends the first N +latent frames of each new chunk toward the previous chunk's last frame. Generally not +recommended as it introduces its own artifacts (grid patterns, brightness shift). ### 2. Color warmth / saturation diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index 5d02358..908341f 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -146,7 +146,7 @@ def generate_video( amplify_first_chunk: bool = False, guidance_scale: float = 1.0, negative_prompt: str = "", - chunk_blend: int = 2, + chunk_blend: int = 0, debug: bool = False, ): """Generate video using Helios autoregressive pipeline with pyramid denoising. @@ -509,7 +509,7 @@ def generate_video( if tiling == "none": tiling_config = None elif tiling == "auto": - tiling_config = TilingConfig.auto(height, width, num_frames) + tiling_config = TilingConfig.auto(height, width, frames_per_chunk) elif tiling == "default": tiling_config = TilingConfig.default() elif tiling == "aggressive": @@ -517,53 +517,54 @@ def generate_video( elif tiling == "conservative": tiling_config = TilingConfig.conservative() else: - tiling_config = TilingConfig.auto(height, width, num_frames) + tiling_config = TilingConfig.auto(height, width, frames_per_chunk) - # Concatenate all chunks: each is [C, F_lat, H_lat, W_lat] - all_latents = mx.concatenate(all_latent_chunks, axis=1) # [C, total_F_lat, H_lat, W_lat] - - # Smooth chunk boundaries in latent space: first latent frames of each new chunk - # are blurrier (and may show grid/brightness artifacts) due to lack of temporal - # context. We blend them toward the previous chunk's last frame to transfer - # spatial detail, then correct per-channel mean to prevent brightness shift. + # Optional: smooth chunk boundaries in latent space (off by default). + # When enabled, blends first N latent frames of each new chunk toward + # the previous chunk's last frame to reduce quality discontinuity. if chunk_blend > 0 and num_chunks > 1: blend_n = min(chunk_blend, num_latent_per_chunk - 1) - latent_np = np.array(all_latents) for b in range(1, num_chunks): - boundary = b * num_latent_per_chunk - ref = latent_np[:, boundary - 1].copy() # [C, H, W] - for k in range(min(blend_n, latent_np.shape[1] - boundary)): - target = latent_np[:, boundary + k] - # Gentle ramp: 40% reference at boundary, decreasing for later frames + ref_np = np.array(all_latent_chunks[b - 1][:, -1]) # [C, H, W] + chunk_np = np.array(all_latent_chunks[b]) # [C, F, H, W] + for k in range(min(blend_n, chunk_np.shape[1])): + target = chunk_np[:, k] ref_weight = 0.4 * (blend_n - k) / blend_n - blended = (1 - ref_weight) * target + ref_weight * ref - # Correct per-channel mean to match target (prevents brightness shift - # while keeping the detail/std boost from blending) + blended = (1 - ref_weight) * target + ref_weight * ref_np for c in range(blended.shape[0]): blended[c] += target[c].mean() - blended[c].mean() - latent_np[:, boundary + k] = blended - all_latents = mx.array(latent_np) + chunk_np[:, k] = blended + all_latent_chunks[b] = mx.array(chunk_np) print(f"{Colors.DIM} Applied chunk boundary blend ({blend_n} latent frames){Colors.RESET}") - # Decode: WanVAE expects [B, C, T, H, W], handles denormalization internally - z = all_latents[None, :, :, :, :] # [1, C, T, H, W] - if tiling_config is not None: - video = vae.decode_tiled(z, tiling_config) - else: - video = vae.decode(z) - mx.eval(video) - print(f"{Colors.DIM} VAE decode: {time.time() - t4:.1f}s{Colors.RESET}") + # Decode each chunk independently (matching reference behavior). + # Per-chunk decoding avoids cross-chunk VAE temporal convolution artifacts + # that occur when the quality discontinuity at boundaries hits the causal conv. + video_chunks = [] + for ci, chunk_latents in enumerate(all_latent_chunks): + z = chunk_latents[None, :, :, :, :] # [1, C, 9, H_lat, W_lat] + if tiling_config is not None: + chunk_video = vae.decode_tiled(z, tiling_config) + else: + chunk_video = vae.decode(z) + mx.eval(chunk_video) - # Convert to numpy: video is [1, 3, T, H, W] in [-1, 1] - video = np.array(video[0]) # [3, T, H, W] + chunk_np = np.array(chunk_video[0]) # [3, T_decoded, H, W] + # Trim VAE warmup frames (causal padding produces stride_t-1 garbage at start) + valid = (num_latent_per_chunk - 1) * vae_stride_t + 1 # 33 + trim = chunk_np.shape[1] - valid + if trim > 0: + chunk_np = chunk_np[:, trim:] + video_chunks.append(chunk_np) + + del chunk_video, z + gc.collect() + mx.clear_cache() + + print(f"{Colors.DIM} VAE decode: {time.time() - t4:.1f}s{Colors.RESET}") - # Trim VAE warmup frames: causal padding produces stride_t-1 garbage frames at start - warmup_frames = vae_stride_t - 1 # 3 - total_decoded = video.shape[1] - valid_frames = (total_latent_frames - 1) * vae_stride_t + 1 - trim_start = total_decoded - valid_frames - if trim_start > 0: - video = video[:, trim_start:] + # Concatenate pixel frames from all chunks + video = np.concatenate(video_chunks, axis=1) # [3, T_total, H, W] video = (video + 1.0) / 2.0 video = np.clip(video * 255.0, 0, 255).astype(np.uint8) @@ -598,7 +599,7 @@ def main(): ) parser.add_argument("--guidance-scale", type=float, default=1.0, help="CFG guidance scale (1.0 = no CFG, default for distilled)") parser.add_argument("--negative-prompt", type=str, default="", help="Negative prompt for CFG") - parser.add_argument("--chunk-blend", type=int, default=2, help="Latent frames to blend at chunk boundaries (0=off, default=2)") + parser.add_argument("--chunk-blend", type=int, default=0, help="Latent frames to blend at chunk boundaries (0=off, default=0)") parser.add_argument("--debug", action="store_true", help="Print per-step latent statistics for debugging") args = parser.parse_args() From f35d10564625d68d07aa4ccfba4c8a0251b8406a Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 6 Mar 2026 10:01:24 +0100 Subject: [PATCH 14/40] Implement adaptive anti-drifting for temporal consistency Port the reference's AdaptiveAntiDrifting mechanism. Tracks per-channel latent mean/variance via EMA across chunks. When both drift beyond L2 thresholds (0.15), adds Gaussian noise (default 10%) to the chunk's latents before saving to history, forcing subsequent chunks to re-anchor to global statistics. Usage: --anti-drifting [--anti-drift-strength 0.1] Off by default (matching reference). Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- docs/HELIOS-DIAGNOSTICS.md | 18 ++++++++++++++- mlx_video/generate_helios.py | 45 ++++++++++++++++++++++++++++++++++++ 2 files changed, 62 insertions(+), 1 deletion(-) diff --git a/docs/HELIOS-DIAGNOSTICS.md b/docs/HELIOS-DIAGNOSTICS.md index 79cb905..0abd2a1 100644 --- a/docs/HELIOS-DIAGNOSTICS.md +++ b/docs/HELIOS-DIAGNOSTICS.md @@ -348,7 +348,23 @@ recommended as it introduces its own artifacts (grid patterns, brightness shift) zero-history timestep embedding, not inherent model behavior. Output now shows proper color variation matching the prompt. -### 3. Generation speed +### 3. Adaptive anti-drifting for temporal consistency + +**Status**: Implemented (`--anti-drifting` flag). Ports the reference's `AdaptiveAntiDrifting` +mechanism to prevent color/style drift across chunks in long videos. + +**How it works**: +- Tracks per-channel latent mean/variance via EMA (momentum=0.9) across chunks +- After each chunk: computes L2 norm of deviation from running average +- If BOTH mean drift > 0.15 AND variance drift > 0.15: adds Gaussian noise to the chunk's + latents (default strength=0.1) before saving to history +- This forces subsequent chunks to re-anchor to the global statistics +- Only triggers on non-final chunks (last chunk doesn't need correction) + +**Usage**: `--anti-drifting` to enable, `--anti-drift-strength 0.1` to control noise amplitude. +Off by default (matching reference). + +### 4. Generation speed **Status**: ~14s/step at 384Γ—640 resolution. This is limited by the full-resolution stages (stages 0-1 at reduced resolution are fast: ~5s/step). diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index 908341f..787aeae 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -147,6 +147,8 @@ def generate_video( guidance_scale: float = 1.0, negative_prompt: str = "", chunk_blend: int = 0, + anti_drifting: bool = False, + anti_drift_strength: float = 0.1, debug: bool = False, ): """Generate video using Helios autoregressive pipeline with pyramid denoising. @@ -165,6 +167,8 @@ def generate_video( guidance_scale: CFG guidance scale (1.0 = no CFG, 5.0 = default) negative_prompt: Negative prompt for CFG (empty string = unconditional) chunk_blend: Number of latent frames to blend at chunk boundaries (0 to disable) + anti_drifting: Enable adaptive anti-drifting for temporal consistency + anti_drift_strength: Noise strength for anti-drifting correction (default 0.1) """ from mlx_video.models.helios.config import HeliosModelConfig @@ -310,6 +314,11 @@ def generate_video( total_generated = 0 image_latents_prefix = None # Set after first chunk for keep_first_frame + # Adaptive anti-drifting: EMA of per-channel latent statistics + drift_global_mean = None + drift_global_var = None + drift_rho = 0.9 # EMA momentum + for chunk_idx in range(num_chunks): t_chunk = time.time() is_first = chunk_idx == 0 @@ -478,6 +487,38 @@ def generate_video( pbar.close() mx.eval(latents) + + # Adaptive anti-drifting: detect and correct latent statistics drift + # between chunks to maintain temporal consistency (matching reference). + if anti_drifting and num_chunks > 1: + # Compute per-channel mean/var: latents is [C, F, H, W] + lat_f32 = latents.astype(mx.float32) + cur_mean = mx.mean(lat_f32, axis=(1, 2, 3)) # [C] + cur_var = mx.var(lat_f32, axis=(1, 2, 3)) # [C] + mx.eval(cur_mean, cur_var) + + if drift_global_mean is None: + drift_global_mean = cur_mean + drift_global_var = cur_var + else: + # Detect drift: L2 norm of deviation from EMA + mean_drift = float(mx.sqrt(mx.sum((cur_mean - drift_global_mean) ** 2)).item()) + var_drift = float(mx.sqrt(mx.sum((cur_var - drift_global_var) ** 2)).item()) + has_drift = mean_drift > 0.15 and var_drift > 0.15 + + if has_drift and chunk_idx < num_chunks - 1: + # Add noise to prevent further drift in subsequent chunks + noise = mx.random.normal(latents.shape) + latents = latents + anti_drift_strength * noise + mx.eval(latents) + print(f"{Colors.DIM} ⚠ Drift detected (mean={mean_drift:.3f}, var={var_drift:.3f}), applied correction{Colors.RESET}") + elif debug: + print(f" [drift] mean={mean_drift:.3f}, var={var_drift:.3f}, threshold=0.15") + + # Update EMA + drift_global_mean = drift_rho * drift_global_mean + (1 - drift_rho) * cur_mean + drift_global_var = drift_rho * drift_global_var + (1 - drift_rho) * cur_var + all_latent_chunks.append(latents) # Update history: append this chunk's latents @@ -600,6 +641,8 @@ def main(): parser.add_argument("--guidance-scale", type=float, default=1.0, help="CFG guidance scale (1.0 = no CFG, default for distilled)") parser.add_argument("--negative-prompt", type=str, default="", help="Negative prompt for CFG") parser.add_argument("--chunk-blend", type=int, default=0, help="Latent frames to blend at chunk boundaries (0=off, default=0)") + parser.add_argument("--anti-drifting", action="store_true", help="Enable adaptive anti-drifting for temporal consistency between chunks") + parser.add_argument("--anti-drift-strength", type=float, default=0.1, help="Noise strength for anti-drifting correction (default=0.1)") parser.add_argument("--debug", action="store_true", help="Print per-step latent statistics for debugging") args = parser.parse_args() @@ -617,6 +660,8 @@ def main(): guidance_scale=args.guidance_scale, negative_prompt=args.negative_prompt, chunk_blend=args.chunk_blend, + anti_drifting=args.anti_drifting, + anti_drift_strength=args.anti_drift_strength, debug=args.debug, ) From 2d5ccf64da43be4d9c222d9a3e4043f9ee848176 Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 6 Mar 2026 10:20:16 +0100 Subject: [PATCH 15/40] Replace anti-drifting noise corruption with history normalization MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The previous approach (adding Gaussian noise when drift detected) degraded output quality and caused camera jumps β€” noise was applied to both decoded output AND history, cascading into future chunks. New approach: - Clean latents always saved for decoding (no output quality impact) - When drift detected, normalize only the HISTORY copy's per-channel mean/var toward the running EMA (deterministic, no random noise) - Blend controlled by --anti-drift-blend (0-1, default 0.5) - Fix EMA update order to match reference (update before detect) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- docs/HELIOS-DIAGNOSTICS.md | 23 +++++++++------- mlx_video/generate_helios.py | 51 ++++++++++++++++++++++-------------- 2 files changed, 45 insertions(+), 29 deletions(-) diff --git a/docs/HELIOS-DIAGNOSTICS.md b/docs/HELIOS-DIAGNOSTICS.md index 0abd2a1..ce018db 100644 --- a/docs/HELIOS-DIAGNOSTICS.md +++ b/docs/HELIOS-DIAGNOSTICS.md @@ -350,18 +350,23 @@ color variation matching the prompt. ### 3. Adaptive anti-drifting for temporal consistency -**Status**: Implemented (`--anti-drifting` flag). Ports the reference's `AdaptiveAntiDrifting` -mechanism to prevent color/style drift across chunks in long videos. +**Status**: Implemented (`--anti-drifting` flag). Prevents color/style drift across chunks in +long videos by normalizing history latent statistics toward a running average. **How it works**: - Tracks per-channel latent mean/variance via EMA (momentum=0.9) across chunks -- After each chunk: computes L2 norm of deviation from running average -- If BOTH mean drift > 0.15 AND variance drift > 0.15: adds Gaussian noise to the chunk's - latents (default strength=0.1) before saving to history -- This forces subsequent chunks to re-anchor to the global statistics -- Only triggers on non-final chunks (last chunk doesn't need correction) - -**Usage**: `--anti-drifting` to enable, `--anti-drift-strength 0.1` to control noise amplitude. +- After each chunk: computes L2 norm of deviation from updated EMA +- If BOTH mean drift > 0.15 AND variance drift > 0.15 AND not last chunk: + - Normalizes the **history copy** (not the decoded output) per-channel to match the EMA + - `normalized = (latents - cur_mean) / cur_std * global_std + global_mean` + - Controlled by `--anti-drift-blend` (0=off, 0.5=half-normalize, 1.0=full normalize) +- Clean latents are always kept for decoding β€” no output quality impact +- Only the history conditioning is adjusted, gently steering future chunks + +**Previous approach** (noise corruption, commit `a9fd911d`) was reverted because adding +Gaussian noise degraded output quality and cascaded noise into subsequent chunks via history. + +**Usage**: `--anti-drifting` to enable, `--anti-drift-blend 0.5` (default) to control strength. Off by default (matching reference). ### 4. Generation speed diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index 787aeae..55d9cbb 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -148,7 +148,7 @@ def generate_video( negative_prompt: str = "", chunk_blend: int = 0, anti_drifting: bool = False, - anti_drift_strength: float = 0.1, + anti_drift_blend: float = 0.5, debug: bool = False, ): """Generate video using Helios autoregressive pipeline with pyramid denoising. @@ -168,7 +168,7 @@ def generate_video( negative_prompt: Negative prompt for CFG (empty string = unconditional) chunk_blend: Number of latent frames to blend at chunk boundaries (0 to disable) anti_drifting: Enable adaptive anti-drifting for temporal consistency - anti_drift_strength: Noise strength for anti-drifting correction (default 0.1) + anti_drift_blend: How much to normalize history toward EMA (0=off, 0.5=half, 1.0=full) """ from mlx_video.models.helios.config import HeliosModelConfig @@ -488,11 +488,13 @@ def generate_video( pbar.close() mx.eval(latents) - # Adaptive anti-drifting: detect and correct latent statistics drift - # between chunks to maintain temporal consistency (matching reference). + # Adaptive anti-drifting: normalize history latent statistics to prevent + # color/style drift between chunks. Clean latents are kept for decoding; + # only the history copy is normalized toward the running EMA. + history_latents_chunk = latents # default: same as output if anti_drifting and num_chunks > 1: - # Compute per-channel mean/var: latents is [C, F, H, W] lat_f32 = latents.astype(mx.float32) + # Per-channel stats: latents is [C, F, H, W] cur_mean = mx.mean(lat_f32, axis=(1, 2, 3)) # [C] cur_var = mx.var(lat_f32, axis=(1, 2, 3)) # [C] mx.eval(cur_mean, cur_var) @@ -501,29 +503,38 @@ def generate_video( drift_global_mean = cur_mean drift_global_var = cur_var else: - # Detect drift: L2 norm of deviation from EMA + # Update EMA BEFORE detection (matching reference order) + drift_global_mean = drift_rho * drift_global_mean + (1 - drift_rho) * cur_mean + drift_global_var = drift_rho * drift_global_var + (1 - drift_rho) * cur_var + + # Detect drift: L2 norm of deviation from updated EMA mean_drift = float(mx.sqrt(mx.sum((cur_mean - drift_global_mean) ** 2)).item()) var_drift = float(mx.sqrt(mx.sum((cur_var - drift_global_var) ** 2)).item()) has_drift = mean_drift > 0.15 and var_drift > 0.15 if has_drift and chunk_idx < num_chunks - 1: - # Add noise to prevent further drift in subsequent chunks - noise = mx.random.normal(latents.shape) - latents = latents + anti_drift_strength * noise - mx.eval(latents) - print(f"{Colors.DIM} ⚠ Drift detected (mean={mean_drift:.3f}, var={var_drift:.3f}), applied correction{Colors.RESET}") + # Normalize history copy toward EMA (deterministic, no noise) + # Per-channel: shift mean and scale variance + cur_mean_4d = cur_mean[:, None, None, None] + cur_std_4d = mx.sqrt(mx.maximum(cur_var, mx.array(1e-8)))[:, None, None, None] + global_mean_4d = drift_global_mean[:, None, None, None] + global_std_4d = mx.sqrt(mx.maximum(drift_global_var, mx.array(1e-8)))[:, None, None, None] + + # Standardize, then rescale to target stats + normalized = (latents - cur_mean_4d) / cur_std_4d * global_std_4d + global_mean_4d + # Blend: 0 = keep raw, 1 = fully normalize to EMA + history_latents_chunk = (1 - anti_drift_blend) * latents + anti_drift_blend * normalized + history_latents_chunk = history_latents_chunk.astype(latents.dtype) + mx.eval(history_latents_chunk) + print(f"{Colors.DIM} ⚠ Drift detected (mean={mean_drift:.3f}, var={var_drift:.3f}), normalized history{Colors.RESET}") elif debug: print(f" [drift] mean={mean_drift:.3f}, var={var_drift:.3f}, threshold=0.15") - # Update EMA - drift_global_mean = drift_rho * drift_global_mean + (1 - drift_rho) * cur_mean - drift_global_var = drift_rho * drift_global_var + (1 - drift_rho) * cur_var - - all_latent_chunks.append(latents) + all_latent_chunks.append(latents) # clean latents for decoding - # Update history: append this chunk's latents + # Update history: use potentially normalized chunk for conditioning total_generated += num_latent_per_chunk - history_latents = mx.concatenate([history_latents, latents], axis=1) + history_latents = mx.concatenate([history_latents, history_latents_chunk], axis=1) # After first chunk, save first frame as prefix for subsequent chunks if is_first and image_latents_prefix is None: @@ -642,7 +653,7 @@ def main(): parser.add_argument("--negative-prompt", type=str, default="", help="Negative prompt for CFG") parser.add_argument("--chunk-blend", type=int, default=0, help="Latent frames to blend at chunk boundaries (0=off, default=0)") parser.add_argument("--anti-drifting", action="store_true", help="Enable adaptive anti-drifting for temporal consistency between chunks") - parser.add_argument("--anti-drift-strength", type=float, default=0.1, help="Noise strength for anti-drifting correction (default=0.1)") + parser.add_argument("--anti-drift-blend", type=float, default=0.5, help="How much to normalize history toward EMA stats (0=off, 0.5=half, 1.0=full; default=0.5)") parser.add_argument("--debug", action="store_true", help="Print per-step latent statistics for debugging") args = parser.parse_args() @@ -661,7 +672,7 @@ def main(): negative_prompt=args.negative_prompt, chunk_blend=args.chunk_blend, anti_drifting=args.anti_drifting, - anti_drift_strength=args.anti_drift_strength, + anti_drift_blend=args.anti_drift_blend, debug=args.debug, ) From 7b868c105bcecc252e8e6a8acc354326ec3acf9b Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 6 Mar 2026 10:44:34 +0100 Subject: [PATCH 16/40] Mitigate camera jumps: amplify first chunk + pixel cross-fade Two mitigations for the inherent autoregressive chunk boundary artifacts: 1. Enable --amplify-first-chunk by default (matching reference distilled script). Doubles DMD steps for the first chunk, providing a better spatial anchor for subsequent chunks via history. 2. Add pixel-space cross-fade (--crossfade-frames 4, default ON). Linear blend of last N frames of chunk K with first N of chunk K+1. Unlike latent-space blending, pixel-space is clean since VAE decode has already resolved block noise patterns. Confirmed NOT a bug: thorough reference comparison shows identical schedules, indices, history, and DMD steps. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- docs/HELIOS-DIAGNOSTICS.md | 22 +++++++++++++++++----- mlx_video/generate_helios.py | 23 ++++++++++++++++++++--- 2 files changed, 37 insertions(+), 8 deletions(-) diff --git a/docs/HELIOS-DIAGNOSTICS.md b/docs/HELIOS-DIAGNOSTICS.md index ce018db..1ba17a5 100644 --- a/docs/HELIOS-DIAGNOSTICS.md +++ b/docs/HELIOS-DIAGNOSTICS.md @@ -379,13 +379,25 @@ Off by default (matching reference). - Quantization (model supports 4/8-bit via convert_helios.py) - Memory-efficient attention -### 4. `amplify_first_chunk` not tested +### 5. Camera jumps at chunk boundaries -The reference recommends `--is-amplify-first-chunk` for distilled models. This doubles the -DMD timestep expansion for the first chunk (2n+1 steps instead of n+1). Not yet tested in -our pipeline. +**Status**: Mitigated with two defaults, inherent to autoregressive generation. -### 5. Non-distilled model not supported +Objects may appear to shift spatial scale or "jump in" at chunk boundaries. This is NOT a bug +β€” thorough comparison with the reference confirms identical schedules, indices, history +handling, and DMD steps. It's an inherent limitation of autoregressive chunked generation +where each chunk has limited temporal context. + +**Mitigations applied**: +- `--amplify-first-chunk` (now ON by default): Doubles DMD steps for the first chunk, + providing a higher-quality anchor for subsequent chunks via history. Reference ALWAYS uses + this for distilled models. +- `--crossfade-frames 4` (default 4): Pixel-space linear cross-fade at chunk boundaries. + Blends the last N frames of chunk K with the first N of chunk K+1. Applied in pixel space + so there are no grid artifacts (unlike latent-space blending). Use `--crossfade-frames 0` + to disable. + +### 6. Non-distilled model not supported Only the distilled model (DMD scheduler, 2+2+2 steps, no CFG) is implemented. The non-distilled model uses Euler/UniPC schedulers with 20+20+20 steps and requires CFG. diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index 55d9cbb..8e76b65 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -143,10 +143,11 @@ def generate_video( seed: int = -1, output_path: str = "output_helios.mp4", tiling: str = "auto", - amplify_first_chunk: bool = False, + amplify_first_chunk: bool = True, guidance_scale: float = 1.0, negative_prompt: str = "", chunk_blend: int = 0, + crossfade_frames: int = 4, anti_drifting: bool = False, anti_drift_blend: float = 0.5, debug: bool = False, @@ -163,10 +164,11 @@ def generate_video( seed: Random seed (-1 for random) output_path: Output video path tiling: VAE tiling mode: auto, none, default, aggressive, conservative - amplify_first_chunk: Double steps for first chunk (better quality) + amplify_first_chunk: Double steps for first chunk (recommended for distilled model) guidance_scale: CFG guidance scale (1.0 = no CFG, 5.0 = default) negative_prompt: Negative prompt for CFG (empty string = unconditional) chunk_blend: Number of latent frames to blend at chunk boundaries (0 to disable) + crossfade_frames: Number of pixel frames to cross-fade between chunks (0 to disable) anti_drifting: Enable adaptive anti-drifting for temporal consistency anti_drift_blend: How much to normalize history toward EMA (0=off, 0.5=half, 1.0=full) """ @@ -615,6 +617,18 @@ def generate_video( print(f"{Colors.DIM} VAE decode: {time.time() - t4:.1f}s{Colors.RESET}") + # Pixel-space cross-fade at chunk boundaries to smooth transitions. + # Unlike latent-space blending, this is clean β€” no grid artifacts since + # the VAE decode has already resolved block noise patterns. + if crossfade_frames > 0 and len(video_chunks) > 1: + cf = min(crossfade_frames, video_chunks[0].shape[1] - 1) + for i in range(1, len(video_chunks)): + for k in range(cf): + # Linear ramp: weight 1β†’0 for previous chunk, 0β†’1 for current + w = (k + 1) / (cf + 1) + video_chunks[i][:, k] = (1 - w) * video_chunks[i - 1][:, -(cf - k)] + w * video_chunks[i][:, k] + print(f"{Colors.DIM} Applied pixel cross-fade ({cf} frames at each boundary){Colors.RESET}") + # Concatenate pixel frames from all chunks video = np.concatenate(video_chunks, axis=1) # [3, T_total, H, W] @@ -641,7 +655,8 @@ def main(): "--pyramid-steps", type=int, nargs="+", default=[2, 2, 2], help="Steps per pyramid stage (default: 2 2 2 for distilled, total 6 forward passes)", ) - parser.add_argument("--amplify-first-chunk", action="store_true", help="Double steps for first chunk (better quality)") + parser.add_argument("--amplify-first-chunk", action="store_true", default=True, help="Double steps for first chunk (default: on, recommended for distilled)") + parser.add_argument("--no-amplify-first-chunk", action="store_false", dest="amplify_first_chunk", help="Disable first chunk amplification") parser.add_argument("--seed", type=int, default=-1, help="Random seed") parser.add_argument("--output-path", type=str, default="output_helios.mp4", help="Output video path") parser.add_argument( @@ -652,6 +667,7 @@ def main(): parser.add_argument("--guidance-scale", type=float, default=1.0, help="CFG guidance scale (1.0 = no CFG, default for distilled)") parser.add_argument("--negative-prompt", type=str, default="", help="Negative prompt for CFG") parser.add_argument("--chunk-blend", type=int, default=0, help="Latent frames to blend at chunk boundaries (0=off, default=0)") + parser.add_argument("--crossfade-frames", type=int, default=4, help="Pixel frames to cross-fade between chunks (0=off, default=4)") parser.add_argument("--anti-drifting", action="store_true", help="Enable adaptive anti-drifting for temporal consistency between chunks") parser.add_argument("--anti-drift-blend", type=float, default=0.5, help="How much to normalize history toward EMA stats (0=off, 0.5=half, 1.0=full; default=0.5)") parser.add_argument("--debug", action="store_true", help="Print per-step latent statistics for debugging") @@ -671,6 +687,7 @@ def main(): guidance_scale=args.guidance_scale, negative_prompt=args.negative_prompt, chunk_blend=args.chunk_blend, + crossfade_frames=args.crossfade_frames, anti_drifting=args.anti_drifting, anti_drift_blend=args.anti_drift_blend, debug=args.debug, From 26807550351cf573f71f0c4859d7d0a00f49a918 Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 6 Mar 2026 13:49:59 +0100 Subject: [PATCH 17/40] Fix zoom bug: use float32 for residual connections in transformer blocks MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The Helios transformer block was computing residual connections (self-attn, cross-attn, FFN) entirely in bfloat16. The reference PyTorch implementation promotes to float32 for these additions (.float() + ...).type_as()). In bfloat16 (7 bits mantissa), small residual updates from attention/FFN are systematically truncated. Over 48 blocks Γ— 3 residuals Γ— 6 model calls per chunk, this truncation preferentially removes high-frequency spatial content. When these smoothed latents become history for the next chunk, the effect compounds, producing a progressive 'zooming in' artifact. Fix: cast to float32 before residual additions, matching the reference: x = (x.astype(float32) + attn_out * gate).astype(w_dtype) Verified via ablation: - Frozen history (same history all chunks): NO zoom β†’ proves history causes it - Model forward pass: cos_sim=0.996 vs reference β†’ model correct per-call - After fix: spatial gradient 2x higher, declining slope 3x smaller Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/models/helios/transformer.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/mlx_video/models/helios/transformer.py b/mlx_video/models/helios/transformer.py index 21a6bb4..4798939 100644 --- a/mlx_video/models/helios/transformer.py +++ b/mlx_video/models/helios/transformer.py @@ -123,24 +123,26 @@ def __call__( rope_cos_sin=rotary_emb, original_context_length=original_context_length, ) - x = (x + attn_out * gate_msa) + # Residual in float32 to match reference (prevents systematic truncation + # of small updates in bfloat16, which compounds across chunks via history) + x = (x.astype(mx.float32) + attn_out * gate_msa).astype(w_dtype) # 2. Cross-attention (history tokens skip cross-attention) if self.guidance_cross_attn and history_seq_len > 0: history_x, current_x = x[:, :history_seq_len], x[:, history_seq_len:] norm_current = self.norm2(current_x) if self.norm2 is not None else current_x cross_out = self.cross_attn(norm_current, encoder_hidden_states, kv_cache=cross_kv_cache) - current_x = current_x + cross_out + current_x = (current_x.astype(mx.float32) + cross_out).astype(w_dtype) x = mx.concatenate([history_x, current_x], axis=1) else: norm_x = self.norm2(x) if self.norm2 is not None else x cross_out = self.cross_attn(norm_x, encoder_hidden_states, kv_cache=cross_kv_cache) - x = x + cross_out + x = (x.astype(mx.float32) + cross_out).astype(w_dtype) # 3. Feed-forward norm_x = (self.norm3(x) * (1 + c_scale) + c_shift).astype(w_dtype) ff_out = self.ffn(norm_x) - x = x + ff_out * c_gate + x = (x.astype(mx.float32) + ff_out.astype(mx.float32) * c_gate).astype(w_dtype) return x From 80421ce0a47eea2f15738f61c14304886592d348 Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 6 Mar 2026 15:05:20 +0100 Subject: [PATCH 18/40] Disable pixel cross-fade by default to fix chunk boundary quality Reference pipeline comparison showed cross-fade was causing 40% sharpness drops at chunk boundaries. Without it, boundaries show the correct pattern: first frame is sharpest (matching reference's UP +16-21% spikes). Cross-fade can still be enabled via --crossfade-frames N if desired. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/generate_helios.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index 8e76b65..c406038 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -147,7 +147,7 @@ def generate_video( guidance_scale: float = 1.0, negative_prompt: str = "", chunk_blend: int = 0, - crossfade_frames: int = 4, + crossfade_frames: int = 0, anti_drifting: bool = False, anti_drift_blend: float = 0.5, debug: bool = False, @@ -667,7 +667,7 @@ def main(): parser.add_argument("--guidance-scale", type=float, default=1.0, help="CFG guidance scale (1.0 = no CFG, default for distilled)") parser.add_argument("--negative-prompt", type=str, default="", help="Negative prompt for CFG") parser.add_argument("--chunk-blend", type=int, default=0, help="Latent frames to blend at chunk boundaries (0=off, default=0)") - parser.add_argument("--crossfade-frames", type=int, default=4, help="Pixel frames to cross-fade between chunks (0=off, default=4)") + parser.add_argument("--crossfade-frames", type=int, default=0, help="Pixel frames to cross-fade between chunks (0=off, default=0)") parser.add_argument("--anti-drifting", action="store_true", help="Enable adaptive anti-drifting for temporal consistency between chunks") parser.add_argument("--anti-drift-blend", type=float, default=0.5, help="How much to normalize history toward EMA stats (0=off, 0.5=half, 1.0=full; default=0.5)") parser.add_argument("--debug", action="store_true", help="Print per-step latent statistics for debugging") From fa0a9fc4c6434919f0e8556decc128fae215ca8f Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 6 Mar 2026 15:06:08 +0100 Subject: [PATCH 19/40] Update diagnostics doc: boundary quality and camera jump issues resolved Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- docs/HELIOS-DIAGNOSTICS.md | 57 +++++++++++++++++++++----------------- 1 file changed, 32 insertions(+), 25 deletions(-) diff --git a/docs/HELIOS-DIAGNOSTICS.md b/docs/HELIOS-DIAGNOSTICS.md index 1ba17a5..fd98767 100644 --- a/docs/HELIOS-DIAGNOSTICS.md +++ b/docs/HELIOS-DIAGNOSTICS.md @@ -323,23 +323,26 @@ values to avoid array overhead. ## Open Problems -### 1. Chunk boundary blur artifacts +### 1. Chunk boundary quality βœ… RESOLVED -**Status**: Improved via per-chunk VAE decoding. First few latent frames of each new chunk -have ~45% less spatial detail than peak frames due to lack of temporal context during -denoising. This is inherent to autoregressive chunking β€” the reference has the same -limitation and does **no post-processing** at chunk boundaries. +**Status**: Fixed. Boundary quality now matches the reference pipeline. -**Key finding**: The reference decodes each chunk independently (9 latent frames at a time), -then concatenates pixel frames. Our initial approach of decoding the full concatenated -latent sequence caused the VAE's causal temporal convolutions to propagate quality -discontinuities across chunk boundaries, adding secondary artifacts (grid, brightness). +**Root cause**: Pixel cross-fade was blending the first N frames of each new chunk with the +tail of the previous chunk. Since frames from different chunks don't spatially align, this +created blur β€” causing a **40% sharpness drop** at every boundary. The reference pipeline +uses **no cross-fade** at all. -**Current approach**: Per-chunk VAE decoding (matching reference). Each chunk is decoded -independently with fresh causal padding, producing cleaner boundary transitions. +**Key finding**: Reference comparison revealed the correct boundary pattern: the first frame +of each chunk should be the **sharpest** (conditioned by high-quality history), not the +blurriest. With cross-fade disabled, our boundary pattern now matches the reference: +- Reference: boundary UP +16-21%, per-chunk means stable (5.2, 5.2, 5.5) +- MLX (fixed): boundary UP +8%, per-chunk means stable (6.6, 6.8, 6.5) +- MLX (old cross-fade): boundary DOWN -40%, declining means -**Optional**: Latent-space blend (`--chunk-blend N`, default 0 = off). Blends the first N -latent frames of each new chunk toward the previous chunk's last frame. Generally not +**Current approach**: Per-chunk VAE decoding (matching reference), no cross-fade (default). +Cross-fade still available via `--crossfade-frames N` but not recommended. + +**Optional**: Latent-space blend (`--chunk-blend N`, default 0 = off). Generally not recommended as it introduces its own artifacts (grid patterns, brightness shift). ### 2. Color warmth / saturation @@ -379,23 +382,27 @@ Off by default (matching reference). - Quantization (model supports 4/8-bit via convert_helios.py) - Memory-efficient attention -### 5. Camera jumps at chunk boundaries +### 5. Camera jumps at chunk boundaries βœ… RESOLVED + +**Status**: Fixed by two changes: float32 residual connections (commit b24d60a1) and +disabling pixel cross-fade (commit f89eeeb9). -**Status**: Mitigated with two defaults, inherent to autoregressive generation. +**Root cause 1 β€” Gradual zoom**: bfloat16 residual connections systematically truncated +high-frequency spatial content over 48 blocks Γ— 3 residuals Γ— 6 model calls per chunk. +When smoothed output became history for the next chunk, the effect compounded β†’ progressive +zoom. Fixed by promoting residual additions to float32 (matching reference's `.float()` +pattern). -Objects may appear to shift spatial scale or "jump in" at chunk boundaries. This is NOT a bug -β€” thorough comparison with the reference confirms identical schedules, indices, history -handling, and DMD steps. It's an inherent limitation of autoregressive chunked generation -where each chunk has limited temporal context. +**Root cause 2 β€” Boundary jumps**: Pixel cross-fade blended frames from different chunks +that didn't spatially align, creating visible blur/jumps at every boundary. The reference +uses no cross-fade. Disabling it matches reference boundary behavior. -**Mitigations applied**: -- `--amplify-first-chunk` (now ON by default): Doubles DMD steps for the first chunk, +**Mitigations retained**: +- `--amplify-first-chunk` (ON by default): Doubles DMD steps for the first chunk, providing a higher-quality anchor for subsequent chunks via history. Reference ALWAYS uses this for distilled models. -- `--crossfade-frames 4` (default 4): Pixel-space linear cross-fade at chunk boundaries. - Blends the last N frames of chunk K with the first N of chunk K+1. Applied in pixel space - so there are no grid artifacts (unlike latent-space blending). Use `--crossfade-frames 0` - to disable. +- `--crossfade-frames 0` (OFF by default): Can be re-enabled if desired but not recommended + based on reference comparison. ### 6. Non-distilled model not supported From bb912f0d868966e8914c63136b372dee8f905715 Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 6 Mar 2026 15:08:08 +0100 Subject: [PATCH 20/40] Document resolution sensitivity as upstream model limitation Reference: https://github.com/PKU-YuanGroup/Helios/issues/2 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- docs/HELIOS-DIAGNOSTICS.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/docs/HELIOS-DIAGNOSTICS.md b/docs/HELIOS-DIAGNOSTICS.md index fd98767..a501992 100644 --- a/docs/HELIOS-DIAGNOSTICS.md +++ b/docs/HELIOS-DIAGNOSTICS.md @@ -404,6 +404,12 @@ uses no cross-fade. Disabling it matches reference boundary behavior. - `--crossfade-frames 0` (OFF by default): Can be re-enabled if desired but not recommended based on reference comparison. +**Note β€” Resolution sensitivity**: The model was only trained at 384Γ—640. Using non-default +resolutions (even valid multiples of 64, like 640Γ—384 portrait) causes obvious frame jumps +in the reference pipeline too. This is a known upstream limitation +([PKU-YuanGroup/Helios#2](https://github.com/PKU-YuanGroup/Helios/issues/2)). Residual +zoom with complex prompts at the default resolution is also an inherent model behavior. + ### 6. Non-distilled model not supported Only the distilled model (DMD scheduler, 2+2+2 steps, no CFG) is implemented. The From 2f10b4c43cc8e20e312b3ce66ba9f177a8ba308f Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 6 Mar 2026 15:21:39 +0100 Subject: [PATCH 21/40] Drop first pixel frame from each chunk to remove boundary distortion Each chunk's first frame is a distorted reconstruction of the previous chunk's last frame via history conditioning. Dropping it gives 32 frames/chunk (exactly 2s at 16fps) and eliminates visible boundary artifacts. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/generate_helios.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index c406038..80a1d4e 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -609,6 +609,9 @@ def generate_video( trim = chunk_np.shape[1] - valid if trim > 0: chunk_np = chunk_np[:, trim:] + # Drop first pixel frame: it's the overlap/conditioning frame from history + # (distorted duplicate of previous chunk's last frame). 33β†’32 = exact 2s at 16fps. + chunk_np = chunk_np[:, 1:] video_chunks.append(chunk_np) del chunk_video, z From 4f6b27cd2edea7575f7ecb5d4ea12061e661a1fc Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 6 Mar 2026 16:07:10 +0100 Subject: [PATCH 22/40] Fix brightness jumps at chunk boundaries with contrast correction The VAE's causal temporal convolutions lack context at the start of each independently-decoded chunk, causing a ~7% contrast drop in the first few frames. This manifests as visible brightness jumps at chunk boundaries. Correct this by matching each non-first chunk's initial frames to the contrast level of the previous chunk's last frame, with a smooth 6-frame linear ramp. Reduces the contrast discontinuity from 7% to 1.5% and the pixel diff ratio from 4.1x to 3.0x (matching the reference pipeline). Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/generate_helios.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index 80a1d4e..60c080e 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -620,6 +620,27 @@ def generate_video( print(f"{Colors.DIM} VAE decode: {time.time() - t4:.1f}s{Colors.RESET}") + # Correct contrast drop at chunk boundaries caused by VAE causal padding warmup. + # The first few frames of each non-first chunk have ~7% lower contrast (std dev) + # because the causal temporal convolutions lack context. We match their contrast + # to the previous chunk's last frame with a smooth linear ramp. + if len(video_chunks) > 1: + blend_n = 6 # frames over which to ramp correction + for i in range(1, len(video_chunks)): + ref_frame = video_chunks[i - 1][:, -1] # [3, H, W] + ref_std = ref_frame.std() + if ref_std < 1e-6: + continue + for k in range(min(blend_n, video_chunks[i].shape[1])): + frame = video_chunks[i][:, k] + cur_std = frame.std() + if cur_std < 1e-6: + continue + w = 1.0 - k / blend_n # 1.0 β†’ 0.0 + scale = 1.0 + w * (ref_std / cur_std - 1.0) + frame_mean = frame.mean() + video_chunks[i][:, k] = (frame - frame_mean) * scale + frame_mean + # Pixel-space cross-fade at chunk boundaries to smooth transitions. # Unlike latent-space blending, this is clean β€” no grid artifacts since # the VAE decode has already resolved block noise patterns. From c5515196f0eadebfaf821e12f6718d53378023de Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 6 Mar 2026 18:52:08 +0100 Subject: [PATCH 23/40] Upgrade boundary correction to per-channel brightness and contrast matching Replace the global contrast-only correction with per-channel mean and std matching. This eliminates the remaining brightness jump (0.9% -> 0.0%) and color shift (1.8 -> 0.3 pixel max channel shift) at chunk boundaries. The pixel diff ratio improves to 2.4x, better than the reference's 3.0x. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/generate_helios.py | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index 60c080e..995582f 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -620,26 +620,27 @@ def generate_video( print(f"{Colors.DIM} VAE decode: {time.time() - t4:.1f}s{Colors.RESET}") - # Correct contrast drop at chunk boundaries caused by VAE causal padding warmup. - # The first few frames of each non-first chunk have ~7% lower contrast (std dev) - # because the causal temporal convolutions lack context. We match their contrast - # to the previous chunk's last frame with a smooth linear ramp. + # Correct brightness/contrast discontinuity at chunk boundaries caused by VAE + # causal padding warmup. The first few frames of each non-first chunk have ~7% + # lower contrast and slight per-channel color shifts. We match per-channel mean + # and std dev to the previous chunk's last frame with a smooth linear ramp. if len(video_chunks) > 1: blend_n = 6 # frames over which to ramp correction for i in range(1, len(video_chunks)): ref_frame = video_chunks[i - 1][:, -1] # [3, H, W] - ref_std = ref_frame.std() - if ref_std < 1e-6: - continue + # Per-channel stats of reference frame + ref_mean = ref_frame.mean(axis=(1, 2), keepdims=True) # [3, 1, 1] + ref_std = ref_frame.std(axis=(1, 2), keepdims=True) for k in range(min(blend_n, video_chunks[i].shape[1])): frame = video_chunks[i][:, k] - cur_std = frame.std() - if cur_std < 1e-6: - continue + cur_mean = frame.mean(axis=(1, 2), keepdims=True) + cur_std = frame.std(axis=(1, 2), keepdims=True) + cur_std = np.maximum(cur_std, 1e-6) w = 1.0 - k / blend_n # 1.0 β†’ 0.0 - scale = 1.0 + w * (ref_std / cur_std - 1.0) - frame_mean = frame.mean() - video_chunks[i][:, k] = (frame - frame_mean) * scale + frame_mean + # Blend toward reference stats + target_mean = cur_mean + w * (ref_mean - cur_mean) + target_std = cur_std + w * (ref_std - cur_std) + video_chunks[i][:, k] = (frame - cur_mean) * (target_std / cur_std) + target_mean # Pixel-space cross-fade at chunk boundaries to smooth transitions. # Unlike latent-space blending, this is clean β€” no grid artifacts since From b9d3334834802848481b00152e67c7b50139c5c6 Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 6 Mar 2026 19:07:15 +0100 Subject: [PATCH 24/40] Add spatially-varying brightness correction at chunk boundaries The VAE causal warmup causes not just global contrast drops but spatial brightness redistribution (face darkens while background brightens). Fix with a two-stage correction: 1. Low-frequency spatial match: downsample frames 16x, compute per-channel brightness difference vs previous chunk's last frame, upsample and apply as a smooth additive correction 2. Per-channel contrast scaling to match reference std dev Results: center brightness shift reduced from -1.85 to -0.15, periphery from +0.68 to +0.09. Contrast jump improved to -1.0%. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/generate_helios.py | 39 ++++++++++++++++++++++++++---------- 1 file changed, 28 insertions(+), 11 deletions(-) diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index 995582f..82046a2 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -17,6 +17,7 @@ import mlx.core as mx import mlx.nn as nn import numpy as np +import cv2 from tqdm import tqdm from mlx_video.models.helios.loading import ( @@ -621,26 +622,42 @@ def generate_video( print(f"{Colors.DIM} VAE decode: {time.time() - t4:.1f}s{Colors.RESET}") # Correct brightness/contrast discontinuity at chunk boundaries caused by VAE - # causal padding warmup. The first few frames of each non-first chunk have ~7% - # lower contrast and slight per-channel color shifts. We match per-channel mean - # and std dev to the previous chunk's last frame with a smooth linear ramp. + # causal padding warmup. Two-stage correction: + # 1. Spatially-varying brightness: match low-frequency (blurred) brightness per + # channel to the previous chunk's last frame, fixing the "face darkens while + # background brightens" effect from the VAE's spatial redistribution. + # 2. Per-channel contrast: scale std dev to match, fixing the ~7% contrast drop. if len(video_chunks) > 1: blend_n = 6 # frames over which to ramp correction + blur_size = 16 # downscale factor for low-frequency brightness map for i in range(1, len(video_chunks)): ref_frame = video_chunks[i - 1][:, -1] # [3, H, W] - # Per-channel stats of reference frame - ref_mean = ref_frame.mean(axis=(1, 2), keepdims=True) # [3, 1, 1] + _, fh, fw = ref_frame.shape + # Pre-compute low-frequency brightness map of reference + small_h, small_w = max(fh // blur_size, 1), max(fw // blur_size, 1) + ref_lf = np.zeros((3, small_h, small_w), dtype=np.float32) + for c in range(3): + ref_lf[c] = cv2.resize(ref_frame[c], (small_w, small_h), interpolation=cv2.INTER_AREA) + # Per-channel global stats ref_std = ref_frame.std(axis=(1, 2), keepdims=True) + for k in range(min(blend_n, video_chunks[i].shape[1])): frame = video_chunks[i][:, k] - cur_mean = frame.mean(axis=(1, 2), keepdims=True) + ramp = 1.0 - k / blend_n # 1.0 β†’ 0.0 + + # Stage 1: spatially-varying brightness correction + for c in range(3): + cur_lf = cv2.resize(frame[c], (small_w, small_h), interpolation=cv2.INTER_AREA) + diff_small = ref_lf[c] - cur_lf + diff_full = cv2.resize(diff_small, (fw, fh), interpolation=cv2.INTER_LINEAR) + frame[c] = frame[c] + ramp * diff_full + + # Stage 2: per-channel contrast correction cur_std = frame.std(axis=(1, 2), keepdims=True) cur_std = np.maximum(cur_std, 1e-6) - w = 1.0 - k / blend_n # 1.0 β†’ 0.0 - # Blend toward reference stats - target_mean = cur_mean + w * (ref_mean - cur_mean) - target_std = cur_std + w * (ref_std - cur_std) - video_chunks[i][:, k] = (frame - cur_mean) * (target_std / cur_std) + target_mean + target_std = cur_std + ramp * (ref_std - cur_std) + cur_mean = frame.mean(axis=(1, 2), keepdims=True) + video_chunks[i][:, k] = (frame - cur_mean) * (target_std / cur_std) + cur_mean # Pixel-space cross-fade at chunk boundaries to smooth transitions. # Unlike latent-space blending, this is clean β€” no grid artifacts since From 1016d6fa76c2c28468fffdf7ca7087d66f48eb04 Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 6 Mar 2026 20:42:51 +0100 Subject: [PATCH 25/40] Add diagnostic scripts and update HELIOS-DIAGNOSTICS.md - Add scripts/helios/ with 4 consolidated diagnostic tools: analyze_boundaries.py, run_reference.py, compare_pipelines.py, compare_models.py - Update boundary quality section with full fix chain and metrics table - Document failed VAE overlap decode approach - Add diagnostic recipes section referencing new scripts - Update file layout and commit history appendix Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- docs/HELIOS-DIAGNOSTICS.md | 117 +++++++++++-- scripts/helios/analyze_boundaries.py | 250 +++++++++++++++++++++++++++ scripts/helios/compare_models.py | 141 +++++++++++++++ scripts/helios/compare_pipelines.py | 205 ++++++++++++++++++++++ scripts/helios/run_reference.py | 132 ++++++++++++++ 5 files changed, 828 insertions(+), 17 deletions(-) create mode 100644 scripts/helios/analyze_boundaries.py create mode 100644 scripts/helios/compare_models.py create mode 100644 scripts/helios/compare_pipelines.py create mode 100644 scripts/helios/run_reference.py diff --git a/docs/HELIOS-DIAGNOSTICS.md b/docs/HELIOS-DIAGNOSTICS.md index a501992..1dae8f0 100644 --- a/docs/HELIOS-DIAGNOSTICS.md +++ b/docs/HELIOS-DIAGNOSTICS.md @@ -47,7 +47,7 @@ VAE decode β†’ video frames ### File layout (~2500 lines total) ``` -mlx_video/generate_helios.py # Pipeline orchestration (554 lines) +mlx_video/generate_helios.py # Pipeline orchestration (~700 lines) mlx_video/models/helios/ config.py # HeliosModelConfig dataclass (69 lines) transformer.py # 14B DiT backbone (511 lines) @@ -57,6 +57,11 @@ mlx_video/models/helios/ loading.py # Weight loading wrappers (51 lines) mlx_video/convert_helios.py # HFβ†’MLX weight conversion tests/test_helios.py # 46 tests (554 lines) +scripts/helios/ + analyze_boundaries.py # Boundary quality analysis (compare videos) + run_reference.py # Run PyTorch reference pipeline on MPS + compare_pipelines.py # Compare scheduler/pipeline mechanics + compare_models.py # Cross-framework model output comparison ``` --- @@ -325,22 +330,43 @@ values to avoid array overhead. ### 1. Chunk boundary quality βœ… RESOLVED -**Status**: Fixed. Boundary quality now matches the reference pipeline. - -**Root cause**: Pixel cross-fade was blending the first N frames of each new chunk with the -tail of the previous chunk. Since frames from different chunks don't spatially align, this -created blur β€” causing a **40% sharpness drop** at every boundary. The reference pipeline -uses **no cross-fade** at all. - -**Key finding**: Reference comparison revealed the correct boundary pattern: the first frame -of each chunk should be the **sharpest** (conditioned by high-quality history), not the -blurriest. With cross-fade disabled, our boundary pattern now matches the reference: -- Reference: boundary UP +16-21%, per-chunk means stable (5.2, 5.2, 5.5) -- MLX (fixed): boundary UP +8%, per-chunk means stable (6.6, 6.8, 6.5) -- MLX (old cross-fade): boundary DOWN -40%, declining means - -**Current approach**: Per-chunk VAE decoding (matching reference), no cross-fade (default). -Cross-fade still available via `--crossfade-frames N` but not recommended. +**Status**: Fixed. Four-layer fix eliminates visible boundary artifacts. + +**Root cause (cross-fade)**: Pixel cross-fade was blending the first N frames of each new +chunk with the tail of the previous chunk. Since frames from different chunks don't +spatially align, this created blur β€” causing a **40% sharpness drop** at every boundary. +The reference pipeline uses **no cross-fade** at all. + +**Root cause (conditioning frame)**: The first pixel frame of each non-first chunk is a +distorted reconstruction of the previous chunk's last frame (via history conditioning). +The reference keeps it as a bridge frame, but it creates visual stutter. Dropping it gives +exactly 32 frames per chunk = 2 seconds at 16 fps. + +**Root cause (VAE warmup)**: The WanVAE decoder uses causal temporal convolutions. When +decoding each chunk independently, the first few frames lack temporal context (only zero +padding). This causes a **~7% contrast drop** in the first frames of each chunk, plus a +spatial brightness redistribution (face darkens, background brightens). + +**Fix layers** (applied in order during VAE decode): +1. **No cross-fade** (default `--crossfade-frames 0`) β€” matches reference +2. **First-frame trim** β€” drops conditioning frame from each chunk (33 β†’ 32 frames) +3. **Spatially-varying brightness correction** β€” matches low-frequency per-channel brightness + between chunks via downscale/diff/upscale additive correction (6-frame ramp) +4. **Per-channel contrast correction** β€” scales std dev to match previous chunk's last frame + +**Results** (measured with `scripts/helios/analyze_boundaries.py`): +| Metric | No fix | With all fixes | +|--------|--------|----------------| +| Contrast jump | -7.0% | **-1.0%** | +| Brightness jump | +0.7% | **+0.0%** | +| Max color shift | 1.8 px | **0.3 px** | +| Frame diff ratio | 4.1Γ— | **2.5Γ—** | +| Center brightness shift | -0.90 | **-0.15** | +| Periphery brightness shift | +1.39 | **+0.09** | + +**Note**: VAE overlap decode (prepending previous chunk's last latent frames as temporal +context) was tested but made things **worse** (22% contrast drop). The VAE's causal +convolutions see conflicting content from different chunks and create larger artifacts. **Optional**: Latent-space blend (`--chunk-blend N`, default 0 = off). Generally not recommended as it introduces its own artifacts (grid patterns, brightness shift). @@ -556,6 +582,48 @@ def _time_shift(mu, t): ## Diagnostic Recipes +### Diagnostic scripts + +Consolidated diagnostic scripts live in `scripts/helios/`: + +| Script | Purpose | +|--------|---------| +| `analyze_boundaries.py` | Measure boundary quality (contrast, brightness, color, spatial). Compare multiple videos side-by-side. | +| `run_reference.py` | Run the PyTorch reference pipeline on MPS for ground-truth comparison. Patches float64 β†’ float32 for MPS. | +| `compare_pipelines.py` | Compare scheduler/pipeline mechanics between MLX and PyTorch using identical dummy inputs. No model needed. | +| `compare_models.py` | Feed identical inputs to both MLX and PyTorch transformer models and compare flow predictions. | + +**Analyze chunk boundaries** (most commonly used): + +```bash +# Single video +python scripts/helios/analyze_boundaries.py /tmp/helios_output.mp4 + +# Compare before/after fix +python scripts/helios/analyze_boundaries.py \ + /tmp/before_fix.mp4 /tmp/after_fix.mp4 + +# Reference pipeline uses 33 frames/chunk (no first-frame trim) +python scripts/helios/analyze_boundaries.py --chunk-size 33 /tmp/ref.mp4 +``` + +**Run reference pipeline for comparison**: + +```bash +# Requires: pip install diffusers transformers torch accelerate +python scripts/helios/run_reference.py \ + --model-dir /path/to/Helios-Distilled \ + --prompt "A golden retriever running on a sunny beach" \ + --output /tmp/helios_ref.mp4 +``` + +**Compare pipeline mechanics** (no model weights needed): + +```bash +python scripts/helios/compare_pipelines.py \ + --helios-dir /path/to/Helios +``` + ### Check latent channel statistics per step Add this inside the denoising loop (after `scheduler.step_dmd`): @@ -639,3 +707,18 @@ Expected output should match the values in the [Verified Components](#3-schedule | `fcefee27` | Add CFG support and VAE frame trimming | | `e61eb33b` | Fix pyramid color distortion (restrict_self_attn, float32 precision, int timestep) | | `c5acde72` | Fix color bias (normalized start_point + bfloat16 inputs) | +| `24012f96` | Add diagnostics and engineering notes | +| `35f700be` | Fix pure noise: remove start-point normalization, add debug mode | +| `061f191b` | Fix zero-history timestep embedding and scheduler precision | +| `d6f9b4e2` | Mitigate chunk boundary blur with latent-space temporal blend | +| `38d454d0` | Switch to per-chunk VAE decoding, revert blend default to off | +| `a9fd911d` | Implement adaptive anti-drifting for temporal consistency | +| `a7c9086e` | Replace anti-drifting noise corruption with history normalization | +| `4415b746` | Amplify first chunk + pixel cross-fade for camera jumps | +| `b24d60a1` | Fix zoom bug: float32 residual connections in transformer blocks | +| `f89eeeb9` | Disable pixel cross-fade by default (matches reference) | +| `811c4deb` | Document resolution sensitivity as upstream model limitation | +| `e60688fd` | Drop first pixel frame from each chunk to remove boundary distortion | +| `a716216b` | Fix brightness jumps: global contrast correction at boundaries | +| `ff101b2a` | Upgrade to per-channel brightness and contrast matching | +| `8cb3ca96` | Add spatially-varying brightness correction at chunk boundaries | diff --git a/scripts/helios/analyze_boundaries.py b/scripts/helios/analyze_boundaries.py new file mode 100644 index 0000000..a7a73fa --- /dev/null +++ b/scripts/helios/analyze_boundaries.py @@ -0,0 +1,250 @@ +#!/usr/bin/env python3 +"""Analyze chunk boundary quality in Helios-generated videos. + +Measures brightness, contrast, color shifts, spatial distribution, and +frame-to-frame differences at chunk boundaries. Compares multiple videos +side-by-side when given multiple paths. + +This was the primary diagnostic tool used to identify and fix: +- 40% contrast drops from pixel cross-fade (β†’ disabled cross-fade) +- 7% contrast drops from VAE causal padding warmup (β†’ contrast correction) +- Per-channel color shifts at boundaries (β†’ per-channel matching) +- Spatial brightness redistribution (β†’ low-frequency spatial correction) + +Usage: + # Analyze a single video + python scripts/helios/analyze_boundaries.py /tmp/helios_output.mp4 + + # Compare multiple videos + python scripts/helios/analyze_boundaries.py \ + /tmp/helios_before.mp4 /tmp/helios_after.mp4 + + # Custom chunk size (default: 32 frames per chunk) + python scripts/helios/analyze_boundaries.py --chunk-size 33 /tmp/ref.mp4 +""" + +import argparse +import sys + +import cv2 +import numpy as np + + +def analyze_video(path, chunk_size=32): + """Analyze boundary quality metrics for a video.""" + vid = cv2.VideoCapture(path) + if not vid.isOpened(): + print(f"Error: cannot open {path}", file=sys.stderr) + return None + + frames = [] + while True: + ret, f = vid.read() + if not ret: + break + frames.append(f) + vid.release() + + n = len(frames) + if n == 0: + print(f"Error: no frames in {path}", file=sys.stderr) + return None + + # Compute per-frame statistics + means = np.zeros(n) + stds = np.zeros(n) + ch_means = np.zeros((n, 3)) + diffs = np.zeros(n - 1) + + for i, f in enumerate(frames): + gray = cv2.cvtColor(f, cv2.COLOR_BGR2GRAY).astype(np.float64) + means[i] = gray.mean() + stds[i] = gray.std() + ch_means[i] = [f[:, :, c].mean() for c in range(3)] + if i > 0: + prev_gray = cv2.cvtColor(frames[i - 1], cv2.COLOR_BGR2GRAY).astype(np.float64) + diffs[i - 1] = np.abs(gray - prev_gray).mean() + + # Find chunk boundaries + boundaries = [] + b = chunk_size - 1 + while b < n - 1: + boundaries.append(b) + b += chunk_size + + results = { + "path": path, + "num_frames": n, + "chunk_size": chunk_size, + "boundaries": [], + } + + for b in boundaries: + if b >= n - 1: + break + + # Contrast + pre_std = stds[max(0, b - 2) : b + 1] + post_std = stds[b + 1 : min(n, b + 4)] + contrast_jump = post_std[0] - pre_std[-1] + contrast_pct = contrast_jump / max(pre_std[-1], 1e-6) * 100 + + # Brightness + bright_jump = means[b + 1] - means[b] + bright_pct = bright_jump / max(means[b], 1e-6) * 100 + + # Per-channel color shift + ch_shifts = ch_means[b + 1] - ch_means[b] # B, G, R + + # Frame diff ratio + boundary_diff = diffs[b] + window = 3 + nearby_indices = list(range(max(0, b - window), b)) + list( + range(b + 1, min(len(diffs), b + 1 + window)) + ) + nearby_avg = np.mean(diffs[nearby_indices]) if nearby_indices else 1.0 + diff_ratio = boundary_diff / max(nearby_avg, 1e-6) + + # Spatial analysis + f_pre = frames[b].astype(np.float64) + f_post = frames[b + 1].astype(np.float64) + gray_diff = cv2.cvtColor(frames[b + 1], cv2.COLOR_BGR2GRAY).astype( + np.float64 + ) - cv2.cvtColor(frames[b], cv2.COLOR_BGR2GRAY).astype(np.float64) + h, w = gray_diff.shape + ch, cw = h // 4, w // 4 + center_shift = gray_diff[ch : 3 * ch, cw : 3 * cw].mean() + periph_mask = np.ones_like(gray_diff, dtype=bool) + periph_mask[ch : 3 * ch, cw : 3 * cw] = False + periph_shift = gray_diff[periph_mask].mean() + + results["boundaries"].append( + { + "frame": b, + "contrast_pct": contrast_pct, + "bright_pct": bright_pct, + "ch_shifts_bgr": ch_shifts.tolist(), + "diff_ratio": diff_ratio, + "center_shift": center_shift, + "periph_shift": periph_shift, + "boundary_diff": boundary_diff, + "nearby_diff": nearby_avg, + } + ) + + # Per-chunk stats + chunk_stats = [] + for c in range(0, n, chunk_size): + end = min(c + chunk_size, n) + chunk_stats.append( + { + "frames": f"{c}-{end - 1}", + "mean_bright": means[c:end].mean(), + "mean_contrast": stds[c:end].mean(), + "first_contrast": stds[c], + "last_contrast": stds[end - 1], + } + ) + results["chunk_stats"] = chunk_stats + + return results + + +def print_results(results): + """Pretty-print analysis results.""" + print(f"\n{'=' * 70}") + print(f" {results['path']}") + print(f" {results['num_frames']} frames, chunk size = {results['chunk_size']}") + print(f"{'=' * 70}") + + for bd in results["boundaries"]: + b = bd["frame"] + print(f"\n Boundary {b}β†’{b + 1}:") + print(f" Contrast jump: {bd['contrast_pct']:+.1f}%") + print(f" Brightness jump: {bd['bright_pct']:+.1f}%") + print( + f" Color shift B/G/R: {bd['ch_shifts_bgr'][0]:+.1f} / " + f"{bd['ch_shifts_bgr'][1]:+.1f} / {bd['ch_shifts_bgr'][2]:+.1f}" + ) + print( + f" Frame diff: {bd['boundary_diff']:.1f} vs nearby " + f"{bd['nearby_diff']:.1f} ({bd['diff_ratio']:.1f}Γ—)" + ) + print( + f" Spatial: center {bd['center_shift']:+.2f}, " + f"periphery {bd['periph_shift']:+.2f}" + ) + + print(f"\n Per-chunk summary:") + for cs in results["chunk_stats"]: + print( + f" Frames {cs['frames']:>7s}: brightness={cs['mean_bright']:.1f}, " + f"contrast={cs['mean_contrast']:.1f} " + f"(first={cs['first_contrast']:.1f}, last={cs['last_contrast']:.1f})" + ) + + +def print_comparison(all_results): + """Print side-by-side comparison table.""" + if len(all_results) < 2: + return + + print(f"\n{'=' * 70}") + print(" COMPARISON SUMMARY") + print(f"{'=' * 70}") + + # Header + labels = [r["path"].split("/")[-1] for r in all_results] + header = f"{'Metric':<25s}" + for label in labels: + header += f" {label:>18s}" + print(f"\n{header}") + print("-" * (25 + 20 * len(labels))) + + # For each boundary index + max_boundaries = max(len(r["boundaries"]) for r in all_results) + for bi in range(max_boundaries): + print(f"\n Boundary {bi + 1}:") + for metric, key, fmt in [ + ("Contrast jump", "contrast_pct", "{:+.1f}%"), + ("Brightness jump", "bright_pct", "{:+.1f}%"), + ("Frame diff ratio", "diff_ratio", "{:.1f}Γ—"), + ("Center shift", "center_shift", "{:+.2f}"), + ("Periphery shift", "periph_shift", "{:+.2f}"), + ]: + row = f" {metric:<23s}" + for r in all_results: + if bi < len(r["boundaries"]): + val = r["boundaries"][bi][key] + row += f" {fmt.format(val):>18s}" + else: + row += f" {'N/A':>18s}" + print(row) + + +def main(): + parser = argparse.ArgumentParser( + description="Analyze chunk boundary quality in Helios videos" + ) + parser.add_argument("videos", nargs="+", help="Video file paths to analyze") + parser.add_argument( + "--chunk-size", + type=int, + default=32, + help="Frames per chunk (default: 32, use 33 for reference pipeline)", + ) + args = parser.parse_args() + + all_results = [] + for path in args.videos: + results = analyze_video(path, args.chunk_size) + if results is not None: + print_results(results) + all_results.append(results) + + if len(all_results) > 1: + print_comparison(all_results) + + +if __name__ == "__main__": + main() diff --git a/scripts/helios/compare_models.py b/scripts/helios/compare_models.py new file mode 100644 index 0000000..91e4927 --- /dev/null +++ b/scripts/helios/compare_models.py @@ -0,0 +1,141 @@ +#!/usr/bin/env python3 +"""Cross-framework model comparison: feed identical inputs to MLX and PyTorch models. + +Saves intermediate tensors from the MLX pipeline, loads them into the PyTorch +reference model, and compares outputs. Used to verify the MLX transformer +produces numerically equivalent flow predictions. + +Workflow: + 1. Run MLX generation with --debug to save /tmp/helios_model_inputs.npz + and /tmp/helios_mlx_output.npy + 2. Run this script to load inputs into PyTorch model and compare + +Requirements: + - Reference Helios weights (original PyTorch format) + - diffusers, torch + - Saved inputs from MLX debug run + +Usage: + # Step 1: Generate with debug to save inputs + python -m mlx_video.generate_helios \ + --model-dir /path/to/Helios-Distilled-MLX \ + --prompt "A beautiful sunset over the ocean" \ + --debug --num-frames 33 \ + --output-path /tmp/debug_test.mp4 + + # Step 2: Compare with PyTorch + python scripts/helios/compare_models.py \ + --model-dir /path/to/Helios-Distilled \ + --prompt "A beautiful sunset over the ocean" \ + --inputs /tmp/helios_model_inputs.npz \ + --mlx-output /tmp/helios_mlx_output.npy +""" + +import argparse +import sys + +import numpy as np +import torch + + +def main(): + parser = argparse.ArgumentParser(description="Compare MLX vs PyTorch model outputs") + parser.add_argument("--model-dir", required=True, help="Path to original Helios weights") + parser.add_argument("--prompt", required=True, help="Same prompt used for MLX debug run") + parser.add_argument("--inputs", default="/tmp/helios_model_inputs.npz", help="Saved MLX inputs") + parser.add_argument("--mlx-output", default="/tmp/helios_mlx_output.npy", help="Saved MLX output") + args = parser.parse_args() + + # Load saved MLX inputs + data = np.load(args.inputs) + print("Loaded inputs:") + for k in data.files: + print(f" {k}: shape={data[k].shape}, dtype={data[k].dtype}") + + # Load reference model + print("\nLoading reference pipeline...") + from diffusers import DiffusionPipeline + + pipe = DiffusionPipeline.from_pretrained( + args.model_dir, torch_dtype=torch.float16 + ).to("mps") + transformer = pipe.transformer + + # Convert inputs to torch tensors (MLX: [C,F,H,W] β†’ PT: [B,C,F,H,W]) + latents_pt = torch.from_numpy(data["latents"]).unsqueeze(0).to("mps") + timestep_pt = torch.tensor([int(data["timestep"][0])], dtype=torch.int64, device="mps") + hist_short = torch.from_numpy(data["hist_short"]).unsqueeze(0).to("mps") + hist_mid = torch.from_numpy(data["hist_mid"]).unsqueeze(0).to("mps") + hist_long = torch.from_numpy(data["hist_long"]).unsqueeze(0).to("mps") + idx_current = torch.from_numpy(data["idx_current"]).unsqueeze(0).to("mps") + idx_short = torch.from_numpy(data["idx_short"]).unsqueeze(0).to("mps") + idx_mid = torch.from_numpy(data["idx_mid"]).unsqueeze(0).to("mps") + idx_long = torch.from_numpy(data["idx_long"]).unsqueeze(0).to("mps") + + # Encode prompt with reference text encoder + print("\nEncoding prompt with reference T5...") + prompt_embeds, _ = pipe.encode_prompt( + prompt=args.prompt, do_classifier_free_guidance=False, device="mps" + ) + print(f" prompt_embeds: {prompt_embeds.shape}") + + # Run reference forward pass + print("Running reference model...") + transformer.eval() + with torch.no_grad(): + output = transformer( + hidden_states=latents_pt.half(), + timestep=timestep_pt, + encoder_hidden_states=prompt_embeds.half(), + return_dict=False, + indices_hidden_states=idx_current, + indices_latents_history_short=idx_short, + indices_latents_history_mid=idx_mid, + indices_latents_history_long=idx_long, + latents_history_short=hist_short.half(), + latents_history_mid=hist_mid.half(), + latents_history_long=hist_long.half(), + ) + + pt_output = output[0].float().cpu().numpy().squeeze(0) + + # Load MLX output + mlx_output = np.load(args.mlx_output) + + print(f"\n{'=' * 50}") + print(f"MLX: shape={mlx_output.shape}, mean={mlx_output.mean():.6f}, std={mlx_output.std():.6f}") + print(f"PT: shape={pt_output.shape}, mean={pt_output.mean():.6f}, std={pt_output.std():.6f}") + + diff = mlx_output - pt_output + rmse = np.sqrt(np.mean(diff**2)) + mae = np.mean(np.abs(diff)) + cos_sim = np.sum(mlx_output * pt_output) / ( + np.linalg.norm(mlx_output) * np.linalg.norm(pt_output) + ) + + print(f"\nRMSE: {rmse:.6f}") + print(f"MAE: {mae:.6f}") + print(f"Cosine similarity: {cos_sim:.6f}") + print(f"Max abs diff: {np.abs(diff).max():.6f}") + + if cos_sim > 0.999: + print("\nβœ“ Models produce equivalent outputs") + elif cos_sim > 0.99: + print("\n⚠ Minor differences (likely precision-related)") + else: + print("\nβœ— Significant differences β€” investigate!") + + # Per-channel breakdown + print(f"\nPer-channel (first 4):") + for c in range(min(4, mlx_output.shape[0])): + c_cos = np.sum(mlx_output[c] * pt_output[c]) / ( + np.linalg.norm(mlx_output[c]) * np.linalg.norm(pt_output[c]) + 1e-8 + ) + print( + f" Ch {c}: MLX mean={mlx_output[c].mean():.4f} " + f"PT mean={pt_output[c].mean():.4f} cos={c_cos:.4f}" + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/helios/compare_pipelines.py b/scripts/helios/compare_pipelines.py new file mode 100644 index 0000000..d2936d1 --- /dev/null +++ b/scripts/helios/compare_pipelines.py @@ -0,0 +1,205 @@ +#!/usr/bin/env python3 +"""Compare Helios pipeline mechanics: PyTorch reference vs MLX. + +Runs both schedulers with identical inputs and fixed dummy model outputs to +isolate pipeline logic differences (downsampling, upsampling, alpha/beta +blending, DMD stepping). No model weights needed. + +This was used to verify that the MLX scheduler produces numerically identical +results to the PyTorch reference, ruling out pipeline mechanics as a source of +output quality differences. + +Requirements: + - MLX video package (this repo) + - Reference Helios repo on sys.path (--helios-dir) + - PyTorch + diffusers + +Usage: + python scripts/helios/compare_pipelines.py \ + --helios-dir /path/to/Helios + + # Custom parameters + python scripts/helios/compare_pipelines.py \ + --helios-dir /path/to/Helios \ + --seed 123 --stages 3 --steps 2 2 2 +""" + +import argparse +import math +import sys + +import numpy as np + + +def calculate_shift( + image_seq_len, + base_seq_len=256, + max_seq_len=4096, + base_shift=0.5, + max_shift=1.15, +): + m = (max_shift - base_shift) / (max_seq_len - base_seq_len) + b = base_shift - m * base_seq_len + return image_seq_len * m + b + + +def main(): + parser = argparse.ArgumentParser(description="Compare pipeline mechanics") + parser.add_argument("--helios-dir", required=True, help="Path to reference Helios repo") + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--stages", type=int, default=3) + parser.add_argument("--steps", type=int, nargs="+", default=[2, 2, 2]) + args = parser.parse_args() + + sys.path.insert(0, args.helios_dir) + sys.path.insert(0, ".") + + import torch + import torch.nn.functional as F + import mlx.core as mx + + from helios.diffusers_version.scheduling_helios_diffusers import ( + HeliosScheduler as PTScheduler, + ) + from mlx_video.generate_helios import ( + _bilinear_downsample_2d, + _nearest_upsample_2d, + _spatial_reshape, + _spatial_unreshape, + ) + from mlx_video.models.helios.scheduler import HeliosScheduler as MLXScheduler + + C, NL, H, W = 16, 9, 48, 80 + PATCH_SIZE = (1, 2, 2) + GAMMA = 1 / 3 + STAGES = args.stages + PYRAMID_STEPS = args.steps + + # Create identical initial noise from numpy + rng = np.random.RandomState(args.seed) + noise_np = rng.randn(C, NL, H, W).astype(np.float32) + mx_latents = mx.array(noise_np) + pt_latents = torch.from_numpy(noise_np).unsqueeze(0) + + print(f"Initial noise: mean={noise_np.mean():.6f} std={noise_np.std():.6f}") + + # Downsample β€” MLX + cur_h, cur_w = H, W + mx_flat = _spatial_reshape(mx_latents, NL, C) + for _ in range(STAGES - 1): + cur_h //= 2 + cur_w //= 2 + mx_flat = _bilinear_downsample_2d(mx_flat, cur_h, cur_w) * 2 + mx_latents = _spatial_unreshape(mx_flat, NL, C, cur_h, cur_w) + mx.eval(mx_latents) + + # Downsample β€” PyTorch + cur_h_pt, cur_w_pt = H, W + pt_flat = pt_latents.permute(0, 2, 1, 3, 4).reshape(NL, C, H, W) + for _ in range(STAGES - 1): + cur_h_pt //= 2 + cur_w_pt //= 2 + pt_flat = F.interpolate(pt_flat, size=(cur_h_pt, cur_w_pt), mode="bilinear") * 2 + pt_latents = pt_flat.reshape(1, NL, C, cur_h_pt, cur_w_pt).permute(0, 2, 1, 3, 4) + + mx_np = np.array(mx_latents) + pt_np = pt_latents.squeeze(0).numpy() + diff = np.abs(mx_np - pt_np) + print(f"After downsample to {cur_h}Γ—{cur_w}: diff max={diff.max():.8f} mean={diff.mean():.8f}") + + # Initialize schedulers + mlx_sched = MLXScheduler( + num_train_timesteps=1000, shift=1.0, stages=STAGES, + gamma=GAMMA, use_dynamic_shifting=True, + ) + pt_sched = PTScheduler( + num_train_timesteps=1000, shift=1.0, stages=STAGES, + gamma=GAMMA, use_dynamic_shifting=True, scheduler_type="dmd", + ) + + mx_start_points = [mx_latents] + pt_start_points = [pt_latents.clone()] + max_diff = 0.0 + + for i_s in range(STAGES): + seq_len = (NL * cur_h * cur_w) // math.prod(PATCH_SIZE) + mu = calculate_shift(seq_len) + + mlx_sched.set_timesteps(PYRAMID_STEPS[i_s], stage_index=i_s, image_seq_len=seq_len) + pt_sched.set_timesteps(PYRAMID_STEPS[i_s], i_s, device="cpu", mu=mu) + + print(f"\nStage {i_s}: {cur_h}Γ—{cur_w}, seq_len={seq_len}") + print(f" MLX sigmas: {mlx_sched.sigmas.tolist()}") + print(f" PT sigmas: {pt_sched.sigmas.tolist()}") + + if i_s > 0: + cur_h *= 2 + cur_w *= 2 + cur_h_pt *= 2 + cur_w_pt *= 2 + + # Upsample + mx_flat = _spatial_reshape(mx_latents, NL, C) + mx_flat = _nearest_upsample_2d(mx_flat, cur_h, cur_w) + mx_latents = _spatial_unreshape(mx_flat, NL, C, cur_h, cur_w) + + pt_flat = pt_latents.permute(0, 2, 1, 3, 4).reshape(NL, C, cur_h_pt // 2, cur_w_pt // 2) + pt_flat = F.interpolate(pt_flat, size=(cur_h_pt, cur_w_pt), mode="nearest") + pt_latents = pt_flat.reshape(1, NL, C, cur_h_pt, cur_w_pt).permute(0, 2, 1, 3, 4) + + # Alpha/beta blending with same noise + ori_sigma = 1 - mlx_sched.ori_start_sigmas[i_s] + alpha = 1 / (math.sqrt(1 + (1 / GAMMA)) * (1 - ori_sigma) + ori_sigma) + beta = alpha * (1 - ori_sigma) / math.sqrt(GAMMA) + + noise_np2 = np.random.RandomState(args.seed + i_s * 1000).randn( + C, NL, cur_h, cur_w + ).astype(np.float32) + mx_latents = alpha * mx_latents + beta * mx.array(noise_np2) + pt_latents = alpha * pt_latents + beta * torch.from_numpy(noise_np2).unsqueeze(0) + + mx.eval(mx_latents) + mx_start_points.append(mx_latents) + pt_start_points.append(pt_latents.clone()) + + d = np.abs(np.array(mx_latents) - pt_latents.squeeze(0).numpy()) + print(f" After upsample+mix: diff max={d.max():.8f} mean={d.mean():.8f}") + + # DMD steps with fixed model output + for idx in range(len(mlx_sched.timesteps)): + t_pt = pt_sched.timesteps[idx] + + mx_pred = mx.full(mx_latents.shape, 0.05) + pt_pred = torch.full(pt_latents.shape, 0.05) + + mx_latents = mlx_sched.step_dmd(mx_pred, mx_latents, idx, mx_start_points[i_s]) + mx.eval(mx_latents) + + pt_latents = pt_sched.step( + pt_pred, t_pt, pt_latents, return_dict=False, + cur_sampling_step=idx, + dmd_noisy_tensor=pt_start_points[i_s], + dmd_sigmas=pt_sched.sigmas, + dmd_timesteps=pt_sched.timesteps, + all_timesteps=pt_sched.timesteps, + )[0] + + d = np.abs(np.array(mx_latents) - pt_latents.squeeze(0).numpy()) + max_diff = max(max_diff, d.max()) + print( + f" Step {idx}: MLX mean={mx_latents.mean().item():.6f} " + f"PT mean={pt_latents.mean():.6f} diff max={d.max():.8f}" + ) + + print(f"\n{'=' * 50}") + print(f"Maximum difference across all stages/steps: {max_diff:.8f}") + if max_diff < 1e-4: + print("βœ“ Pipelines are numerically equivalent") + elif max_diff < 1e-2: + print("⚠ Small differences (likely floating point precision)") + else: + print("βœ— Significant differences detected β€” investigate!") + + +if __name__ == "__main__": + main() diff --git a/scripts/helios/run_reference.py b/scripts/helios/run_reference.py new file mode 100644 index 0000000..ecf435b --- /dev/null +++ b/scripts/helios/run_reference.py @@ -0,0 +1,132 @@ +#!/usr/bin/env python3 +"""Run the reference Helios pipeline on MPS for comparison. + +Generates a video using the original PyTorch/diffusers Helios pipeline on Apple +MPS, with necessary float64β†’float32 patches for MPS compatibility. Useful for +comparing output quality against the MLX implementation. + +Requirements: + pip install diffusers transformers torch accelerate + +Usage: + python scripts/helios/run_reference.py \ + --model-dir /path/to/Helios-Distilled \ + --prompt "A golden retriever running on a sunny beach" \ + --output /tmp/helios_ref.mp4 + + # Compare against MLX output + python -m mlx_video.generate_helios \ + --model-dir /path/to/Helios-Distilled-MLX \ + --prompt "A golden retriever running on a sunny beach" \ + --output-path /tmp/helios_mlx.mp4 + python scripts/helios/analyze_boundaries.py \ + /tmp/helios_ref.mp4 /tmp/helios_mlx.mp4 +""" + +import argparse + +import cv2 +import numpy as np +import torch + + +def patch_scheduler_for_mps(): + """Patch the Helios DMD scheduler to work on MPS (no float64 support).""" + import diffusers.schedulers.scheduling_helios_dmd as sched_mod + + _orig_set_ts = sched_mod.HeliosDMDScheduler.set_timesteps + + def _patched_set_ts( + self, + num_inference_steps, + stage_index=None, + device=None, + sigmas=None, + mu=None, + is_amplify_first_chunk=False, + ): + real_device = device + _orig_set_ts( + self, + num_inference_steps, + stage_index=stage_index, + device="cpu", + sigmas=sigmas, + mu=mu, + is_amplify_first_chunk=is_amplify_first_chunk, + ) + self.timesteps = self.timesteps.float() + self.sigmas = self.sigmas.float() + if real_device is not None and str(real_device) != "cpu": + self.timesteps = self.timesteps.to(real_device) + self.sigmas = self.sigmas.to(real_device) + + sched_mod.HeliosDMDScheduler.set_timesteps = _patched_set_ts + + def _patched_convert_flow(self, flow_pred, xt, timestep, sigmas, timesteps): + original_dtype = flow_pred.dtype + device = flow_pred.device + flow_pred, xt, sigmas, timesteps = ( + x.float().to(device) for x in (flow_pred, xt, sigmas, timesteps) + ) + timestep_id = torch.argmin( + (timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1 + ) + sigma_t = sigmas[timestep_id].reshape(-1, 1, 1, 1, 1) + x0_pred = xt - sigma_t * flow_pred + return x0_pred.to(original_dtype) + + sched_mod.HeliosDMDScheduler.convert_flow_pred_to_x0 = _patched_convert_flow + + +def main(): + parser = argparse.ArgumentParser(description="Run Helios reference pipeline on MPS") + parser.add_argument("--model-dir", required=True, help="Path to Helios-Distilled weights") + parser.add_argument("--prompt", required=True, help="Text prompt") + parser.add_argument("--output", default="/tmp/helios_ref.mp4", help="Output video path") + parser.add_argument("--height", type=int, default=384) + parser.add_argument("--width", type=int, default=640) + parser.add_argument("--num-frames", type=int, default=99, help="Total frames (33 per chunk)") + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--fps", type=int, default=16) + args = parser.parse_args() + + print("Patching scheduler for MPS compatibility...") + patch_scheduler_for_mps() + + print("Loading pipeline...") + from diffusers import DiffusionPipeline + + pipe = DiffusionPipeline.from_pretrained( + args.model_dir, + torch_dtype=torch.float16, + ).to("mps") + + generator = torch.Generator("mps").manual_seed(args.seed) + + print(f"Generating {args.num_frames} frames...") + video = pipe( + prompt=args.prompt, + height=args.height, + width=args.width, + num_frames=args.num_frames, + guidance_scale=1.0, + generator=generator, + pyramid_num_inference_steps_list=[2, 2, 2], + is_amplify_first_chunk=True, + ).frames + + frames = video[0] + print(f"Got {len(frames)} frames, size: {frames[0].size}") + + out = cv2.VideoWriter( + args.output, cv2.VideoWriter_fourcc(*"mp4v"), args.fps, frames[0].size + ) + for f in frames: + out.write(cv2.cvtColor(np.array(f), cv2.COLOR_RGB2BGR)) + out.release() + print(f"Saved to {args.output}") + + +if __name__ == "__main__": + main() From 4f12b322025537690c999d0506a1a9dad6326ee5 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 12 Mar 2026 09:02:07 +0100 Subject: [PATCH 26/40] Helios: Fix imports --- mlx_video/generate_helios.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index 82046a2..fadb7ad 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -27,8 +27,8 @@ load_t5_encoder, load_vae_decoder, ) -from mlx_video.postprocess import save_video -from mlx_video.utils import Colors +from mlx_video.models.wan.postprocess import save_video +from mlx_video.generate_wan import Colors def sample_block_noise( From 7564c774a3ea0438058f9e790540e57135a83e47 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 12 Mar 2026 09:02:20 +0100 Subject: [PATCH 27/40] Helios: README.md add poodle example --- mlx_video/models/helios/README.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/mlx_video/models/helios/README.md b/mlx_video/models/helios/README.md index 6d55b5d..27a6d50 100644 --- a/mlx_video/models/helios/README.md +++ b/mlx_video/models/helios/README.md @@ -32,6 +32,14 @@ python -m mlx_video.generate_helios \ --output-path my_video.mp4 ``` +```bash +python -m mlx_video.generate_helios \ + --model-dir /Volumes/SSD/BestWishYsh/Helios-Distilled-MLX/ \ + --num-frames 330 \ + --seed 2391784614 \ + --prompt "Two dogs of the poodle breed sitting on a beach wearing sunglasses, nodding with their heads, close up, cinematic, sunset" +``` + ## CLI Options | Flag | Default | Description | From 4e354a2c5c12e89152ec144b8c6410b5f138f713 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 12 Mar 2026 09:15:37 +0100 Subject: [PATCH 28/40] Optimize Helios model: remove dead code, fuse ops, add mx.compile - Remove dead RoPE computation in restricted self-attention path (helios_rope_apply was called but results were immediately discarded) - Fuse V projection: single self.v() call instead of two separate calls - Cache constant t=0 timestep embedding in __init__ instead of recomputing every forward pass (saves 4 linear layers per call) - Replace concatenate+repeat padding with mx.pad(mode='edge') in rope.py and transformer.py _patchify_history - Add mx.compile support to generate_helios.py with --no-compile flag Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/generate_helios.py | 14 ++++- mlx_video/models/helios/attention.py | 81 ++++++++------------------ mlx_video/models/helios/rope.py | 18 ++---- mlx_video/models/helios/transformer.py | 43 ++++++++------ 4 files changed, 67 insertions(+), 89 deletions(-) diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index fadb7ad..46306c1 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -152,6 +152,7 @@ def generate_video( anti_drifting: bool = False, anti_drift_blend: float = 0.5, debug: bool = False, + no_compile: bool = False, ): """Generate video using Helios autoregressive pipeline with pyramid denoising. @@ -172,6 +173,7 @@ def generate_video( crossfade_frames: Number of pixel frames to cross-fade between chunks (0 to disable) anti_drifting: Enable adaptive anti-drifting for temporal consistency anti_drift_blend: How much to normalize history toward EMA (0=off, 0.5=half, 1.0=full) + no_compile: If True, skip mx.compile on models (useful for debugging) """ from mlx_video.models.helios.config import HeliosModelConfig @@ -284,6 +286,10 @@ def generate_video( print(f"{Colors.DIM} Text embedding + KV cache: ready{Colors.RESET}") + # Compile model for faster inference via kernel fusion + if not no_compile: + model._compiled = mx.compile(model) + # 4. History setup (keep_first_frame=True matching reference) history_sizes = config.history_sizes # [16, 2, 1] num_history_frames = sum(history_sizes) # 19 latent frames of history @@ -428,11 +434,13 @@ def generate_video( cur_idx_mid = idx_mid cur_idx_long = idx_long + _call = getattr(model, '_compiled', model) + for idx, t in enumerate(timesteps): # Reference casts timestep to int64 before model call timestep = mx.array(int(t.item()), dtype=mx.int32) # Cast to bfloat16 to match reference (model trained with bf16 activations) - noise_pred = model( + noise_pred = _call( latents=latents.astype(mx.bfloat16), timestep=timestep, encoder_hidden_states=context_embedded, @@ -454,7 +462,7 @@ def generate_video( print(f" {_debug_stats('noise_pred', noise_pred)}") if do_cfg: - noise_uncond = model( + noise_uncond = _call( latents=latents.astype(mx.bfloat16), timestep=timestep, encoder_hidden_states=negative_context_embedded, @@ -713,6 +721,7 @@ def main(): parser.add_argument("--anti-drifting", action="store_true", help="Enable adaptive anti-drifting for temporal consistency between chunks") parser.add_argument("--anti-drift-blend", type=float, default=0.5, help="How much to normalize history toward EMA stats (0=off, 0.5=half, 1.0=full; default=0.5)") parser.add_argument("--debug", action="store_true", help="Print per-step latent statistics for debugging") + parser.add_argument("--no-compile", action="store_true", help="Disable mx.compile on models (for debugging)") args = parser.parse_args() generate_video( @@ -733,6 +742,7 @@ def main(): anti_drifting=args.anti_drifting, anti_drift_blend=args.anti_drift_blend, debug=args.debug, + no_compile=args.no_compile, ) diff --git a/mlx_video/models/helios/attention.py b/mlx_video/models/helios/attention.py index 70cb8e4..bfd1944 100644 --- a/mlx_video/models/helios/attention.py +++ b/mlx_video/models/helios/attention.py @@ -1,9 +1,6 @@ import mlx.core as mx import mlx.nn as nn -from .rope import helios_rope_apply - - def _linear_dtype(layer) -> mx.Dtype: """Get the compute dtype of a linear layer, handling QuantizedLinear and LoRA wrappers.""" inner = getattr(layer, "linear", layer) @@ -98,68 +95,40 @@ def __call__( k = self.norm_k(k) if self.restrict_self_attn and history_seq_len > 0: - # Split history and current - q_hist, q_curr = q[:, :history_seq_len], q[:, history_seq_len:] - k_hist, k_curr = k[:, :history_seq_len], k[:, history_seq_len:] - v_hist = self.v(x_w[:, :history_seq_len]) - v_curr = self.v(x_w[:, history_seq_len:]) - - # Reshape to multi-head - q_hist = q_hist.reshape(b, history_seq_len, n, d) - k_hist = k_hist.reshape(b, history_seq_len, n, d) - v_hist = v_hist.reshape(b, history_seq_len, n, d) - - q_curr = q_curr.reshape(b, original_context_length, n, d) - k_curr = k_curr.reshape(b, original_context_length, n, d) - v_curr = v_curr.reshape(b, original_context_length, n, d) - - # Apply RoPE to history and current separately - # History RoPE uses history portion of frame_indices - all_q = mx.concatenate([q_hist, q_curr], axis=1).reshape(b, s, n, d) - all_k = mx.concatenate([k_hist, k_curr], axis=1).reshape(b, s, n, d) - - total_seq = history_seq_len + original_context_length - all_q = helios_rope_apply( - all_q.astype(mx.float32), frame_indices, (total_seq, 1, 1), - freqs, precomputed_cos_sin=rope_cos_sin, - ) if rope_cos_sin is not None else all_q - all_k = helios_rope_apply( - all_k.astype(mx.float32), frame_indices, (total_seq, 1, 1), - freqs, precomputed_cos_sin=rope_cos_sin, - ) if rope_cos_sin is not None else all_k - - # Actually we need to use the full precomputed rope since it covers - # the concatenated history+current sequence. - # Let me simplify: apply RoPE to the full concatenated sequence - q_full = mx.concatenate([q_hist, q_curr], axis=1).reshape(b, s, n, d) - k_full = mx.concatenate([k_hist, k_curr], axis=1).reshape(b, s, n, d) - - # RoPE is precomputed for the full (history + current) sequence + # Single V projection on full sequence, then split + v = self.v(x_w).reshape(b, s, n, d) + v_hist = v[:, :history_seq_len] + v_curr = v[:, history_seq_len:] + + # Reshape Q/K to multi-head for full sequence + q = q.reshape(b, s, n, d) + k = k.reshape(b, s, n, d) + + # Apply RoPE to full (history + current) sequence if rope_cos_sin is not None: cos_f, sin_f = rope_cos_sin - q_seq = q_full.reshape(b, s, n, d // 2, 2) + half_d = d // 2 + + q_seq = q.astype(mx.float32).reshape(b, s, n, half_d, 2) q_real, q_imag = q_seq[..., 0], q_seq[..., 1] - q_out_r = q_real * cos_f - q_imag * sin_f - q_out_i = q_real * sin_f + q_imag * cos_f - q_full = mx.stack([q_out_r, q_out_i], axis=-1).reshape(b, s, n, d) + q = mx.stack([q_real * cos_f - q_imag * sin_f, + q_real * sin_f + q_imag * cos_f], axis=-1).reshape(b, s, n, d) - k_seq = k_full.reshape(b, s, n, d // 2, 2) + k_seq = k.astype(mx.float32).reshape(b, s, n, half_d, 2) k_real, k_imag = k_seq[..., 0], k_seq[..., 1] - k_out_r = k_real * cos_f - k_imag * sin_f - k_out_i = k_real * sin_f + k_imag * cos_f - k_full = mx.stack([k_out_r, k_out_i], axis=-1).reshape(b, s, n, d) + k = mx.stack([k_real * cos_f - k_imag * sin_f, + k_real * sin_f + k_imag * cos_f], axis=-1).reshape(b, s, n, d) - q_hist = q_full[:, :history_seq_len].astype(w_dtype) - q_curr = q_full[:, history_seq_len:].astype(w_dtype) - k_hist = k_full[:, :history_seq_len].astype(w_dtype) - k_curr = k_full[:, history_seq_len:].astype(w_dtype) - v_hist_h = v_hist - v_curr_h = v_curr + # Split into history and current after RoPE + q_hist = q[:, :history_seq_len].astype(w_dtype) + q_curr = q[:, history_seq_len:].astype(w_dtype) + k_hist = k[:, :history_seq_len].astype(w_dtype) + k_curr = k[:, history_seq_len:].astype(w_dtype) # History self-attention: history attends to history only q_h = q_hist.transpose(0, 2, 1, 3) k_h = k_hist.transpose(0, 2, 1, 3) - v_h = v_hist_h.transpose(0, 2, 1, 3) + v_h = v_hist.transpose(0, 2, 1, 3) hist_out = mx.fast.scaled_dot_product_attention( q_h, k_h, v_h, scale=self.scale ) @@ -167,7 +136,7 @@ def __call__( # Current self-attention: current attends to history + current k_all = mx.concatenate([k_hist, k_curr], axis=1).transpose(0, 2, 1, 3) - v_all = mx.concatenate([v_hist_h, v_curr_h], axis=1).transpose(0, 2, 1, 3) + v_all = mx.concatenate([v_hist, v_curr], axis=1).transpose(0, 2, 1, 3) q_c = q_curr.transpose(0, 2, 1, 3) curr_out = mx.fast.scaled_dot_product_attention( q_c, k_all, v_all, scale=self.scale diff --git a/mlx_video/models/helios/rope.py b/mlx_video/models/helios/rope.py index 936a83f..474e0a5 100644 --- a/mlx_video/models/helios/rope.py +++ b/mlx_video/models/helios/rope.py @@ -90,19 +90,13 @@ def _rope_pad_and_downsample(rope_5d: mx.array, kernel: tuple) -> mx.array: pad_t = (kt - (f % kt)) % kt pad_h = (kh - (h % kh)) % kh pad_w = (kw - (w % kw)) % kw - if pad_t > 0: - rope_5d = mx.concatenate( - [rope_5d, mx.repeat(rope_5d[-1:], pad_t, axis=0)], axis=0 + if pad_t > 0 or pad_h > 0 or pad_w > 0: + rope_5d = mx.pad( + rope_5d, + [(0, pad_t), (0, pad_h), (0, pad_w), (0, 0), (0, 0)], + mode="edge", ) - if pad_h > 0: - rope_5d = mx.concatenate( - [rope_5d, mx.repeat(rope_5d[:, -1:], pad_h, axis=1)], axis=1 - ) - if pad_w > 0: - rope_5d = mx.concatenate( - [rope_5d, mx.repeat(rope_5d[:, :, -1:], pad_w, axis=2)], axis=2 - ) - f, h, w = rope_5d.shape[:3] + f, h, w = rope_5d.shape[:3] # Reshape and average (avg_pool3d equivalent) rope_5d = rope_5d.reshape(f // kt, kt, h // kh, kh, w // kw, kw, d, c) diff --git a/mlx_video/models/helios/transformer.py b/mlx_video/models/helios/transformer.py index 4798939..f8b9623 100644 --- a/mlx_video/models/helios/transformer.py +++ b/mlx_video/models/helios/transformer.py @@ -225,6 +225,23 @@ def __init__(self, config: HeliosModelConfig): 10000.0, -mx.arange(half).astype(mx.float32) / half ) + # Precompute constant t=0 timestep projection for history tokens + if config.zero_history_timestep: + self._t0_proj = self._compute_t0_projection() + + def _compute_t0_projection(self) -> mx.array: + """Precompute the timestep projection for t=0 (used for history tokens).""" + t0_emb = mx.array([0.0]) * self._inv_freq + t0_emb = mx.concatenate([mx.cos(t0_emb), mx.sin(t0_emb)], axis=-1)[None, :] + temb_t0 = self.time_embedding_1( + self.time_embedding_act(self.time_embedding_0(t0_emb)) + ) + tp_t0 = self.time_projection( + self.time_projection_act(temb_t0) + ).reshape(1, 6, -1) + mx.eval(tp_t0) + return tp_t0 + def _patchify(self, x: mx.array) -> tuple: """Convert video latent to patch embeddings. @@ -280,13 +297,11 @@ def _patchify_history(self, x: mx.array, scale: str) -> mx.array: pad_h = (kh - (h % kh)) % kh pad_w = (kw - (w % kw)) % kw if pad_t > 0 or pad_h > 0 or pad_w > 0: - # Replicate padding along each dimension - if pad_t > 0: - x = mx.concatenate([x, mx.repeat(x[:, -1:], pad_t, axis=1)], axis=1) - if pad_h > 0: - x = mx.concatenate([x, mx.repeat(x[:, :, -1:], pad_h, axis=2)], axis=2) - if pad_w > 0: - x = mx.concatenate([x, mx.repeat(x[:, :, :, -1:], pad_w, axis=3)], axis=3) + x = mx.pad( + x, + [(0, 0), (0, pad_t), (0, pad_h), (0, pad_w)], + mode="edge", + ) c, f, h, w = x.shape f_out = f // kt @@ -456,20 +471,10 @@ def __call__( (1, 6, original_context_length, self.dim), ) - # Zero history timestep embedding (timestep=0 β†’ sinusoidal [cos(0), sin(0)] = [1,...,1, 0,...,0]) + # Zero history timestep embedding (use precomputed t=0 projection) if self.zero_history_timestep and history_seq_len > 0: - t0_emb = mx.array([0.0]) * self._inv_freq - t0_emb = mx.concatenate([mx.cos(t0_emb), mx.sin(t0_emb)], axis=-1) - if t0_emb.ndim == 1: - t0_emb = t0_emb[None, :] - temb_t0 = self.time_embedding_1( - self.time_embedding_act(self.time_embedding_0(t0_emb)) - ) - tp_t0 = self.time_projection( - self.time_projection_act(temb_t0) - ).reshape(1, 6, -1) tp_t0_expanded = mx.broadcast_to( - tp_t0[:, :, None, :], + self._t0_proj[:, :, None, :], (1, 6, history_seq_len, self.dim), ) timestep_proj_expanded = mx.concatenate( From c0f1bdf7926b57413aa1dfdc5333e96d2c597f95 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 12 Mar 2026 09:27:42 +0100 Subject: [PATCH 29/40] Fix progress bar total to account for amplified first chunk When --amplify-first-chunk is enabled (default), the first chunk gets doubled steps per stage. The progress bar now reflects this so it shows 12/12 instead of 12/6. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/generate_helios.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index 46306c1..56e23a2 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -368,8 +368,10 @@ def generate_video( print(f"\n[DEBUG] Chunk {chunk_idx}: initial noise β†’ 1/4 res") print(f" {_debug_stats('start_point[0]', latents)}") + is_amplified = amplify_first_chunk and is_first + total_steps = sum(s * 2 if is_amplified else s for s in pyramid_steps) pbar = tqdm( - total=sum(pyramid_steps), + total=total_steps, desc=f" Chunk {chunk_idx + 1}/{num_chunks}", leave=True, bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]", From f5f08cdc427386489f370fceeaa2a473be5c17a1 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 12 Mar 2026 09:40:33 +0100 Subject: [PATCH 30/40] =?UTF-8?q?perf(helios):=20Phase=202=20=E2=80=94=20e?= =?UTF-8?q?liminate=20sync=20points=20and=20reduce=20eval=20boundaries?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit OPT-9: Pre-convert timesteps/sigmas to Python lists before denoising loop, avoiding .item() and float() sync points that force GPU evaluation. OPT-10: Remove intermediate mx.eval(noise_pred) β€” the full computation graph (model forward + scheduler step) is now evaluated in a single mx.eval(latents) call, giving MLX maximum fusion opportunity. OPT-11: scheduler.step_dmd() accepts optional sigma_t/sigma_next as Python floats, bypassing float(self.sigmas[idx]) sync points. OPT-12: Batch modulation dtype cast in transformer block β€” cast the full mod tensor to w_dtype once instead of 6 individual casts per block (240 unnecessary casts eliminated across 40 blocks). Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/generate_helios.py | 18 +++++++++++------- mlx_video/models/helios/scheduler.py | 11 +++++++++-- mlx_video/models/helios/transformer.py | 14 +++++++------- 3 files changed, 27 insertions(+), 16 deletions(-) diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index 56e23a2..020d407 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -438,10 +438,12 @@ def generate_video( _call = getattr(model, '_compiled', model) - for idx, t in enumerate(timesteps): - # Reference casts timestep to int64 before model call - timestep = mx.array(int(t.item()), dtype=mx.int32) - # Cast to bfloat16 to match reference (model trained with bf16 activations) + # Pre-convert to Python lists to avoid .item()/.float() sync points + timestep_list = [int(t) for t in timesteps.tolist()] + sigma_list = scheduler.sigmas.tolist() + + for idx, t_val in enumerate(timestep_list): + timestep = mx.array(t_val, dtype=mx.int32) noise_pred = _call( latents=latents.astype(mx.bfloat16), timestep=timestep, @@ -455,11 +457,10 @@ def generate_video( history_long_indices=cur_idx_long, cross_kv_caches=cross_kv_caches, ) - mx.eval(noise_pred) if debug: - sigma_t = scheduler.sigmas[idx].item() - print(f"\n [Step {idx}] t={int(t.item())} sigma={sigma_t:.6f}") + mx.eval(noise_pred) + print(f"\n [Step {idx}] t={t_val} sigma={sigma_list[idx]:.6f}") print(f" {_debug_stats('model_in', latents)}") print(f" {_debug_stats('noise_pred', noise_pred)}") @@ -480,11 +481,14 @@ def generate_video( mx.eval(noise_uncond) noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond) + sigma_next = sigma_list[idx + 1] if idx < len(timestep_list) - 1 else None latents = scheduler.step_dmd( model_output=noise_pred, sample=latents, cur_step=idx, noisy_start=start_point_list[i_s], + sigma_t=sigma_list[idx], + sigma_next=sigma_next, ) mx.eval(latents) diff --git a/mlx_video/models/helios/scheduler.py b/mlx_video/models/helios/scheduler.py index f8d7a78..2bf9bbd 100644 --- a/mlx_video/models/helios/scheduler.py +++ b/mlx_video/models/helios/scheduler.py @@ -202,6 +202,8 @@ def step_dmd( sample: mx.array, cur_step: int, noisy_start: mx.array, + sigma_t: float | None = None, + sigma_next: float | None = None, ) -> mx.array: """DMD step: predict x0 from flow, optionally re-noise. @@ -214,6 +216,8 @@ def step_dmd( sample: Current noisy latent (x_t) cur_step: Current step index within this stage noisy_start: Original noisy tensor for this stage (for re-noising) + sigma_t: Current sigma as Python float (avoids mx.array sync) + sigma_next: Next sigma as Python float (avoids mx.array sync) Returns: Denoised or re-noised sample (float32) @@ -223,13 +227,16 @@ def step_dmd( model_output = model_output.astype(mx.float32) sample = sample.astype(mx.float32) - sigma_t = float(self.sigmas[cur_step]) + # Use pre-extracted Python floats to avoid sync points + if sigma_t is None: + sigma_t = float(self.sigmas[cur_step]) x0_pred = sample - sigma_t * model_output num_timesteps = len(self.timesteps) if cur_step < num_timesteps - 1: # Re-noise: blend x0_pred with original noisy tensor at next sigma - sigma_next = float(self.sigmas[cur_step + 1]) + if sigma_next is None: + sigma_next = float(self.sigmas[cur_step + 1]) noisy_start = noisy_start.astype(mx.float32) prev_sample = (1 - sigma_next) * x0_pred + sigma_next * noisy_start else: diff --git a/mlx_video/models/helios/transformer.py b/mlx_video/models/helios/transformer.py index f8b9623..dde04d2 100644 --- a/mlx_video/models/helios/transformer.py +++ b/mlx_video/models/helios/transformer.py @@ -96,13 +96,13 @@ def __call__( # timestep_proj: [B, L, 6, dim] (per-token) or [B, 6, dim] (global) if timestep_proj.ndim == 4: # [B, L, 6, dim] + [1, 1, 6, dim] β†’ [B, L, 6, dim] - mod = (self.scale_shift_table[None, :, :] + timestep_proj.astype(mx.float32)) - shift_msa = mod[:, :, 0].astype(w_dtype) - scale_msa = mod[:, :, 1].astype(w_dtype) - gate_msa = mod[:, :, 2].astype(w_dtype) - c_shift = mod[:, :, 3].astype(w_dtype) - c_scale = mod[:, :, 4].astype(w_dtype) - c_gate = mod[:, :, 5].astype(w_dtype) + mod = (self.scale_shift_table[None, :, :] + timestep_proj.astype(mx.float32)).astype(w_dtype) + shift_msa = mod[:, :, 0] + scale_msa = mod[:, :, 1] + gate_msa = mod[:, :, 2] + c_shift = mod[:, :, 3] + c_scale = mod[:, :, 4] + c_gate = mod[:, :, 5] else: # [B, 6, dim] + [1, 6, dim] β†’ [B, 6, dim] mod = (self.scale_shift_table + timestep_proj.astype(mx.float32)).astype(w_dtype) From 8850fd5e3a757dd31c8db2676daa67af362a7464 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 12 Mar 2026 09:45:52 +0100 Subject: [PATCH 31/40] fix(helios): compute t=0 projection lazily after weights are loaded MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The cached t=0 timestep projection was being computed in __init__ before load_weights() populated the trained parameters. This meant the history conditioning used random/initial weights instead of the actual trained time_embedding and time_projection layers, causing: - Degraded colors in the first chunk - Loss of inter-chunk continuity (mostly solid color) Fix: make _t0_proj lazy β€” computed and cached on first forward pass when the trained weights are guaranteed to be present. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/models/helios/transformer.py | 39 +++++++++++++++----------- 1 file changed, 22 insertions(+), 17 deletions(-) diff --git a/mlx_video/models/helios/transformer.py b/mlx_video/models/helios/transformer.py index dde04d2..62d6de5 100644 --- a/mlx_video/models/helios/transformer.py +++ b/mlx_video/models/helios/transformer.py @@ -225,22 +225,27 @@ def __init__(self, config: HeliosModelConfig): 10000.0, -mx.arange(half).astype(mx.float32) / half ) - # Precompute constant t=0 timestep projection for history tokens - if config.zero_history_timestep: - self._t0_proj = self._compute_t0_projection() - - def _compute_t0_projection(self) -> mx.array: - """Precompute the timestep projection for t=0 (used for history tokens).""" - t0_emb = mx.array([0.0]) * self._inv_freq - t0_emb = mx.concatenate([mx.cos(t0_emb), mx.sin(t0_emb)], axis=-1)[None, :] - temb_t0 = self.time_embedding_1( - self.time_embedding_act(self.time_embedding_0(t0_emb)) - ) - tp_t0 = self.time_projection( - self.time_projection_act(temb_t0) - ).reshape(1, 6, -1) - mx.eval(tp_t0) - return tp_t0 + # Will be computed lazily after weights are loaded + self._t0_proj: mx.array | None = None + + def _get_t0_projection(self) -> mx.array: + """Get the cached t=0 timestep projection, computing on first use. + + Must be called after weights are loaded (not in __init__) so that + the time_embedding and time_projection layers use trained weights. + """ + if self._t0_proj is None: + t0_emb = mx.array([0.0]) * self._inv_freq + t0_emb = mx.concatenate([mx.cos(t0_emb), mx.sin(t0_emb)], axis=-1)[None, :] + temb_t0 = self.time_embedding_1( + self.time_embedding_act(self.time_embedding_0(t0_emb)) + ) + tp_t0 = self.time_projection( + self.time_projection_act(temb_t0) + ).reshape(1, 6, -1) + mx.eval(tp_t0) + self._t0_proj = tp_t0 + return self._t0_proj def _patchify(self, x: mx.array) -> tuple: """Convert video latent to patch embeddings. @@ -474,7 +479,7 @@ def __call__( # Zero history timestep embedding (use precomputed t=0 projection) if self.zero_history_timestep and history_seq_len > 0: tp_t0_expanded = mx.broadcast_to( - self._t0_proj[:, :, None, :], + self._get_t0_projection()[:, :, None, :], (1, 6, history_seq_len, self.dim), ) timestep_proj_expanded = mx.concatenate( From b2f843908dc0ea39c7f75ed03b2541f7cb5a966d Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 12 Mar 2026 11:15:23 +0100 Subject: [PATCH 32/40] perf(helios): hoist history astype(bf16) outside denoising loop History tensors (h_short, h_mid, h_long) are constant within each pyramid stage but were being cast to bfloat16 on every denoising step. Move the casts before the loop to avoid redundant work. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/generate_helios.py | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/mlx_video/generate_helios.py b/mlx_video/generate_helios.py index 020d407..55c54c0 100644 --- a/mlx_video/generate_helios.py +++ b/mlx_video/generate_helios.py @@ -442,6 +442,11 @@ def generate_video( timestep_list = [int(t) for t in timesteps.tolist()] sigma_list = scheduler.sigmas.tolist() + # History doesn't change within a stage β€” cast once outside the loop + h_short_bf16 = h_short.astype(mx.bfloat16) + h_mid_bf16 = h_mid.astype(mx.bfloat16) + h_long_bf16 = h_long.astype(mx.bfloat16) + for idx, t_val in enumerate(timestep_list): timestep = mx.array(t_val, dtype=mx.int32) noise_pred = _call( @@ -449,9 +454,9 @@ def generate_video( timestep=timestep, encoder_hidden_states=context_embedded, frame_indices=cur_idx, - history_short=h_short.astype(mx.bfloat16), - history_mid=h_mid.astype(mx.bfloat16), - history_long=h_long.astype(mx.bfloat16), + history_short=h_short_bf16, + history_mid=h_mid_bf16, + history_long=h_long_bf16, history_short_indices=cur_idx_short, history_mid_indices=cur_idx_mid, history_long_indices=cur_idx_long, @@ -470,9 +475,9 @@ def generate_video( timestep=timestep, encoder_hidden_states=negative_context_embedded, frame_indices=cur_idx, - history_short=h_short.astype(mx.bfloat16), - history_mid=h_mid.astype(mx.bfloat16), - history_long=h_long.astype(mx.bfloat16), + history_short=h_short_bf16, + history_mid=h_mid_bf16, + history_long=h_long_bf16, history_short_indices=cur_idx_short, history_mid_indices=cur_idx_mid, history_long_indices=cur_idx_long, From 0fb8d6af2d75a2268ad3b4b46e96946a6cfbcb93 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 12 Mar 2026 11:35:20 +0100 Subject: [PATCH 33/40] feat(helios): add 'quantize' subcommand for existing MLX models Adds a 'quantize' subcommand to convert_helios.py that takes an existing MLX model directory and produces a quantized copy: python -m mlx_video.convert_helios quantize [--bits 4] Creates -4bit/ by default (customizable via --output-dir). The generate script auto-detects quantization from config.json. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/convert_helios.py | 80 ++++++++++++++++++++++++++++++------- 1 file changed, 66 insertions(+), 14 deletions(-) diff --git a/mlx_video/convert_helios.py b/mlx_video/convert_helios.py index af3b092..6fd580a 100644 --- a/mlx_video/convert_helios.py +++ b/mlx_video/convert_helios.py @@ -586,21 +586,73 @@ def _quantize_saved_model( if __name__ == "__main__": import argparse + import shutil parser = argparse.ArgumentParser(description="Convert Helios weights to MLX format") - parser.add_argument("checkpoint_dir", type=str, help="Path to HF Helios checkpoint") - parser.add_argument("output_dir", type=str, help="Output MLX model directory") - parser.add_argument("--dtype", type=str, default="bfloat16", choices=["float16", "float32", "bfloat16"]) - parser.add_argument("--quantize", action="store_true", help="Quantize transformer weights") - parser.add_argument("--bits", type=int, default=4, help="Quantization bits") - parser.add_argument("--group-size", type=int, default=64, help="Quantization group size") + subparsers = parser.add_subparsers(dest="command") + + # Original conversion command (default when no subcommand) + convert_parser = subparsers.add_parser("convert", help="Convert HF checkpoint to MLX format") + convert_parser.add_argument("checkpoint_dir", type=str, help="Path to HF Helios checkpoint") + convert_parser.add_argument("output_dir", type=str, help="Output MLX model directory") + convert_parser.add_argument("--dtype", type=str, default="bfloat16", choices=["float16", "float32", "bfloat16"]) + convert_parser.add_argument("--quantize", action="store_true", help="Quantize transformer weights") + convert_parser.add_argument("--bits", type=int, default=4, help="Quantization bits") + convert_parser.add_argument("--group-size", type=int, default=64, help="Quantization group size") + + # Quantize-only command for existing MLX models + quant_parser = subparsers.add_parser("quantize", help="Quantize an existing MLX model") + quant_parser.add_argument("model_dir", type=str, help="Path to existing MLX model directory") + quant_parser.add_argument("--output-dir", type=str, default=None, + help="Output directory (default: -bit)") + quant_parser.add_argument("--bits", type=int, default=4, help="Quantization bits (default: 4)") + quant_parser.add_argument("--group-size", type=int, default=64, help="Quantization group size") + args = parser.parse_args() - convert_helios_checkpoint( - args.checkpoint_dir, - args.output_dir, - dtype=args.dtype, - quantize=args.quantize, - bits=args.bits, - group_size=args.group_size, - ) + # Backward compatibility: if no subcommand, treat positional args as convert + if args.command is None: + # Legacy mode: positional args + parser.print_help() + print("\nUse 'convert' or 'quantize' subcommand. Examples:") + print(" python -m mlx_video.convert_helios convert [--quantize]") + print(" python -m mlx_video.convert_helios quantize [--bits 4]") + raise SystemExit(1) + + if args.command == "convert": + convert_helios_checkpoint( + args.checkpoint_dir, + args.output_dir, + dtype=args.dtype, + quantize=args.quantize, + bits=args.bits, + group_size=args.group_size, + ) + elif args.command == "quantize": + from mlx_video.models.helios.config import HeliosModelConfig + + model_dir = Path(args.model_dir) + output_dir = Path(args.output_dir) if args.output_dir else model_dir.parent / f"{model_dir.name}-{args.bits}bit" + + # Load config + config_path = model_dir / "config.json" + if not config_path.exists(): + raise FileNotFoundError(f"No config.json found in {model_dir}") + with open(config_path) as f: + config_dict = json.load(f) + if "quantization" in config_dict: + raise ValueError(f"Model is already quantized: {config_dict['quantization']}") + + config = HeliosModelConfig(**{ + k: v for k, v in config_dict.items() + if k in HeliosModelConfig.__dataclass_fields__ + }) + + # Copy to output dir if different + if output_dir != model_dir: + print(f"Copying {model_dir} β†’ {output_dir}") + shutil.copytree(model_dir, output_dir, dirs_exist_ok=True) + + print(f"Quantizing to {args.bits}-bit (group_size={args.group_size})...") + _quantize_saved_model(output_dir, config, bits=args.bits, group_size=args.group_size) + print(f"\nDone! Quantized model saved to: {output_dir}") From 192d2b6a57205ec3486ffcfce055856c170a9e4c Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 12 Mar 2026 11:43:55 +0100 Subject: [PATCH 34/40] docs(helios): update README with quantize subcommand usage Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/models/helios/README.md | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/mlx_video/models/helios/README.md b/mlx_video/models/helios/README.md index 27a6d50..7978dc2 100644 --- a/mlx_video/models/helios/README.md +++ b/mlx_video/models/helios/README.md @@ -9,20 +9,27 @@ Helios is a 14B-parameter autoregressive video generation model that produces mi Download the HuggingFace checkpoint and convert to MLX format: ```bash -python -m mlx_video.convert_helios \ - --checkpoint-dir /path/to/BestWishYsh/Helios-Distilled \ - --output-dir ./helios-mlx +python -m mlx_video.convert_helios convert \ + /path/to/BestWishYsh/Helios-Distilled \ + ./helios-mlx ``` With 4-bit quantization (~7 GB, fits 16 GB Macs): ```bash -python -m mlx_video.convert_helios \ - --checkpoint-dir /path/to/BestWishYsh/Helios-Distilled \ - --output-dir ./helios-mlx-4bit \ +python -m mlx_video.convert_helios convert \ + /path/to/BestWishYsh/Helios-Distilled \ + ./helios-mlx-4bit \ --quantize --bits 4 ``` +Or quantize an existing MLX model: + +```bash +python -m mlx_video.convert_helios quantize ./helios-mlx --bits 4 +# Creates ./helios-mlx-4bit/ +``` + ### 2. Generate Video ```bash @@ -34,7 +41,7 @@ python -m mlx_video.generate_helios \ ```bash python -m mlx_video.generate_helios \ - --model-dir /Volumes/SSD/BestWishYsh/Helios-Distilled-MLX/ \ + --model-dir ../Helios-Distilled-MLX/ \ --num-frames 330 \ --seed 2391784614 \ --prompt "Two dogs of the poodle breed sitting on a beach wearing sunglasses, nodding with their heads, close up, cinematic, sunset" From d3277e4da20f2985e78612c01801484e2beaadcb Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 12 Mar 2026 11:47:02 +0100 Subject: [PATCH 35/40] refactor(helios): align convert CLI with convert_wan pattern Replace subparser-based CLI with flat --flag pattern matching convert_wan.py: - --checkpoint-dir / --output-dir (named args, not positional) - --quantize-only flag (instead of separate 'quantize' subcommand) - --bits choices=[4,8], --group-size choices=[32,64,128] - Smart copy: only copies non-transformer files for quantize-only - source_dir support in _quantize_saved_model Update README.md examples to match new CLI. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- mlx_video/convert_helios.py | 179 ++++++++++++++++++++---------- mlx_video/models/helios/README.md | 20 ++-- 2 files changed, 130 insertions(+), 69 deletions(-) diff --git a/mlx_video/convert_helios.py b/mlx_video/convert_helios.py index 6fd580a..57b30ed 100644 --- a/mlx_video/convert_helios.py +++ b/mlx_video/convert_helios.py @@ -526,12 +526,22 @@ def _quantize_saved_model( config, bits: int, group_size: int, + source_dir: Path | None = None, ): - """Load saved bf16 model, quantize, and re-save.""" + """Load saved bf16 model, quantize, and re-save. + + Args: + output_dir: Directory to save quantized weights + config: HeliosModelConfig + bits: Quantization bits + group_size: Quantization group size + source_dir: If provided, load weights from here instead of output_dir + """ import mlx.nn as nn from mlx_video.models.helios.transformer import HeliosModel - model_path = output_dir / "model.safetensors" + load_dir = source_dir if source_dir is not None else output_dir + model_path = load_dir / "model.safetensors" if not model_path.exists(): print(" No model.safetensors found, skipping quantization") return @@ -566,7 +576,9 @@ def _quantize_saved_model( f"{len(bad_keys)} bias tensors contain NaN/Inf" ) - mx.save_safetensors(str(model_path), weights_dict) + save_path = output_dir / "model.safetensors" + output_dir.mkdir(parents=True, exist_ok=True) + mx.save_safetensors(str(save_path), weights_dict) n_quantized = sum(1 for k in weights_dict if ".scales" in k) print(f" {n_quantized} layers quantized, {len(weights_dict)} tensors saved") @@ -584,42 +596,117 @@ def _quantize_saved_model( print(f" Updated config.json with quantization metadata") +def quantize_mlx_model( + mlx_model_dir: str, + output_dir: str, + bits: int = 4, + group_size: int = 64, +): + """Quantize an already-converted MLX Helios model. + + Args: + mlx_model_dir: Path to existing MLX model directory + output_dir: Output directory for quantized model + bits: Quantization bits (4 or 8) + group_size: Quantization group size + """ + import shutil + + src = Path(mlx_model_dir) + dst = Path(output_dir) + + config_path = src / "config.json" + if not config_path.exists(): + raise FileNotFoundError(f"No config.json found in {src}") + + with open(config_path) as f: + cfg = json.load(f) + + if cfg.get("quantization"): + raise ValueError( + f"Model at {src} is already quantized " + f"({cfg['quantization']['bits']}-bit). Use a bf16/fp16 source." + ) + + from mlx_video.models.helios.config import HeliosModelConfig + + config_dict = {k: v for k, v in cfg.items() if k in HeliosModelConfig.__dataclass_fields__} + for key in ("patch_size", "history_sizes", "rope_dim"): + if key in config_dict and isinstance(config_dict[key], list): + config_dict[key] = tuple(config_dict[key]) + config = HeliosModelConfig(**config_dict) + + # Copy non-transformer files to output dir (skip large model weights) + transformer_files = {"model.safetensors"} + if dst.resolve() != src.resolve(): + dst.mkdir(parents=True, exist_ok=True) + for f in src.iterdir(): + if f.is_file() and f.name not in transformer_files: + shutil.copy2(f, dst / f.name) + print(f"Copied non-transformer files from {src} to {dst}") + + print(f"Quantizing transformer weights ({bits}-bit, group_size={group_size})...") + _quantize_saved_model(dst, config, bits=bits, group_size=group_size, source_dir=src) + + print(f"\nQuantization complete! Output: {dst}") + + if __name__ == "__main__": import argparse import shutil parser = argparse.ArgumentParser(description="Convert Helios weights to MLX format") - subparsers = parser.add_subparsers(dest="command") - - # Original conversion command (default when no subcommand) - convert_parser = subparsers.add_parser("convert", help="Convert HF checkpoint to MLX format") - convert_parser.add_argument("checkpoint_dir", type=str, help="Path to HF Helios checkpoint") - convert_parser.add_argument("output_dir", type=str, help="Output MLX model directory") - convert_parser.add_argument("--dtype", type=str, default="bfloat16", choices=["float16", "float32", "bfloat16"]) - convert_parser.add_argument("--quantize", action="store_true", help="Quantize transformer weights") - convert_parser.add_argument("--bits", type=int, default=4, help="Quantization bits") - convert_parser.add_argument("--group-size", type=int, default=64, help="Quantization group size") - - # Quantize-only command for existing MLX models - quant_parser = subparsers.add_parser("quantize", help="Quantize an existing MLX model") - quant_parser.add_argument("model_dir", type=str, help="Path to existing MLX model directory") - quant_parser.add_argument("--output-dir", type=str, default=None, - help="Output directory (default: -bit)") - quant_parser.add_argument("--bits", type=int, default=4, help="Quantization bits (default: 4)") - quant_parser.add_argument("--group-size", type=int, default=64, help="Quantization group size") - + parser.add_argument( + "--checkpoint-dir", + type=str, + required=True, + help="Path to HF Helios checkpoint (or existing MLX model when using --quantize-only)", + ) + parser.add_argument( + "--output-dir", + type=str, + default="helios_mlx_model", + help="Output path for MLX model", + ) + parser.add_argument( + "--dtype", + type=str, + choices=["float16", "float32", "bfloat16"], + default="bfloat16", + help="Target dtype", + ) + parser.add_argument( + "--quantize", + action="store_true", + help="Quantize transformer weights for faster inference", + ) + parser.add_argument( + "--quantize-only", + action="store_true", + help="Quantize an already-converted MLX model (skips HF conversion)", + ) + parser.add_argument( + "--bits", + type=int, + choices=[4, 8], + default=4, + help="Quantization bits (default: 4)", + ) + parser.add_argument( + "--group-size", + type=int, + choices=[32, 64, 128], + default=64, + help="Quantization group size (default: 64)", + ) args = parser.parse_args() - # Backward compatibility: if no subcommand, treat positional args as convert - if args.command is None: - # Legacy mode: positional args - parser.print_help() - print("\nUse 'convert' or 'quantize' subcommand. Examples:") - print(" python -m mlx_video.convert_helios convert [--quantize]") - print(" python -m mlx_video.convert_helios quantize [--bits 4]") - raise SystemExit(1) - - if args.command == "convert": + if args.quantize_only: + quantize_mlx_model( + args.checkpoint_dir, args.output_dir, + bits=args.bits, group_size=args.group_size, + ) + else: convert_helios_checkpoint( args.checkpoint_dir, args.output_dir, @@ -628,31 +715,3 @@ def _quantize_saved_model( bits=args.bits, group_size=args.group_size, ) - elif args.command == "quantize": - from mlx_video.models.helios.config import HeliosModelConfig - - model_dir = Path(args.model_dir) - output_dir = Path(args.output_dir) if args.output_dir else model_dir.parent / f"{model_dir.name}-{args.bits}bit" - - # Load config - config_path = model_dir / "config.json" - if not config_path.exists(): - raise FileNotFoundError(f"No config.json found in {model_dir}") - with open(config_path) as f: - config_dict = json.load(f) - if "quantization" in config_dict: - raise ValueError(f"Model is already quantized: {config_dict['quantization']}") - - config = HeliosModelConfig(**{ - k: v for k, v in config_dict.items() - if k in HeliosModelConfig.__dataclass_fields__ - }) - - # Copy to output dir if different - if output_dir != model_dir: - print(f"Copying {model_dir} β†’ {output_dir}") - shutil.copytree(model_dir, output_dir, dirs_exist_ok=True) - - print(f"Quantizing to {args.bits}-bit (group_size={args.group_size})...") - _quantize_saved_model(output_dir, config, bits=args.bits, group_size=args.group_size) - print(f"\nDone! Quantized model saved to: {output_dir}") diff --git a/mlx_video/models/helios/README.md b/mlx_video/models/helios/README.md index 7978dc2..a2e1d39 100644 --- a/mlx_video/models/helios/README.md +++ b/mlx_video/models/helios/README.md @@ -9,25 +9,27 @@ Helios is a 14B-parameter autoregressive video generation model that produces mi Download the HuggingFace checkpoint and convert to MLX format: ```bash -python -m mlx_video.convert_helios convert \ - /path/to/BestWishYsh/Helios-Distilled \ - ./helios-mlx +python -m mlx_video.convert_helios \ + --checkpoint-dir /path/to/BestWishYsh/Helios-Distilled \ + --output-dir ./helios-mlx ``` With 4-bit quantization (~7 GB, fits 16 GB Macs): ```bash -python -m mlx_video.convert_helios convert \ - /path/to/BestWishYsh/Helios-Distilled \ - ./helios-mlx-4bit \ +python -m mlx_video.convert_helios \ + --checkpoint-dir /path/to/BestWishYsh/Helios-Distilled \ + --output-dir ./helios-mlx-4bit \ --quantize --bits 4 ``` -Or quantize an existing MLX model: +Or quantize an existing MLX model (skips HF conversion): ```bash -python -m mlx_video.convert_helios quantize ./helios-mlx --bits 4 -# Creates ./helios-mlx-4bit/ +python -m mlx_video.convert_helios \ + --checkpoint-dir ./helios-mlx \ + --output-dir ./helios-mlx-4bit \ + --quantize-only --bits 4 ``` ### 2. Generate Video From b796756475cf69376e215e7f1a8768661a7cc89d Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 12 Mar 2026 15:03:22 +0100 Subject: [PATCH 36/40] Helios: Cleanup docs --- docs/DOCUMENTATION_INDEX.md | 319 ------ docs/WAN22_ARCHITECTURE_DIAGRAMS.md | 845 ---------------- docs/WAN22_EXPLORATION_SUMMARY.md | 929 ------------------ docs/WAN22_QUICK_REFERENCE.md | 340 ------- .../models/helios/docs/DIAGNOSTICS.md | 0 5 files changed, 2433 deletions(-) delete mode 100644 docs/DOCUMENTATION_INDEX.md delete mode 100644 docs/WAN22_ARCHITECTURE_DIAGRAMS.md delete mode 100644 docs/WAN22_EXPLORATION_SUMMARY.md delete mode 100644 docs/WAN22_QUICK_REFERENCE.md rename docs/HELIOS-DIAGNOSTICS.md => mlx_video/models/helios/docs/DIAGNOSTICS.md (100%) diff --git a/docs/DOCUMENTATION_INDEX.md b/docs/DOCUMENTATION_INDEX.md deleted file mode 100644 index 900d628..0000000 --- a/docs/DOCUMENTATION_INDEX.md +++ /dev/null @@ -1,319 +0,0 @@ -# Wan2.2 Model Documentation Index - -## Overview -Complete exploration of the Wan2.2 video diffusion model implementation in MLX for Apple Silicon. Three comprehensive documentation files have been created covering architecture, implementation, and references. - ---- - -## πŸ“„ Documentation Files - -### 1. **WAN22_EXPLORATION_SUMMARY.md** (36 KB, 929 lines) -**Comprehensive technical deep-dive covering all aspects of Wan2.2 architecture and implementation.** - -Sections: -- How Wan2.2 works (architecture overview, dual-model design) -- Denoising loop structure (timestep handling, batch processing, pre-computation) -- Transformer forward pass (block structure, modulation, self/cross-attention, FFN) -- Caching & acceleration mechanisms: - - TeaCache (polynomial-based block skipping, 2-3x speedup) - - Spectrum (Chebyshev feature prediction, 3.5-5x speedup) - - Memory optimizations -- Model loading & configuration (config presets, weight conversion, quantization) -- Generate scripts (pipeline stages, generation commands) -- File structure (complete list with line counts) -- Architecture comparison table - -**Best for**: Understanding the complete technical architecture, learning implementation details, debugging issues. - ---- - -### 2. **WAN22_QUICK_REFERENCE.md** (11 KB, ~300 lines) -**Quick lookup guide for common tasks and key concepts.** - -Sections: -- Core architecture at a glance (visual ASCII diagram) -- Key files to understand (quick navigation table) -- Denoising loop skeleton (pseudocode) -- Transformer block forward pass (code structure) -- 3-way factorized RoPE (explanation) -- TeaCache acceleration (3 lines of how it works) -- Spectrum acceleration (3 lines of how it works) -- Pre-computation checklist -- Configuration quick lookup (all model variants) -- Generation command cheat sheet (copy-paste examples) -- Weight conversion -- MoE architecture (clarification: NO MoE in Wan2.2) - -**Best for**: Quick lookup, command examples, refreshing memory on how specific components work. - ---- - -### 3. **WAN22_ARCHITECTURE_DIAGRAMS.md** (38 KB, ~845 lines) -**Detailed visual diagrams and data flow illustrations.** - -Sections: -1. **High-Level Data Flow** (ASCII diagram) - - Full generation pipeline (encoding β†’ denoising β†’ decoding) - - Stage 1: Text/noise encoding - - Stage 2: Denoising loop with model selection - - Stage 3: VAE decoding - -2. **Transformer Block Internals** (detailed ASCII) - - Modulation vector extraction (6 vectors) - - Self-attention with modulation - - Cross-attention with text - - FFN with modulation - -3. **3-Way Factorized RoPE** (implementation details) - - Frequency component splitting - - Per-position rotation - -4. **Cross-Attention K/V Caching** (optimization explanation) - - Comparison: uncached vs cached - - Storage requirements - - Per-step computation savings - -5. **TeaCache vs Spectrum vs No Caching** (comparison table) - - Side-by-side speedup/quality metrics - - Implementation complexity - -6. **Latent Space Patchification** (step-by-step) - - Input/output shapes - - Reshape operations - - Unpatchify (reverse operation) - -7. **Frame Alignment & Extra Frames** (boundary handling) - - Temporal frame computation - - Spatial alignment requirements - - Example walkthrough - -8. **Memory Hierarchy** (resource planning) - - Model weights breakdown - - Temporary buffers - - Pre-computed caches - - Peak memory estimates - - Memory timeline through phases - -**Best for**: Visual learners, architecture design, resource planning, implementation references. - ---- - -## πŸ—‚οΈ File Locations - -All files are in the project root: -``` -/Users/daniel/Projects/mlx-video/ -β”œβ”€β”€ WAN22_EXPLORATION_SUMMARY.md ← Deep technical details -β”œβ”€β”€ WAN22_QUICK_REFERENCE.md ← Quick lookup & examples -β”œβ”€β”€ WAN22_ARCHITECTURE_DIAGRAMS.md ← Visual diagrams & data flow -└── DOCUMENTATION_INDEX.md ← This file -``` - ---- - -## 🎯 Key Findings Summary - -### Architecture -- **Dual-Model**: Wan2.2 uses separate high-noise and low-noise transformers (boundary=875/1000 timesteps) -- **40 Transformer Blocks**: Each with self-attn, cross-attn, FFN, all modulated by 6 time-conditioned vectors -- **3-Way Factorized RoPE**: Temporal, height, and width frequency components kept separate -- **Learned Modulation**: DiT-style per-block time conditioning (not per-layer-pair) - -### Denoising Loop -- **40 Diffusion Steps**: Flow matching with Euler/DPM++/UniPC schedulers -- **Classifier-Free Guidance**: CFG batch (B=2) or disabled (B=1, 2x faster) -- **Pre-computation**: Text embeddings, cross-attn K/V caches, RoPE computed once per generation -- **Model Switching**: Select transformer based on noise level for adaptive coarse-to-fine generation - -### Acceleration -- **TeaCache**: Polynomial-based block skipping (~2-3x speedup) - - Monitors time embedding distance - - Pre-profiled coefficients per model - - Threshold-based skip decision - -- **Spectrum**: Chebyshev polynomial feature prediction (~3.5-5x speedup) - - Warmup: 5 steps always compute - - Fit: Chebyshev polynomials to cached features - - Predict: Use fitted model + Taylor extrapolation - - Adaptive windowing: Grows confidence over time - -### No MoE -- **Important**: Wan2.2 uses standard **gated FFN**, NOT Mixture of Experts -- FFN: [5120 β†’ 13824 β†’ 5120] with GELU activation -- No expert routing or sparse computation - -### Caching -- **Cross-attention K/V**: Pre-computed once (40 blocks Γ— 2 models) - - Eliminates 80 linear projections across all 40 denoising steps - - Storage: ~50 MB, saves ~30% of transformer compute - -- **RoPE Frequencies**: Pre-computed for constant grid sizes - - Eliminates per-step broadcast/concat - -- **Text Embeddings**: Pre-computed once - - T5 encoder runs once, freed immediately - - Reused 40 times across denoising - -### Configuration Variants -``` -Wan2.2 T2V 14B (default, dual-model) : dim=5120, heads=40, 40 layers -Wan2.2 I2V 14B (image-to-video) : in_dim=36, dual-model -Wan2.2 TI2V 5B (text+image, single) : dim=3072, heads=24, 30 layers -Wan2.1 T2V 14B (backward compat) : dim=5120, single-model -Wan2.1 T2V 1.3B (smaller) : dim=1536, 30 layers, single-model -``` - ---- - -## πŸ” Code Navigation - -### Main Entry Points -- **Generation**: `mlx_video/generate_wan.py` (38 KB) - - Main `generate_video()` function - - Denoising loop (lines 589-693) - -- **Model**: `mlx_video/models/wan/model.py` (518 lines) - - `WanModel` class (main forward pass) - - `TeaCacheState` (lines 15-59) - - `_patchify()` and `unpatchify()` methods - -### Architecture Components -- **Transformer Blocks**: `mlx_video/models/wan/transformer.py` (96 lines) - - `WanAttentionBlock` (self-attn + cross-attn + FFN) - - `WanFFN` (gated feed-forward) - -- **Attention**: `mlx_video/models/wan/attention.py` (207 lines) - - `WanSelfAttention` (QK norm + 3-way RoPE) - - `WanCrossAttention` (pre-cached K/V support) - -- **RoPE**: `mlx_video/models/wan/rope.py` (178 lines) - - 3-way factorized positional encoding - - Pre-computation utilities - -### Acceleration -- **TeaCache**: Lines 450-507 in `model.py` - - Block skipping logic - - Residual reuse - -- **Spectrum**: `mlx_video/models/wan/spectrum.py` (288 lines) - - `ChebyshevForecaster` (polynomial fitting) - - `SpectrumForecaster` (blended prediction) - - `SpectrumState` (scheduling) - -### Support -- **Configuration**: `mlx_video/models/wan/config.py` (157 lines) - - Model presets for T2V, I2V, TI2V variants - -- **Loading**: `mlx_video/models/wan/loading.py` (183 lines) - - Model loading with quantization support - - T5 encoder initialization - - Text encoding utilities - -- **Conversion**: `mlx_video/convert_wan.py` (27 KB) - - PyTorch β†’ MLX weight conversion - - Quantization configuration - - LoRA merging - -- **Schedulers**: `mlx_video/models/wan/scheduler.py` (428 lines) - - Flow matching: Euler, DPM++, UniPC - ---- - -## πŸ“Š At a Glance - -| Aspect | Details | -|--------|---------| -| **Model Type** | Diffusion Transformer (DiT-style) | -| **Conditioning** | Text (T5), Time (sinusoidal), Image (optional) | -| **Architecture** | 40 blocks Γ— 40 heads, dim=5120 | -| **Patch Size** | (1, 2, 2) temporal/height/width | -| **Dual Model** | Yes (high-noise, low-noise) | -| **Attention Types** | Self-attn (3-way RoPE), Cross-attn (text) | -| **FFN Type** | Standard gated (no MoE) | -| **Time Modulation** | 6 learned vectors per block | -| **Diffusion Steps** | 40 (customizable) | -| **Schedulers** | Euler, DPM++, UniPC | -| **CFG Support** | Yes (batch or disabled for 2x speedup) | -| **TeaCache** | 2-3x speedup with threshold | -| **Spectrum** | 3.5-5x speedup with adaptive windowing | -| **Quantization** | 4-bit or 8-bit on transformer blocks | -| **LoRA Support** | Yes (runtime or weight-merge) | -| **VAE** | Wan2.2: 48-dim latent (4Γ—16Γ—16), Wan2.1: 16-dim (4Γ—8Γ—8) | -| **Peak Memory** | ~74 GB (quantized: ~35 GB, with tiling: ~16 GB) | - ---- - -## πŸŽ“ Learning Path - -**For beginners:** -1. Start with **Quick Reference** (architecture overview) -2. Read **Architecture Diagrams** (visual understanding) -3. Reference specific details in **Exploration Summary** as needed - -**For implementers:** -1. **Exploration Summary** (complete context) -2. **Architecture Diagrams** (data flow verification) -3. Code navigation in **Quick Reference** - -**For optimizers:** -1. Memory Hierarchy (Architecture Diagrams section 8) -2. TeaCache/Spectrum (Quick Reference sections 6-7) -3. Pre-computation (Quick Reference section 8) -4. Acceleration mechanisms (Exploration Summary section 4) - ---- - -## πŸ”— Cross-References - -### Within Documentation -- Line numbers: Use "Ctrl+G" (VS Code) to jump to specific lines -- Search: Use "Ctrl+F" to find terms across files -- Sections: Use markdown outline navigation - -### To Source Code -- Files referenced in documentation have absolute paths -- All paths start with `/Users/daniel/Projects/mlx-video/` -- Use symbolic links or environment variables for portability - -### External References -- **Paper**: Spectrum (CVPR 2026, Han et al.) -- **Reference**: Wan2.2 official implementation (HuggingFace Diffusers) -- **Framework**: MLX (Apple Machine Learning Framework) - ---- - -## βœ… Completeness Checklist - -- [x] How Wan2.2 model works (architecture overview) -- [x] Denoising loop structure (complete walkthrough) -- [x] Transformer forward pass (all components) -- [x] 3-way factorized RoPE (detailed explanation) -- [x] TeaCache acceleration (implementation & theory) -- [x] Spectrum acceleration (Chebyshev + Taylor) -- [x] Model loading & configuration (all variants) -- [x] Generate scripts (pipeline stages) -- [x] MoE clarification (NO MoE present) -- [x] Pre-computation strategies (caching) -- [x] Memory hierarchy (resource planning) -- [x] File structure (complete navigation) -- [x] Visual diagrams (data flow) -- [x] Quick reference (command examples) -- [x] Cross-attention K/V caching -- [x] Frame alignment & VAE handling -- [x] Quantization & LoRA support - ---- - -## πŸ“ Notes - -- All documentation was created from source code analysis (no external copying) -- Line numbers and file paths are current as of March 6, 2025 -- Code examples are pseudocode/simplified for clarity (refer to source for exact implementation) -- Performance metrics (speedup, memory) are approximate and system-dependent - ---- - -Created: March 6, 2025 -Project: mlx-video -Format: Markdown -Total Documentation: ~3 files, ~75 KB, ~2000 lines diff --git a/docs/WAN22_ARCHITECTURE_DIAGRAMS.md b/docs/WAN22_ARCHITECTURE_DIAGRAMS.md deleted file mode 100644 index bfce13b..0000000 --- a/docs/WAN22_ARCHITECTURE_DIAGRAMS.md +++ /dev/null @@ -1,845 +0,0 @@ -# Wan2.2 Architecture Diagrams & Visualizations - -## 1. HIGH-LEVEL DATA FLOW - -``` -β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” -β”‚ GENERATION PIPELINE β”‚ -β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - -STAGE 1: ENCODING -═══════════════════════════════════════════════════════════════════════════════ - - TEXT PROMPT NOISE + I2V CONDITION - "A cat running" ──────┐ Random [48, T, H, W] ──────┐ - ↓ β”‚ ↓ β”‚ - T5 Encoder [2] VAE Encode [4] - (24 layers) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β†’ "Patchify" β”‚ - float32 β”‚ β”‚ Reshapeβ†’Linear Proj β”‚ - ↓ β”‚ β”‚ ↓ β”‚ - [512, 4096] β”‚ └─────[1, L, 5120] β”‚ - ↓ β”‚ β”‚ - Text MLP β”‚ β”‚ - (layer norm) β”‚ β”‚ - ↓ β”‚ β”‚ - [2, 512, 5120] β”‚ TIME EMBEDDING β”‚ - (cond+uncond) β”‚ ════════════════════════════ β”‚ - β”‚ β”‚ Timestep t ∈ [0, 1000] β”‚ - β”‚ β”‚ ↓ β”‚ - β”‚ β”‚ Sinusoidal Positional Encoding β”‚ - β”‚ β”‚ + MLP β”‚ - β”‚ β”‚ ↓ β”‚ - β”‚ β”‚ [B, 5120] (scalar for step) β”‚ - β”‚ β”‚ ↓ β”‚ - β”‚ β”‚ Time Projection (Γ—6 scale) β”‚ - β”‚ └─────→[B, 1, 6, 5120] β”‚ - β”‚ (6 modulation vectors per block) - β”‚ - └─────→ PREPARE K/V CACHES (once, pre-loop) - For each of 40 blocks, cross-attn: - k_cache, v_cache = block.cross_attn.prepare_kv(context_emb) - - -STAGE 2: DENOISING LOOP -═══════════════════════════════════════════════════════════════════════════════ - - FOR STEP t IN 40 TIMESTEPS: - - SELECT MODEL (dual-model only) - ═══════════════════════════════ - if t >= 875: - β”œβ”€ USE: HIGH_NOISE_MODEL (high-noise, timesteps 875-1000) - β”œβ”€ PURPOSE: Coarse structure generation - └─ GUIDANCE: guide_scale[1] - else: - β”œβ”€ USE: LOW_NOISE_MODEL (low-noise, timesteps 0-875) - β”œβ”€ PURPOSE: Fine detail refinement - └─ GUIDANCE: guide_scale[0] - - - FORWARD PASS - ═════════════════════════════════ - β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” - β”‚ INPUT: latents [B, L, 5120] β”‚ - β”‚ time_emb [B, 1, 6, 5120] β”‚ - β”‚ context [B, 512, 5120] β”‚ - β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - ↓ - β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” - β”‚ FOR i IN 0..39 BLOCKS: β”‚ - β”‚ β”‚ - β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ - β”‚ β”‚ MODULATION (6 vectors) β”‚ β”‚ - β”‚ β”‚ mod = self.modulation[i] + time_emb β”‚ β”‚ - β”‚ β”‚ e0,e1,e2,e3,e4,e5 = mod.split() β”‚ β”‚ - β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ - β”‚ ↓ β”‚ - β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ - β”‚ β”‚ SELF-ATTENTION (with modulation) β”‚ β”‚ - β”‚ β”‚ β”‚ β”‚ - β”‚ β”‚ x_norm = norm1(x) * (1 + e1) + e0 β”‚ β”‚ - β”‚ β”‚ attn_out = self_attn(x_norm) β”‚ β”‚ - β”‚ β”‚ x = x + e2 * attn_out (gated residual) β”‚ β”‚ - β”‚ β”‚ β”‚ β”‚ - β”‚ β”‚ Inside self_attn: β”‚ β”‚ - β”‚ β”‚ - Q, K, V projections β”‚ β”‚ - β”‚ β”‚ - QK normalization (RMS norm) β”‚ β”‚ - β”‚ β”‚ - Apply 3-way factorized RoPE β”‚ β”‚ - β”‚ β”‚ - Scaled-dot-product attention β”‚ β”‚ - β”‚ β”‚ - Output projection β”‚ β”‚ - β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ - β”‚ ↓ β”‚ - β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ - β”‚ β”‚ CROSS-ATTENTION (text conditioning) β”‚ β”‚ - β”‚ β”‚ β”‚ β”‚ - β”‚ β”‚ x_cross = norm3(x) if norm3 else x β”‚ β”‚ - β”‚ β”‚ cross_out = cross_attn(x_cross, context) β”‚ β”‚ - β”‚ β”‚ x = x + cross_out β”‚ β”‚ - β”‚ β”‚ β”‚ β”‚ - β”‚ β”‚ Inside cross_attn: β”‚ β”‚ - β”‚ β”‚ - Q from x_cross (use pre-cached K,V) β”‚ β”‚ - β”‚ β”‚ - K,V from context (from K/V cache!) β”‚ β”‚ - β”‚ β”‚ - Scaled-dot-product attention β”‚ β”‚ - β”‚ β”‚ - Output projection β”‚ β”‚ - β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ - β”‚ ↓ β”‚ - β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ - β”‚ β”‚ FFN (Gated Feed-Forward with modulation) β”‚ β”‚ - β”‚ β”‚ β”‚ β”‚ - β”‚ β”‚ x_norm = norm2(x) * (1 + e4) + e3 β”‚ β”‚ - β”‚ β”‚ ffn_out = fc2(GELU(fc1(x_norm))) β”‚ β”‚ - β”‚ β”‚ x = x + e5 * ffn_out (gated residual) β”‚ β”‚ - β”‚ β”‚ β”‚ β”‚ - β”‚ β”‚ fc1: [5120] β†’ [13824] β”‚ β”‚ - β”‚ β”‚ fc2: [13824] β†’ [5120] β”‚ β”‚ - β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ - β”‚ ↓ β”‚ - β”‚ [OPTIONAL] TeaCache/Spectrum decision β”‚ β”‚ - β”‚ (skip block or use prediction) β”‚ β”‚ - β”‚ β”‚ - β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - ↓ - β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” - β”‚ OUTPUT HEAD β”‚ - β”‚ ═════════════════════════════════════════ β”‚ - β”‚ x_out = head(x, time_emb) β”‚ - β”‚ β”‚ - β”‚ Inside head: β”‚ - β”‚ - x_norm = norm(x) β”‚ - β”‚ - Modulation with time_emb β”‚ - β”‚ - Linear proj: [5120] β†’ [out_tokens] β”‚ - β”‚ ↓ β”‚ - β”‚ [B, L, 64] β”‚ - β”‚ (16 channels Γ— patch_size) - β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - - - CLASSIFIER-FREE GUIDANCE - ═════════════════════════════════ - if CFG_DISABLED (guide_scale ≀ 1.0): - noise_pred = output_uncond # Just uncond, B=1 forward - else: - noise_pred_cond = output[0] - noise_pred_uncond = output[1] - noise_pred = noise_uncond + guide_scale Γ— (cond - uncond) - - - SCHEDULER STEP - ═════════════════════════════════ - latents = scheduler.step(noise_pred, timestep, latents) - - # Euler: x_next = x + (Οƒ_next - Οƒ_cur) * v - # DPM++: x_next with 2nd-order correction - # UniPC: Higher-order solver - - - [OPTIONAL] I2V MASK REAPPLY (TI2V-5B only) - ═════════════════════════════════════════════ - if is_i2v_mask_blend: - latents = (1 - mask) * z_img + mask * latents - - -STAGE 3: DECODING -═══════════════════════════════════════════════════════════════════════════════ - - Latents [48, T_lat, H_lat, W_lat] (output of denoising loop) - ↓ - Denormalize (per-channel mean/std) - ↓ - Wan2.2 VAE Decoder: - - CausalConv3d blocks (per-frame + temporal) - - Upsampling Γ— 4 (time), Γ— 16 (spatial) - ↓ - Video Frames [3, T_pix, H_pix, W_pix] - ↓ - Save MP4 -``` - ---- - -## 2. SINGLE TRANSFORMER BLOCK INTERNALS - -``` -β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” -β”‚ WANATTENTIONBLOCK FORWARD PASS β”‚ -β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - -INPUT -══════════════════════════════════════════════════════════════════════════ -x [B, L, 5120] ← hidden states (latent patch embeddings) -e [B, 1, 6, 5120] ← modulation vectors (time-conditioned) -context [B, 512, 5120] ← text embeddings (from T5) -cross_kv_cache: (k, v) tuples ← pre-computed cross-attention caches - - -STEP 1: COMPUTE MODULATION VECTORS -════════════════════════════════════════════════════════════════════════ - - mod = self.modulation[1, 6, 5120] + e[B, 1, 6, 5120] - ↓ - Broadcast + element-wise add: [1, 6, 5120] β†’ [B, 1, 6, 5120] - ↓ - Extract 6 vectors: - β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” - β”‚ e0 = mod[:, 0, :] [B, 5120] shift β”‚ - β”‚ e1 = mod[:, 1, :] [B, 5120] scale β”‚ - β”‚ e2 = mod[:, 2, :] [B, 5120] gate β”‚ - β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€ - β”‚ e3 = mod[:, 3, :] [B, 5120] shift β”‚ - β”‚ e4 = mod[:, 4, :] [B, 5120] scale β”‚ - β”‚ e5 = mod[:, 5, :] [B, 5120] gate β”‚ - β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - ↓ - These control self-attn, cross-attn, and FFN paths - - -STEP 2: SELF-ATTENTION WITH MODULATION -════════════════════════════════════════════════════════════════════════ - - Input residual saved: - residual1 = x - - Apply affine transformation: - x_norm = norm1(x) [B, L, 5120] (layer norm) - x_norm = x_norm * (1 + e1) (scale by learned gain) - x_norm = x_norm + e0 (shift by learned bias) - - Self-attention: - x_attn = self_attn(x_norm, grid_sizes, freqs, rope_cos_sin, attn_mask) - β”œβ”€ Q = linear(x_norm) - β”œβ”€ K = linear(x_norm) - β”œβ”€ V = linear(x_norm) - β”œβ”€ QK norm (RMS) - β”œβ”€ Apply 3-way RoPE (temporal, height, width) - β”œβ”€ Scaled-dot-product attention - └─ Output projection - - Apply gated residual: - x = residual1 + e2 * x_attn (gate controls contribution) - - -STEP 3: CROSS-ATTENTION (TEXT CONDITIONING) -════════════════════════════════════════════════════════════════════════ - - (Optional norm, depends on cross_attn_norm flag) - x_cross = norm3(x) if norm3 else x - - Cross-attention: - x_cross_out = cross_attn(x_cross, context, kv_cache=cross_kv_cache) - β”œβ”€ Q = linear(x_cross) [B, L, 5120] - β”œβ”€ K, V from cache: - β”‚ (if kv_cache) use pre-computed [B, 40, 512, 128] - β”‚ (else) compute from context - β”œβ”€ Scaled-dot-product attention - └─ Output projection - - Residual: - x = x + x_cross_out (no gating on cross-attn) - - -STEP 4: FFN WITH MODULATION -════════════════════════════════════════════════════════════════════════ - - Input residual saved: - residual2 = x - - Apply affine transformation: - x_norm = norm2(x) - x_norm = x_norm * (1 + e4) (scale) - x_norm = x_norm + e3 (shift) - - FFN: - x_ffn = fc2(GELU(fc1(x_norm))) - β”œβ”€ fc1: [5120] β†’ [13824] (4Γ— expansion) - β”œβ”€ GELU activation (approx='tanh') - └─ fc2: [13824] β†’ [5120] (project back) - - Apply gated residual: - x = residual2 + e5 * x_ffn (gate controls contribution) - - -OUTPUT -══════════════════════════════════════════════════════════════════════════ -x [B, L, 5120] ← updated hidden states (one block's output = next block's input) -``` - ---- - -## 3. SELF-ATTENTION WITH 3-WAY FACTORIZED ROPE - -``` -β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” -β”‚ WAN SELF-ATTENTION WITH 3-WAY ROPE β”‚ -β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - -ROPE PRECOMPUTATION (once per generation) -═════════════════════════════════════════════════════════════════════════ - -Grid sizes: [F_grid, H_grid, W_grid] e.g., [3, 10, 20] for (4D, 2D, 2D) -Seq length: L = F_grid * H_grid * W_grid = 3 Γ— 10 Γ— 20 = 600 - -Freqs split into 3 parts: - head_dim_half = 64 - d_t = 64 - 2Γ—(64//3) β‰ˆ 38 (temporal gets more capacity) - d_h = 64 // 3 β‰ˆ 21 (height) - d_w = 64 // 3 β‰ˆ 21 (width) - -For each position (f, h, w) ∈ grid: - β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” - β”‚ Temporal component: freqs_t[f] β”‚ - β”‚ Height component: freqs_h[h] β”‚ - β”‚ Width component: freqs_w[w] β”‚ - β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - Concatenate: [38 + 21 + 21 = 64] dimensions - - -SELF-ATTENTION COMPUTATION -═════════════════════════════════════════════════════════════════════════ - -Inputs: - x [B, L, 5120] - grid_sizes = [F_grid, H_grid, W_grid] - freqs [1024, 64, 2] (precomputed cos/sin pairs) - rope_cos_sin (precomputed, optional) - attn_mask [B, 1, 1, L] (optional, for variable seq lengths) - -Step 1: Project to Q, K, V - Q = linear(x) [B, L, 5120] - K = linear(x) [B, L, 5120] - V = linear(x) [B, L, 5120] - -Step 2: Apply QK normalization (RMS norm) - Q = norm_q(Q) (if qk_norm=True) - K = norm_k(K) (if qk_norm=True) - -Step 3: Reshape to heads - Q = Q.reshape(B, L, 40, 128) [B, 40, L, 128] - K = K.reshape(B, L, 40, 128) - V = V.reshape(B, L, 40, 128) - -Step 4: Apply 3-way RoPE - β”Œβ”€ Compute cos/sin for each (f, h, w) position - β”œβ”€ Three separate frequency components - β”œβ”€ Interleaved as: [temp freq] + [height freq] + [width freq] - └─ Rotate Q and K by these frequencies (complex multiplication) - - For each head and position: - cos_t, sin_t = freqs_t[f] (temporal rotation) - cos_h, sin_h = freqs_h[h] (height rotation) - cos_w, sin_w = freqs_w[w] (width rotation) - - Apply rotation: z' = z * e^(iΞΈ) = cos(ΞΈ)z + sin(ΞΈ)z_rotated - -Step 5: Scaled-dot-product attention - scale = 1/√(head_dim) = 1/√128 β‰ˆ 0.088 - - scores = Q @ K^T Γ— scale [B, 40, L, L] - - (Optional) Add attention mask: - scores[i, :, :, j:] += mask[i, :, :, j:] (βˆ’βˆž for masked positions) - - attn = softmax(scores, dim=-1) - out = attn @ V - -Step 6: Output projection - out = out.reshape(B, L, 5120) - out = linear(out) [B, L, 5120] - -Output: [B, L, 5120] ← gated and summed into residual -``` - ---- - -## 4. CROSS-ATTENTION K/V CACHING - -``` -β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” -β”‚ CROSS-ATTENTION K/V PRE-COMPUTATION β”‚ -β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - -NORMAL (UNCACHED) CROSS-ATTENTION -═════════════════════════════════════════════════════════════════════════ - -For each denoising step: - Q = linear(x) [B, L, 5120] - K = linear(context) [B, 512, 5120] ← EXPENSIVE! (40 times) - V = linear(context) [B, 512, 5120] ← EXPENSIVE! (40 times) - attn = Q @ K^T - out = softmax(attn) @ V - -Cost: 2 Γ— 40 = 80 linear projections across all blocks and steps - - -WAN2.2 OPTIMIZATION: PRE-COMPUTE K/V -═════════════════════════════════════════════════════════════════════════ - -Step 1: Before denoising loop (once per generation) - - context_emb = embed_text([text, neg_text]) [2, 512, 5120] - - For each block (40 blocks): - K = block.cross_attn.linear_k(context_emb) [2, 512, 5120] - V = block.cross_attn.linear_v(context_emb) [2, 512, 5120] - - Reshape to attention format: - K = K.reshape(2, 512, 40_heads, 128).transpose(0, 2, 1, 3) - = [2, 40, 512, 128] ← ready for attention - V = V.reshape(2, 512, 40_heads, 128).transpose(0, 2, 1, 3) - = [2, 40, 512, 128] - - cross_kv_caches[block_idx] = (K, V) - - Total storage: 40 blocks Γ— 2 tensors Γ— [2, 40, 512, 128] - = 40 Γ— 2 Γ— 2 Γ— 40 Γ— 512 Γ— 128 β‰ˆ 13 million params (cache) - - -Step 2: During denoising loop (for each step, 40 times) - - For each block: - K, V = cross_kv_caches[block_idx] ← retrieve cached - - Q = block.cross_attn.linear_q(x) [B, L, 5120] - Q = Q.reshape(B, L, 40_heads, 128).transpose(0, 2, 1, 3) - = [B, 40, L, 128] - - attn = Q @ K^T / √128 [B, 40, L, 512] - out = softmax(attn) @ V [B, 40, L, 128] - out = out.transpose(...).reshape(B, L, 5120) - out = block.cross_attn.linear_out(out) ← only this projection per step - - -Benefit: - OLD: 80 linear projections (K and V, all 40 steps, both models) - NEW: 2 linear projections (K and V, once before loop) - - Speedup factor: 40Γ— on context projection overhead! - (Text encoder inference is free after T5 embed) -``` - ---- - -## 5. TEACACHE vs SPECTRUM vs NO CACHING - -``` -β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” -β”‚ CACHING COMPARISON β”‚ -β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - -NO CACHING (Default, if no acceleration) -═══════════════════════════════════════════════════════════════════════════ - - FOR EACH STEP (40 iterations): - RUN ALL 40 TRANSFORMER BLOCKS - β”œβ”€ 40 self-attn forward passes - β”œβ”€ 40 cross-attn forward passes - β”œβ”€ 40 FFN forward passes - └─ Total: 120 forward passes per step - - Total forwards: 40 steps Γ— 120 = 4,800 block forwards - - -TEACACHE ACCELERATION (2-3x speedup) -═══════════════════════════════════════════════════════════════════════════ - - Pre-profile: Train polynomial to map - rel_l1_distance(time_embedding) β†’ output_distance - - FOR EACH STEP: - β”œβ”€ Compute e0 (projected time embedding) - β”œβ”€ Compare with previous e0: - β”‚ rel_l1 = ||e0 - prev_e0|| / ||prev_e0|| - β”œβ”€ Map through polynomial: - β”‚ rescaled_distance = poly(rel_l1) - β”œβ”€ Accumulate: - β”‚ total_distance += rescaled_distance - β”‚ - β”œβ”€ DECISION: - β”‚ if total_distance < threshold: - β”‚ SKIP ALL BLOCKS - β”‚ x = x + cached_residual ← instant (no transformer) - β”‚ else: - β”‚ RUN ALL 40 BLOCKS (as normal) - β”‚ cached_residual = x_output - x_input - β”‚ total_distance = 0 (reset) - β”‚ - └─ Statistics: - β”œβ”€ threshold=0.1 β†’ ~40% of steps skipped β†’ 2x speedup - β”œβ”€ threshold=0.2 β†’ ~60% of steps skipped β†’ 3x speedup - └─ Higher threshold = more skips = faster but lower quality - - Why it works: - Time embeddings change smoothly across steps. - When change is small, relative contribution to output is small. - Cached residual from previous step is a good approximation! - - -SPECTRUM ACCELERATION (3.5-5x speedup) -═══════════════════════════════════════════════════════════════════════════ - - Warmup (steps 0-4): - └─ RUN ALL BLOCKS, cache features and timesteps - - Fitting (online, after each compute): - β”œβ”€ Collect: (t_i, h_i) pairs - β”œβ”€ Fit Chebyshev polynomial: h = Ξ£ c_k T_k(Ο„) - β”‚ where Ο„ ∈ [-1, 1] normalized timestep - β”‚ Solve: (X^T X + Ξ»I) c = X^T h (Cholesky-based ridge) - └─ Storage: coefficients (P=M+1 params per feature) - - Prediction (steps 5+): - β”œβ”€ Use adaptive windowing: - β”‚ window_size starts at 2 - β”‚ grows by flex_window (0.75) after each compute - β”‚ Example: 2, 2.75, 3.5, 4.25, 5.0, ... - β”‚ - β”œβ”€ Prediction happens every window_size steps - β”‚ PREDICT: - β”‚ h_taylor = h_recent + (step - t_recent) * dh/dt - β”‚ h_cheb = sum(c_k * T_k(Ο„_step)) - β”‚ h_pred = (1-w) * h_taylor + w * h_cheb - β”‚ x = x + h_pred ← no transformer needed! - β”‚ - └─ Compute only on boundaries (every ~2-5 steps) - - Results: - β”œβ”€ flex_window=0.75 β†’ ~3.5x speedup (high quality) - β”œβ”€ flex_window=3.0 β†’ ~5x speedup (experimental, lower quality) - └─ Warmup prevents cold-start artifacts - - -COMPARISON TABLE -═══════════════════════════════════════════════════════════════════════════ - -Method β”‚ Speedup β”‚ Quality β”‚ Implementation Complexity -──────────────┼──────────┼─────────┼───────────────────────────── -No caching β”‚ 1.0x β”‚ Reference -TeaCache β”‚ 2-3x β”‚ Good β”‚ Polynomial threshold (pre-profiled) -Spectrum β”‚ 3.5-5x β”‚ Great β”‚ Chebyshev+Taylor blending -──────────────┴──────────┴─────────┴───────────────────────────── -``` - ---- - -## 6. LATENT SPACE PATCHIFICATION - -``` -β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” -β”‚ PATCHIFY OPERATION β”‚ -β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - -INPUT: Video latent [C, F, H, W] -═════════════════════════════════════════════════════════════════════════ - -Example: [48, 21, 90, 160] - C = 48 (latent channel depth, from VAE) - F = 21 (temporal frames in latent space) - H = 90 (height in latent space) - W = 160 (width in latent space) - -Patch size = (1, 2, 2) - pt = 1 (temporal patch size) - ph = 2 (height patch size) - pw = 2 (width patch size) - - -RESHAPE OPERATION -═════════════════════════════════════════════════════════════════════════ - -Step 1: Compute output grid dimensions - f_out = F / pt = 21 / 1 = 21 - h_out = H / ph = 90 / 2 = 45 - w_out = W / pw = 160 / 2 = 80 - - grid_size = (f_out, h_out, w_out) = (21, 45, 80) - total_patches = 21 Γ— 45 Γ— 80 = 75,600 patches - - -Step 2: Reshape to separate patch dimensions - [48, 21, 90, 160] - ↓ - Reshape: [48, 21Γ·1, 1, 90Γ·2, 2, 160Γ·2, 2] - = [48, 21, 1, 45, 2, 80, 2] - ↓ - Transpose to group: [F', H', W', C, pt, ph, pw] - [21, 45, 80, 48, 1, 2, 2] - - -Step 3: Flatten patch dimension - [21, 45, 80, 48, 1, 2, 2] - ↓ - Reshape to: [21Γ—45Γ—80, 48Γ—1Γ—2Γ—2] - = [75600, 192] (L, CΓ—patch_product) - - -Step 4: Project through linear layer - weights: [5120, 192] (output_dim, input_dim) - [75600, 192] @ [192, 5120] - ↓ - [75600, 5120] (L, dim) - - -Step 5: Add batch dimension - [1, 75600, 5120] (batch_size=1, seq_len=L, dim) - -OUTPUT -═════════════════════════════════════════════════════════════════════════ - -patches: [1, 75600, 5120] -grid_sizes: (21, 45, 80) - -Used for: - β”œβ”€ Input to transformer blocks (L = 75,600 tokens) - β”œβ”€ RoPE grid construction - β”œβ”€ Attention masking for variable lengths - └─ Unpatchify to reconstruct video - - -UNPATCHIFY (REVERSE OPERATION) -═════════════════════════════════════════════════════════════════════════ - -Input: [B, L, out_dim * patch_product] - [1, 75600, 64] (where 64 = 16 channels Γ— patch_product) - -Step 1: Extract sequence length for this batch - seq_len = grid_size[0] * grid_size[1] * grid_size[2] - = 21 * 45 * 80 = 75600 - -Step 2: Reshape - [75600, 64] β†’ [21, 45, 80, 1, 2, 2, 16] - (f, h, w, pt, ph, pw, C) - -Step 3: Transpose to group channels and patch dims - [16, 21, 1, 45, 2, 80, 2] (C, F', pt, H', ph, W', pw) - -Step 4: Flatten to reconstruct spatial dimensions - [16, 21Γ—1, 45Γ—2, 80Γ—2] - [16, 21, 90, 160] (C, F, H, W) - -Output: [16, 21, 90, 160] ← back to latent space - -This is then decoded through VAE decoder to get pixel-space video. -``` - ---- - -## 7. FRAME ALIGNMENT & EXTRA FRAMES - -``` -β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” -β”‚ FRAME HANDLING IN WAN2.2 β”‚ -β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - -USER REQUEST: num_frames=81 - -VAE STRIDE: (4, 16, 16) for Wan2.2 - -COMPUTE LATENT FRAMES -═════════════════════════════════════════════════════════════════════════ - -User requested 81 pixel frames with VAE stride 4 in temporal: - - gen_frames = 81 - extra_frames = 4 (VAE stride[0]) ← T2V: add extra for boundary artifacts - - total_latent_frames = gen_frames + extra_frames = 85 pixel frames - t_latent = (total_latent_frames - 1) // 4 + 1 - = (85 - 1) // 4 + 1 - = 84 // 4 + 1 - = 21 + 1 = 22 latent frames - -WHY EXTRA FRAMES? -───────────────── -The VAE uses causal convolution (temporal causality). -At boundaries (first/last frame), padding creates artifacts. -Extra frames are generated and later discarded: - - First 4 extra frames absorbed by VAE padding - - Original 81 frames remain clean - - Final 4 frames discarded - -For I2V (image-to-video): - - extra_frames = 0 (reference image provides real first frame) - - No need for padding - - -ALIGNMENT REQUIREMENT -═════════════════════════════════════════════════════════════════════════ - -Dimensions must align to patch_size Γ— vae_stride: - - Spatial: - align_h = patch_size[1] * vae_stride[1] = 2 * 16 = 32 - align_w = patch_size[2] * vae_stride[2] = 2 * 16 = 32 - - User requests: 720 Γ— 1280 - - Check: - 720 % 32 = 0 βœ“ - 1280 % 32 = 0 βœ“ - - If not aligned, auto-round down: - height_aligned = (720 // 32) * 32 = 22 * 32 = 704 - width_aligned = (1280 // 32) * 32 = 40 * 32 = 1280 - - Temporal: (not usually constrained for T2V, constrained for I2V) - - -EXAMPLE GENERATION -═════════════════════════════════════════════════════════════════════════ - -User requests: 1280Γ—720, 81 frames, T2V - -Alignment check: - βœ“ 1280 % 32 = 0 - βœ“ 720 % 32 = 0 - -Dimension computation: - vae_stride = (4, 16, 16) - gen_frames = 81 + 4 = 85 - - h_latent = 720 / 16 = 45 - w_latent = 1280 / 16 = 80 - t_latent = (85 - 1) / 4 + 1 = 22 - z_dim = 48 - - target_shape = (48, 22, 45, 80) - -Patchification: - patch_size = (1, 2, 2) - f_grid = 22 / 1 = 22 - h_grid = 45 / 2 = 22.5 ← ERROR! Not divisible - - Actually: h_grid = 45 / 2 = 22 (integer division) - Remainder frames are padding (handled by attn_mask) - -Generation: - Denoising loop: 40 steps - β†’ Output shape: [48, 22, 45, 80] latent frames - -VAE Decode: - [48, 22, 45, 80] - ↓ Upscale spatial Γ— 16: [48, 22, 720, 1280] - ↓ Upscale temporal Γ— 4: [48, 88, 720, 1280] - - (Extra frames included; trim after decode) - ↓ Final: [3, 81, 720, 1280] (RGB, 81 frames) -``` - ---- - -## 8. MEMORY HIERARCHY - -``` -β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” -β”‚ MEMORY USAGE BREAKDOWN β”‚ -β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - -Model weights (Wan2.2 T2V 14B Γ— 2 models): - High-noise model: - Transformer (40 blocks): ~26 GB (bf16) - Text embeddings: ~1 MB - Low-noise model: - Transformer (40 blocks): ~26 GB (bf16) - Text embeddings: ~1 MB - - With 4-bit quantization: ~6.5 GB per model (80% reduction) - - -Temporary buffers (per denoising step): - Input latents: [2, 48, 22, 45, 80] = ~7.5 MB (CFG batch) - Patches: [2, 75600, 5120] = ~1.5 GB - Transformer hidden states: [2, 75600, 5120] = ~1.5 GB - Attention maps: [2, 40 heads, 75600, 75600] = ~21 GB ← peak! - - Intermediate buffers are discarded after each step. - - -Pre-computed caches (reused across all steps): - Text embeddings (embedded T5 output): - [2, 512, 5120] per model = ~10 MB - - Cross-attn K/V caches (all 40 blocks): - Per model: 40 blocks Γ— 2 tensors Γ— [2, 40, 512, 128] = ~20 MB - Total (2 models): ~40 MB - - RoPE frequencies: - [seq_len, 1, head_dim/2] β‰ˆ 3 MB - - Total cache: ~50 MB (negligible compared to model weights) - - -PEAK MEMORY ESTIMATE -═════════════════════════════════════════════════════════════════════════ - -Scenario: Wan2.2 T2V 14B, dual-model, no quantization - - Model weights (in memory): 2 Γ— 26 GB = 52 GB - Transformer hidden states: ~1.5 GB - Attention maps (peak): ~21 GB - ───────────────────────────────────── - Total peak: ~74.5 GB ← too much for most GPUs! - - Solutions: - β”œβ”€ Quantization (4-bit): 2 Γ— 6.5 GB = 13 GB + temp = ~35 GB βœ“ - β”œβ”€ Single model (no dual): 26 GB + temp = ~48 GB βœ“ - β”œβ”€ Spectrum (skip blocks): reduces temp buffers - └─ Tiling VAE (spatial/temporal): processes in chunks - -With 4-bit quantization + Apple Silicon optimizations: - β†’ Fits in ~8-16 GB systems - - -MEMORY TIMELINE -═════════════════════════════════════════════════════════════════════════ - -Phase 1: T5 Encoding - β”œβ”€ Load T5: ~15 GB - β”œβ”€ Encode prompts: ~1 GB temp - └─ Free T5: Release 15 GB βœ“ - -Phase 2: Model Loading - β”œβ”€ Load transformers: ~13 GB (quantized) - β”œβ”€ Precompute caches: ~50 MB - └─ Total: ~13 GB - -Phase 3: Denoising Loop (per step) - β”œβ”€ Latents: ~7 MB - β”œβ”€ Transformer forward: ~1.5 GB peak - β”œβ”€ Clean temp: Release between steps - └─ Steady state: ~13 GB - -Phase 4: VAE Decode - β”œβ”€ Load VAE: ~2 GB - β”œβ”€ Decode latents: ~2-3 GB - β”œβ”€ Free transformers: Release ~13 GB - └─ Total: ~4 GB - -Phase 5: Video Saving - β”œβ”€ Write MP4: CPU-based, minimal GPU memory - └─ ~500 MB for frame buffer -``` - ---- - -Created: March 6, 2025 -Project: mlx-video (Wan2.2 Implementation on MLX) -Filename: WAN22_ARCHITECTURE_DIAGRAMS.md diff --git a/docs/WAN22_EXPLORATION_SUMMARY.md b/docs/WAN22_EXPLORATION_SUMMARY.md deleted file mode 100644 index 718d4f7..0000000 --- a/docs/WAN22_EXPLORATION_SUMMARY.md +++ /dev/null @@ -1,929 +0,0 @@ -# Wan2.2 Model Architecture & Implementation - Comprehensive Exploration - -## Executive Summary -The mlx-video project contains a complete implementation of Wan2.2 (Text-to-Video & Image-to-Video generation) diffusion models on Apple Silicon using MLX. The architecture includes dual-model configurations, advanced caching mechanisms (TeaCache and Spectrum), and supports both quantization and LoRA fine-tuning. - ---- - -## 1. HOW WAN2.2 MODEL WORKS - -### 1.1 High-Level Architecture -Wan2.2 is a **flow-matching diffusion model** with: -- **Dual-model configuration**: Separate high-noise and low-noise transformer models for different stages of denoising -- **Single-model fallback**: Wan2.1 uses single transformer for compatibility -- **Condition types**: Text embeddings (from UMT5-XXL T5 encoder) + time embeddings + optional image conditioning (I2V) - -### 1.2 Input Flow (Text-to-Video Pipeline) -``` -User Prompt β†’ T5 Encoder β†’ Text Embeddings [B, text_len, 4096] - ↓ - Embed Layer (linear projection) - ↓ - Transformer-friendly [B, text_len, dim] - -Noise [C, T, H, W] β†’ VAE Encode β†’ Latents [z_dim, T_latent, H_latent, W_latent] - ↓ - Patchify - ↓ - Patch Embeddings [1, L, dim] - -Time Step (continuous 0-1000) β†’ Sinusoidal Embedding [B, freq_dim] - ↓ - Time embedding MLP β†’ [B, dim] - ↓ - Time projection (6x scale) β†’ [B, 1, 6, dim] -``` - -### 1.3 Core Configuration -**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/config.py` - -Key Wan2.2 T2V 14B configuration: -- **Model Dimension**: 5120 -- **Number of Transformer Blocks**: 40 -- **Attention Heads**: 40 (head_dim = 128) -- **FFN Dimension**: 13,824 -- **Patch Size**: (1, 2, 2) - temporal, height, width -- **Input Channels**: 16 (VAE latent depth) -- **Output Channels**: 16 -- **Text Length**: 512 tokens -- **Text Embedding Dimension**: 4096 (from T5) -- **Frequency Dimension**: 256 (for time embedding sinusoids) -- **Number of Training Timesteps**: 1000 - -**Configuration Variants**: -```python -WanModelConfig.wan22_t2v_14b() # Text-to-Video (default, dual-model) -WanModelConfig.wan22_i2v_14b() # Image-to-Video (dual-model, in_dim=36) -WanModelConfig.wan22_ti2v_5b() # Text+Image-to-Video (single-model, dim=3072) -WanModelConfig.wan21_t2v_14b() # Wan2.1 14B (single-model) -WanModelConfig.wan21_t2v_1_3b() # Wan2.1 1.3B (single-model, dim=1536) -``` - -### 1.4 Dual Model Architecture (Wan2.2 Specific) -**File**: `/Users/daniel/Projects/mlx-video/mlx_video/generate_wan.py` lines 407-419 - -Wan2.2 uses **adaptive model switching** based on noise levels: -- **High-Noise Model** (early denoising steps): Handles coarse structure generation from pure noise - - Used when: `timestep_val >= boundary` (default: 87.5% of training timesteps) - - More sensitive to global structure - -- **Low-Noise Model** (late denoising steps): Handles fine details and refinement - - Used when: `timestep_val < boundary` - - More sensitive to local details - -**Boundary Calculation**: `boundary = config.boundary * config.num_train_timesteps = 0.875 * 1000 = 875` - -**Guidance Scales**: Can differ per model -- Example: `guide_scale=(3.0, 4.0)` means (low_noise_scale, high_noise_scale) - ---- - -## 2. DENOISING LOOP STRUCTURE - -### 2.1 Main Denoising Loop -**File**: `/Users/daniel/Projects/mlx-video/mlx_video/generate_wan.py` lines 589-693 - -```python -for i, t in enumerate(tqdm(range(steps), desc="Diffusion")): - timestep_val = timestep_list[i] - - # Select model based on noise level - if is_dual: - if timestep_val >= boundary: - model = high_noise_model - kv = cross_kv_high - rcs = rope_cos_sin_high - else: - model = low_noise_model - kv = cross_kv_low - rcs = rope_cos_sin_low - - # CFG or direct inference - if cfg_disabled: - # B=1 forward (faster) - noise_pred = forward_pass([latents], timestep_val, context_cond) - else: - # B=2 forward: conditional + unconditional - noise_pred = forward_pass( - [latents, latents], - [timestep_val, timestep_val], - [context_cond, context_uncond] - ) - # Classifier-free guidance - noise_pred = noise_uncond + guide_scale * (noise_cond - noise_uncond) - - # Scheduler step (Euler, DPM++, or UniPC) - latents = scheduler.step(noise_pred, timestep_val, latents) - - # (I2V only) Freeze first frame - if is_i2v_mask_blend: - latents = (1 - mask) * z_img + mask * latents -``` - -### 2.2 Timestep Handling -- **Timesteps are contiguous values** (not discrete indices) from 0-1000 -- **Reverse scheduling**: Start at high noise (β‰ˆ1000), end at low noise (β‰ˆ0) -- **Scheduler converts to sigma** (noise level): Used for denoising step size -- **Step index tracking**: Pre-converted to Python list to avoid GPU sync per step - ```python - timestep_list = sched.timesteps.tolist() # Pre-convert to avoid .item() sync - ``` - -### 2.3 Batch Processing Optimization (CFG) -**No-CFG Mode** (`guide_scale ≀ 1.0`): -- Batch size = 1 (only conditional) -- **2x faster** than CFG mode -- Skips unconditional forward pass - -**CFG Mode** (standard): -- Batch size = 2 (conditional + unconditional in same forward) -- Single forward pass processes both, then blends - -### 2.4 Pre-computation to Avoid Per-Step Overhead -**Text Embeddings**: -```python -context_emb = model.embed_text([context, context_null]) # Once, reused all steps -``` - -**Cross-Attention K/V Cache**: -```python -cross_kv = model.prepare_cross_kv(context_emb) # Once, indices passed to each block -``` - -**RoPE Frequencies**: -```python -rope_cos_sin = model.prepare_rope(grid_sizes) # Once (grid sizes constant) -``` - ---- - -## 3. TRANSFORMER FORWARD PASS FOR WAN2.2 - -### 3.1 Main Forward Pass -**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/model.py` lines 295-518 - -```python -def __call__( - self, - x_list: list, # Video latents [C, F, H, W] - t: mx.array, # Timesteps [B] or [B, L] - context: list | mx.array, # Text embeddings [B, text_len, dim] - seq_len: int, # Max sequence length - cross_kv_caches: list | None = None, # Pre-computed K/V - y: list | None = None, # I2V conditioning - rope_cos_sin: tuple | None = None, # Pre-computed RoPE -) β†’ list: -``` - -### 3.2 Input Processing - -**Step 1: Detect CFG Batching** -- Check if all batch elements are identical (common for CFG batch of 2) -- Broadcast optimization if `all_same` - -**Step 2: Channel Concatenation (I2V)** -```python -if y is not None: # Image-to-Video conditioning - x_list = [mx.concatenate([u, v], axis=0) for u, v in zip(x_list, y)] - # Now x has extra channels from conditioning image -``` - -**Step 3: Patchification** -```python -# Input: [C, F, H, W] -# Output: [1, L, dim] where L = (F/patch_t) * (H/patch_h) * (W/patch_w) -patches, grid_sizes = model._patchify(x_list[0]) -``` - -### 3.3 Transformer Block Structure -**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/transformer.py` lines 7-96 - -Each `WanAttentionBlock` contains: - -```python -class WanAttentionBlock(nn.Module): - def __init__(self, dim, ffn_dim, num_heads, ...): - # Self-attention path - self.norm1 = WanLayerNorm(dim) - self.self_attn = WanSelfAttention(dim, num_heads) - - # Cross-attention path - self.norm3 = WanLayerNorm(dim) if cross_attn_norm else None - self.cross_attn = WanCrossAttention(dim, num_heads) - - # FFN path - self.norm2 = WanLayerNorm(dim) - self.ffn = WanFFN(dim, ffn_dim) - - # Learned modulation (6 vectors Γ— dim) - self.modulation = mx.random.normal((1, 6, dim)) * (dim**-0.5) - - def __call__(self, x, e, context, cross_kv_cache=None, ...): - # Modulation (compute in float32 for precision) - mod = (self.modulation + e) # Add time embedding - e0, e1, e2, e3, e4, e5 = mod.split() # 6 modulation vectors - - # Self-attention with modulation - x_norm = self.norm1(x) * (1 + e1) + e0 # Affine transform - x = x + e2 * self.self_attn(x_norm) - - # Cross-attention (conditioned on text) - x_norm = self.norm3(x) if self.norm3 else x - x = x + self.cross_attn(x_norm, context, kv_cache=cross_kv_cache) - - # FFN with modulation - x_norm = self.norm2(x) * (1 + e4) + e3 - x = x + e5 * self.ffn(x_norm) - - return x -``` - -### 3.4 Block Processing Loop -**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/model.py` lines 508-511 - -Standard path (no caching): -```python -for i, block in enumerate(self.blocks): - kv = cross_kv_caches[i] if cross_kv_caches is not None else None - x = block(x, cross_kv_cache=kv, **kwargs) -``` - -With **TeaCache** enabled (lines 450-507): -- Skip blocks if time embedding change is small -- Reuse residual from previous step - -With **Spectrum** enabled (lines 430-449): -- Predict features with Chebyshev polynomials -- Full compute only at strategic intervals - -### 3.5 Learned Modulation (DiT-style) -The 6 modulation vectors control: -1. **e0**: Self-attention shift (residual connection bias) -2. **e1**: Self-attention scale (learnable gain) -3. **e2**: Self-attention gate (residual weight) -4. **e3**: FFN shift -5. **e4**: FFN scale -6. **e5**: FFN gate - -This is a **Diffusion Transformer (DiT)** style architecture where modulation is **per-block, not per-layer-pair**. - -### 3.6 Self-Attention Details -**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/attention.py` lines 46-121 - -```python -class WanSelfAttention(nn.Module): - def __call__(self, x, seq_lens, grid_sizes, freqs, rope_cos_sin=None, attn_mask=None): - b, s, _ = x.shape - - # Project to Q, K, V - q = self.q(x) - k = self.k(x) - v = self.v(x) - - # QK normalization (optional) - if self.norm_q: - q = self.norm_q(q) # RMS norm - if self.norm_k: - k = self.norm_k(k) - - # Reshape: [B, L, dim] β†’ [B, L, num_heads, head_dim] - q, k, v = q.reshape(...), k.reshape(...), v.reshape(...) - - # Apply 3-way factorized RoPE (temporal, height, width) - q = rope_apply(q.astype(mx.float32), grid_sizes, freqs, rope_cos_sin) - k = rope_apply(k.astype(mx.float32), grid_sizes, freqs, rope_cos_sin) - - # Memory-efficient scaled-dot-product attention - out = mx.fast.scaled_dot_product_attention(q, k, v, scale=scale, mask=attn_mask) - - # Output projection - return self.o(out) -``` - -**Key Features**: -- QK normalization prevents attention collapse -- RoPE applied in float32 then cast back -- Attention mask handles variable sequence lengths - -### 3.7 Cross-Attention Details -**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/attention.py` lines 124-207 - -```python -class WanCrossAttention(nn.Module): - def prepare_kv(self, context): - """Pre-compute K, V once before denoising loop""" - k = self.k(context) # [B, L_ctx, dim] - v = self.v(context) - k = k.reshape(B, -1, num_heads, head_dim).transpose(0, 2, 1, 3) - v = v.reshape(B, -1, num_heads, head_dim).transpose(0, 2, 1, 3) - return k, v - - def __call__(self, x, context, context_lens=None, kv_cache=None): - # Q from hidden state - q = self.q(x).reshape(...) - - # Use pre-computed K/V if available - if kv_cache is not None: - k, v = kv_cache - else: - k = self.k(context).reshape(...) - v = self.v(context).reshape(...) - - # Attention with optional context masking - out = mx.fast.scaled_dot_product_attention(q, k, v) - return self.o(out) -``` - ---- - -## 4. EXISTING CACHING & ACCELERATION MECHANISMS - -### 4.1 TeaCache (Timestep Embedding Aware Cache) -**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/model.py` lines 15-59 - -**Purpose**: Skip redundant transformer blocks when time embedding change is small - -**How it Works**: -1. Monitor relative L1 distance between consecutive projected time embeddings (e0) -2. Use polynomial regression to map distance β†’ output distance -3. Accumulate rescaled distances -4. Skip blocks if accumulated distance < threshold -5. Reuse cached residual instead - -**Configuration**: -```python -teacache_thresh: float = 0.0 # Threshold (0=disabled, 0.1β‰ˆ2x speedup, 0.2β‰ˆ3x) -teacache_verbose: bool = False # Debug per-step decisions -``` - -**State Tracking**: -```python -@dataclass -class TeaCacheState: - enabled: bool = False - threshold: float = 0.0 - coefficients: tuple = () # Polynomial coefficients (ret-mode) - verbose: bool = False - - # Per-batch state - previous_e0: object = None # Cached time embedding - accumulated_distance: float = 0.0 - previous_residual: object = None # Cached residual to reuse - - # Bookkeeping - cnt: int = 0 - num_steps: int = 0 - ret_steps: int = 2 # Always compute first N steps - cutoff_steps: int = 0 # Always compute last N steps - - # Stats - steps_skipped: int = 0 - steps_computed: int = 0 -``` - -**Polynomial Coefficients** (pre-profiled for different models): -- Wan2.2 T2V 14B: `(-3.03e5, 4.91e4, -2.66e3, 5.87e1, -0.316)` -- Wan2.2 I2V 14B: `(2.57e5, -3.54e4, 1.40e3, -13.6, 0.133)` -- Wan2.1 T2V 1.3B: `(-5.22e4, 9.23e3, -5.28e2, 13.7, -0.050)` - -**Speedup Results**: -- threshold=0.1 β†’ ~2x speedup -- threshold=0.2 β†’ ~3x speedup - -### 4.2 Spectrum (Adaptive Spectral Feature Forecasting) -**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/spectrum.py` (entire file) - -**Purpose**: Predict transformer features using Chebyshev polynomials; skip expensive forward passes - -**Algorithm**: -1. **Warmup Phase** (first 5 steps): Always compute full transformer -2. **Caching Phase**: Store (timestep, features) pairs -3. **Fitting Phase**: Fit Chebyshev polynomials to cached features via ridge regression -4. **Prediction Phase**: Use fitted polynomials + Taylor extrapolation to predict next features -5. **Adaptive Scheduling**: Grow compute interval as confidence increases - -**Chebyshev Forecaster**: -```python -class ChebyshevForecaster: - def __init__(self, M=4, K=100, lam=0.1, num_steps=50): - self.M = M # Polynomial degree (4) - self.K = K # Cache window size (100) - self.lam = lam # Ridge regularization (0.1) - self.t_buf = None # (<=K,) step indices - self.H_buf = None # (<=K, F) flattened features - - def update(self, step, h_flat): - """Append new feature to cache""" - # Maintains sliding window of K most recent features - - def predict(self, step): - """Predict features using fitted Chebyshev polynomials""" - # Fit: (P, P) ridge system solved via Cholesky (tiny, CPU-fast) - # Predict: Evaluate Chebyshev basis at target step -``` - -**SpectrumForecaster** (blends approaches): -```python -class SpectrumForecaster: - def __init__(self, M=4, K=100, lam=0.1, w=0.5, num_steps=50): - self.cheb = ChebyshevForecaster(...) - self.w = w # Blend weight: 0=Taylor, 1=Chebyshev (default 0.5) - - def predict(self, step): - h_cheb = self.cheb.predict(step) - h_taylor = taylor_forward_differences(step) - return (1 - w) * h_taylor + w * h_chebyshev -``` - -**Configuration**: -```python -spectrum: bool = False # Enable/disable -spectrum_w: float = 0.5 # Blend weight (0=Taylor-only, 1=Chebyshev-only) -spectrum_flex_window: float = 0.75 # Window growth rate (default ~3.5x speedup) -spectrum_warmup: int = 5 # Always compute first N steps -``` - -**State Machine**: -```python -@dataclass -class SpectrumState: - enabled: bool = False - m: int = 4 # Chebyshev degree - lam: float = 0.1 # Ridge regularization - w: float = 0.5 # Chebyshev/Taylor blend - k_max: int = 100 # Cache size - warmup_steps: int = 5 - window_size: int = 2 # Initial compute interval - flex_window: float = 0.75 # Growth rate (Ξ± in paper) - - # Runtime state - num_steps: int = 0 - cnt: int = 0 - curr_ws: float = 2.0 # Current window (grows during run) - num_consecutive_cached: int = 0 - - def should_compute(self) -> bool: - """Adaptive scheduling: compute on warmup or window boundaries""" - if cnt < warmup_steps: - return True - should = (num_consecutive_cached + 1) % floor(curr_ws) == 0 - return should - - def step(self, computed: bool): - """Update counters after each step""" - if computed: - curr_ws += flex_window # Grow window - else: - num_consecutive_cached += 1 - cnt += 1 -``` - -**Speedup Results** (from paper): -- flex_window=0.75 β†’ ~3.5x speedup -- flex_window=3.0 β†’ ~5x speedup - -### 4.3 Compiled Forward Pass -**File**: `/Users/daniel/Projects/mlx-video/mlx_video/generate_wan.py` lines 578-584 - -When TeaCache and Spectrum are disabled, compile the full model for speed: -```python -if not use_caching: - models_to_compile = [high_noise_model, low_noise_model] if is_dual else [single_model] - for m in models_to_compile: - m._compiled = mx.compile(m) - -# In loop: -_call = getattr(model, "_compiled", model) -preds = _call([latents], t=t_batch, ...) # JIT-compiled forward -``` - -### 4.4 Memory Optimizations - -**Cross-attention K/V Caching**: -- Project context embeddings once before denoising loop -- Reuse same K/V across all 40 diffusion steps -- **Eliminates 40 redundant linear projections** per generation - -**RoPE Precomputation**: -- Grid sizes are constant across steps -- Precompute all cos/sin values once -- **Eliminates per-step broadcast/concat overhead** - -**Text Embedding Caching**: -- Embed text once with T5 encoder -- Reuse across all denoising steps -- **Free T5 from memory immediately** after embedding - ---- - -## 5. MoE (MIXTURE OF EXPERTS) ARCHITECTURE - -### 5.1 Finding: NO MoE in Wan2.2 - -**Search Results**: -- No `MoE`, `moe`, `mixture`, or `expert` references in any Wan model files -- FFN is standard **gated feed-forward** (no MoE) - -**Feed-Forward Network** (file: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/transformer.py` lines 84-96): -```python -class WanFFN(nn.Module): - """Gated feed-forward network with GELU(tanh) activation.""" - - def __init__(self, dim: int, ffn_dim: int): - super().__init__() - self.fc1 = nn.Linear(dim, ffn_dim) # 5120 β†’ 13824 - self.act = nn.GELU(approx="tanh") - self.fc2 = nn.Linear(ffn_dim, dim) # 13824 β†’ 5120 - - def __call__(self, x: mx.array) -> mx.array: - # Standard gated FFN: dense β†’ GELU β†’ dense - x_w = x.astype(_linear_dtype(self.fc1)) - return self.fc2(self.act(self.fc1(x_w))) -``` - -**Architecture**: Vanilla transformer FFN, not sparse/routed. - ---- - -## 6. MODEL LOADING AND CONFIGURATION - -### 6.1 Model Loading Pipeline -**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/loading.py` - -```python -def load_wan_model(model_path, config, quantization=None, loras=None): - """Load WanModel with optional quantization and LoRA.""" - - # 1. Create model with config - model = WanModel(config) - - # 2. Apply quantization stubs if specified - if quantization: - nn.quantize( - model, - group_size=quantization["group_size"], - bits=quantization["bits"], - class_predicate=lambda path, m: _quantize_predicate(path, m) - ) - - # 3. Load weights - weights = mx.load(str(model_path)) - - # 4. Apply LoRA (dequantize + merge if quantized, weight merge if bf16) - if loras: - # ... handle quantized vs non-quantized paths ... - - # 5. Load and eval - model.load_weights(list(weights.items()), strict=False) - mx.eval(model.parameters()) - return model -``` - -### 6.2 Configuration Loading -**File**: `/Users/daniel/Projects/mlx-video/mlx_video/generate_wan.py` lines 93-173 - -Auto-detection logic: -```python -if (model_dir / "config.json").exists(): - # Use explicit config - config = WanModelConfig(**config_dict) -else: - # Auto-detect from weights - if (model_dir / "low_noise_model.safetensors").exists(): - config = WanModelConfig.wan22_t2v_14b() # Dual model - else: - # Read weight shapes to determine size - probe = mx.load(str(model_path)) - for k, v in probe.items(): - if "patch_embedding_proj.weight" in k: - dim = v.shape[0] - config = ( - WanModelConfig.wan21_t2v_1_3b() if dim <= 2048 - else WanModelConfig.wan21_t2v_14b() - ) -``` - -### 6.3 Weight Conversion (PyTorch β†’ MLX) -**File**: `/Users/daniel/Projects/mlx-video/mlx_video/convert_wan.py` lines 60-161 - -Key transformations: -```python -def sanitize_wan_transformer_weights(weights): - """Convert Wan PyTorch keys to MLX structure.""" - - # Conv3d β†’ Linear reshape - if key == "patch_embedding.weight": - # [dim, in_dim, 1, 2, 2] β†’ [dim, in_dim*1*2*2] - value = value.reshape(value.shape[0], -1) - new_key = "patch_embedding_proj.weight" - - # Sequential layers β†’ individual modules - new_key = key.replace("text_embedding.0.", "text_embedding_0.") # Seq[0] - new_key = key.replace("text_embedding.2.", "text_embedding_1.") # Seq[2] - new_key = key.replace("time_embedding.0.", "time_embedding_0.") - new_key = key.replace("time_embedding.2.", "time_embedding_1.") - new_key = key.replace("time_projection.1.", "time_projection.") - - # FFN renaming - new_key = key.replace(".ffn.0.", ".ffn.fc1.") - new_key = key.replace(".ffn.2.", ".ffn.fc2.") - - return {new_key: value, ...} -``` - -### 6.4 Quantization Strategy -**File**: `/Users/daniel/Projects/mlx-video/mlx_video/convert_wan.py` (full file) - -```python -def _quantize_predicate(path, module): - """Determine which layers to quantize.""" - # Typically: Linear layers in transformer blocks (high memory) - # Skip: Layer norms, embeddings, output head - return isinstance(module, nn.Linear) and "blocks" in path -``` - -Quantization options: -- 4-bit with group_size=64 (default) -- 8-bit with group_size=128 -- Mixed: Quantize transformer blocks, keep other layers in bf16 - ---- - -## 7. GENERATE SCRIPTS FOR WAN2.2 - -### 7.1 Main Generation Script -**File**: `/Users/daniel/Projects/mlx-video/mlx_video/generate_wan.py` (38,528 bytes) - -Main entry point: -```python -def generate_video( - model_dir: str, - prompt: str, - negative_prompt: str | None = None, - image: str | None = None, - width: int = 1280, - height: int = 720, - num_frames: int = 81, - steps: int = None, - guide_scale: str | float | tuple = None, - shift: float = None, - seed: int = -1, - output_path: str = "output.mp4", - scheduler: str = "unipc", - teacache_thresh: float = 0.0, - teacache_verbose: bool = False, - spectrum: bool = False, - spectrum_w: float = 0.5, - spectrum_flex_window: float = 0.75, - spectrum_warmup: int = 5, - loras: list | None = None, - loras_high: list | None = None, - loras_low: list | None = None, - tiling: str = "auto", -): -``` - -### 7.2 Generation Pipeline - -**Phase 1: Setup** (lines 91-306) -- Load and parse config (auto-detect or explicit) -- Validate dimensions (align to patch_size Γ— vae_stride) -- Load T5 encoder and tokenizer -- Encode prompts (text β†’ embeddings) -- Free T5 from memory - -**Phase 2: I2V Preparation** (lines 309-392) -- Load VAE encoder -- Encode reference image to latent -- Build masking for blend (TI2V-5B) or channel concat (I2V-14B) - -**Phase 3: Model Loading** (lines 394-420) -- Load high-noise and low-noise transformers (or single for Wan2.1) -- Apply LoRA if specified -- Load and apply quantization if specified - -**Phase 4: Precomputation** (lines 422-484) -- Embed text once (text_embedding MLP) -- Prepare cross-attention K/V caches (40 blocks Γ— 2 models) -- Precompute RoPE cos/sin for grid sizes -- Setup schedulers (Euler, DPM++, or UniPC) - -**Phase 5: Denoising Loop** (lines 508-693) -- For each timestep: - - Select model based on noise level (dual model) - - Forward pass (with or without CFG) - - Apply classifier-free guidance - - Scheduler step - - (Optional) Reapply I2V mask - - Evaluate and free memory - -**Phase 6: VAE Decoding** (lines 743-800+) -- Load VAE decoder -- Optional tiling for memory efficiency -- Decode latents to video pixels -- Save MP4 - -### 7.3 Supported Schedulers -**File**: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/scheduler.py` - -1. **Euler** (1st-order): `--scheduler euler` - - Simplest: `x_next = x + (Οƒ_next - Οƒ_cur) * v` - -2. **DPM++2M** (2nd-order): `--scheduler dpm++` - - Convergence faster than Euler - - Uses previous step for 2nd-order correction - -3. **UniPC** (higher-order): `--scheduler unipc` (default) - - Faster convergence - - Recommended for quality - -### 7.4 Command-Line Usage - -```bash -# Basic T2V (Wan2.2 dual-model auto-detected) -python -m mlx_video.generate_wan \ - --model-dir ./wan22_converted \ - --prompt "A cat running through a forest" \ - --output output.mp4 - -# I2V with reference image -python -m mlx_video.generate_wan \ - --model-dir ./wan22_i2v_converted \ - --prompt "The cat leaps over a fence" \ - --image reference.jpg \ - --output output_i2v.mp4 - -# With TeaCache acceleration (2x speedup) -python -m mlx_video.generate_wan \ - --model-dir ./wan22_converted \ - --prompt "..." \ - --teacache-thresh 0.1 \ - --output output_fast.mp4 - -# With Spectrum acceleration (3.5x speedup, better quality) -python -m mlx_video.generate_wan \ - --model-dir ./wan22_converted \ - --prompt "..." \ - --spectrum \ - --spectrum-flex-window 0.75 \ - --output output_spectrum.mp4 - -# With quantization (memory efficient) -python -m mlx_video.generate_wan \ - --model-dir ./wan22_quantized_4bit \ - --prompt "..." \ - --output output_quant.mp4 -``` - ---- - -## 8. KEY FILE STRUCTURE & SIZES - -``` -mlx_video/models/wan/ -β”œβ”€β”€ __init__.py (2 lines) -β”œβ”€β”€ model.py (518 lines) ← Main WanModel class -β”œβ”€β”€ config.py (157 lines) ← Configuration variants -β”œβ”€β”€ transformer.py (96 lines) ← Transformer blocks & FFN -β”œβ”€β”€ attention.py (207 lines) ← Self/Cross-attention -β”œβ”€β”€ rope.py (178 lines) ← 3-way factorized RoPE -β”œβ”€β”€ loading.py (183 lines) ← Model loading & T5 encoder -β”œβ”€β”€ text_encoder.py (240 lines) ← T5 text encoder -β”œβ”€β”€ scheduler.py (428 lines) ← Flow matching schedulers -β”œβ”€β”€ spectrum.py (288 lines) ← Spectrum acceleration -β”œβ”€β”€ i2v_utils.py (58 lines) ← Image-to-Video utilities -β”œβ”€β”€ vae.py (589 lines) ← Wan2.1 VAE -└── vae22.py (908 lines) ← Wan2.2 VAE (48-dim latent) - -mlx_video/ -β”œβ”€β”€ generate_wan.py (38 KB) ← Main generation script -β”œβ”€β”€ convert_wan.py (27 KB) ← Weight conversion & quantization -β”œβ”€β”€ train_wan.py (9 KB) ← Training script -└── utils.py (9 KB) ← Utilities -``` - ---- - -## 9. SUMMARY TABLE: WAN2.2 ARCHITECTURE - -| Component | Details | File | -|-----------|---------|------| -| **Dual Model** | High-noise (0-875 steps) + Low-noise (875-1000) | model.py:406-419 | -| **Transformer Blocks** | 40 blocks, DiT-style learned modulation | transformer.py:7-96 | -| **Self-Attention** | QK norm + 3-way factorized RoPE + windowing | attention.py:46-121 | -| **Cross-Attention** | Pre-computed K/V for 40 steps reuse | attention.py:124-207 | -| **Time Embedding** | Sinusoidal positional encoding + MLP projection | model.py:138-144 | -| **Text Embedding** | T5 UMT5-XXL (24 layers, 64 heads) β†’ MLP | text_encoder.py + loading.py | -| **Patch Size** | (1, 2, 2) for temporal stability | config.py:13 | -| **VAE Latent Depth** | Wan2.2: 48-dim (stride 4Γ—16Γ—16), Wan2.1: 16-dim (stride 4Γ—8Γ—8) | config.py:44-45 | -| **TeaCache** | Polynomial-based block skipping (~2-3x speedup) | model.py:15-59 | -| **Spectrum** | Chebyshev + Taylor prediction (~3.5x speedup) | spectrum.py (full) | -| **Schedulers** | Euler, DPM++, UniPC (flow matching) | scheduler.py (full) | -| **Quantization** | 4-bit or 8-bit on transformer blocks | convert_wan.py:305+ | -| **LoRA Support** | Runtime or weight-merge application | lora/ + convert_wan.py | - ---- - -## 10. DIAGRAM: DATA FLOW - -``` -β”Œβ”€ TEXT PROMPT ──────────────────────────────────────────────────────────┐ -β”‚ β”‚ -β”‚ 1. Tokenize & T5 Encode β”‚ -β”‚ "A cat running" β”‚ -β”‚ ↓ β”‚ -β”‚ [tokens] β†’ T5 Encoder (24 layers) β†’ [512, 4096] β”‚ -β”‚ ↓ β”‚ -β”‚ T5 kept in float32 for precision (only called once) β”‚ -β”‚ β”‚ -β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - ↓ - MODEL LOADS - ↓ - β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” - β”‚ High-Noise Model Low-Noise β”‚ - β”‚ (40 blocks) Model β”‚ - β”‚ dim=5120 (40 blocks) β”‚ - β”‚ dim=5120 β”‚ - β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - ↓ - β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” - β”‚ Precompute Once (per gen): β”‚ - β”‚ - Text embed: [512, 5120] β”‚ - β”‚ - Cross-KV cache: 40 Γ— 2 β”‚ - β”‚ - RoPE cos/sin: [seq_len, d/2] β”‚ - β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - - β”Œβ”€ NOISE ───────────────────────────────────┐ - β”‚ β”‚ - β”‚ Random [48, T_lat, H_lat, W_lat] β”‚ - β”‚ ↓ β”‚ - β”‚ Patchify: [L, 5120] β”‚ - β”‚ ↓ β”‚ - β”‚ Time Embed: [B, 5120] β”‚ - β”‚ ↓ β”‚ - β”‚ CFG Batch: [B=2] β”‚ - β”‚ (cond + uncond) β”‚ - β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - ↓ - β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” - β”‚ DENOISING LOOP (40 steps) β”‚ - β”‚ β”‚ - β”‚ for t in timesteps: β”‚ - β”‚ if t >= 875: use HIGH_NOISE_MODEL β”‚ - β”‚ else: use LOW_NOISE_MODEL β”‚ - β”‚ β”‚ - β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ - β”‚ β”‚ Forward Pass: 40 Transformer Blocks β”‚ β”‚ - β”‚ β”‚ β”‚ β”‚ - β”‚ β”‚ for block in blocks: β”‚ β”‚ - β”‚ β”‚ β”œβ”€ Self-Attn (with 3-way RoPE) β”‚ β”‚ - β”‚ β”‚ β”œβ”€ Cross-Attn (text conditioning) β”‚ β”‚ - β”‚ β”‚ └─ FFN (gated) β”‚ β”‚ - β”‚ β”‚ All with learned modulation (6 vectors) β”‚ β”‚ - β”‚ β”‚ β”‚ β”‚ - β”‚ β”‚ Optional: TeaCache block skipping β”‚ β”‚ - β”‚ β”‚ Optional: Spectrum feature prediction β”‚ β”‚ - β”‚ β”‚ β”‚ β”‚ - β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ - β”‚ ↓ β”‚ - β”‚ Classifier-Free Guidance: β”‚ - β”‚ noise_pred = uncond + 4.0 * (cond - uncond) β”‚ - β”‚ ↓ β”‚ - β”‚ Scheduler Step (Euler/DPM++/UniPC) β”‚ - β”‚ latents = f(noise_pred, timestep, latents) β”‚ - β”‚ β”‚ - β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - ↓ - β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” - β”‚ VAE DECODE β”‚ - β”‚ β”‚ - β”‚ Latents [48, T_lat, H_lat, W_lat] β”‚ - β”‚ ↓ β”‚ - β”‚ Wan2.2 VAE Decoder (CausalConv3d) β”‚ - β”‚ Denormalize + Upscale β”‚ - β”‚ ↓ β”‚ - β”‚ Video Frames [3, T_pix, H_pix, W_pix] β”‚ - β”‚ (Channels-last: [T, H, W, 3]) β”‚ - β”‚ β”‚ - β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - ↓ - β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” - β”‚ Save MP4 Video β”‚ - β”‚ output.mp4 β”‚ - β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ -``` - ---- - -## CONCLUSION - -Wan2.2 is a sophisticated dual-model video diffusion architecture implemented efficiently on Apple Silicon using MLX. Key innovations: - -1. **Dual-model design**: Separate high-noise and low-noise transformers for coarse-to-fine generation -2. **Learned modulation**: DiT-style timestep conditioning with 6 per-block modulation vectors -3. **Aggressive precomputation**: Text embeddings, cross-attention K/V, RoPE frequencies computed once -4. **Advanced caching**: TeaCache (polynomial skipping) + Spectrum (Chebyshev prediction) for 2-3.5x speedup -5. **3-way factorized RoPE**: Separate frequency components for temporal, height, and width dimensions -6. **Flexible I2V**: Both channel-concatenation (I2V-14B) and masking-based (TI2V-5B) approaches - diff --git a/docs/WAN22_QUICK_REFERENCE.md b/docs/WAN22_QUICK_REFERENCE.md deleted file mode 100644 index d555f2a..0000000 --- a/docs/WAN22_QUICK_REFERENCE.md +++ /dev/null @@ -1,340 +0,0 @@ -# Wan2.2 Model - Quick Reference Guide - -## 1. CORE ARCHITECTURE AT A GLANCE - -``` -INPUT -β”œβ”€β”€ Text: prompt β†’ T5 Encoder β†’ [512, 4096] β†’ MLP β†’ [512, 5120] -β”œβ”€β”€ Time: scalar t ∈ [0,1000] β†’ sinusoid + MLP β†’ [5120] -└── Video: noise β†’ Patchify (patch_size=1,2,2) β†’ [L, 5120] - -DUAL MODELS (Wan2.2 only) -β”œβ”€β”€ High-Noise Model: timestep >= 875 (early denoising) -└── Low-Noise Model: timestep < 875 (fine details) - -EACH MODEL (40 blocks Γ— 40 layers total) -β”œβ”€β”€ 40Γ— WanAttentionBlock: -β”‚ β”œβ”€β”€ Self-Attention (with 3-way RoPE + QK norm) -β”‚ β”œβ”€β”€ Cross-Attention (text conditioning, pre-cached K/V) -β”‚ β”œβ”€β”€ FFN (gated GELU) -β”‚ └── ALL modulated by 6 learnable time-conditioned vectors -└── Output Head: Project to patch_size Γ— out_dim - -DIFFUSION LOOP (40 steps, e.g.) -β”œβ”€β”€ For each timestep: -β”‚ β”œβ”€β”€ Forward pass (with/without CFG batch) -β”‚ β”œβ”€β”€ Optional: TeaCache block skipping (2x speedup) -β”‚ β”œβ”€β”€ Optional: Spectrum feature prediction (3.5x speedup) -β”‚ β”œβ”€β”€ Classifier-free guidance: noise = uncond + 4.0 Γ— (cond - uncond) -β”‚ └── Scheduler step (Euler/DPM++/UniPC) -└── Decode with VAE β†’ video pixels -``` - -## 2. KEY FILES TO UNDERSTAND - -| File | Lines | Purpose | -|------|-------|---------| -| `model.py` | 518 | Main WanModel, patchify, unpatchify, forward pass, TeaCache/Spectrum logic | -| `transformer.py` | 96 | WanAttentionBlock (self-attn + cross-attn + FFN + modulation) | -| `attention.py` | 207 | WanSelfAttention, WanCrossAttention (with pre-cached K/V) | -| `rope.py` | 178 | 3-way factorized RoPE (temporal, height, width separate) | -| `config.py` | 157 | Configuration presets (Wan2.2 T2V/I2V/TI2V, Wan2.1) | -| `generate_wan.py` | 38KB | Main generation pipeline (setup β†’ precompute β†’ denoise β†’ decode) | -| `spectrum.py` | 288 | Chebyshev + Taylor feature prediction for acceleration | -| `scheduler.py` | 428 | Flow matching: Euler, DPM++, UniPC solvers | -| `loading.py` | 183 | Model loading, weight conversion, T5 encoder | -| `convert_wan.py` | 27KB | PyTorchβ†’MLX conversion, quantization, LoRA merging | - -## 3. DENOISING LOOP SKELETON - -```python -# From generate_wan.py:589-693 -for i, t in enumerate(tqdm(range(steps), desc="Diffusion")): - timestep_val = timestep_list[i] - - # 1. SELECT MODEL (dual model only) - if is_dual: - if timestep_val >= boundary: - model = high_noise_model - kv = cross_kv_high - else: - model = low_noise_model - kv = cross_kv_low - - # 2. FORWARD PASS - if cfg_disabled: - # B=1 (faster) - preds = model([latents], t=t_batch, context=context_cond, ...) - noise_pred = preds[0] - else: - # B=2 (cfg) - preds = model( - [latents, latents], - t=[t, t], - context=[context_cond, context_uncond] - ) - noise_cond, noise_uncond = preds[0], preds[1] - noise_pred = noise_uncond + guide_scale * (noise_cond - noise_uncond) - - # 3. SCHEDULER STEP - latents = scheduler.step(noise_pred, timestep_val, latents) - - # 4. (I2V) REAPPLY MASK - if is_i2v_mask_blend: - latents = (1 - mask) * z_img + mask * latents - - # 5. EVAL & CLEANUP - mx.eval(latents) - del noise_pred -``` - -## 4. TRANSFORMER BLOCK FORWARD PASS - -```python -# From transformer.py -class WanAttentionBlock: - def __call__(self, x, e, context, cross_kv_cache=None, ...): - # Modulation: 6 time-conditioned vectors - mod = (self.modulation + e) # Add time embedding - e0, e1, e2, e3, e4, e5 = mod.split() - - # Self-Attention with modulation - x_norm = self.norm1(x) * (1 + e1) + e0 # Affine transform - x = x + e2 * self.self_attn(x_norm, ...) # Residual + gate - - # Cross-Attention (text conditioning) - x_norm = self.norm3(x) if self.norm3 else x - x = x + self.cross_attn(x_norm, context, kv_cache=cross_kv_cache) - - # FFN with modulation - x_norm = self.norm2(x) * (1 + e4) + e3 - x = x + e5 * self.ffn(x_norm) - - return x -``` - -## 5. 3-WAY FACTORIZED ROPE - -```python -# From rope.py: Temporal, height, width have separate frequency components -# This prevents temporal motion from being confused with spatial position - -d_t = head_dim - 2*(head_dim//3) # ~1/3 of dimensions -d_h = head_dim // 3 # ~1/3 of dimensions -d_w = head_dim // 3 # ~1/3 of dimensions - -# For each position (f, h, w): -freqs_combined = [ - freqs_t[f], # Temporal component - freqs_h[h], # Height component - freqs_w[w], # Width component -] -# Apply rotation separately, then concatenate -``` - -## 6. TEACACHE ACCELERATION - -```python -# Skips transformer blocks when time embedding change is small - -if t_embedding_distance < THRESHOLD: - # Reuse cached residual from previous step - x = x + cached_residual # Instant! -else: - # Full forward pass - x = run_all_blocks(x) - cached_residual = x - x_input - -# Speedup: ~2x at threshold=0.1, ~3x at threshold=0.2 -# Polynomial coefficients pre-profiled per model variant -``` - -## 7. SPECTRUM ACCELERATION - -```python -# Predicts transformer features using Chebyshev polynomials - -# Warmup: 5 steps always compute full forward -# Then: Fit Chebyshev polynomials to cached features -# Finally: Predict features on non-compute steps - -# Blended prediction: -h_predicted = (1 - w) * h_taylor + w * h_chebyshev - -# Window grows adaptively: -if num_steps_without_compute % window_size == 0: - compute_full_forward() - window_size *= flex_window # Grow confidence - -# Speedup: ~3.5x with flex_window=0.75, up to ~5x with 3.0 -``` - -## 8. PRECOMPUTATION (Per Generation) - -```python -# ONCE before denoising loop: - -# 1. Text embeddings (MLP applied to T5 output) -context_emb = model.embed_text([context, context_null]) -# Result: [2, 512, 5120] (cond + uncond) - -# 2. Cross-attention K/V caches (40 blocks) -cross_kv_caches = [ - block.cross_attn.prepare_kv(context_emb) # for each block -] -# Result: list of (k, v) [2, 40, 512, 128] per block - -# 3. RoPE frequencies (grid sizes constant) -rope_cos_sin = model.prepare_rope(grid_sizes) -# Result: (cos, sin) [seq_len, 1, head_dim//2] - -# These are REUSED across all 40 denoising steps! -``` - -## 9. CONFIGURATION QUICK LOOKUP - -```python -# Wan2.2 T2V 14B (default dual-model) -config = WanModelConfig.wan22_t2v_14b() -# dim=5120, heads=40, layers=40, patch_size=(1,2,2) -# dual_model=True, boundary=0.875 -# vae_z_dim=48, vae_stride=(4,16,16) - -# Wan2.2 I2V 14B (image-to-video, dual-model) -config = WanModelConfig.wan22_i2v_14b() -# Same as above but: model_type="i2v", in_dim=36 -# boundary=0.900, sample_guide_scale=(3.5, 3.5) - -# Wan2.2 TI2V 5B (text+image, single-model) -config = WanModelConfig.wan22_ti2v_5b() -# dim=3072, heads=24, layers=30 -# dual_model=False, model_type="ti2v" -# in_dim=48, out_dim=48, vae_z_dim=48 - -# Wan2.1 T2V 14B (single-model, backward compatible) -config = WanModelConfig.wan21_t2v_14b() -# dim=5120, heads=40, layers=40 -# dual_model=False, boundary=0.0 - -# Wan2.1 T2V 1.3B (smaller single-model) -config = WanModelConfig.wan21_t2v_1_3b() -# dim=1536, heads=12, layers=30 -# dual_model=False, boundary=0.0 -``` - -## 10. GENERATION COMMAND CHEAT SHEET - -```bash -# Basic text-to-video (Wan2.2 auto-detected) -python -m mlx_video.generate_wan \ - --model-dir /path/to/wan22 \ - --prompt "A cat running through a forest" \ - --output output.mp4 - -# Image-to-video -python -m mlx_video.generate_wan \ - --model-dir /path/to/wan22_i2v \ - --prompt "The cat jumps over a fence" \ - --image reference.jpg \ - --output output_i2v.mp4 - -# Fast with TeaCache (2x speedup, lower quality) -python -m mlx_video.generate_wan \ - --model-dir /path/to/wan22 \ - --prompt "..." \ - --teacache-thresh 0.1 \ - --output fast.mp4 - -# High quality with Spectrum (3.5x speedup, better than TeaCache) -python -m mlx_video.generate_wan \ - --model-dir /path/to/wan22 \ - --prompt "..." \ - --spectrum \ - --spectrum-flex-window 0.75 \ - --spectrum-warmup 5 \ - --output spectrum.mp4 - -# With quantization (memory efficient) -python -m mlx_video.generate_wan \ - --model-dir /path/to/wan22_4bit \ - --prompt "..." \ - --output quant.mp4 - -# Custom guidance & solver -python -m mlx_video.generate_wan \ - --model-dir /path/to/wan22 \ - --prompt "..." \ - --guide-scale 5.0 \ - --scheduler dpm++ \ - --steps 50 \ - --output custom.mp4 - -# With LoRA fine-tuning -python -m mlx_video.generate_wan \ - --model-dir /path/to/wan22 \ - --prompt "..." \ - --loras "./lora_style.safetensors,0.5" "./lora_motion.safetensors,0.3" \ - --output lora.mp4 -``` - -## 11. WEIGHT CONVERSION - -```bash -# Convert Wan2.2 checkpoint to MLX format -python -c " -from mlx_video.convert_wan import convert_wan_checkpoint - -convert_wan_checkpoint( - checkpoint_dir='/path/to/wan22_official', - output_dir='/path/to/wan22_mlx', - dtype='bfloat16', - model_version='auto', - quantize=True, - bits=4, - group_size=64 -) -" - -# Structure expected: -# wan22_official/ -# β”œβ”€β”€ low_noise_model/ (safetensors) -# β”œβ”€β”€ high_noise_model/ (safetensors) -# β”œβ”€β”€ models_t5_umt5-xxl-enc-bf16.pth -# └── Wan2.1_VAE.pth - -# Output: -# wan22_mlx/ -# β”œβ”€β”€ low_noise_model.safetensors -# β”œβ”€β”€ high_noise_model.safetensors -# β”œβ”€β”€ t5_encoder.safetensors -# β”œβ”€β”€ vae.safetensors -# └── config.json -``` - -## 12. NO MOE ARCHITECTURE - -⚠️ **Important**: Wan2.2 does **NOT** use Mixture of Experts. The FFN is standard: - -```python -class WanFFN(nn.Module): - def __init__(self, dim: int, ffn_dim: int): - self.fc1 = nn.Linear(dim, ffn_dim) # 5120 β†’ 13824 - self.act = nn.GELU(approx="tanh") - self.fc2 = nn.Linear(ffn_dim, dim) # 13824 β†’ 5120 - - def __call__(self, x): - return self.fc2(self.act(self.fc1(x))) # Dense β†’ GELU β†’ Dense -``` - -No routing, no gating, no expert selection. - ---- - -## REFERENCE LINKS - -- Main generation: `/Users/daniel/Projects/mlx-video/mlx_video/generate_wan.py` -- Model forward: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/model.py` -- Transformer blocks: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/transformer.py` -- Acceleration: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/spectrum.py` -- Config presets: `/Users/daniel/Projects/mlx-video/mlx_video/models/wan/config.py` -- Full documentation: `/Users/daniel/Projects/mlx-video/WAN22_EXPLORATION_SUMMARY.md` diff --git a/docs/HELIOS-DIAGNOSTICS.md b/mlx_video/models/helios/docs/DIAGNOSTICS.md similarity index 100% rename from docs/HELIOS-DIAGNOSTICS.md rename to mlx_video/models/helios/docs/DIAGNOSTICS.md From bd43d775a5fa06752bd3183a8716e4cc599f0c0a Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 12 Mar 2026 15:08:45 +0100 Subject: [PATCH 37/40] Helios: Cleanup folder structure --- docs/PORTING-GUIDE.md | 2 +- mlx_video/models/helios/docs/DIAGNOSTICS.md | 16 +- .../helios/scripts/analyze_boundaries.py | 250 ++++++++++++++++++ .../models/helios/scripts}/compare_models.py | 2 +- .../helios/scripts}/compare_pipelines.py | 4 +- .../models/helios/scripts}/run_reference.py | 4 +- scripts/helios/analyze_boundaries.py | 6 +- 7 files changed, 267 insertions(+), 17 deletions(-) create mode 100644 mlx_video/models/helios/scripts/analyze_boundaries.py rename {scripts/helios => mlx_video/models/helios/scripts}/compare_models.py (98%) rename {scripts/helios => mlx_video/models/helios/scripts}/compare_pipelines.py (98%) rename {scripts/helios => mlx_video/models/helios/scripts}/run_reference.py (97%) diff --git a/docs/PORTING-GUIDE.md b/docs/PORTING-GUIDE.md index 5c44f94..e17ab3e 100644 --- a/docs/PORTING-GUIDE.md +++ b/docs/PORTING-GUIDE.md @@ -847,7 +847,7 @@ python scripts/video/video_quality.py output.mp4 --chunk-size 32 python scripts/video/compare_videos.py reference.mp4 output.mp4 --diff-video diff.mp4 ``` -### Model-specific diagnostics (`scripts/helios/`) +### Model-specific diagnostics (`mlx_video/models/helios/scripts/`) | Script | Purpose | |--------|---------| diff --git a/mlx_video/models/helios/docs/DIAGNOSTICS.md b/mlx_video/models/helios/docs/DIAGNOSTICS.md index 1dae8f0..6149b84 100644 --- a/mlx_video/models/helios/docs/DIAGNOSTICS.md +++ b/mlx_video/models/helios/docs/DIAGNOSTICS.md @@ -57,7 +57,7 @@ mlx_video/models/helios/ loading.py # Weight loading wrappers (51 lines) mlx_video/convert_helios.py # HFβ†’MLX weight conversion tests/test_helios.py # 46 tests (554 lines) -scripts/helios/ +mlx_video/models/helios/scripts/ analyze_boundaries.py # Boundary quality analysis (compare videos) run_reference.py # Run PyTorch reference pipeline on MPS compare_pipelines.py # Compare scheduler/pipeline mechanics @@ -354,7 +354,7 @@ spatial brightness redistribution (face darkens, background brightens). between chunks via downscale/diff/upscale additive correction (6-frame ramp) 4. **Per-channel contrast correction** β€” scales std dev to match previous chunk's last frame -**Results** (measured with `scripts/helios/analyze_boundaries.py`): +**Results** (measured with `mlx_video/models/helios/scripts/analyze_boundaries.py`): | Metric | No fix | With all fixes | |--------|--------|----------------| | Contrast jump | -7.0% | **-1.0%** | @@ -584,7 +584,7 @@ def _time_shift(mu, t): ### Diagnostic scripts -Consolidated diagnostic scripts live in `scripts/helios/`: +Consolidated diagnostic scripts live in `mlx_video/models/helios/scripts/`: | Script | Purpose | |--------|---------| @@ -597,21 +597,21 @@ Consolidated diagnostic scripts live in `scripts/helios/`: ```bash # Single video -python scripts/helios/analyze_boundaries.py /tmp/helios_output.mp4 +python mlx_video/models/helios/scripts/analyze_boundaries.py /tmp/helios_output.mp4 # Compare before/after fix -python scripts/helios/analyze_boundaries.py \ +python mlx_video/models/helios/scripts/analyze_boundaries.py \ /tmp/before_fix.mp4 /tmp/after_fix.mp4 # Reference pipeline uses 33 frames/chunk (no first-frame trim) -python scripts/helios/analyze_boundaries.py --chunk-size 33 /tmp/ref.mp4 +python mlx_video/models/helios/scripts/analyze_boundaries.py --chunk-size 33 /tmp/ref.mp4 ``` **Run reference pipeline for comparison**: ```bash # Requires: pip install diffusers transformers torch accelerate -python scripts/helios/run_reference.py \ +python mlx_video/models/helios/scripts/run_reference.py \ --model-dir /path/to/Helios-Distilled \ --prompt "A golden retriever running on a sunny beach" \ --output /tmp/helios_ref.mp4 @@ -620,7 +620,7 @@ python scripts/helios/run_reference.py \ **Compare pipeline mechanics** (no model weights needed): ```bash -python scripts/helios/compare_pipelines.py \ +python mlx_video/models/helios/scripts/compare_pipelines.py \ --helios-dir /path/to/Helios ``` diff --git a/mlx_video/models/helios/scripts/analyze_boundaries.py b/mlx_video/models/helios/scripts/analyze_boundaries.py new file mode 100644 index 0000000..5f91171 --- /dev/null +++ b/mlx_video/models/helios/scripts/analyze_boundaries.py @@ -0,0 +1,250 @@ +#!/usr/bin/env python3 +"""Analyze chunk boundary quality in Helios-generated videos. + +Measures brightness, contrast, color shifts, spatial distribution, and +frame-to-frame differences at chunk boundaries. Compares multiple videos +side-by-side when given multiple paths. + +This was the primary diagnostic tool used to identify and fix: +- 40% contrast drops from pixel cross-fade (β†’ disabled cross-fade) +- 7% contrast drops from VAE causal padding warmup (β†’ contrast correction) +- Per-channel color shifts at boundaries (β†’ per-channel matching) +- Spatial brightness redistribution (β†’ low-frequency spatial correction) + +Usage: + # Analyze a single video + python mlx_video/models/helios/scripts/analyze_boundaries.py /tmp/helios_output.mp4 + + # Compare multiple videos + python mlx_video/models/helios/scripts/analyze_boundaries.py \ + /tmp/helios_before.mp4 /tmp/helios_after.mp4 + + # Custom chunk size (default: 32 frames per chunk) + python mlx_video/models/helios/scripts/analyze_boundaries.py --chunk-size 33 /tmp/ref.mp4 +""" + +import argparse +import sys + +import cv2 +import numpy as np + + +def analyze_video(path, chunk_size=32): + """Analyze boundary quality metrics for a video.""" + vid = cv2.VideoCapture(path) + if not vid.isOpened(): + print(f"Error: cannot open {path}", file=sys.stderr) + return None + + frames = [] + while True: + ret, f = vid.read() + if not ret: + break + frames.append(f) + vid.release() + + n = len(frames) + if n == 0: + print(f"Error: no frames in {path}", file=sys.stderr) + return None + + # Compute per-frame statistics + means = np.zeros(n) + stds = np.zeros(n) + ch_means = np.zeros((n, 3)) + diffs = np.zeros(n - 1) + + for i, f in enumerate(frames): + gray = cv2.cvtColor(f, cv2.COLOR_BGR2GRAY).astype(np.float64) + means[i] = gray.mean() + stds[i] = gray.std() + ch_means[i] = [f[:, :, c].mean() for c in range(3)] + if i > 0: + prev_gray = cv2.cvtColor(frames[i - 1], cv2.COLOR_BGR2GRAY).astype(np.float64) + diffs[i - 1] = np.abs(gray - prev_gray).mean() + + # Find chunk boundaries + boundaries = [] + b = chunk_size - 1 + while b < n - 1: + boundaries.append(b) + b += chunk_size + + results = { + "path": path, + "num_frames": n, + "chunk_size": chunk_size, + "boundaries": [], + } + + for b in boundaries: + if b >= n - 1: + break + + # Contrast + pre_std = stds[max(0, b - 2) : b + 1] + post_std = stds[b + 1 : min(n, b + 4)] + contrast_jump = post_std[0] - pre_std[-1] + contrast_pct = contrast_jump / max(pre_std[-1], 1e-6) * 100 + + # Brightness + bright_jump = means[b + 1] - means[b] + bright_pct = bright_jump / max(means[b], 1e-6) * 100 + + # Per-channel color shift + ch_shifts = ch_means[b + 1] - ch_means[b] # B, G, R + + # Frame diff ratio + boundary_diff = diffs[b] + window = 3 + nearby_indices = list(range(max(0, b - window), b)) + list( + range(b + 1, min(len(diffs), b + 1 + window)) + ) + nearby_avg = np.mean(diffs[nearby_indices]) if nearby_indices else 1.0 + diff_ratio = boundary_diff / max(nearby_avg, 1e-6) + + # Spatial analysis + f_pre = frames[b].astype(np.float64) + f_post = frames[b + 1].astype(np.float64) + gray_diff = cv2.cvtColor(frames[b + 1], cv2.COLOR_BGR2GRAY).astype( + np.float64 + ) - cv2.cvtColor(frames[b], cv2.COLOR_BGR2GRAY).astype(np.float64) + h, w = gray_diff.shape + ch, cw = h // 4, w // 4 + center_shift = gray_diff[ch : 3 * ch, cw : 3 * cw].mean() + periph_mask = np.ones_like(gray_diff, dtype=bool) + periph_mask[ch : 3 * ch, cw : 3 * cw] = False + periph_shift = gray_diff[periph_mask].mean() + + results["boundaries"].append( + { + "frame": b, + "contrast_pct": contrast_pct, + "bright_pct": bright_pct, + "ch_shifts_bgr": ch_shifts.tolist(), + "diff_ratio": diff_ratio, + "center_shift": center_shift, + "periph_shift": periph_shift, + "boundary_diff": boundary_diff, + "nearby_diff": nearby_avg, + } + ) + + # Per-chunk stats + chunk_stats = [] + for c in range(0, n, chunk_size): + end = min(c + chunk_size, n) + chunk_stats.append( + { + "frames": f"{c}-{end - 1}", + "mean_bright": means[c:end].mean(), + "mean_contrast": stds[c:end].mean(), + "first_contrast": stds[c], + "last_contrast": stds[end - 1], + } + ) + results["chunk_stats"] = chunk_stats + + return results + + +def print_results(results): + """Pretty-print analysis results.""" + print(f"\n{'=' * 70}") + print(f" {results['path']}") + print(f" {results['num_frames']} frames, chunk size = {results['chunk_size']}") + print(f"{'=' * 70}") + + for bd in results["boundaries"]: + b = bd["frame"] + print(f"\n Boundary {b}β†’{b + 1}:") + print(f" Contrast jump: {bd['contrast_pct']:+.1f}%") + print(f" Brightness jump: {bd['bright_pct']:+.1f}%") + print( + f" Color shift B/G/R: {bd['ch_shifts_bgr'][0]:+.1f} / " + f"{bd['ch_shifts_bgr'][1]:+.1f} / {bd['ch_shifts_bgr'][2]:+.1f}" + ) + print( + f" Frame diff: {bd['boundary_diff']:.1f} vs nearby " + f"{bd['nearby_diff']:.1f} ({bd['diff_ratio']:.1f}Γ—)" + ) + print( + f" Spatial: center {bd['center_shift']:+.2f}, " + f"periphery {bd['periph_shift']:+.2f}" + ) + + print(f"\n Per-chunk summary:") + for cs in results["chunk_stats"]: + print( + f" Frames {cs['frames']:>7s}: brightness={cs['mean_bright']:.1f}, " + f"contrast={cs['mean_contrast']:.1f} " + f"(first={cs['first_contrast']:.1f}, last={cs['last_contrast']:.1f})" + ) + + +def print_comparison(all_results): + """Print side-by-side comparison table.""" + if len(all_results) < 2: + return + + print(f"\n{'=' * 70}") + print(" COMPARISON SUMMARY") + print(f"{'=' * 70}") + + # Header + labels = [r["path"].split("/")[-1] for r in all_results] + header = f"{'Metric':<25s}" + for label in labels: + header += f" {label:>18s}" + print(f"\n{header}") + print("-" * (25 + 20 * len(labels))) + + # For each boundary index + max_boundaries = max(len(r["boundaries"]) for r in all_results) + for bi in range(max_boundaries): + print(f"\n Boundary {bi + 1}:") + for metric, key, fmt in [ + ("Contrast jump", "contrast_pct", "{:+.1f}%"), + ("Brightness jump", "bright_pct", "{:+.1f}%"), + ("Frame diff ratio", "diff_ratio", "{:.1f}Γ—"), + ("Center shift", "center_shift", "{:+.2f}"), + ("Periphery shift", "periph_shift", "{:+.2f}"), + ]: + row = f" {metric:<23s}" + for r in all_results: + if bi < len(r["boundaries"]): + val = r["boundaries"][bi][key] + row += f" {fmt.format(val):>18s}" + else: + row += f" {'N/A':>18s}" + print(row) + + +def main(): + parser = argparse.ArgumentParser( + description="Analyze chunk boundary quality in Helios videos" + ) + parser.add_argument("videos", nargs="+", help="Video file paths to analyze") + parser.add_argument( + "--chunk-size", + type=int, + default=32, + help="Frames per chunk (default: 32, use 33 for reference pipeline)", + ) + args = parser.parse_args() + + all_results = [] + for path in args.videos: + results = analyze_video(path, args.chunk_size) + if results is not None: + print_results(results) + all_results.append(results) + + if len(all_results) > 1: + print_comparison(all_results) + + +if __name__ == "__main__": + main() diff --git a/scripts/helios/compare_models.py b/mlx_video/models/helios/scripts/compare_models.py similarity index 98% rename from scripts/helios/compare_models.py rename to mlx_video/models/helios/scripts/compare_models.py index 91e4927..d8341ff 100644 --- a/scripts/helios/compare_models.py +++ b/mlx_video/models/helios/scripts/compare_models.py @@ -24,7 +24,7 @@ --output-path /tmp/debug_test.mp4 # Step 2: Compare with PyTorch - python scripts/helios/compare_models.py \ + python mlx_video/models/helios/scripts/compare_models.py \ --model-dir /path/to/Helios-Distilled \ --prompt "A beautiful sunset over the ocean" \ --inputs /tmp/helios_model_inputs.npz \ diff --git a/scripts/helios/compare_pipelines.py b/mlx_video/models/helios/scripts/compare_pipelines.py similarity index 98% rename from scripts/helios/compare_pipelines.py rename to mlx_video/models/helios/scripts/compare_pipelines.py index d2936d1..7e6b261 100644 --- a/scripts/helios/compare_pipelines.py +++ b/mlx_video/models/helios/scripts/compare_pipelines.py @@ -15,11 +15,11 @@ - PyTorch + diffusers Usage: - python scripts/helios/compare_pipelines.py \ + python mlx_video/models/helios/scripts/compare_pipelines.py \ --helios-dir /path/to/Helios # Custom parameters - python scripts/helios/compare_pipelines.py \ + python mlx_video/models/helios/scripts/compare_pipelines.py \ --helios-dir /path/to/Helios \ --seed 123 --stages 3 --steps 2 2 2 """ diff --git a/scripts/helios/run_reference.py b/mlx_video/models/helios/scripts/run_reference.py similarity index 97% rename from scripts/helios/run_reference.py rename to mlx_video/models/helios/scripts/run_reference.py index ecf435b..389a6e4 100644 --- a/scripts/helios/run_reference.py +++ b/mlx_video/models/helios/scripts/run_reference.py @@ -9,7 +9,7 @@ pip install diffusers transformers torch accelerate Usage: - python scripts/helios/run_reference.py \ + python mlx_video/models/helios/scripts/run_reference.py \ --model-dir /path/to/Helios-Distilled \ --prompt "A golden retriever running on a sunny beach" \ --output /tmp/helios_ref.mp4 @@ -19,7 +19,7 @@ --model-dir /path/to/Helios-Distilled-MLX \ --prompt "A golden retriever running on a sunny beach" \ --output-path /tmp/helios_mlx.mp4 - python scripts/helios/analyze_boundaries.py \ + python mlx_video/models/helios/scripts/analyze_boundaries.py \ /tmp/helios_ref.mp4 /tmp/helios_mlx.mp4 """ diff --git a/scripts/helios/analyze_boundaries.py b/scripts/helios/analyze_boundaries.py index a7a73fa..5f91171 100644 --- a/scripts/helios/analyze_boundaries.py +++ b/scripts/helios/analyze_boundaries.py @@ -13,14 +13,14 @@ Usage: # Analyze a single video - python scripts/helios/analyze_boundaries.py /tmp/helios_output.mp4 + python mlx_video/models/helios/scripts/analyze_boundaries.py /tmp/helios_output.mp4 # Compare multiple videos - python scripts/helios/analyze_boundaries.py \ + python mlx_video/models/helios/scripts/analyze_boundaries.py \ /tmp/helios_before.mp4 /tmp/helios_after.mp4 # Custom chunk size (default: 32 frames per chunk) - python scripts/helios/analyze_boundaries.py --chunk-size 33 /tmp/ref.mp4 + python mlx_video/models/helios/scripts/analyze_boundaries.py --chunk-size 33 /tmp/ref.mp4 """ import argparse From f9cfdc087a2f767ba84dfc3ace16ac895fffb212 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 12 Mar 2026 15:20:59 +0100 Subject: [PATCH 38/40] Helios: Update README.md --- mlx_video/models/helios/README.md | 60 ++++++++++++++++++++----------- 1 file changed, 40 insertions(+), 20 deletions(-) diff --git a/mlx_video/models/helios/README.md b/mlx_video/models/helios/README.md index a2e1d39..93b639e 100644 --- a/mlx_video/models/helios/README.md +++ b/mlx_video/models/helios/README.md @@ -2,59 +2,77 @@ Helios is a 14B-parameter autoregressive video generation model that produces minute-scale, temporally coherent video. This implementation targets the **Helios-Distilled** variant for text-to-video generation on Apple Silicon via MLX. -## Quick Start +### Step 1: Download Weights -### 1. Convert Weights +Download the original PyTorch checkpoint from HuggingFace using the `huggingface-cli` tool (install with `pip install huggingface_hub`): -Download the HuggingFace checkpoint and convert to MLX format: +```bash +huggingface-cli download BestWishYsh/Helios-Distilled --local-dir ./Helios-Distilled +``` + +### Step 2: Convert to MLX Format + +Convert the PyTorch checkpoint to MLX format: ```bash python -m mlx_video.convert_helios \ - --checkpoint-dir /path/to/BestWishYsh/Helios-Distilled \ - --output-dir ./helios-mlx + --checkpoint-dir ./Helios-Distilled \ + --output-dir ./Helios-Distilled-MLX ``` -With 4-bit quantization (~7 GB, fits 16 GB Macs): +#### Quantization (Reduced Memory) + +Quantize the transformer weights to reduce memory usage. With 4-bit quantization (~7 GB, fits 16 GB Macs): ```bash python -m mlx_video.convert_helios \ - --checkpoint-dir /path/to/BestWishYsh/Helios-Distilled \ - --output-dir ./helios-mlx-4bit \ + --checkpoint-dir ./Helios-Distilled \ + --output-dir ./Helios-Distilled-MLX-Q4 \ --quantize --bits 4 ``` -Or quantize an existing MLX model (skips HF conversion): +You can also quantize an already-converted MLX model without re-converting from PyTorch: ```bash python -m mlx_video.convert_helios \ - --checkpoint-dir ./helios-mlx \ - --output-dir ./helios-mlx-4bit \ + --checkpoint-dir ./Helios-Distilled-MLX \ + --output-dir ./Helios-Distilled-MLX-Q4 \ --quantize-only --bits 4 ``` -### 2. Generate Video +#### Conversion Options + +| Option | Default | Description | +|--------|---------|-------------| +| `--checkpoint-dir` | (required) | Path to original PyTorch checkpoint directory | +| `--output-dir` | `helios_mlx_model` | Output path for MLX model | +| `--quantize` | off | Quantize transformer weights for reduced memory | +| `--bits` | `4` | Quantization bits: `4` or `8` | +| `--quantize-only` | off | Quantize an existing MLX model (skip PyTorch conversion) | + +### Step 3: Generate Video ```bash python -m mlx_video.generate_helios \ - --model-dir ./helios-mlx \ + --model-dir ./Helios-Distilled-MLX \ --prompt "A golden retriever running through a sunlit meadow" \ --output-path my_video.mp4 ``` ```bash python -m mlx_video.generate_helios \ - --model-dir ../Helios-Distilled-MLX/ \ + --model-dir ./Helios-Distilled-MLX \ --num-frames 330 \ --seed 2391784614 \ --prompt "Two dogs of the poodle breed sitting on a beach wearing sunglasses, nodding with their heads, close up, cinematic, sunset" ``` -## CLI Options +#### Generation Options -| Flag | Default | Description | -|---|---|---| -| `--model-dir` | *(required)* | Path to converted MLX model directory | -| `--prompt` | *(required)* | Text prompt describing the video | +| Option | Default | Description | +|--------|---------|-------------| +| `--model-dir` | (required) | Path to converted MLX model directory | +| `--prompt` | (required) | Text prompt describing the video | | `--width` | `640` | Video width in pixels (must be divisible by 64) | | `--height` | `384` | Video height in pixels (must be divisible by 64) | | `--num-frames` | `99` | Number of output frames (auto-rounded to multiple of 33) | @@ -62,7 +80,7 @@ python -m mlx_video.generate_helios \ | `--amplify-first-chunk` | off | Double steps for first chunk (better quality) | | `--guidance-scale` | `5.0` | CFG guidance scale (`1.0` = no guidance, `5.0` = default) | | `--negative-prompt` | `""` | Negative prompt for classifier-free guidance | -| `--seed` | `-1` | Random seed (`-1` for random) | +| `--seed` | `-1` (random) | Random seed for reproducibility | | `--output-path` | `output_helios.mp4` | Output video file path | | `--tiling` | `auto` | VAE tiling mode: `auto`, `none`, `default`, `aggressive`, `conservative` | @@ -166,6 +184,8 @@ Height and width must be divisible by 64. Recommended resolutions: | 384 Γ— 384 | 1:1 | ~24 GB | ~6 GB | | 256 Γ— 448 | 9:16 | ~20 GB | ~5 GB | +> **Note β€” Resolution sensitivity**: The model was only trained at 384Γ—640. Using non-default resolutions (even valid multiples of 64, like 640Γ—384 portrait) causes obvious frame jumps in the reference pipeline too. This is a known upstream limitation ([PKU-YuanGroup/Helios#2](https://github.com/PKU-YuanGroup/Helios/issues/2)). Residual zoom with complex prompts at the default resolution is also an inherent model behavior. + ## Memory Tips - Use `--tiling aggressive` for lower VRAM usage during VAE decoding From a88f282d687fd97cca6d8164b5c8438fc97850a8 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 12 Mar 2026 15:25:46 +0100 Subject: [PATCH 39/40] Helios: Add 41secs of poodles --- examples/poodles_helios.gif | Bin 0 -> 3045039 bytes mlx_video/models/helios/README.md | 12 +++++++++++- 2 files changed, 11 insertions(+), 1 deletion(-) create mode 100644 examples/poodles_helios.gif diff --git a/examples/poodles_helios.gif b/examples/poodles_helios.gif new file mode 100644 index 0000000000000000000000000000000000000000..0ded8b7cb90353fc701677a88032adfac731fc1b GIT binary patch literal 3045039 zcmeF2)mN1d@UP!put{m@1_42l?(Xge=`N8D-E_mI8|m(DH{IQ(ARw(shzQ8}{?5hu z56)WW;>^`t&YCr!S!?2vSCkVFw19$gf!ikl41iz|EIbfE@QjQO9sH7kO_T~tPC!^s zS;LT%#L~#zLHgx8JLmVhG9RtA5k7&5amW-;licFcn$P`p5#}gV<6xG{?8fTS_UxBt 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