docs: add XL (4B DiT) model documentation

README.md

@@ -113,13 +113,16 @@ Open http://localhost:7860 (Gradio) or http://localhost:8001 (API).
 
 ### 💡 Which Model Should I Choose?
 
-| Your GPU VRAM | Recommended LM Model | Backend | Notes |
-|---------------|---------------------|---------|-------|
-| **≤6GB** | None (DiT only) | — | LM disabled by default; INT8 quantization + full CPU offload |
-| **6-8GB** | `acestep-5Hz-lm-0.6B` | `pt` | Lightweight LM with PyTorch backend |
-| **8-16GB** | `acestep-5Hz-lm-0.6B` / `1.7B` | `vllm` | 0.6B for 8-12GB, 1.7B for 12-16GB |
-| **16-24GB** | `acestep-5Hz-lm-1.7B` | `vllm` | 4B available on 20GB+; no offload needed on 20GB+ |
-| **≥24GB** | `acestep-5Hz-lm-4B` | `vllm` | Best quality, all models fit without offload |
+| Your GPU VRAM | Recommended DiT | Recommended LM Model | Backend | Notes |
+|---------------|----------------|---------------------|---------|-------|
+| **≤6GB** | 2B turbo | None (DiT only) | — | LM disabled by default; INT8 quantization + full CPU offload |
+| **6-8GB** | 2B turbo | `acestep-5Hz-lm-0.6B` | `pt` | Lightweight LM with PyTorch backend |
+| **8-16GB** | 2B turbo/sft | `acestep-5Hz-lm-0.6B` / `1.7B` | `vllm` | 0.6B for 8-12GB, 1.7B for 12-16GB |
+| **16-20GB** | 2B sft or XL turbo | `acestep-5Hz-lm-1.7B` | `vllm` | XL requires CPU offload below 20GB |
+| **20-24GB** | XL turbo/sft | `acestep-5Hz-lm-1.7B` | `vllm` | XL fits without offload; 4B LM available |
+| **≥24GB** | XL sft (or xl-base for extract/lego/complete) | `acestep-5Hz-lm-4B` | `vllm` | Best quality, all models fit without offload |
+
+> **XL (4B) models** (`acestep-v15-xl-*`) offer higher audio quality with ~9GB VRAM for weights (vs ~4.7GB for 2B). They require ≥12GB VRAM (with offload + quantization) or ≥20GB (without offload). All LM models are fully compatible with XL.
 
 The UI automatically selects the best configuration for your GPU. All settings (LM model, backend, offloading, quantization) are tier-aware and pre-configured.
 
@@ -244,6 +247,16 @@ See also the **LoRA Training** tab in Gradio UI for one-click training, or [Grad
 | `acestep-v15-turbo` | ✅ | ✅ | ❌ | ❌ | 8 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | Very High | Medium | Medium | [Link](https://huggingface.co/ACE-Step/Ace-Step1.5) |
 | `acestep-v15-turbo-rl` | ✅ | ✅ | ✅ | ❌ | 8 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | Very High | Medium | Medium | To be released |
 
+### XL (4B) DiT Models
+
+> XL models use a larger 4B-parameter DiT decoder (~9GB bf16) for higher audio quality. They require ≥12GB VRAM (with offload + quantization) or ≥20GB (without offload). All LM models are fully compatible.
+
+| DiT Model | Pre-Training | SFT | RL | CFG | Step | Refer audio | Text2Music | Cover | Repaint | Extract | Lego | Complete | Quality | Diversity | Fine-Tunability | Hugging Face |
+|-----------|:------------:|:---:|:--:|:---:|:----:|:-----------:|:----------:|:-----:|:-------:|:-------:|:----:|:--------:|:-------:|:---------:|:---------------:|--------------|
+| `acestep-v15-xl-base` | ✅ | ❌ | ❌ | ✅ | 50 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | High | High | Easy | [Link](https://huggingface.co/ACE-Step/acestep-v15-xl-base) |
+| `acestep-v15-xl-sft` | ✅ | ✅ | ❌ | ✅ | 50 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | Very High | Medium | Easy | [Link](https://huggingface.co/ACE-Step/acestep-v15-xl-sft) |
+| `acestep-v15-xl-turbo` | ✅ | ✅ | ❌ | ❌ | 8 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | Very High | Medium | Medium | [Link](https://huggingface.co/ACE-Step/acestep-v15-xl-turbo) |
+
 ### LM Models
 
 | LM Model | Pretrain from | Pre-Training | SFT | RL | CoT metas | Query rewrite | Audio Understanding | Composition Capability | Copy Melody | Hugging Face |

docs/en/GPU_COMPATIBILITY.md

@@ -4,16 +4,18 @@ ACE-Step 1.5 automatically adapts to your GPU's available VRAM, adjusting genera
 
 ## GPU Tier Configuration
 
-| VRAM | Tier | LM Models | Recommended LM | Backend | Max Duration (LM / No LM) | Max Batch (LM / No LM) | Offload | Quantization |
-|------|------|-----------|-----------------|---------|----------------------------|-------------------------|---------|--------------|
-| ≤4GB | Tier 1 | None | — | pt | 4 min / 6 min | 1 / 1 | CPU + DiT | INT8 |
-| 4-6GB | Tier 2 | None | — | pt | 8 min / 10 min | 1 / 1 | CPU + DiT | INT8 |
-| 6-8GB | Tier 3 | 0.6B | 0.6B | pt | 8 min / 10 min | 2 / 2 | CPU + DiT | INT8 |
-| 8-12GB | Tier 4 | 0.6B | 0.6B | vllm | 8 min / 10 min | 2 / 4 | CPU + DiT | INT8 |
-| 12-16GB | Tier 5 | 0.6B, 1.7B | 1.7B | vllm | 8 min / 10 min | 4 / 4 | CPU | INT8 |
-| 16-20GB | Tier 6a | 0.6B, 1.7B | 1.7B | vllm | 8 min / 10 min | 4 / 8 | CPU | INT8 |
-| 20-24GB | Tier 6b | 0.6B, 1.7B, 4B | 1.7B | vllm | 8 min / 8 min | 8 / 8 | None | None |
-| ≥24GB | Unlimited | All (0.6B, 1.7B, 4B) | 4B | vllm | 10 min / 10 min | 8 / 8 | None | None |
+| VRAM | Tier | XL (4B) DiT | LM Models | Recommended LM | Backend | Max Duration (LM / No LM) | Max Batch (LM / No LM) | Offload | Quantization |
+|------|------|:-----------:|-----------|-----------------|---------|----------------------------|-------------------------|---------|--------------|
+| ≤4GB | Tier 1 | ❌ | None | — | pt | 4 min / 6 min | 1 / 1 | CPU + DiT | INT8 |
+| 4-6GB | Tier 2 | ❌ | None | — | pt | 8 min / 10 min | 1 / 1 | CPU + DiT | INT8 |
+| 6-8GB | Tier 3 | ❌ | 0.6B | 0.6B | pt | 8 min / 10 min | 2 / 2 | CPU + DiT | INT8 |
+| 8-12GB | Tier 4 | ❌ | 0.6B | 0.6B | vllm | 8 min / 10 min | 2 / 4 | CPU + DiT | INT8 |
+| 12-16GB | Tier 5 | ⚠️ | 0.6B, 1.7B | 1.7B | vllm | 8 min / 10 min | 4 / 4 | CPU | INT8 |
+| 16-20GB | Tier 6a | ✅ (offload) | 0.6B, 1.7B | 1.7B | vllm | 8 min / 10 min | 4 / 8 | CPU | INT8 |
+| 20-24GB | Tier 6b | ✅ | 0.6B, 1.7B, 4B | 1.7B | vllm | 8 min / 8 min | 8 / 8 | None | None |
+| ≥24GB | Unlimited | ✅ | All (0.6B, 1.7B, 4B) | 4B | vllm | 10 min / 10 min | 8 / 8 | None | None |
+
+> **XL (4B) DiT column**: ❌ = not supported, ⚠️ = marginal (offload + quantization required, reduced batch; works on 12-16GB with aggressive offload), ✅ (offload) = supported with CPU offload, ✅ = fully supported. XL models use ~9GB VRAM for weights (vs ~4.7GB for 2B). All LM models are compatible with XL.
 
 ### Column Descriptions
 
@@ -75,8 +77,8 @@ The detection happens at startup in `acestep/gpu_config.py` (`is_legacy_cuda_gpu
 1. **Very Low VRAM (≤6GB)**: Use DiT-only mode without LM initialization. INT8 quantization and full CPU offload are mandatory. VAE decode may fall back to CPU automatically.
 2. **Low VRAM (6-8GB)**: The 0.6B LM model can be used with `pt` backend. Keep offload enabled.
 3. **Medium VRAM (8-16GB)**: Use the 0.6B or 1.7B LM model. `vllm` backend works well on Tier 4+.
-4. **High VRAM (16-24GB)**: Enable larger LM models (1.7B recommended). Quantization becomes optional on 20GB+.
-5. **Very High VRAM (≥24GB)**: All models fit without offloading or quantization. Use 4B LM for best quality.
+4. **High VRAM (16-24GB)**: Enable larger LM models (1.7B recommended). Quantization becomes optional on 20GB+. XL (4B) DiT models are supported — with offload on 16GB, without offload on 20GB+.
+5. **Very High VRAM (≥24GB)**: All models fit without offloading or quantization. Use XL DiT + 4B LM for best quality.
 
 ## Debug Mode: Simulating Different GPU Configurations
 

docs/en/INSTALL.md

@@ -650,9 +650,14 @@ python -m acestep.model_downloader --all              # Download all models
 # Main model (vae, Qwen3-Embedding-0.6B, acestep-v15-turbo, acestep-5Hz-lm-1.7B)
 huggingface-cli download ACE-Step/Ace-Step1.5 --local-dir ./checkpoints
 
-# Optional models
+# Optional LM models
 huggingface-cli download ACE-Step/acestep-5Hz-lm-0.6B --local-dir ./checkpoints/acestep-5Hz-lm-0.6B
 huggingface-cli download ACE-Step/acestep-5Hz-lm-4B --local-dir ./checkpoints/acestep-5Hz-lm-4B
+
+# XL (4B) DiT models - requires ≥12GB VRAM (with offload)
+huggingface-cli download ACE-Step/acestep-v15-xl-base --local-dir ./checkpoints/acestep-v15-xl-base
+huggingface-cli download ACE-Step/acestep-v15-xl-sft --local-dir ./checkpoints/acestep-v15-xl-sft
+huggingface-cli download ACE-Step/acestep-v15-xl-turbo --local-dir ./checkpoints/acestep-v15-xl-turbo
 ```
 
 ### Available Models
@@ -667,20 +672,24 @@ huggingface-cli download ACE-Step/acestep-5Hz-lm-4B --local-dir ./checkpoints/ac
 | acestep-v15-turbo-shift1 | Turbo DiT with shift1 | [Link](https://huggingface.co/ACE-Step/acestep-v15-turbo-shift1) |
 | acestep-v15-turbo-shift3 | Turbo DiT with shift3 | [Link](https://huggingface.co/ACE-Step/acestep-v15-turbo-shift3) |
 | acestep-v15-turbo-continuous | Turbo DiT with continuous shift (1-5) | [Link](https://huggingface.co/ACE-Step/acestep-v15-turbo-continuous) |
+| **acestep-v15-xl-base** | XL (4B) Base DiT — higher quality, ≥12GB VRAM | [Link](https://huggingface.co/ACE-Step/acestep-v15-xl-base) |
+| **acestep-v15-xl-sft** | XL (4B) SFT DiT — higher quality, ≥12GB VRAM | [Link](https://huggingface.co/ACE-Step/acestep-v15-xl-sft) |
+| **acestep-v15-xl-turbo** | XL (4B) Turbo DiT — higher quality, ≥12GB VRAM | [Link](https://huggingface.co/ACE-Step/acestep-v15-xl-turbo) |
 
 ---
 
 ## 💡 Which Model Should I Choose?
 
 ACE-Step automatically adapts to your GPU's VRAM. The UI pre-configures all settings (LM model, backend, offloading, quantization) based on your detected GPU tier:
 
-| Your GPU VRAM | Recommended LM Model | Backend | Notes |
-|---------------|---------------------|---------|-------|
-| **≤6GB** | None (DiT only) | — | LM disabled by default; INT8 quantization + full CPU offload |
-| **6-8GB** | `acestep-5Hz-lm-0.6B` | `pt` | Lightweight LM with PyTorch backend |
-| **8-16GB** | `0.6B` / `1.7B` | `vllm` | 0.6B for 8-12GB, 1.7B for 12-16GB |
-| **16-24GB** | `acestep-5Hz-lm-1.7B` | `vllm` | 4B available on 20GB+; no offload on 20GB+ |
-| **≥24GB** | `acestep-5Hz-lm-4B` | `vllm` | Best quality, all models fit without offload |
+| Your GPU VRAM | Recommended DiT | Recommended LM Model | Backend | Notes |
+|---------------|----------------|---------------------|---------|-------|
+| **≤6GB** | 2B turbo | None (DiT only) | — | LM disabled; INT8 quantization + full CPU offload |
+| **6-8GB** | 2B turbo | `acestep-5Hz-lm-0.6B` | `pt` | Lightweight LM with PyTorch backend |
+| **8-16GB** | 2B turbo/sft | `0.6B` / `1.7B` | `vllm` | 0.6B for 8-12GB, 1.7B for 12-16GB |
+| **16-20GB** | 2B sft or XL turbo | `acestep-5Hz-lm-1.7B` | `vllm` | XL requires CPU offload below 20GB |
+| **20-24GB** | XL turbo/sft | `acestep-5Hz-lm-1.7B` | `vllm` | XL fits without offload; 4B LM available |
+| **≥24GB** | XL sft (or xl-base for extract/lego/complete) | `acestep-5Hz-lm-4B` | `vllm` | Best quality, all models fit without offload |
 
 > 📖 For detailed GPU compatibility information (tier table, duration limits, batch sizes, adaptive UI defaults, memory optimization), see [GPU Compatibility Guide](GPU_COMPATIBILITY.md).
 

docs/en/Tutorial.md

@@ -188,7 +188,7 @@ Based on your hardware:
 
 With a planning scheme, you still need to choose an executor. DiT is the core of ACE-Step 1.5—it handles various tasks and decides how to interpret LM-generated codes.
 
-We've open-sourced **4 Turbo models**, **1 SFT model**, and **1 Base model**.
+We've open-sourced **4 Turbo models**, **1 SFT model**, and **1 Base model** — plus their **XL (4B)** counterparts for higher audio quality.
 
 #### Turbo Series (Recommended for Daily Use)
 
@@ -250,13 +250,28 @@ This greatly expands **customization and playability**—train a model unique to
 
 > For the detailed LoRA training guide, see the [LoRA Training Tutorial](./LoRA_Training_Tutorial.md). You can also use the "LoRA Training" tab in Gradio UI for one-click training.
 
+#### XL (4B) Models
+
+XL models use a larger 4B-parameter DiT decoder for higher audio quality. They come in the same three variants (base, sft, turbo) and behave identically — just with better generation quality. **All LM models (0.6B / 1.7B / 4B) are fully compatible with XL.**
+
+**Requirements:** XL models need ~9GB VRAM for weights (vs ~4.7GB for 2B). Minimum 12GB VRAM with offload + quantization, 20GB+ recommended.
+
+| XL Model (full name) | Steps | CFG | VRAM | Notes |
+|----------------------|:-----:|:---:|:----:|-------|
+| `acestep-v15-xl-turbo` | 8 | ❌ | ≥12GB | Fast + high quality, best daily driver on 20GB+ GPUs |
+| `acestep-v15-xl-sft` | 50 | ✅ | ≥12GB | Highest quality, tunable CFG |
+| `acestep-v15-xl-base` | 50 | ✅ | ≥12GB | All tasks including extract/lego/complete |
+
 #### DiT Selection Summary
 
 | Model | Steps | CFG | Speed | Exclusive Tasks | Recommended Scenarios |
 |-------|:-----:|:---:|:-----:|-----------------|----------------------|
 | `turbo` (default) | 8 | ❌ | ⚡⚡⚡ | — | Daily use, rapid iteration |
 | `sft` | 50 | ✅ | ⚡ | — | Pursuing details, like tuning |
 | `base` | 50 | ✅ | ⚡ | extract, lego, complete | Special tasks, large-scale fine-tuning |
+| **`acestep-v15-xl-turbo`** | 8 | ❌ | ⚡⚡ | — | Best daily driver on 20GB+ GPUs |
+| **`acestep-v15-xl-sft`** | 50 | ✅ | ⚡ | — | Highest quality, ≥12GB VRAM |
+| **`acestep-v15-xl-base`** | 50 | ✅ | ⚡ | extract, lego, complete | All tasks with higher quality, ≥12GB VRAM |
 
 ### Combination Strategies
 

docs/ja/GPU_COMPATIBILITY.md

@@ -4,16 +4,18 @@ ACE-Step 1.5 は GPU の VRAM に自動的に適応し、生成時間の制限
 
 ## GPU ティア構成
 
-| VRAM | ティア | LM モデル | 推奨 LM | バックエンド | 最大時間 (LM有 / LM無) | 最大バッチ (LM有 / LM無) | オフロード | 量子化 |
-|------|--------|-----------|---------|-------------|------------------------|--------------------------|------------|--------|
-| ≤4GB | Tier 1 | なし | — | pt | 4分 / 6分 | 1 / 1 | CPU + DiT | INT8 |
-| 4-6GB | Tier 2 | なし | — | pt | 8分 / 10分 | 1 / 1 | CPU + DiT | INT8 |
-| 6-8GB | Tier 3 | 0.6B | 0.6B | pt | 8分 / 10分 | 2 / 2 | CPU + DiT | INT8 |
-| 8-12GB | Tier 4 | 0.6B | 0.6B | vllm | 8分 / 10分 | 2 / 4 | CPU + DiT | INT8 |
-| 12-16GB | Tier 5 | 0.6B, 1.7B | 1.7B | vllm | 8分 / 10分 | 4 / 4 | CPU | INT8 |
-| 16-20GB | Tier 6a | 0.6B, 1.7B | 1.7B | vllm | 8分 / 10分 | 4 / 8 | CPU | INT8 |
-| 20-24GB | Tier 6b | 0.6B, 1.7B, 4B | 1.7B | vllm | 8分 / 8分 | 8 / 8 | なし | なし |
-| ≥24GB | 無制限 | 全モデル (0.6B, 1.7B, 4B) | 4B | vllm | 10分 / 10分 | 8 / 8 | なし | なし |
+| VRAM | ティア | XL (4B) DiT | LM モデル | 推奨 LM | バックエンド | 最大時間 (LM有 / LM無) | 最大バッチ (LM有 / LM無) | オフロード | 量子化 |
+|------|--------|:-----------:|-----------|---------|-------------|------------------------|--------------------------|------------|--------|
+| ≤4GB | Tier 1 | ❌ | なし | — | pt | 4分 / 6分 | 1 / 1 | CPU + DiT | INT8 |
+| 4-6GB | Tier 2 | ❌ | なし | — | pt | 8分 / 10分 | 1 / 1 | CPU + DiT | INT8 |
+| 6-8GB | Tier 3 | ❌ | 0.6B | 0.6B | pt | 8分 / 10分 | 2 / 2 | CPU + DiT | INT8 |
+| 8-12GB | Tier 4 | ❌ | 0.6B | 0.6B | vllm | 8分 / 10分 | 2 / 4 | CPU + DiT | INT8 |
+| 12-16GB | Tier 5 | ⚠️ | 0.6B, 1.7B | 1.7B | vllm | 8分 / 10分 | 4 / 4 | CPU | INT8 |
+| 16-20GB | Tier 6a | ✅ (オフロード) | 0.6B, 1.7B | 1.7B | vllm | 8分 / 10分 | 4 / 8 | CPU | INT8 |
+| 20-24GB | Tier 6b | ✅ | 0.6B, 1.7B, 4B | 1.7B | vllm | 8分 / 8分 | 8 / 8 | なし | なし |
+| ≥24GB | 無制限 | ✅ | 全モデル (0.6B, 1.7B, 4B) | 4B | vllm | 10分 / 10分 | 8 / 8 | なし | なし |
+
+> **XL (4B) DiT 列**: ❌ = 非対応, ⚠️ = 限定的（オフロード + 量子化が必要、12-16GBでは積極的なオフロードで動作可能）, ✅ (オフロード) = CPUオフロードで対応, ✅ = 完全対応。XLモデルの重みは約9GB（bf16）、2Bは約4.7GB。すべてのLMモデルがXLと互換性があります。
 
 ### 列の説明
 

docs/ja/INSTALL.md

@@ -531,13 +531,14 @@ huggingface-cli download ACE-Step/acestep-5Hz-lm-4B --local-dir ./checkpoints/ac
 
 ACE-Step は GPU の VRAM に自動適応します。UI は検出された GPU ティアに基づいてすべての設定（LM モデル、バックエンド、オフロード、量子化）を事前構成します：
 
-| GPU VRAM | 推奨 LM モデル | バックエンド | 備考 |
-|----------|---------------|-------------|------|
-| **≤6GB** | なし（DiTのみ） | — | LM はデフォルトで無効；INT8 量子化 + 完全 CPU オフロード |
-| **6-8GB** | `acestep-5Hz-lm-0.6B` | `pt` | 軽量 LM、PyTorch バックエンド |
-| **8-16GB** | `0.6B` / `1.7B` | `vllm` | 8-12GB は 0.6B、12-16GB は 1.7B |
-| **16-24GB** | `acestep-5Hz-lm-1.7B` | `vllm` | 20GB+ で 4B 利用可能；20GB+ でオフロード不要 |
-| **≥24GB** | `acestep-5Hz-lm-4B` | `vllm` | 最高品質、すべてのモデルがオフロードなしで動作 |
+| GPU VRAM | 推奨 DiT | 推奨 LM モデル | バックエンド | 備考 |
+|----------|---------|---------------|-------------|------|
+| **≤6GB** | 2B turbo | なし（DiTのみ） | — | LM はデフォルトで無効；INT8 量子化 + 完全 CPU オフロード |
+| **6-8GB** | 2B turbo | `acestep-5Hz-lm-0.6B` | `pt` | 軽量 LM、PyTorch バックエンド |
+| **8-16GB** | 2B turbo/sft | `0.6B` / `1.7B` | `vllm` | 8-12GB は 0.6B、12-16GB は 1.7B |
+| **16-20GB** | 2B sft または XL turbo | `acestep-5Hz-lm-1.7B` | `vllm` | XL は 20GB 未満で CPU オフロードが必要 |
+| **20-24GB** | XL turbo/sft | `acestep-5Hz-lm-1.7B` | `vllm` | XL はオフロード不要；4B LM 利用可能 |
+| **≥24GB** | XL sft（extract/lego/complete には xl-base） | `acestep-5Hz-lm-4B` | `vllm` | 最高品質、すべてのモデルがオフロードなしで動作 |
 
 > 📖 GPU 互換性の詳細（ティアテーブル、時間制限、バッチサイズ、アダプティブ UI デフォルト、メモリ最適化）は [GPU 互換性ガイド](GPU_COMPATIBILITY.md) を参照してください。
 

docs/ja/Tutorial.md

@@ -250,13 +250,28 @@ Baseは**タスクの集大成**で、SFTとTurboを超える3つの独占タス
 
 > LoRA訓練の詳細ガイドについては、[LoRA トレーニングチュートリアル](./LoRA_Training_Tutorial.md)を参照してください。Gradio UIの「LoRA Training」タブからワンクリックで訓練することもできます。
 
+#### XL (4B) モデル
+
+XLモデルは、より大きな4BパラメータのDiTデコーダを使用し、生成品質が向上します。標準モデルと同じ3つのバリアント（base、sft、turbo）で、動作は完全に同じです。**すべてのLMモデル（0.6B / 1.7B / 4B）がXLと完全互換です。**
+
+**必要条件:** XLモデルの重みは約9GB（bf16）、2Bは約4.7GB。最小12GB VRAM（オフロード + 量子化が必要）、20GB+推奨。
+
+| XLモデル（フルネーム） | ステップ | CFG | VRAM | 備考 |
+|----------------------|:-------:|:---:|:----:|------|
+| `acestep-v15-xl-turbo` | 8 | ❌ | ≥12GB | 高速 + 高品質、20GB+ GPUの最適な日常選択 |
+| `acestep-v15-xl-sft` | 50 | ✅ | ≥12GB | 最高品質、CFG調整可能 |
+| `acestep-v15-xl-base` | 50 | ✅ | ≥12GB | extract/lego/completeを含む全タスク対応 |
+
 #### DiT選択のまとめ
 
 | モデル | ステップ | CFG | 速度 | 独占タスク | 推奨シナリオ |
 |--------|:-------:|:---:|:----:|----------|------------|
 | `turbo`（デフォルト） | 8 | ❌ | ⚡⚡⚡ | — | 日常使用、迅速な反復 |
 | `sft` | 50 | ✅ | ⚡ | — | 詳細を追求、調整が好き |
 | `base` | 50 | ✅ | ⚡ | extract, lego, complete | 特殊タスク、大規模微調整 |
+| **`acestep-v15-xl-turbo`** | 8 | ❌ | ⚡⚡ | — | 20GB+ GPUの最適な日常選択 |
+| **`acestep-v15-xl-sft`** | 50 | ✅ | ⚡ | — | 最高品質、≥12GB VRAM |
+| **`acestep-v15-xl-base`** | 50 | ✅ | ⚡ | extract, lego, complete | より高品質な全タスク対応、≥12GB VRAM |
 
 ### 組み合わせ戦略