diff --git a/frontend/package-lock.json b/frontend/package-lock.json index 169d1f2dd..0f971b9e1 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -89,6 +89,7 @@ "integrity": "sha512-2BCOP7TN8M+gVDj7/ht3hsaO/B/n5oDbiAyyvnRlNOs+u1o+JWNYTQrmpuNp1/Wq2gcFrI01JAW+paEKDMx/CA==", "dev": true, "license": "MIT", + "peer": true, "dependencies": { "@babel/code-frame": "^7.27.1", "@babel/generator": "^7.28.3", @@ -2557,6 +2558,7 @@ "integrity": "sha512-hHkbU/eoO3EG5/MZkuFSKmYqPbSVk5byPFa3e7y/8TybHiLMACgI8seVYlicwk7H5K/rI2px9xrQp/C+AUDTiQ==", "devOptional": true, "license": "MIT", + "peer": true, "dependencies": { "csstype": "^3.0.2" } @@ -2567,6 +2569,7 @@ "integrity": "sha512-qXRuZaOsAdXKFyOhRBg6Lqqc0yay13vN7KrIg4L7N4aaHN68ma9OK3NE1BoDFgFOTfM7zg+3/8+2n8rLUH3OKQ==", "devOptional": true, "license": "MIT", + "peer": true, "peerDependencies": { "@types/react": "^19.0.0" } @@ -2617,6 +2620,7 @@ "integrity": "sha512-B7RIQiTsCBBmY+yW4+ILd6mF5h1FUwJsVvpqkrgpszYifetQ2Ke+Z4u6aZh0CblkUGIdR59iYVyXqqZGkZ3aBw==", "dev": true, "license": "MIT", + "peer": true, "dependencies": { "@typescript-eslint/scope-manager": "8.43.0", "@typescript-eslint/types": "8.43.0", @@ -2869,6 +2873,7 @@ "integrity": "sha512-NZyJarBfL7nWwIq+FDL6Zp/yHEhePMNnnJ0y3qfieCrmNvYct8uvtiV41UvlSe6apAfk0fY1FbWx+NwfmpvtTg==", "dev": true, "license": "MIT", + "peer": true, "bin": { "acorn": "bin/acorn" }, @@ -3037,6 +3042,7 @@ } ], "license": "MIT", + "peer": true, "dependencies": { "baseline-browser-mapping": "^2.8.2", "caniuse-lite": "^1.0.30001741", @@ -3319,6 +3325,7 @@ "integrity": "sha512-QePbBFMJFjgmlE+cXAlbHZbHpdFVS2E/6vzCy7aKlebddvl1vadiC4JFV5u/wqTkNUwEV8WrQi257jf5f06hrg==", "dev": true, "license": "MIT", + "peer": true, "dependencies": { "@eslint-community/eslint-utils": "^4.8.0", "@eslint-community/regexpp": "^4.12.1", @@ -4455,6 +4462,7 @@ } ], "license": "MIT", + "peer": true, "dependencies": { "nanoid": "^3.3.11", "picocolors": "^1.1.1", @@ -4533,6 +4541,7 @@ "resolved": "https://registry.npmjs.org/react/-/react-19.1.1.tgz", "integrity": "sha512-w8nqGImo45dmMIfljjMwOGtbmC/mk4CMYhWIicdSflH91J9TyCyczcPFXJzrZ/ZXcgGRFeP6BU0BEJTw6tZdfQ==", "license": "MIT", + "peer": true, "engines": { "node": ">=0.10.0" } @@ -4542,6 +4551,7 @@ "resolved": "https://registry.npmjs.org/react-dom/-/react-dom-19.1.1.tgz", "integrity": "sha512-Dlq/5LAZgF0Gaz6yiqZCf6VCcZs1ghAJyrsu84Q/GT0gV+mCxbfmKNoGRKBYMJ8IEdGPqu49YWXD02GCknEDkw==", "license": "MIT", + "peer": true, "dependencies": { "scheduler": "^0.26.0" }, @@ -4899,6 +4909,7 @@ "integrity": "sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q==", "dev": true, "license": "MIT", + "peer": true, "engines": { "node": ">=12" }, @@ -4957,6 +4968,7 @@ "integrity": "sha512-p1diW6TqL9L07nNxvRMM7hMMw4c5XOo/1ibL4aAIGmSAt9slTE1Xgw5KWuof2uTOvCg9BY7ZRi+GaF+7sfgPeQ==", "dev": true, "license": "Apache-2.0", + "peer": true, "bin": { "tsc": "bin/tsc", "tsserver": "bin/tsserver" @@ -5079,6 +5091,7 @@ "integrity": "sha512-4cKBO9wR75r0BeIWWWId9XK9Lj6La5X846Zw9dFfzMRw38IlTk2iCcUt6hsyiDRcPidc55ZParFYDXi0nXOeLQ==", "dev": true, "license": "MIT", + "peer": true, "dependencies": { "esbuild": "^0.25.0", "fdir": "^6.5.0", @@ -5172,6 +5185,7 @@ "integrity": "sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q==", "dev": true, "license": "MIT", + "peer": true, "engines": { "node": ">=12" }, diff --git a/frontend/src/components/DownloadDialog.tsx b/frontend/src/components/DownloadDialog.tsx index d94362e84..556b7c26a 100644 --- a/frontend/src/components/DownloadDialog.tsx +++ b/frontend/src/components/DownloadDialog.tsx @@ -9,11 +9,12 @@ import { DialogTitle, } from "./ui/dialog"; import { Progress } from "./ui/progress"; -import { PIPELINES } from "../data/pipelines"; import type { PipelineId, DownloadProgress } from "../types"; +import type { PipelineInfo } from "../hooks/usePipelines"; interface DownloadDialogProps { open: boolean; + pipelines: Record | null; pipelineId: PipelineId; onClose: () => void; onDownload: () => void; @@ -23,13 +24,14 @@ interface DownloadDialogProps { export function DownloadDialog({ open, + pipelines, pipelineId, onClose, onDownload, isDownloading = false, progress = null, }: DownloadDialogProps) { - const pipelineInfo = PIPELINES[pipelineId]; + const pipelineInfo = pipelines?.[pipelineId]; if (!pipelineInfo) return null; return ( diff --git a/frontend/src/components/InputAndControlsPanel.tsx b/frontend/src/components/InputAndControlsPanel.tsx index ca3a0e060..2d00d5d79 100644 --- a/frontend/src/components/InputAndControlsPanel.tsx +++ b/frontend/src/components/InputAndControlsPanel.tsx @@ -15,7 +15,7 @@ import { LabelWithTooltip } from "./ui/label-with-tooltip"; import type { VideoSourceMode } from "../hooks/useVideoSource"; import type { PromptItem, PromptTransition } from "../lib/api"; import type { InputMode } from "../types"; -import { pipelineIsMultiMode } from "../data/pipelines"; +import type { PipelineInfo } from "../hooks/usePipelines"; import { PromptInput } from "./PromptInput"; import { TimelinePromptEditor } from "./TimelinePromptEditor"; import type { TimelinePrompt } from "./PromptTimeline"; @@ -24,6 +24,7 @@ import { Button } from "./ui/button"; interface InputAndControlsPanelProps { className?: string; + pipelines: Record | null; localStream: MediaStream | null; isInitializing: boolean; error: string | null; @@ -73,6 +74,7 @@ interface InputAndControlsPanelProps { export function InputAndControlsPanel({ className = "", + pipelines, localStream, isInitializing, error, @@ -128,7 +130,8 @@ export function InputAndControlsPanel({ const videoRef = useRef(null); // Check if this pipeline supports multiple input modes - const isMultiMode = pipelineIsMultiMode(pipelineId); + const pipeline = pipelines?.[pipelineId]; + const isMultiMode = (pipeline?.supportedModes?.length ?? 0) > 1; useEffect(() => { if (videoRef.current && localStream) { @@ -329,6 +332,11 @@ export function InputAndControlsPanel({ // The Input can have two states: Append (default) and Edit (when a prompt is selected and the video is paused) const isEditMode = selectedTimelinePrompt && isVideoPaused; + // Hide prompts section if pipeline doesn't support prompts + if (pipeline?.supportsPrompts === false) { + return null; + } + return (
@@ -358,7 +366,6 @@ export function InputAndControlsPanel({ onPromptsSubmit={onPromptsSubmit} onTransitionSubmit={onTransitionSubmit} disabled={ - pipelineId === "passthrough" || (_isTimelinePlaying && !isVideoPaused && !isAtEndOfTimeline()) || diff --git a/frontend/src/components/SettingsPanel.tsx b/frontend/src/components/SettingsPanel.tsx index d72e6768c..a59776620 100644 --- a/frontend/src/components/SettingsPanel.tsx +++ b/frontend/src/components/SettingsPanel.tsx @@ -19,12 +19,7 @@ import { Input } from "./ui/input"; import { Button } from "./ui/button"; import { Toggle } from "./ui/toggle"; import { SliderWithInput } from "./ui/slider-with-input"; -import { Hammer, Info, Minus, Plus, RotateCcw } from "lucide-react"; -import { - PIPELINES, - pipelineSupportsLoRA, - pipelineSupportsVACE, -} from "../data/pipelines"; +import { Info, Minus, Plus, RotateCcw } from "lucide-react"; import { PARAMETER_METADATA } from "../data/parameterMetadata"; import { DenoisingStepsSlider } from "./DenoisingStepsSlider"; import { useLocalSliderValue } from "../hooks/useLocalSliderValue"; @@ -35,12 +30,14 @@ import type { SettingsState, InputMode, } from "../types"; +import type { PipelineInfo } from "../hooks/usePipelines"; import { LoRAManager } from "./LoRAManager"; const MIN_DIMENSION = 16; interface SettingsPanelProps { className?: string; + pipelines: Record | null; pipelineId: PipelineId; onPipelineIdChange?: (pipelineId: PipelineId) => void; isStreaming?: boolean; @@ -89,6 +86,7 @@ interface SettingsPanelProps { export function SettingsPanel({ className = "", + pipelines, pipelineId, onPipelineIdChange, isStreaming = false, @@ -141,7 +139,7 @@ export function SettingsPanel({ const [seedError, setSeedError] = useState(null); const handlePipelineIdChange = (value: string) => { - if (value in PIPELINES) { + if (pipelines && value in pipelines) { onPipelineIdChange?.(value as PipelineId); } }; @@ -235,7 +233,7 @@ export function SettingsPanel({ handleSeedChange(newValue); }; - const currentPipeline = PIPELINES[pipelineId]; + const currentPipeline = pipelines?.[pipelineId]; return ( @@ -254,11 +252,12 @@ export function SettingsPanel({ - {Object.keys(PIPELINES).map(id => ( - - {id} - - ))} + {pipelines && + Object.keys(pipelines).map(id => ( + + {id} + + ))}
@@ -273,9 +272,7 @@ export function SettingsPanel({
- {(currentPipeline.about || - currentPipeline.docsUrl || - currentPipeline.modified) && ( + {(currentPipeline.about || currentPipeline.docsUrl) && (
{currentPipeline.about && ( @@ -294,26 +291,6 @@ export function SettingsPanel({ )} - {currentPipeline.modified && ( - - - - - - - - -

- This pipeline contains modifications based on the - original project. -

- - - - )} {currentPipeline.docsUrl && (
)} - {pipelineSupportsLoRA(pipelineId) && ( + {currentPipeline?.supportsLoRA && (
= { text: "A 3D animated scene. A **panda** walks along a path towards the camera in a park on a spring day.", video: "A 3D animated scene. A **panda** sitting in the grass, looking around.", }; -export interface PipelineInfo { - name: string; - about: string; - docsUrl?: string; - modified?: boolean; - estimatedVram?: number; // GB - requiresModels?: boolean; // Whether this pipeline requires models to be downloaded - defaultTemporalInterpolationMethod?: "linear" | "slerp"; // Default method for temporal interpolation - defaultTemporalInterpolationSteps?: number; // Default number of steps for temporal interpolation - supportsLoRA?: boolean; // Whether this pipeline supports LoRA adapters - supportsVACE?: boolean; // Whether this pipeline supports VACE (Video All-In-One Creation and Editing) - - // Multi-mode support - supportedModes: InputMode[]; - defaultMode: InputMode; -} - -export const PIPELINES: Record = { - streamdiffusionv2: { - name: "StreamDiffusionV2", - docsUrl: - "https://github.com/daydreamlive/scope/blob/main/src/scope/core/pipelines/streamdiffusionv2/docs/usage.md", - about: - "A streaming pipeline and autoregressive video diffusion model from the creators of the original StreamDiffusion project. The model is trained using Self-Forcing on Wan2.1 1.3b with modifications to support streaming. Includes VACE (All-In-One Video Creation and Editing) for reference image conditioning and structural guidance (depth, flow, pose).", - modified: true, - estimatedVram: 20, - requiresModels: true, - defaultTemporalInterpolationMethod: "slerp", - defaultTemporalInterpolationSteps: 0, - supportsLoRA: true, - supportsVACE: true, - // Multi-mode support - supportedModes: ["text", "video"], - defaultMode: "video", - }, - longlive: { - name: "LongLive", - docsUrl: - "https://github.com/daydreamlive/scope/blob/main/src/scope/core/pipelines/longlive/docs/usage.md", - about: - "A streaming pipeline and autoregressive video diffusion model from Nvidia, MIT, HKUST, HKU and THU. The model is trained using Self-Forcing on Wan2.1 1.3b with modifications to support smoother prompt switching and improved quality over longer time periods while maintaining fast generation. Includes VACE (All-In-One Video Creation and Editing) for reference image conditioning and structural guidance (depth, flow, pose).", - modified: true, - estimatedVram: 20, - requiresModels: true, - defaultTemporalInterpolationMethod: "slerp", - defaultTemporalInterpolationSteps: 0, - supportsLoRA: true, - supportsVACE: true, - // Multi-mode support - supportedModes: ["text", "video"], - defaultMode: "text", - }, - "krea-realtime-video": { - name: "Krea Realtime Video", - docsUrl: - "https://github.com/daydreamlive/scope/blob/main/src/scope/core/pipelines/krea_realtime_video/docs/usage.md", - about: - "A streaming pipeline and autoregressive video diffusion model from Krea. The model is trained using Self-Forcing on Wan2.1 14b.", - modified: true, - estimatedVram: 32, - requiresModels: true, - defaultTemporalInterpolationMethod: "linear", - defaultTemporalInterpolationSteps: 4, - supportsLoRA: true, - // Multi-mode support - supportedModes: ["text", "video"], - defaultMode: "text", - }, - "reward-forcing": { - name: "RewardForcing", - docsUrl: - "https://github.com/daydreamlive/scope/blob/main/src/scope/core/pipelines/reward_forcing/docs/usage.md", - about: - "A streaming pipeline and autoregressive video diffusion model from ZJU, Ant Group, SIAS-ZJU, HUST and SJTU. The model is trained with Rewarded Distribution Matching Distillation using Wan2.1 1.3b as the base model. Includes VACE (All-In-One Video Creation and Editing) for reference image conditioning and structural guidance (depth, flow, pose).", - modified: true, - estimatedVram: 20, - requiresModels: true, - defaultTemporalInterpolationMethod: "slerp", - defaultTemporalInterpolationSteps: 0, - supportsLoRA: true, - supportsVACE: true, - // Multi-mode support - supportedModes: ["text", "video"], - defaultMode: "text", - }, - passthrough: { - name: "Passthrough", - about: - "A pipeline that returns the input video without any processing that is useful for testing and debugging.", - requiresModels: false, - // Video-only pipeline - supportedModes: ["video"], - defaultMode: "video", - }, -}; - -export function pipelineSupportsLoRA(pipelineId: string): boolean { - return PIPELINES[pipelineId]?.supportsLoRA === true; -} - -export function pipelineSupportsVACE(pipelineId: string): boolean { - return PIPELINES[pipelineId]?.supportsVACE === true; -} - -export function pipelineSupportsMode( - pipelineId: string, - mode: InputMode -): boolean { - return PIPELINES[pipelineId]?.supportedModes?.includes(mode) ?? false; -} - -export function pipelineIsMultiMode(pipelineId: string): boolean { - const modes = PIPELINES[pipelineId]?.supportedModes ?? []; - return modes.length > 1; -} - -export function getPipelineDefaultMode(pipelineId: string): InputMode { - return PIPELINES[pipelineId]?.defaultMode ?? "text"; -} - export function getDefaultPromptForMode(mode: InputMode): string { return DEFAULT_PROMPTS[mode]; } diff --git a/frontend/src/hooks/usePipeline.ts b/frontend/src/hooks/usePipeline.ts index ccd681bb2..57e4b6e92 100644 --- a/frontend/src/hooks/usePipeline.ts +++ b/frontend/src/hooks/usePipeline.ts @@ -32,15 +32,16 @@ export function usePipeline(options: UsePipelineOptions = {}) { if (statusResponse.status === "error") { const errorMessage = statusResponse.error || "Unknown pipeline error"; - const fullMessage = `${errorMessage}. If this error persists, consider removing the models directory and re-downloading models.`; // Show toast if we haven't shown this error yet if (shownErrorRef.current !== errorMessage) { toast.error("Pipeline Error", { - description: fullMessage, - duration: 8000, + description: errorMessage, + duration: 10000, // Show longer for detailed errors }); shownErrorRef.current = errorMessage; } + // Ensure loading state is cleared when error occurs + setIsLoading(false); // Don't set error in state - it's shown as toast and cleared on backend setError(null); } else { @@ -55,7 +56,7 @@ export function usePipeline(options: UsePipelineOptions = {}) { if (shownErrorRef.current !== errorMessage) { toast.error("Pipeline Error", { description: errorMessage, - duration: 5000, + duration: 10000, // Show longer for detailed errors }); shownErrorRef.current = errorMessage; } @@ -88,15 +89,16 @@ export function usePipeline(options: UsePipelineOptions = {}) { if (statusResponse.status === "error") { const errorMessage = statusResponse.error || "Unknown pipeline error"; - const fullMessage = `${errorMessage}. If this error persists, consider removing the models directory and re-downloading models.`; // Show toast if we haven't shown this error yet if (shownErrorRef.current !== errorMessage) { toast.error("Pipeline Error", { - description: fullMessage, - duration: 8000, + description: errorMessage, + duration: 10000, // Show longer for detailed errors }); shownErrorRef.current = errorMessage; } + // Ensure loading state is cleared when error occurs + setIsLoading(false); // Don't set error in state - it's shown as toast and cleared on backend setError(null); } else { @@ -110,6 +112,7 @@ export function usePipeline(options: UsePipelineOptions = {}) { statusResponse.status === "error" ) { stopPolling(); + setIsLoading(false); // Ensure loading is stopped return; } } catch (err) { @@ -120,7 +123,7 @@ export function usePipeline(options: UsePipelineOptions = {}) { if (shownErrorRef.current !== errorMessage) { toast.error("Pipeline Error", { description: errorMessage, - duration: 5000, + duration: 10000, // Show longer for detailed errors }); shownErrorRef.current = errorMessage; } @@ -180,15 +183,15 @@ export function usePipeline(options: UsePipelineOptions = {}) { resolve(true); } else if (currentStatus.status === "error") { const errorMsg = currentStatus.error || "Pipeline load failed"; - const fullMessage = `${errorMsg}. If this error persists, consider removing the models directory and re-downloading models.`; // Show toast for load completion errors if (shownErrorRef.current !== errorMsg) { toast.error("Pipeline Error", { - description: fullMessage, - duration: 8000, + description: errorMsg, + duration: 10000, // Show longer for detailed errors }); shownErrorRef.current = errorMsg; } + setIsLoading(false); // Ensure loading is stopped on error reject(new Error(errorMsg)); } else { // Continue polling @@ -215,13 +218,12 @@ export function usePipeline(options: UsePipelineOptions = {}) { } catch (err) { const errorMessage = err instanceof Error ? err.message : "Failed to load pipeline"; - const fullMessage = `${errorMessage}. If this error persists, consider removing the models directory and re-downloading models.`; - console.error("Pipeline load error:", fullMessage); + console.error("Pipeline load error:", errorMessage); // Show toast for load errors if (shownErrorRef.current !== errorMessage) { toast.error("Pipeline Error", { - description: fullMessage, - duration: 8000, + description: errorMessage, + duration: 10000, // Show longer for detailed errors }); shownErrorRef.current = errorMessage; } diff --git a/frontend/src/hooks/usePipelines.ts b/frontend/src/hooks/usePipelines.ts new file mode 100644 index 000000000..c07d24f66 --- /dev/null +++ b/frontend/src/hooks/usePipelines.ts @@ -0,0 +1,82 @@ +import { useState, useEffect } from "react"; +import { getPipelineSchemas } from "../lib/api"; +import type { InputMode } from "../types"; + +export interface PipelineInfo { + name: string; + about: string; + docsUrl?: string | null; + estimatedVram?: number | null; + requiresModels?: boolean; + supportsPrompts?: boolean; + defaultTemporalInterpolationMethod?: "linear" | "slerp"; + defaultTemporalInterpolationSteps?: number; + supportsLoRA?: boolean; + supportsVACE?: boolean; + supportedModes: InputMode[]; + defaultMode: InputMode; +} + +export function usePipelines() { + const [pipelines, setPipelines] = useState | null>(null); + const [isLoading, setIsLoading] = useState(true); + const [error, setError] = useState(null); + + useEffect(() => { + let mounted = true; + + async function fetchPipelines() { + try { + setIsLoading(true); + const schemas = await getPipelineSchemas(); + + if (!mounted) return; + + // Transform to camelCase for TypeScript conventions + const transformed: Record = {}; + for (const [id, schema] of Object.entries(schemas.pipelines)) { + transformed[id] = { + name: schema.name, + about: schema.description, + supportedModes: schema.supported_modes as InputMode[], + defaultMode: schema.default_mode as InputMode, + supportsPrompts: schema.supports_prompts, + defaultTemporalInterpolationMethod: + schema.default_temporal_interpolation_method, + defaultTemporalInterpolationSteps: + schema.default_temporal_interpolation_steps, + docsUrl: schema.docs_url, + estimatedVram: schema.estimated_vram_gb, + requiresModels: schema.requires_models, + supportsLoRA: schema.supports_lora, + supportsVACE: schema.supports_vace, + }; + } + + setPipelines(transformed); + setError(null); + } catch (err) { + if (!mounted) return; + const errorMessage = + err instanceof Error ? err.message : "Failed to fetch pipelines"; + setError(errorMessage); + console.error("Failed to fetch pipelines:", err); + } finally { + if (mounted) { + setIsLoading(false); + } + } + } + + fetchPipelines(); + + return () => { + mounted = false; + }; + }, []); + + return { pipelines, isLoading, error }; +} diff --git a/frontend/src/hooks/useStreamState.ts b/frontend/src/hooks/useStreamState.ts index 95c7e60cb..c8c0fe310 100644 --- a/frontend/src/hooks/useStreamState.ts +++ b/frontend/src/hooks/useStreamState.ts @@ -13,11 +13,9 @@ import { type HardwareInfoResponse, type PipelineSchemasResponse, } from "../lib/api"; -import { getPipelineDefaultMode } from "../data/pipelines"; // Generic fallback defaults used before schemas are loaded. -// Resolution and denoising steps use conservative values; mode-specific -// values are derived from pipelines.ts when possible. +// Resolution and denoising steps use conservative values. const BASE_FALLBACK = { height: 512, width: 512, @@ -26,9 +24,9 @@ const BASE_FALLBACK = { }; // Get fallback defaults for a pipeline before schemas are loaded -// Derives mode from pipelines.ts to stay in sync with frontend definitions -function getFallbackDefaults(pipelineId: PipelineId, mode?: InputMode) { - const effectiveMode = mode ?? getPipelineDefaultMode(pipelineId); +function getFallbackDefaults(mode?: InputMode) { + // Default to text mode if no mode specified (will be corrected when schemas load) + const effectiveMode = mode ?? "text"; const isVideoMode = effectiveMode === "video"; // Video mode gets noise controls, text mode doesn't @@ -107,8 +105,7 @@ export function useStreamState() { }; } // Fallback to derived defaults if schemas not loaded - // Mode is derived from pipelines.ts to stay in sync - return getFallbackDefaults(pipelineId, mode); + return getFallbackDefaults(mode); }, [pipelineSchemas] ); @@ -137,7 +134,7 @@ export function useStreamState() { ); // Get initial defaults (use fallback since schemas haven't loaded yet) - const initialDefaults = getFallbackDefaults("streamdiffusionv2"); + const initialDefaults = getFallbackDefaults(); const [settings, setSettings] = useState({ pipelineId: "streamdiffusionv2", @@ -177,7 +174,27 @@ export function useStreamState() { ]); if (schemasResult.status === "fulfilled") { - setPipelineSchemas(schemasResult.value); + const schemas = schemasResult.value; + setPipelineSchemas(schemas); + + // Check if the default pipeline (streamdiffusionv2) is available + // If not, switch to the first available pipeline + const availablePipelines = Object.keys(schemas.pipelines); + const preferredPipeline = "streamdiffusionv2"; + + if ( + !availablePipelines.includes(preferredPipeline) && + availablePipelines.length > 0 + ) { + const firstPipelineId = availablePipelines[0] as PipelineId; + const firstPipelineSchema = schemas.pipelines[firstPipelineId]; + + setSettings(prev => ({ + ...prev, + pipelineId: firstPipelineId, + inputMode: firstPipelineSchema.default_mode, + })); + } } else { console.error( "useStreamState: Failed to fetch pipeline schemas:", @@ -201,6 +218,21 @@ export function useStreamState() { fetchInitialData(); }, []); + // Update inputMode when schemas load or pipeline changes + // This sets the correct default mode for the pipeline + useEffect(() => { + if (pipelineSchemas) { + const schema = pipelineSchemas.pipelines[settings.pipelineId]; + if (schema?.default_mode) { + setSettings(prev => ({ + ...prev, + inputMode: schema.default_mode, + })); + } + } + // Only run when schemas load or pipeline changes, NOT when inputMode changes + }, [pipelineSchemas, settings.pipelineId]); + // Set recommended quantization when krea-realtime-video is selected // Reset to null when switching to other pipelines useEffect(() => { diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts index bdd19f7f0..c36f320f7 100644 --- a/frontend/src/lib/api.ts +++ b/frontend/src/lib/api.ts @@ -423,10 +423,20 @@ export interface PipelineSchemaInfo { name: string; description: string; version: string; + docs_url: string | null; + estimated_vram_gb: number | null; + requires_models: boolean; + supports_lora: boolean; + supports_vace: boolean; + // Pipeline config schema config_schema: PipelineConfigSchema; // Mode support - comes from config class supported_modes: ("text" | "video")[]; default_mode: "text" | "video"; + // Prompt and temporal interpolation support + supports_prompts: boolean; + default_temporal_interpolation_method: "linear" | "slerp"; + default_temporal_interpolation_steps: number; // Mode-specific default overrides (optional) mode_defaults?: Record<"text" | "video", ModeDefaults>; } diff --git a/frontend/src/pages/StreamPage.tsx b/frontend/src/pages/StreamPage.tsx index a583ec5c4..1b30c9b02 100644 --- a/frontend/src/pages/StreamPage.tsx +++ b/frontend/src/pages/StreamPage.tsx @@ -12,12 +12,8 @@ import { useVideoSource } from "../hooks/useVideoSource"; import { useWebRTCStats } from "../hooks/useWebRTCStats"; import { usePipeline } from "../hooks/usePipeline"; import { useStreamState } from "../hooks/useStreamState"; -import { - PIPELINES, - getPipelineDefaultMode, - getDefaultPromptForMode, - pipelineSupportsVACE, -} from "../data/pipelines"; +import { usePipelines } from "../hooks/usePipelines"; +import { getDefaultPromptForMode } from "../data/pipelines"; import type { InputMode, PipelineId, @@ -66,6 +62,14 @@ function getVaceParams( } export function StreamPage() { + // Fetch available pipelines dynamically + const { pipelines } = usePipelines(); + + // Helper to get default mode for a pipeline + const getPipelineDefaultMode = (pipelineId: string): InputMode => { + return pipelines?.[pipelineId]?.defaultMode ?? "text"; + }; + // Use the stream state hook for settings management const { settings, @@ -253,7 +257,7 @@ export function StreamPage() { stopStream(); } - const newPipeline = PIPELINES[pipelineId]; + const newPipeline = pipelines?.[pipelineId]; const modeToUse = newPipeline?.defaultMode || "text"; const currentMode = settings.inputMode || "text"; @@ -339,7 +343,7 @@ export function StreamPage() { // Preserve the current input mode that the user selected before download // Only fall back to pipeline's default mode if no mode is currently set - const newPipeline = PIPELINES[pipelineId]; + const newPipeline = pipelines?.[pipelineId]; const currentMode = settings.inputMode || newPipeline?.defaultMode || "text"; const defaults = getDefaults(pipelineId, currentMode); @@ -610,9 +614,9 @@ export function StreamPage() { return () => document.removeEventListener("keydown", handleKeyDown); }, [selectedTimelinePrompt]); - // Update temporal interpolation defaults when pipeline changes + // Update temporal interpolation defaults and clear prompts when pipeline changes useEffect(() => { - const pipeline = PIPELINES[settings.pipelineId]; + const pipeline = pipelines?.[settings.pipelineId]; if (pipeline) { const defaultMethod = pipeline.defaultTemporalInterpolationMethod || "slerp"; @@ -620,8 +624,13 @@ export function StreamPage() { setTemporalInterpolationMethod(defaultMethod); setTransitionSteps(defaultSteps); + + // Clear prompts if pipeline doesn't support them + if (pipeline.supportsPrompts === false) { + setPromptItems([{ text: "", weight: 1.0 }]); + } } - }, [settings.pipelineId]); + }, [settings.pipelineId, pipelines]); const handlePlayPauseToggle = () => { const newPausedState = !settings.paused; @@ -661,7 +670,7 @@ export function StreamPage() { try { // Check if models are needed but not downloaded - const pipelineInfo = PIPELINES[pipelineIdToUse]; + const pipelineInfo = pipelines?.[pipelineIdToUse]; if (pipelineInfo?.requiresModels) { try { const status = await checkModelStatus(pipelineIdToUse); @@ -693,7 +702,7 @@ export function StreamPage() { // Compute VACE enabled state once - enabled by default for text mode on VACE-supporting pipelines const vaceEnabled = settings.vaceEnabled ?? - (pipelineSupportsVACE(pipelineIdToUse) && currentMode !== "video"); + (pipelines?.[pipelineIdToUse]?.supportsVACE && currentMode !== "video"); if (pipelineIdToUse === "streamdiffusionv2" && resolution) { loadParams = { @@ -801,7 +810,7 @@ export function StreamPage() { }; // Common parameters for pipelines that support prompts - if (pipelineIdToUse !== "passthrough") { + if (pipelineInfo?.supportsPrompts !== false) { initialParameters.prompts = promptItems; initialParameters.prompt_interpolation_method = interpolationMethod; initialParameters.denoising_step_list = settings.denoisingSteps || [ @@ -872,6 +881,7 @@ export function StreamPage() {
=1.13.0", "fastapi>=0.116.1", "httpx>=0.28.1", + "pyzmq>=26.0.0", "twilio>=9.8.0", "uvicorn>=0.35.0", "torch==2.8.0", - "torchvision==0.23.0", - "easydict>=1.13", - "diffusers>=0.31.0", - "ftfy>=6.3.1", - "transformers>=4.49.0", - "einops>=0.8.1", - "lmdb>=1.7.3", - "omegaconf>=2.3.0", - "accelerate>=1.1.1", - "flash-attn==2.8.3; sys_platform == 'linux' or sys_platform == 'win32'", - "sageattention==2.2.0; sys_platform == 'linux' or sys_platform == 'win32'", - "safetensors>=0.6.2", "huggingface_hub>=0.25.0", - "peft>=0.17.1", - "torchao==0.13.0", - "kernels>=0.10.4", - "triton==3.4.0; sys_platform == 'linux'", - "triton-windows==3.4.0.post21; sys_platform == 'win32'", - "SpoutGL>=0.1.1; sys_platform == 'win32'", - "PyOpenGL>=3.1.10; sys_platform == 'win32'", + "pluggy>=1.5.0", + "click>=8.3.1", + "tomli>=2.0.0; python_version < '3.11'", ] [project.scripts] @@ -66,6 +51,9 @@ Homepage = "https://github.com/daydreamlive/scope" Repository = "https://github.com/daydreamlive/scope" Issues = "https://github.com/daydreamlive/scope/issues" +[tool.uv] +preview = true + [tool.uv.extra-build-dependencies] flash-attn = [{ requirement = "torch", match-runtime = true, marker = "sys_platform == 'linux' or sys_platform == 'win32'" }] @@ -76,21 +64,6 @@ flash-attn = { FLASH_ATTENTION_SKIP_CUDA_BUILD = "TRUE" } torch = [ { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, ] -torchvision = [ - { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, -] -flash-attn = [ - # Prebuilt Linux wheels from https://github.com/Dao-AILab/flash-attention - { url = "https://github.com/Dao-AILab/flash-attention/releases/download/v2.8.3/flash_attn-2.8.3+cu12torch2.8cxx11abiFALSE-cp310-cp310-linux_x86_64.whl", marker = "sys_platform == 'linux'" }, - # Prebuilt Windows wheels from https://github.com/kingbri1/flash-attention - { url = "https://github.com/kingbri1/flash-attention/releases/download/v2.8.3/flash_attn-2.8.3+cu128torch2.8.0cxx11abiFALSE-cp310-cp310-win_amd64.whl", marker = "sys_platform == 'win32'" }, -] -sageattention = [ - # Prebuilt Linux wheels from https://github.com/daydreamlive/SageAttention - { url = "https://github.com/daydreamlive/SageAttention/releases/download/v2.2.0-linux/sageattention-2.2.0-cp310-cp310-linux_x86_64.whl", marker = "sys_platform == 'linux'" }, - # Prebuilt Windows wheels from https://github.com/woct0rdho/SageAttention/releases - { url = "https://github.com/woct0rdho/SageAttention/releases/download/v2.2.0-windows.post3/sageattention-2.2.0+cu128torch2.8.0.post3-cp39-abi3-win_amd64.whl", marker = "sys_platform == 'win32'" }, -] [[tool.uv.index]] name = "pytorch-cu128" diff --git a/src/scope/core/__init__.py b/src/scope/core/__init__.py index e69de29bb..4abf2c701 100644 --- a/src/scope/core/__init__.py +++ b/src/scope/core/__init__.py @@ -0,0 +1,13 @@ +"""Core functionality for Scope.""" + + +def __getattr__(name): + """Lazy import for plugin hooks to avoid requiring pluggy in worker environments.""" + if name == "hookimpl": + from scope.core.plugins import hookimpl + + return hookimpl + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") + + +__all__ = ["hookimpl"] diff --git a/src/scope/core/pipelines/krea_realtime_video/pipeline.py b/src/scope/core/pipelines/krea_realtime_video/pipeline.py index 0db8065c7..5ab26ccaa 100644 --- a/src/scope/core/pipelines/krea_realtime_video/pipeline.py +++ b/src/scope/core/pipelines/krea_realtime_video/pipeline.py @@ -21,7 +21,6 @@ from ..wan2_1.lora.mixin import LoRAEnabledPipeline from ..wan2_1.vae import WanVAEWrapper from .modular_blocks import KreaRealtimeVideoBlocks -from .modules.causal_model import CausalWanModel if TYPE_CHECKING: from ..schema import BasePipelineConfig @@ -49,6 +48,8 @@ def __init__( device: torch.device | None = None, dtype: torch.dtype = torch.bfloat16, ): + from .modules.causal_model import CausalWanModel + model_dir = getattr(config, "model_dir", None) generator_path = getattr(config, "generator_path", None) text_encoder_path = getattr(config, "text_encoder_path", None) diff --git a/src/scope/core/pipelines/krea_realtime_video/pyproject.toml b/src/scope/core/pipelines/krea_realtime_video/pyproject.toml new file mode 100644 index 000000000..a65c36f9b --- /dev/null +++ b/src/scope/core/pipelines/krea_realtime_video/pyproject.toml @@ -0,0 +1,51 @@ +[project] +name = "scope-pipeline-krea-realtime-video" +version = "0.1.0" +description = "KREA Realtime Video pipeline for daydream-scope" +requires-python = ">=3.10.12,<3.11" +dependencies = [ + "scope-worker-base", + "aiortc>=1.13.0", + "torch==2.8.0", + "torchvision==0.23.0", + "easydict>=1.13", + "diffusers>=0.31.0", + "ftfy>=6.3.1", + "transformers>=4.49.0", + "einops>=0.8.1", + "lmdb>=1.7.3", + "omegaconf>=2.3.0", + "accelerate>=1.1.1", + "flash-attn==2.8.3; sys_platform == 'linux' or sys_platform == 'win32'", + "sageattention==2.2.0; sys_platform == 'linux' or sys_platform == 'win32'", + "safetensors>=0.6.2", + "huggingface_hub>=0.25.0", + "peft>=0.17.1", + "torchcodec==0.7.0", + "torchao==0.13.0", + "kernels>=0.10.4", + "triton==3.4.0; sys_platform != 'win32'", + "triton-windows==3.4.0.post21; sys_platform == 'win32'", +] + +[tool.uv.sources] +scope-worker-base = { path = "../../../../../worker_env", editable = true } +torch = [ + { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, +] +torchvision = [ + { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, +] +flash-attn = [ + { url = "https://github.com/Dao-AILab/flash-attention/releases/download/v2.8.3/flash_attn-2.8.3+cu12torch2.8cxx11abiFALSE-cp310-cp310-linux_x86_64.whl", marker = "sys_platform == 'linux'" }, + { url = "https://github.com/kingbri1/flash-attention/releases/download/v2.8.3/flash_attn-2.8.3+cu128torch2.8.0cxx11abiFALSE-cp310-cp310-win_amd64.whl", marker = "sys_platform == 'win32'" }, +] +sageattention = [ + { url = "https://github.com/daydreamlive/SageAttention/releases/download/v2.2.0-linux/sageattention-2.2.0-cp310-cp310-linux_x86_64.whl", marker = "sys_platform == 'linux'" }, + { url = "https://github.com/woct0rdho/SageAttention/releases/download/v2.2.0-windows.post3/sageattention-2.2.0+cu128torch2.8.0.post3-cp39-abi3-win_amd64.whl", marker = "sys_platform == 'win32'" }, +] + +[[tool.uv.index]] +name = "pytorch-cu128" +url = "https://download.pytorch.org/whl/cu128" +explicit = true diff --git a/src/scope/core/pipelines/longlive/pipeline.py b/src/scope/core/pipelines/longlive/pipeline.py index 67d76b9a8..b2dfe99fd 100644 --- a/src/scope/core/pipelines/longlive/pipeline.py +++ b/src/scope/core/pipelines/longlive/pipeline.py @@ -23,7 +23,6 @@ from ..wan2_1.vace import VACEEnabledPipeline from ..wan2_1.vae import WanVAEWrapper from .modular_blocks import LongLiveBlocks -from .modules.causal_model import CausalWanModel if TYPE_CHECKING: from ..schema import BasePipelineConfig @@ -45,6 +44,8 @@ def __init__( device: torch.device | None = None, dtype: torch.dtype = torch.bfloat16, ): + from .modules.causal_model import CausalWanModel + model_dir = getattr(config, "model_dir", None) generator_path = getattr(config, "generator_path", None) lora_path = getattr(config, "lora_path", None) diff --git a/src/scope/core/pipelines/longlive/pyproject.toml b/src/scope/core/pipelines/longlive/pyproject.toml new file mode 100644 index 000000000..c082545b4 --- /dev/null +++ b/src/scope/core/pipelines/longlive/pyproject.toml @@ -0,0 +1,51 @@ +[project] +name = "scope-pipeline-longlive" +version = "0.1.0" +description = "LongLive pipeline for daydream-scope" +requires-python = ">=3.10.12,<3.11" +dependencies = [ + "scope-worker-base", + "aiortc>=1.13.0", + "torch==2.8.0", + "torchvision==0.23.0", + "easydict>=1.13", + "diffusers>=0.31.0", + "ftfy>=6.3.1", + "transformers>=4.49.0", + "einops>=0.8.1", + "lmdb>=1.7.3", + "omegaconf>=2.3.0", + "accelerate>=1.1.1", + "flash-attn==2.8.3; sys_platform == 'linux' or sys_platform == 'win32'", + "sageattention==2.2.0; sys_platform == 'linux' or sys_platform == 'win32'", + "safetensors>=0.6.2", + "huggingface_hub>=0.25.0", + "peft>=0.17.1", + "torchcodec==0.7.0", + "torchao==0.13.0", + "kernels>=0.10.4", + "triton==3.4.0; sys_platform != 'win32'", + "triton-windows==3.4.0.post21; sys_platform == 'win32'", +] + +[tool.uv.sources] +scope-worker-base = { path = "../../../../../worker_env", editable = true } +torch = [ + { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, +] +torchvision = [ + { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, +] +flash-attn = [ + { url = "https://github.com/Dao-AILab/flash-attention/releases/download/v2.8.3/flash_attn-2.8.3+cu12torch2.8cxx11abiFALSE-cp310-cp310-linux_x86_64.whl", marker = "sys_platform == 'linux'" }, + { url = "https://github.com/kingbri1/flash-attention/releases/download/v2.8.3/flash_attn-2.8.3+cu128torch2.8.0cxx11abiFALSE-cp310-cp310-win_amd64.whl", marker = "sys_platform == 'win32'" }, +] +sageattention = [ + { url = "https://github.com/daydreamlive/SageAttention/releases/download/v2.2.0-linux/sageattention-2.2.0-cp310-cp310-linux_x86_64.whl", marker = "sys_platform == 'linux'" }, + { url = "https://github.com/woct0rdho/SageAttention/releases/download/v2.2.0-windows.post3/sageattention-2.2.0+cu128torch2.8.0.post3-cp39-abi3-win_amd64.whl", marker = "sys_platform == 'win32'" }, +] + +[[tool.uv.index]] +name = "pytorch-cu128" +url = "https://download.pytorch.org/whl/cu128" +explicit = true diff --git a/src/scope/core/pipelines/longlive/uv.lock b/src/scope/core/pipelines/longlive/uv.lock new file mode 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It enables dynamic pipeline discovery and metadata retrieval. + +The registry separates config classes (lightweight, no heavy imports) from +pipeline classes (heavy imports, loaded lazily only when needed by worker). """ +import importlib +import logging from typing import TYPE_CHECKING +import torch + if TYPE_CHECKING: from .interface import Pipeline from .schema import BasePipelineConfig +logger = logging.getLogger(__name__) + class PipelineRegistry: - """Registry for managing available pipelines.""" + """Registry for managing available pipelines. - _pipelines: dict[str, type["Pipeline"]] = {} + Stores config classes (lightweight) and module paths for lazy loading + pipeline implementations (heavy imports) only when needed. + """ + + # Map pipeline_id -> config class (lightweight, always available) + _configs: dict[str, type["BasePipelineConfig"]] = {} + + # Map pipeline_id -> (module_path, class_name) for lazy loading + _pipeline_modules: dict[str, tuple[str, str]] = {} @classmethod - def register(cls, pipeline_id: str, pipeline_class: type["Pipeline"]) -> None: - """Register a pipeline class with its ID. + def register( + cls, + pipeline_id: str, + config_class: type["BasePipelineConfig"], + module_path: str | None = None, + class_name: str | None = None, + ) -> None: + """Register a pipeline with its config class and optional module path. Args: pipeline_id: Unique identifier for the pipeline - pipeline_class: Pipeline class to register + config_class: Pipeline config class (lightweight, from schema.py) + module_path: Optional module path for lazy loading pipeline class + class_name: Optional class name for lazy loading pipeline class """ - cls._pipelines[pipeline_id] = pipeline_class + cls._configs[pipeline_id] = config_class + if module_path and class_name: + cls._pipeline_modules[pipeline_id] = (module_path, class_name) @classmethod def get(cls, pipeline_id: str) -> type["Pipeline"] | None: - """Get a pipeline class by its ID. + """Get a pipeline class by its ID (lazy loads the module). + + This should only be called by the worker process, not the main server. Args: pipeline_id: Pipeline identifier @@ -37,22 +66,31 @@ def get(cls, pipeline_id: str) -> type["Pipeline"] | None: Returns: Pipeline class if found, None otherwise """ - return cls._pipelines.get(pipeline_id) + if pipeline_id not in cls._pipeline_modules: + return None + + module_path, class_name = cls._pipeline_modules[pipeline_id] + try: + module = importlib.import_module(module_path, package=__package__) + return getattr(module, class_name) + except ImportError as e: + logger.error(f"Failed to import pipeline {pipeline_id}: {e}") + return None @classmethod def get_config_class(cls, pipeline_id: str) -> type["BasePipelineConfig"] | None: """Get config class for a specific pipeline. + This is safe to call from the main server as it doesn't trigger + heavy pipeline imports. + Args: pipeline_id: Pipeline identifier Returns: Pydantic config class if found, None otherwise """ - pipeline_class = cls.get(pipeline_id) - if pipeline_class is None: - return None - return pipeline_class.get_config_class() + return cls._configs.get(pipeline_id) @classmethod def list_pipelines(cls) -> list[str]: @@ -61,30 +99,180 @@ def list_pipelines(cls) -> list[str]: Returns: List of pipeline IDs """ - return list(cls._pipelines.keys()) + return list(cls._configs.keys()) + + +def _get_gpu_vram_gb() -> float | None: + """Get total GPU VRAM in GB if available. + + Returns: + Total VRAM in GB if GPU is available, None otherwise + """ + try: + if torch.cuda.is_available(): + _, total_mem = torch.cuda.mem_get_info(0) + return total_mem / (1024**3) + except Exception as e: + logger.warning(f"Failed to get GPU VRAM info: {e}") + return None + + +def _should_register_pipeline( + estimated_vram_gb: float | None, vram_gb: float | None +) -> bool: + """Determine if a pipeline should be registered based on GPU requirements. + + Args: + estimated_vram_gb: Estimated/required VRAM in GB from pipeline config, + or None if no requirement + vram_gb: Total GPU VRAM in GB, or None if no GPU + + Returns: + True if the pipeline should be registered, False otherwise + """ + return estimated_vram_gb is None or vram_gb is not None # Register all available pipelines def _register_pipelines(): - """Register all built-in pipelines.""" - # Import lazily to avoid circular imports and heavy dependencies - from .krea_realtime_video.pipeline import KreaRealtimeVideoPipeline - from .longlive.pipeline import LongLivePipeline - from .passthrough.pipeline import PassthroughPipeline - from .reward_forcing.pipeline import RewardForcingPipeline - from .streamdiffusionv2.pipeline import StreamDiffusionV2Pipeline - - # Register each pipeline with its ID from its config class - for pipeline_class in [ - LongLivePipeline, - KreaRealtimeVideoPipeline, - StreamDiffusionV2Pipeline, - PassthroughPipeline, - RewardForcingPipeline, - ]: - config_class = pipeline_class.get_config_class() - PipelineRegistry.register(config_class.pipeline_id, pipeline_class) + """Register pipelines based on GPU availability and requirements. + + Uses config classes from schema.py (no heavy imports) and stores + module paths for lazy loading when worker needs them. + """ + from .schema import ( + PIPELINE_CONFIGS, + KreaRealtimeVideoConfig, + LongLiveConfig, + PassthroughConfig, + RewardForcingConfig, + StreamDiffusionV2Config, + ) + + # Check GPU VRAM + vram_gb = _get_gpu_vram_gb() + + if vram_gb is not None: + logger.info(f"GPU detected with {vram_gb:.1f} GB VRAM") + else: + logger.info("No GPU detected") + + # Define pipeline module paths for lazy loading + # Maps config class -> (module_path, class_name) + pipeline_module_paths = { + PassthroughConfig: (".passthrough.pipeline", "PassthroughPipeline"), + LongLiveConfig: (".longlive.pipeline", "LongLivePipeline"), + KreaRealtimeVideoConfig: ( + ".krea_realtime_video.pipeline", + "KreaRealtimeVideoPipeline", + ), + StreamDiffusionV2Config: ( + ".streamdiffusionv2.pipeline", + "StreamDiffusionV2Pipeline", + ), + RewardForcingConfig: (".reward_forcing.pipeline", "RewardForcingPipeline"), + } + + # Register each pipeline using its config class + for pipeline_id, config_class in PIPELINE_CONFIGS.items(): + estimated_vram_gb = config_class.estimated_vram_gb + + # Check if pipeline meets GPU requirements + should_register = _should_register_pipeline(estimated_vram_gb, vram_gb) + if not should_register: + logger.debug( + f"Skipping {pipeline_id} pipeline - " + f"does not meet GPU requirements " + f"(required: {estimated_vram_gb} GB, " + f"available: {vram_gb} GB)" + ) + continue + + # Get module path for lazy loading + module_info = pipeline_module_paths.get(config_class) + if module_info: + module_path, class_name = module_info + else: + module_path, class_name = None, None + + # Register the pipeline with its config class and module path + PipelineRegistry.register( + pipeline_id, config_class, module_path, class_name + ) + logger.debug(f"Registered {pipeline_id} pipeline") + + +def _create_plugin_config_class(pipeline_id: str, name: str) -> type["BasePipelineConfig"]: + """Create a minimal config class for a plugin pipeline. + + This allows plugins to appear in the pipelines list without importing + their heavy dependencies. + + Args: + pipeline_id: The pipeline identifier + name: Display name for the pipeline + + Returns: + A dynamically created config class + """ + from .schema import BasePipelineConfig + + # Create a new class that inherits from BasePipelineConfig + config_class = type( + f"{name.replace('-', '_').title()}Config", + (BasePipelineConfig,), + { + "pipeline_id": pipeline_id, + "pipeline_name": name, + "pipeline_description": f"Plugin pipeline: {name}", + "requires_models": True, # Assume plugins need models + }, + ) + return config_class + + +def _register_directory_plugins(): + """Register plugins discovered from the plugins directory. + + These plugins are just copied to the plugins dir, not installed as packages. + We create minimal config classes so they appear in the pipelines list. + """ + from scope.core.plugins import discover_directory_plugins + + for plugin_info in discover_directory_plugins(): + pipeline_id = plugin_info["pipeline_id"] + name = plugin_info.get("name", pipeline_id) + + # Skip if already registered (e.g., via entry points) + if pipeline_id in PipelineRegistry._configs: + logger.debug(f"Plugin {pipeline_id} already registered, skipping") + continue + + # Create a minimal config class for the plugin + config_class = _create_plugin_config_class(pipeline_id, name) + + # Register with no module path - the worker will discover it from the plugins dir + PipelineRegistry.register(pipeline_id, config_class) + logger.debug(f"Registered directory plugin: {pipeline_id}") + + +def _initialize_registry(): + """Initialize registry with built-in pipelines and plugins.""" + # Register built-in pipelines first + _register_pipelines() + + # Load and register plugin pipelines via entry points (installed packages) + from scope.core.plugins import load_plugins, register_plugin_pipelines + + load_plugins() + register_plugin_pipelines(PipelineRegistry) + + # Also discover plugins from the plugins directory (copied plugins) + _register_directory_plugins() + + pipeline_count = len(PipelineRegistry.list_pipelines()) + logger.info(f"Registry initialized with {pipeline_count} pipeline(s)") # Auto-register pipelines on module import -_register_pipelines() +_initialize_registry() diff --git a/src/scope/core/pipelines/reward_forcing/pipeline.py b/src/scope/core/pipelines/reward_forcing/pipeline.py index 094f40bc8..ddb281928 100644 --- a/src/scope/core/pipelines/reward_forcing/pipeline.py +++ b/src/scope/core/pipelines/reward_forcing/pipeline.py @@ -22,7 +22,6 @@ from ..wan2_1.vace.mixin import VACEEnabledPipeline from ..wan2_1.vae import WanVAEWrapper from .modular_blocks import RewardForcingBlocks -from .modules.causal_model import CausalWanModel if TYPE_CHECKING: from ..schema import BasePipelineConfig @@ -44,6 +43,8 @@ def __init__( device: torch.device | None = None, dtype: torch.dtype = torch.bfloat16, ): + from .modules.causal_model import CausalWanModel + model_dir = getattr(config, "model_dir", None) generator_path = getattr(config, "generator_path", None) text_encoder_path = getattr(config, "text_encoder_path", None) diff --git a/src/scope/core/pipelines/schema.py b/src/scope/core/pipelines/schema.py index 73b9e68aa..9cfec81ab 100644 --- a/src/scope/core/pipelines/schema.py +++ b/src/scope/core/pipelines/schema.py @@ -59,11 +59,23 @@ class BasePipelineConfig(BaseModel): pipeline_name: ClassVar[str] = "Base Pipeline" pipeline_description: ClassVar[str] = "Base pipeline configuration" pipeline_version: ClassVar[str] = "1.0.0" + docs_url: ClassVar[str | None] = None + estimated_vram_gb: ClassVar[float | None] = None + requires_models: ClassVar[bool] = False + supports_lora: ClassVar[bool] = False + supports_vace: ClassVar[bool] = False # Mode support - override in subclasses supported_modes: ClassVar[list[InputMode]] = ["text"] default_mode: ClassVar[InputMode] = "text" + # Prompt and temporal interpolation support + supports_prompts: ClassVar[bool] = True + default_temporal_interpolation_method: ClassVar[Literal["linear", "slerp"]] = ( + "slerp" + ) + default_temporal_interpolation_steps: ClassVar[int] = 0 + # Resolution settings height: int = Field(default=512, ge=1, description="Output height in pixels") width: int = Field(default=512, ge=1, description="Output width in pixels") @@ -154,16 +166,23 @@ def get_schema_with_metadata(cls) -> dict[str, Any]: This is the primary method for API/UI schema generation. Returns: - Dict containing: - - Pipeline metadata (id, name, description, version) - - supported_modes: List of supported input modes - - default_mode: Default input mode - - mode_defaults: Dict of mode-specific default overrides - - config_schema: Full JSON schema for the config model + Dict containing pipeline metadata """ metadata = cls.get_pipeline_metadata() metadata["supported_modes"] = cls.supported_modes metadata["default_mode"] = cls.default_mode + metadata["supports_prompts"] = cls.supports_prompts + metadata["default_temporal_interpolation_method"] = ( + cls.default_temporal_interpolation_method + ) + metadata["default_temporal_interpolation_steps"] = ( + cls.default_temporal_interpolation_steps + ) + metadata["docs_url"] = cls.docs_url + metadata["estimated_vram_gb"] = cls.estimated_vram_gb + metadata["requires_models"] = cls.requires_models + metadata["supports_lora"] = cls.supports_lora + metadata["supports_vace"] = cls.supports_vace metadata["config_schema"] = cls.model_json_schema() # Include mode-specific defaults if defined @@ -198,8 +217,17 @@ class LongLiveConfig(BasePipelineConfig): pipeline_id: ClassVar[str] = "longlive" pipeline_name: ClassVar[str] = "LongLive" pipeline_description: ClassVar[str] = ( - "Long-form video generation with temporal consistency" + "A streaming pipeline and autoregressive video diffusion model from Nvidia, MIT, HKUST, HKU and THU. " + "The model is trained using Self-Forcing on Wan2.1 1.3b with modifications to support smoother prompt " + "switching and improved quality over longer time periods while maintaining fast generation." + ) + docs_url: ClassVar[str | None] = ( + "https://github.com/daydreamlive/scope/blob/main/src/scope/core/pipelines/longlive/docs/usage.md" ) + estimated_vram_gb: ClassVar[float | None] = 20.0 + requires_models: ClassVar[bool] = True + supports_lora: ClassVar[bool] = True + supports_vace: ClassVar[bool] = True # Mode support supported_modes: ClassVar[list[InputMode]] = ["text", "video"] @@ -258,10 +286,19 @@ class StreamDiffusionV2Config(BasePipelineConfig): """ pipeline_id: ClassVar[str] = "streamdiffusionv2" - pipeline_name: ClassVar[str] = "StreamDiffusion V2" + pipeline_name: ClassVar[str] = "StreamDiffusionV2" pipeline_description: ClassVar[str] = ( - "Real-time video-to-video generation with temporal consistency" + "A streaming pipeline and autoregressive video diffusion model from the creators of the original " + "StreamDiffusion project. The model is trained using Self-Forcing on Wan2.1 1.3b with modifications " + "to support streaming." ) + docs_url: ClassVar[str | None] = ( + "https://github.com/daydreamlive/scope/blob/main/src/scope/core/pipelines/streamdiffusionv2/docs/usage.md" + ) + estimated_vram_gb: ClassVar[float | None] = 20.0 + requires_models: ClassVar[bool] = True + supports_lora: ClassVar[bool] = True + supports_vace: ClassVar[bool] = True # Mode support supported_modes: ClassVar[list[InputMode]] = ["text", "video"] @@ -329,8 +366,20 @@ class KreaRealtimeVideoConfig(BasePipelineConfig): pipeline_id: ClassVar[str] = "krea-realtime-video" pipeline_name: ClassVar[str] = "Krea Realtime Video" pipeline_description: ClassVar[str] = ( - "High-quality real-time video generation with 14B model" + "A streaming pipeline and autoregressive video diffusion model from Krea. " + "The model is trained using Self-Forcing on Wan2.1 14b." + ) + docs_url: ClassVar[str | None] = ( + "https://github.com/daydreamlive/scope/blob/main/src/scope/core/pipelines/krea_realtime_video/docs/usage.md" ) + estimated_vram_gb: ClassVar[float | None] = 32.0 + requires_models: ClassVar[bool] = True + supports_lora: ClassVar[bool] = True + + default_temporal_interpolation_method: ClassVar[Literal["linear", "slerp"]] = ( + "linear" + ) + default_temporal_interpolation_steps: ClassVar[int] = 4 # Mode support supported_modes: ClassVar[list[InputMode]] = ["text", "video"] @@ -379,8 +428,16 @@ class RewardForcingConfig(BasePipelineConfig): pipeline_id: ClassVar[str] = "reward-forcing" pipeline_name: ClassVar[str] = "RewardForcing" pipeline_description: ClassVar[str] = ( - "Efficient streaming video generation with rewarded distribution matching distillation" + "A streaming pipeline and autoregressive video diffusion model from ZJU, Ant Group, SIAS-ZJU, HUST and SJTU. " + "The model is trained with Rewarded Distribution Matching Distillation using Wan2.1 1.3b as the base model." + ) + docs_url: ClassVar[str | None] = ( + "https://github.com/daydreamlive/scope/blob/main/src/scope/core/pipelines/reward_forcing/docs/usage.md" ) + estimated_vram_gb: ClassVar[float | None] = 20.0 + requires_models: ClassVar[bool] = True + supports_lora: ClassVar[bool] = True + supports_vace: ClassVar[bool] = True # Mode support supported_modes: ClassVar[list[InputMode]] = ["text", "video"] @@ -439,12 +496,17 @@ class PassthroughConfig(BasePipelineConfig): pipeline_id: ClassVar[str] = "passthrough" pipeline_name: ClassVar[str] = "Passthrough" - pipeline_description: ClassVar[str] = "Passthrough pipeline for testing" + pipeline_description: ClassVar[str] = ( + "A pipeline that returns the input video without any processing that is useful for testing and debugging." + ) # Mode support - video only supported_modes: ClassVar[list[InputMode]] = ["video"] default_mode: ClassVar[InputMode] = "video" + # Does not support prompts + supports_prompts: ClassVar[bool] = False + # Passthrough defaults - requires video input (distinct from StreamDiffusionV2) height: int = Field(default=512, ge=1, description="Output height in pixels") width: int = Field(default=512, ge=1, description="Output width in pixels") diff --git a/src/scope/core/pipelines/streamdiffusionv2/pipeline.py b/src/scope/core/pipelines/streamdiffusionv2/pipeline.py index 55085eeaa..0055f96f0 100644 --- a/src/scope/core/pipelines/streamdiffusionv2/pipeline.py +++ b/src/scope/core/pipelines/streamdiffusionv2/pipeline.py @@ -22,7 +22,6 @@ from ..wan2_1.vace import VACEEnabledPipeline from .components import StreamDiffusionV2WanVAEWrapper from .modular_blocks import StreamDiffusionV2Blocks -from .modules.causal_model import CausalWanModel if TYPE_CHECKING: from ..schema import BasePipelineConfig @@ -44,6 +43,8 @@ def __init__( device: torch.device | None = None, dtype: torch.dtype = torch.bfloat16, ): + from .modules.causal_model import CausalWanModel + model_dir = getattr(config, "model_dir", None) generator_path = getattr(config, "generator_path", None) text_encoder_path = getattr(config, "text_encoder_path", None) diff --git a/src/scope/core/pipelines/streamdiffusionv2/pyproject.toml b/src/scope/core/pipelines/streamdiffusionv2/pyproject.toml new file mode 100644 index 000000000..b3ebc0291 --- /dev/null +++ b/src/scope/core/pipelines/streamdiffusionv2/pyproject.toml @@ -0,0 +1,51 @@ +[project] +name = "scope-pipeline-streamdiffusionv2" +version = "0.1.0" +description = "StreamDiffusionV2 pipeline for daydream-scope" +requires-python = ">=3.10.12,<3.11" +dependencies = [ + "scope-worker-base", + "aiortc>=1.13.0", + "torch==2.8.0", + "torchvision==0.23.0", + "easydict>=1.13", + "diffusers>=0.31.0", + "ftfy>=6.3.1", + "transformers>=4.49.0", + "einops>=0.8.1", + "lmdb>=1.7.3", + "omegaconf>=2.3.0", + "accelerate>=1.1.1", + "flash-attn==2.8.3; sys_platform == 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specifications for the Scope plugin system.""" + +import pluggy + +hookspec = pluggy.HookspecMarker("scope") +hookimpl = pluggy.HookimplMarker("scope") + + +class ScopeHookSpec: + """Hook specifications for Scope plugins.""" + + @hookspec + def register_pipelines(self, register): + """Register custom pipeline implementations. + + Args: + register: Callback to register pipeline classes. + Usage: register(PipelineClass) + + Example: + @scope.core.hookimpl + def register_pipelines(register): + register(MyPipeline) + """ diff --git a/src/scope/core/plugins/manager.py b/src/scope/core/plugins/manager.py new file mode 100644 index 000000000..9758fd676 --- /dev/null +++ b/src/scope/core/plugins/manager.py @@ -0,0 +1,114 @@ +"""Plugin manager for discovering and loading Scope plugins.""" + +import logging +from pathlib import Path + +import pluggy + +from .hookspecs import ScopeHookSpec + +logger = logging.getLogger(__name__) + +# Create the plugin manager singleton +pm = pluggy.PluginManager("scope") +pm.add_hookspecs(ScopeHookSpec) + + +def load_plugins(): + """Discover and load all plugins via entry points.""" + pm.load_setuptools_entrypoints("scope") + logger.info(f"Loaded {len(pm.get_plugins())} plugin(s)") + + +def _get_plugins_dir() -> Path: + """Get the plugins directory path.""" + # Import here to avoid circular imports + from scope.server.models_config import get_plugins_dir + + return get_plugins_dir() + + +def _parse_plugin_pyproject(pyproject_path: Path) -> dict | None: + """Parse a plugin's pyproject.toml to extract metadata. + + Returns: + Dict with 'pipeline_id' and 'name' if found, None otherwise + """ + try: + # Try tomllib (Python 3.11+) first, fall back to tomli for Python 3.10 + try: + import tomllib + except ImportError: + import tomli as tomllib + + with open(pyproject_path, "rb") as f: + data = tomllib.load(f) + + # Get entry points to find pipeline_id + entry_points = data.get("project", {}).get("entry-points", {}).get("scope", {}) + if entry_points: + # Use the first entry point name as pipeline_id + pipeline_id = next(iter(entry_points.keys())) + return { + "pipeline_id": pipeline_id, + "name": data.get("project", {}).get("name", pipeline_id), + } + return None + except Exception as e: + logger.warning(f"Failed to parse {pyproject_path}: {e}") + return None + + +def discover_directory_plugins() -> list[dict]: + """Discover plugins installed in the plugins directory. + + Returns: + List of dicts with plugin metadata (pipeline_id, name, path) + """ + plugins = [] + plugins_dir = _get_plugins_dir() + + if not plugins_dir.exists(): + return plugins + + for plugin_dir in plugins_dir.iterdir(): + if not plugin_dir.is_dir(): + continue + + pyproject_path = plugin_dir / "pyproject.toml" + if not pyproject_path.exists(): + continue + + metadata = _parse_plugin_pyproject(pyproject_path) + if metadata: + metadata["path"] = plugin_dir + plugins.append(metadata) + logger.debug(f"Discovered plugin: {metadata['pipeline_id']} at {plugin_dir}") + + return plugins + + +def register_plugin_pipelines(registry): + """Call register_pipelines hook for all plugins. + + Args: + registry: PipelineRegistry to register pipelines with + """ + + def register_callback(pipeline_class): + """Callback function passed to plugins. + + For plugins, we register the config class and store the pipeline class + module info for lazy loading by the worker. + """ + config_class = pipeline_class.get_config_class() + pipeline_id = config_class.pipeline_id + + # Get module path and class name for lazy loading + module_path = pipeline_class.__module__ + class_name = pipeline_class.__name__ + + registry.register(pipeline_id, config_class, module_path, class_name) + logger.info(f"Registered plugin pipeline: {pipeline_id}") + + pm.hook.register_pipelines(register=register_callback) diff --git a/src/scope/server/app.py b/src/scope/server/app.py index 16f0e4536..7a96a6321 100644 --- a/src/scope/server/app.py +++ b/src/scope/server/app.py @@ -1,18 +1,22 @@ -import argparse import asyncio +import contextlib +import io import logging import os import subprocess import sys import threading import time +import warnings import webbrowser from contextlib import asynccontextmanager from datetime import datetime +from functools import wraps from importlib.metadata import version from logging.handlers import RotatingFileHandler from pathlib import Path +import click import torch import uvicorn from fastapi import Depends, FastAPI, HTTPException, Query, Request @@ -117,6 +121,28 @@ def filter(self, record): logger = logging.getLogger(__name__) +def suppress_init_output(func): + """Decorator to suppress all initialization output (logging, warnings, stdout/stderr).""" + + @wraps(func) + def wrapper(*args, **kwargs): + with ( + contextlib.redirect_stdout(io.StringIO()), + contextlib.redirect_stderr(io.StringIO()), + warnings.catch_warnings(), + ): + warnings.simplefilter("ignore") + # Temporarily disable all logging + logging.disable(logging.CRITICAL) + try: + return func(*args, **kwargs) + finally: + # Re-enable logging + logging.disable(logging.NOTSET) + + return wrapper + + def get_git_commit_hash() -> str: """ Get the current git commit hash. @@ -346,15 +372,17 @@ async def get_pipeline_schemas(): The frontend should use this as the source of truth for parameter defaults. """ - from scope.core.pipelines.schema import PIPELINE_CONFIGS + from scope.core.pipelines.registry import PipelineRegistry pipelines: dict = {} - for pipeline_id, config_class in PIPELINE_CONFIGS.items(): - # get_schema_with_metadata() now includes supported_modes, default_mode, - # and mode_defaults directly from the config class - schema_data = config_class.get_schema_with_metadata() - pipelines[pipeline_id] = schema_data + for pipeline_id in PipelineRegistry.list_pipelines(): + config_class = PipelineRegistry.get_config_class(pipeline_id) + if config_class: + # get_schema_with_metadata() includes supported_modes, default_mode, + # and mode_defaults directly from the config class + schema_data = config_class.get_schema_with_metadata() + pipelines[pipeline_id] = schema_data return PipelineSchemasResponse(pipelines=pipelines) @@ -845,40 +873,12 @@ def open_browser_when_ready(host: str, port: int, server): logger.info(f"🌐 UI is available at: {url}") -def main(): - """Main entry point for the daydream-scope command.""" - parser = argparse.ArgumentParser( - description="A tool for running and customizing real-time, interactive generative AI pipelines and models" - ) - parser.add_argument( - "--version", - action="store_true", - help="Show version information and exit", - ) - parser.add_argument( - "--reload", - action="store_true", - help="Enable auto-reload for development (default: False)", - ) - parser.add_argument( - "--host", default="0.0.0.0", help="Host to bind to (default: 0.0.0.0)" - ) - parser.add_argument( - "--port", type=int, default=8000, help="Port to bind to (default: 8000)" - ) - parser.add_argument( - "-N", - "--no-browser", - action="store_true", - help="Do not automatically open a browser window after the server starts", - ) - - args = parser.parse_args() +def run_server(reload: bool, host: str, port: int, no_browser: bool): + """Run the Daydream Scope server.""" - # Handle version flag - if args.version: - print_version_info() - sys.exit(0) + from scope.core.pipelines.registry import ( + PipelineRegistry, # noqa: F401 - imported for side effects (registry initialization) + ) # Configure static file serving configure_static_files() @@ -891,18 +891,18 @@ def main(): # Create server instance for production mode config = uvicorn.Config( "scope.server.app:app", - host=args.host, - port=args.port, - reload=args.reload, + host=host, + port=port, + reload=reload, log_config=None, # Use our logging config, don't override it ) server = uvicorn.Server(config) # Start browser opening thread (unless disabled) - if not args.no_browser: + if not no_browser: browser_thread = threading.Thread( target=open_browser_when_ready, - args=(args.host, args.port, server), + args=(host, port, server), daemon=True, ) browser_thread.start() @@ -918,12 +918,324 @@ def main(): # Development mode - just run normally uvicorn.run( "scope.server.app:app", - host=args.host, - port=args.port, - reload=args.reload, + host=host, + port=port, + reload=reload, log_config=None, # Use our logging config, don't override it ) +@click.group(invoke_without_command=True) +@click.option("--version", is_flag=True, help="Show version information and exit") +@click.option( + "--reload", is_flag=True, help="Enable auto-reload for development (default: False)" +) +@click.option("--host", default="0.0.0.0", help="Host to bind to (default: 0.0.0.0)") +@click.option("--port", default=8000, help="Port to bind to (default: 8000)") +@click.option( + "-N", + "--no-browser", + is_flag=True, + help="Do not automatically open a browser window after the server starts", +) +@click.pass_context +def main(ctx, version: bool, reload: bool, host: str, port: int, no_browser: bool): + # Handle version flag + if version: + print_version_info() + sys.exit(0) + + # If no subcommand was invoked, run the server + if ctx.invoked_subcommand is None: + run_server(reload, host, port, no_browser) + + +@main.command() +def plugins(): + """List all installed plugins.""" + from .models_config import get_plugins_dir + + plugins_dir = get_plugins_dir() + + if not plugins_dir.exists(): + click.echo("No plugins installed.") + return + + # List plugin directories + plugin_dirs = [ + d for d in plugins_dir.iterdir() + if d.is_dir() and (d / "pyproject.toml").exists() + ] + + if not plugin_dirs: + click.echo("No plugins installed.") + return + + click.echo(f"{len(plugin_dirs)} plugin(s) installed:\n") + + for plugin_dir in sorted(plugin_dirs): + plugin_id = plugin_dir.name + click.echo(f" • {plugin_id}") + click.echo(f" Location: {plugin_dir}") + + +@main.command() +def pipelines(): + """List all available pipelines.""" + + @suppress_init_output + def _load_pipelines(): + from scope.core.pipelines.registry import PipelineRegistry + + return PipelineRegistry.list_pipelines() + + all_pipelines = _load_pipelines() + + if not all_pipelines: + click.echo("No pipelines available.") + return + + click.echo(f"{len(all_pipelines)} pipeline(s) available:\n") + + # List all pipelines + for pipeline_id in all_pipelines: + click.echo(f" • {pipeline_id}") + + +def _get_plugin_id_from_pyproject(plugin_path: Path) -> str | None: + """Extract plugin ID from pyproject.toml. + + Looks for a pipeline_id in the project metadata or uses the project name. + """ + pyproject_path = plugin_path / "pyproject.toml" + if not pyproject_path.exists(): + return None + + try: + # Try tomllib (Python 3.11+) first, fall back to tomli for Python 3.10 + try: + import tomllib + except ImportError: + import tomli as tomllib + + with open(pyproject_path, "rb") as f: + data = tomllib.load(f) + + # Try to get pipeline_id from project.entry-points or use project name + project = data.get("project", {}) + name = project.get("name", "") + + # Use project name as plugin ID (strip common prefixes) + plugin_id = name + for prefix in ["scope-plugin-", "daydream-scope-plugin-", "scope-"]: + if plugin_id.startswith(prefix): + plugin_id = plugin_id[len(prefix) :] + break + + return plugin_id if plugin_id else None + except Exception as e: + logger.warning(f"Failed to parse pyproject.toml: {e}") + return None + + +def _copy_plugin_to_plugins_dir(source_path: Path, plugin_id: str) -> Path: + """Copy plugin source to the plugins directory. + + Args: + source_path: Path to the plugin source + plugin_id: Unique identifier for the plugin + + Returns: + Path to the installed plugin directory + """ + import shutil + + from .models_config import ensure_plugins_dir + + plugins_dir = ensure_plugins_dir() + target_dir = plugins_dir / plugin_id + + # Remove existing plugin if it exists + if target_dir.exists(): + shutil.rmtree(target_dir) + + # Copy the plugin directory + shutil.copytree(source_path, target_dir) + + logger.info(f"Plugin '{plugin_id}' installed to {target_dir}") + return target_dir + + +def _clone_git_repo(git_url: str) -> Path: + """Clone a git repository to a temporary directory. + + Args: + git_url: Git URL (e.g., git+https://github.com/user/repo.git) + + Returns: + Path to the cloned directory + """ + import tempfile + + # Remove git+ prefix if present + if git_url.startswith("git+"): + git_url = git_url[4:] + + # Create a temporary directory + temp_dir = tempfile.mkdtemp(prefix="scope-plugin-") + temp_path = Path(temp_dir) + + click.echo(f"Cloning {git_url}...") + + # Clone the repository + result = subprocess.run( + ["git", "clone", "--depth", "1", git_url, str(temp_path / "repo")], + capture_output=True, + text=True, + ) + + if result.returncode != 0: + click.echo(f"Error cloning repository: {result.stderr}", err=True) + # Clean up temp directory + import shutil + + shutil.rmtree(temp_dir, ignore_errors=True) + sys.exit(1) + + return temp_path / "repo" + + +@main.command() +@click.argument("source", required=True) +@click.option( + "--plugin-id", + help="Override the plugin ID (default: extracted from pyproject.toml)", +) +@click.option("--force", is_flag=True, help="Force reinstall if plugin already exists") +def install(source, plugin_id, force): + """Install a plugin from a local path or git URL. + + SOURCE can be: + - A local path to a plugin directory containing pyproject.toml + - A git URL (e.g., git+https://github.com/user/repo.git) + + The plugin will be copied to ~/.daydream-scope/plugins// + and will run in its own isolated environment when loaded. + + Examples: + daydream-scope install ./my-plugin + daydream-scope install /path/to/scope-personalive --plugin-id personalive + daydream-scope install git+https://github.com/user/scope-plugin.git + """ + import shutil + + from .models_config import get_plugins_dir + + temp_clone_dir = None + + # Check if source is a git URL + if source.startswith("git+") or source.startswith("https://github.com"): + # Add git+ prefix if missing for github URLs + if source.startswith("https://github.com") and not source.startswith("git+"): + source = f"git+{source}" + temp_clone_dir = _clone_git_repo(source) + source_path = temp_clone_dir + else: + source_path = Path(source).expanduser().resolve() + + try: + if not source_path.exists(): + click.echo(f"Error: Source path does not exist: {source_path}", err=True) + sys.exit(1) + + if not source_path.is_dir(): + click.echo(f"Error: Source must be a directory: {source_path}", err=True) + sys.exit(1) + + pyproject_path = source_path / "pyproject.toml" + if not pyproject_path.exists(): + click.echo( + f"Error: No pyproject.toml found in {source_path}. " + "Plugins must have a pyproject.toml file.", + err=True, + ) + sys.exit(1) + + # Determine plugin ID + if plugin_id is None: + plugin_id = _get_plugin_id_from_pyproject(source_path) + if plugin_id is None: + click.echo( + "Error: Could not determine plugin ID from pyproject.toml. " + "Please specify --plugin-id explicitly.", + err=True, + ) + sys.exit(1) + + # Check if plugin already exists + plugins_dir = get_plugins_dir() + target_dir = plugins_dir / plugin_id + if target_dir.exists() and not force: + click.echo( + f"Plugin '{plugin_id}' already exists at {target_dir}. " + "Use --force to reinstall.", + err=True, + ) + sys.exit(1) + + # Copy plugin to plugins directory + installed_path = _copy_plugin_to_plugins_dir(source_path, plugin_id) + click.echo(f"✓ Plugin '{plugin_id}' installed successfully.") + click.echo(f" Location: {installed_path}") + click.echo( + f"\n The plugin will be available after restarting the server." + ) + + except Exception as e: + click.echo(f"Error installing plugin: {e}", err=True) + sys.exit(1) + + finally: + # Clean up temporary clone directory if we cloned from git + if temp_clone_dir is not None: + parent_dir = temp_clone_dir.parent + shutil.rmtree(parent_dir, ignore_errors=True) + + +@main.command() +@click.argument("plugin_id", required=True) +@click.option("-y", "--yes", is_flag=True, help="Don't ask for confirmation") +def uninstall(plugin_id, yes): + """Uninstall a plugin. + + PLUGIN_ID is the identifier of the plugin to uninstall. + + Example: + daydream-scope uninstall personalive + """ + import shutil + + from .models_config import get_plugins_dir + + plugins_dir = get_plugins_dir() + plugin_dir = plugins_dir / plugin_id + + if not plugin_dir.exists(): + click.echo(f"Error: Plugin '{plugin_id}' is not installed.", err=True) + sys.exit(1) + + if not yes: + if not click.confirm(f"Uninstall plugin '{plugin_id}' from {plugin_dir}?"): + click.echo("Cancelled.") + return + + try: + shutil.rmtree(plugin_dir) + click.echo(f"✓ Plugin '{plugin_id}' uninstalled successfully.") + except Exception as e: + click.echo(f"Error uninstalling plugin: {e}", err=True) + sys.exit(1) + + if __name__ == "__main__": main() diff --git a/src/scope/server/frame_processor_proxy.py b/src/scope/server/frame_processor_proxy.py new file mode 100644 index 000000000..a7232a7c2 --- /dev/null +++ b/src/scope/server/frame_processor_proxy.py @@ -0,0 +1,198 @@ +"""FrameProcessor Proxy - Communicates with FrameProcessor in worker process via ZMQ.""" + +import json +import logging +from enum import Enum + +import numpy as np +import torch +import zmq +from aiortc.mediastreams import VideoFrame + +logger = logging.getLogger(__name__) + +# Constants +DEFAULT_TIMEOUT = 60000 # milliseconds + + +class WorkerCommand(Enum): + """Commands that can be sent to the worker process.""" + + LOAD_PIPELINE = "load_pipeline" + UNLOAD_PIPELINE = "unload_pipeline" + CREATE_FRAME_PROCESSOR = "create_frame_processor" + DESTROY_FRAME_PROCESSOR = "destroy_frame_processor" + PUT_FRAME = "put_frame" + GET_FRAME = "get_frame" + UPDATE_PARAMETERS = "update_parameters" + GET_FPS = "get_fps" + SHUTDOWN = "shutdown" + + +class WorkerResponse(Enum): + """Response types from worker process.""" + + SUCCESS = "success" + ERROR = "error" + PIPELINE_LOADED = "pipeline_loaded" + PIPELINE_NOT_LOADED = "pipeline_not_loaded" + RESULT = "result" + FRAME_PROCESSOR_CREATED = "frame_processor_created" + FRAME = "frame" + + +class FrameProcessorProxy: + """Proxy object that communicates with FrameProcessor in worker process via ZMQ.""" + + def __init__( + self, + frame_processor_id: str, + command_socket: zmq.Socket, + response_socket: zmq.Socket, + ): + self.frame_processor_id = frame_processor_id + self._command_socket = command_socket + self._response_socket = response_socket + + def put(self, frame: VideoFrame) -> bool: + """Put a frame into the FrameProcessor buffer.""" + try: + # Serialize VideoFrame to binary for efficient transfer + frame_array = frame.to_ndarray(format="rgb24") + + # Send command with binary frame data + command = { + "command": WorkerCommand.PUT_FRAME.value, + "frame_processor_id": self.frame_processor_id, + "__has_binary__": True, + "frame_info": { + "dtype": str(frame_array.dtype), + "shape": frame_array.shape, + }, + } + self._command_socket.send(json.dumps(command).encode("utf-8"), zmq.SNDMORE) + self._command_socket.send(frame_array.tobytes()) + return True + except Exception as e: + logger.error(f"Error putting frame: {e}") + return False + + def get(self) -> torch.Tensor | None: + """Get a processed frame from the FrameProcessor.""" + try: + # Request a frame + command = { + "command": WorkerCommand.GET_FRAME.value, + "frame_processor_id": self.frame_processor_id, + } + self._command_socket.send(json.dumps(command).encode("utf-8")) + + # Wait for response with timeout + if self._response_socket.poll(timeout=1000): # 1 second + message = self._response_socket.recv() + response = json.loads(message.decode("utf-8")) + + if response["status"] == WorkerResponse.FRAME.value: + if response.get("__has_binary__"): + # Receive binary frame data + binary_data = self._response_socket.recv() + frame_info = response.get("frame_info", {}) + arr = np.frombuffer( + binary_data, dtype=frame_info["dtype"] + ).reshape(frame_info["shape"]) + return torch.from_numpy(arr.copy()) + return None + elif response["status"] == WorkerResponse.RESULT.value: + # No frame available + return None + elif response["status"] == WorkerResponse.ERROR.value: + error_msg = response.get("error", "Unknown error") + logger.error(f"Error getting frame: {error_msg}") + return None + else: + logger.warning(f"Unexpected response status: {response['status']}") + return None + else: + # Timeout - no frame available + return None + + except Exception as e: + logger.error(f"Error getting frame: {e}") + return None + + def get_current_pipeline_fps(self) -> float: + """Get the current dynamically calculated pipeline FPS.""" + try: + command = { + "command": WorkerCommand.GET_FPS.value, + "frame_processor_id": self.frame_processor_id, + } + self._command_socket.send(json.dumps(command).encode("utf-8")) + + if self._response_socket.poll(timeout=DEFAULT_TIMEOUT): + message = self._response_socket.recv() + response = json.loads(message.decode("utf-8")) + + if response["status"] == WorkerResponse.RESULT.value: + return response.get("result", 30.0) + elif response["status"] == WorkerResponse.ERROR.value: + error_msg = response.get("error", "Unknown error") + logger.error(f"Error getting FPS: {error_msg}") + return 30.0 + else: + logger.warning(f"Unexpected response status: {response['status']}") + return 30.0 + else: + logger.error("Timeout waiting for FPS response") + return 30.0 + + except Exception as e: + logger.error(f"Error getting FPS: {e}") + return 30.0 + + def get_output_fps(self) -> float: + """Get the output FPS (alias for get_current_pipeline_fps).""" + return self.get_current_pipeline_fps() + + def update_parameters(self, parameters: dict): + """Update parameters that will be used in the next pipeline call.""" + try: + command = { + "command": WorkerCommand.UPDATE_PARAMETERS.value, + "frame_processor_id": self.frame_processor_id, + "parameters": parameters, + } + self._command_socket.send(json.dumps(command).encode("utf-8")) + return True + except Exception as e: + logger.error(f"Error updating parameters: {e}") + return False + + def start(self): + """Start the FrameProcessor (already started when created).""" + pass + + def stop(self): + """Stop and destroy the FrameProcessor.""" + try: + command = { + "command": WorkerCommand.DESTROY_FRAME_PROCESSOR.value, + "frame_processor_id": self.frame_processor_id, + } + self._command_socket.send(json.dumps(command).encode("utf-8")) + + # Wait for response + if self._response_socket.poll(timeout=DEFAULT_TIMEOUT): + message = self._response_socket.recv() + response = json.loads(message.decode("utf-8")) + + if response["status"] == WorkerResponse.SUCCESS.value: + logger.info(f"FrameProcessor {self.frame_processor_id} stopped") + else: + error_msg = response.get("error", "Unknown error") + logger.warning(f"Error stopping FrameProcessor: {error_msg}") + else: + logger.warning("Timeout waiting for FrameProcessor stop response") + + except Exception as e: + logger.error(f"Error stopping FrameProcessor: {e}") diff --git a/src/scope/server/models_config.py b/src/scope/server/models_config.py index ace85ffc6..a64109f46 100644 --- a/src/scope/server/models_config.py +++ b/src/scope/server/models_config.py @@ -89,6 +89,34 @@ def get_assets_dir() -> Path: return assets_dir +def get_plugins_dir() -> Path: + """ + Get the plugins directory path (at the same level as models directory). + + Plugin pipelines are installed to this directory with their own pyproject.toml + and isolated environment. Each plugin has its own subdirectory. + + Returns: + Path: Absolute path to the plugins directory (e.g., ~/.daydream-scope/plugins) + """ + models_dir = get_models_dir() + # Get the parent directory (e.g., ~/.daydream-scope) and create plugins directory there + plugins_dir = models_dir.parent / "plugins" + return plugins_dir + + +def ensure_plugins_dir() -> Path: + """ + Get the plugins directory path and ensure it exists. + + Returns: + Path: Absolute path to the plugins directory + """ + plugins_dir = get_plugins_dir() + plugins_dir.mkdir(parents=True, exist_ok=True) + return plugins_dir + + def get_required_model_files(pipeline_id: str | None = None) -> list[Path]: """ Get the list of required model files that should exist for a given pipeline. diff --git a/src/scope/server/pipeline_manager.py b/src/scope/server/pipeline_manager.py index 383eb9951..86069fb88 100644 --- a/src/scope/server/pipeline_manager.py +++ b/src/scope/server/pipeline_manager.py @@ -1,22 +1,52 @@ """Pipeline Manager for lazy loading and managing ML pipelines.""" import asyncio -import gc +import json import logging import os +import subprocess import threading +import time +import traceback from enum import Enum +from pathlib import Path from typing import Any -import torch -from omegaconf import OmegaConf +import zmq logger = logging.getLogger(__name__) +# Constants +PIPELINE_LOAD_TIMEOUT = 300 # 5 minutes +WORKER_SHUTDOWN_TIMEOUT = 5 # seconds +WORKER_TERMINATE_TIMEOUT = 3 # seconds +WORKER_KILL_TIMEOUT = 1 # seconds -def get_device() -> torch.device: - """Get the appropriate device (CUDA if available, CPU otherwise).""" - return torch.device("cuda" if torch.cuda.is_available() else "cpu") + +class WorkerCommand(Enum): + """Commands that can be sent to the worker process.""" + + LOAD_PIPELINE = "load_pipeline" + UNLOAD_PIPELINE = "unload_pipeline" + CREATE_FRAME_PROCESSOR = "create_frame_processor" + DESTROY_FRAME_PROCESSOR = "destroy_frame_processor" + PUT_FRAME = "put_frame" + GET_FRAME = "get_frame" + UPDATE_PARAMETERS = "update_parameters" + GET_FPS = "get_fps" + SHUTDOWN = "shutdown" + + +class WorkerResponse(Enum): + """Response types from worker process.""" + + SUCCESS = "success" + ERROR = "error" + PIPELINE_LOADED = "pipeline_loaded" + PIPELINE_NOT_LOADED = "pipeline_not_loaded" + RESULT = "result" + FRAME_PROCESSOR_CREATED = "frame_processor_created" + FRAME = "frame" class PipelineNotAvailableException(Exception): @@ -35,7 +65,7 @@ class PipelineStatus(Enum): class PipelineManager: - """Manager for ML pipeline lifecycle.""" + """Manager for ML pipeline lifecycle using separate process for GPU isolation.""" def __init__(self): self._status = PipelineStatus.NOT_LOADED @@ -45,6 +75,16 @@ def __init__(self): self._error_message = None self._lock = threading.RLock() # Single reentrant lock for all access + # Worker process management + self._worker_process: subprocess.Popen | None = None + + # ZMQ context and sockets + self._zmq_context: zmq.Context | None = None + self._command_socket: zmq.Socket | None = None + self._response_socket: zmq.Socket | None = None + self._command_port: int | None = None + self._response_port: int | None = None + @property def status(self) -> PipelineStatus: """Get current pipeline status.""" @@ -61,13 +101,19 @@ def error_message(self) -> str | None: return self._error_message def get_pipeline(self): - """Get the loaded pipeline instance (thread-safe).""" + """Get the loaded pipeline instance (thread-safe). + + Note: Pipeline is now loaded in worker process. Use create_frame_processor() + to get a FrameProcessor that uses the pipeline directly in the worker process. + """ with self._lock: - if self._status != PipelineStatus.LOADED or self._pipeline is None: + if self._status != PipelineStatus.LOADED or self._worker_process is None: raise PipelineNotAvailableException( f"Pipeline not available. Status: {self._status.value}" ) - return self._pipeline + # Pipeline is in worker process, return a placeholder to indicate it's loaded + # Actual pipeline access is through FrameProcessor in worker process + return None def get_status_info(self) -> dict[str, Any]: """Get detailed status information (thread-safe). @@ -83,13 +129,9 @@ def get_status_info(self) -> dict[str, Any]: load_params = self._load_params # Capture loaded LoRA adapters if pipeline exposes them + # Note: With worker process, we can't directly access pipeline attributes + # This would require a worker command to query the pipeline state loaded_lora_adapters = None - if self._pipeline is not None and hasattr( - self._pipeline, "loaded_lora_adapters" - ): - loaded_lora_adapters = getattr( - self._pipeline, "loaded_lora_adapters", None - ) # If there's an error, clear it after capturing it # This ensures errors don't persist across page reloads @@ -177,401 +219,460 @@ def _load_pipeline_sync_wrapper( self._error_message = None # Release lock during slow loading operation - logger.info(f"Loading pipeline: {pipeline_id}") + logger.info(f"Loading pipeline in worker process: {pipeline_id}") try: - # Load the pipeline synchronously (we're already in executor thread) - pipeline = self._load_pipeline_implementation(pipeline_id, load_params) + # Start worker process and load pipeline + # This will raise RuntimeError if loading fails + self._start_worker_and_load_pipeline(pipeline_id, load_params) # Hold lock while updating state with loaded pipeline with self._lock: - self._pipeline = pipeline self._pipeline_id = pipeline_id self._load_params = load_params self._status = PipelineStatus.LOADED - logger.info(f"Pipeline {pipeline_id} loaded successfully") + logger.info(f"Pipeline {pipeline_id} loaded successfully in worker process") return True except Exception as e: - from .models_config import get_models_dir - - models_dir = get_models_dir() - error_msg = f"Failed to load pipeline {pipeline_id}: {e}" - logger.error( - f"{error_msg}. If this error persists, consider removing the models " - f"directory '{models_dir}' and re-downloading models." - ) + # Capture full error message including traceback for better debugging + error_details = str(e) + # If the error already contains detailed info, use it; otherwise add traceback + if ( + "traceback" not in error_details.lower() + and "Worker process" not in error_details + ): + error_details = f"{error_details}\n{traceback.format_exc()}" + error_msg = f"Failed to load pipeline {pipeline_id}: {error_details}" + logger.error(error_msg) # Hold lock while updating state with error with self._lock: self._status = PipelineStatus.ERROR self._error_message = error_msg - self._pipeline = None self._pipeline_id = None self._load_params = None - return False - - def _get_vace_checkpoint_path(self) -> str: - """Get the path to the VACE module checkpoint. - - Returns: - str: Path to VACE module checkpoint file (contains only VACE weights) - """ - from .models_config import get_model_file_path - - return str( - get_model_file_path( - "WanVideo_comfy/Wan2_1-VACE_module_1_3B_bf16.safetensors" - ) - ) - - def _configure_vace(self, config: dict, load_params: dict | None = None) -> None: - """Configure VACE support for a pipeline. - - Adds vace_path to config and optionally extracts VACE-specific parameters - from load_params (ref_images, vace_context_scale). + # Cleanup worker on failure + self._stop_worker() - Args: - config: Pipeline configuration dict to modify - load_params: Optional load parameters containing VACE settings - """ - config["vace_path"] = self._get_vace_checkpoint_path() - logger.debug(f"_configure_vace: Using VACE checkpoint at {config['vace_path']}") - - # Extract VACE-specific parameters from load_params if present - if load_params: - ref_images = load_params.get("ref_images", []) - if ref_images: - config["ref_images"] = ref_images - config["vace_context_scale"] = load_params.get( - "vace_context_scale", 1.0 - ) - logger.info( - f"_configure_vace: VACE parameters from load_params: " - f"ref_images count={len(ref_images)}, " - f"vace_context_scale={config.get('vace_context_scale', 1.0)}" - ) + return False - def _apply_load_params( - self, - config: dict, - load_params: dict | None, - default_height: int, - default_width: int, - default_seed: int = 42, - ) -> None: - """Extract and apply common load parameters (resolution, seed, LoRAs) to config. + def _unload_pipeline_unsafe(self): + """Unload the current pipeline. Must be called with lock held. - Args: - config: Pipeline config dict to update - load_params: Load parameters dict (may contain height, width, seed, loras, lora_merge_mode) - default_height: Default height if not in load_params - default_width: Default width if not in load_params - default_seed: Default seed if not in load_params + This will kill the worker process to ensure proper VRAM cleanup. """ - height = default_height - width = default_width - seed = default_seed - loras = None - lora_merge_mode = "permanent_merge" - - if load_params: - height = load_params.get("height", default_height) - width = load_params.get("width", default_width) - seed = load_params.get("seed", default_seed) - loras = load_params.get("loras", None) - lora_merge_mode = load_params.get("lora_merge_mode", lora_merge_mode) - - config["height"] = height - config["width"] = width - config["seed"] = seed - if loras: - config["loras"] = loras - # Pass merge_mode directly to mixin, not via config - config["_lora_merge_mode"] = lora_merge_mode - - def _unload_pipeline_unsafe(self): - """Unload the current pipeline. Must be called with lock held.""" - if self._pipeline: + if self._pipeline_id: logger.info(f"Unloading pipeline: {self._pipeline_id}") - # Change status and pipeline atomically + # Stop the worker process (this ensures VRAM is cleaned up) + self._stop_worker() + + # Reset state self._status = PipelineStatus.NOT_LOADED self._pipeline = None self._pipeline_id = None self._load_params = None self._error_message = None - # Cleanup resources - gc.collect() - if torch.cuda.is_available(): - try: - torch.cuda.empty_cache() - torch.cuda.synchronize() - logger.info("CUDA cache cleared") - except Exception as e: - logger.warning(f"CUDA cleanup failed: {e}") + def _find_free_port(self) -> int: + """Find a free port to use for ZMQ communication.""" + import socket - def _load_pipeline_implementation( - self, pipeline_id: str, load_params: dict | None = None - ): - """Synchronous pipeline loading (runs in thread executor).""" - if pipeline_id == "streamdiffusionv2": - from scope.core.pipelines import ( - StreamDiffusionV2Pipeline, - ) + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind(("127.0.0.1", 0)) + return s.getsockname()[1] - from .models_config import get_model_file_path, get_models_dir + def _get_pipeline_env_dir(self, pipeline_id: str) -> tuple[Path, bool]: + """Get the environment directory for a specific pipeline. - models_dir = get_models_dir() - config = OmegaConf.create( - { - "model_dir": str(models_dir), - "generator_path": str( - get_model_file_path( - "StreamDiffusionV2/wan_causal_dmd_v2v/model.pt" - ) - ), - "text_encoder_path": str( - get_model_file_path( - "WanVideo_comfy/umt5-xxl-enc-fp8_e4m3fn.safetensors" - ) - ), - "tokenizer_path": str( - get_model_file_path("Wan2.1-T2V-1.3B/google/umt5-xxl") - ), - } - ) + Each pipeline has its own pyproject.toml with pipeline-specific dependencies. + For plugin pipelines, this returns the plugin's installed directory. - # Configure VACE support if enabled in load_params (default: True) - # Note: VACE is not available for StreamDiffusion in video mode (enforced by frontend) - vace_enabled = True - if load_params: - vace_enabled = load_params.get("vace_enabled", True) + Args: + pipeline_id: The pipeline identifier - if vace_enabled: - self._configure_vace(config, load_params) - else: - logger.info("VACE disabled by load_params, skipping VACE configuration") - - # Apply load parameters (resolution, seed, LoRAs) to config - self._apply_load_params( - config, - load_params, - default_height=512, - default_width=512, - default_seed=42, + Returns: + Tuple of (path to the pipeline's environment directory, is_plugin) + """ + from .models_config import get_plugins_dir + + project_root = Path(__file__).parent.parent.parent.parent + + # Map built-in pipeline IDs to their directory names + builtin_pipeline_dir_map = { + "passthrough": "passthrough", + "streamdiffusionv2": "streamdiffusionv2", + "longlive": "longlive", + "krea-realtime-video": "krea_realtime_video", + "reward-forcing": "reward_forcing", + } + + # Check if it's a built-in pipeline + if pipeline_id in builtin_pipeline_dir_map: + pipeline_dir = ( + project_root + / "src" + / "scope" + / "core" + / "pipelines" + / builtin_pipeline_dir_map[pipeline_id] ) + if pipeline_dir.exists() and (pipeline_dir / "pyproject.toml").exists(): + return pipeline_dir, False - quantization = None - if load_params: - quantization = load_params.get("quantization", None) + # Check if it's a plugin pipeline + plugins_dir = get_plugins_dir() + plugin_dir = plugins_dir / pipeline_id + if plugin_dir.exists() and (plugin_dir / "pyproject.toml").exists(): + logger.info(f"Found plugin pipeline at: {plugin_dir}") + return plugin_dir, True - pipeline = StreamDiffusionV2Pipeline( - config, - quantization=quantization, - device=torch.device("cuda"), - dtype=torch.bfloat16, - ) - logger.info("StreamDiffusionV2 pipeline initialized") - return pipeline - - elif pipeline_id == "passthrough": - from scope.core.pipelines import PassthroughPipeline - - # Use load parameters for resolution, default to 512x512 - height = 512 - width = 512 - if load_params: - height = load_params.get("height", 512) - width = load_params.get("width", 512) - - pipeline = PassthroughPipeline( - height=height, - width=width, - device=get_device(), - dtype=torch.bfloat16, - ) - logger.info("Passthrough pipeline initialized") - return pipeline - - elif pipeline_id == "longlive": - from scope.core.pipelines import LongLivePipeline - - from .models_config import get_model_file_path, get_models_dir - - models_dir = get_models_dir() - config = OmegaConf.create( - { - "model_dir": str(models_dir), - "generator_path": str( - get_model_file_path("LongLive-1.3B/models/longlive_base.pt") - ), - "lora_path": str( - get_model_file_path("LongLive-1.3B/models/lora.pt") - ), - "text_encoder_path": str( - get_model_file_path( - "WanVideo_comfy/umt5-xxl-enc-fp8_e4m3fn.safetensors" - ) - ), - "tokenizer_path": str( - get_model_file_path("Wan2.1-T2V-1.3B/google/umt5-xxl") - ), - } - ) + # Fallback to base worker_env for unknown pipelines + worker_env_dir = project_root / "worker_env" + if not worker_env_dir.exists(): + raise RuntimeError(f"Worker environment directory not found: {worker_env_dir}") + return worker_env_dir, False - # Configure VACE support if enabled in load_params (default: True) - vace_enabled = True - if load_params: - vace_enabled = load_params.get("vace_enabled", True) + def _prepare_plugin_environment(self, plugin_dir: Path) -> None: + """Prepare a plugin's environment by updating pyproject.toml with source overrides. - if vace_enabled: - self._configure_vace(config, load_params) - else: - logger.info("VACE disabled by load_params, skipping VACE configuration") - - # Apply load parameters (resolution, seed, LoRAs) to config - self._apply_load_params( - config, - load_params, - default_height=320, - default_width=576, - default_seed=42, - ) + Plugins may depend on 'daydream-scope' or 'scope-worker-base', but these + aren't published to PyPI. We update the plugin's pyproject.toml to add + source overrides pointing to the local packages. - quantization = None - if load_params: - quantization = load_params.get("quantization", None) + Args: + plugin_dir: Path to the plugin directory + """ + # Try tomllib (Python 3.11+) first, fall back to tomli for Python 3.10 + try: + import tomllib + except ImportError: + import tomli as tomllib + + project_root = Path(__file__).parent.parent.parent.parent + worker_env_dir = project_root / "worker_env" + + pyproject_path = plugin_dir / "pyproject.toml" + if not pyproject_path.exists(): + logger.warning(f"No pyproject.toml found at {pyproject_path}") + return + + # Read existing pyproject.toml + with open(pyproject_path, "rb") as f: + pyproject_data = tomllib.load(f) + + # Check if we've already added source overrides + existing_sources = pyproject_data.get("tool", {}).get("uv", {}).get("sources", {}) + if "daydream-scope" in existing_sources: + logger.debug(f"Source overrides already exist in {pyproject_path}") + return + + # Check if scope-worker-base is in dependencies + dependencies = pyproject_data.get("project", {}).get("dependencies", []) + needs_worker_base = not any( + "scope-worker-base" in dep for dep in dependencies + ) - pipeline = LongLivePipeline( - config, - quantization=quantization, - device=torch.device("cuda"), - dtype=torch.bfloat16, - ) - logger.info("LongLive pipeline initialized") - return pipeline + # Append source overrides and scope-worker-base dependency to pyproject.toml + additions = "\n# Auto-generated by daydream-scope for plugin dependency resolution\n" + + if needs_worker_base: + # Rewrite dependencies section to include scope-worker-base + new_deps = dependencies + ["scope-worker-base"] + deps_str = ", ".join(f'"{d}"' for d in new_deps) + # We'll add scope-worker-base via the sources mechanism + + additions += f'''[tool.uv.sources] +daydream-scope = {{ path = "{project_root}", editable = true }} +scope-worker-base = {{ path = "{worker_env_dir}", editable = true }} +''' + + # If dependencies need updating, we need to rewrite the file + if needs_worker_base: + # Read file as text and add scope-worker-base to dependencies + with open(pyproject_path, "r") as f: + content = f.read() + + # Add scope-worker-base to dependencies list + import re + + # Find the dependencies line and add scope-worker-base + dep_pattern = r'(dependencies\s*=\s*\[)([^\]]*)(])' + + def add_worker_base(match): + prefix = match.group(1) + deps = match.group(2) + suffix = match.group(3) + # Add scope-worker-base if not already there + if "scope-worker-base" not in deps: + deps = deps.rstrip() + if deps and not deps.endswith(","): + deps += "," + deps += '\n "scope-worker-base",\n' + return prefix + deps + suffix + + content = re.sub(dep_pattern, add_worker_base, content) + content += additions + + with open(pyproject_path, "w") as f: + f.write(content) + else: + with open(pyproject_path, "a") as f: + f.write(additions) - elif pipeline_id == "krea-realtime-video": - from scope.core.pipelines import ( - KreaRealtimeVideoPipeline, - ) + # Remove any existing .venv and uv.lock so they get regenerated + venv_path = plugin_dir / ".venv" + lock_path = plugin_dir / "uv.lock" + if venv_path.exists(): + import shutil - from .models_config import get_model_file_path, get_models_dir + shutil.rmtree(venv_path) + logger.debug(f"Removed existing .venv at {venv_path}") + if lock_path.exists(): + lock_path.unlink() + logger.debug(f"Removed existing uv.lock at {lock_path}") - config = OmegaConf.create( - { - "model_dir": str(get_models_dir()), - "generator_path": str( - get_model_file_path( - "krea-realtime-video/krea-realtime-video-14b.safetensors" - ) - ), - "text_encoder_path": str( - get_model_file_path( - "WanVideo_comfy/umt5-xxl-enc-fp8_e4m3fn.safetensors" - ) - ), - "tokenizer_path": str( - get_model_file_path("Wan2.1-T2V-1.3B/google/umt5-xxl") - ), - "vae_path": str( - get_model_file_path("Wan2.1-T2V-1.3B/Wan2.1_VAE.pth") - ), - } - ) + logger.info(f"Added source overrides to {pyproject_path}") - # Apply load parameters (resolution, seed, LoRAs) to config - self._apply_load_params( - config, - load_params, - default_height=512, - default_width=512, - default_seed=42, - ) + def _start_worker_and_load_pipeline( + self, pipeline_id: str, load_params: dict | None = None + ) -> bool: + """Start worker process and load pipeline in it. - quantization = None - if load_params: - quantization = load_params.get("quantization", None) - - pipeline = KreaRealtimeVideoPipeline( - config, - quantization=quantization, - # Only compile diffusion model for hopper right now - compile=any( - x in torch.cuda.get_device_name(0).lower() - for x in ("h100", "hopper") - ), - device=torch.device("cuda"), - dtype=torch.bfloat16, - ) - logger.info("krea-realtime-video pipeline initialized") - return pipeline + Returns: + bool: True if successful, False otherwise - elif pipeline_id == "reward-forcing": - from scope.core.pipelines import ( - RewardForcingPipeline, - ) + Raises: + RuntimeError: If worker process dies unexpectedly or fails to load pipeline + """ + # Stop any existing worker first + self._stop_worker() + + # Get the pipeline-specific environment directory + project_root = Path(__file__).parent.parent.parent.parent + pipeline_env_dir, is_plugin = self._get_pipeline_env_dir(pipeline_id) + + # For plugins, prepare the environment with source overrides + if is_plugin: + self._prepare_plugin_environment(pipeline_env_dir) + + logger.info(f"Using pipeline environment: {pipeline_env_dir}") + + # Set up ZMQ context and sockets + self._zmq_context = zmq.Context() + + # Find free ports for communication + self._command_port = self._find_free_port() + self._response_port = self._find_free_port() + + # PUSH socket for sending commands (bind) + self._command_socket = self._zmq_context.socket(zmq.PUSH) + self._command_socket.bind(f"tcp://127.0.0.1:{self._command_port}") + + # PULL socket for receiving responses (bind) + self._response_socket = self._zmq_context.socket(zmq.PULL) + self._response_socket.bind(f"tcp://127.0.0.1:{self._response_port}") + + # Get models directory from environment or config + from .models_config import get_models_dir + + models_dir = str(get_models_dir()) + + # Build environment for the worker process + env = os.environ.copy() + env["SCOPE_PROJECT_ROOT"] = str(project_root) + env["MODELS_DIR"] = models_dir + + # Start worker process using uv run + cmd = [ + "uv", + "run", + "scope-worker", + "--command-port", + str(self._command_port), + "--response-port", + str(self._response_port), + ] + + logger.info(f"Starting worker process: {' '.join(cmd)}") + logger.info(f"Working directory: {pipeline_env_dir}") + + self._worker_process = subprocess.Popen( + cmd, + cwd=str(pipeline_env_dir), + env=env, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + ) - from .models_config import get_model_file_path, get_models_dir - - config = OmegaConf.create( - { - "model_dir": str(get_models_dir()), - "generator_path": str( - get_model_file_path("Reward-Forcing-T2V-1.3B/rewardforcing.pt") - ), - "text_encoder_path": str( - get_model_file_path( - "WanVideo_comfy/umt5-xxl-enc-fp8_e4m3fn.safetensors" + # Start a thread to log worker output + def log_worker_output(): + if self._worker_process and self._worker_process.stdout: + for line in iter(self._worker_process.stdout.readline, b""): + if line: + logger.info(f"[Worker] {line.decode('utf-8').rstrip()}") + + output_thread = threading.Thread(target=log_worker_output, daemon=True) + output_thread.start() + + logger.info(f"Started worker process (PID: {self._worker_process.pid})") + + # Give the worker a moment to start and connect + time.sleep(0.5) + + # Send load command + command = { + "command": WorkerCommand.LOAD_PIPELINE.value, + "pipeline_id": pipeline_id, + "load_params": load_params, + } + self._command_socket.send(json.dumps(command).encode("utf-8")) + + # Wait for response with timeout, checking if worker is still alive + start_time = time.time() + check_interval = 500 # milliseconds for ZMQ poll + + while True: + # Check if worker process is still alive + if self._worker_process.poll() is not None: + # Worker died unexpectedly - get exit code + exit_code = self._worker_process.returncode + if exit_code != 0: + # Process crashed (e.g., OOM, segmentation fault) + if exit_code == -9: # SIGKILL (often OOM killer) + error_msg = ( + f"Worker process was killed (likely out of memory). " + f"Exit code: {exit_code}. " + f"Pipeline loading failed for {pipeline_id}." ) - ), - "tokenizer_path": str( - get_model_file_path("Wan2.1-T2V-1.3B/google/umt5-xxl") - ), - "vae_path": str( - get_model_file_path("Wan2.1-T2V-1.3B/Wan2.1_VAE.pth") - ), - } - ) + elif exit_code < 0: + error_msg = ( + f"Worker process crashed with signal {abs(exit_code)}. " + f"Pipeline loading failed for {pipeline_id}." + ) + else: + error_msg = ( + f"Worker process exited unexpectedly with code {exit_code}. " + f"Pipeline loading failed for {pipeline_id}." + ) + logger.error(error_msg) + raise RuntimeError(error_msg) + else: + # Process exited normally but we didn't get a response + error_msg = ( + f"Worker process exited before sending response. " + f"Pipeline loading failed for {pipeline_id}." + ) + logger.error(error_msg) + raise RuntimeError(error_msg) + + # Check for timeout + elapsed = time.time() - start_time + if elapsed >= PIPELINE_LOAD_TIMEOUT: + error_msg = ( + f"Timeout waiting for pipeline load response after {PIPELINE_LOAD_TIMEOUT}s. " + f"Pipeline loading failed for {pipeline_id}." + ) + logger.error(error_msg) + raise RuntimeError(error_msg) + + # Try to get response with short timeout to allow periodic worker checks + if self._response_socket.poll(timeout=check_interval): + message = self._response_socket.recv() + response = json.loads(message.decode("utf-8")) + + if response["status"] == WorkerResponse.SUCCESS.value: + logger.info( + f"Pipeline loaded successfully in worker: {response.get('message')}" + ) + return True + else: + # Worker sent an error response + error_msg = response.get("error", "Unknown error") + logger.error(f"Failed to load pipeline in worker: {error_msg}") + raise RuntimeError(f"Pipeline loading failed: {error_msg}") + + def _stop_worker(self): + """Stop the worker process if it's running. + + This ensures proper VRAM cleanup by killing the process. + """ + if self._worker_process is not None and self._worker_process.poll() is None: + logger.info(f"Stopping worker process (PID: {self._worker_process.pid})") - # Configure VACE support if enabled in load_params (default: True) - vace_enabled = True - if load_params: - vace_enabled = load_params.get("vace_enabled", True) + # Try graceful shutdown first by sending shutdown command + try: + if self._command_socket: + # Set linger to 0 for immediate close (avoid blocking on pending messages) + self._command_socket.setsockopt(zmq.LINGER, 0) + command = {"command": WorkerCommand.SHUTDOWN.value} + # Use NOBLOCK to avoid blocking if receiver is not consuming + self._command_socket.send( + json.dumps(command).encode("utf-8"), zmq.NOBLOCK + ) + # Wait for process to exit + try: + self._worker_process.wait(timeout=WORKER_SHUTDOWN_TIMEOUT) + except subprocess.TimeoutExpired: + pass + except zmq.ZMQError: + # Socket error (e.g., would block), skip graceful shutdown + pass + except Exception as e: + logger.warning(f"Error during graceful shutdown: {e}") - if vace_enabled: - self._configure_vace(config, load_params) - else: - logger.info("VACE disabled by load_params, skipping VACE configuration") - - # Apply load parameters (resolution, seed, LoRAs) to config - self._apply_load_params( - config, - load_params, - default_height=320, - default_width=576, - default_seed=42, - ) + # Force terminate if still alive + if self._worker_process.poll() is None: + logger.warning( + "Worker process did not shut down gracefully, terminating..." + ) + self._worker_process.terminate() + try: + self._worker_process.wait(timeout=WORKER_TERMINATE_TIMEOUT) + except subprocess.TimeoutExpired: + pass + + # Final kill if still alive + if self._worker_process.poll() is None: + logger.warning("Worker process did not terminate, killing...") + self._worker_process.kill() + try: + self._worker_process.wait(timeout=WORKER_KILL_TIMEOUT) + except subprocess.TimeoutExpired: + pass + + logger.info("Worker process stopped") + + # Clean up ZMQ sockets - set LINGER to 0 first to avoid blocking on close + if self._command_socket is not None: + try: + self._command_socket.setsockopt(zmq.LINGER, 0) + self._command_socket.close() + except Exception: + pass + self._command_socket = None - quantization = None - if load_params: - quantization = load_params.get("quantization", None) + if self._response_socket is not None: + try: + self._response_socket.setsockopt(zmq.LINGER, 0) + self._response_socket.close() + except Exception: + pass + self._response_socket = None - pipeline = RewardForcingPipeline( - config, - quantization=quantization, - device=torch.device("cuda"), - dtype=torch.bfloat16, - ) - logger.info("RewardForcing pipeline initialized") - return pipeline + if self._zmq_context is not None: + try: + self._zmq_context.term() + except Exception: + pass + self._zmq_context = None - else: - raise ValueError(f"Invalid pipeline ID: {pipeline_id}") + self._worker_process = None + self._command_port = None + self._response_port = None def unload_pipeline(self): """Unload the current pipeline (thread-safe).""" @@ -582,3 +683,46 @@ def is_loaded(self) -> bool: """Check if pipeline is loaded and ready (thread-safe).""" with self._lock: return self._status == PipelineStatus.LOADED + + def create_frame_processor(self, frame_processor_id: str, initial_parameters: dict = None): + """Create a FrameProcessor in the worker process (thread-safe). + + Args: + frame_processor_id: Unique identifier for this FrameProcessor + initial_parameters: Initial parameters for the FrameProcessor + + Returns: + FrameProcessorProxy instance + """ + from .frame_processor_proxy import FrameProcessorProxy + + with self._lock: + if self._status != PipelineStatus.LOADED or self._worker_process is None: + raise PipelineNotAvailableException( + f"Pipeline not available. Status: {self._status.value}" + ) + + # Send command to create FrameProcessor in worker + command = { + "command": WorkerCommand.CREATE_FRAME_PROCESSOR.value, + "frame_processor_id": frame_processor_id, + "initial_parameters": initial_parameters or {}, + } + self._command_socket.send(json.dumps(command).encode("utf-8")) + + # Wait for response + if self._response_socket.poll(timeout=60000): # 60 seconds + message = self._response_socket.recv() + response = json.loads(message.decode("utf-8")) + + if response["status"] == WorkerResponse.FRAME_PROCESSOR_CREATED.value: + return FrameProcessorProxy( + frame_processor_id=frame_processor_id, + command_socket=self._command_socket, + response_socket=self._response_socket, + ) + else: + error_msg = response.get("error", "Unknown error") + raise RuntimeError(f"Failed to create FrameProcessor: {error_msg}") + else: + raise RuntimeError("Timeout waiting for FrameProcessor creation") diff --git a/src/scope/server/pipeline_worker.py b/src/scope/server/pipeline_worker.py new file mode 100644 index 000000000..e871eb211 --- /dev/null +++ b/src/scope/server/pipeline_worker.py @@ -0,0 +1,882 @@ +"""Pipeline Worker - Runs pipeline and FrameProcessor in a separate process for proper VRAM cleanup.""" + +import logging +import multiprocessing as mp +import os +import queue +import threading +import time +import traceback +from collections import deque +from enum import Enum + +import torch +from omegaconf import OmegaConf + +# Configure logging for worker process +logger = logging.getLogger(__name__) + + +class WorkerCommand(Enum): + """Commands that can be sent to the worker process.""" + + LOAD_PIPELINE = "load_pipeline" + UNLOAD_PIPELINE = "unload_pipeline" + CREATE_FRAME_PROCESSOR = "create_frame_processor" + DESTROY_FRAME_PROCESSOR = "destroy_frame_processor" + PUT_FRAME = "put_frame" + GET_FRAME = "get_frame" + UPDATE_PARAMETERS = "update_parameters" + GET_FPS = "get_fps" + SHUTDOWN = "shutdown" + + +class WorkerResponse(Enum): + """Response types from worker process.""" + + SUCCESS = "success" + ERROR = "error" + PIPELINE_LOADED = "pipeline_loaded" + PIPELINE_NOT_LOADED = "pipeline_not_loaded" + RESULT = "result" + FRAME_PROCESSOR_CREATED = "frame_processor_created" + FRAME = "frame" + + +# Constants for FrameProcessor +OUTPUT_QUEUE_MAX_SIZE_FACTOR = 3 +MIN_FPS = 1.0 +MAX_FPS = 60.0 +DEFAULT_FPS = 30.0 +SLEEP_TIME = 0.01 + + +class WorkerFrameProcessor: + """FrameProcessor that runs in the worker process and uses pipeline directly.""" + + def __init__( + self, + pipeline, + max_output_queue_size: int = 8, + max_parameter_queue_size: int = 8, + max_buffer_size: int = 30, + initial_parameters: dict = None, + ): + self.pipeline = pipeline + + self.frame_buffer = deque(maxlen=max_buffer_size) + self.frame_buffer_lock = threading.Lock() + self.output_queue = queue.Queue(maxsize=max_output_queue_size) + + # Current parameters used by processing thread + self.parameters = initial_parameters or {} + # Queue for parameter updates from external threads + self.parameters_queue = queue.Queue(maxsize=max_parameter_queue_size) + + self.worker_thread: threading.Thread | None = None + self.shutdown_event = threading.Event() + self.running = False + + self.is_prepared = False + + # FPS tracking variables + self.processing_time_per_frame = deque(maxlen=2) + self.last_fps_update = time.time() + self.fps_update_interval = 0.5 + self.min_fps = MIN_FPS + self.max_fps = MAX_FPS + self.current_pipeline_fps = DEFAULT_FPS + self.fps_lock = threading.Lock() + + self.paused = False + + def start(self): + if self.running: + return + + self.running = True + self.shutdown_event.clear() + self.worker_thread = threading.Thread(target=self.worker_loop, daemon=True) + self.worker_thread.start() + + logger.info("WorkerFrameProcessor started") + + def stop(self): + if not self.running: + return + + self.running = False + self.shutdown_event.set() + + if self.worker_thread and self.worker_thread.is_alive(): + if threading.current_thread() != self.worker_thread: + self.worker_thread.join(timeout=5.0) + + while not self.output_queue.empty(): + try: + self.output_queue.get_nowait() + except queue.Empty: + break + + with self.frame_buffer_lock: + self.frame_buffer.clear() + + logger.info("WorkerFrameProcessor stopped") + + def put(self, frame_data: dict) -> bool: + """Put a frame into the buffer. frame_data is a serialized VideoFrame.""" + if not self.running: + return False + + # Deserialize frame from dict + frame_array = frame_data.get("array") + if frame_array is None: + return False + + with self.frame_buffer_lock: + # Store as dict for now, will convert to tensor when processing + self.frame_buffer.append(frame_data) + return True + + def get(self) -> dict | None: + """Get a processed frame. Returns serialized tensor data.""" + if not self.running: + return None + + try: + frame_tensor = self.output_queue.get_nowait() + # Serialize tensor to dict for inter-process communication + return {"__tensor__": True, "data": frame_tensor.cpu().numpy()} + except queue.Empty: + return None + + def get_current_pipeline_fps(self) -> float: + """Get the current dynamically calculated pipeline FPS""" + with self.fps_lock: + return self.current_pipeline_fps + + def _calculate_pipeline_fps(self, start_time: float, num_frames: int): + """Calculate FPS based on processing time and number of frames created""" + processing_time = time.time() - start_time + if processing_time <= 0 or num_frames <= 0: + return + + time_per_frame = processing_time / num_frames + self.processing_time_per_frame.append(time_per_frame) + + current_time = time.time() + if current_time - self.last_fps_update >= self.fps_update_interval: + if len(self.processing_time_per_frame) >= 1: + avg_time_per_frame = sum(self.processing_time_per_frame) / len( + self.processing_time_per_frame + ) + + with self.fps_lock: + current_fps = self.current_pipeline_fps + estimated_fps = ( + 1.0 / avg_time_per_frame if avg_time_per_frame > 0 else current_fps + ) + + estimated_fps = max(self.min_fps, min(self.max_fps, estimated_fps)) + with self.fps_lock: + self.current_pipeline_fps = estimated_fps + + self.last_fps_update = current_time + + def update_parameters(self, parameters: dict): + """Update parameters that will be used in the next pipeline call.""" + try: + self.parameters_queue.put_nowait(parameters) + except queue.Full: + logger.info("Parameter queue full, dropping parameter update") + return False + + def worker_loop(self): + logger.info("WorkerFrameProcessor worker thread started") + + while self.running and not self.shutdown_event.is_set(): + try: + self.process_chunk() + + except Exception as e: + if self._is_recoverable(e): + logger.error(f"Error in worker loop: {e}") + continue + else: + logger.error( + f"Non-recoverable error in worker loop: {e}, stopping frame processor" + ) + self.stop() + break + logger.info("WorkerFrameProcessor worker thread stopped") + + def process_chunk(self): + start_time = time.time() + try: + # Check if there are new parameters + try: + new_parameters = self.parameters_queue.get_nowait() + if new_parameters != self.parameters: + if ( + "prompts" in new_parameters + and "transition" not in new_parameters + and "transition" in self.parameters + ): + self.parameters.pop("transition", None) + + self.parameters = {**self.parameters, **new_parameters} + except queue.Empty: + pass + + # Pause or resume the processing + paused = self.parameters.pop("paused", None) + if paused is not None and paused != self.paused: + self.paused = paused + if self.paused: + self.shutdown_event.wait(SLEEP_TIME) + return + + reset_cache = self.parameters.pop("reset_cache", None) + lora_scales = self.parameters.pop("lora_scales", None) + + if reset_cache: + logger.info("Clearing output buffer queue due to reset_cache request") + while not self.output_queue.empty(): + try: + self.output_queue.get_nowait() + except queue.Empty: + break + + requirements = None + if hasattr(self.pipeline, "prepare"): + requirements = self.pipeline.prepare(**self.parameters) + + video_input = None + if requirements is not None: + current_chunk_size = requirements.input_size + with self.frame_buffer_lock: + if not self.frame_buffer or len(self.frame_buffer) < current_chunk_size: + self.shutdown_event.wait(SLEEP_TIME) + return + video_input = self.prepare_chunk(current_chunk_size) + + call_params = dict(self.parameters.items()) + call_params["init_cache"] = not self.is_prepared + if reset_cache is not None: + call_params["init_cache"] = reset_cache + + if lora_scales is not None: + call_params["lora_scales"] = lora_scales + + if video_input is not None: + call_params["video"] = video_input + + # Call pipeline directly - no proxy needed! + output = self.pipeline(**call_params) + + # Clear transition when complete + if "transition" in call_params and "transition" in self.parameters: + transition_active = False + if hasattr(self.pipeline, "state"): + transition_active = self.pipeline.state.get("_transition_active", False) + + transition = call_params.get("transition") + if not transition_active or transition is None: + self.parameters.pop("transition", None) + + processing_time = time.time() - start_time + num_frames = output.shape[0] + logger.debug( + f"Processed pipeline in {processing_time:.4f}s, {num_frames} frames" + ) + + # Normalize to [0, 255] and convert to uint8 + output = ( + (output * 255.0) + .clamp(0, 255) + .to(dtype=torch.uint8) + .contiguous() + .detach() + .cpu() + ) + + # Resize output queue to meet target max size + target_output_queue_max_size = num_frames * OUTPUT_QUEUE_MAX_SIZE_FACTOR + if self.output_queue.maxsize < target_output_queue_max_size: + logger.info( + f"Increasing output queue size to {target_output_queue_max_size}, current size {self.output_queue.maxsize}, num_frames {num_frames}" + ) + + old_queue = self.output_queue + self.output_queue = queue.Queue(maxsize=target_output_queue_max_size) + while not old_queue.empty(): + try: + frame = old_queue.get_nowait() + self.output_queue.put_nowait(frame) + except queue.Empty: + break + + for frame in output: + try: + self.output_queue.put_nowait(frame) + except queue.Full: + logger.warning("Output queue full, dropping processed frame") + self._calculate_pipeline_fps(start_time, num_frames) + continue + + self._calculate_pipeline_fps(start_time, num_frames) + except Exception as e: + if self._is_recoverable(e): + logger.error(f"Error processing chunk: {e}", exc_info=True) + else: + raise e + + self.is_prepared = True + + def prepare_chunk(self, chunk_size: int) -> list[torch.Tensor]: + """Sample frames uniformly from the buffer and convert them to tensors.""" + step = len(self.frame_buffer) / chunk_size + indices = [round(i * step) for i in range(chunk_size)] + video_frames_data = [self.frame_buffer[i] for i in indices] + + last_idx = indices[-1] + for _ in range(last_idx + 1): + self.frame_buffer.popleft() + + tensor_frames = [] + for frame_data in video_frames_data: + # Convert frame data to tensor + frame_array = frame_data.get("array") + if frame_array is not None: + tensor = torch.from_numpy(frame_array).float().unsqueeze(0) + tensor_frames.append(tensor) + + return tensor_frames + + @staticmethod + def _is_recoverable(error: Exception) -> bool: + """Check if an error is recoverable.""" + if isinstance(error, torch.cuda.OutOfMemoryError): + return False + return True + + +def _load_pipeline_implementation(pipeline_id: str, load_params: dict | None = None): + """Load a pipeline in the worker process. + + This is the same logic as in PipelineManager._load_pipeline_implementation + but runs in a separate process for proper VRAM isolation. + """ + if pipeline_id == "streamdiffusionv2": + from scope.core.pipelines import ( + StreamDiffusionV2Pipeline, + ) + + from scope.server.models_config import get_model_file_path, get_models_dir + + models_dir = get_models_dir() + config = OmegaConf.create( + { + "model_dir": str(models_dir), + "generator_path": str( + get_model_file_path( + "StreamDiffusionV2/wan_causal_dmd_v2v/model.pt" + ) + ), + "text_encoder_path": str( + get_model_file_path( + "WanVideo_comfy/umt5-xxl-enc-fp8_e4m3fn.safetensors" + ) + ), + "tokenizer_path": str( + get_model_file_path("Wan2.1-T2V-1.3B/google/umt5-xxl") + ), + } + ) + + # Apply load parameters (resolution, seed, LoRAs) to config + height = 512 + width = 512 + seed = 42 + loras = None + lora_merge_mode = "permanent_merge" + + if load_params: + height = load_params.get("height", 512) + width = load_params.get("width", 512) + seed = load_params.get("seed", 42) + loras = load_params.get("loras", None) + lora_merge_mode = load_params.get("lora_merge_mode", lora_merge_mode) + + config["height"] = height + config["width"] = width + config["seed"] = seed + if loras: + config["loras"] = loras + config["_lora_merge_mode"] = lora_merge_mode + + pipeline = StreamDiffusionV2Pipeline( + config, device=torch.device("cuda"), dtype=torch.bfloat16 + ) + logger.info("StreamDiffusionV2 pipeline initialized in worker process") + return pipeline + + elif pipeline_id == "passthrough": + from scope.core.pipelines import PassthroughPipeline + + # Use load parameters for resolution, default to 512x512 + height = 512 + width = 512 + if load_params: + height = load_params.get("height", 512) + width = load_params.get("width", 512) + + pipeline = PassthroughPipeline( + height=height, + width=width, + device=torch.device("cuda"), + dtype=torch.bfloat16, + ) + logger.info("Passthrough pipeline initialized in worker process") + return pipeline + + elif pipeline_id == "longlive": + from scope.core.pipelines import LongLivePipeline + + from scope.server.models_config import get_model_file_path, get_models_dir + + config = OmegaConf.create( + { + "model_dir": str(get_models_dir()), + "generator_path": str( + get_model_file_path("LongLive-1.3B/models/longlive_base.pt") + ), + "lora_path": str( + get_model_file_path("LongLive-1.3B/models/lora.pt") + ), + "text_encoder_path": str( + get_model_file_path( + "WanVideo_comfy/umt5-xxl-enc-fp8_e4m3fn.safetensors" + ) + ), + "tokenizer_path": str( + get_model_file_path("Wan2.1-T2V-1.3B/google/umt5-xxl") + ), + } + ) + + # Apply load parameters (resolution, seed, LoRAs) to config + height = 320 + width = 576 + seed = 42 + loras = None + lora_merge_mode = "permanent_merge" + + if load_params: + height = load_params.get("height", 320) + width = load_params.get("width", 576) + seed = load_params.get("seed", 42) + loras = load_params.get("loras", None) + lora_merge_mode = load_params.get("lora_merge_mode", lora_merge_mode) + + config["height"] = height + config["width"] = width + config["seed"] = seed + if loras: + config["loras"] = loras + config["_lora_merge_mode"] = lora_merge_mode + + pipeline = LongLivePipeline( + config, device=torch.device("cuda"), dtype=torch.bfloat16 + ) + logger.info("LongLive pipeline initialized in worker process") + return pipeline + + elif pipeline_id == "krea-realtime-video": + from scope.core.pipelines import ( + KreaRealtimeVideoPipeline, + ) + + from scope.server.models_config import get_model_file_path, get_models_dir + + config = OmegaConf.create( + { + "model_dir": str(get_models_dir()), + "generator_path": str( + get_model_file_path( + "krea-realtime-video/krea-realtime-video-14b.safetensors" + ) + ), + "text_encoder_path": str( + get_model_file_path( + "WanVideo_comfy/umt5-xxl-enc-fp8_e4m3fn.safetensors" + ) + ), + "tokenizer_path": str( + get_model_file_path("Wan2.1-T2V-1.3B/google/umt5-xxl") + ), + "vae_path": str( + get_model_file_path("Wan2.1-T2V-1.3B/Wan2.1_VAE.pth") + ), + } + ) + + # Apply load parameters (resolution, seed, LoRAs) to config + height = 512 + width = 512 + seed = 42 + loras = None + lora_merge_mode = "permanent_merge" + quantization = None + + if load_params: + height = load_params.get("height", 512) + width = load_params.get("width", 512) + seed = load_params.get("seed", 42) + loras = load_params.get("loras", None) + lora_merge_mode = load_params.get("lora_merge_mode", lora_merge_mode) + quantization = load_params.get("quantization", None) + + config["height"] = height + config["width"] = width + config["seed"] = seed + if loras: + config["loras"] = loras + config["_lora_merge_mode"] = lora_merge_mode + + pipeline = KreaRealtimeVideoPipeline( + config, + quantization=quantization, + # Only compile diffusion model for hopper right now + compile=any( + x in torch.cuda.get_device_name(0).lower() + for x in ("h100", "hopper") + ), + device=torch.device("cuda"), + dtype=torch.bfloat16, + ) + logger.info("krea-realtime-video pipeline initialized in worker process") + return pipeline + + else: + raise ValueError(f"Invalid pipeline ID: {pipeline_id}") + + +def pipeline_worker_process(command_queue: mp.Queue, response_queue: mp.Queue): + """Main worker process function that handles pipeline and FrameProcessor operations. + + This process runs in isolation and can be killed to ensure proper VRAM cleanup. + + Args: + command_queue: Queue for receiving commands from main process + response_queue: Queue for sending responses back to main process + """ + # Set up logging for worker process + logging.basicConfig( + level=logging.INFO, + format=f"%(asctime)s - [Worker-{os.getpid()}] - %(name)s - %(levelname)s - %(message)s", + ) + + logger.info(f"Pipeline worker process started (PID: {os.getpid()})") + + pipeline = None + pipeline_id = None + frame_processors: dict[str, WorkerFrameProcessor] = {} + + try: + while True: + try: + # Wait for commands from main process + command_data = command_queue.get() + + if command_data is None: + logger.info("Received shutdown signal") + break + + command = command_data.get("command") + + if command == WorkerCommand.LOAD_PIPELINE.value: + # Load pipeline + pipeline_id_to_load = command_data.get("pipeline_id") + load_params = command_data.get("load_params") + + logger.info( + f"Loading pipeline: {pipeline_id_to_load} with params: {load_params}" + ) + + try: + # Unload existing pipeline if any + if pipeline is not None: + logger.info(f"Unloading existing pipeline: {pipeline_id}") + # Stop all frame processors + for fp_id, fp in list(frame_processors.items()): + fp.stop() + del frame_processors[fp_id] + del pipeline + pipeline = None + pipeline_id = None + + # Load new pipeline + pipeline = _load_pipeline_implementation( + pipeline_id_to_load, load_params + ) + pipeline_id = pipeline_id_to_load + + response_queue.put( + { + "status": WorkerResponse.SUCCESS.value, + "message": f"Pipeline {pipeline_id} loaded successfully", + } + ) + logger.info(f"Pipeline {pipeline_id} loaded successfully") + + except Exception as e: + error_msg = ( + f"Failed to load pipeline: {str(e)}\n{traceback.format_exc()}" + ) + logger.error(error_msg) + response_queue.put( + {"status": WorkerResponse.ERROR.value, "error": error_msg} + ) + + elif command == WorkerCommand.CREATE_FRAME_PROCESSOR.value: + # Create a new FrameProcessor instance + if pipeline is None: + response_queue.put( + { + "status": WorkerResponse.ERROR.value, + "error": "Pipeline not loaded", + } + ) + continue + + try: + fp_id = command_data.get("frame_processor_id") + initial_parameters = command_data.get("initial_parameters", {}) + + if fp_id in frame_processors: + logger.warning(f"FrameProcessor {fp_id} already exists, stopping old one") + frame_processors[fp_id].stop() + + frame_processor = WorkerFrameProcessor( + pipeline=pipeline, + initial_parameters=initial_parameters, + ) + frame_processor.start() + frame_processors[fp_id] = frame_processor + + response_queue.put( + { + "status": WorkerResponse.FRAME_PROCESSOR_CREATED.value, + "frame_processor_id": fp_id, + } + ) + logger.info(f"Created FrameProcessor {fp_id}") + + except Exception as e: + error_msg = ( + f"Failed to create FrameProcessor: {str(e)}\n{traceback.format_exc()}" + ) + logger.error(error_msg) + response_queue.put( + {"status": WorkerResponse.ERROR.value, "error": error_msg} + ) + + elif command == WorkerCommand.DESTROY_FRAME_PROCESSOR.value: + # Destroy a FrameProcessor instance + fp_id = command_data.get("frame_processor_id") + if fp_id in frame_processors: + frame_processors[fp_id].stop() + del frame_processors[fp_id] + response_queue.put( + { + "status": WorkerResponse.SUCCESS.value, + "message": f"FrameProcessor {fp_id} destroyed", + } + ) + logger.info(f"Destroyed FrameProcessor {fp_id}") + else: + response_queue.put( + { + "status": WorkerResponse.ERROR.value, + "error": f"FrameProcessor {fp_id} not found", + } + ) + + elif command == WorkerCommand.PUT_FRAME.value: + # Put a frame into a FrameProcessor + fp_id = command_data.get("frame_processor_id") + frame_data = command_data.get("frame_data") + + if fp_id not in frame_processors: + response_queue.put( + { + "status": WorkerResponse.ERROR.value, + "error": f"FrameProcessor {fp_id} not found", + } + ) + continue + + try: + success = frame_processors[fp_id].put(frame_data) + # Don't send response for every frame to avoid queue buildup + # Only send response if there's an error + if not success: + response_queue.put( + { + "status": WorkerResponse.ERROR.value, + "error": "Failed to put frame", + } + ) + except Exception as e: + error_msg = f"Error putting frame: {str(e)}" + logger.error(error_msg) + response_queue.put( + {"status": WorkerResponse.ERROR.value, "error": error_msg} + ) + + elif command == WorkerCommand.GET_FRAME.value: + # Get a processed frame from a FrameProcessor + fp_id = command_data.get("frame_processor_id") + + if fp_id not in frame_processors: + response_queue.put( + { + "status": WorkerResponse.ERROR.value, + "error": f"FrameProcessor {fp_id} not found", + } + ) + continue + + try: + frame_data = frame_processors[fp_id].get() + if frame_data is not None: + response_queue.put( + { + "status": WorkerResponse.FRAME.value, + "frame_data": frame_data, + } + ) + else: + # No frame available - send empty response + response_queue.put( + { + "status": WorkerResponse.RESULT.value, + "result": None, + } + ) + except Exception as e: + error_msg = f"Error getting frame: {str(e)}" + logger.error(error_msg) + response_queue.put( + {"status": WorkerResponse.ERROR.value, "error": error_msg} + ) + + elif command == WorkerCommand.UPDATE_PARAMETERS.value: + # Update parameters for a FrameProcessor + fp_id = command_data.get("frame_processor_id") + parameters = command_data.get("parameters", {}) + + if fp_id not in frame_processors: + response_queue.put( + { + "status": WorkerResponse.ERROR.value, + "error": f"FrameProcessor {fp_id} not found", + } + ) + continue + + try: + frame_processors[fp_id].update_parameters(parameters) + # Don't send response for parameter updates to avoid queue buildup + except Exception as e: + error_msg = f"Error updating parameters: {str(e)}" + logger.error(error_msg) + response_queue.put( + {"status": WorkerResponse.ERROR.value, "error": error_msg} + ) + + elif command == WorkerCommand.GET_FPS.value: + # Get current FPS from a FrameProcessor + fp_id = command_data.get("frame_processor_id") + + if fp_id not in frame_processors: + response_queue.put( + { + "status": WorkerResponse.ERROR.value, + "error": f"FrameProcessor {fp_id} not found", + } + ) + continue + + try: + fps = frame_processors[fp_id].get_current_pipeline_fps() + response_queue.put( + { + "status": WorkerResponse.RESULT.value, + "result": fps, + } + ) + except Exception as e: + error_msg = f"Error getting FPS: {str(e)}" + logger.error(error_msg) + response_queue.put( + {"status": WorkerResponse.ERROR.value, "error": error_msg} + ) + + elif command == WorkerCommand.SHUTDOWN.value: + logger.info("Received shutdown command") + break + + except Exception as e: + error_msg = ( + f"Error processing command: {str(e)}\n{traceback.format_exc()}" + ) + logger.error(error_msg) + response_queue.put( + {"status": WorkerResponse.ERROR.value, "error": error_msg} + ) + + finally: + # Cleanup on exit + logger.info("Cleaning up worker process...") + # Stop all frame processors + for fp_id, fp in list(frame_processors.items()): + fp.stop() + frame_processors.clear() + if pipeline is not None: + del pipeline + + logger.info("Pipeline worker process shutting down") + + +def _serialize_tensors(obj): + """Serialize torch tensors for inter-process communication. + + For CUDA tensors, we move them to CPU first for serialization. + """ + if isinstance(obj, torch.Tensor): + # Move to CPU for serialization + return {"__tensor__": True, "data": obj.cpu().numpy()} + elif isinstance(obj, dict): + return {k: _serialize_tensors(v) for k, v in obj.items()} + elif isinstance(obj, list): + return [_serialize_tensors(item) for item in obj] + elif isinstance(obj, tuple): + return tuple(_serialize_tensors(item) for item in obj) + else: + return obj + + +def _deserialize_tensors(obj): + """Deserialize torch tensors from inter-process communication.""" + if isinstance(obj, dict): + if obj.get("__tensor__"): + # Reconstruct tensor from numpy array + return torch.from_numpy(obj["data"]) + return {k: _deserialize_tensors(v) for k, v in obj.items()} + elif isinstance(obj, list): + return [_deserialize_tensors(item) for item in obj] + elif isinstance(obj, tuple): + return tuple(_deserialize_tensors(item) for item in obj) + return obj diff --git a/src/scope/server/schema.py b/src/scope/server/schema.py index ca0bc1f9c..409c4cce9 100644 --- a/src/scope/server/schema.py +++ b/src/scope/server/schema.py @@ -10,7 +10,12 @@ LongLiveConfig, StreamDiffusionV2Config, ) -from scope.core.pipelines.utils import Quantization + + +class Quantization(str, Enum): + """Quantization method enumeration.""" + + FP8_E4M3FN = "fp8_e4m3fn" class HealthResponse(BaseModel): diff --git a/src/scope/server/tracks.py b/src/scope/server/tracks.py index 6269ba9e4..df7046dd3 100644 --- a/src/scope/server/tracks.py +++ b/src/scope/server/tracks.py @@ -3,12 +3,12 @@ import logging import threading import time +import uuid from aiortc import MediaStreamTrack from aiortc.mediastreams import VIDEO_CLOCK_RATE, VIDEO_TIME_BASE, MediaStreamError from av import VideoFrame -from .frame_processor import FrameProcessor from .pipeline_manager import PipelineManager logger = logging.getLogger(__name__) @@ -33,6 +33,7 @@ def __init__( self.frame_ptime = 1.0 / fps self.frame_processor = None + self.frame_processor_id = str(uuid.uuid4()) self.input_task = None self.input_task_running = False self._paused = False @@ -94,10 +95,10 @@ async def next_timestamp(self) -> tuple[int, fractions.Fraction]: def initialize_output_processing(self): if not self.frame_processor: - self.frame_processor = FrameProcessor( - pipeline_manager=self.pipeline_manager, + # Create FrameProcessor in worker process via PipelineManager + self.frame_processor = self.pipeline_manager.create_frame_processor( + frame_processor_id=self.frame_processor_id, initial_parameters=self.initial_parameters, - notification_callback=self.notification_callback, ) self.frame_processor.start() @@ -171,5 +172,6 @@ async def stop(self): if self.frame_processor is not None: self.frame_processor.stop() + self.frame_processor = None await super().stop() diff --git a/uv.lock b/uv.lock index 400751bac..dfc9d36c6 100644 --- a/uv.lock +++ b/uv.lock @@ -13,26 +13,6 @@ resolution-markers = [ "python_full_version < '3.11' and sys_platform != 'linux' and sys_platform != 'win32'", ] -[[package]] -name = "accelerate" -version = "1.11.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "huggingface-hub" }, - { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, - { name = "numpy", version = "2.3.4", source = { registry = 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+description = "Base worker process for daydream-scope pipeline execution" +requires-python = ">=3.10.12" +dependencies = [ + "pyzmq>=26.0.0", + "pydantic>=2.0.0", + "numpy>=1.24.0", + "torch", + "omegaconf>=2.3.0", + "pluggy>=1.5.0", +] + +[project.scripts] +scope-worker = "scope_worker.worker:main" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/scope_worker"] diff --git a/worker_env/src/scope_worker/__init__.py b/worker_env/src/scope_worker/__init__.py new file mode 100644 index 000000000..a235881df --- /dev/null +++ b/worker_env/src/scope_worker/__init__.py @@ -0,0 +1,5 @@ +"""Scope Worker - Pipeline worker process for daydream-scope.""" + +from .plugins import hookimpl, hookspec + +__all__ = ["hookimpl", "hookspec"] diff --git a/worker_env/src/scope_worker/pipelines/__init__.py b/worker_env/src/scope_worker/pipelines/__init__.py new file mode 100644 index 000000000..90e9463ac --- /dev/null +++ b/worker_env/src/scope_worker/pipelines/__init__.py @@ -0,0 +1,16 @@ +"""Pipeline interfaces for Scope worker plugins. + +This module provides the base classes needed for creating Scope pipeline plugins. +""" + +from .interface import Pipeline, Requirements +from .schema import BasePipelineConfig, InputMode, ModeDefaults + +__all__ = [ + "Pipeline", + "Requirements", + "BasePipelineConfig", + "InputMode", + "ModeDefaults", +] + diff --git a/worker_env/src/scope_worker/pipelines/interface.py b/worker_env/src/scope_worker/pipelines/interface.py new file mode 100644 index 000000000..01519c666 --- /dev/null +++ b/worker_env/src/scope_worker/pipelines/interface.py @@ -0,0 +1,78 @@ +"""Base interface for all pipelines.""" + +from abc import ABC, abstractmethod +from typing import TYPE_CHECKING + +import torch +from pydantic import BaseModel + +if TYPE_CHECKING: + from .schema import BasePipelineConfig + + +class Requirements(BaseModel): + """Requirements for pipeline configuration.""" + + input_size: int + + +class Pipeline(ABC): + """Abstract base class for all pipelines. + + Pipelines must implement get_config_class() to return their Pydantic config model. + This enables: + - Validation via model_validate() / model_validate_json() + - JSON Schema generation via model_json_schema() + - Type-safe configuration access + - API introspection and automatic UI generation + + See schema.py for the BasePipelineConfig model and pipeline-specific configs. + For multi-mode pipeline support (text/video), pipelines use helper functions + from defaults.py (resolve_input_mode, apply_mode_defaults_to_state, etc.). + """ + + @classmethod + def get_config_class(cls) -> type["BasePipelineConfig"]: + """Return the Pydantic config class for this pipeline. + + The config class should inherit from BasePipelineConfig and define: + - pipeline_id: Unique identifier + - pipeline_name: Human-readable name + - pipeline_description: Capabilities description + - pipeline_version: Version string + - Default parameter values for the pipeline + + Returns: + Pydantic config model class + + Note: + Subclasses should override this method to return their config class. + The default implementation returns BasePipelineConfig. + + Example: + from .schema import MyPipelineConfig + + @classmethod + def get_config_class(cls) -> type[BasePipelineConfig]: + return MyPipelineConfig + """ + from .schema import BasePipelineConfig + + return BasePipelineConfig + + @abstractmethod + def __call__( + self, input: torch.Tensor | list[torch.Tensor] | None = None, **kwargs + ) -> torch.Tensor: + """ + Process a chunk of video frames. + + Args: + input: A tensor in BCTHW format OR a list of frame tensors in THWC format (in [0, 255] range), or None + **kwargs: Additional parameters + + Returns: + A processed chunk tensor in THWC format and [0, 1] range + """ + pass + diff --git a/worker_env/src/scope_worker/pipelines/schema.py b/worker_env/src/scope_worker/pipelines/schema.py new file mode 100644 index 000000000..deb3fa0e4 --- /dev/null +++ b/worker_env/src/scope_worker/pipelines/schema.py @@ -0,0 +1,205 @@ +"""Pydantic-based schema models for pipeline configuration. + +This module provides Pydantic models for pipeline configuration that can be used for: +- Validation of pipeline parameters via model_validate() / model_validate_json() +- JSON Schema generation via model_json_schema() +- Type-safe configuration access +- API introspection and automatic UI generation + +Pipeline-specific configs inherit from BasePipelineConfig and override defaults. +Each pipeline defines its supported modes and can provide mode-specific defaults. +""" + +from typing import Annotated, Any, ClassVar, Literal + +from pydantic import BaseModel, ConfigDict, Field + +# Type alias for input modes +InputMode = Literal["text", "video"] + + +class ModeDefaults(BaseModel): + """Mode-specific default values. + + These override the base config defaults when operating in a specific mode. + Only non-None values will override the base defaults. + """ + + model_config = ConfigDict(extra="forbid") + + # Resolution can differ per mode + height: int | None = None + width: int | None = None + + # Core parameters + denoising_steps: list[int] | None = None + + # Video mode parameters + noise_scale: float | None = None + noise_controller: bool | None = None + + +class BasePipelineConfig(BaseModel): + """Base configuration for all pipelines. + + This provides common parameters shared across all pipeline modes. + Pipeline-specific configs inherit from this and override defaults. + + Mode support is declared via class variables: + - supported_modes: List of modes this pipeline supports ("text", "video") + - default_mode: The mode to use by default in the UI + + Mode-specific defaults can be provided via the get_mode_defaults() class method. + """ + + model_config = ConfigDict(extra="forbid") + + # Pipeline metadata - not configuration parameters, used for identification + pipeline_id: ClassVar[str] = "base" + pipeline_name: ClassVar[str] = "Base Pipeline" + pipeline_description: ClassVar[str] = "Base pipeline configuration" + pipeline_version: ClassVar[str] = "1.0.0" + docs_url: ClassVar[str | None] = None + estimated_vram_gb: ClassVar[float | None] = None + requires_models: ClassVar[bool] = False + supports_lora: ClassVar[bool] = False + supports_vace: ClassVar[bool] = False + + # Mode support - override in subclasses + supported_modes: ClassVar[list[InputMode]] = ["text"] + default_mode: ClassVar[InputMode] = "text" + + # Prompt and temporal interpolation support + supports_prompts: ClassVar[bool] = True + default_temporal_interpolation_method: ClassVar[Literal["linear", "slerp"]] = ( + "slerp" + ) + default_temporal_interpolation_steps: ClassVar[int] = 0 + + # Resolution settings + height: int = Field(default=512, ge=1, description="Output height in pixels") + width: int = Field(default=512, ge=1, description="Output width in pixels") + + # Core parameters + manage_cache: bool = Field( + default=True, + description="Enable automatic cache management for performance optimization", + ) + base_seed: Annotated[int, Field(ge=0)] = Field( + default=42, + description="Base random seed for reproducible generation", + ) + denoising_steps: list[int] | None = Field( + default=None, + description="Denoising step schedule for progressive generation", + ) + + # Video mode parameters (None means not applicable/text mode) + noise_scale: Annotated[float, Field(ge=0.0, le=1.0)] | None = Field( + default=None, + description="Amount of noise to add during video generation (video mode only)", + ) + noise_controller: bool | None = Field( + default=None, + description="Enable dynamic noise control during generation (video mode only)", + ) + input_size: int | None = Field( + default=None, + description="Expected input video frame count (video mode only)", + ) + + @classmethod + def get_pipeline_metadata(cls) -> dict[str, str]: + """Return pipeline identification metadata. + + Returns: + Dict with id, name, description, version + """ + return { + "id": cls.pipeline_id, + "name": cls.pipeline_name, + "description": cls.pipeline_description, + "version": cls.pipeline_version, + } + + @classmethod + def get_mode_defaults(cls) -> dict[InputMode, ModeDefaults]: + """Return mode-specific default overrides. + + Override in subclasses to provide different defaults per mode. + Values in ModeDefaults override the base config defaults. + + Returns: + Dict mapping mode name to ModeDefaults with override values + """ + return {} + + @classmethod + def get_defaults_for_mode(cls, mode: InputMode) -> dict[str, Any]: + """Get effective defaults for a specific mode. + + Merges base config defaults with mode-specific overrides. + + Args: + mode: The input mode ("text" or "video") + + Returns: + Dict of parameter names to their effective default values + """ + # Start with base defaults from model fields + base_instance = cls() + defaults = base_instance.model_dump() + + # Apply mode-specific overrides + mode_defaults = cls.get_mode_defaults().get(mode) + if mode_defaults: + for field_name, value in mode_defaults.model_dump().items(): + if value is not None: + defaults[field_name] = value + + return defaults + + @classmethod + def get_schema_with_metadata(cls) -> dict[str, Any]: + """Return complete schema with pipeline metadata and JSON schema. + + This is the primary method for API/UI schema generation. + + Returns: + Dict containing pipeline metadata + """ + metadata = cls.get_pipeline_metadata() + metadata["supported_modes"] = cls.supported_modes + metadata["default_mode"] = cls.default_mode + metadata["supports_prompts"] = cls.supports_prompts + metadata["default_temporal_interpolation_method"] = ( + cls.default_temporal_interpolation_method + ) + metadata["default_temporal_interpolation_steps"] = ( + cls.default_temporal_interpolation_steps + ) + metadata["docs_url"] = cls.docs_url + metadata["estimated_vram_gb"] = cls.estimated_vram_gb + metadata["requires_models"] = cls.requires_models + metadata["supports_lora"] = cls.supports_lora + metadata["supports_vace"] = cls.supports_vace + metadata["config_schema"] = cls.model_json_schema() + + # Include mode-specific defaults if defined + mode_defaults = cls.get_mode_defaults() + if mode_defaults: + metadata["mode_defaults"] = { + mode: defaults.model_dump(exclude_none=True) + for mode, defaults in mode_defaults.items() + } + + return metadata + + def is_video_mode(self) -> bool: + """Check if this config represents video mode. + + Returns: + True if video mode parameters are set + """ + return self.input_size is not None + diff --git a/worker_env/src/scope_worker/plugins.py b/worker_env/src/scope_worker/plugins.py new file mode 100644 index 000000000..8643cf184 --- /dev/null +++ b/worker_env/src/scope_worker/plugins.py @@ -0,0 +1,25 @@ +"""Hook specifications for the Scope plugin system.""" + +import pluggy + +hookspec = pluggy.HookspecMarker("scope") +hookimpl = pluggy.HookimplMarker("scope") + + +class ScopeHookSpec: + """Hook specifications for Scope plugins.""" + + @hookspec + def register_pipelines(self, register): + """Register custom pipeline implementations. + + Args: + register: Callback to register pipeline classes. + Usage: register(PipelineClass) + + Example: + @scope_worker.hookimpl + def register_pipelines(register): + register(MyPipeline) + """ + diff --git a/worker_env/src/scope_worker/worker.py b/worker_env/src/scope_worker/worker.py new file mode 100644 index 000000000..665f19b1c --- /dev/null +++ b/worker_env/src/scope_worker/worker.py @@ -0,0 +1,1076 @@ +"""Pipeline Worker - Runs pipeline and FrameProcessor in a separate process using ZMQ for IPC.""" + +import argparse +import json +import logging +import os +import queue +import sys +import threading +import time +import traceback +from collections import deque +from enum import Enum + +import numpy as np +import torch +import zmq +from omegaconf import OmegaConf + +# Configure logging for worker process +logger = logging.getLogger(__name__) + + +class WorkerCommand(Enum): + """Commands that can be sent to the worker process.""" + + LOAD_PIPELINE = "load_pipeline" + UNLOAD_PIPELINE = "unload_pipeline" + CREATE_FRAME_PROCESSOR = "create_frame_processor" + DESTROY_FRAME_PROCESSOR = "destroy_frame_processor" + PUT_FRAME = "put_frame" + GET_FRAME = "get_frame" + UPDATE_PARAMETERS = "update_parameters" + GET_FPS = "get_fps" + SHUTDOWN = "shutdown" + + +class WorkerResponse(Enum): + """Response types from worker process.""" + + SUCCESS = "success" + ERROR = "error" + PIPELINE_LOADED = "pipeline_loaded" + PIPELINE_NOT_LOADED = "pipeline_not_loaded" + RESULT = "result" + FRAME_PROCESSOR_CREATED = "frame_processor_created" + FRAME = "frame" + + +# Constants for FrameProcessor +OUTPUT_QUEUE_MAX_SIZE_FACTOR = 3 +MIN_FPS = 1.0 +MAX_FPS = 60.0 +DEFAULT_FPS = 30.0 +SLEEP_TIME = 0.01 + + +class WorkerFrameProcessor: + """FrameProcessor that runs in the worker process and uses pipeline directly.""" + + def __init__( + self, + pipeline, + max_output_queue_size: int = 8, + max_parameter_queue_size: int = 8, + max_buffer_size: int = 30, + initial_parameters: dict = None, + ): + self.pipeline = pipeline + + self.frame_buffer = deque(maxlen=max_buffer_size) + self.frame_buffer_lock = threading.Lock() + self.output_queue = queue.Queue(maxsize=max_output_queue_size) + + # Current parameters used by processing thread + self.parameters = initial_parameters or {} + # Queue for parameter updates from external threads + self.parameters_queue = queue.Queue(maxsize=max_parameter_queue_size) + + self.worker_thread: threading.Thread | None = None + self.shutdown_event = threading.Event() + self.running = False + + self.is_prepared = False + + # FPS tracking variables + self.processing_time_per_frame = deque(maxlen=2) + self.last_fps_update = time.time() + self.fps_update_interval = 0.5 + self.min_fps = MIN_FPS + self.max_fps = MAX_FPS + self.current_pipeline_fps = DEFAULT_FPS + self.fps_lock = threading.Lock() + + self.paused = False + + def start(self): + if self.running: + return + + self.running = True + self.shutdown_event.clear() + self.worker_thread = threading.Thread(target=self.worker_loop, daemon=True) + self.worker_thread.start() + + logger.info("WorkerFrameProcessor started") + + def stop(self): + if not self.running: + return + + self.running = False + self.shutdown_event.set() + + if self.worker_thread and self.worker_thread.is_alive(): + if threading.current_thread() != self.worker_thread: + self.worker_thread.join(timeout=5.0) + + while not self.output_queue.empty(): + try: + self.output_queue.get_nowait() + except queue.Empty: + break + + with self.frame_buffer_lock: + self.frame_buffer.clear() + + logger.info("WorkerFrameProcessor stopped") + + def put(self, frame_data: dict) -> bool: + """Put a frame into the buffer. frame_data is a serialized VideoFrame.""" + if not self.running: + return False + + # Deserialize frame from dict + frame_array = frame_data.get("array") + if frame_array is None: + return False + + with self.frame_buffer_lock: + # Store as dict for now, will convert to tensor when processing + self.frame_buffer.append(frame_data) + return True + + def get(self) -> dict | None: + """Get a processed frame. Returns serialized tensor data.""" + if not self.running: + return None + + try: + frame_tensor = self.output_queue.get_nowait() + # Serialize tensor to dict for inter-process communication + return {"__tensor__": True, "data": frame_tensor.cpu().numpy()} + except queue.Empty: + return None + + def get_current_pipeline_fps(self) -> float: + """Get the current dynamically calculated pipeline FPS""" + with self.fps_lock: + return self.current_pipeline_fps + + def _calculate_pipeline_fps(self, start_time: float, num_frames: int): + """Calculate FPS based on processing time and number of frames created""" + processing_time = time.time() - start_time + if processing_time <= 0 or num_frames <= 0: + return + + time_per_frame = processing_time / num_frames + self.processing_time_per_frame.append(time_per_frame) + + current_time = time.time() + if current_time - self.last_fps_update >= self.fps_update_interval: + if len(self.processing_time_per_frame) >= 1: + avg_time_per_frame = sum(self.processing_time_per_frame) / len( + self.processing_time_per_frame + ) + + with self.fps_lock: + current_fps = self.current_pipeline_fps + estimated_fps = ( + 1.0 / avg_time_per_frame if avg_time_per_frame > 0 else current_fps + ) + + estimated_fps = max(self.min_fps, min(self.max_fps, estimated_fps)) + with self.fps_lock: + self.current_pipeline_fps = estimated_fps + + self.last_fps_update = current_time + + def update_parameters(self, parameters: dict): + """Update parameters that will be used in the next pipeline call.""" + try: + self.parameters_queue.put_nowait(parameters) + except queue.Full: + logger.info("Parameter queue full, dropping parameter update") + return False + + def worker_loop(self): + logger.info("WorkerFrameProcessor worker thread started") + + while self.running and not self.shutdown_event.is_set(): + try: + self.process_chunk() + + except Exception as e: + if self._is_recoverable(e): + logger.error(f"Error in worker loop: {e}") + continue + else: + logger.error( + f"Non-recoverable error in worker loop: {e}, stopping frame processor" + ) + self.stop() + break + logger.info("WorkerFrameProcessor worker thread stopped") + + def process_chunk(self): + start_time = time.time() + try: + # Check if there are new parameters + try: + new_parameters = self.parameters_queue.get_nowait() + if new_parameters != self.parameters: + if ( + "prompts" in new_parameters + and "transition" not in new_parameters + and "transition" in self.parameters + ): + self.parameters.pop("transition", None) + + self.parameters = {**self.parameters, **new_parameters} + except queue.Empty: + pass + + # Pause or resume the processing + paused = self.parameters.pop("paused", None) + if paused is not None and paused != self.paused: + self.paused = paused + if self.paused: + self.shutdown_event.wait(SLEEP_TIME) + return + + reset_cache = self.parameters.pop("reset_cache", None) + lora_scales = self.parameters.pop("lora_scales", None) + + if reset_cache: + logger.info("Clearing output buffer queue due to reset_cache request") + while not self.output_queue.empty(): + try: + self.output_queue.get_nowait() + except queue.Empty: + break + + requirements = None + if hasattr(self.pipeline, "prepare"): + requirements = self.pipeline.prepare(**self.parameters) + + video_input = None + if requirements is not None: + current_chunk_size = requirements.input_size + with self.frame_buffer_lock: + if not self.frame_buffer or len(self.frame_buffer) < current_chunk_size: + self.shutdown_event.wait(SLEEP_TIME) + return + video_input = self.prepare_chunk(current_chunk_size) + + call_params = dict(self.parameters.items()) + call_params["init_cache"] = not self.is_prepared + if reset_cache is not None: + call_params["init_cache"] = reset_cache + + if lora_scales is not None: + call_params["lora_scales"] = lora_scales + + if video_input is not None: + call_params["video"] = video_input + + # Call pipeline directly - no proxy needed! + output = self.pipeline(**call_params) + + # Clear transition when complete + if "transition" in call_params and "transition" in self.parameters: + transition_active = False + if hasattr(self.pipeline, "state"): + transition_active = self.pipeline.state.get("_transition_active", False) + + transition = call_params.get("transition") + if not transition_active or transition is None: + self.parameters.pop("transition", None) + + processing_time = time.time() - start_time + num_frames = output.shape[0] + logger.debug( + f"Processed pipeline in {processing_time:.4f}s, {num_frames} frames" + ) + + # Normalize to [0, 255] and convert to uint8 + output = ( + (output * 255.0) + .clamp(0, 255) + .to(dtype=torch.uint8) + .contiguous() + .detach() + .cpu() + ) + + # Resize output queue to meet target max size + target_output_queue_max_size = num_frames * OUTPUT_QUEUE_MAX_SIZE_FACTOR + if self.output_queue.maxsize < target_output_queue_max_size: + logger.info( + f"Increasing output queue size to {target_output_queue_max_size}, current size {self.output_queue.maxsize}, num_frames {num_frames}" + ) + + old_queue = self.output_queue + self.output_queue = queue.Queue(maxsize=target_output_queue_max_size) + while not old_queue.empty(): + try: + frame = old_queue.get_nowait() + self.output_queue.put_nowait(frame) + except queue.Empty: + break + + for frame in output: + try: + self.output_queue.put_nowait(frame) + except queue.Full: + logger.warning("Output queue full, dropping processed frame") + self._calculate_pipeline_fps(start_time, num_frames) + continue + + self._calculate_pipeline_fps(start_time, num_frames) + except Exception as e: + if self._is_recoverable(e): + logger.error(f"Error processing chunk: {e}", exc_info=True) + else: + raise e + + self.is_prepared = True + + def prepare_chunk(self, chunk_size: int) -> list[torch.Tensor]: + """Sample frames uniformly from the buffer and convert them to tensors.""" + step = len(self.frame_buffer) / chunk_size + indices = [round(i * step) for i in range(chunk_size)] + video_frames_data = [self.frame_buffer[i] for i in indices] + + last_idx = indices[-1] + for _ in range(last_idx + 1): + self.frame_buffer.popleft() + + tensor_frames = [] + for frame_data in video_frames_data: + # Convert frame data to tensor + frame_array = frame_data.get("array") + if frame_array is not None: + tensor = torch.from_numpy(frame_array).float().unsqueeze(0) + tensor_frames.append(tensor) + + return tensor_frames + + @staticmethod + def _is_recoverable(error: Exception) -> bool: + """Check if an error is recoverable.""" + if isinstance(error, torch.cuda.OutOfMemoryError): + return False + return True + + +def _get_model_file_path(relative_path: str) -> str: + """Get the absolute path to a model file.""" + models_dir = os.environ.get("MODELS_DIR", os.path.expanduser("~/.daydream-scope/models")) + return os.path.join(models_dir, relative_path) + + +def _get_models_dir() -> str: + """Get the models directory.""" + return os.environ.get("MODELS_DIR", os.path.expanduser("~/.daydream-scope/models")) + + +def _get_plugins_dir() -> str: + """Get the plugins directory.""" + models_dir = _get_models_dir() + # Get parent directory of models and add plugins + parent_dir = os.path.dirname(models_dir) + return os.path.join(parent_dir, "plugins") + + +def _load_plugin_pipeline(pipeline_id: str, load_params: dict | None = None): + """Try to load a plugin pipeline from the plugins directory or current working directory. + + The worker is started with cwd set to the plugin directory, so we can import + the pipeline module directly. + """ + import importlib.util + + # First, try to find a pipeline.py in the current working directory + cwd = os.getcwd() + pipeline_module_path = os.path.join(cwd, "pipeline.py") + + if not os.path.exists(pipeline_module_path): + # Try src//pipeline.py pattern (standard Python package layout) + src_dir = os.path.join(cwd, "src") + if os.path.exists(src_dir): + for name in os.listdir(src_dir): + candidate = os.path.join(src_dir, name, "pipeline.py") + if os.path.exists(candidate): + pipeline_module_path = candidate + break + + if not os.path.exists(pipeline_module_path): + # Try /pipeline.py pattern (flat layout, e.g. scope_test_generator/) + for name in os.listdir(cwd): + candidate_dir = os.path.join(cwd, name) + if os.path.isdir(candidate_dir) and not name.startswith("."): + candidate = os.path.join(candidate_dir, "pipeline.py") + if os.path.exists(candidate): + pipeline_module_path = candidate + break + + if not os.path.exists(pipeline_module_path): + raise ValueError(f"No pipeline.py found for plugin pipeline: {pipeline_id}") + + logger.info(f"Loading plugin pipeline from: {pipeline_module_path}") + + # Dynamically import the pipeline module + spec = importlib.util.spec_from_file_location("plugin_pipeline", pipeline_module_path) + if spec is None or spec.loader is None: + raise ValueError(f"Could not load pipeline module from: {pipeline_module_path}") + + module = importlib.util.module_from_spec(spec) + sys.modules["plugin_pipeline"] = module + spec.loader.exec_module(module) + + # Look for a Pipeline class in the module + pipeline_class = None + for name in dir(module): + obj = getattr(module, name) + if isinstance(obj, type) and name.endswith("Pipeline") and name != "Pipeline": + pipeline_class = obj + break + + if pipeline_class is None: + raise ValueError(f"No Pipeline class found in {pipeline_module_path}") + + logger.info(f"Found pipeline class: {pipeline_class.__name__}") + + # Instantiate the pipeline with load_params + load_params = load_params or {} + pipeline = pipeline_class(**load_params) + + logger.info(f"Plugin pipeline '{pipeline_id}' initialized in worker process") + return pipeline + + +def _load_pipeline_implementation(pipeline_id: str, load_params: dict | None = None): + """Load a pipeline in the worker process.""" + # Add the main project src to path so we can import scope modules + project_root = os.environ.get("SCOPE_PROJECT_ROOT", "") + if project_root: + src_path = os.path.join(project_root, "src") + if src_path not in sys.path: + sys.path.insert(0, src_path) + + # List of built-in pipelines + BUILTIN_PIPELINES = { + "streamdiffusionv2", + "passthrough", + "longlive", + "krea-realtime-video", + "reward-forcing", + } + + # If not a built-in pipeline, try to load as plugin + if pipeline_id not in BUILTIN_PIPELINES: + return _load_plugin_pipeline(pipeline_id, load_params) + + if pipeline_id == "streamdiffusionv2": + from scope.core.pipelines import StreamDiffusionV2Pipeline + + models_dir = _get_models_dir() + config = OmegaConf.create( + { + "model_dir": str(models_dir), + "generator_path": str( + _get_model_file_path( + "StreamDiffusionV2/wan_causal_dmd_v2v/model.pt" + ) + ), + "text_encoder_path": str( + _get_model_file_path( + "WanVideo_comfy/umt5-xxl-enc-fp8_e4m3fn.safetensors" + ) + ), + "tokenizer_path": str( + _get_model_file_path("Wan2.1-T2V-1.3B/google/umt5-xxl") + ), + } + ) + + # Apply load parameters (resolution, seed, LoRAs) to config + height = 512 + width = 512 + seed = 42 + loras = None + lora_merge_mode = "permanent_merge" + + if load_params: + height = load_params.get("height", 512) + width = load_params.get("width", 512) + seed = load_params.get("seed", 42) + loras = load_params.get("loras", None) + lora_merge_mode = load_params.get("lora_merge_mode", lora_merge_mode) + + config["height"] = height + config["width"] = width + config["seed"] = seed + if loras: + config["loras"] = loras + config["_lora_merge_mode"] = lora_merge_mode + + pipeline = StreamDiffusionV2Pipeline( + config, device=torch.device("cuda"), dtype=torch.bfloat16 + ) + logger.info("StreamDiffusionV2 pipeline initialized in worker process") + return pipeline + + elif pipeline_id == "passthrough": + from scope.core.pipelines import PassthroughPipeline + + # Use load parameters for resolution, default to 512x512 + height = 512 + width = 512 + if load_params: + height = load_params.get("height", 512) + width = load_params.get("width", 512) + + pipeline = PassthroughPipeline( + height=height, + width=width, + device=torch.device("cuda"), + dtype=torch.bfloat16, + ) + logger.info("Passthrough pipeline initialized in worker process") + return pipeline + + elif pipeline_id == "longlive": + from scope.core.pipelines import LongLivePipeline + + config = OmegaConf.create( + { + "model_dir": str(_get_models_dir()), + "generator_path": str( + _get_model_file_path("LongLive-1.3B/models/longlive_base.pt") + ), + "lora_path": str( + _get_model_file_path("LongLive-1.3B/models/lora.pt") + ), + "text_encoder_path": str( + _get_model_file_path( + "WanVideo_comfy/umt5-xxl-enc-fp8_e4m3fn.safetensors" + ) + ), + "tokenizer_path": str( + _get_model_file_path("Wan2.1-T2V-1.3B/google/umt5-xxl") + ), + } + ) + + # Apply load parameters (resolution, seed, LoRAs) to config + height = 320 + width = 576 + seed = 42 + loras = None + lora_merge_mode = "permanent_merge" + + if load_params: + height = load_params.get("height", 320) + width = load_params.get("width", 576) + seed = load_params.get("seed", 42) + loras = load_params.get("loras", None) + lora_merge_mode = load_params.get("lora_merge_mode", lora_merge_mode) + + config["height"] = height + config["width"] = width + config["seed"] = seed + if loras: + config["loras"] = loras + config["_lora_merge_mode"] = lora_merge_mode + + pipeline = LongLivePipeline( + config, device=torch.device("cuda"), dtype=torch.bfloat16 + ) + logger.info("LongLive pipeline initialized in worker process") + return pipeline + + elif pipeline_id == "krea-realtime-video": + from scope.core.pipelines import KreaRealtimeVideoPipeline + + config = OmegaConf.create( + { + "model_dir": str(_get_models_dir()), + "generator_path": str( + _get_model_file_path( + "krea-realtime-video/krea-realtime-video-14b.safetensors" + ) + ), + "text_encoder_path": str( + _get_model_file_path( + "WanVideo_comfy/umt5-xxl-enc-fp8_e4m3fn.safetensors" + ) + ), + "tokenizer_path": str( + _get_model_file_path("Wan2.1-T2V-1.3B/google/umt5-xxl") + ), + "vae_path": str( + _get_model_file_path("Wan2.1-T2V-1.3B/Wan2.1_VAE.pth") + ), + } + ) + + # Apply load parameters (resolution, seed, LoRAs) to config + height = 512 + width = 512 + seed = 42 + loras = None + lora_merge_mode = "permanent_merge" + quantization = None + + if load_params: + height = load_params.get("height", 512) + width = load_params.get("width", 512) + seed = load_params.get("seed", 42) + loras = load_params.get("loras", None) + lora_merge_mode = load_params.get("lora_merge_mode", lora_merge_mode) + quantization = load_params.get("quantization", None) + + config["height"] = height + config["width"] = width + config["seed"] = seed + if loras: + config["loras"] = loras + config["_lora_merge_mode"] = lora_merge_mode + + pipeline = KreaRealtimeVideoPipeline( + config, + quantization=quantization, + # Only compile diffusion model for hopper right now + compile=any( + x in torch.cuda.get_device_name(0).lower() + for x in ("h100", "hopper") + ), + device=torch.device("cuda"), + dtype=torch.bfloat16, + ) + logger.info("krea-realtime-video pipeline initialized in worker process") + return pipeline + + elif pipeline_id == "reward-forcing": + from scope.core.pipelines import RewardForcingPipeline + + config = OmegaConf.create( + { + "model_dir": str(_get_models_dir()), + "generator_path": str( + _get_model_file_path("Reward-Forcing-T2V-1.3B/rewardforcing.pt") + ), + "text_encoder_path": str( + _get_model_file_path( + "WanVideo_comfy/umt5-xxl-enc-fp8_e4m3fn.safetensors" + ) + ), + "tokenizer_path": str( + _get_model_file_path("Wan2.1-T2V-1.3B/google/umt5-xxl") + ), + "vae_path": str( + _get_model_file_path("Wan2.1-T2V-1.3B/Wan2.1_VAE.pth") + ), + } + ) + + # Apply load parameters (resolution, seed, LoRAs) to config + height = 320 + width = 576 + seed = 42 + loras = None + lora_merge_mode = "permanent_merge" + quantization = None + + if load_params: + height = load_params.get("height", 320) + width = load_params.get("width", 576) + seed = load_params.get("seed", 42) + loras = load_params.get("loras", None) + lora_merge_mode = load_params.get("lora_merge_mode", lora_merge_mode) + quantization = load_params.get("quantization", None) + + config["height"] = height + config["width"] = width + config["seed"] = seed + if loras: + config["loras"] = loras + config["_lora_merge_mode"] = lora_merge_mode + + pipeline = RewardForcingPipeline( + config, + quantization=quantization, + device=torch.device("cuda"), + dtype=torch.bfloat16, + ) + logger.info("RewardForcing pipeline initialized in worker process") + return pipeline + + else: + # Fallback: try to load as plugin pipeline + return _load_plugin_pipeline(pipeline_id, load_params) + + +def serialize_for_zmq(obj): + """Serialize an object for ZMQ transmission.""" + if isinstance(obj, np.ndarray): + return {"__ndarray__": True, "data": obj.tobytes(), "dtype": str(obj.dtype), "shape": obj.shape} + elif isinstance(obj, torch.Tensor): + arr = obj.cpu().numpy() + return {"__ndarray__": True, "data": arr.tobytes(), "dtype": str(arr.dtype), "shape": arr.shape} + elif isinstance(obj, dict): + return {k: serialize_for_zmq(v) for k, v in obj.items()} + elif isinstance(obj, list): + return [serialize_for_zmq(item) for item in obj] + elif isinstance(obj, tuple): + return {"__tuple__": True, "items": [serialize_for_zmq(item) for item in obj]} + else: + return obj + + +def deserialize_from_zmq(obj): + """Deserialize an object from ZMQ transmission.""" + if isinstance(obj, dict): + if obj.get("__ndarray__"): + return np.frombuffer(obj["data"], dtype=obj["dtype"]).reshape(obj["shape"]) + elif obj.get("__tuple__"): + return tuple(deserialize_from_zmq(item) for item in obj["items"]) + return {k: deserialize_from_zmq(v) for k, v in obj.items()} + elif isinstance(obj, list): + return [deserialize_from_zmq(item) for item in obj] + return obj + + +def worker_main(command_port: int, response_port: int): + """Main worker process function that handles pipeline and FrameProcessor operations via ZMQ. + + Args: + command_port: ZMQ port for receiving commands + response_port: ZMQ port for sending responses + """ + # Set up logging for worker process + logging.basicConfig( + level=logging.INFO, + format=f"%(asctime)s - [Worker-{os.getpid()}] - %(name)s - %(levelname)s - %(message)s", + ) + + logger.info(f"Pipeline worker process started (PID: {os.getpid()})") + logger.info(f"Connecting to command port {command_port}, response port {response_port}") + + # Set up ZMQ context and sockets + context = zmq.Context() + + # PULL socket for receiving commands + command_socket = context.socket(zmq.PULL) + command_socket.connect(f"tcp://127.0.0.1:{command_port}") + + # PUSH socket for sending responses + response_socket = context.socket(zmq.PUSH) + response_socket.connect(f"tcp://127.0.0.1:{response_port}") + + pipeline = None + pipeline_id = None + frame_processors: dict[str, WorkerFrameProcessor] = {} + + try: + while True: + try: + # Wait for commands from main process + # Use a timeout so we can check for shutdown signals + if command_socket.poll(timeout=100): + message = command_socket.recv() + command_data = json.loads(message.decode("utf-8")) + + # Handle binary data separately if present + if command_data.get("__has_binary__"): + binary_data = command_socket.recv() + # Reconstruct the frame data + frame_info = command_data.get("frame_info", {}) + if frame_info: + frame_array = np.frombuffer( + binary_data, + dtype=frame_info["dtype"] + ).reshape(frame_info["shape"]) + command_data["frame_data"] = {"array": frame_array} + else: + continue + + if command_data is None: + logger.info("Received shutdown signal") + break + + command = command_data.get("command") + + if command == WorkerCommand.LOAD_PIPELINE.value: + # Load pipeline + pipeline_id_to_load = command_data.get("pipeline_id") + load_params = command_data.get("load_params") + + logger.info( + f"Loading pipeline: {pipeline_id_to_load} with params: {load_params}" + ) + + try: + # Unload existing pipeline if any + if pipeline is not None: + logger.info(f"Unloading existing pipeline: {pipeline_id}") + # Stop all frame processors + for fp_id, fp in list(frame_processors.items()): + fp.stop() + del frame_processors[fp_id] + del pipeline + pipeline = None + pipeline_id = None + + # Load new pipeline + pipeline = _load_pipeline_implementation( + pipeline_id_to_load, load_params + ) + pipeline_id = pipeline_id_to_load + + response = { + "status": WorkerResponse.SUCCESS.value, + "message": f"Pipeline {pipeline_id} loaded successfully", + } + response_socket.send(json.dumps(response).encode("utf-8")) + logger.info(f"Pipeline {pipeline_id} loaded successfully") + + except Exception as e: + error_msg = ( + f"Failed to load pipeline: {str(e)}\n{traceback.format_exc()}" + ) + logger.error(error_msg) + response = {"status": WorkerResponse.ERROR.value, "error": error_msg} + response_socket.send(json.dumps(response).encode("utf-8")) + + elif command == WorkerCommand.CREATE_FRAME_PROCESSOR.value: + # Create a new FrameProcessor instance + if pipeline is None: + response = { + "status": WorkerResponse.ERROR.value, + "error": "Pipeline not loaded", + } + response_socket.send(json.dumps(response).encode("utf-8")) + continue + + try: + fp_id = command_data.get("frame_processor_id") + initial_parameters = command_data.get("initial_parameters", {}) + + if fp_id in frame_processors: + logger.warning(f"FrameProcessor {fp_id} already exists, stopping old one") + frame_processors[fp_id].stop() + + frame_processor = WorkerFrameProcessor( + pipeline=pipeline, + initial_parameters=initial_parameters, + ) + frame_processor.start() + frame_processors[fp_id] = frame_processor + + response = { + "status": WorkerResponse.FRAME_PROCESSOR_CREATED.value, + "frame_processor_id": fp_id, + } + response_socket.send(json.dumps(response).encode("utf-8")) + logger.info(f"Created FrameProcessor {fp_id}") + + except Exception as e: + error_msg = ( + f"Failed to create FrameProcessor: {str(e)}\n{traceback.format_exc()}" + ) + logger.error(error_msg) + response = {"status": WorkerResponse.ERROR.value, "error": error_msg} + response_socket.send(json.dumps(response).encode("utf-8")) + + elif command == WorkerCommand.DESTROY_FRAME_PROCESSOR.value: + # Destroy a FrameProcessor instance + fp_id = command_data.get("frame_processor_id") + if fp_id in frame_processors: + frame_processors[fp_id].stop() + del frame_processors[fp_id] + response = { + "status": WorkerResponse.SUCCESS.value, + "message": f"FrameProcessor {fp_id} destroyed", + } + response_socket.send(json.dumps(response).encode("utf-8")) + logger.info(f"Destroyed FrameProcessor {fp_id}") + else: + response = { + "status": WorkerResponse.ERROR.value, + "error": f"FrameProcessor {fp_id} not found", + } + response_socket.send(json.dumps(response).encode("utf-8")) + + elif command == WorkerCommand.PUT_FRAME.value: + # Put a frame into a FrameProcessor + fp_id = command_data.get("frame_processor_id") + frame_data = command_data.get("frame_data") + + if fp_id not in frame_processors: + response = { + "status": WorkerResponse.ERROR.value, + "error": f"FrameProcessor {fp_id} not found", + } + response_socket.send(json.dumps(response).encode("utf-8")) + continue + + try: + success = frame_processors[fp_id].put(frame_data) + # Don't send response for every frame to avoid queue buildup + # Only send response if there's an error + if not success: + response = { + "status": WorkerResponse.ERROR.value, + "error": "Failed to put frame", + } + response_socket.send(json.dumps(response).encode("utf-8")) + except Exception as e: + error_msg = f"Error putting frame: {str(e)}" + logger.error(error_msg) + response = {"status": WorkerResponse.ERROR.value, "error": error_msg} + response_socket.send(json.dumps(response).encode("utf-8")) + + elif command == WorkerCommand.GET_FRAME.value: + # Get a processed frame from a FrameProcessor + fp_id = command_data.get("frame_processor_id") + + if fp_id not in frame_processors: + response = { + "status": WorkerResponse.ERROR.value, + "error": f"FrameProcessor {fp_id} not found", + } + response_socket.send(json.dumps(response).encode("utf-8")) + continue + + try: + frame_data = frame_processors[fp_id].get() + if frame_data is not None: + # Send frame data as binary for efficiency + arr = frame_data["data"] + header = { + "status": WorkerResponse.FRAME.value, + "__has_binary__": True, + "frame_info": { + "dtype": str(arr.dtype), + "shape": arr.shape, + }, + } + response_socket.send(json.dumps(header).encode("utf-8"), zmq.SNDMORE) + response_socket.send(arr.tobytes()) + else: + # No frame available - send empty response + response = { + "status": WorkerResponse.RESULT.value, + "result": None, + } + response_socket.send(json.dumps(response).encode("utf-8")) + except Exception as e: + error_msg = f"Error getting frame: {str(e)}" + logger.error(error_msg) + response = {"status": WorkerResponse.ERROR.value, "error": error_msg} + response_socket.send(json.dumps(response).encode("utf-8")) + + elif command == WorkerCommand.UPDATE_PARAMETERS.value: + # Update parameters for a FrameProcessor + fp_id = command_data.get("frame_processor_id") + parameters = command_data.get("parameters", {}) + + if fp_id not in frame_processors: + response = { + "status": WorkerResponse.ERROR.value, + "error": f"FrameProcessor {fp_id} not found", + } + response_socket.send(json.dumps(response).encode("utf-8")) + continue + + try: + frame_processors[fp_id].update_parameters(parameters) + # Don't send response for parameter updates to avoid queue buildup + except Exception as e: + error_msg = f"Error updating parameters: {str(e)}" + logger.error(error_msg) + response = {"status": WorkerResponse.ERROR.value, "error": error_msg} + response_socket.send(json.dumps(response).encode("utf-8")) + + elif command == WorkerCommand.GET_FPS.value: + # Get current FPS from a FrameProcessor + fp_id = command_data.get("frame_processor_id") + + if fp_id not in frame_processors: + response = { + "status": WorkerResponse.ERROR.value, + "error": f"FrameProcessor {fp_id} not found", + } + response_socket.send(json.dumps(response).encode("utf-8")) + continue + + try: + fps = frame_processors[fp_id].get_current_pipeline_fps() + response = { + "status": WorkerResponse.RESULT.value, + "result": fps, + } + response_socket.send(json.dumps(response).encode("utf-8")) + except Exception as e: + error_msg = f"Error getting FPS: {str(e)}" + logger.error(error_msg) + response = {"status": WorkerResponse.ERROR.value, "error": error_msg} + response_socket.send(json.dumps(response).encode("utf-8")) + + elif command == WorkerCommand.SHUTDOWN.value: + logger.info("Received shutdown command") + break + + except zmq.ZMQError as e: + if e.errno == zmq.EAGAIN: + # No message available, continue polling + continue + else: + error_msg = f"ZMQ error: {str(e)}\n{traceback.format_exc()}" + logger.error(error_msg) + response = {"status": WorkerResponse.ERROR.value, "error": error_msg} + response_socket.send(json.dumps(response).encode("utf-8")) + + except Exception as e: + error_msg = ( + f"Error processing command: {str(e)}\n{traceback.format_exc()}" + ) + logger.error(error_msg) + response = {"status": WorkerResponse.ERROR.value, "error": error_msg} + response_socket.send(json.dumps(response).encode("utf-8")) + + finally: + # Cleanup on exit + logger.info("Cleaning up worker process...") + # Stop all frame processors + for fp_id, fp in list(frame_processors.items()): + fp.stop() + frame_processors.clear() + if pipeline is not None: + del pipeline + + # Close ZMQ sockets + command_socket.close() + response_socket.close() + context.term() + + logger.info("Pipeline worker process shutting down") + + +def main(): + """Entry point for the worker process.""" + parser = argparse.ArgumentParser(description="Scope Pipeline Worker") + parser.add_argument("--command-port", type=int, required=True, help="ZMQ command port") + parser.add_argument("--response-port", type=int, required=True, help="ZMQ response port") + + args = parser.parse_args() + + worker_main(args.command_port, args.response_port) + + +if __name__ == "__main__": + main() diff --git a/worker_env/uv.lock b/worker_env/uv.lock new file mode 100644 index 000000000..09eaf7dea --- /dev/null +++ b/worker_env/uv.lock @@ -0,0 +1,980 @@ +version = 1 +revision = 3 +requires-python = ">=3.10.12" +resolution-markers = [ + "python_full_version >= '3.12' and sys_platform == 'linux'", + "python_full_version == '3.11.*' and sys_platform == 'linux'", + "python_full_version < '3.11' and sys_platform == 'linux'", + "python_full_version >= '3.12' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + 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