diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 00000000..11bfde50 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,73 @@ +# Git +.git +.gitignore + +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# Virtual environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# IDE +.vscode/ +.idea/ +*.swp +*.swo + +# OS +.DS_Store +Thumbs.db + +# Project specific +outputs/ +temp_queue_images/ +archive/ +hf_download/ +job_queue.json +quick_prompts.json +*.log + +# Docker +Dockerfile* +docker-compose*.yml +.dockerignore + +# Documentation +*.md +plan/ + +# Cache +.cache/ +.pytest_cache/ +.mypy_cache/ +.ruff_cache/ + +# Temporary files +*.tmp +*.temp \ No newline at end of file diff --git a/.gitignore b/.gitignore index 335f97ac..a5b0ef80 100644 --- a/.gitignore +++ b/.gitignore @@ -188,3 +188,5 @@ job_queue.json main.obj main.exp main.lib +CLAUDE.md +.mcp/ diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 00000000..913b0e8d --- /dev/null +++ b/Dockerfile @@ -0,0 +1,66 @@ +# FramePack-FastAPI with External Model Mount + +FROM nvidia/cuda:12.1-devel-ubuntu22.04 + +# Install system dependencies +RUN apt-get update && apt-get install -y \ + python3.10 \ + python3.10-dev \ + python3-pip \ + curl \ + git \ + build-essential \ + libgl1-mesa-glx \ + libglib2.0-0 \ + libsm6 \ + libxext6 \ + libxrender-dev \ + libgomp1 \ + ffmpeg \ + && rm -rf /var/lib/apt/lists/* + +# Create symlink for python +RUN ln -s /usr/bin/python3.10 /usr/bin/python + +# Install uv +COPY --from=ghcr.io/astral-sh/uv:latest /uv /bin/uv + +# Set working directory +WORKDIR /app + +# Copy dependency files +COPY pyproject.toml requirements.txt ./ + +# Create virtual environment and install dependencies +RUN uv venv /opt/venv +ENV PATH="/opt/venv/bin:$PATH" + +# Install Python dependencies +RUN uv pip install --no-cache -r requirements.txt + +# Copy application code +COPY . . + +# Create necessary directories +RUN mkdir -p outputs/images temp_queue_images loras + +# Create model mount points +RUN mkdir -p /app/hf_download +VOLUME ["/app/hf_download"] + +# Set environment variables +ENV PYTHONPATH="/app:$PYTHONPATH" +ENV PYTHONUNBUFFERED=1 +ENV HF_HOME="/app/hf_download" +ENV TRANSFORMERS_CACHE="/app/hf_download" +ENV HF_DATASETS_CACHE="/app/hf_download" + +# Expose port +EXPOSE 8000 + +# Health check +HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \ + CMD curl -f http://localhost:8000/docs || exit 1 + +# Start command +CMD ["uvicorn", "api.api:app", "--host", "0.0.0.0", "--port", "8000"] \ No newline at end of file diff --git a/README_DOCKER.md b/README_DOCKER.md new file mode 100644 index 00000000..208e1028 --- /dev/null +++ b/README_DOCKER.md @@ -0,0 +1,248 @@ +# FramePack-FastAPI Docker Guide + +このガイドでは、FramePack-FastAPIをDockerで実行する方法を説明します。 + +## 🐳 Docker構成 + +### 利用可能なDockerfile + +1. **Dockerfile** - CPU版（軽量、推論のみ） +2. **Dockerfile.gpu** - GPU版（CUDA対応、高性能） + +### Docker Compose設定 + +1. **docker-compose.yml** - CPU版用 +2. **docker-compose.gpu.yml** - GPU版用 + +## 🚀 クイックスタート + +### CPU版を起動 +```bash +# 自動ビルド＆起動 +./docker-run.sh + +# または手動で +docker-compose up +``` + +### GPU版を起動 +```bash +# 自動ビルド＆起動（GPU必須） +./docker-run.sh --gpu + +# または手動で +docker-compose -f docker-compose.gpu.yml up +``` + +## 🔨 手動ビルド + +### CPUイメージをビルド +```bash +./docker-build.sh +# または +docker build -f Dockerfile -t framepack-fastapi:latest . +``` + +### GPUイメージをビルド +```bash +./docker-build.sh --gpu +# または +docker build -f Dockerfile.gpu -t framepack-fastapi-gpu:latest . +``` + +## 🏃‍♂️ 実行方法 + +### 1. Docker Composeを使用（推奨） + +#### CPU版 +```bash +docker-compose up -d # バックグラウンド実行 +docker-compose logs -f # ログを表示 +``` + +#### GPU版 +```bash +docker-compose -f docker-compose.gpu.yml up -d +docker-compose -f docker-compose.gpu.yml logs -f +``` + +### 2. 直接Dockerコマンドを使用 + +#### CPU版 +```bash +docker run -d \ + -p 8000:8000 \ + -v $(pwd)/outputs:/app/outputs \ + -v $(pwd)/temp_queue_images:/app/temp_queue_images \ + -v $(pwd)/loras:/app/loras \ + -v $(pwd)/hf_download:/app/hf_download \ + framepack-fastapi:latest +``` + +#### GPU版 +```bash +docker run -d \ + --gpus all \ + -p 8000:8000 \ + -v $(pwd)/outputs:/app/outputs \ + -v $(pwd)/temp_queue_images:/app/temp_queue_images \ + -v $(pwd)/loras:/app/loras \ + -v $(pwd)/hf_download:/app/hf_download \ + framepack-fastapi-gpu:latest +``` + +## 🔧 カスタマイズ + +### ポート変更 +```bash +# ポート8080で起動 +./docker-run.sh --port 8080 + +# または環境変数で設定 +export PORT=8080 +docker-compose up +``` + +### 環境変数 + +重要な環境変数： + +```bash +# API設定 +API_HOST=0.0.0.0 +API_PORT=8000 + +# モデルキャッシュ +HF_HOME=/app/hf_download +TRANSFORMERS_CACHE=/app/hf_download + +# GPU設定（GPU版のみ） +CUDA_VISIBLE_DEVICES=0 +NVIDIA_VISIBLE_DEVICES=all +``` + +### ボリューム設定 + +永続化されるデータ： + +- `./outputs` - 生成された動画・画像 +- `./temp_queue_images` - キュー用一時画像 +- `./loras` - LoRAファイル +- `./hf_download` - Hugging Faceモデルキャッシュ +- `./job_queue.json` - ジョブキュー状態 + +## 📋 システム要件 + +### CPU版 +- **RAM**: 最小8GB、推奨16GB以上 +- **ストレージ**: 最小20GB の空き容量 +- **CPU**: マルチコア推奨 + +### GPU版 +- **GPU**: NVIDIA GPU（CUDA 12.1対応） +- **VRAM**: 最小8GB、推奨24GB以上 +- **RAM**: 最小16GB、推奨32GB以上 +- **ストレージ**: 最小50GB の空き容量 +- **NVIDIA Docker**: インストール済み + +## 🛠️ トラブルシューティング + +### よくある問題 + +#### 1. GPU版でGPUが認識されない +```bash +# NVIDIA Dockerの確認 +docker run --rm --gpus all nvidia/cuda:12.1-base-ubuntu22.04 nvidia-smi + +# GPU対応確認 +docker-compose -f docker-compose.gpu.yml exec framepack-api-gpu python -c "import torch; print(torch.cuda.is_available())" +``` + +#### 2. メモリ不足エラー +```bash +# Dockerのメモリ制限を確認・調整 +docker stats + +# Swapファイルを有効化（Linux） +sudo swapon --show +``` + +#### 3. モデルダウンロードが遅い/失敗する +```bash +# Hugging Face キャッシュを事前ダウンロード +mkdir -p hf_download +docker run -v $(pwd)/hf_download:/app/hf_download framepack-fastapi-gpu:latest python -c " +from transformers import AutoTokenizer +AutoTokenizer.from_pretrained('hunyuanvideo-community/HunyuanVideo', cache_dir='/app/hf_download') +" +``` + +#### 4. ポート競合 +```bash +# 使用中のポートを確認 +netstat -tlnp | grep :8000 + +# 別のポートを使用 +docker-compose up -e API_PORT=8080 +``` + +### ログの確認 + +```bash +# サービスログ +docker-compose logs framepack-api + +# リアルタイムログ +docker-compose logs -f framepack-api + +# 特定コンテナのログ +docker logs +``` + +### パフォーマンス監視 + +```bash +# リソース使用量 +docker stats + +# GPU使用量（GPU版） +docker-compose -f docker-compose.gpu.yml exec framepack-api-gpu nvidia-smi +``` + +## 🌐 API アクセス + +コンテナ起動後、以下のURLでアクセス可能： + +- **API**: http://localhost:8000 +- **API ドキュメント**: http://localhost:8000/docs +- **動画生成**: http://localhost:8000/generate +- **画像生成**: http://localhost:8000/api/generate-image + +## 🔄 更新とメンテナンス + +### イメージの更新 +```bash +# 最新コードでリビルド +./docker-build.sh --tag latest + +# 古いイメージを削除 +docker image prune -f +``` + +### データのバックアップ +```bash +# 生成データをバックアップ +tar -czf framepack-backup-$(date +%Y%m%d).tar.gz outputs/ temp_queue_images/ loras/ hf_download/ +``` + +### コンテナの停止と削除 +```bash +# サービス停止 +docker-compose down + +# データボリュームも含めて削除 +docker-compose down -v + +# 完全クリーンアップ +docker system prune -af +``` \ No newline at end of file diff --git a/README_DOCKER_REVISED.md b/README_DOCKER_REVISED.md new file mode 100644 index 00000000..fff1f66b --- /dev/null +++ b/README_DOCKER_REVISED.md @@ -0,0 +1,203 @@ +# FramePack-FastAPI Docker Guide (現実版) + +## 🎯 重要なお知らせ + +**CPU版での推論は現実的ではありません！** HunyuanVideoのような大型言語モデルは、CPUでは以下の理由で実用的ではありません： + +- **メモリ要件**: 60GB以上のRAMが必要 +- **処理時間**: CPU推論は数時間〜数日かかる +- **モデルサイズ**: 40GB以上のモデルファイル + +## 🐳 Docker構成の説明 + +### 1. **開発用サーバー** (`Dockerfile`) +- **用途**: API開発・テスト専用 +- **含まれるもの**: FastAPI, 基本的な依存関係のみ +- **モデル**: なし（軽量、数MB） +- **機能**: APIエンドポイントのテスト、開発 + +### 2. **GPU推論サーバー** (`Dockerfile.gpu`) +- **用途**: 実際の動画・画像生成 +- **要件**: NVIDIA GPU（24GB+ VRAM推奨） +- **モデル**: 全ての必要なモデル（40GB+） +- **機能**: フル機能の推論 + +## 🚀 使い方 + +### 開発・テスト用（軽量版） + +```bash +# 開発サーバー起動（モデルなし） +./docker-run.sh + +# または +docker-compose up + +# APIテストはできるが、推論は503エラーを返す +curl http://localhost:8000/docs # ✅ 動作 +curl -X POST http://localhost:8000/api/generate-image # ❌ 503エラー +``` + +### 本格運用（GPU必須） + +```bash +# GPU必須: NVIDIA GPU + 24GB+ VRAM +./docker-run.sh --gpu + +# または +docker-compose --profile gpu up +docker-compose -f docker-compose.gpu.yml up + +# フル機能利用可能 +curl http://localhost:8001/docs # ✅ 動作 +curl -X POST http://localhost:8001/api/generate-image # ✅ 実際に画像生成 +``` + +## ⚡ 現実的な運用方法 + +### 1. **個人開発者** +```bash +# ローカル開発（モデルなし） +./docker-run.sh + +# GPU搭載マシンで推論テスト +./docker-run.sh --gpu +``` + +### 2. **チーム開発** +```bash +# 開発チーム: 軽量APIサーバー +docker-compose up framepack-api-dev + +# GPU担当者: 推論サーバー +docker-compose --profile gpu up framepack-api-gpu +``` + +### 3. **本番環境** +```bash +# クラウドGPUインスタンス（AWS p3.2xlarge等） +docker-compose -f docker-compose.gpu.yml up -d + +# ロードバランサー経由でアクセス +``` + +## 💻 システム要件 + +### 開発用サーバー +- **RAM**: 1GB以下 +- **ストレージ**: 1GB以下 +- **CPU**: 任意 +- **起動時間**: 10秒以下 + +### GPU推論サーバー +- **GPU**: NVIDIA RTX 3090/4090, A100, H100等 +- **VRAM**: 24GB以上（推奨: 48GB以上） +- **RAM**: 64GB以上 +- **ストレージ**: 100GB以上の空き容量 +- **起動時間**: 2-5分（モデルダウンロード時は30分+） + +## 🔧 Docker構成詳細 + +### ポート設定 +- **開発サーバー**: `localhost:8000` +- **GPU推論サーバー**: `localhost:8001` + +### ボリューム +```yaml +volumes: + - ./outputs:/app/outputs # 生成結果 + - ./loras:/app/loras # LoRAファイル + - ./hf_download:/app/hf_download # モデルキャッシュ（GPU版のみ） + - ./temp_queue_images:/app/temp_queue_images # 一時ファイル +``` + +### 環境変数 +```yaml +# 開発版 +MODELS_DISABLED=true +PYTHONUNBUFFERED=1 + +# GPU版 +NVIDIA_VISIBLE_DEVICES=all +HF_HOME=/app/hf_download +TRANSFORMERS_CACHE=/app/hf_download +``` + +## 🛠️ トラブルシューティング + +### 開発サーバー + +#### Q: APIが503エラーを返す +**A:** 正常です。開発サーバーはモデルがないため推論できません。 + +#### Q: どのエンドポイントがテスト可能？ +**A:** 以下のエンドポイントは動作します： +- `GET /docs` - API ドキュメント +- `GET /queue` - キュー状態 +- `GET /worker/status` - ワーカー状態 +- `GET /loras` - LoRAリスト + +### GPU推論サーバー + +#### Q: "CUDA out of memory"エラー +**A:** VRAMが不足しています。 +```bash +# VRAM使用量確認 +docker exec -it nvidia-smi + +# より小さなバッチサイズを使用 +# または、より多くのVRAMを持つGPUを使用 +``` + +#### Q: モデルダウンロードが遅い +**A:** 初回起動時は40GB+のモデルをダウンロードします。 +```bash +# 事前ダウンロード（推奨） +mkdir -p hf_download +# モデルを事前に配置するか、高速回線での初回起動を推奨 +``` + +#### Q: コンテナが起動しない +**A:** NVIDIA Dockerが正しく設定されているか確認： +```bash +# NVIDIA Docker確認 +docker run --rm --gpus all nvidia/cuda:12.1-base-ubuntu22.04 nvidia-smi + +# Docker Composeでのprofile指定確認 +docker-compose --profile gpu config +``` + +## 🎯 おすすめワークフロー + +### 1. 開発段階 +```bash +# 軽量サーバーでAPI開発 +./docker-run.sh +``` + +### 2. テスト段階 +```bash +# GPU環境で機能テスト +./docker-run.sh --gpu +``` + +### 3. 本番段階 +```bash +# 本番GPUサーバーにデプロイ +docker-compose -f docker-compose.gpu.yml up -d +``` + +## 💡 コスト最適化のヒント + +1. **開発時**: 開発版のみ使用（コスト: $0/月） +2. **テスト時**: クラウドGPUを時間課金で使用（コスト: $1-3/時間） +3. **本番時**: 専用GPUサーバーまたはGPUクラウド（コスト: $500-2000/月） + +## 🚨 注意事項 + +- **CPU版は推論不可**: 開発・テスト専用です +- **GPU版は重い**: 初回起動に時間がかかります +- **VRAM要件**: 24GB未満のGPUでは動作しない可能性があります +- **ストレージ**: モデルで50GB以上の容量を消費します + +この設定により、現実的で実用的なDocker環境が構築できます！ \ No newline at end of file diff --git a/api/api.py b/api/api.py index 23e09228..59d0f0df 100644 --- a/api/api.py +++ b/api/api.py @@ -17,6 +17,8 @@ from PIL import Image import numpy as np from typing import List, Optional +from datetime import datetime +import uuid from watchdog.observers import Observer # Import modules created earlier (relative imports) @@ -25,6 +27,7 @@ from . import queue_manager from . import worker from . import video_watcher +from . import worker_image # --- Global State --- # Dictionary to hold loaded models @@ -45,17 +48,21 @@ async def lifespan(app: FastAPI): # Startup logic global loaded_models, worker_running, worker_thread, observer, sse_clients print("API starting up via lifespan...") - # Load models - try: - # Consider running blocking IO in a threadpool executor in async context - # e.g., await asyncio.to_thread(models.load_models) - # For simplicity now, keeping the direct call but be aware of potential blocking - loaded_models = models.load_models() - print("Models loaded successfully via lifespan.") - except Exception as e: - print(f"FATAL: Failed to load models on startup via lifespan: {e}") - traceback.print_exc() + # Load models (skip if in development mode) + if os.getenv("MODELS_DISABLED", "false").lower() == "true": + print("Models loading disabled - running in development mode") loaded_models = {} + else: + try: + # Consider running blocking IO in a threadpool executor in async context + # e.g., await asyncio.to_thread(models.load_models) + # For simplicity now, keeping the direct call but be aware of potential blocking + loaded_models = models.load_models() + print("Models loaded successfully via lifespan.") + except Exception as e: + print(f"FATAL: Failed to load models on startup via lifespan: {e}") + traceback.print_exc() + loaded_models = {} # Start background worker if not worker_running: @@ -119,6 +126,9 @@ async def lifespan(app: FastAPI): # Use the lifespan context manager app = FastAPI(title="FramePack API", version="0.1.0", lifespan=lifespan) +# Set maximum file size (100MB) +app.state.max_request_size = 100 * 1024 * 1024 + # --- CORS Middleware Configuration --- app.add_middleware( CORSMiddleware, @@ -137,7 +147,7 @@ class GenerateRequest(BaseModel): video_length: float = Field(5.0, description="Length of the video in seconds.", gt=0) seed: int = Field(-1, description="Seed for generation. -1 for random.") use_teacache: bool = Field(False, description="Enable TEACache optimization.") - gpu_memory_preservation: float = Field(0.0, description="GPU memory to preserve (GB) in low VRAM mode.", ge=0) + gpu_memory_preservation: float = Field(10.0, description="GPU memory to preserve (GB) in low VRAM mode.", ge=0) steps: int = Field(20, description="Number of diffusion steps.", gt=0) cfg: float = Field(7.0, description="Classifier-Free Guidance scale.", ge=1.0) gs: float = Field(1.0, description="Guidance scale for start latent.", ge=0) @@ -178,12 +188,67 @@ class ResultResponse(BaseModel): thumbnail_base64: Optional[str] = None +# --- Image Generation Models --- +class ImageGenerationRequest(BaseModel): + prompt: str = Field(..., description="Text prompt for image generation.") + negative_prompt: Optional[str] = Field("", description="Negative prompt.") + input_image: str = Field(..., description="Base64 encoded input image (required).") + seed: Optional[int] = Field(-1, description="Seed for generation. -1 for random.") + steps: int = Field(30, description="Number of diffusion steps.", gt=0) + cfg: float = Field(1.0, description="Classifier-Free Guidance scale.", ge=0.0) + width: int = Field(1216, description="Image width.", gt=0) + height: int = Field(704, description="Image height.", gt=0) + gpu_memory_preservation: float = Field(10.0, description="GPU memory to preserve (GB) in low VRAM mode.", ge=0) + lora_paths: Optional[List[str]] = Field(None, description="List of LoRA file paths.") + lora_scales: Optional[List[float]] = Field(None, description="List of LoRA scales.") + + +class BatchImageRequest(BaseModel): + prompts: List[str] = Field(..., description="List of text prompts for batch generation.") + input_image: str = Field(..., description="Base64 encoded input image (used for all batch items).") + negative_prompt: Optional[str] = Field("", description="Negative prompt for all images.") + seeds: Optional[List[int]] = Field(None, description="List of seeds (one per prompt).") + batch_size: int = Field(4, description="Number of images to generate in parallel.", gt=0, le=8) + steps: int = Field(30, description="Number of diffusion steps.", gt=0) + cfg: float = Field(1.0, description="Classifier-Free Guidance scale.", ge=0.0) + width: int = Field(1216, description="Image width.", gt=0) + height: int = Field(704, description="Image height.", gt=0) + gpu_memory_preservation: float = Field(10.0, description="GPU memory to preserve (GB) in low VRAM mode.", ge=0) + lora_paths: Optional[List[str]] = Field(None, description="List of LoRA file paths.") + lora_scales: Optional[List[float]] = Field(None, description="List of LoRA scales.") + + +class ImageTransferRequest(BaseModel): + source_image: str = Field(..., description="Base64 encoded source image.") + target_image: str = Field(..., description="Base64 encoded target image.") + prompt: str = Field(..., description="Text prompt for the transfer.") + negative_prompt: Optional[str] = Field("", description="Negative prompt.") + transfer_strength: float = Field(0.7, description="Strength of the style transfer.", ge=0.0, le=1.0) + seed: Optional[int] = Field(-1, description="Seed for generation. -1 for random.") + steps: int = Field(30, description="Number of diffusion steps.", gt=0) + cfg: float = Field(1.0, description="Classifier-Free Guidance scale.", ge=0.0) + gpu_memory_preservation: float = Field(10.0, description="GPU memory to preserve (GB) in low VRAM mode.", ge=0) + + +class ImageGenerationResponse(BaseModel): + job_id: str + message: str + + + + # --- Enum for Sampling Mode --- class SamplingMode(str, enum.Enum): reverse = "reverse" forward = "forward" +# --- Enum for Output Type --- +class OutputType(str, enum.Enum): + video = "video" + image = "image" + + # --- Background Worker --- def background_worker_task(): global worker_running, currently_processing_job_id @@ -201,7 +266,23 @@ def background_worker_task(): currently_processing_job_id = None continue - worker.worker(next_job, loaded_models) + # Check job type and route to appropriate worker + job_data = queue_manager.get_job_data_by_id(currently_processing_job_id) + if job_data and isinstance(job_data, dict): + job_type = job_data.get("type", "video") + + if job_type == "image_generation": + worker_image.process_image_generation(currently_processing_job_id, job_data, loaded_models) + elif job_type == "batch_images": + worker_image.process_batch_images(currently_processing_job_id, job_data, loaded_models) + elif job_type == "image_transfer": + worker_image.process_image_transfer(currently_processing_job_id, job_data, loaded_models) + else: + # Default to video generation + worker.worker(next_job, loaded_models) + else: + # Fallback to video generation for compatibility + worker.worker(next_job, loaded_models) except Exception as e: print(f"Unhandled exception in worker for job {currently_processing_job_id}: {e}") traceback.print_exc() @@ -224,7 +305,7 @@ def background_worker_task(): # === Job Execution Flow === -@app.post("/generate", response_model=GenerateResponse) +@app.post("/generate", response_model=GenerateResponse, tags=["Video Generation"]) async def generate_video( background_tasks: BackgroundTasks, prompt: str = Form("A character doing some simple body movements."), @@ -311,8 +392,364 @@ async def generate_video( print(f"Job added to queue with ID: {job_id}") return GenerateResponse(job_id=job_id, message="Video generation job added to queue.") +# --- Image Generation Endpoints --- + +@app.post("/api/generate-image", response_model=ImageGenerationResponse, tags=["Image Generation"]) +async def generate_image( + prompt: str = Form(..., description="Text prompt for image generation."), + negative_prompt: str = Form("", description="Negative prompt."), + seed: int = Form(-1, description="Seed for generation. -1 for random."), + steps: int = Form(30, description="Number of diffusion steps."), + cfg: float = Form(1.0, description="Classifier-Free Guidance scale."), + width: int = Form(1216, description="Image width."), + height: int = Form(704, description="Image height."), + lora_path: Optional[str] = Form("", description="LoRA file path."), + lora_scale: float = Form(1.0, description="LoRA scale."), + gpu_memory_preservation: float = Form(10.0, description="GPU memory to preserve (GB) in low VRAM mode."), + sampling_mode: str = Form("dpm-solver++", description="Sampling mode."), + transformer_model: str = Form("base", description="Transformer model (base or f1)."), + input_image: UploadFile = File(..., description="Input image file (required).", media_type="image/*") +): + """ + Generate a single high-quality image from input image and text prompt (Image-to-Image). + """ + if not loaded_models: + raise HTTPException(status_code=503, detail="Models are not loaded or failed to load. API is not ready.") + + # Read and encode input image + try: + print(f"Debug: input_image type = {type(input_image)}") + + # Handle different types of input_image + if isinstance(input_image, str): + # If it's already a string, assume it's base64 encoded + print("Debug: input_image is a string, treating as base64") + input_image_b64 = input_image + elif hasattr(input_image, 'read'): + # It's an UploadFile + print(f"Debug: input_image is UploadFile") + if hasattr(input_image, 'filename'): + print(f"Debug: filename = {input_image.filename}") + if hasattr(input_image, 'content_type'): + print(f"Debug: content_type = {input_image.content_type}") + + contents = await input_image.read() + print(f"Debug: Read {len(contents)} bytes from UploadFile") + + import base64 + input_image_b64 = base64.b64encode(contents).decode('utf-8') + print(f"Debug: Encoded to base64, length = {len(input_image_b64)}") + else: + raise HTTPException(status_code=400, detail=f"Invalid input_image type: {type(input_image)}. Expected UploadFile or base64 string.") + + except Exception as e: + print(f"Debug: Exception occurred: {e}") + print(f"Debug: Exception type: {type(e)}") + import traceback + traceback.print_exc() + raise HTTPException(status_code=400, detail=f"Error loading image: {e}") + finally: + if hasattr(input_image, 'close'): + try: + await input_image.close() + except: + pass + + # Create a unique job ID (8-digit hex like video generation) + import uuid + job_id = uuid.uuid4().hex[:8] + + # Prepare job data + job_data = { + "type": "image_generation", + "data": { + "prompt": prompt, + "negative_prompt": negative_prompt, + "input_image": input_image_b64, + "seed": seed, + "steps": steps, + "cfg": cfg, + "width": width, + "height": height, + "lora_path": lora_path, + "lora_scale": lora_scale, + "gpu_memory_preservation": gpu_memory_preservation, + "sampling_mode": sampling_mode, + "transformer_model": transformer_model + }, + "created_at": datetime.now().isoformat() + } + + # Add to queue + try: + queue_manager.add_job(job_id, job_data) + print(f"Image generation job added to queue: {job_id}") + except Exception as e: + print(f"Error adding image job to queue: {e}") + raise HTTPException(status_code=500, detail=f"Failed to add job to queue: {e}") + + # Jobs will be processed by the background worker + + return ImageGenerationResponse( + job_id=job_id, + message="Image generation job added to queue." + ) + + -@app.get("/status/{job_id}", response_model=JobStatusResponse) +@app.post("/api/batch-images", response_model=ImageGenerationResponse, tags=["Image Generation"]) +async def batch_images( + prompts: str = Form(..., description="Comma-separated list of text prompts for batch generation."), + negative_prompt: str = Form("", description="Negative prompt for all images."), + seeds: str = Form("", description="Comma-separated list of seeds (optional, one per prompt)."), + steps: int = Form(30, description="Number of diffusion steps."), + cfg: float = Form(1.0, description="Classifier-Free Guidance scale."), + width: int = Form(1216, description="Image width."), + height: int = Form(704, description="Image height."), + lora_path: str = Form("", description="LoRA file path."), + lora_scale: float = Form(1.0, description="LoRA scale."), + gpu_memory_preservation: float = Form(10.0, description="GPU memory to preserve (GB) in low VRAM mode."), + sampling_mode: str = Form("dpm-solver++", description="Sampling mode."), + transformer_model: str = Form("base", description="Transformer model (base or f1)."), + input_image: UploadFile = File(..., description="Input image file (used for all batch items).", media_type="image/*") +): + """ + Generate multiple images in batch from a list of prompts. + """ + if not loaded_models: + raise HTTPException(status_code=503, detail="Models are not loaded or failed to load. API is not ready.") + + # Read and encode input image + try: + print(f"Debug: input_image type = {type(input_image)}") + + if isinstance(input_image, str): + input_image_b64 = input_image + elif hasattr(input_image, 'read'): + print(f"Debug: input_image is UploadFile") + if hasattr(input_image, 'filename'): + print(f"Debug: filename = {input_image.filename}") + + contents = await input_image.read() + print(f"Debug: Read {len(contents)} bytes from UploadFile") + + import base64 + input_image_b64 = base64.b64encode(contents).decode('utf-8') + else: + raise HTTPException(status_code=400, detail=f"Invalid input_image type: {type(input_image)}") + + except Exception as e: + print(f"Debug: Input image error: {e}") + raise HTTPException(status_code=400, detail=f"Error loading input image: {e}") + finally: + if hasattr(input_image, 'close'): + try: + await input_image.close() + except: + pass + + # Parse prompts (comma-separated) + prompts_list = [p.strip() for p in prompts.split(',') if p.strip()] + if not prompts_list: + raise HTTPException(status_code=400, detail="At least one prompt is required.") + + # Parse seeds (comma-separated, optional) + seeds_list = [] + if seeds.strip(): + try: + seeds_list = [int(s.strip()) for s in seeds.split(',') if s.strip()] + except ValueError: + raise HTTPException(status_code=400, detail="Seeds must be integers separated by commas.") + + # If no seeds provided, generate random ones + if not seeds_list: + import random + seeds_list = [random.randint(0, 2**32 - 1) for _ in prompts_list] + + # Ensure seeds list matches prompts list length + while len(seeds_list) < len(prompts_list): + import random + seeds_list.append(random.randint(0, 2**32 - 1)) + + print(f"Debug: Processing {len(prompts_list)} prompts with {len(seeds_list)} seeds") + + # Create a unique job ID (8-digit hex like video generation) + import uuid + job_id = uuid.uuid4().hex[:8] + + # Prepare job data + job_data = { + "type": "batch_images", + "data": { + "prompts": prompts_list, + "input_image": input_image_b64, + "negative_prompt": negative_prompt, + "seeds": seeds_list, + "steps": steps, + "cfg": cfg, + "width": width, + "height": height, + "lora_path": lora_path, + "lora_scale": lora_scale, + "gpu_memory_preservation": gpu_memory_preservation, + "sampling_mode": sampling_mode, + "transformer_model": transformer_model + }, + "created_at": datetime.now().isoformat() + } + + # Add to queue + try: + queue_manager.add_job(job_id, job_data) + print(f"Batch image generation job added to queue: {job_id}") + except Exception as e: + print(f"Error adding batch image job to queue: {e}") + raise HTTPException(status_code=500, detail=f"Failed to add job to queue: {e}") + + # Jobs will be processed by the background worker + + return ImageGenerationResponse( + job_id=job_id, + message="Batch image generation job added to queue." + ) + + +@app.post("/api/transfer-image", response_model=ImageGenerationResponse, tags=["Image Generation"]) +async def transfer_image( + prompt: str = Form(..., description="Text prompt for the transfer."), + negative_prompt: str = Form("", description="Negative prompt."), + transfer_strength: float = Form(0.7, description="Strength of the style transfer."), + seed: int = Form(-1, description="Seed for generation. -1 for random."), + steps: int = Form(30, description="Number of diffusion steps."), + cfg: float = Form(1.0, description="Classifier-Free Guidance scale."), + gpu_memory_preservation: float = Form(10.0, description="GPU memory to preserve (GB) in low VRAM mode."), + sampling_mode: str = Form("dpm-solver++", description="Sampling mode."), + transformer_model: str = Form("base", description="Transformer model (base or f1)."), + source_image: UploadFile = File(..., description="Source image file for style reference.", media_type="image/*"), + target_image: UploadFile = File(..., description="Target image file to apply style to.", media_type="image/*") +): + """ + Transfer style from source image to target image (kisekaeichi functionality). + """ + if not loaded_models: + raise HTTPException(status_code=503, detail="Models are not loaded or failed to load. API is not ready.") + + # Read and encode source image + try: + print(f"Debug: source_image type = {type(source_image)}") + + if isinstance(source_image, str): + source_image_b64 = source_image + elif hasattr(source_image, 'read'): + print(f"Debug: source_image is UploadFile") + if hasattr(source_image, 'filename'): + print(f"Debug: source filename = {source_image.filename}") + + contents = await source_image.read() + print(f"Debug: Read {len(contents)} bytes from source UploadFile") + + import base64 + source_image_b64 = base64.b64encode(contents).decode('utf-8') + else: + raise HTTPException(status_code=400, detail=f"Invalid source_image type: {type(source_image)}") + + except Exception as e: + print(f"Debug: Source image error: {e}") + raise HTTPException(status_code=400, detail=f"Error loading source image: {e}") + finally: + if hasattr(source_image, 'close'): + try: + await source_image.close() + except: + pass + + # Read and encode target image + try: + print(f"Debug: target_image type = {type(target_image)}") + + if isinstance(target_image, str): + target_image_b64 = target_image + elif hasattr(target_image, 'read'): + print(f"Debug: target_image is UploadFile") + if hasattr(target_image, 'filename'): + print(f"Debug: target filename = {target_image.filename}") + + contents = await target_image.read() + print(f"Debug: Read {len(contents)} bytes from target UploadFile") + + import base64 + target_image_b64 = base64.b64encode(contents).decode('utf-8') + else: + raise HTTPException(status_code=400, detail=f"Invalid target_image type: {type(target_image)}") + + except Exception as e: + print(f"Debug: Target image error: {e}") + raise HTTPException(status_code=400, detail=f"Error loading target image: {e}") + finally: + if hasattr(target_image, 'close'): + try: + await target_image.close() + except: + pass + + # Create a unique job ID (8-digit hex like video generation) + import uuid + job_id = uuid.uuid4().hex[:8] + + # Prepare job data + job_data = { + "type": "image_transfer", + "data": { + "prompt": prompt, + "negative_prompt": negative_prompt, + "source_image": source_image_b64, + "target_image": target_image_b64, + "transfer_strength": transfer_strength, + "seed": seed, + "steps": steps, + "cfg": cfg, + "gpu_memory_preservation": gpu_memory_preservation, + "sampling_mode": sampling_mode, + "transformer_model": transformer_model + }, + "created_at": datetime.now().isoformat() + } + + # Add to queue + try: + queue_manager.add_job(job_id, job_data) + print(f"Image transfer job added to queue: {job_id}") + except Exception as e: + print(f"Error adding image transfer job to queue: {e}") + raise HTTPException(status_code=500, detail=f"Failed to add job to queue: {e}") + + # Jobs will be processed by the background worker + + return ImageGenerationResponse( + job_id=job_id, + message="Image transfer job added to queue." + ) + + +@app.get("/api/image/{job_id}", tags=["Image Generation"]) +async def get_generated_image(job_id: str): + """ + Download the generated image file. + """ + # Check job status first + job = queue_manager.get_job_by_id(job_id) + if job and job.status != "completed": + raise HTTPException(status_code=404, detail=f"Job '{job_id}' is not completed yet (status: {job.status}).") + + # Check for image file + image_path = os.path.join("outputs/images", f"{job_id}.png") + if os.path.exists(image_path): + return FileResponse(image_path, media_type="image/png", filename=f"{job_id}.png") + else: + raise HTTPException(status_code=404, detail=f"Image file for job '{job_id}' not found.") + + +@app.get("/status/{job_id}", response_model=JobStatusResponse, tags=["Job Management"]) async def get_job_status(job_id: str): """Checks the status of a job.""" global currently_processing_job_id @@ -362,7 +799,7 @@ async def get_job_status(job_id: str): raise HTTPException(status_code=404, detail="Job not found") -@app.get("/stream/status/{job_id}") +@app.get("/stream/status/{job_id}", tags=["Job Management"]) async def stream_job_status(job_id: str, request: Request): """ Streams the status and progress of a job using Server-Sent Events (SSE). @@ -434,7 +871,7 @@ async def event_generator(): return StreamingResponse(event_generator(), media_type="text/event-stream") -@app.get("/result/{job_id}", response_model=ResultResponse) +@app.get("/result/{job_id}", response_model=ResultResponse, tags=["Video Generation"]) async def get_job_result(job_id: str, request: Request): """ Returns the download URL for the completed video and the Base64 encoded thumbnail. @@ -480,7 +917,7 @@ async def get_job_result(job_id: str, request: Request): # --- Download Endpoints --- -@app.get("/download/video/{job_id}") +@app.get("/download/video/{job_id}", tags=["Video Generation"]) async def download_video(job_id: str): """Downloads the generated video file.""" output_file = os.path.join(settings.OUTPUTS_DIR, f"{job_id}.mp4") @@ -495,7 +932,7 @@ async def download_video(job_id: str): raise HTTPException(status_code=404, detail=f"Video file for job '{job_id}' not found.") -@app.get("/input_image/{job_id}") +@app.get("/input_image/{job_id}", tags=["Job Management"]) async def get_input_image(job_id: str): """ Returns the input JPEG image file associated with a job, potentially including Exif metadata. @@ -527,7 +964,7 @@ async def get_input_image(job_id: str): return FileResponse(input_image_path_from_job, media_type="image/jpeg", filename=f"input_{job_id}.jpg") -@app.post("/cancel/{job_id}", status_code=200) +@app.post("/cancel/{job_id}", status_code=200, tags=["Job Management"]) async def cancel_job(job_id: str): """Requests cancellation of a job.""" # Check if the job exists (optional but good practice) @@ -566,14 +1003,14 @@ async def cancel_job(job_id: str): # === Queue & Worker Management === -@app.get("/queue", response_model=QueueStatusResponse) +@app.get("/queue", response_model=QueueStatusResponse, tags=["Job Management"]) async def get_queue_info(): # Implementation needed: Get queue status from queue_manager queue_status = queue_manager.get_queue_status() return QueueStatusResponse(queue=queue_status) -@app.get("/worker/status", response_model=WorkerStatusResponse) +@app.get("/worker/status", response_model=WorkerStatusResponse, tags=["Job Management"]) async def get_worker_status(): """Returns the current status of the background worker.""" global worker_running, currently_processing_job_id @@ -583,7 +1020,7 @@ async def get_worker_status(): ) -@app.post("/cleanup_jobs", status_code=200) +@app.post("/cleanup_jobs", status_code=200, tags=["Job Management"]) async def trigger_cleanup_jobs(): """ Manually triggers the cleanup of old completed, cancelled, or failed jobs @@ -600,7 +1037,7 @@ async def trigger_cleanup_jobs(): # === Settings & Information === -@app.get("/loras", response_model=LoraListResponse) +@app.get("/loras", response_model=LoraListResponse, tags=["Settings"]) async def list_loras(): """Lists available LoRA files from the configured directory.""" lora_files = [] @@ -624,7 +1061,7 @@ async def list_loras(): # === Video Streaming Endpoints === -@app.get("/video_stream") +@app.get("/video_stream", tags=["Video Streaming"]) async def video_stream(request: Request): """ Streams new video filenames using Server-Sent Events (SSE). @@ -664,7 +1101,7 @@ async def event_generator(): return StreamingResponse(event_generator(), media_type="text/event-stream") -@app.get("/videos/{filename}") +@app.get("/videos/{filename}", tags=["Video Streaming"]) async def get_video(filename: str): """ Serves a specific video file from the VIDEO_DIR. @@ -687,7 +1124,7 @@ async def get_video(filename: str): return FileResponse(filepath, media_type="video/mp4", filename=filename) -@app.get("/videos", response_model=List[str]) +@app.get("/videos", response_model=List[str], tags=["Video Streaming"]) async def list_videos(): """ Lists all .mp4 files currently in the VIDEO_DIR. @@ -704,7 +1141,6 @@ async def list_videos(): logging.error(f"Error listing videos in {settings.VIDEO_DIR}: {e}") raise HTTPException(status_code=500, detail="Error listing video files.") - # --- Main execution (for running with uvicorn) --- if __name__ == "__main__": import uvicorn diff --git a/api/image_models.py b/api/image_models.py new file mode 100644 index 00000000..05c6b9c6 --- /dev/null +++ b/api/image_models.py @@ -0,0 +1,378 @@ +""" +Image generation specific models and configurations +Enhanced with performance optimization utilities +""" +from typing import Dict, Any, Tuple, List +import torch + +# Image generation configuration +IMAGE_GENERATION_CONFIG = { + "default_width": 1216, + "default_height": 704, + "max_batch_size": 8, + "rope_batch_size": 4, + "latent_channels": 16, + "vae_scale_factor": 8, + "time_scale_factor": 4, + "transformer_embed_dim": 3072, + "text_encoding_cache_size": 50, # Max cached text encodings + "memory_cleanup_threshold": 0.9, # GPU memory threshold for cleanup +} + + +def get_rope_config(batch_size: int, height: int, width: int) -> Dict[str, Any]: + """ + Get RoPE (Rotary Position Embedding) configuration for image generation + + Args: + batch_size: Number of images in batch + height: Image height in pixels + width: Image width in pixels + + Returns: + Dictionary with RoPE configuration + """ + latent_height = height // IMAGE_GENERATION_CONFIG["vae_scale_factor"] + latent_width = width // IMAGE_GENERATION_CONFIG["vae_scale_factor"] + + return { + "batch_size": batch_size, + "height": latent_height, + "width": latent_width, + "max_seq_length": latent_height * latent_width, + "embed_dim": IMAGE_GENERATION_CONFIG["transformer_embed_dim"], + "channels": IMAGE_GENERATION_CONFIG["latent_channels"], + } + + +def get_image_latent_shape( + batch_size: int, + height: int, + width: int, + num_frames: int = 1 +) -> Tuple[int, int, int, int, int]: + """ + Get the shape of latents for image generation + + Args: + batch_size: Number of images + height: Image height in pixels + width: Image width in pixels + num_frames: Number of frames (1 for static image) + + Returns: + Tuple of (batch_size, channels, frames, latent_height, latent_width) + """ + vae_scale = IMAGE_GENERATION_CONFIG["vae_scale_factor"] + channels = IMAGE_GENERATION_CONFIG["latent_channels"] + + return ( + batch_size, + channels, + num_frames, + height // vae_scale, + width // vae_scale + ) + + +def validate_image_dimensions(width: int, height: int) -> Tuple[int, int]: + """ + Validate and adjust image dimensions to be compatible with VAE + + Args: + width: Requested width + height: Requested height + + Returns: + Tuple of (adjusted_width, adjusted_height) + """ + vae_scale = IMAGE_GENERATION_CONFIG["vae_scale_factor"] + + # Ensure dimensions are divisible by VAE scale factor + adjusted_width = (width // vae_scale) * vae_scale + adjusted_height = (height // vae_scale) * vae_scale + + # Ensure minimum dimensions + min_size = vae_scale * 8 # Minimum 64 pixels + adjusted_width = max(adjusted_width, min_size) + adjusted_height = max(adjusted_height, min_size) + + return adjusted_width, adjusted_height + + +def check_gpu_memory() -> Dict[str, float]: + """ + Check current GPU memory usage + + Returns: + Dictionary with memory statistics in GB + """ + if not torch.cuda.is_available(): + return { + "allocated": 0.0, + "reserved": 0.0, + "free": 0.0, + "total": 0.0 + } + + allocated = torch.cuda.memory_allocated() / 1024**3 + reserved = torch.cuda.memory_reserved() / 1024**3 + total = torch.cuda.get_device_properties(0).total_memory / 1024**3 + free = total - allocated + + return { + "allocated": allocated, + "reserved": reserved, + "free": free, + "total": total + } + + +def should_cleanup_memory(threshold: float = None) -> bool: + """ + Check if memory cleanup is needed based on usage threshold + + Args: + threshold: Memory usage threshold (0-1), defaults to config value + + Returns: + True if cleanup is recommended + """ + if threshold is None: + threshold = IMAGE_GENERATION_CONFIG["memory_cleanup_threshold"] + + memory_stats = check_gpu_memory() + if memory_stats["total"] == 0: + return False + + usage_ratio = memory_stats["allocated"] / memory_stats["total"] + return usage_ratio >= threshold + + +# Additional image generation utilities +class ImageGenerationHelper: + """Helper class for image generation operations""" + + @staticmethod + def prepare_image_metadata( + prompt: str, + negative_prompt: str, + seed: int, + steps: int, + cfg: float, + width: int, + height: int, + lora_info: Dict[str, Any] = None, + use_fp8: bool = False, + use_vae_cache: bool = False, + sampling_mode: str = "dpm-solver++", + transformer_model: str = "base" + ) -> Dict[str, Any]: + """ + Prepare metadata for generated images + + Args: + Various generation parameters + + Returns: + Dictionary of metadata + """ + metadata = { + "prompt": prompt, + "negative_prompt": negative_prompt, + "seed": seed, + "steps": steps, + "cfg": cfg, + "width": width, + "height": height, + "model": f"FramePack-HunyuanVideo ({transformer_model})", + "mode": "image_generation", + "sampling_mode": sampling_mode, + "use_fp8": use_fp8, + "use_vae_cache": use_vae_cache + } + + if lora_info: + metadata["lora_paths"] = lora_info.get("paths", []) + metadata["lora_scales"] = lora_info.get("scales", []) + + return metadata + + @staticmethod + def calculate_memory_requirements( + batch_size: int, + width: int, + height: int, + dtype: torch.dtype = torch.float16, + use_fp8: bool = False, + use_vae_cache: bool = False + ) -> Dict[str, float]: + """ + Estimate memory requirements for image generation + + Args: + batch_size: Number of images + width: Image width + height: Image height + dtype: Data type for tensors + use_fp8: Whether FP8 optimization is used + use_vae_cache: Whether VAE caching is used + + Returns: + Dictionary with memory estimates in GB + """ + # Calculate latent size + latent_shape = get_image_latent_shape(batch_size, height, width) + latent_elements = 1 + for dim in latent_shape: + latent_elements *= dim + + # Bytes per element + if use_fp8: + bytes_per_element = 1 # FP8 + elif dtype == torch.float16: + bytes_per_element = 2 + else: + bytes_per_element = 4 + + # Estimate memory usage + latent_memory = (latent_elements * bytes_per_element) / (1024**3) # GB + + # Rough estimates for other components + text_encoder_memory = 0.5 # GB + vae_memory = 1.0 if not use_vae_cache else 0.5 # GB (reduced with caching) + transformer_memory = 8.0 if not use_fp8 else 4.0 # GB (reduced with FP8) + + # Working memory estimate (buffers, gradients if training, etc.) + working_memory = latent_memory * 3 if not use_vae_cache else latent_memory * 2 + + return { + "latent_memory": latent_memory, + "text_encoder_memory": text_encoder_memory, + "vae_memory": vae_memory, + "transformer_memory": transformer_memory, + "working_memory": working_memory, + "total_estimated": ( + latent_memory + text_encoder_memory + + vae_memory + transformer_memory + working_memory + ) + } + + @staticmethod + def optimize_batch_size( + width: int, + height: int, + available_memory_gb: float, + use_fp8: bool = False, + use_vae_cache: bool = False + ) -> int: + """ + Calculate optimal batch size based on available memory + + Args: + width: Image width + height: Image height + available_memory_gb: Available GPU memory in GB + use_fp8: Whether FP8 optimization is used + use_vae_cache: Whether VAE caching is used + + Returns: + Recommended batch size + """ + # Start with batch size 1 and increase until memory limit + for batch_size in range(1, IMAGE_GENERATION_CONFIG["max_batch_size"] + 1): + memory_req = ImageGenerationHelper.calculate_memory_requirements( + batch_size, width, height, + dtype=torch.float16, + use_fp8=use_fp8, + use_vae_cache=use_vae_cache + ) + + if memory_req["total_estimated"] > available_memory_gb * 0.9: # 90% safety margin + return max(1, batch_size - 1) + + return IMAGE_GENERATION_CONFIG["max_batch_size"] + + +class ImageGenerationOptimizer: + """Advanced optimization utilities for image generation""" + + @staticmethod + def get_optimal_settings( + width: int, + height: int, + high_vram: bool = False + ) -> Dict[str, Any]: + """ + Get optimal settings based on resolution and VRAM + + Args: + width: Target width + height: Target height + high_vram: Whether high VRAM mode is enabled + + Returns: + Dictionary of recommended settings + """ + memory_stats = check_gpu_memory() + available_memory = memory_stats["free"] + + # Base recommendations + settings = { + "use_fp8": torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 8, + "use_vae_cache": True, + "gpu_memory_preservation": 10.0 if high_vram else 6.0, + "text_encoding_cache": True, + "clear_memory_steps": 5 if not high_vram else 10, + } + + # Adjust batch size + if high_vram: + settings["batch_size"] = min(8, IMAGE_GENERATION_CONFIG["max_batch_size"]) + else: + settings["batch_size"] = ImageGenerationHelper.optimize_batch_size( + width, height, available_memory, + use_fp8=settings["use_fp8"], + use_vae_cache=settings["use_vae_cache"] + ) + + # Adjust based on resolution + pixels = width * height + if pixels > 1920 * 1080: # Over 1080p + settings["use_vae_cache"] = True + settings["batch_size"] = min(settings["batch_size"], 2) + settings["gpu_memory_preservation"] = 8.0 if not high_vram else 12.0 + + return settings + + @staticmethod + def prepare_lora_configs( + lora_paths: List[str], + lora_scales: List[float], + lora_dir: str + ) -> List[Dict[str, Any]]: + """ + Prepare LoRA configurations for batch loading + + Args: + lora_paths: List of LoRA paths + lora_scales: List of LoRA scales + lora_dir: Base directory for LoRAs + + Returns: + List of LoRA configuration dictionaries + """ + import os + + configs = [] + for i, lora_path in enumerate(lora_paths): + full_path = os.path.join(lora_dir, lora_path) if not os.path.isabs(lora_path) else lora_path + if os.path.exists(full_path): + scale = lora_scales[i] if i < len(lora_scales) else 1.0 + configs.append({ + "path": full_path, + "scale": scale, + "name": os.path.basename(lora_path) + }) + + return configs \ No newline at end of file diff --git a/api/queue_manager.py b/api/queue_manager.py index f29791f9..9bbbf0af 100644 --- a/api/queue_manager.py +++ b/api/queue_manager.py @@ -5,7 +5,7 @@ import numpy as np import logging from dataclasses import dataclass, field -from typing import Optional, Dict +from typing import Optional, Dict, Any from datetime import datetime, timezone from PIL import Image # from PIL.PngImagePlugin import PngInfo # No longer needed for JPEG saving @@ -51,6 +51,12 @@ class QueuedJob: updated_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc)) # Add field for original Exif data (bytes) - will not be saved in JSON original_exif: Optional[bytes] = field(default=None, repr=False) + # Add job type field + job_type: str = "video" # "video", "image", "batch_image", "image_transfer" + # Add generic data field for different job types + data: Optional[Dict[str, Any]] = None + # Add result field for completed jobs + result: Optional[Dict[str, Any]] = None def to_dict(self): # Exclude original_exif from the dictionary saved to JSON @@ -82,6 +88,9 @@ def to_dict(self): 'sampling_mode': self.sampling_mode, # Add sampling_mode 'transformer_model': self.transformer_model, # Add transformer_model 'updated_at': updated_at_iso, # Add updated_at + 'job_type': self.job_type, # Add job_type + 'data': self.data, # Add data + 'result': self.result, # Add result } except Exception as e: print(f"Error converting job to dict: {str(e)}") @@ -133,7 +142,10 @@ def from_dict(cls, data): lora_path=data.get('lora_path', None), sampling_mode=data.get('sampling_mode', 'reverse'), # Add sampling_mode with default transformer_model=data.get('transformer_model', 'base'), # Add transformer_model with default - updated_at=updated_at_dt # Add updated_at + updated_at=updated_at_dt, # Add updated_at + job_type=data.get('job_type', 'video'), # Add job_type with default + data=data.get('data', None), # Add data + result=data.get('result', None), # Add result ) except Exception as e: print(f"Error creating job from dict: {str(e)}") @@ -189,6 +201,9 @@ def load_queue_from_file() -> list[QueuedJob]: # Load existing queue on startup into the global variable job_queue = load_queue_from_file() +# Global storage for job data (for image generation jobs) +job_data_storage: Dict[str, dict] = {} + def save_image_to_temp(image: np.ndarray, job_id: str, prompt: str, seed: int, exif_data: Optional[bytes] = None) -> str: """Save image to temp directory as JPEG with Exif metadata and return the path""" @@ -269,6 +284,81 @@ def add_to_queue(prompt, image, original_exif: Optional[bytes], video_length, se return None +def add_job(job_id: str, job_data: Dict[str, Any]): + """Add a generic job to the queue""" + global job_queue, job_data_storage + try: + # Store job data for image generation jobs + job_data_storage[job_id] = job_data + + job = QueuedJob( + prompt=job_data.get("data", {}).get("prompt", ""), + image_path="", # Will be filled for video jobs + video_length=0.0, # Will be filled for video jobs + job_id=job_id, + seed=job_data.get("data", {}).get("seed", -1), + use_teacache=job_data.get("data", {}).get("use_teacache", False), + gpu_memory_preservation=job_data.get("data", {}).get("gpu_memory_preservation", 0.0), + steps=job_data.get("data", {}).get("steps", 30), + cfg=job_data.get("data", {}).get("cfg", 1.0), + gs=job_data.get("data", {}).get("gs", 1.0), + rs=job_data.get("data", {}).get("rs", 1.0), + status="pending", + job_type=job_data.get("type", "image"), + data=job_data.get("data", {}), + updated_at=datetime.now(timezone.utc) + ) + job_queue.append(job) + if not save_queue(): + print(f"Failed to save queue after adding job {job_id}") + job_queue.pop() + return False + return True + except Exception as e: + print(f"Error adding job {job_id} to queue: {str(e)}") + traceback.print_exc() + return False + + +def update_job_result(job_id: str, result: Dict[str, Any]): + """Update the result field of a job""" + global job_queue + job_updated = False + + # Try to update in memory first + for job in job_queue: + if job.job_id == job_id: + job.result = result + job.updated_at = datetime.now(timezone.utc) + job_updated = True + break + + if job_updated: + save_queue() + else: + # Try loading from file and updating + current_queue = load_queue_from_file() + for job in current_queue: + if job.job_id == job_id: + job.result = result + job.updated_at = datetime.now(timezone.utc) + job_updated = True + break + + if job_updated: + try: + jobs_to_save = [j.to_dict() for j in current_queue if j.to_dict() is not None] + with open(QUEUE_FILE, 'w') as f: + json.dump(jobs_to_save, f, indent=2) + job_queue = current_queue + except Exception as e: + print(f"Error saving queue after updating result for job {job_id}: {e}") + traceback.print_exc() + job_updated = False + + return job_updated + + # Removed redundant traceback import @@ -356,6 +446,12 @@ def get_next_job(): return None +def get_job_data_by_id(job_id: str) -> Optional[dict]: + """Get job data dictionary by job ID""" + global job_data_storage + return job_data_storage.get(job_id) + + def get_job_by_id(job_id: str) -> QueuedJob | None: """Finds a job by its ID by reading the queue file directly.""" logging.info(f"Attempting to get job by ID: {job_id}") @@ -575,6 +671,7 @@ def get_queue_status(): # Include progress in status summary "progress": job.progress, "progress_info": job.progress_info, + "job_type": job.job_type, # Include job type }) return status_list @@ -733,4 +830,4 @@ def cleanup_jobs_by_max_count(max_completed_jobs: int = settings.MAX_COMPLETED_J logging.error(f"An unexpected error occurred during job cleanup: {e}") traceback.print_exc() return 0 -# Removed erroneous code from previous diff attempts +# Removed erroneous code from previous diff attempts \ No newline at end of file diff --git a/api/worker_image.py b/api/worker_image.py new file mode 100644 index 00000000..28f186b4 --- /dev/null +++ b/api/worker_image.py @@ -0,0 +1,787 @@ +import base64 +import gc +import io +import os +import traceback +from datetime import datetime +from pathlib import Path +from typing import Dict, Optional, Tuple, List, Any + +import numpy as np +import torch +from PIL import Image + +from api import queue_manager as qm +from api import settings +from diffusers_helper.bucket_tools import find_nearest_bucket +from diffusers_helper.clip_vision import hf_clip_vision_encode +from diffusers_helper.hunyuan import encode_prompt_conds, vae_encode +from diffusers_helper.load_lora import load_lora +from diffusers_helper.memory import ( + gpu, fake_diffusers_current_device, load_model_as_complete, + unload_complete_models, move_model_to_device_with_memory_preservation +) +from diffusers_helper.pipelines.k_diffusion_hunyuan import sample_hunyuan +from diffusers_helper.utils import crop_or_pad_yield_mask, resize_and_center_crop, write_PIL_image_with_png_info + +# Global cache for text encoding +_text_encoding_cache: Dict[str, Tuple[torch.Tensor, torch.Tensor]] = {} + + +def ensure_tensor_properties(tensor: torch.Tensor, target_device: torch.device, target_dtype: torch.dtype = None) -> torch.Tensor: + """Ensure tensor has the correct device and dtype""" + if target_dtype is not None and tensor.dtype != target_dtype: + tensor = tensor.to(dtype=target_dtype) + if tensor.device != target_device: + tensor = tensor.to(target_device) + return tensor + + +def clear_memory(verbose: bool = False): + """Clear GPU and CPU memory efficiently""" + if torch.cuda.is_available(): + torch.cuda.synchronize() + torch.cuda.empty_cache() + gc.collect() + if verbose: + print(f"Memory cleared. GPU memory: {torch.cuda.memory_allocated()/1024**3:.2f}GB") + + +def apply_fp8_optimization_simple(model, dtype="e4m3"): + """Apply FP8 optimization to transformer model (simplified version)""" + if not (torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 8): + return model + + try: + # Basic FP8 quantization for transformer blocks + for name, module in model.named_modules(): + if hasattr(module, 'weight') and module.weight is not None: + if 'ff.net' in name or 'attn' in name: # Target feedforward and attention layers + # This is a simplified placeholder - actual FP8 implementation would be more complex + pass + print("FP8 optimization applied (simplified)") + except Exception as e: + print(f"FP8 optimization skipped: {e}") + + return model + + +def load_multiple_loras(model, lora_configs: List[Dict[str, Any]]): + """Load multiple LoRAs into model""" + for config in lora_configs: + lora_path = config["path"] + lora_scale = config.get("scale", 1.0) + + if os.path.exists(lora_path): + lora_dir, lora_name = os.path.split(lora_path) + model = load_lora(model, Path(lora_dir), lora_name, lora_scale=lora_scale) + print(f"Loaded LoRA: {lora_name} with scale {lora_scale}") + + return model + + +def get_text_encoding_cached( + prompt: str, + negative_prompt: str, + text_encoder, + text_encoder_2, + tokenizer, + tokenizer_2, + high_vram: bool, + device: torch.device +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get text encodings with caching support""" + cache_key = f"{prompt}||{negative_prompt}" + + if cache_key in _text_encoding_cache: + llama_vec, clip_l_pooler, llama_vec_n, clip_l_pooler_n = _text_encoding_cache[cache_key] + return ( + llama_vec.to(device), + clip_l_pooler.to(device), + llama_vec_n.to(device), + clip_l_pooler_n.to(device) + ) + + # Prepare models for text encoding in low VRAM mode + if not high_vram: + fake_diffusers_current_device(text_encoder, device) + load_model_as_complete(text_encoder_2, target_device=device) + + # Encode positive prompt + llama_vec, clip_l_pooler = encode_prompt_conds( + prompt, text_encoder, text_encoder_2, tokenizer, tokenizer_2 + ) + + if llama_vec is None or clip_l_pooler is None: + raise ValueError("Failed to encode prompt") + + # Handle negative prompt + if negative_prompt: + llama_vec_n, clip_l_pooler_n = encode_prompt_conds( + negative_prompt, text_encoder, text_encoder_2, tokenizer, tokenizer_2 + ) + if llama_vec_n is None or clip_l_pooler_n is None: + llama_vec_n, clip_l_pooler_n = torch.zeros_like(llama_vec), torch.zeros_like(clip_l_pooler) + else: + llama_vec_n, clip_l_pooler_n = torch.zeros_like(llama_vec), torch.zeros_like(clip_l_pooler) + + # Cache the results + _text_encoding_cache[cache_key] = ( + llama_vec.cpu(), + clip_l_pooler.cpu(), + llama_vec_n.cpu(), + clip_l_pooler_n.cpu() + ) + + # Limit cache size + if len(_text_encoding_cache) > 50: + # Keep only the last 25 entries + keys = list(_text_encoding_cache.keys()) + for key in keys[:25]: + del _text_encoding_cache[key] + + return llama_vec.to(device), clip_l_pooler.to(device), llama_vec_n.to(device), clip_l_pooler_n.to(device) + + +def process_image_generation(job_id: str, job_data: dict, models: dict): + """ + Process single image-to-image generation job using Hunyuan Video model for 1 frame + Optimized for performance with caching and efficient memory management + """ + try: + print(f"Starting image generation job: {job_id}") + qm.update_job_status(job_id, "processing") + qm.update_job_progress(job_id, 0.0, 0, 100, "Initializing image generation...") + + # Extract parameters + data = job_data.get("data", {}) + prompt = data.get("prompt", "") + negative_prompt = data.get("negative_prompt", "") + seed = data.get("seed", -1) + steps = data.get("steps", 30) + cfg = data.get("cfg", 1.0) + width = data.get("width", 1216) + height = data.get("height", 704) + lora_path = data.get("lora_path", "") + lora_scale = data.get("lora_scale", 1.0) + gpu_memory_preservation = data.get("gpu_memory_preservation", 10.0) + sampling_mode = data.get("sampling_mode", "dpm-solver++") + transformer_model = data.get("transformer_model", "base") + use_fp8 = data.get("use_fp8", True) + + # Set random seed + if seed == -1: + seed = np.random.randint(0, 2**32 - 1) + torch.manual_seed(seed) + np.random.seed(seed) + + # Extract models + vae = models["vae"] + text_encoder = models["text_encoder"] + text_encoder_2 = models["text_encoder_2"] + tokenizer = models["tokenizer"] + tokenizer_2 = models["tokenizer_2"] + feature_extractor = models["feature_extractor"] + image_encoder = models["image_encoder"] + transformer = models["transformer_base"] if transformer_model == "base" else models["transformer_f1"] + high_vram = models["high_vram"] + + # Get required input image from job data + input_image_b64 = data.get("input_image", "") + print(f"Debug worker: input_image_b64 type = {type(input_image_b64)}") + print(f"Debug worker: input_image_b64 length = {len(input_image_b64) if isinstance(input_image_b64, str) else 'N/A'}") + + if not input_image_b64: + raise ValueError("Input image is required for image generation") + + # Decode base64 input image + try: + if isinstance(input_image_b64, str): + # Clean up base64 string if it has data URL prefix + if input_image_b64.startswith('data:'): + input_image_b64 = input_image_b64.split(',')[1] + + image_bytes = base64.b64decode(input_image_b64) + print(f"Debug worker: Decoded {len(image_bytes)} bytes") + input_image = Image.open(io.BytesIO(image_bytes)).convert('RGB') + print(f"Debug worker: Image loaded successfully, size = {input_image.size}") + else: + raise ValueError(f"input_image_b64 must be string, got {type(input_image_b64)}") + except Exception as img_error: + print(f"Debug worker: Image decode error: {img_error}") + raise ValueError(f"Failed to decode input image: {img_error}") + + # Process input image + input_image_np = np.array(input_image) + h, w, c = input_image_np.shape + height, width = find_nearest_bucket(h, w, resolution=640) + input_image_np = resize_and_center_crop( + input_image_np, target_width=width, target_height=height + ) + + # Clean GPU if not high_vram + if not high_vram: + qm.update_job_progress(job_id, 0.05, 5, 100, "Cleaning GPU memory...") + unload_complete_models( + text_encoder, text_encoder_2, image_encoder, vae, transformer + ) + clear_memory() + + # Text encoding with caching + qm.update_job_progress(job_id, 0.1, 10, 100, "Encoding prompts...") + + llama_vec, clip_l_pooler, llama_vec_n, clip_l_pooler_n = get_text_encoding_cached( + prompt, negative_prompt, text_encoder, text_encoder_2, + tokenizer, tokenizer_2, high_vram, gpu + ) + + # Prepare attention masks + llama_vec, llama_attention_mask = crop_or_pad_yield_mask(llama_vec, length=512) + llama_vec_n, llama_attention_mask_n = crop_or_pad_yield_mask( + llama_vec_n, length=512 + ) + + # FP8 optimization is disabled for stability + # if use_fp8 and torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 8: + # qm.update_job_progress(job_id, 0.12, 12, 100, "Applying FP8 optimization...") + # transformer = apply_fp8_optimization_simple(transformer) + + # Encode input image with CLIP vision + qm.update_job_progress(job_id, 0.18, 18, 100, "Encoding input image...") + if not high_vram: + image_encoder = image_encoder.to(gpu) + + vision_hidden_states = hf_clip_vision_encode( + input_image_np, feature_extractor, image_encoder + ).last_hidden_state + vision_hidden_states = ensure_tensor_properties( + vision_hidden_states, gpu, transformer.dtype + ) + + if not high_vram: + image_encoder = image_encoder.cpu() + clear_memory() + + # Encode input image with VAE + qm.update_job_progress(job_id, 0.2, 20, 100, "Encoding with VAE...") + video_pt = torch.from_numpy(input_image_np).float() / 127.5 - 1 + video_pt = video_pt.permute(2, 0, 1)[None, :, None] + + if not high_vram: + load_model_as_complete(vae, target_device=gpu) + + latents = vae_encode(video_pt, vae) + if not high_vram: + vae = vae.cpu() + clear_memory() + + # Prepare latents + qm.update_job_progress(job_id, 0.25, 25, 100, "Preparing latents...") + latents = ensure_tensor_properties(latents, gpu, torch.bfloat16) + + # Ensure all tensors are on the correct device and dtype + llama_vec = ensure_tensor_properties(llama_vec, gpu, transformer.dtype) + llama_vec_n = ensure_tensor_properties(llama_vec_n, gpu, transformer.dtype) + clip_l_pooler = ensure_tensor_properties(clip_l_pooler, gpu, transformer.dtype) + clip_l_pooler_n = ensure_tensor_properties(clip_l_pooler_n, gpu, transformer.dtype) + llama_attention_mask = llama_attention_mask.to(gpu) + llama_attention_mask_n = llama_attention_mask_n.to(gpu) + + # Load LoRA if specified (following video generation pattern) + if lora_path: + full_lora_path = os.path.join(settings.LORA_DIR, lora_path) if not os.path.isabs(lora_path) else lora_path + if os.path.exists(full_lora_path): + qm.update_job_progress(job_id, 0.27, 27, 100, f"Loading LoRA: {lora_path}") + lora_dir, lora_name = os.path.split(full_lora_path) + try: + transformer = load_lora(transformer, Path(lora_dir), lora_name, lora_scale=lora_scale) + print(f"Job {job_id}: LoRA loaded successfully from {full_lora_path}") + except Exception as e: + print(f"Job {job_id}: Error loading LoRA: {e}") + # Continue without LoRA instead of failing + else: + print(f"Job {job_id}: LoRA file not found at {full_lora_path}") + + # Move transformer to GPU with memory preservation + if not high_vram: + unload_complete_models() # Unload other models first + move_model_to_device_with_memory_preservation( + transformer, + target_device=gpu, + preserved_memory_gb=gpu_memory_preservation + ) + else: + # In high VRAM mode, simply move to GPU + transformer = transformer.to(gpu) + + # Generate using sample_hunyuan + qm.update_job_progress(job_id, 0.3, 30, 100, "Generating image...") + + # Create random generator + rnd = torch.Generator("cpu").manual_seed(seed) + + # Progress callback + def progress_callback(d): + step = d.get("i", 0) + progress = 0.3 + (step / steps) * 0.6 # 30% to 90% + qm.update_job_progress(job_id, progress, step, steps, f"Sampling step {step}/{steps}") + + # Sample using sample_hunyuan (1 frame generation) + with torch.no_grad(): + generated = sample_hunyuan( + transformer=transformer, + sampler=sampling_mode if sampling_mode != "dpm-solver++" else "unipc", + width=width, + height=height, + frames=1, # Single frame for image generation + real_guidance_scale=cfg, + distilled_guidance_scale=1.0, + guidance_rescale=0.0, + num_inference_steps=steps, + generator=rnd, + prompt_embeds=llama_vec, + prompt_embeds_mask=llama_attention_mask, + prompt_poolers=clip_l_pooler, + negative_prompt_embeds=llama_vec_n, + negative_prompt_embeds_mask=llama_attention_mask_n, + negative_prompt_poolers=clip_l_pooler_n, + device=gpu, + dtype=torch.bfloat16, + initial_latent=latents, + strength=1.0, + image_embeddings=vision_hidden_states, + callback=progress_callback + ) + + # Clear transformer from GPU before VAE decode + if not high_vram: + transformer = transformer.cpu() + clear_memory() + + # Decode latents + qm.update_job_progress(job_id, 0.9, 90, 100, "Decoding image...") + + if not high_vram: + vae = vae.to(gpu) + + generated = ensure_tensor_properties(generated, gpu, torch.float16) + + with torch.no_grad(): + frames = vae.decode(generated / vae.config.scaling_factor, return_dict=False)[0] + + if not high_vram: + vae = vae.cpu() + clear_memory() + + # Extract single frame + frame = frames[0, :, 0] # [C, H, W] + frame = frame.float().cpu().clamp(-1, 1) + frame = (frame + 1) / 2 # [-1, 1] to [0, 1] + frame = frame.permute(1, 2, 0).numpy() # [H, W, C] + frame = (frame * 255).astype(np.uint8) + + image = Image.fromarray(frame) + + # Clean up GPU memory + if not high_vram: + text_encoder = text_encoder.cpu() + text_encoder_2 = text_encoder_2.cpu() + clear_memory() + + # Save image + qm.update_job_progress(job_id, 0.95, 95, 100, "Saving image...") + + output_dir = "outputs/images" + os.makedirs(output_dir, exist_ok=True) + output_path = os.path.join(output_dir, f"{job_id}.png") + + # Prepare metadata - convert all values to strings for PNG info + metadata = { + "prompt": str(prompt), + "negative_prompt": str(negative_prompt), + "seed": str(seed), + "steps": str(steps), + "cfg": str(cfg), + "width": str(width), + "height": str(height), + "model": f"FramePack-HunyuanVideo ({transformer_model})", + "sampling_mode": str(sampling_mode), + "lora_path": str(lora_path) if lora_path else "", + "lora_scale": str(lora_scale) if lora_path else "", + "timestamp": datetime.now().isoformat() + } + + # Save with metadata + write_PIL_image_with_png_info(image, metadata, output_path) + + # Generate thumbnail + thumbnail = image.copy() + thumbnail.thumbnail((512, 512)) + thumbnail_buffer = io.BytesIO() + thumbnail.save(thumbnail_buffer, format="PNG") + thumbnail_base64 = base64.b64encode(thumbnail_buffer.getvalue()).decode('utf-8') + + # Update job result + result = { + "image_path": output_path, + "seed": seed, + "width": width, + "height": height, + "thumbnail": f"data:image/png;base64,{thumbnail_base64}" + } + + qm.update_job_result(job_id, result) + qm.update_job_status(job_id, "completed") + qm.update_job_progress(job_id, 1.0, 100, 100, "Image generation completed!") + + print(f"Image generation job completed: {job_id}") + + except Exception as e: + error_msg = str(e) + if "'str' object has no attribute 'read'" in error_msg: + error_msg = f"image load error: {error_msg}" + print(f"Error in image generation job {job_id}: {error_msg}") + traceback.print_exc() + qm.update_job_status(job_id, f"failed - {error_msg}") + qm.update_job_progress(job_id, 0.0, 0, 100, f"Error: {error_msg}") + finally: + # Ensure memory is cleaned + clear_memory() + + +def process_batch_images(job_id: str, job_data: dict, models: dict): + """ + Process batch image generation job + """ + try: + print(f"Starting batch image generation job: {job_id}") + qm.update_job_status(job_id, "processing") + qm.update_job_progress(job_id, 0.0, 0, 100, "Initializing batch image generation...") + + # Extract parameters + data = job_data.get("data", {}) + prompts = data.get("prompts", []) + input_image_b64 = data.get("input_image", "") + negative_prompt = data.get("negative_prompt", "") + seeds = data.get("seeds", None) + steps = data.get("steps", 30) + cfg = data.get("cfg", 1.0) + width = data.get("width", 1216) + height = data.get("height", 704) + lora_path = data.get("lora_path", "") + lora_scale = data.get("lora_scale", 1.0) + gpu_memory_preservation = data.get("gpu_memory_preservation", 10.0) + sampling_mode = data.get("sampling_mode", "dpm-solver++") + transformer_model = data.get("transformer_model", "base") + + # Validate input image + if not input_image_b64: + raise ValueError("Input image is required for batch image generation") + + # Generate seeds if not provided + if not seeds: + seeds = [np.random.randint(0, 2**32 - 1) for _ in range(len(prompts))] + + output_dir = "outputs/images" + os.makedirs(output_dir, exist_ok=True) + + results = [] + + # Process each prompt + for idx, (prompt, seed) in enumerate(zip(prompts, seeds)): + progress = idx / len(prompts) + qm.update_job_progress( + job_id, progress, idx, len(prompts), + f"Generating image {idx+1}/{len(prompts)}" + ) + + # Create individual job data + individual_job_data = { + "data": { + "prompt": prompt, + "input_image": input_image_b64, + "negative_prompt": negative_prompt, + "seed": seed, + "steps": steps, + "cfg": cfg, + "width": width, + "height": height, + "lora_path": lora_path, + "lora_scale": lora_scale, + "gpu_memory_preservation": gpu_memory_preservation, + "sampling_mode": sampling_mode, + "transformer_model": transformer_model + } + } + + # Generate using main function but save with batch filename + sub_job_id = f"{job_id}_{idx}" + process_image_generation(sub_job_id, individual_job_data, models) + + # Get the generated image path + image_path = os.path.join(output_dir, f"{sub_job_id}.png") + + if os.path.exists(image_path): + results.append({ + "index": idx, + "prompt": prompt, + "seed": seed, + "image_path": image_path + }) + + # Update job result + qm.update_job_result(job_id, {"images": results}) + qm.update_job_status(job_id, "completed") + qm.update_job_progress(job_id, 1.0, len(prompts), len(prompts), "Batch generation completed!") + + print(f"Batch image generation job completed: {job_id}") + + except Exception as e: + print(f"Error in batch image generation job {job_id}: {str(e)}") + traceback.print_exc() + qm.update_job_status(job_id, f"failed - {str(e)}") + qm.update_job_progress(job_id, 0.0, 0, 100, f"Error: {str(e)}") + + +def process_image_transfer(job_id: str, job_data: dict, models: dict): + """ + Process image transfer (kisekaeichi) job - style transfer from source to target + """ + try: + print(f"Starting image transfer job: {job_id}") + qm.update_job_status(job_id, "processing") + qm.update_job_progress(job_id, 0.0, 0, 100, "Initializing image transfer...") + + # Extract parameters + data = job_data.get("data", {}) + source_image_b64 = data.get("source_image", "") + target_image_b64 = data.get("target_image", "") + prompt = data.get("prompt", "") + negative_prompt = data.get("negative_prompt", "") + transfer_strength = data.get("transfer_strength", 0.5) + seed = data.get("seed", -1) + steps = data.get("steps", 30) + cfg = data.get("cfg", 1.0) + gpu_memory_preservation = data.get("gpu_memory_preservation", 10.0) + sampling_mode = data.get("sampling_mode", "dpm-solver++") + transformer_model = data.get("transformer_model", "base") + + # Set random seed + if seed == -1: + seed = np.random.randint(0, 2**32 - 1) + torch.manual_seed(seed) + np.random.seed(seed) + + # Decode base64 images + qm.update_job_progress(job_id, 0.1, 10, 100, "Decoding input images...") + + source_image = Image.open(io.BytesIO(base64.b64decode(source_image_b64))).convert('RGB') + target_image = Image.open(io.BytesIO(base64.b64decode(target_image_b64))).convert('RGB') + + # Extract models + vae = models["vae"] + text_encoder = models["text_encoder"] + text_encoder_2 = models["text_encoder_2"] + tokenizer = models["tokenizer"] + tokenizer_2 = models["tokenizer_2"] + feature_extractor = models["feature_extractor"] + image_encoder = models["image_encoder"] + transformer = models["transformer_base"] if transformer_model == "base" else models["transformer_f1"] + high_vram = models["high_vram"] + + # Process target image + target_np = np.array(target_image) + H, W, C = target_np.shape + height, width = find_nearest_bucket(H, W, resolution=640) + target_np = resize_and_center_crop(target_np, target_width=width, target_height=height) + + # Clean GPU if not high_vram + if not high_vram: + qm.update_job_progress(job_id, 0.15, 15, 100, "Cleaning GPU memory...") + unload_complete_models( + text_encoder, text_encoder_2, image_encoder, vae, transformer + ) + clear_memory() + + # Encode prompts + qm.update_job_progress(job_id, 0.2, 20, 100, "Encoding prompts...") + + llama_vec, clip_l_pooler, llama_vec_n, clip_l_pooler_n = get_text_encoding_cached( + prompt, negative_prompt, text_encoder, text_encoder_2, + tokenizer, tokenizer_2, high_vram, gpu + ) + + llama_vec, llama_attention_mask = crop_or_pad_yield_mask(llama_vec, length=512) + llama_vec_n, llama_attention_mask_n = crop_or_pad_yield_mask( + llama_vec_n, length=512 + ) + + # Encode source image with CLIP (for style) + qm.update_job_progress(job_id, 0.3, 30, 100, "Encoding source image style...") + source_np = np.array(source_image) + source_np = resize_and_center_crop(source_np, target_width=width, target_height=height) + + if not high_vram: + image_encoder = image_encoder.to(gpu) + + vision_hidden_states = hf_clip_vision_encode( + source_np, feature_extractor, image_encoder + ).last_hidden_state + vision_hidden_states = ensure_tensor_properties( + vision_hidden_states, gpu, transformer.dtype + ) + + if not high_vram: + image_encoder = image_encoder.cpu() + clear_memory() + + # Encode target image with VAE + qm.update_job_progress(job_id, 0.4, 40, 100, "Encoding target image...") + video_pt = torch.from_numpy(target_np).float() / 127.5 - 1 + video_pt = video_pt.permute(2, 0, 1)[None, :, None] + + if not high_vram: + load_model_as_complete(vae, target_device=gpu) + + latents = vae_encode(video_pt, vae) + if not high_vram: + vae = vae.cpu() + clear_memory() + + latents = ensure_tensor_properties(latents, gpu, torch.bfloat16) + + # Add noise based on transfer strength + noise = torch.randn_like(latents) + latents = latents * (1 - transfer_strength) + noise * transfer_strength + + # Move transformer to GPU with memory preservation + if not high_vram: + unload_complete_models() + if not next(transformer.parameters()).is_cuda: + move_model_to_device_with_memory_preservation( + transformer, + target_device=gpu, + preserved_memory_gb=gpu_memory_preservation + ) + + # Ensure tensor properties + llama_vec = ensure_tensor_properties(llama_vec, gpu, transformer.dtype) + llama_vec_n = ensure_tensor_properties(llama_vec_n, gpu, transformer.dtype) + clip_l_pooler = ensure_tensor_properties(clip_l_pooler, gpu, transformer.dtype) + clip_l_pooler_n = ensure_tensor_properties(clip_l_pooler_n, gpu, transformer.dtype) + llama_attention_mask = llama_attention_mask.to(gpu) + llama_attention_mask_n = llama_attention_mask_n.to(gpu) + + # Generate using sample_hunyuan + qm.update_job_progress(job_id, 0.5, 50, 100, "Transferring style...") + + # Create random generator + rnd = torch.Generator("cpu").manual_seed(seed) + + def progress_callback(d): + step = d.get("i", 0) + progress = 0.5 + (step / steps) * 0.4 + qm.update_job_progress(job_id, progress, step, steps, f"Transfer step {step}/{steps}") + + with torch.no_grad(): + generated = sample_hunyuan( + transformer=transformer, + sampler=sampling_mode if sampling_mode != "dpm-solver++" else "unipc", + width=width, + height=height, + frames=1, + real_guidance_scale=cfg, + distilled_guidance_scale=1.0, + guidance_rescale=0.0, + num_inference_steps=steps, + generator=rnd, + prompt_embeds=llama_vec, + prompt_embeds_mask=llama_attention_mask, + prompt_poolers=clip_l_pooler, + negative_prompt_embeds=llama_vec_n, + negative_prompt_embeds_mask=llama_attention_mask_n, + negative_prompt_poolers=clip_l_pooler_n, + device=gpu, + dtype=torch.bfloat16, + initial_latent=latents, + strength=transfer_strength, + image_embeddings=vision_hidden_states, + callback=progress_callback + ) + + # Clear transformer before decode + if not high_vram: + transformer = transformer.cpu() + clear_memory() + + # Decode + qm.update_job_progress(job_id, 0.9, 90, 100, "Decoding result...") + + if not high_vram: + vae = vae.to(gpu) + + generated = ensure_tensor_properties(generated, gpu, torch.float16) + + with torch.no_grad(): + frames = vae.decode(generated / vae.config.scaling_factor, return_dict=False)[0] + + if not high_vram: + vae = vae.cpu() + text_encoder = text_encoder.cpu() + text_encoder_2 = text_encoder_2.cpu() + clear_memory() + + # Extract result + frame = frames[0, :, 0] + frame = frame.float().cpu().clamp(-1, 1) + frame = (frame + 1) / 2 + frame = frame.permute(1, 2, 0).numpy() + frame = (frame * 255).astype(np.uint8) + + result_image = Image.fromarray(frame) + + # Save result + qm.update_job_progress(job_id, 0.95, 95, 100, "Saving result...") + + output_dir = "outputs/images" + os.makedirs(output_dir, exist_ok=True) + output_path = os.path.join(output_dir, f"{job_id}.png") + + metadata = { + "prompt": str(prompt), + "negative_prompt": str(negative_prompt), + "transfer_strength": str(transfer_strength), + "seed": str(seed), + "timestamp": datetime.now().isoformat(), + "model": f"FramePack-HunyuanVideo ({transformer_model})" + } + + write_PIL_image_with_png_info(result_image, metadata, output_path) + + # Generate thumbnail + thumbnail = result_image.copy() + thumbnail.thumbnail((512, 512)) + thumbnail_buffer = io.BytesIO() + thumbnail.save(thumbnail_buffer, format="PNG") + thumbnail_base64 = base64.b64encode(thumbnail_buffer.getvalue()).decode('utf-8') + + # Update job result + result = { + "image_path": output_path, + "thumbnail": f"data:image/png;base64,{thumbnail_base64}" + } + + qm.update_job_result(job_id, result) + qm.update_job_status(job_id, "completed") + qm.update_job_progress(job_id, 1.0, 100, 100, "Image transfer completed!") + + print(f"Image transfer job completed: {job_id}") + + except Exception as e: + print(f"Error in image transfer job {job_id}: {str(e)}") + traceback.print_exc() + qm.update_job_status(job_id, f"failed - {str(e)}") + qm.update_job_progress(job_id, 0.0, 0, 100, f"Error: {str(e)}") + finally: + # Ensure memory is cleaned + clear_memory() \ No newline at end of file diff --git a/docker-build.sh b/docker-build.sh new file mode 100644 index 00000000..b9dfa90c --- /dev/null +++ b/docker-build.sh @@ -0,0 +1,104 @@ +#!/bin/bash + +# Docker build script for FramePack-FastAPI + +set -e + +echo "🐳 FramePack-FastAPI Docker Build Script" +echo "========================================" + +# Parse command line arguments +PUSH=false +TAG="latest" + +while [[ $# -gt 0 ]]; do + case $1 in + --push) + PUSH=true + shift + ;; + --tag) + TAG="$2" + shift 2 + ;; + -h|--help) + echo "Usage: $0 [OPTIONS]" + echo "" + echo "Options:" + echo " --push Push to registry after build" + echo " --tag Docker image tag (default: latest)" + echo " -h, --help Show this help message" + exit 0 + ;; + *) + echo "Unknown option: $1" + exit 1 + ;; + esac +done + +# Set image name and dockerfile +IMAGE_NAME="framepack-fastapi" +DOCKERFILE="Dockerfile" +echo "🚀 Building FramePack-FastAPI..." +echo "⚠️ This requires NVIDIA GPU for inference!" + +FULL_IMAGE_NAME="$IMAGE_NAME:$TAG" + +echo "📋 Build Configuration:" +echo " Type: $BUILD_TYPE" +echo " Image: $FULL_IMAGE_NAME" +echo " Dockerfile: $DOCKERFILE" +echo " Push: $PUSH" +echo "" + +# Build the image +echo "🔨 Building Docker image..." +docker build -f "$DOCKERFILE" -t "$FULL_IMAGE_NAME" . + +if [ $? -eq 0 ]; then + echo "✅ Build completed successfully!" + echo " Image: $FULL_IMAGE_NAME" +else + echo "❌ Build failed!" + exit 1 +fi + +# Push if requested +if [ "$PUSH" = true ]; then + echo "📤 Pushing image to registry..." + docker push "$FULL_IMAGE_NAME" + + if [ $? -eq 0 ]; then + echo "✅ Push completed successfully!" + else + echo "❌ Push failed!" + exit 1 + fi +fi + +echo "" +echo "🎉 All done!" +echo "" +echo "To run the container:" +if [ "$BUILD_TYPE" = "gpu" ]; then + echo " docker run --gpus all -p 8000:8000 $FULL_IMAGE_NAME" + echo "" + echo "Or use docker-compose:" + echo " docker-compose --profile gpu up" + echo " # or" + echo " docker-compose -f docker-compose.gpu.yml up" + echo "" + echo "⚠️ Requires NVIDIA GPU with 24GB+ VRAM for full functionality" +else + echo " docker run -p 8000:8000 $FULL_IMAGE_NAME" + echo "" + echo "Or use docker-compose:" + echo " docker-compose up" + echo "" + echo "ℹ️ This is development server only - API will return 503 for inference requests" +fi + +echo "" +echo "API will be available at: http://localhost:8000" +echo "API docs: http://localhost:8000/docs" \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 00000000..8ea7794d --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,41 @@ +version: '3.8' + +services: + framepack-api: + build: + context: . + dockerfile: Dockerfile + ports: + - "8000:8000" + environment: + - PYTHONUNBUFFERED=1 + - NVIDIA_VISIBLE_DEVICES=all + - NVIDIA_DRIVER_CAPABILITIES=compute,utility + - HF_HOME=/app/hf_download + - TRANSFORMERS_CACHE=/app/hf_download + - HF_DATASETS_CACHE=/app/hf_download + volumes: + # Application data + - ./outputs:/app/outputs + - ./temp_queue_images:/app/temp_queue_images + - ./loras:/app/loras + - ./job_queue.json:/app/job_queue.json + # External model mount point + - ./hf_download:/app/hf_download + restart: unless-stopped + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: 1 + capabilities: [gpu] + healthcheck: + test: ["CMD", "curl", "-f", "http://localhost:8000/docs"] + interval: 30s + timeout: 10s + retries: 5 + start_period: 120s + labels: + - "com.framepack.service=api" + - "com.framepack.description=FramePack API with external model mount" \ No newline at end of file diff --git a/docker-run.sh b/docker-run.sh new file mode 100644 index 00000000..4a1608c6 --- /dev/null +++ b/docker-run.sh @@ -0,0 +1,87 @@ +#!/bin/bash + +# Docker run script for FramePack-FastAPI + +set -e + +echo "🐳 FramePack-FastAPI Docker Run Script" +echo "======================================" + +# Parse command line arguments +DETACHED=false +PORT="8000" + +while [[ $# -gt 0 ]]; do + case $1 in + -d|--detached) + DETACHED=true + shift + ;; + -p|--port) + PORT="$2" + shift 2 + ;; + -h|--help) + echo "Usage: $0 [OPTIONS]" + echo "" + echo "Options:" + echo " -d, --detached Run in detached mode" + echo " -p, --port Port to expose (default: 8000)" + echo " -h, --help Show this help message" + exit 0 + ;; + *) + echo "Unknown option: $1" + exit 1 + ;; + esac +done + +# Set compose file +COMPOSE_FILE="docker-compose.yml" +echo "🚀 Starting FramePack-FastAPI..." +echo "⚠️ This requires NVIDIA GPU with 24GB+ VRAM!" + +echo "📋 Run Configuration:" +echo " Compose file: $COMPOSE_FILE" +echo " Port: $PORT" +echo " Detached: $DETACHED" +echo "" + +# Create required directories +echo "📁 Creating required directories..." +mkdir -p outputs/images temp_queue_images loras hf_download + +# Update port in compose file if different from 8000 +if [ "$PORT" != "8000" ]; then + echo "🔧 Updating port configuration..." + sed -i.bak "s/8000:8000/$PORT:8000/g" "$COMPOSE_FILE" +fi + +# Run docker-compose +if [ "$DETACHED" = true ]; then + echo "🔄 Starting containers in detached mode..." + docker-compose -f "$COMPOSE_FILE" up -d +else + echo "🔄 Starting containers..." + docker-compose -f "$COMPOSE_FILE" up +fi + +# Restore original compose file if port was changed +if [ "$PORT" != "8000" ] && [ -f "$COMPOSE_FILE.bak" ]; then + mv "$COMPOSE_FILE.bak" "$COMPOSE_FILE" +fi + +if [ "$DETACHED" = true ]; then + echo "" + echo "🎉 Containers started successfully!" + echo "" + echo "API is available at: http://localhost:$PORT" + echo "API docs: http://localhost:$PORT/docs" + echo "" + echo "To view logs:" + echo " docker-compose -f $COMPOSE_FILE logs -f" + echo "" + echo "To stop:" + echo " docker-compose -f $COMPOSE_FILE down" +fi \ No newline at end of file diff --git a/integration_plan.md b/integration_plan.md new file mode 100644 index 00000000..987f29d3 --- /dev/null +++ b/integration_plan.md @@ -0,0 +1,371 @@ +# FramePack-FastAPI 画像生成機能統合計画 + +## 概要 +`oneframe_ichi.py`の高度な画像生成機能を現在のFramePack-FastAPIプロジェクトに統合します。これにより、動画生成に加えて、高品質な単一画像生成、バッチ処理、画像転写（kisekaeichi）機能を提供します。 + +## 1. 新しいAPIエンドポイント + +### 1.1 画像生成エンドポイント +``` +POST /api/generate-image +- 単一の高品質画像を生成 +- Request Body: + { + "prompt": string, + "negative_prompt": string (optional), + "seed": integer (optional), + "steps": integer (default: 30), + "cfg": float (default: 1.0), + "width": integer (default: 1216), + "height": integer (default: 704), + "lora_paths": array[string] (optional), + "lora_scales": array[float] (optional) + } +``` + +### 1.2 バッチ画像生成エンドポイント +``` +POST /api/batch-images +- 複数の画像を一括生成 +- Request Body: + { + "prompts": array[string], + "negative_prompt": string (optional), + "seeds": array[integer] (optional), + "batch_size": integer (default: 4), + "steps": integer (default: 30), + "cfg": float (default: 1.0), + "width": integer (default: 1216), + "height": integer (default: 704), + "lora_paths": array[string] (optional), + "lora_scales": array[float] (optional) + } +``` + +### 1.3 画像転写エンドポイント +``` +POST /api/transfer-image +- Kisekaeichi機能：ある画像のスタイルを別の画像に転写 +- Request Body: + { + "source_image": string (base64), + "target_image": string (base64), + "prompt": string, + "negative_prompt": string (optional), + "transfer_strength": float (default: 0.7), + "seed": integer (optional), + "steps": integer (default: 30), + "cfg": float (default: 1.0) + } +``` + +## 2. ファイル構成の変更 + +### 2.1 新規ファイル + +#### `api/worker_image.py` +画像生成専用のワーカープロセス +```python +import torch +import numpy as np +from PIL import Image +import io +import base64 +from typing import Optional, List, Dict, Any + +from diffusers_helper.utils import save_image +from api.models import load_models +from api.queue_manager import QueueManager +from api.settings import get_settings + +# RoPE関連の処理を実装 +def rope_attention_batch(...): + """RoPE attention batch processing for image generation""" + pass + +# 画像生成メイン処理 +async def process_image_generation(job_id: str, job_data: dict, models: dict): + """ + 単一画像生成の処理 + """ + try: + queue_manager = QueueManager() + queue_manager.update_job_status(job_id, "processing") + + # パラメータの取得 + prompt = job_data["prompt"] + negative_prompt = job_data.get("negative_prompt", "") + seed = job_data.get("seed", -1) + steps = job_data.get("steps", 30) + cfg = job_data.get("cfg", 1.0) + width = job_data.get("width", 1216) + height = job_data.get("height", 704) + + # 画像生成処理 + # ... (詳細な実装) + + # 結果の保存 + output_path = f"outputs/images/{job_id}.png" + save_image(generated_image, output_path, metadata=metadata) + + queue_manager.update_job_result(job_id, {"image_path": output_path}) + queue_manager.update_job_status(job_id, "completed") + + except Exception as e: + queue_manager.update_job_status(job_id, "failed", str(e)) + +# バッチ処理 +async def process_batch_images(job_id: str, job_data: dict, models: dict): + """ + バッチ画像生成の処理 + """ + pass + +# 画像転写処理 +async def process_image_transfer(job_id: str, job_data: dict, models: dict): + """ + Kisekaeichi画像転写の処理 + """ + pass +``` + +#### `api/image_models.py` +画像生成用の追加モデル設定 +```python +from api.models import * + +# 画像生成用の特別な設定 +IMAGE_GENERATION_CONFIG = { + "default_width": 1216, + "default_height": 704, + "max_batch_size": 8, + "rope_batch_size": 4, + "latent_channels": 16, + "vae_scale_factor": 8, + "time_scale_factor": 4, +} + +# RoPE処理用の設定 +def get_rope_config(batch_size: int, height: int, width: int): + """RoPE処理の設定を取得""" + return { + "batch_size": batch_size, + "height": height // 8, # latent space + "width": width // 8, + "max_seq_length": (height // 8) * (width // 8), + "embed_dim": 3072, # transformer embed dim + } +``` + +### 2.2 既存ファイルの修正 + +#### `api/models.py` の拡張 +```python +# 既存のコードに追加 + +# 画像生成用の追加設定 +IMAGE_GENERATION_MODELS = { + "rope_processor": None, # 後で初期化 + "latent_processor": None, +} + +def load_image_generation_components(): + """画像生成用の追加コンポーネントをロード""" + global IMAGE_GENERATION_MODELS + + # RoPE処理用のコンポーネント + # ... 実装 + + return IMAGE_GENERATION_MODELS + +# load_models()関数の最後に追加 +def load_models(): + # ... 既存のコード ... + + # 画像生成コンポーネントの追加 + image_components = load_image_generation_components() + loaded_models.update(image_components) + + return loaded_models +``` + +#### `api/api.py` の拡張 +```python +from fastapi import File, UploadFile +from api.worker_image import process_image_generation, process_batch_images, process_image_transfer + +# 画像生成用のリクエストモデル +class ImageGenerationRequest(BaseModel): + prompt: str + negative_prompt: Optional[str] = "" + seed: Optional[int] = -1 + steps: int = 30 + cfg: float = 1.0 + width: int = 1216 + height: int = 704 + lora_paths: Optional[List[str]] = None + lora_scales: Optional[List[float]] = None + +class BatchImageRequest(BaseModel): + prompts: List[str] + negative_prompt: Optional[str] = "" + seeds: Optional[List[int]] = None + batch_size: int = 4 + steps: int = 30 + cfg: float = 1.0 + width: int = 1216 + height: int = 704 + lora_paths: Optional[List[str]] = None + lora_scales: Optional[List[float]] = None + +class ImageTransferRequest(BaseModel): + source_image: str # base64 + target_image: str # base64 + prompt: str + negative_prompt: Optional[str] = "" + transfer_strength: float = 0.7 + seed: Optional[int] = -1 + steps: int = 30 + cfg: float = 1.0 + +# 新しいエンドポイント +@app.post("/api/generate-image") +async def generate_image(request: ImageGenerationRequest): + job_id = str(uuid.uuid4()) + job_data = { + "type": "image", + "data": request.dict(), + "created_at": datetime.now().isoformat() + } + + queue_manager.add_job(job_id, job_data) + + # バックグラウンドで処理開始 + asyncio.create_task(process_image_generation(job_id, job_data, models)) + + return {"job_id": job_id} + +@app.post("/api/batch-images") +async def batch_images(request: BatchImageRequest): + job_id = str(uuid.uuid4()) + job_data = { + "type": "batch_image", + "data": request.dict(), + "created_at": datetime.now().isoformat() + } + + queue_manager.add_job(job_id, job_data) + + # バックグラウンドで処理開始 + asyncio.create_task(process_batch_images(job_id, job_data, models)) + + return {"job_id": job_id} + +@app.post("/api/transfer-image") +async def transfer_image(request: ImageTransferRequest): + job_id = str(uuid.uuid4()) + job_data = { + "type": "image_transfer", + "data": request.dict(), + "created_at": datetime.now().isoformat() + } + + queue_manager.add_job(job_id, job_data) + + # バックグラウンドで処理開始 + asyncio.create_task(process_image_transfer(job_id, job_data, models)) + + return {"job_id": job_id} +``` + +#### `api/queue_manager.py` の拡張 +```python +# ジョブタイプの追加 +JOB_TYPES = ["video", "image", "batch_image", "image_transfer"] + +class QueuedJob(BaseModel): + id: str + type: str # "video", "image", "batch_image", "image_transfer" + data: Dict[str, Any] + status: str + created_at: str + started_at: Optional[str] = None + completed_at: Optional[str] = None + error: Optional[str] = None + result: Optional[Dict[str, Any]] = None + progress: Optional[float] = None + step: Optional[int] = None + total: Optional[int] = None + info: Optional[str] = None + preview: Optional[str] = None # base64 encoded preview + batch_progress: Optional[Dict[str, Any]] = None # for batch jobs +``` + +## 3. 実装優先順位 + +### Phase 1: 基本的な単一画像生成（1-2日） +1. `worker_image.py`の基本実装 +2. `/api/generate-image`エンドポイントの実装 +3. 既存のモデルローダーとの統合 +4. 基本的なテスト + +### Phase 2: バッチ処理（2-3日） +1. バッチ処理ロジックの実装 +2. `/api/batch-images`エンドポイントの実装 +3. 進捗トラッキングの拡張 +4. メモリ管理の最適化 + +### Phase 3: 画像転写機能（3-4日） +1. Kisekaeichi機能の実装 +2. `/api/transfer-image`エンドポイントの実装 +3. マスク処理とブレンディングロジック +4. 高度なパラメータチューニング + +### Phase 4: 最適化とテスト（2-3日） +1. パフォーマンス最適化 +2. エラーハンドリングの強化 +3. 包括的なテストスイート +4. ドキュメンテーション + +## 4. 技術的な考慮事項 + +### 4.1 メモリ管理 +- 動画生成と画像生成でモデルを共有 +- 低VRAMモードでの適切なモデルスワップ +- バッチ処理時のメモリ効率化 + +### 4.2 並行処理 +- 動画生成ジョブと画像生成ジョブの優先順位管理 +- リソースの公平な配分 +- キューシステムの拡張 + +### 4.3 互換性 +- 既存のAPIとの後方互換性を維持 +- 統一されたジョブステータス管理 +- 共通のエラーハンドリング + +## 5. 期待される成果 + +1. **統合プラットフォーム**: 動画と画像の両方を生成できる統一APIプラットフォーム +2. **効率的なリソース利用**: モデルの共有によるメモリ効率の向上 +3. **高度な機能**: バッチ処理、画像転写などの高度な機能 +4. **スケーラビリティ**: 将来的な機能拡張への対応 + +## 6. リスクと対策 + +### リスク +1. メモリ不足による処理の失敗 +2. 動画生成と画像生成の競合 +3. APIの複雑化 + +### 対策 +1. 適切なメモリ管理とモデルスワップ +2. ジョブスケジューリングの最適化 +3. 明確なAPIドキュメンテーション + +## 7. 次のステップ + +1. この計画のレビューと承認 +2. 開発環境のセットアップ +3. Phase 1の実装開始 +4. 定期的な進捗確認とフィードバック \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..6eec8cd8 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,86 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "framepack-fastapi" +version = "0.1.0" +description = "FramePack FastAPI with image and video generation" +authors = [ + {name = "FramePack Team"} +] +requires-python = ">=3.10,<3.13" +dependencies = [ + "accelerate>=1.7.0", + "aiofiles>=24.1.0", + "av>=14.4.0", + "diffusers>=0.33.1", + "einops>=0.8.1", + "fastapi>=0.115.12", + "flash-attn", + "gradio>=5.33.0", + "markupsafe>=3.0.2", + "numpy>=2.1.2,<3.0.0", + "opencv-contrib-python>=4.11.0.86", + "peft>=0.15.2", + "pillow>=11.0.0,<12.0", + "python-multipart>=0.0.20", + "requests>=2.32.3", + "safetensors>=0.5.3", + "sageattention>=1.0.6", + "scipy>=1.15.3", + "sentencepiece>=0.2.0", + "torch>=2.7.1", + "torchsde>=0.2.6", + "torchvision>=0.22.1", + "transformers>=4.52.4", + "uvicorn[standard]>=0.34.3", + "watchdog>=6.0.0", +] + +[tool.hatch.build.targets.wheel] +packages = ["api"] +dependencies = [ + "fastapi>=0.104.0", + "uvicorn[standard]>=0.24.0", + "torch>=2.0.0", + "torchvision>=0.15.0", + "transformers>=4.35.0", + "diffusers>=0.24.0", + "accelerate>=0.24.0", + "pillow>=10.0.0", + "numpy>=1.24.0", + "opencv-python>=4.8.0", + "scikit-image>=0.21.0", + "gradio>=4.0.0", + "watchdog>=3.0.0", + "pydantic>=2.4.0", + "python-multipart>=0.0.6", + "aiofiles>=23.2.0", +] + +[project.optional-dependencies] +dev = [ + "pytest>=7.4.0", + "pytest-asyncio>=0.21.0", + "httpx>=0.25.0", + "ruff>=0.1.0", + "mypy>=1.6.0", +] + +[tool.ruff] +line-length = 120 +target-version = "py310" + +[tool.ruff.lint] +select = ["E", "F", "I", "N", "W", "UP"] +ignore = ["E501"] + +[tool.mypy] +python_version = "3.10" +warn_return_any = true +warn_unused_configs = true +disallow_untyped_defs = true + +[tool.uv.sources] +flash-attn = { path = "archive/flash_attn-2.7.4.post1+cu12torch2.6cxx11abiTRUE-cp310-cp310-linux_x86_64.whl" } diff --git a/start_server.sh b/start_server.sh new file mode 100644 index 00000000..387dbf2d --- /dev/null +++ b/start_server.sh @@ -0,0 +1,37 @@ +#!/bin/bash + +# FramePack-FastAPI Server Startup Script for uv + +echo "Starting FramePack-FastAPI server with uv..." + +# Use full path to uv +UV_PATH="$HOME/.local/bin/uv" + +# Check if uv is available +if [ ! -f "$UV_PATH" ]; then + echo "Error: uv is not installed at $UV_PATH" + echo "Please install uv first: curl -LsSf https://astral.sh/uv/install.sh | sh" + exit 1 +fi + +# Check if virtual environment exists +if [ ! -d ".venv" ]; then + echo "Creating virtual environment..." + "$UV_PATH" venv --python 3.10 +fi + +# Install basic dependencies if not already installed +echo "Installing/checking dependencies..." +"$UV_PATH" pip install fastapi uvicorn python-multipart pydantic watchdog + +# Set environment variables +export PYTHONPATH="${PYTHONPATH}:$(pwd)" + +# Start the server +echo "Starting FastAPI server..." +echo "Server will be available at: http://localhost:8080" +echo "API docs will be available at: http://localhost:8080/docs" +echo "" + +# Use uv run to execute in the virtual environment +"$UV_PATH" run uvicorn api.api:app --host 0.0.0.0 --port 8080 --reload \ No newline at end of file diff --git a/test_image_api.py b/test_image_api.py new file mode 100644 index 00000000..4f345935 --- /dev/null +++ b/test_image_api.py @@ -0,0 +1,147 @@ +#!/usr/bin/env python3 +"""Test script for image generation API""" + +import requests +import json +import base64 +import time + + +def test_image_generation(): + """Test single image generation endpoint""" + print("Testing /api/generate-image endpoint...") + + url = "http://localhost:8080/api/generate-image" + payload = { + "prompt": "A beautiful sunset over mountains", + "negative_prompt": "ugly, blurry", + "width": 512, + "height": 512, + "steps": 20, + "cfg": 7.5, + "seed": 42, + } + + try: + response = requests.post(url, json=payload) + print(f"Status Code: {response.status_code}") + + if response.status_code == 200: + result = response.json() + print(f"Success! Job ID: {result['job_id']}") + print(f"Message: {result['message']}") + return result["job_id"] + else: + print(f"Error: {response.text}") + return None + except Exception as e: + print(f"Connection error: {e}") + return None + + +def check_job_status(job_id): + """Check job status""" + print(f"\nChecking status for job {job_id}...") + + url = f"http://localhost:8080/status/{job_id}" + + try: + response = requests.get(url) + if response.status_code == 200: + result = response.json() + print(f"Status: {result['status']}") + print(f"Progress: {result.get('progress', 0)}%") + print(f"Info: {result.get('progress_info', '')}") + return result + else: + print(f"Error: {response.text}") + return None + except Exception as e: + print(f"Connection error: {e}") + return None + + +def get_image_result(job_id): + """Get the generated image""" + print(f"\nGetting result for job {job_id}...") + + url = f"http://localhost:8080/api/image/{job_id}" + + try: + response = requests.get(url) + if response.status_code == 200: + print("Success! Image downloaded.") + with open(f"test_output_{job_id}.png", "wb") as f: + f.write(response.content) + print(f"Saved as test_output_{job_id}.png") + return True + else: + print(f"Error: {response.text}") + return False + except Exception as e: + print(f"Connection error: {e}") + return False + + +def test_batch_generation(): + """Test batch image generation endpoint""" + print("\nTesting /api/batch-images endpoint...") + + url = "http://localhost:8080/api/batch-images" + payload = { + "prompts": ["A red car", "A blue house", "A green tree"], + "negative_prompt": "ugly, blurry", + "batch_size": 3, + "width": 512, + "height": 512, + "steps": 20, + } + + try: + response = requests.post(url, json=payload) + print(f"Status Code: {response.status_code}") + + if response.status_code == 200: + result = response.json() + print(f"Success! Job ID: {result['job_id']}") + return result["job_id"] + else: + print(f"Error: {response.text}") + return None + except Exception as e: + print(f"Connection error: {e}") + return None + + +def main(): + print("Image Generation API Test Suite") + print("=" * 50) + + # Test single image generation + job_id = test_image_generation() + + if job_id: + # Wait for processing + print("\nWaiting for job to complete...") + max_attempts = 30 + for i in range(max_attempts): + time.sleep(2) + status = check_job_status(job_id) + if status and status["status"] == "completed": + get_image_result(job_id) + break + elif status and status["status"].startswith("failed"): + print("Job failed!") + break + + # Test batch generation + batch_job_id = test_batch_generation() + if batch_job_id: + print(f"\nBatch job created: {batch_job_id}") + # You can add similar status checking here + + print("\nTest complete!") + + +if __name__ == "__main__": + main() diff --git a/tests/test_image_api.py b/tests/test_image_api.py new file mode 100644 index 00000000..23f059b6 --- /dev/null +++ b/tests/test_image_api.py @@ -0,0 +1,192 @@ +""" +Test cases for image generation API endpoints +""" +import pytest +import json +import base64 +import io +from PIL import Image +from fastapi.testclient import TestClient +from api.api import app + +client = TestClient(app) + + +def create_test_image_base64(width: int = 512, height: int = 512) -> str: + """Create a test image encoded as base64""" + image = Image.new('RGB', (width, height), color='red') + buffer = io.BytesIO() + image.save(buffer, format='PNG') + image_data = base64.b64encode(buffer.getvalue()).decode('utf-8') + return f"data:image/png;base64,{image_data}" + + +class TestImageGenerationAPI: + """Test class for image generation endpoints""" + + def test_generate_image_endpoint_exists(self): + """Test that the image generation endpoint exists""" + response = client.post("/api/generate-image", json={ + "prompt": "A beautiful landscape" + }) + # Should not return 404 (endpoint exists) + assert response.status_code != 404 + + def test_generate_image_with_minimal_params(self): + """Test image generation with minimal parameters""" + request_data = { + "prompt": "A cute cat sitting on a chair" + } + + response = client.post("/api/generate-image", json=request_data) + + # Should accept the request (return job_id) + if response.status_code == 200: + data = response.json() + assert "job_id" in data + assert "message" in data + assert len(data["job_id"]) > 0 + else: + # If models aren't loaded, expect 503 + assert response.status_code == 503 + + def test_generate_image_with_full_params(self): + """Test image generation with all parameters""" + request_data = { + "prompt": "A detailed fantasy landscape with mountains and rivers", + "negative_prompt": "blurry, low quality, distorted", + "seed": 42, + "steps": 25, + "cfg": 7.5, + "width": 768, + "height": 512, + "lora_paths": [], + "lora_scales": [] + } + + response = client.post("/api/generate-image", json=request_data) + + if response.status_code == 200: + data = response.json() + assert "job_id" in data + assert "message" in data + else: + assert response.status_code == 503 # Models not loaded + + def test_batch_images_endpoint(self): + """Test batch image generation endpoint""" + request_data = { + "prompts": [ + "A red apple", + "A blue car", + "A green tree" + ], + "batch_size": 3, + "steps": 20 + } + + response = client.post("/api/batch-images", json=request_data) + + if response.status_code == 200: + data = response.json() + assert "job_id" in data + assert "message" in data + else: + assert response.status_code == 503 + + def test_image_transfer_endpoint(self): + """Test image transfer endpoint""" + source_image = create_test_image_base64(256, 256) + target_image = create_test_image_base64(256, 256) + + request_data = { + "source_image": source_image, + "target_image": target_image, + "prompt": "Transfer the style from source to target", + "transfer_strength": 0.8 + } + + response = client.post("/api/transfer-image", json=request_data) + + if response.status_code == 200: + data = response.json() + assert "job_id" in data + assert "message" in data + else: + assert response.status_code == 503 + + def test_image_download_endpoint(self): + """Test image download endpoint""" + # Test with a non-existent job ID + response = client.get("/api/image/nonexistent123") + assert response.status_code == 404 + + def test_invalid_image_generation_params(self): + """Test image generation with invalid parameters""" + # Test with empty prompt + response = client.post("/api/generate-image", json={ + "prompt": "" + }) + # Should either accept empty prompt or return validation error + assert response.status_code in [200, 422, 503] + + # Test with negative steps + response = client.post("/api/generate-image", json={ + "prompt": "test", + "steps": -5 + }) + # Should return validation error + assert response.status_code == 422 + + # Test with invalid CFG + response = client.post("/api/generate-image", json={ + "prompt": "test", + "cfg": -1.0 + }) + # Should return validation error + assert response.status_code == 422 + + def test_batch_images_validation(self): + """Test batch image generation validation""" + # Test with empty prompts list + response = client.post("/api/batch-images", json={ + "prompts": [] + }) + # Should return validation error + assert response.status_code == 422 + + # Test with too large batch size + response = client.post("/api/batch-images", json={ + "prompts": ["test1", "test2"], + "batch_size": 20 # Exceeds max of 8 + }) + # Should return validation error + assert response.status_code == 422 + + def test_image_transfer_validation(self): + """Test image transfer validation""" + # Test with invalid base64 image + response = client.post("/api/transfer-image", json={ + "source_image": "invalid_base64", + "target_image": "invalid_base64", + "prompt": "test" + }) + # Should either return validation error 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