# ComfyUI RunPod Serverless Project

## Project Overview
Build a RunPod Serverless endpoint running ComfyUI with SageAttention 2.2 for image/video generation. Self-hosted frontend will call the RunPod API.

## Architecture
- **RunPod Serverless**: Hosts ComfyUI worker with GPU inference
- **Network Volume**: Mounts at `/userdata`
- **Gitea Registry**: Hosts Docker image
- **Frontend**: Self-hosted on home server, calls RunPod API over HTTPS

## Reference Environment (extracted from working pod)

### Base System
- Ubuntu 22.04.5 LTS (Jammy)
- Python 3.12.12
- CUDA 12.8 (nvcc 12.8.93)
- cuDNN 9.8.0.87
- NCCL 2.25.1

### PyTorch Stack
- torch==2.8.0+cu128
- torchvision==0.23.0+cu128
- torchaudio==2.8.0+cu128
- triton==3.4.0

### Key Dependencies
- transformers==4.56.2
- diffusers==0.35.2
- accelerate==1.11.0
- safetensors==0.6.2
- onnxruntime-gpu==1.23.2
- opencv-python==4.12.0.88
- mediapipe==0.10.14
- insightface==0.7.3
- spandrel==0.4.1
- kornia==0.8.2
- einops==0.8.1
- timm==1.0.22
- peft==0.17.1
- gguf==0.17.1
- av==16.0.1 (video)
- imageio-ffmpeg==0.6.0

### Nunchaku (prebuilt wheel)
```
nunchaku @ https://github.com/nunchaku-tech/nunchaku/releases/download/v1.0.2/nunchaku-1.0.2+torch2.8-cp312-cp312-linux_x86_64.whl
```

### ComfyUI
- Location: `/workspace/ComfyUI`
- Uses venv at `/workspace/ComfyUI/venv`
- Commit: 532e2850794c7b497174a0a42ac0cb1fe5b62499 (Dec 24, 2025)

### Custom Nodes (from CUSTOM_NODES env var + actual install)
```
ltdrdata/ComfyUI-Manager
jnxmx/ComfyUI_HuggingFace_Downloader
kijai/ComfyUI-KJNodes
Fannovel16/comfyui_controlnet_aux
crystian/ComfyUI-Crystools
Kosinkadink/ComfyUI-VideoHelperSuite
willmiao/ComfyUI-Lora-Manager
city96/ComfyUI-GGUF
Fannovel16/ComfyUI-Frame-Interpolation
nunchaku-tech/ComfyUI-nunchaku
evanspearman/ComfyMath
ssitu/ComfyUI_UltimateSDUpscale
```

### Environment Variables (relevant)
```bash
HF_HOME=/workspace/.cache/huggingface
HF_HUB_ENABLE_HF_TRANSFER=1
TRANSFORMERS_CACHE=/workspace/.cache/huggingface/transformers
PYTHONUNBUFFERED=1
LD_LIBRARY_PATH=/usr/local/cuda/lib64
LIBRARY_PATH=/usr/local/cuda/lib64/stubs
```

### Network Volume Mount
- Mount point: `/userdata`

## Technical Requirements

### SageAttention 2.2 (Critical)
Must be compiled from source with no build isolation:
```bash
git clone https://github.com/thu-ml/SageAttention.git
cd SageAttention
pip install triton
export EXT_PARALLEL=4 NVCC_APPEND_FLAGS="--threads 8" MAX_JOBS=32
pip install --no-build-isolation -e .
```

### Network Volume Structure
```
/userdata/
├── models/
│   ├── checkpoints/
│   ├── loras/
│   ├── vae/
│   ├── controlnet/
│   ├── clip/
│   └── upscale_models/
└── .cache/
    └── huggingface/
```

### Handler Requirements
- Accept JSON input: `{"image": "base64", "prompt": "string", "workflow": {}}`
- Image upload to ComfyUI if provided
- Inject prompt into workflow at specified node
- Queue workflow, poll for completion
- Return output as base64:
  - Images: PNG/JPEG base64
  - Videos: MP4 base64 (or presigned URL if >10MB)
- Detect output type from workflow output node
- Timeout handling (max 600s for video generation)

### Dockerfile Requirements
- Base: `nvidia/cuda:12.8.1-devel-ubuntu22.04` (or equivalent with CUDA 12.8 devel)
- Python 3.12
- PyTorch 2.8.0+cu128 from pytorch index
- Install nunchaku from GitHub wheel
- Compile SageAttention with --no-build-isolation
- Symlink model directories to /userdata
- Clone and install all custom nodes
- Install ffmpeg for video handling
- Expose handler as entrypoint

## File Structure
```
/project
├── Dockerfile
├── handler.py
├── requirements.txt
├── scripts/
│   └── install_custom_nodes.sh
├── workflows/
│   └── default_workflow_api.json
└── README.md
```

## Tasks
1. Create Dockerfile matching reference environment (CUDA 12.8, Python 3.12, PyTorch 2.8)
2. Create requirements.txt from extracted pip freeze (pruned to essentials)
3. Create install_custom_nodes.sh for all listed custom nodes
4. Create handler.py with ComfyUI API integration (image + video output support)
5. Document deployment steps in README.md

## Notes
- Nick is a Principal Systems Engineer, prefers direct technical communication
- Target deployment: RunPod Serverless with 5090 GPU
- Development machine: RTX 3080 (forward compatible)
- Registry: Self-hosted Gitea
- Output will likely be video - ensure ffmpeg installed and handler detects output type
- Reference pod uses venv - serverless image can install globally

## Claude Code Init Command
```
Read PROJECT.md fully. Build the Dockerfile first, matching the reference environment exactly: CUDA 12.8.1, Python 3.12, PyTorch 2.8.0+cu128, triton 3.4.0. Install nunchaku from the GitHub wheel URL. Compile SageAttention 2.2 with --no-build-isolation. Install all custom nodes listed. Symlink model paths to /userdata. Do not use a venv in the container.
```