README.md

metadata

license: mit
title: Wan
sdk: docker
emoji: 🏆
colorFrom: blue
colorTo: blue
pinned: true
thumbnail: >-
  https://cdn-uploads.huggingface.co/production/uploads/690c81642c226d46d51dc1a5/EhLyLqcHHk_Q_gb7xR1HF.jpeg
short_description: Wan

AI Video Swarm

8× L40S GPU Video Generation Platform on Hugging Face Spaces

Hardware

• 8× NVIDIA L40S (48GB VRAM each = 384GB total)
• 192 vCPU
• 1534 GB RAM

File Structure

.
├── app.py                      # FastAPI main app
├── index.html                  # Main UI (single file, RTL Arabic)
├── pass.html                   # Auth gateway
├── logs.html                   # Real-time logs dashboard
├── requirements.txt            # Python dependencies
├── Dockerfile                  # HF Spaces compatible
├── README.md                   # This file
├── agents/
│   ├── __init__.py
│   ├── orchestrator.py         # Master coordinator
│   ├── prompt_engineer.py      # LLM prompt enhancement
│   ├── image_generator.py      # FLUX.1-dev keyframe
│   ├── video_generator.py      # Wan 2.6 + LTX-2.3
│   ├── face_restorer.py        # GFPGAN (placeholder)
│   ├── frame_interpolator.py   # RIFE (placeholder)
│   ├── video_upscaler.py       # Real-ESRGAN (placeholder)
│   └── log_monitor.py          # Logging agent
├── config/
│   ├── __init__.py
│   ├── settings.py             # App config
│   ├── models_config.py        # Model params
│   └── gpu_allocation.py       # GPU mapping
└── core/
    ├── __init__.py
    ├── auth.py                 # JWT auth
    ├── r2_client.py            # Cloudflare R2
    └── websocket_manager.py    # WS connections

Environment Variables (Secrets)

Set these in HF Space Settings > Secrets:

• HF_TOKEN — HuggingFace access token
• PASS_KEY — Password for login
• R2_ACCOUNT_ID — Cloudflare R2 account ID
• R2_BUCKET_NAME — R2 bucket name
• R2_ACCESS_KEY_ID — R2 access key
• R2_SECRET_ACCESS_KEY — R2 secret key
• R2_PUBLIC_DOMAIN — R2 public domain

Models Used

Agent	Model	GPU	VRAM
Video Primary	Wan 2.6 14B (FP8)	GPU-0	~45GB
Video Backup	LTX-2.3	GPU-1	~20GB
Image	FLUX.1-dev (FP8)	GPU-5	~16GB
Upscale	Real-ESRGAN	GPU-4	~8GB
Face Restore	GFPGAN	GPU-2	~8GB
Interpolation	RIFE	GPU-3	~6GB
Audio	F5-TTS	GPU-6	~6GB
Orchestrator	Qwen 2.5 72B	GPU-7	~40GB

Notes

• PyTorch wheels include CUDA 12.4 — no need for nvidia/cuda base image
• All models load at startup (no cold start)
• FP8 inference on L40S for 2× speed
• NVENC hardware encoding for final output