VIDEO_GENERATION_SOLUTION.md

?? LIGHTWEIGHT VIDEO GENERATION SOLUTION

?? Goal: Enable REAL Video Generation on HF Spaces

You''re absolutely right - the whole point is video generation! Here''s how we can achieve it within HF Spaces 50GB limit:

?? Storage-Optimized Model Selection

? Previous Problem (30GB+ models):

• Wan2.1-T2V-14B: ~28GB
• OmniAvatar-14B: ~2GB
• Total: 30GB+ (exceeded limits)

? New Solution (15GB total):

• Video Generation: stabilityai/stable-video-diffusion-img2vid-xt (~4.7GB)
• Avatar Animation: Moore-AnimateAnyone/AnimateAnyone (~3.8GB)
• Audio Processing: facebook/wav2vec2-base (~0.36GB)
• TTS: microsoft/speecht5_tts (~0.5GB)
• System overhead: ~5GB
• TOTAL: ~14.4GB (well within 50GB limit!)

?? Implementation Strategy

1. Lightweight Video Engine

• lightweight_video_engine.py: Uses smaller, efficient models
• Storage check before model loading
• Graceful fallback to TTS if needed
• Memory optimization with torch.float16

2. Smart Model Selection

• hf_spaces_models.py: Curated list of HF Spaces compatible models
• Multiple configuration options (minimal/recommended/maximum)
• Automatic storage calculation

3. Intelligent Startup

• smart_startup.py: Detects environment and configures optimal models
• Storage analysis before model loading
• Clear user feedback about capabilities

?? Expected Video Generation Flow

1. Text Input: "Professional teacher explaining math"
2. TTS Generation: Convert text to speech
3. Image Selection: Use provided image or generate default avatar
4. Video Generation: Use Stable Video Diffusion for base video
5. Avatar Animation: Apply AnimateAnyone for realistic movement
6. Lip Sync: Synchronize audio with mouth movement
7. Output: High-quality avatar video within HF Spaces

? Benefits of This Approach

• ? Real Video Generation: Not just TTS, actual avatar videos
• ? HF Spaces Compatible: ~15GB total vs 30GB+ before
• ? High Quality: Using proven models like Stable Video Diffusion
• ? Reliable: Storage checks and graceful fallbacks
• ? Scalable: Can add more models as space allows

?? Technical Advantages

Stable Video Diffusion (4.7GB)

• Proven model from Stability AI
• High-quality video generation
• Optimized for deployment
• Good documentation and community support

AnimateAnyone (3.8GB)

• Specifically designed for human avatar animation
• Excellent lip synchronization
• Natural movement patterns
• Optimized inference speed

Memory Optimizations

• torch.float16 (half precision) saves 50% memory
• Selective model loading (only what''s needed)
• Automatic cleanup after generation
• Device mapping for optimal GPU usage

?? Expected API Response (Success!)

{
  "message": "? Video generated successfully with lightweight models!",
  "output_path": "/outputs/avatar_video_123456.mp4",
  "processing_time": 15.2,
  "audio_generated": true,
  "tts_method": "Lightweight Video Generation (HF Spaces Compatible)"
}

?? Next Steps

This solution should give you:

1. Actual video generation capability on HF Spaces
2. Professional avatar videos with lip sync and natural movement
3. Reliable deployment within storage constraints
4. Scalable architecture for future model additions

The key insight is using smaller, specialized models instead of one massive 28GB model. Multiple 3-5GB models can achieve the same results while fitting comfortably in HF Spaces!