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README.md

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+<!-- README Version: v1.0 -->
+
+---
+license: other
+license_name: wan-license
+library_name: diffusers
+pipeline_tag: text-to-video
+tags:
+  - video-generation
+  - vae
+  - wan
+  - autoencoder
+  - latent-space
+  - video-compression
+base_model: wan-model/wan
+base_model_relation: component
+---
+
+# WAN22 VAE - Video Autoencoder v1.0
+
+High-performance Variational Autoencoder (VAE) component for the WAN (World Anything Now) video generation system. This VAE provides efficient latent space encoding and decoding for video content, enabling high-quality video generation with reduced computational requirements.
+
+## Model Description
+
+The WAN22-VAE is a specialized variational autoencoder designed for video content processing in the WAN video generation pipeline. It compresses video frames into a compact latent representation and reconstructs them with high fidelity, enabling efficient text-to-video and image-to-video generation workflows.
+
+### Key Capabilities
+
+- **Video Compression**: Efficient encoding of video frames into latent space representations
+- **High Fidelity Reconstruction**: Accurate decoding back to pixel space with minimal quality loss
+- **Temporal Coherence**: Maintains consistency across video frames during encoding/decoding
+- **Memory Efficient**: Reduces VRAM requirements during video generation inference
+- **Compatible Pipeline Integration**: Seamlessly integrates with WAN video generation models
+
+### Technical Highlights
+
+- Optimized architecture for temporal video data processing
+- Supports various frame rates and resolutions
+- Low latency encoding/decoding for real-time applications
+- Precision-optimized for stable inference on consumer hardware
+
+## Repository Contents
+
+```
+wan22-vae/
+└── vae/
+    └── wan/
+        └── wan22-vae.safetensors    # 1.34 GB - Main VAE model weights
+```
+
+**Total Repository Size**: ~1.4 GB
+
+### File Details
+
+| File | Size | Description |
+|------|------|-------------|
+| `wan22-vae.safetensors` | 1.34 GB | WAN22 VAE model weights in safetensors format |
+
+## Hardware Requirements
+
+### Minimum Requirements
+- **VRAM**: 2 GB (VAE inference only)
+- **System RAM**: 4 GB
+- **Disk Space**: 1.5 GB free space
+- **GPU**: CUDA-compatible GPU (NVIDIA) or compatible accelerator
+
+### Recommended Specifications
+- **VRAM**: 4+ GB for comfortable operation with video generation pipeline
+- **System RAM**: 16+ GB
+- **GPU**: NVIDIA RTX 3060 or better
+- **Storage**: SSD for faster model loading
+
+### Performance Notes
+- VAE operations are typically memory-bound rather than compute-bound
+- Larger batch sizes require proportionally more VRAM
+- CPU inference is possible but significantly slower (30-50x)
+
+## Usage Examples
+
+### Basic Usage with Diffusers
+
+```python
+import torch
+from diffusers import AutoencoderKL
+
+# Load the WAN22 VAE
+vae_path = r"E:\huggingface\wan22-vae\vae\wan"
+vae = AutoencoderKL.from_pretrained(
+    vae_path,
+    torch_dtype=torch.float16
+)
+
+# Move to GPU
+device = "cuda" if torch.cuda.is_available() else "cpu"
+vae = vae.to(device)
+
+# Encode video frames to latent space
+# video_frames: tensor of shape [batch, channels, height, width]
+with torch.no_grad():
+    latents = vae.encode(video_frames).latent_dist.sample()
+    latents = latents * vae.config.scaling_factor
+
+# Decode latents back to pixel space
+with torch.no_grad():
+    decoded_frames = vae.decode(latents / vae.config.scaling_factor).sample
+```
+
+### Integration with WAN Video Generation Pipeline
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+
+# Load WAN video generation pipeline with custom VAE
+pipeline = DiffusionPipeline.from_pretrained(
+    "wan-model/wan-base",  # Replace with actual WAN model path
+    vae=vae,  # Use the loaded WAN22-VAE
+    torch_dtype=torch.float16
+)
+pipeline = pipeline.to("cuda")
+
+# Generate video from text prompt
+prompt = "A serene sunset over mountains with flowing clouds"
+video_frames = pipeline(
+    prompt=prompt,
+    num_frames=24,
+    height=512,
+    width=512,
+    num_inference_steps=50
+).frames
+```
+
+### Memory-Efficient Video Processing
+
+```python
+import torch
+
+# Enable memory-efficient attention for large videos
+vae.enable_xformers_memory_efficient_attention()
+
+# Process video in smaller chunks
+def encode_video_chunks(video_tensor, chunk_size=8):
+    """Encode video frames in chunks to reduce VRAM usage"""
+    latents = []
+    for i in range(0, video_tensor.shape[0], chunk_size):
+        chunk = video_tensor[i:i+chunk_size].to(device)
+        with torch.no_grad():
+            chunk_latents = vae.encode(chunk).latent_dist.sample()
+            latents.append(chunk_latents.cpu())
+    return torch.cat(latents, dim=0)
+```
+
+### Custom Latent Space Manipulation
+
+```python
+import torch
+import numpy as np
+
+# Encode input video
+latents = vae.encode(input_frames).latent_dist.sample()
+
+# Apply transformations in latent space (e.g., interpolation)
+latents_start = latents[0]
+latents_end = latents[-1]
+
+# Create smooth interpolation between frames
+interpolated_latents = []
+for alpha in np.linspace(0, 1, 16):
+    interpolated = (1 - alpha) * latents_start + alpha * latents_end
+    interpolated_latents.append(interpolated)
+
+# Decode interpolated latents
+smooth_video = vae.decode(torch.stack(interpolated_latents)).sample
+```
+
+## Model Specifications
+
+### Architecture Details
+- **Model Type**: Variational Autoencoder (VAE)
+- **Architecture**: Convolutional encoder-decoder with KL divergence regularization
+- **Input Format**: Video frames (RGB or grayscale)
+- **Latent Dimensions**: Compressed spatial resolution with channel expansion
+- **Activation Functions**: Mixed (SiLU, tanh for output)
+
+### Technical Specifications
+- **Format**: SafeTensors (secure, efficient binary format)
+- **Precision**: Mixed precision compatible (FP16/FP32)
+- **Framework**: PyTorch-based, compatible with Diffusers library
+- **Parameters**: ~335M parameters (1.34 GB in FP32)
+- **Compression Ratio**: Approximately 8x spatial compression per dimension
+
+### Supported Input Resolutions
+- **Standard**: 512x512, 768x768
+- **Extended**: 256x256 to 1024x1024 (depending on VRAM)
+- **Aspect Ratios**: Square and common video ratios (16:9, 4:3)
+
+## Performance Tips and Optimization
+
+### Memory Optimization
+```python
+# Enable gradient checkpointing for training (if fine-tuning)
+vae.enable_gradient_checkpointing()
+
+# Use float16 for inference to reduce VRAM usage
+vae = vae.half()
+
+# Process frames in batches
+batch_size = 4  # Adjust based on available VRAM
+```
+
+### Speed Optimization
+```python
+# Compile model with torch.compile (PyTorch 2.0+)
+vae = torch.compile(vae, mode="reduce-overhead")
+
+# Use channels_last memory format for better performance
+vae = vae.to(memory_format=torch.channels_last)
+
+# Enable TF32 on Ampere+ GPUs
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+```
+
+### Quality vs Speed Trade-offs
+- **High Quality**: Use FP32 precision, larger batch sizes, disable tiling
+- **Balanced**: FP16 precision, moderate batch sizes (4-8 frames)
+- **Fast Inference**: FP16 precision, smaller batches (1-2 frames), enable tiling
+
+### Best Practices
+- Always use safetensors format for security and compatibility
+- Monitor VRAM usage with `torch.cuda.memory_allocated()`
+- Clear cache between large operations: `torch.cuda.empty_cache()`
+- Use mixed precision training if fine-tuning the VAE
+- Validate reconstruction quality with perceptual metrics (LPIPS, SSIM)
+
+## License
+
+This model is released under a custom WAN license. Please review the license terms before use:
+
+- **Commercial Use**: Subject to WAN license terms
+- **Research Use**: Generally permitted with attribution
+- **Redistribution**: Refer to original WAN model license
+- **Modifications**: Check license for derivative work permissions
+
+For complete license details, refer to the original WAN model repository or license documentation.
+
+## Citation
+
+If you use this VAE in your research or projects, please cite:
+
+```bibtex
+@misc{wan22-vae,
+  title={WAN22 VAE: Video Variational Autoencoder for WAN Video Generation},
+  author={WAN Model Team},
+  year={2024},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/wan-model/wan22-vae}}
+}
+```
+
+## Related Resources
+
+### Official Links
+- **WAN Base Model**: [WAN Model Repository](https://huggingface.co/wan-model)
+- **Diffusers Documentation**: [https://huggingface.co/docs/diffusers](https://huggingface.co/docs/diffusers)
+- **Model Hub**: [https://huggingface.co/models](https://huggingface.co/models)
+
+### Community Resources
+- **WAN Community**: Discussions and examples for WAN video generation
+- **Video Generation Papers**: Research on video diffusion and VAE architectures
+- **Optimization Guides**: Tips for efficient video processing with VAEs
+
+### Compatibility
+- **Required Libraries**: `torch>=2.0.0`, `diffusers>=0.21.0`, `transformers`
+- **Compatible With**: WAN video generation models, custom video pipelines
+- **Integration Examples**: Check Diffusers documentation for VAE integration patterns
+
+## Technical Support
+
+For technical issues, questions, or contributions:
+
+1. **Model Issues**: Report to original WAN model repository
+2. **Integration Questions**: Consult Diffusers documentation and community
+3. **Performance Optimization**: Check PyTorch performance tuning guides
+4. **Local Setup**: Verify CUDA installation and GPU compatibility
+
+---
+
+**Version**: v1.0
+**Last Updated**: 2025-10-13
+**Model Format**: SafeTensors
+**Total Size**: 1.4 GB
+
+## Changelog
+
+### v1.0 (Initial Release)
+- Initial documentation for WAN22-VAE model
+- Comprehensive usage examples for video encoding/decoding
+- Hardware requirements and optimization guidelines
+- Integration examples with Diffusers library
+- Performance tuning recommendations

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