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README.md

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+---
+license: other
+library_name: diffusers
+pipeline_tag: image-to-video
+tags:
+  - wan
+  - wan22
+  - image-to-video
+  - video-generation
+  - fp16
+---
+
+<!-- README Version: v1.3 -->
+
+# WAN 2.2 FP16 - Image-to-Video Models (Maximum Quality)
+
+High-quality image-to-video (I2V) generation models in full FP16 precision for maximum quality video generation. This repository contains the core I2V diffusion models optimized for research-grade and archival quality video synthesis.
+
+## Model Description
+
+WAN 2.2 FP16 is a 14-billion parameter video generation model based on diffusion architecture, providing full FP16 precision for maximum quality image-to-video generation. This repository contains the essential I2V diffusion models for high-end video generation workloads.
+
+**Key Features**:
+- 14B parameter diffusion-based architecture
+- Full FP16 precision for maximum quality (27GB per model)
+- Dedicated high-noise (creative) and low-noise (faithful) generation modes
+- Image-to-video capabilities with cinematic quality output
+- Optimized for research, archival quality, and final production renders
+
+**Model Statistics**:
+- **Total Repository Size**: ~54GB
+- **Model Architecture**: Diffusion-based image-to-video generation
+- **Format**: `.safetensors` (FP16)
+- **Parameters**: 14 billion
+- **Precision**: FP16 (full precision, no quantization)
+- **Input**: Images + text prompts
+- **Output**: Video sequences (typically 16-24 frames)
+
+## Repository Contents
+
+### Diffusion Models
+
+Located in `diffusion_models/wan/`
+
+| File | Size | Type | VRAM Required | Description |
+|------|------|------|---------------|-------------|
+| `wan22-i2v-14b-fp16-high.safetensors` | 27GB | FP16 I2V | 24GB+ | High-noise variant - Creative generation with higher variance |
+| `wan22-i2v-14b-fp16-low.safetensors` | 27GB | FP16 I2V | 24GB+ | Low-noise variant - Faithful reproduction with consistent results |
+
+**Total Size**: ~54GB
+
+## Hardware Requirements
+
+### Minimum Requirements
+
+| Component | Requirement |
+|-----------|-------------|
+| **GPU VRAM** | 24GB minimum |
+| **Recommended VRAM** | 32GB+ |
+| **Disk Space** | 54GB free space |
+| **System RAM** | 32GB+ recommended |
+| **CUDA** | 11.8+ or 12.1+ |
+| **PyTorch** | 2.0+ with FP16 support |
+
+### Compatible GPUs
+
+**Minimum (24GB VRAM)**:
+- NVIDIA RTX 4090 (24GB)
+- NVIDIA RTX A5000 (24GB)
+- NVIDIA RTX 6000 Ada (48GB)
+- NVIDIA A6000 (48GB)
+
+**Recommended (32GB+ VRAM)**:
+- NVIDIA A100 (40GB/80GB)
+- NVIDIA H100 (80GB)
+- NVIDIA RTX 6000 Ada (48GB)
+- Multi-GPU setups
+
+**Not Compatible**:
+- GPUs with less than 24GB VRAM (RTX 4080, RTX 3090, etc.)
+- For lower VRAM requirements, see GGUF quantized variants in other repositories
+
+## Usage Examples
+
+### Basic Image-to-Video Generation
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+from PIL import Image
+
+# Load input image
+input_image = Image.open("path/to/your/image.jpg")
+
+# Load I2V pipeline with FP16 precision
+pipe = DiffusionPipeline.from_pretrained(
+    "path-to-base-wan22-model",
+    torch_dtype=torch.float16
+)
+
+# Load WAN 2.2 FP16 I2V model (high-noise variant for creative generation)
+pipe.unet = torch.load(
+    "E:/huggingface/wan22-fp16-i2v/diffusion_models/wan/wan22-i2v-14b-fp16-high.safetensors"
+)
+
+pipe.to("cuda")
+
+# Generate video from image
+video = pipe(
+    image=input_image,
+    prompt="cinematic shot, high quality, detailed",
+    num_inference_steps=50,
+    num_frames=16
+).frames
+
+# Save video
+from diffusers.utils import export_to_video
+export_to_video(video, "output.mp4", fps=8)
+```
+
+### Using Low-Noise Variant
+
+```python
+# Load low-noise variant for more faithful reproduction
+pipe.unet = torch.load(
+    "E:/huggingface/wan22-fp16-i2v/diffusion_models/wan/wan22-i2v-14b-fp16-low.safetensors"
+)
+
+# Generate video with consistent, faithful results
+video = pipe(
+    image=input_image,
+    prompt="realistic scene, photographic quality",
+    num_inference_steps=50,
+    num_frames=16
+).frames
+```
+
+### Memory Optimization
+
+```python
+# Enable CPU offloading if running into VRAM limits
+pipe.enable_model_cpu_offload()
+
+# Enable attention slicing for memory efficiency
+pipe.enable_attention_slicing()
+
+# For systems with 24GB VRAM, reduce frame count
+video = pipe(
+    image=input_image,
+    prompt="your prompt",
+    num_inference_steps=50,
+    num_frames=12  # Reduced from 16 for memory efficiency
+).frames
+```
+
+## Model Specifications
+
+### Architecture Details
+
+- **Model Type**: Diffusion transformer for image-to-video generation
+- **Parameters**: 14 billion
+- **Precision**: FP16 (IEEE 754 half-precision floating point)
+- **Format**: SafeTensors (secure tensor serialization format)
+- **Context Length**: Image conditioning + text prompt
+- **Output Format**: Video frame sequences
+
+### Noise Schedule Variants
+
+**High-Noise Model** (`wan22-i2v-14b-fp16-high.safetensors`):
+- Greater noise variance during diffusion
+- More creative interpretation of input
+- Better for abstract, stylized, or artistic content
+- Higher output variance across generations
+
+**Low-Noise Model** (`wan22-i2v-14b-fp16-low.safetensors`):
+- Lower noise variance during diffusion
+- More faithful to input image and prompt
+- Better for realistic, photographic content
+- More consistent and predictable results
+
+## Performance Tips
+
+### Quality Optimization
+
+1. **FP16 Precision**: These models provide maximum quality with no quantization artifacts
+2. **Inference Steps**: Use 50-100 steps for best quality, 20-30 for rapid prototyping
+3. **Noise Variant Selection**:
+   - Use high-noise for creative, artistic outputs
+   - Use low-noise for realistic, consistent results
+4. **Prompt Engineering**: Detailed, specific prompts yield better results
+
+### Speed Optimization
+
+1. **Enable xFormers**: `pipe.enable_xformers_memory_efficient_attention()`
+2. **Reduce Inference Steps**: Start with 20-30 steps for testing
+3. **Optimize Frame Count**: Use 8-12 frames for faster generation
+4. **Batch Processing**: Generate multiple videos sequentially to amortize model loading
+
+### Memory Management
+
+1. **CPU Offloading**: `pipe.enable_model_cpu_offload()` for VRAM management
+2. **Attention Slicing**: `pipe.enable_attention_slicing()` for memory efficiency
+3. **Gradient Checkpointing**: Enable if fine-tuning
+4. **Clear Cache**: `torch.cuda.empty_cache()` between generations
+
+### GPU-Specific Tips
+
+**RTX 4090 (24GB)**:
+- Optimal performance with FP16 models
+- Reduce frame count to 12-14 for stability
+- Enable attention slicing for safety margin
+
+**RTX 6000 Ada / A6000 (48GB)**:
+- Full frame counts (16-24) without issues
+- Can run batch processing or parallel pipelines
+- Optimal for production workloads
+
+**A100 / H100 (40GB-80GB)**:
+- Maximum performance and flexibility
+- Suitable for research and large-scale production
+- Can handle extended frame sequences
+
+## Prompting Guidelines
+
+### Effective Prompt Structure
+
+```
+[Style/Quality] [Subject/Scene] [Action/Motion] [Technical Details]
+```
+
+### Example Prompts
+
+**Cinematic**:
+- "cinematic shot, high quality, detailed lighting, professional cinematography"
+- "film-like quality, dramatic shadows, cinematic color grading"
+
+**Realistic**:
+- "photorealistic, natural lighting, high detail, realistic motion"
+- "documentary style, authentic atmosphere, lifelike movement"
+
+**Artistic**:
+- "stylized art, creative interpretation, abstract motion, artistic flair"
+- "surreal atmosphere, dreamlike quality, artistic vision"
+
+### Prompt Tips
+
+1. **Be Specific**: Detailed prompts yield better results
+2. **Include Quality Terms**: "high quality", "detailed", "cinematic"
+3. **Describe Motion**: Specify desired movement or action
+4. **Lighting Description**: Mention lighting conditions for better results
+5. **Avoid Negatives**: Focus on what you want, not what you don't want
+
+## Intended Uses
+
+### Direct Use
+
+WAN 2.2 FP16 is designed for:
+- **Research**: Academic research in video generation and diffusion models
+- **Archival Quality**: Maximum quality video generation for preservation
+- **Final Production**: High-end content creation and professional video production
+- **Quality Benchmarking**: Reference standard for video generation quality assessment
+
+### Downstream Use
+
+- Fine-tuning on specialized datasets
+- Quality baseline for model comparison
+- Integration with high-end video production pipelines
+- Training data generation for downstream tasks
+
+### Out-of-Scope Use
+
+The model should **NOT** be used for:
+- Generating deceptive, harmful, or misleading video content
+- Creating deepfakes or non-consensual content of individuals
+- Producing content that violates copyright or intellectual property rights
+- Generating content intended to harass, abuse, or discriminate
+- Creating videos for illegal purposes or activities
+- Systems with insufficient VRAM (<24GB) - use quantized variants instead
+
+## Limitations and Considerations
+
+### Technical Limitations
+
+**Hardware Constraints**:
+- **Requires 24GB+ VRAM**: Not accessible on consumer GPUs below RTX 4090 tier
+- **Large Model Size**: 27GB per model requires substantial disk space and loading time
+- **Inference Speed**: FP16 precision trades speed for quality
+- **Memory Intensive**: May require memory management techniques on 24GB systems
+
+**Generation Quality**:
+- **Temporal Consistency**: May produce flickering in complex motion sequences
+- **Fine Details**: Small objects or intricate textures may lack perfect consistency
+- **Physical Realism**: Generated physics may not always follow real-world rules
+- **Text Rendering**: Cannot reliably render readable text within videos
+- **Face Quality**: Faces may show artifacts (LoRAs can help but not included in this repo)
+
+### Content Limitations
+
+- Training data biases may affect representation diversity
+- May struggle with uncommon objects or rare scenarios
+- Generated content may reflect biases present in training data
+- No built-in content filtering or moderation
+
+## Risks and Mitigations
+
+### Misuse Risks
+
+**Deepfakes and Misinformation**:
+- Risk: Model could generate deceptive content
+- Mitigation: Implement watermarking, content authentication, usage monitoring
+
+**Copyright Infringement**:
+- Risk: May generate content similar to copyrighted material
+- Mitigation: Content filtering, responsible use guidelines
+
+**Harmful Content**:
+- Risk: Could generate disturbing or inappropriate content
+- Mitigation: Safety filters, content moderation, ethical usage policies
+
+### Ethical Considerations
+
+- Obtain appropriate permissions before generating videos of identifiable individuals
+- Label AI-generated content clearly to prevent deception
+- Consider environmental impact of compute-intensive inference
+- Respect privacy, consent, and intellectual property rights
+
+### Recommendations
+
+1. Implement content moderation in production deployments
+2. Add visible/invisible watermarks to identify AI-generated content
+3. Provide clear disclaimers about AI generation
+4. Monitor for misuse and enforce usage policies
+5. Validate outputs for unintended biases before distribution
+6. Consider carbon offset for high-volume production use
+
+## Training Details
+
+### Training Data
+
+Specific training data details are not publicly available. Typical video diffusion models of this scale are trained on:
+- Large-scale video datasets with diverse content
+- Text-video pairs for caption conditioning
+- Image-video pairs for I2V tasks
+
+**Note**: Contact original model authors for specific training dataset information.
+
+### Training Procedure
+
+**Architecture**:
+- Diffusion transformer with 14B parameters
+- FP16 precision training
+- Separate noise schedules for high-noise and low-noise variants
+
+**Noise Schedules**:
+- **High-noise**: Greater variance for creative generation
+- **Low-noise**: Lower variance for faithful reproduction
+
+## Environmental Impact
+
+Video generation models require significant computational resources.
+
+### Resource Consumption
+
+- **Model Size**: 54GB total (two 27GB models)
+- **Inference Power**: 350-450W per generation (high-end GPUs)
+- **Training Impact**: Not disclosed (training carbon footprint unknown)
+- **Inference Carbon**: Varies by energy source and usage patterns
+
+### Recommendations for Reducing Impact
+
+1. **Use Quantized Models**: Consider GGUF variants for efficiency (not in this repo)
+2. **Batch Processing**: Amortize overhead across multiple generations
+3. **Optimize Inference**: Use fewer steps for non-critical applications
+4. **Energy-Efficient Hardware**: Use modern GPUs with better performance-per-watt
+5. **Carbon Offset**: Consider offsetting for production deployments
+6. **On-Demand Usage**: Load models only when needed, unload after use
+
+## License
+
+This repository uses the "other" license tag with license name "wan-license". Please check the original WAN 2.2 model repository for specific license terms, usage restrictions, and commercial use guidelines.
+
+**Important**: Verify license compatibility before using in commercial or production applications.
+
+## Citation
+
+If you use WAN 2.2 in your research or applications, please cite the original model:
+
+```bibtex
+@misc{wan22,
+  title={WAN 2.2: Image-to-Video and Text-to-Video Generation},
+  author={WAN Team},
+  year={2024},
+  howpublished={Hugging Face Model Repository}
+}
+```
+
+## Troubleshooting
+
+### Out of Memory Errors
+
+**Problem**: CUDA out of memory during inference
+
+**Solutions**:
+1. Enable CPU offloading: `pipe.enable_model_cpu_offload()`
+2. Enable attention slicing: `pipe.enable_attention_slicing()`
+3. Reduce frame count: Use 8-12 frames instead of 16
+4. Clear CUDA cache: `torch.cuda.empty_cache()`
+5. Use sequential CPU offload: `pipe.enable_sequential_cpu_offload()`
+6. Consider GGUF quantized models (available in other repositories)
+
+**Note**: If errors persist with 24GB VRAM, these FP16 models may not be suitable for your hardware. Consider GGUF Q8 or Q4 variants.
+
+### Slow Generation Speed
+
+**Problem**: Video generation takes too long
+
+**Solutions**:
+1. Enable xFormers: `pipe.enable_xformers_memory_efficient_attention()`
+2. Reduce inference steps: Start with 20-30 steps
+3. Reduce frame count: Use 8-12 frames for faster generation
+4. Optimize CUDA: Ensure CUDA 12.1+ for best performance
+5. Consider GGUF Q4 models for faster inference (not in this repo)
+
+### Quality Issues
+
+**Problem**: Generated videos lack quality or consistency
+
+**Solutions**:
+1. **Try both noise variants**: Test high-noise and low-noise models
+2. **Increase inference steps**: Use 50-100 steps for best quality
+3. **Improve prompts**: Be more specific and detailed
+4. **Check model loading**: Ensure FP16 model loaded correctly
+5. **Verify input image**: High-quality input yields better output
+
+**Note**: FP16 models provide maximum quality. If quality is still insufficient, issue may be prompt engineering or input image quality.
+
+### Model Loading Issues
+
+**Problem**: Error loading SafeTensors files
+
+**Solutions**:
+1. Verify file integrity: Check file size matches 27GB
+2. Ensure sufficient disk space: Need 27GB+ free space
+3. Update dependencies: `pip install --upgrade diffusers safetensors torch`
+4. Check PyTorch version: Requires PyTorch 2.0+ with FP16 support
+5. Verify CUDA installation: Ensure CUDA 11.8+ or 12.1+
+
+## Related Repositories
+
+### Other WAN 2.2 Repositories
+
+- **wan22-fp8**: FP8 and GGUF quantized I2V + T2V models with LoRAs (~89GB)
+  - Includes text-to-video models
+  - Includes 10 enhancement LoRAs (camera control, lighting, etc.)
+  - 16GB VRAM requirement for FP8 models
+
+### Previous WAN Versions
+
+- **wan21-fp16**: WAN 2.1 FP16 models (camera control v1, I2V only)
+- **wan21-fp8**: WAN 2.1 FP8 models (camera control v1, I2V only)
+
+### Complementary Resources
+
+For complete WAN 2.2 ecosystem:
+- **VAE Models**: Available in wan22-fp8 repository
+- **LoRA Adapters**: Available in wan22-fp8 repository (camera control, lighting, face enhancement)
+- **Text-to-Video**: Available in wan22-fp8 repository
+
+## Model Card Information
+
+**Model Card Authors**: Repository maintainer
+**Model Card Contact**: Please open an issue in the repository
+**Last Updated**: October 2024
+**Model Version**: WAN 2.2 FP16 (v1.0)
+**Repository Type**: Full Precision Model Weights
+
+## Support
+
+For issues, questions, or contributions:
+- Check the troubleshooting section above
+- Refer to the main Hugging Face model repository
+- Open an issue in this repository
+- Consult the diffusers library documentation
+
+## Summary
+
+**WAN 2.2 FP16 - Maximum Quality I2V Models**
+
+This repository contains WAN 2.2 image-to-video models in full FP16 precision for maximum quality video generation:
+
+- **2 Models**: High-noise and low-noise variants
+- **54GB Total**: 27GB per model
+- **FP16 Precision**: No quantization, maximum quality
+- **24GB+ VRAM Required**: High-end GPUs only (RTX 4090, A5000, A6000+)
+- **Research Grade**: Archival quality and final production renders
+- **Image-to-Video Only**: For text-to-video and LoRAs, see wan22-fp8
+
+**Recommended For**:
+- Research and academic applications
+- Archival quality video generation
+- Final production renders
+- Quality benchmarking and reference standards
+- High-end video production workflows
+
+**Not Recommended For**:
+- Systems with <24GB VRAM (use GGUF quantized variants)
+- Rapid prototyping (use GGUF Q4 variants)
+- Budget or consumer GPUs (use FP8 or GGUF variants)
+
+**Quality Hierarchy**: FP16 (this repo) > FP8 > GGUF Q8 > GGUF Q4
+
+---
+
+**Repository Statistics**:
+- **Total Size**: ~54GB
+- **File Count**: 2 models
+- **Format**: SafeTensors (FP16)
+- **Primary Use Case**: Maximum quality I2V generation for research and production