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

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

# 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

# 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

FP16 Precision: These models provide maximum quality with no quantization artifacts
Inference Steps: Use 50-100 steps for best quality, 20-30 for rapid prototyping
Noise Variant Selection:
- Use high-noise for creative, artistic outputs
- Use low-noise for realistic, consistent results
Prompt Engineering: Detailed, specific prompts yield better results

Speed Optimization

Enable xFormers: pipe.enable_xformers_memory_efficient_attention()
Reduce Inference Steps: Start with 20-30 steps for testing
Optimize Frame Count: Use 8-12 frames for faster generation
Batch Processing: Generate multiple videos sequentially to amortize model loading

Memory Management

CPU Offloading: pipe.enable_model_cpu_offload() for VRAM management
Attention Slicing: pipe.enable_attention_slicing() for memory efficiency
Gradient Checkpointing: Enable if fine-tuning
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

Be Specific: Detailed prompts yield better results
Include Quality Terms: "high quality", "detailed", "cinematic"
Describe Motion: Specify desired movement or action
Lighting Description: Mention lighting conditions for better results
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

Implement content moderation in production deployments
Add visible/invisible watermarks to identify AI-generated content
Provide clear disclaimers about AI generation
Monitor for misuse and enforce usage policies
Validate outputs for unintended biases before distribution
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

Use Quantized Models: Consider GGUF variants for efficiency (not in this repo)
Batch Processing: Amortize overhead across multiple generations
Optimize Inference: Use fewer steps for non-critical applications
Energy-Efficient Hardware: Use modern GPUs with better performance-per-watt
Carbon Offset: Consider offsetting for production deployments
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:

@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:

Enable CPU offloading: pipe.enable_model_cpu_offload()
Enable attention slicing: pipe.enable_attention_slicing()
Reduce frame count: Use 8-12 frames instead of 16
Clear CUDA cache: torch.cuda.empty_cache()
Use sequential CPU offload: pipe.enable_sequential_cpu_offload()
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:

Enable xFormers: pipe.enable_xformers_memory_efficient_attention()
Reduce inference steps: Start with 20-30 steps
Reduce frame count: Use 8-12 frames for faster generation
Optimize CUDA: Ensure CUDA 12.1+ for best performance
Consider GGUF Q4 models for faster inference (not in this repo)

Quality Issues

Problem: Generated videos lack quality or consistency

Solutions:

Try both noise variants: Test high-noise and low-noise models
Increase inference steps: Use 50-100 steps for best quality
Improve prompts: Be more specific and detailed
Check model loading: Ensure FP16 model loaded correctly
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:

Verify file integrity: Check file size matches 27GB
Ensure sufficient disk space: Need 27GB+ free space
Update dependencies: pip install --upgrade diffusers safetensors torch
Check PyTorch version: Requires PyTorch 2.0+ with FP16 support
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

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