wangkanai/wan21-fp16-480p

WAN 2.1 FP16 480p - Image-to-Video Diffusion Model

High-fidelity 480p image-to-video generation model in full FP16 precision (14 billion parameters). Part of the WAN (Wan An) 2.1 model family for transforming static images into dynamic videos.

Model Description

WAN 2.1 I2V 480p is a 14-billion parameter transformer-based diffusion model that generates videos from static images. This FP16 variant provides maximum numerical precision and generation quality for research and high-quality video synthesis applications. The 480p resolution offers a balanced approach between quality and computational requirements.

Key Capabilities:

• Image-to-video generation with temporal coherence
• 480p resolution output (balanced quality/performance)
• Full FP16 precision (16-bit floating point)
• Compatible with camera control LoRAs for cinematic effects
• Optimized for research and professional production workflows

Repository Contents

wan21-fp16-480p/
└── diffusion_models/
    └── wan/
        └── wan21-i2v-480p-14b-fp16.safetensors  (31.0 GB)

Total Repository Size: 31.0 GB

Model Files

File	Size	Description
wan21-i2v-480p-14b-fp16.safetensors	31.0 GB	WAN 2.1 I2V 480p diffusion model (14B parameters, FP16 precision)

Hardware Requirements

Minimum Requirements

• VRAM: 32 GB (for basic inference)
• System RAM: 32 GB
• Disk Space: 31 GB for model file
• GPU: NVIDIA GPU with FP16 support (RTX 3090, A6000, or better)

Recommended Requirements

• VRAM: 40 GB+ (for optimal performance and batch processing)
• System RAM: 64 GB
• GPU: High-end NVIDIA GPU (RTX 4090, A6000, A100)
• Storage: SSD for faster model loading

Performance Notes

• FP16 precision requires more VRAM than quantized variants (FP8)
• Enable memory optimization techniques for 24GB GPUs (gradient checkpointing, attention slicing)
• For production deployment with lower VRAM, consider FP8 quantized variants

Usage Examples

Basic Image-to-Video Generation

from diffusers import DiffusionPipeline
from PIL import Image
import torch

# Load the WAN 2.1 I2V 480p FP16 model
pipe = DiffusionPipeline.from_single_file(
    "E:/huggingface/wan21-fp16-480p/diffusion_models/wan/wan21-i2v-480p-14b-fp16.safetensors",
    torch_dtype=torch.float16,
    use_safetensors=True
)

pipe.to("cuda")

# Load input image
input_image = Image.open("path/to/your/image.jpg")

# Generate video from image
video = pipe(
    image=input_image,
    prompt="smooth camera movement, cinematic lighting",
    num_frames=24,
    num_inference_steps=50,
    guidance_scale=7.5
).frames[0]

# Export video
from diffusers.utils import export_to_video
export_to_video(video, "output_video.mp4", fps=8)

With Memory Optimization (for lower VRAM)

from diffusers import DiffusionPipeline
import torch

# Load model with memory optimizations
pipe = DiffusionPipeline.from_single_file(
    "E:/huggingface/wan21-fp16-480p/diffusion_models/wan/wan21-i2v-480p-14b-fp16.safetensors",
    torch_dtype=torch.float16,
    use_safetensors=True
)

# Enable memory-efficient attention
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()

# For even lower VRAM usage
pipe.enable_model_cpu_offload()

pipe.to("cuda")

# Generate video with optimizations
video = pipe(
    image=input_image,
    prompt="your prompt here",
    num_frames=16,  # Reduce frames for lower memory
    num_inference_steps=30,  # Fewer steps for faster generation
    guidance_scale=7.5
).frames[0]

With Camera Control LoRAs

from diffusers import DiffusionPipeline
from PIL import Image
import torch

# Load base model
pipe = DiffusionPipeline.from_single_file(
    "E:/huggingface/wan21-fp16-480p/diffusion_models/wan/wan21-i2v-480p-14b-fp16.safetensors",
    torch_dtype=torch.float16,
    use_safetensors=True
)

pipe.to("cuda")

# Load camera control LoRA (requires separate download)
# Example: rotation, arc shot, or drone camera movements
pipe.load_lora_weights(
    "path/to/wan21-camera-rotation-rank16-v1.safetensors"
)

# Generate with camera control
video = pipe(
    image=input_image,
    prompt="rotating camera around the subject, cinematic",
    num_frames=24,
    num_inference_steps=50,
    guidance_scale=7.5
).frames[0]

export_to_video(video, "output_rotating.mp4", fps=8)

Model Specifications

Specification	Value
Architecture	Transformer-based image-to-video diffusion model
Parameters	14 billion
Precision	FP16 (16-bit floating point)
Resolution	480p (video output)
Format	SafeTensors
Model Size	31.0 GB
Task	Image-to-video generation
Library	diffusers
Compatible LoRAs	WAN 2.1 camera control LoRAs (rotation, arc shot, drone)

Technical Details

• FP16 Format: 1 sign bit, 5-bit exponent, 10-bit mantissa
• Numerical Range: ±65,504 (max value)
• Precision: ~3-4 decimal digits
• Quality: Full precision without quantization artifacts
• Compatibility: All modern PyTorch versions with CUDA support

Installation

# Install required dependencies
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
pip install diffusers transformers accelerate safetensors pillow

# For video export
pip install opencv-python imageio imageio-ffmpeg

Requirements

• Python 3.8+
• PyTorch 2.0+
• diffusers >= 0.21.0
• transformers
• accelerate
• safetensors
• PIL/Pillow
• CUDA 11.8+ (or compatible version)

Performance Tips

1. Memory Optimization

   • Enable attention_slicing() and vae_slicing() for lower VRAM usage
   • Use enable_model_cpu_offload() for 24GB GPUs
   • Reduce num_frames and num_inference_steps for faster generation

2. Quality Optimization

   • Use guidance_scale between 7.0-9.0 for best results
   • Higher num_inference_steps (50-75) improves quality but increases time
   • Experiment with different sampling schedulers (DDIM, DPM++, Euler)

3. Speed Optimization

   • Use fewer inference steps (25-30) for faster generation
   • Reduce frame count for shorter videos
   • Consider FP8 quantized variants for production deployment

4. Prompt Engineering

   • Include motion descriptions: "smooth movement", "slow pan", "camera tracking"
   • Specify lighting: "cinematic lighting", "natural light", "dramatic shadows"
   • Add quality tokens: "high quality", "detailed", "professional"

Version Comparison

WAN 2.1 Variants

Variant	Precision	Size	VRAM	Use Case
FP16 480p (this)	FP16	31 GB	32 GB+	Research, archival quality
FP16 720p	FP16	31 GB	40 GB+	Maximum quality output
FP8 480p	FP8	~16 GB	18 GB+	Production, deployment
FP8 720p	FP8	~16 GB	24 GB+	Production, high quality

Precision Trade-offs

FP16 Advantages:

• Maximum generation quality
• Full numerical precision
• No quantization artifacts
• Research standard

FP16 Disadvantages:

• Higher VRAM requirements (2x vs FP8)
• Larger file size (2x vs FP8)
• Slower inference on tensor core GPUs
• Higher deployment costs

When to Use FP16 480p

• Research and development
• Quality benchmarking
• Archival/professional production
• GPU with 32GB+ VRAM available
• Maximum quality requirements

When to Consider Alternatives

• FP8 variants: Production deployment, VRAM constraints, batch processing
• 720p variants: Higher resolution requirements
• WAN 2.2: Enhanced camera controls, quality improvements

Compatibility

Compatible Components

• VAE: WAN 2.1 VAE (separate download required)
• LoRAs: WAN 2.1 camera control LoRAs
  • Camera rotation (rank-16)
  • Arc shot (rank-16)
  • Drone shot (rank-16)
• Frameworks: diffusers, ComfyUI (with appropriate nodes)

Camera Control LoRAs

This model is compatible with WAN 2.1 camera control LoRAs for cinematic effects:

• Rotation: Orbital camera movements around subjects
• Arc Shot: Smooth curved dolly movements
• Drone: Aerial and elevated perspectives

Note: LoRAs are not included and must be downloaded separately.

License

This model uses a custom WAN license (wan-license). Please review the official WAN license terms before use. This may differ from standard open-source licenses and may include restrictions on commercial use, redistribution, or specific applications.

Citation

If you use this model in your research or projects, please cite:

@software{wan21_i2v_480p_fp16,
  title={WAN 2.1 Image-to-Video 480p FP16},
  year={2024},
  note={14B parameter image-to-video diffusion model in full FP16 precision},
  url={https://huggingface.co/wan21-fp16-480p}
}

Related Resources

WAN Model Family

• WAN 2.1 FP16 720p - Higher resolution variant (31 GB, 40 GB+ VRAM)
• WAN 2.1 FP8 - Quantized variants for efficient deployment (~50% smaller)
• WAN 2.2 - Enhanced camera controls and quality improvements
• WAN LightX2V - CFG step distillation adapters for faster generation

Additional Components

• WAN 2.1 VAE - Video variational autoencoder (243 MB, separate download)
• Camera Control LoRAs - Cinematic camera movement adapters (343 MB each)
• Enhancement LoRAs - Lighting, face quality, action improvements (WAN 2.2)

Documentation

• WAN Official Documentation
• diffusers Library Documentation
• Camera Control LoRA Guide

Troubleshooting

Common Issues

Out of Memory Errors:

# Enable all memory optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.enable_model_cpu_offload()

# Reduce generation parameters
num_frames=16  # Instead of 24
num_inference_steps=30  # Instead of 50

Slow Generation:

• Reduce num_inference_steps
• Use fewer frames
• Disable CPU offload if you have sufficient VRAM
• Consider FP8 variants for faster inference

Quality Issues:

• Increase num_inference_steps (50-75)
• Adjust guidance_scale (try 7.0-9.0)
• Improve prompt quality and specificity
• Ensure input image is high quality

Best Practices

1. Image Input: Use high-quality input images (1024x1024 or higher)
2. Prompts: Be specific about motion, lighting, and camera movement
3. Memory Management: Monitor VRAM usage and enable optimizations as needed
4. Experimentation: Test different schedulers and parameters for your use case
5. Responsible Use: Follow ethical AI guidelines and license terms

Technical Notes

FP16 Precision Benefits

• Numerical Accuracy: Full 16-bit floating point precision
• Quality: No quantization artifacts or edge cases
• Compatibility: Broad GPU and software ecosystem support
• Research Standard: Industry standard for development and benchmarking

VRAM Optimization Techniques

# Technique 1: Attention slicing (5-10% VRAM reduction)
pipe.enable_attention_slicing()

# Technique 2: VAE slicing (additional 5-10% VRAM reduction)
pipe.enable_vae_slicing()

# Technique 3: Model CPU offload (significant VRAM reduction, slower)
pipe.enable_model_cpu_offload()

# Technique 4: Sequential CPU offload (maximum VRAM reduction, slowest)
pipe.enable_sequential_cpu_offload()

Changelog

v1.0 (Current)

• Initial release of WAN 2.1 I2V 480p FP16 model
• 14 billion parameters
• Full FP16 precision
• 480p resolution output
• Compatible with WAN 2.1 camera control LoRAs

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Model Version: v1.0 Last Updated: 2024-08-12 Maintained By: WAN Model Team

For questions, issues, or contributions, please refer to the official WAN model repositories and community forums.

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⚠️ Important: This is a high-precision model requiring significant computational resources. Ensure your hardware meets the minimum requirements before attempting to load and run this model. For production deployment or resource-constrained environments, consider the FP8 quantized variants.