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wangkanai/
wan22-fp16-i2v-loras

Name: wan22-fp16-i2v-loras
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WAN v2.2 FP16 Image-to-Video LoRA Collection

Specialized LoRA adapters for the WAN (Wanniwatch) v2.2 video generation model, providing enhanced camera control, lighting effects, quality improvements, and character actions for text-to-video and image-to-video generation.

Model Description

This repository contains 9 specialized LoRA adapters designed to enhance WAN v2.2 video generation capabilities. These adapters provide fine-grained control over camera movements, lighting conditions, facial animation, and overall video quality without requiring retraining of the base model.

Key Capabilities

Camera Control: 5 specialized camera movement LoRAs (rotation, drone shots, arc shots, aerial perspectives, earth zoom-out)
Lighting Enhancement: Volumetric lighting effects for cinematic quality
Quality Improvement: Realism boost and upscaling for enhanced video fidelity
Character Animation: Facial naturalizer and action-specific LoRAs (wink animation)
FP16 Precision: All models use float16 precision for efficient inference with minimal quality loss

Repository Contents

wan22-fp16-i2v-loras/
└── loras/
    └── wan/
        ├── wan22-action-wink-i2v-v1-low.safetensors        (147 MB)
        ├── wan22-camera-adr1a-v1.safetensors               (293 MB)
        ├── wan22-camera-arcshot-rank16-v2-high.safetensors (293 MB)
        ├── wan22-camera-drone-rank16-v2.safetensors        (293 MB)
        ├── wan22-camera-earthzoomout.safetensors           (293 MB)
        ├── wan22-camera-rotation-rank16-v2.safetensors     (293 MB)
        ├── wan22-face-naturalizer.safetensors              (586 MB)
        ├── wan22-light-volumetric.safetensors              (293 MB)
        └── wan22-upscale-realismboost-t2v-14b.safetensors  (293 MB)

Total Repository Size: 2.8 GB

Model Files

File	Size	Purpose
`wan22-action-wink-i2v-v1-low.safetensors`	147 MB	Character wink animation for image-to-video
`wan22-camera-adr1a-v1.safetensors`	293 MB	Advanced camera movement control (ADR1A system)
`wan22-camera-arcshot-rank16-v2-high.safetensors`	293 MB	Cinematic arc/circular camera movements
`wan22-camera-drone-rank16-v2.safetensors`	293 MB	Drone-style aerial camera movements
`wan22-camera-earthzoomout.safetensors`	293 MB	Earth zoom-out perspective (space-to-ground)
`wan22-camera-rotation-rank16-v2.safetensors`	293 MB	Object/camera rotation around subject
`wan22-face-naturalizer.safetensors`	586 MB	Facial animation quality and naturalness
`wan22-light-volumetric.safetensors`	293 MB	Volumetric lighting and atmospheric effects
`wan22-upscale-realismboost-t2v-14b.safetensors`	293 MB	Quality enhancement and realism for text-to-video

Hardware Requirements

Minimum Requirements

VRAM: 12 GB (for single LoRA usage with WAN base model)
RAM: 16 GB system memory
Disk Space: 3 GB for LoRA collection
GPU: NVIDIA RTX 3060 (12GB) or equivalent

Recommended Requirements

VRAM: 24 GB (for multiple simultaneous LoRAs)
RAM: 32 GB system memory
Disk Space: 5 GB (with base model cache)
GPU: NVIDIA RTX 4090, A6000, or equivalent

Base Model Requirements

These LoRAs require the WAN v2.2 base model (separate download):

WAN base model: ~14 GB additional disk space
Combined VRAM usage: 16-24 GB depending on configuration

Usage Examples

Basic LoRA Loading with Diffusers

python

from diffusers import WANPipeline
import torch

# Load base WAN pipeline
pipe = WANPipeline.from_pretrained(
    "Wanniwatch/WAN22",
    torch_dtype=torch.float16,
    variant="fp16"
).to("cuda")

# Load camera rotation LoRA
pipe.load_lora_weights(
    "E:/huggingface/wan22-fp16-i2v-loras/loras/wan",
    weight_name="wan22-camera-rotation-rank16-v2.safetensors",
    adapter_name="camera_rotation"
)

# Generate video with camera rotation
prompt = "A majestic lion on a savanna, cinematic camera rotation around subject"
video = pipe(
    prompt=prompt,
    num_frames=64,
    height=512,
    width=512,
    guidance_scale=7.5,
    lora_scale=0.8  # Adjust strength (0.0-1.0)
).frames[0]

Combining Multiple LoRAs

python

# Load multiple LoRAs for enhanced control
lora_configs = [
    ("wan22-camera-drone-rank16-v2.safetensors", "drone", 0.7),
    ("wan22-light-volumetric.safetensors", "lighting", 0.6),
    ("wan22-upscale-realismboost-t2v-14b.safetensors", "quality", 0.5)
]

for weight_name, adapter_name, scale in lora_configs:
    pipe.load_lora_weights(
        "E:/huggingface/wan22-fp16-i2v-loras/loras/wan",
        weight_name=weight_name,
        adapter_name=adapter_name
    )

# Set adapter weights
pipe.set_adapters(
    ["drone", "lighting", "quality"],
    adapter_weights=[0.7, 0.6, 0.5]
)

prompt = "Aerial drone shot descending over a misty forest at dawn, volumetric god rays"
video = pipe(prompt=prompt, num_frames=64, height=768, width=768).frames[0]

Image-to-Video with Action LoRA

python

from PIL import Image

# Load image-to-video pipeline
pipe = WANPipeline.from_pretrained(
    "Wanniwatch/WAN22",
    torch_dtype=torch.float16
).to("cuda")

# Load wink action LoRA
pipe.load_lora_weights(
    "E:/huggingface/wan22-fp16-i2v-loras/loras/wan",
    weight_name="wan22-action-wink-i2v-v1-low.safetensors",
    adapter_name="wink"
)

# Load input image
image = Image.open("portrait.jpg")

# Generate video with wink animation
video = pipe(
    prompt="Portrait, person winking at camera",
    image=image,
    num_frames=32,
    lora_scale=0.6
).frames[0]

Advanced Camera Control

python

# Earth zoom-out effect
pipe.load_lora_weights(
    "E:/huggingface/wan22-fp16-i2v-loras/loras/wan",
    weight_name="wan22-camera-earthzoomout.safetensors"
)

prompt = "Starting from street level, camera zooms out revealing city, country, Earth from space"
video = pipe(
    prompt=prompt,
    num_frames=96,  # Longer sequence for zoom effect
    height=1024,
    width=1024,
    lora_scale=0.85
).frames[0]

Cinematic Lighting Enhancement

python

# Volumetric lighting
pipe.load_lora_weights(
    "E:/huggingface/wan22-fp16-i2v-loras/loras/wan",
    weight_name="wan22-light-volumetric.safetensors"
)

prompt = "Dark cathedral interior, volumetric light beams through stained glass windows"
video = pipe(
    prompt=prompt,
    num_frames=64,
    guidance_scale=8.5,  # Higher guidance for dramatic lighting
    lora_scale=0.9
).frames[0]

Quality Enhancement Pipeline

python

# Realism boost for text-to-video
pipe.load_lora_weights(
    "E:/huggingface/wan22-fp16-i2v-loras/loras/wan",
    weight_name="wan22-upscale-realismboost-t2v-14b.safetensors"
)

# Combine with face naturalizer for character videos
pipe.load_lora_weights(
    "E:/huggingface/wan22-fp16-i2v-loras/loras/wan",
    weight_name="wan22-face-naturalizer.safetensors",
    adapter_name="face"
)

prompt = "Close-up of a person smiling naturally in golden hour lighting"
video = pipe(
    prompt=prompt,
    num_frames=48,
    height=768,
    width=512,
    lora_scale=0.7
).frames[0]

Model Specifications

Architecture

Base Model: WAN v2.2 (Wanniwatch video generation model)
LoRA Rank: Rank 16 for most camera/lighting LoRAs
Adapter Type: Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning
Precision: FP16 (float16) for all models
Format: SafeTensors (secure tensor serialization)

LoRA Categories

Camera Control (5 LoRAs)

Rotation: 360° camera rotation around subjects
Drone: Aerial drone cinematography movements
Arc Shot: Curved/circular camera paths around subjects
ADR1A: Advanced dynamic camera control system
Earth Zoom: Extreme zoom-out from ground to orbital perspective

Quality Enhancement (3 LoRAs)

Realism Boost: Enhanced photorealism for text-to-video generation
Face Naturalizer: Improved facial animation quality and natural expressions
Upscale: Quality enhancement for higher resolution output

Effects (1 LoRA)

Volumetric Lighting: Atmospheric lighting with light shafts and fog effects

Actions (1 LoRA)

Wink Animation: Character winking for image-to-video generation

Technical Details

All LoRAs use rank-16 decomposition (except action LoRA which is lower rank)
Compatible with diffusers library version ≥0.25.0
Supports both text-to-video and image-to-video pipelines
LoRA scaling factor: 0.5-1.0 (adjustable per use case)

Performance Tips and Optimization

LoRA Scaling Guidelines

Camera LoRAs: 0.7-0.9 for pronounced effects, 0.4-0.6 for subtle movements
Lighting LoRAs: 0.6-0.9 for dramatic effects, 0.3-0.5 for natural lighting
Quality LoRAs: 0.5-0.8 for balanced enhancement
Action LoRAs: 0.5-0.7 for controlled character animation

Combining LoRAs

Maximum Recommended: 3-4 simultaneous LoRAs to avoid conflicts
Complementary Combinations:
- Camera + Lighting + Quality (cinematic results)
- Camera + Face Naturalizer (character focus)
- Multiple camera LoRAs (complex camera movements)
Avoid Conflicts: Don't combine multiple quality enhancement LoRAs simultaneously

Memory Optimization

Use torch.float16 for all operations
Enable xformers memory efficient attention: pipe.enable_xformers_memory_efficient_attention()
Reduce resolution for testing: Start with 512x512 before scaling to 1024x1024
Process fewer frames: 32-64 frames for testing, 96+ for final renders
Unload unused LoRAs: pipe.unload_lora_weights() between generations

Inference Speed

Expected generation time (RTX 4090, 64 frames, 768x768): 45-60 seconds
Single LoRA overhead: ~5-10% slower than base model
Multiple LoRAs: ~10-20% slower than base model
Batch processing: Not recommended due to VRAM constraints

Prompt Engineering

Camera LoRAs: Include camera movement keywords (e.g., "rotating camera", "drone shot")
Lighting LoRAs: Specify lighting conditions (e.g., "volumetric rays", "god beams")
Quality LoRAs: Focus on detail keywords (e.g., "photorealistic", "highly detailed")
Action LoRAs: Explicitly describe the action (e.g., "person winking")

License

This model collection is released under the WAN License (other). Please refer to the official WAN v2.2 license terms from Wanniwatch for usage restrictions and commercial licensing.

License Terms Summary

Research and non-commercial use permitted
Commercial use may require separate licensing
Redistribution must preserve original attribution
No warranty or liability provided

For complete license details, visit: Wanniwatch/WAN22 on Hugging Face

Citation

If you use these LoRAs in your research or projects, please cite:

bibtex

@misc{wan22-loras,
  title={WAN v2.2 LoRA Collection for Video Generation},
  author={Wanniwatch Team},
  year={2024},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/Wanniwatch/WAN22}}
}

Related Resources

Official Links

Base Model: Wanniwatch/WAN22
Documentation: WAN v2.2 Official Docs
Community: WAN Discord Server

Recommended Complementary Models

VAE: Enhanced video VAE for improved quality
Upscalers: RealESRGAN for post-processing enhancement
Controlnets: Depth/pose control for structured video generation

Additional LoRA Collections

WAN v2.2 FP8 LoRAs (lower precision, faster inference)
WAN v2.2 Style LoRAs (artistic styles and aesthetics)
WAN v2.2 Motion LoRAs (specialized motion patterns)

Troubleshooting

Common Issues

Out of Memory Errors:

Reduce resolution or frame count
Use single LoRA instead of multiple
Enable memory efficient attention
Close other GPU applications

Low Quality Output:

Increase guidance scale (7.5-9.0)
Adjust LoRA scale (try 0.7-0.9)
Use quality enhancement LoRA
Increase resolution if VRAM allows

LoRA Not Loading:

Verify absolute file path is correct
Check diffusers version (≥0.25.0)
Ensure base model is loaded correctly
Confirm safetensors format compatibility

Unexpected Camera Movements:

Lower LoRA scale for subtler effects
Refine prompt with specific camera keywords
Avoid conflicting camera LoRAs simultaneously

Contact and Support

For questions, issues, or contributions:

Issues: Report technical problems via GitHub Issues
Community: Join the WAN Discord for community support
Email: support@wanniwatch.ai for commercial inquiries

Last Updated: October 2025 README Version: v1.4 Model Version: WAN v2.2 LoRA Collection: 9 specialized adapters

wangkanai/wan22-fp16-i2v-loras

WAN v2.2 FP16 Image-to-Video LoRA Collection

Model Description

Key Capabilities

Repository Contents

Model Files

Hardware Requirements

Minimum Requirements

Recommended Requirements

Base Model Requirements

Usage Examples

Basic LoRA Loading with Diffusers

Combining Multiple LoRAs

Image-to-Video with Action LoRA

Advanced Camera Control

Cinematic Lighting Enhancement

Quality Enhancement Pipeline

Model Specifications

Architecture

LoRA Categories

Camera Control (5 LoRAs)

Quality Enhancement (3 LoRAs)

Effects (1 LoRA)

Actions (1 LoRA)

Technical Details

Performance Tips and Optimization

LoRA Scaling Guidelines

Combining LoRAs

Memory Optimization

Inference Speed

Prompt Engineering

License

License Terms Summary

Citation

Related Resources

Official Links

Recommended Complementary Models

Additional LoRA Collections

Troubleshooting

Common Issues

Contact and Support

wangkanai/
wan22-fp16-i2v-loras