WAN LoRA Collection v2.2 - Image-to-Video (I2V) Adapters (FP8)

A curated collection of specialized LoRA adapters for the WAN (Wangfuyun Animate) video generation model, focusing on camera control, lighting effects, and action animation for image-to-video (I2V) generation.

Model Description

This repository contains 6 specialized LoRA adapters designed to enhance WAN video generation capabilities for image-to-video workflows:

Camera Control LoRAs: 4 models for precise camera movement control (arc shots, drone perspective, earth zoom-out, rotation)
Lighting LoRAs: 1 model for cinematic lens flare effects
Action LoRAs: 1 model for facial animation control (wink action)

These LoRAs are optimized for FP8 precision models and provide fine-grained control over video generation parameters while maintaining high quality and performance.

Repository Contents

wan22-fp8-i2v-loras/
└── loras/
    └── wan/
        ├── wan22-action-wink-i2v-v1-low.safetensors                     (147 MB)
        ├── wan22-camera-arcshot-rank16-i2v-a14b-high.safetensors       (293 MB)
        ├── wan22-camera-drone-rank16-i2v-a14b.safetensors              (293 MB)
        ├── wan22-camera-earthzoomout.safetensors                       (293 MB)
        ├── wan22-camera-rotation-rank16-i2v-a14b.safetensors           (293 MB)
        └── wan22-light-cinematicflare-i2v-low.safetensors              (293 MB)

Total Repository Size: 1.6 GB

LoRA Descriptions

Camera Control LoRAs:

wan22-camera-arcshot-rank16-i2v-a14b-high: High-quality arc shot movements with curved camera paths (rank-16, optimized for 14B models)
wan22-camera-drone-rank16-i2v-a14b: Aerial drone-style perspective shots with smooth movement (rank-16)
wan22-camera-earthzoomout: Dramatic earth zoom-out effect for cinematic establishing shots
wan22-camera-rotation-rank16-i2v-a14b: Circular rotation around subject with stable framing (rank-16)

Lighting LoRAs:

wan22-light-cinematicflare-i2v-low: Cinematic lens flare effects for I2V (low-noise variant, optimized for subtle lighting enhancement)

Action LoRAs:

wan22-action-wink-i2v-v1-low: Wink facial animation for I2V with low-noise optimization (147 MB, half-size for efficient loading)

Hardware Requirements

Minimum Requirements

VRAM: 12 GB (for FP8 base WAN model + 1-2 LoRAs)
System RAM: 16 GB
Disk Space: 1.6 GB (for LoRA collection)
GPU: NVIDIA RTX 3060 12GB / 4060 Ti 16GB or equivalent

Recommended Requirements

VRAM: 16-24 GB (RTX 4080, 4090, A5000, or better)
System RAM: 32 GB
Disk Space: 50+ GB (including base WAN models and video outputs)
GPU: NVIDIA RTX 4090, A6000, or higher

Note: Each LoRA adds approximately 150-300 MB to VRAM usage. FP8 base models typically require 6-10 GB VRAM.

Usage Examples

Loading with Diffusers (Python)

from diffusers import DiffusionPipeline
import torch
from PIL import Image

# Load base WAN model with FP8 precision
pipe = DiffusionPipeline.from_pretrained(
    "wangfuyun/AnimateLCM-SVD-xt",
    torch_dtype=torch.float8_e4m3fn,
    variant="fp8"
)
pipe.to("cuda")

# Load camera arc shot LoRA
pipe.load_lora_weights(
    r"E:\huggingface\wan22-fp8-i2v-loras\loras\wan",
    weight_name="wan22-camera-arcshot-rank16-i2v-a14b-high.safetensors",
    adapter_name="camera_arcshot"
)

# Set LoRA strength (0.0-1.0, recommended: 0.6-0.8)
pipe.set_adapters(["camera_arcshot"], adapter_weights=[0.75])

# Load source image
image = Image.open("portrait.jpg").resize((512, 512))

# Generate I2V video with arc shot camera movement
video = pipe(
    prompt="Cinematic arc shot around person, dramatic lighting",
    image=image,
    num_frames=24,
    guidance_scale=7.5,
    num_inference_steps=25
).frames

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

Combining Multiple LoRAs for Cinematic Effect

# Load camera rotation LoRA
pipe.load_lora_weights(
    r"E:\huggingface\wan22-fp8-i2v-loras\loras\wan",
    weight_name="wan22-camera-rotation-rank16-i2v-a14b.safetensors",
    adapter_name="camera_rotation"
)

# Load cinematic lens flare LoRA
pipe.load_lora_weights(
    r"E:\huggingface\wan22-fp8-i2v-loras\loras\wan",
    weight_name="wan22-light-cinematicflare-i2v-low.safetensors",
    adapter_name="lens_flare"
)

# Set multiple adapters with individual weights
pipe.set_adapters(
    ["camera_rotation", "lens_flare"],
    adapter_weights=[0.7, 0.5]
)

# Load scene image
image = Image.open("landscape.jpg")

# Generate video with rotation + lens flare
video = pipe(
    prompt="Rotating camera view with golden hour lens flare, cinematic atmosphere",
    image=image,
    num_frames=32,
    guidance_scale=8.0,
    num_inference_steps=30
).frames

export_to_video(video, "cinematic_rotation.mp4", fps=12)

Facial Animation with Wink Action

# Load wink action LoRA
pipe.load_lora_weights(
    r"E:\huggingface\wan22-fp8-i2v-loras\loras\wan",
    weight_name="wan22-action-wink-i2v-v1-low.safetensors",
    adapter_name="wink_action"
)

pipe.set_adapters(["wink_action"], adapter_weights=[0.85])

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

# Generate wink animation
video = pipe(
    prompt="Person winking naturally with subtle facial movement",
    image=image,
    num_frames=16,
    guidance_scale=7.0,
    num_inference_steps=20
).frames

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

Aerial Drone Shot with Earth Zoom-Out

# Load earth zoom-out LoRA for dramatic establishing shot
pipe.load_lora_weights(
    r"E:\huggingface\wan22-fp8-i2v-loras\loras\wan",
    weight_name="wan22-camera-earthzoomout.safetensors",
    adapter_name="earth_zoom"
)

# Or use drone camera LoRA for aerial perspective
pipe.load_lora_weights(
    r"E:\huggingface\wan22-fp8-i2v-loras\loras\wan",
    weight_name="wan22-camera-drone-rank16-i2v-a14b.safetensors",
    adapter_name="drone_camera"
)

pipe.set_adapters(["drone_camera"], adapter_weights=[0.8])

# Load aerial or landscape image
image = Image.open("city_skyline.jpg")

# Generate dramatic aerial movement
video = pipe(
    prompt="Smooth drone camera pullback revealing vast landscape, cinematic movement",
    image=image,
    num_frames=48,
    guidance_scale=8.5,
    num_inference_steps=30
).frames

export_to_video(video, "aerial_drone.mp4", fps=12)

Model Specifications

Technical Details

Format: SafeTensors (secure, memory-efficient binary format)
Precision: FP8-compatible (optimized for FP8 base models)
Architecture: LoRA (Low-Rank Adaptation) adapters
Rank: Rank-16 for most models (efficient parameter count)
Compatibility: WAN/AnimateLCM-SVD-xt FP8 base models
Model Variants: I2V-optimized (image-to-video conditioning)

LoRA Strength Guidelines

Camera Control LoRAs: Recommended strength 0.6-0.8 (higher for stronger camera movement)
Lighting LoRAs: Recommended strength 0.4-0.6 (lower for subtle effects)
Action LoRAs: Recommended strength 0.7-0.9 (higher for clear facial animation)

Combination Guidelines

Camera + Lighting: Safe to combine (reduce camera to 0.6-0.7, lighting to 0.4-0.5)
Multiple Camera LoRAs: Not recommended (conflicting motion vectors)
Action + Camera: Can combine for animated portraits with camera movement
Earth Zoom + Lighting: Excellent for dramatic establishing shots

Performance Tips

LoRA Selection: Use 1-2 LoRAs maximum per generation to avoid conflicts
Strength Tuning: Start at recommended strength and adjust ±0.1 based on desired intensity
Camera Movement: Use one camera LoRA at a time for consistent, smooth motion
Lighting Effects: Keep lighting LoRA strength lower (0.4-0.5) for natural-looking enhancement
Action Animation: Use action LoRAs with close-up portrait images for best results
FP8 Optimization: These LoRAs are specifically tuned for FP8 inference (2x faster than FP16)
Frame Count: Use 16-24 frames for action animations, 24-48 frames for camera movements
Guidance Scale: Use 7.0-8.5 for I2V (higher values increase prompt adherence)

Optimization Recommendations

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

# Use FP16 for intermediate calculations (FP8 for weights)
pipe.unet.to(dtype=torch.float16)
pipe.vae.to(dtype=torch.float16)

# Enable xformers for faster attention (requires xformers package)
try:
    pipe.enable_xformers_memory_efficient_attention()
except:
    print("xformers not available, using default attention")

# Reduce VRAM usage for longer videos
pipe.enable_sequential_cpu_offload()  # For <12GB VRAM systems

Troubleshooting

Issue: Camera movement too strong or jittery

Solution: Reduce LoRA strength to 0.5-0.6, increase num_inference_steps to 30-35

Issue: Lighting effects too subtle or invisible

Solution: Increase LoRA strength to 0.6-0.7, use prompts with specific lighting keywords

Issue: Action animation not visible or too exaggerated

Solution: Adjust strength (0.7-0.9 range), ensure image is close-up portrait, use clear action prompt

Issue: Out of memory errors

Solution: Enable CPU offloading, reduce num_frames, use only one LoRA at a time

License

This LoRA collection follows the WAN (Wangfuyun Animate) license terms. Please refer to the base model license at: https://huggingface.co/wangfuyun/AnimateLCM-SVD-xt

Usage Restrictions:

Free for research and non-commercial use
Commercial use may require permission from original authors
Derivative works should credit the original WAN model and LoRA creators
Do not use for generating harmful, illegal, or unethical content
Respect privacy rights when using facial animation features

Citation

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

@misc{wan22-i2v-lora-collection,
  title={WAN LoRA Collection v2.2: Image-to-Video Camera Control and Enhancement},
  author={Wangfuyun AnimateLCM Team},
  year={2024},
  howpublished={\url{https://huggingface.co/wangfuyun/AnimateLCM-SVD-xt}},
  note={LoRA adapters for I2V camera control, lighting, and action animation}
}

Resources

Base Model: wangfuyun/AnimateLCM-SVD-xt
Diffusers Documentation: https://huggingface.co/docs/diffusers
LoRA Training Guide: https://huggingface.co/docs/diffusers/training/lora
WAN Community: Hugging Face Discussions
FP8 Inference Guide: https://huggingface.co/docs/diffusers/optimization/fp8

Changelog

Version 1.4 (2025-10-28)

Verified and corrected temporal references in changelog
Confirmed all metadata fields comply with HuggingFace standards
Validated YAML frontmatter format and positioning
Updated README version header to v1.4

Version 1.3 (2024-10-14)

Updated README to reflect actual repository contents (6 LoRAs, 1.6 GB total)
Corrected file inventory and sizes based on directory analysis
Focused documentation on I2V (image-to-video) workflow
Added comprehensive usage examples for all 6 LoRA adapters
Enhanced troubleshooting section with common issues and solutions
Verified YAML frontmatter compliance with HuggingFace standards
Removed references to non-existent LoRAs (face-naturalizer, upscale, volumetric light, etc.)
Updated hardware requirements to reflect actual FP8 model sizes

Version 1.2 (2024-10-14)

Verified YAML metadata compliance with HuggingFace standards
Confirmed base_model fields for LoRA adapters

Version 1.1 (2024-10-14)

Fixed YAML frontmatter position (moved to line 1)
Updated version header placement

Version 1.0 (2024-10-13)

Initial comprehensive README with Hugging Face metadata

Model Version: v2.2 README Version: v1.4 Last Updated: 2025-10-28 Repository Size: 1.6 GB LoRA Count: 6 specialized I2V adapters

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