WAN LightX2V I2V LoRA Adapters (480p - All Ranks)

Complete collection of LoRA (Low-Rank Adaptation) adapters for the LightX2V 14B image-to-video generation model at 480p resolution. This repository contains all 7 rank variants (4, 8, 16, 32, 64, 128, 256) enabling flexible quality/performance trade-offs through CFG (Classifier-Free Guidance) step distillation.

📦 Model Information

Base Model: LightX2V I2V 14B
Type: CFG Step Distillation LoRA Adapters
Version: v1
Precision: BF16 (Brain Floating Point 16)
Resolution: 480p (854x480)
Available Ranks: 4, 8, 16, 32, 64, 128, 256 (all ranks included)
Total Models: 7 adapters
Repository Size: ~5.5GB

📁 Repository Contents

wan21-lightx2v-i2v-14b-480p/
└── loras/
    └── wan/
        ├── wan21-lightx2v-i2v-14b-480p-cfg-step-distill-rank4-bf16.safetensors (52MB)
        ├── wan21-lightx2v-i2v-14b-480p-cfg-step-distill-rank8-bf16.safetensors (96MB)
        ├── wan21-lightx2v-i2v-14b-480p-cfg-step-distill-rank16-bf16.safetensors (183MB)
        ├── wan21-lightx2v-i2v-14b-480p-cfg-step-distill-rank32-bf16.safetensors (357MB)
        ├── wan21-lightx2v-i2v-14b-480p-cfg-step-distill-rank64-bf16.safetensors (704MB)
        ├── wan21-lightx2v-i2v-14b-480p-cfg-step-distill-rank128-bf16.safetensors (1.4GB)
        └── wan21-lightx2v-i2v-14b-480p-cfg-step-distill-rank256-bf16.safetensors (2.8GB)

🎯 LoRA Rank Selection Guide

Choose the appropriate rank based on your hardware and quality requirements:

Rank	File Size	Quality	Speed	VRAM Usage	Use Case
4	52MB	Basic	Fastest	Minimal	Rapid prototyping, severe memory constraints
8	96MB	Good	Very Fast	Low	Quick testing, low-resource systems
16	183MB	Better	Fast	Low	Balanced performance/quality
32	357MB	High	Moderate	Medium	General production use (recommended)
64	704MB	Very High	Slower	Higher	Quality-focused applications
128	1.4GB	Excellent	Slow	High	Maximum quality, ample resources
256	2.8GB	Maximum	Slowest	Very High	Research, highest fidelity needs

Recommendation: Start with rank-32 for the best quality/performance balance. Scale up to 64/128/256 if quality is paramount, or down to 16/8/4 for faster iteration or limited resources.

🚀 Usage Examples

Image-to-Video (I2V) with Diffusers

from diffusers import DiffusionPipeline
from PIL import Image
import torch

# Load base I2V model
pipe = DiffusionPipeline.from_pretrained(
    "lightx2v/lightx2v-i2v-14b",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Load I2V LoRA adapter (rank-32 recommended)
pipe.load_lora_weights(
    "E:/huggingface/wan21-lightx2v-i2v-14b-480p/loras/wan/wan21-lightx2v-i2v-14b-480p-cfg-step-distill-rank32-bf16.safetensors"
)

# Load input image
input_image = Image.open("your_image.jpg").resize((854, 480))  # 480p resolution

# Generate video from image
prompt = "Camera slowly zooms in, gentle wind movement, cinematic 480p quality"
video = pipe(
    prompt=prompt,
    image=input_image,
    num_inference_steps=20,  # Reduced steps thanks to distillation
    guidance_scale=7.5,
    num_frames=24,
    height=480,
    width=854
).frames

# Save video
from diffusers.utils import export_to_video
export_to_video(video, "output_i2v_480p.mp4", fps=8)

Using Different Ranks

import os

# Define base path
LORA_PATH = "E:/huggingface/wan21-lightx2v-i2v-14b-480p/loras/wan"

# Select rank based on your needs
rank = 32  # Options: 4, 8, 16, 32, 64, 128, 256

lora_file = f"{LORA_PATH}/wan21-lightx2v-i2v-14b-480p-cfg-step-distill-rank{rank}-bf16.safetensors"
pipe.load_lora_weights(lora_file)

Comparing Ranks

# Test multiple ranks to find optimal balance
ranks_to_test = [16, 32, 64, 128]

for rank in ranks_to_test:
    lora_file = f"{LORA_PATH}/wan21-lightx2v-i2v-14b-480p-cfg-step-distill-rank{rank}-bf16.safetensors"
    pipe.load_lora_weights(lora_file)

    # Generate and compare
    video = pipe(prompt, image=input_image, num_inference_steps=20, num_frames=24).frames
    export_to_video(video, f"output_rank{rank}.mp4", fps=8)

ComfyUI Integration

Download LoRA file:
- Recommended: wan21-lightx2v-i2v-14b-480p-cfg-step-distill-rank32-bf16.safetensors
- Or choose any rank based on your needs

Installation:

ComfyUI/models/loras/wan/
└── wan21-lightx2v-i2v-480p-rank32-bf16.safetensors

Workflow Setup:
- Add "Load LoRA" node to your workflow
- Select the LoRA file (any rank)
- Set LoRA strength: 0.8-1.0 (recommended)
- Connect to your LightX2V I2V model nodes
- Set resolution to 854x480 (480p)
Parameters:
- Steps: 15-25 (distilled model requires fewer steps)
- CFG Scale: 6.0-8.0
- LoRA Strength: 0.8-1.0
- Resolution: 854x480 (480p)

⚙️ Technical Details

CFG Step Distillation

These LoRAs utilize Classifier-Free Guidance (CFG) step distillation, which:

Reduces inference steps from 50-100 down to 15-30 steps
Maintains quality while accelerating generation by 2-3x
Optimizes guidance behavior for better prompt adherence
Improves consistency across different CFG scale values

Benefits:

Faster iteration during creative workflows
Lower computational costs
Suitable for real-time and interactive applications

BF16 Precision

All adapters use Brain Floating Point 16 (BF16) format:

Better stability than FP16 for training and inference
Wider dynamic range prevents numerical overflow
Hardware optimized for NVIDIA Ampere/Ada/Hopper architectures
Mixed precision ready for efficient memory usage

Rank Architecture

LoRA rank determines the adapter's capacity:

Low rank (4-16): Captures essential patterns, minimal overhead
Medium rank (32-64): Balances detail capture with efficiency
High rank (128-256): Maximum expressiveness, requires more resources

💻 Hardware Requirements

Minimum Requirements (Rank 8-16)

GPU: NVIDIA RTX 3060 (12GB VRAM) or equivalent
RAM: 16GB system RAM
Storage: 500MB for adapters + base model space
Precision: BF16 support (Ampere architecture or newer)

Recommended (Rank 32-64)

GPU: NVIDIA RTX 4070 Ti (16GB VRAM) or RTX 3090 (24GB)
RAM: 32GB system RAM
Storage: 1-2GB for adapters + base model space

High-End (Rank 128-256)

GPU: NVIDIA RTX 4090 (24GB VRAM) or A100 (40GB)
RAM: 64GB system RAM
Storage: 3-5GB for adapters + base model space

Software Requirements

Python: 3.9+ (3.10 recommended)
PyTorch: 2.0+ with CUDA 11.8 or 12.1
Diffusers: 0.25.0+
Transformers: 4.36.0+
CUDA: 11.8+ or 12.1+

# Install dependencies
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install diffusers transformers accelerate safetensors

📊 Performance Benchmarks

I2V Generation Speed (RTX 4090, 24 frames, 480p)

Rank	Steps	Time (seconds)	Quality	VRAM Usage
4	20	~16s	Basic	~12GB
8	20	~17s	Good	~12GB
16	20	~18s	Better	~13GB
32	20	~20s	High	~14GB
64	20	~23s	Very High	~15GB
128	20	~27s	Excellent	~17GB
256	20	~34s	Maximum	~20GB

Note: 480p generation is faster than 720p. Actual performance varies based on prompt complexity, GPU model, and system configuration.

🎨 Prompting Tips

Image-to-Video (I2V) Best Practices

Motion description: Focus on how elements in the image should move or animate
Camera instruction: Specify desired camera movements (zoom, pan, static, dolly)
Consistency: Keep prompts aligned with image content and composition
Quality modifiers: Include "cinematic", "480p quality", "smooth motion", "professional"
Resolution mention: Include "480p" for optimal results at this resolution

Example prompts for 480p:

"Gentle wind blowing through hair and clothing, camera slowly zooming in, cinematic 480p quality"

"Clouds moving across the sky, leaves rustling in the breeze, camera static, smooth motion"

"Water rippling and reflecting light, camera panning slowly across the scene, professional cinematography, 480p quality"

"Person walking forward, camera tracking movement, natural motion, 480p HD quality"

"Fire crackling and flames dancing, camera slowly circling, cinematic quality, 480p resolution"

Prompting by Rank

Rank 4-8: Keep prompts simple and focused on primary motion
Rank 16-32: Add moderate detail about motion and camera movements
Rank 64-128: Include complex motion details, multiple elements, sophisticated camera work
Rank 256: Maximum detail, nuanced motion descriptions, complex interactions

🔧 Troubleshooting

Out of Memory (OOM) Errors

# Solution 1: Use lower rank
pipe.load_lora_weights("...rank16_bf16.safetensors")

# Solution 2: Enable CPU offloading
pipe.enable_model_cpu_offload()

# Solution 3: Reduce batch size/frames
video = pipe(prompt, num_frames=16)  # Instead of 24

Poor Quality Results

Increase rank: Try rank-64, rank-128, or rank-256
Adjust steps: 20-25 steps usually optimal for 480p
Tune CFG scale: 6.5-8.0 range works best
Improve prompts: Add more descriptive motion details and "480p quality"
Check resolution: Ensure input image is 854x480 for best results
Test multiple ranks: Compare outputs from different ranks

Slow Generation

Use lower rank: rank-4, rank-8, or rank-16 for fastest generation
Reduce steps: 15-20 steps sufficient with distillation
Enable optimizations: torch.compile() on PyTorch 2.0+
Consider lower resolution: 480p is already efficient for iteration
Reduce frames: Generate 16 frames instead of 24

Choosing the Right Rank

Speed priority: Use rank-4 or rank-8
Balance: Use rank-16 or rank-32
Quality priority: Use rank-64 or rank-128
Maximum quality: Use rank-256 (research/archival)
Testing: Start with rank-32, adjust based on results

📝 Model Card

Property	Value
Model Type	LoRA Adapters for Video Diffusion
Architecture	Low-Rank Adaptation (LoRA)
Training Method	CFG Step Distillation
Precision	BF16
Resolution	480p (854x480)
Rank Variants	4, 8, 16, 32, 64, 128, 256 (complete set)
Parameter Count	Varies by rank (4M-256M parameters)
License	See base model license
Intended Use	Image-to-video generation at 480p
Languages	Prompt: English (primary)

📄 License

These LoRA adapters are compatible with the LightX2V base model license. Please verify license compliance with:

LightX2V I2V 14B base model license

Usage Restrictions: Follow the base model's terms for commercial/non-commercial use.

🙏 Acknowledgments

LightX2V Team for the exceptional I2V 14B base model
Community contributors for testing and feedback
Hugging Face for hosting infrastructure

📚 Related Resources

LightX2V Base Models: Official LightX2V model repository
WAN 2.1 Models: WAN 2.1 I2V models with camera control
WAN 2.2 Models: WAN 2.2 I2V/T2V models with enhanced features
720p I2V LoRAs: wan21-lightx2v-i2v-14b-720p (for higher resolution)
720p T2V LoRAs: wan21-lightx2v-t2v-14b-720p (for text-to-video)
Diffusers Documentation: https://huggingface.co/docs/diffusers

📧 Support

For questions or issues specific to these adapters, please open an issue in this repository. For base model questions, refer to the official LightX2V documentation.

Summary

This repository contains the complete collection of 7 I2V LoRA adapters optimized for 480p image-to-video generation:

Total Size: ~5.5GB (all 7 adapters)
Available Ranks: 4, 8, 16, 32, 64, 128, 256 (complete set)
Resolution: 480p (854x480)
Precision: BF16
Speed: 2-3x faster than non-distilled models
Flexibility: Choose rank based on quality/speed/VRAM needs
Recommended: Rank-32 for balanced quality/performance

Complete Collection: This repository includes all rank variants from minimal (rank-4, 52MB) to maximum quality (rank-256, 2.8GB), providing complete flexibility for different use cases and hardware configurations.

Note: This repository contains I2V (image-to-video) LoRAs at 480p resolution. For T2V (text-to-video) LoRAs, see the wan21-lightx2v-t2v-14b-720p repository. For higher resolution I2V, see wan21-lightx2v-i2v-14b-720p.

Last Updated: October 2025 Repository Version: 1.4 Total Size: ~5.5GB (7 adapters: ranks 4, 8, 16, 32, 64, 128, 256) Primary Use Case: Image-to-video generation at 480p resolution with flexible quality/performance options

Downloads last month: -

Collection including wangkanai/wan21-lightx2v-i2v-14b-480p

wan-2.1

Collection

WAN 2.1 Video models • 23 items • Updated about 19 hours ago • 1