This repository is designed to store LoRA (Low-Rank Adaptation) adapters for the WAN 2.5 image-to-video generation model in FP16 precision. LoRAs are lightweight model adapters that enhance specific capabilities of the base WAN model without requiring full model retraining.

Current Status: Repository structure initialized, awaiting model file population. This is a storage directory for WAN 2.5 image-to-video (I2V) LoRA adapters in FP16 precision, specifically for NSFW content generation.

What are LoRAs?

LoRAs (Low-Rank Adaptations) are small adapter models that modify the behavior of a base model by learning task-specific adjustments. For image-to-video generation, LoRAs can provide:

Camera Control: Precise control over camera movements (pan, tilt, zoom, dolly)
Motion Enhancement: Improved motion consistency and quality from static images
Lighting Enhancement: Better lighting consistency and quality in generated videos
Style Transfer: Specific visual styles or aesthetics applied to video generation
Quality Improvements: Enhanced detail, coherence, and temporal consistency
Content Specialization: Specialized generation for specific content types (including NSFW)

Key Features

Image-to-Video Focus: Specialized for converting static images into dynamic video sequences
FP16 Precision: Half-precision floating-point for balanced quality and performance
Modular Enhancement: Stack multiple LoRAs for combined effects
Low VRAM Overhead: Minimal additional memory requirements (~100-500MB per LoRA)
Compatible with WAN 2.5: Designed for the WAN image-to-video pipeline
NSFW Capability: Specialized for adult content generation with appropriate safeguards

Repository Contents

Directory Structure

wan25-fp16-i2v-loras-nsfw/
├── README.md                    # This file
└── loras/                       # LoRA model directory
    └── wan/                     # WAN I2V-specific LoRAs
        └── (model files pending)

Expected Model Files

When populated, this repository will contain:

*.safetensors: LoRA weight files in SafeTensors format
*.json: Configuration files for each LoRA
*.txt: Metadata and usage instructions

Example LoRA types for Image-to-Video:

i2v_camera_control_lora.safetensors (~100-300MB)
i2v_motion_enhancement_lora.safetensors (~150-400MB)
i2v_lighting_enhancement_lora.safetensors (~150-400MB)
i2v_quality_boost_lora.safetensors (~200-500MB)
i2v_style_*_lora.safetensors (~100-400MB)

Hardware Requirements

Minimum Requirements

VRAM: 12GB (base WAN model + 1-2 LoRAs)
RAM: 32GB system memory
Disk Space: 2-5GB for LoRA collection
GPU: NVIDIA RTX 3060 12GB or better

Recommended Requirements

VRAM: 24GB (base WAN model + multiple LoRAs)
RAM: 64GB system memory
Disk Space: 10GB for complete LoRA collection
GPU: NVIDIA RTX 4090 or A6000

Performance Notes

Each LoRA adds ~100-500MB VRAM overhead
Multiple LoRAs can be stacked (VRAM overhead accumulates)
FP16 precision provides good quality-to-performance balance

Usage Examples

Basic Image-to-Video LoRA Loading with Diffusers

python

from diffusers import DiffusionPipeline
from PIL import Image
import torch

# Load base WAN 2.5 I2V model
pipe = DiffusionPipeline.from_pretrained(
    "genmoai/wan-video-1.5b",
    torch_dtype=torch.float16,
    variant="fp16"
).to("cuda")

# Load a single I2V LoRA
pipe.load_lora_weights(
    r"E:\huggingface\wan25-fp16-i2v-loras-nsfw\loras\wan",
    weight_name="i2v_camera_control_lora.safetensors",
    adapter_name="camera"
)

# Load input image
input_image = Image.open("input_image.png")

# Generate video from image with LoRA enhancement
prompt = "A cinematic dolly shot moving through the scene"
video = pipe(
    image=input_image,
    prompt=prompt,
    num_frames=49,
    height=480,
    width=832,
    num_inference_steps=30,
    guidance_scale=7.5,
    cross_attention_kwargs={"scale": 0.8}  # LoRA strength
).frames[0]

Stacking Multiple I2V LoRAs

python

from PIL import Image

# Load input image
input_image = Image.open("input_image.png")

# Load multiple I2V LoRAs for combined effects
pipe.load_lora_weights(
    r"E:\huggingface\wan25-fp16-i2v-loras-nsfw\loras\wan",
    weight_name="i2v_camera_control_lora.safetensors",
    adapter_name="camera"
)

pipe.load_lora_weights(
    r"E:\huggingface\wan25-fp16-i2v-loras-nsfw\loras\wan",
    weight_name="i2v_motion_enhancement_lora.safetensors",
    adapter_name="motion"
)

pipe.load_lora_weights(
    r"E:\huggingface\wan25-fp16-i2v-loras-nsfw\loras\wan",
    weight_name="i2v_lighting_enhancement_lora.safetensors",
    adapter_name="lighting"
)

# Set adapter weights
pipe.set_adapters(["camera", "motion", "lighting"], adapter_weights=[0.8, 0.7, 0.6])

# Generate with combined enhancements
video = pipe(
    image=input_image,
    prompt="Dynamic camera movement with enhanced motion and cinematic lighting",
    num_frames=49,
    height=480,
    width=832,
    num_inference_steps=30,
    guidance_scale=7.5
).frames[0]

Adjusting LoRA Strength

python

# LoRA strength controls how much the adapter affects generation
# Range: 0.0 (no effect) to 1.0 (full effect), can go higher for stronger effects

# Subtle enhancement
pipe.set_adapters(["camera"], adapter_weights=[0.3])

# Standard enhancement
pipe.set_adapters(["camera"], adapter_weights=[0.8])

# Strong enhancement
pipe.set_adapters(["camera"], adapter_weights=[1.2])

Saving Videos

python

from diffusers.utils import export_to_video

# Generate video
video_frames = pipe(prompt="...").frames[0]

# Export to MP4
export_to_video(
    video_frames,
    output_video_path="output_video.mp4",
    fps=8
)

Model Specifications

Format Details

Format: SafeTensors (.safetensors)
Precision: FP16 (16-bit floating-point)
Architecture: LoRA adapters for WAN 2.5 DiT (Diffusion Transformer)
Compatibility: WAN 2.5, WAN 1.5B models

LoRA Architecture

Rank: Typically 4-64 (lower = smaller file, higher = more capacity)
Alpha: Scaling parameter for LoRA strength
Target Modules: Attention layers, feed-forward layers
Merge Strategy: Additive (combines with base model weights)

Supported Resolutions

480x832 (standard landscape)
832x480 (portrait)
512x512 (square)
Custom: Other aspect ratios may work with varying quality

Performance Tips

Optimization Strategies

LoRA Selection: Use only LoRAs needed for your task
Strength Tuning: Lower LoRA weights (0.5-0.8) often provide better balance
Memory Management: Unload unused LoRAs with pipe.unload_lora_weights()
Batch Processing: Process multiple prompts in sequence to amortize loading costs

Quality vs Performance Trade-offs

Configuration	VRAM Usage	Quality	Speed
Base model only	~10GB	Good	Fastest
Base + 1 LoRA	~12GB	Better	Fast
Base + 2-3 LoRAs	~14GB	Best	Moderate
Base + 4+ LoRAs	~16GB+	Excellent	Slower

VRAM Optimization

python

# Enable memory-efficient attention
pipe.enable_attention_slicing()

# Enable VAE tiling for higher resolutions
pipe.enable_vae_tiling()

# Use CPU offloading if VRAM is limited
pipe.enable_model_cpu_offload()

# Clear CUDA cache between generations
import torch
torch.cuda.empty_cache()

License

This repository contains LoRA adapters for the WAN video generation model. Usage is governed by the WAN model license.

License Type: Custom WAN License Commercial Use: Check base model license for restrictions Attribution: Required when redistributing or publishing generated content Modifications: Allowed for personal and research use

NSFW Content Notice: This repository may contain or be designed to generate NSFW (Not Safe For Work) content. Users must comply with applicable laws and platform policies.

Base Model License

LoRAs are derivative works of the base WAN model. Review the base model license:

https://huggingface.co/genmoai/wan-video-1.5b

Citation

If you use these LoRAs in your research or projects, please cite both the base WAN model and this LoRA collection:

bibtex

@software{wan25_fp16_loras,
  title = {WAN 2.5 FP16 LoRA Collection (NSFW)},
  author = {Community Contributors},
  year = {2025},
  url = {https://huggingface.co/[your-username]/wan25-fp16-loras-nsfw}
}

@software{wan_video,
  title = {WAN: World Animated Network},
  author = {Genmo AI},
  year = {2024},
  url = {https://huggingface.co/genmoai/wan-video-1.5b}
}

Resources

Official Documentation

WAN Model: https://huggingface.co/genmoai/wan-video-1.5b
Diffusers Library: https://huggingface.co/docs/diffusers/
LoRA Guide: https://huggingface.co/docs/diffusers/training/lora

Community Resources

WAN Discord: Community support and discussions
Example Notebooks: Coming soon
Video Gallery: Generated video examples

Related Repositories

WAN Base Models: E:\huggingface\wan\ (if available)
WAN VAE Models: For encoding/decoding video frames
FLUX Models: For image generation and video frame initialization

Changelog

Version 1.5 (2025-10-28)

Updated to v1.5 specification with image-to-video (I2V) focus
Clarified repository purpose as image-to-video LoRA collection
Updated all code examples to include image input for I2V pipeline
Enhanced LoRA descriptions for motion enhancement capabilities
Updated directory naming and file naming conventions for I2V
Verified YAML frontmatter compliance
Validated metadata accuracy for Hugging Face compatibility

Version 1.4 (2025-10-28)

Updated to v1.4 specification
Verified YAML frontmatter compliance
Confirmed comprehensive documentation structure
Validated metadata accuracy for Hugging Face compatibility

Version 1.3 (2025-10-14)

Fixed YAML frontmatter to remove base_model fields (LoRA adapters, not derived models)
Simplified metadata to core required fields only
Maintained comprehensive documentation structure

Version 1.2 (2025-10-14)

Updated README to v1.2 with enhanced metadata compliance
Clarified repository purpose and current status
Improved documentation structure

Version 1.1 (2025-10-13)

Enhanced YAML frontmatter with comprehensive tags
Added base_model relationship metadata

Version 1.0 (2025-10-13)

Initial repository structure created
README documentation prepared
Awaiting model file population

Repository Status: 🚧 Structure initialized, pending model files Last Updated: 2025-10-28 Maintained By: Local Hugging Face Repository Collection Total Size: ~15KB (directory structure only, no model files yet)

djibeto/wan25-fp16-i2v-loras-nsfw

WAN 2.5 FP16 Image-to-Video LoRAs (NSFW)

Model Description

What are LoRAs?

Key Features

Repository Contents

Directory Structure

Expected Model Files

Hardware Requirements

Minimum Requirements

Recommended Requirements

Performance Notes

Usage Examples

Basic Image-to-Video LoRA Loading with Diffusers

Stacking Multiple I2V LoRAs

Adjusting LoRA Strength

Saving Videos

Model Specifications

Format Details

LoRA Architecture

Supported Resolutions

Performance Tips

Optimization Strategies

Quality vs Performance Trade-offs

VRAM Optimization

License

Base Model License

Citation

Resources

Official Documentation

Community Resources

Related Repositories

Changelog

Version 1.5 (2025-10-28)

Version 1.4 (2025-10-28)

Version 1.3 (2025-10-14)

Version 1.2 (2025-10-14)

Version 1.1 (2025-10-13)

Version 1.0 (2025-10-13)

djibeto/
wan25-fp16-i2v-loras-nsfw