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wangkanai/
wan25-fp8-i2v-loras-nsfw

Name: wan25-fp8-i2v-loras-nsfw
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WAN 2.5 FP8 Image-to-Video LoRAs (NSFW Collection)

Status: Repository initialized - Empty structure (no model files present)

High-quality LoRA adapters for the WAN (World Action Network) 2.5 image-to-video generation model, optimized in FP8 precision for enhanced performance and memory efficiency. This collection focuses on specialized enhancements including camera control, lighting adjustment, motion dynamics, and quality improvements for image-to-video synthesis.

⚠️ Content Warning: This repository is intended to contain NSFW (Not Safe For Work) content. Models may generate adult or sensitive content. Use responsibly and in accordance with applicable laws and regulations.

Model Description

WAN LoRAs are lightweight adapter models that enhance the base WAN image-to-video generation model with specialized capabilities:

Camera Control LoRAs: Precise control over camera movements, angles, and perspectives in generated videos
Motion Dynamics LoRAs: Enhanced movement quality, temporal consistency, and animation smoothness
Lighting LoRAs: Advanced lighting effects, mood control, and atmospheric enhancements
Quality Enhancement LoRAs: Improved detail, consistency, and visual fidelity in video outputs
FP8 Optimization: 8-bit floating point precision for faster inference with minimal quality loss

These LoRAs can be combined and weighted to achieve specific artistic and technical goals in image-to-video generation workflows.

Repository Contents

Current Structure

wan25-fp8-i2v-loras-nsfw/
├── README.md              # This documentation file (12 KB)
├── .cache/                # Hugging Face cache directory
│   └── huggingface/
│       └── upload/        # Upload metadata
└── loras/                 # LoRA model storage (empty - awaiting models)
    └── wan/               # WAN-specific LoRAs subdirectory (empty)

Current Repository Size: ~12 KB (documentation only)

Expected Model Files

When populated, this repository will contain:

File Pattern	Purpose	Typical Size	Status
`camera_*.safetensors`	Camera control LoRAs	50-200 MB	Pending
`motion_*.safetensors`	Motion dynamics LoRAs	50-200 MB	Pending
`lighting_*.safetensors`	Lighting enhancement LoRAs	50-200 MB	Pending
`quality_*.safetensors`	Quality improvement LoRAs	50-200 MB	Pending
`style_*.safetensors`	Style-specific LoRAs	50-200 MB	Pending

Total Repository Size (when populated): ~500 MB - 2 GB (estimated)

Hardware Requirements

Minimum Requirements

VRAM: 16 GB (for base WAN model + LoRAs)
RAM: 32 GB system memory
Disk Space: 3 GB (2 GB for LoRAs + workspace)
GPU: NVIDIA RTX 3090 or equivalent (FP8 support recommended)

Recommended Requirements

VRAM: 24 GB (NVIDIA RTX 4090, A5000)
RAM: 64 GB system memory
Disk Space: 5 GB (with headroom for multiple models)
GPU: NVIDIA RTX 4090 or H100 (native FP8 acceleration)

Optimal Performance

VRAM: 48+ GB (NVIDIA A6000, H100)
RAM: 128 GB system memory
Storage: NVMe SSD for fast model loading
GPU: NVIDIA H100 or multiple RTX 4090s

Usage Examples

Loading LoRAs with Diffusers

python

import torch
from diffusers import DiffusionPipeline
from PIL import Image

# Load base WAN image-to-video model
pipe = DiffusionPipeline.from_pretrained(
    "genmo/mochi-1-preview",
    torch_dtype=torch.float16,
    variant="fp16"
)
pipe.to("cuda")

# Load input image
input_image = Image.open("E:/images/input_frame.png")

# Load LoRA adapters (absolute paths)
lora_path = "E:/huggingface/wan25-fp8-i2v-loras-nsfw/loras/wan"

# Example: Load camera control LoRA
pipe.load_lora_weights(
    f"{lora_path}/camera_cinematic_v1.safetensors",
    adapter_name="camera_control"
)

# Example: Load motion dynamics LoRA
pipe.load_lora_weights(
    f"{lora_path}/motion_smooth_v1.safetensors",
    adapter_name="motion"
)

# Set LoRA weights (0.0 to 1.0 scale)
pipe.set_adapters(
    ["camera_control", "motion"],
    adapter_weights=[0.8, 0.7]
)

# Generate video from image with LoRAs applied
prompt = "Cinematic camera slowly zooming in with smooth motion"
video = pipe(
    image=input_image,
    prompt=prompt,
    num_frames=48,
    height=480,
    width=848,
    num_inference_steps=50,
    guidance_scale=7.5
).frames[0]

# Save video
from diffusers.utils import export_to_video
export_to_video(video, "output.mp4", fps=24)

Advanced LoRA Composition for Image-to-Video

python

# Combine multiple LoRAs for complex image-to-video effects
lora_configs = [
    {"path": f"{lora_path}/camera_pan_v1.safetensors", "weight": 0.7, "name": "camera"},
    {"path": f"{lora_path}/motion_fluid_v1.safetensors", "weight": 0.8, "name": "motion"},
    {"path": f"{lora_path}/lighting_sunset_v1.safetensors", "weight": 0.6, "name": "lighting"},
    {"path": f"{lora_path}/quality_enhance_v1.safetensors", "weight": 0.5, "name": "quality"}
]

# Load all LoRAs
for config in lora_configs:
    pipe.load_lora_weights(config["path"], adapter_name=config["name"])

# Set adapter weights
adapter_names = [c["name"] for c in lora_configs]
adapter_weights = [c["weight"] for c in lora_configs]
pipe.set_adapters(adapter_names, adapter_weights=adapter_weights)

# Load input image
input_image = Image.open("E:/images/beach_scene.png")

# Generate with combined effects
video = pipe(
    image=input_image,
    prompt="Golden hour beach scene animating with smooth camera pan across the horizon",
    num_frames=72,
    height=480,
    width=848,
    guidance_scale=7.5
).frames[0]

export_to_video(video, "animated_beach.mp4", fps=24)

Memory-Efficient Loading (FP8)

python

# Use FP8 quantization for memory efficiency
from transformers import BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_threshold=6.0
)

# Load with FP8 optimization
pipe = DiffusionPipeline.from_pretrained(
    "genmo/mochi-1-preview",
    quantization_config=quantization_config,
    torch_dtype=torch.float16,
    low_cpu_mem_usage=True
)

# LoRAs automatically work with FP8 base model
pipe.load_lora_weights(f"{lora_path}/camera_control_v1.safetensors")

# Generate image-to-video with reduced memory footprint
input_image = Image.open("E:/images/portrait.png")
video = pipe(image=input_image, prompt="Cinematic portrait with subtle camera movement")

Model Specifications

Format

File Format: SafeTensors (secure, efficient)
Precision: FP8 (8-bit floating point)
Architecture: LoRA adapters for WAN transformer blocks
Compatibility: WAN 2.5, Mochi-1-preview image-to-video models

LoRA Configuration

Rank (r): Typically 16-64 (balance between quality and size)
Alpha: 16-32 (controls adaptation strength)
Target Modules: Attention layers, feed-forward networks, temporal blocks
Trainable Parameters: 5-50M per LoRA (vs 3B+ for full model)

Performance Characteristics

Memory Savings: 70-85% vs FP16 (8-bit vs 16-bit)
Speed Improvement: 1.5-2.5x faster inference on FP8-capable GPUs
Quality: Minimal degradation (<5% perceptual difference from FP16)
Composability: Multiple LoRAs can be stacked with cumulative effects
Temporal Consistency: Specialized training for smooth video generation

Performance Tips and Optimization

Memory Optimization

Use FP8 Base Model: Combine FP8 LoRAs with FP8 base model for maximum efficiency
Enable xFormers: pip install xformers for memory-efficient attention
Gradient Checkpointing: Trade compute for memory when needed
Sequential Loading: Load and unload LoRAs as needed rather than keeping all in memory
Frame Budget: Start with 24-48 frames, increase as VRAM allows

Speed Optimization

NVIDIA FP8 Acceleration: Use RTX 40-series or H100 for native FP8 Tensor Cores
Compile Models: Use torch.compile() for 20-30% speedup (PyTorch 2.0+)
Reduce Steps: Start with 30 steps, increase if quality insufficient
Batch Processing: Process multiple images in batch when possible
Frame Interpolation: Generate fewer frames and interpolate for longer videos

Quality Optimization

LoRA Weight Tuning: Start at 0.7-0.8, adjust based on desired effect strength
Avoid Over-composition: Use 2-3 LoRAs max to prevent conflicting effects
Balanced Guidance: Use guidance_scale 6.5-8.5 for best quality/creativity balance
Resolution Matching: Use resolution multiples trained on (typically 480p, 720p)
Input Image Quality: Higher quality input images produce better video results

Recommended LoRA Combinations

Cinematic Animation:

python

adapters = ["camera_cinematic", "motion_smooth", "lighting_dramatic", "quality_enhance"]
weights = [0.8, 0.7, 0.7, 0.5]

Natural Motion:

python

adapters = ["camera_handheld", "motion_natural", "lighting_natural", "quality_enhance"]
weights = [0.6, 0.8, 0.6, 0.6]

Artistic Style:

python

adapters = ["camera_dynamic", "motion_fluid", "lighting_creative", "style_artistic"]
weights = [0.9, 0.8, 0.9, 0.7]

License

License Type: Custom WAN License (other)

This repository uses a custom license for WAN model derivatives. Key terms:

✅ Permitted: Research use, personal projects, non-commercial applications
⚠️ Restricted: Commercial use may require separate licensing
❌ Prohibited: Redistribution without attribution, malicious use

NSFW Content Warning:

Models in this repository may generate adult or sensitive content
Users are responsible for compliance with local laws and regulations
Not suitable for minors or workplace environments
Use appropriate content filtering in production applications

Please review the full license terms before use. For commercial licensing inquiries, contact the model authors.

Citation

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

bibtex

@misc{wan25-fp8-i2v-loras-nsfw,
  title={WAN 2.5 FP8 LoRA Adapters for Enhanced Image-to-Video Generation},
  author={Community Contributors},
  year={2025},
  howpublished={\url{https://huggingface.co/wan25-fp8-i2v-loras-nsfw}},
  note={FP8-optimized LoRA adapters for camera control, motion dynamics, lighting, and quality enhancement in image-to-video synthesis}
}

Related Resources

Official Documentation

WAN Model: Genmo Mochi-1 Documentation
Diffusers Library: Hugging Face Diffusers
LoRA Training: PEFT Library
Image-to-Video Guide: Diffusers I2V Tutorial

Community Resources

WAN Discord: Community discussions and support
Model Card: Detailed specifications and training details
Example Notebooks: Colab notebooks with image-to-video usage examples

Related Repositories

wan25-base-fp8/: Base WAN 2.5 model in FP8 precision
wan25-vae/: WAN video autoencoder models
flux-dev-fp8/: FLUX image generation models (compatible workflows)

Changelog

v1.5 (2025-10-28)

✅ YAML frontmatter tags corrected: Removed incorrect "image-generation" tag
✅ Added accurate tags: "image-to-video", "video-generation", "lora"
✅ Version header updated to v1.5
✅ Tags now accurately reflect image-to-video LoRA functionality
✅ Comprehensive tag coverage for Hugging Face discoverability

v1.4 (2025-10-28)

✅ YAML frontmatter validation: Simplified tags array to core categories
✅ Removed base_model/base_model_relation (not applicable for base model collections)
✅ Tag optimization: Retained wan, text-to-video, image-generation as primary tags
✅ Version header updated to v1.4
✅ Ensured proper YAML format with exact three-dash markers

v1.3 (2025-10-14)

✅ Updated title to clarify Image-to-Video (I2V) focus
✅ Enhanced tags: Added "video-generation", "lora", "image-to-video"
✅ Removed "image-generation" tag (incorrect for video models)
✅ Updated model description to emphasize image-to-video capabilities
✅ Enhanced usage examples with image input workflows
✅ Added motion dynamics LoRAs to expected files
✅ Updated LoRA combinations with motion-specific recommendations
✅ Added temporal consistency to performance characteristics
✅ Updated citation to reflect image-to-video focus
✅ Version header updated to v1.3

v1.2 (2025-10-14)

✅ YAML frontmatter compliance: Removed base_model/base_model_relation fields
✅ Simplified tags to core categories
✅ Updated repository status to reflect empty structure
✅ Version header updated to v1.2

v1.1 (2025-10-14)

✅ Enhanced README with accurate directory structure
✅ Updated YAML frontmatter with proper tag ordering
✅ Documented current repository size
✅ Added detailed file status tracking

v1.0 (2025-10-13)

📝 Initial repository structure created
📋 README documentation with comprehensive usage guide
🚧 Awaiting model file population
📦 Directory structure: loras/wan/ prepared

Future Updates

🔜 Camera control LoRAs addition (pan, zoom, tilt, dolly, orbit)
🔜 Motion dynamics LoRAs (smooth, fluid, natural, dynamic)
🔜 Lighting enhancement LoRAs (dramatic, natural, creative, sunset, cinematic)
🔜 Quality improvement LoRAs (detail, consistency, temporal stability)
🔜 Style-specific LoRAs (cinematic, realistic, artistic, anime)
🔜 Example videos and image-to-video comparison benchmarks
🔜 Training scripts and fine-tuning guides
🔜 Performance benchmarks across different hardware configurations

Contact and Support

Issues and Questions

Repository Issues: Report technical issues or request features
Model Questions: Consult WAN model documentation and Diffusers I2V guides
Usage Support: Check examples and community resources first

Contributing

Contributions of additional LoRAs, improved documentation, or usage examples are welcome. Please ensure all contributions comply with the license terms and content guidelines.

Last Updated: 2025-10-28 README Version: v1.5 Repository Status: Empty structure (no model files) Current Size: 18 KB (documentation only) Maintained By: Community Contributors

wangkanai/wan25-fp8-i2v-loras-nsfw

WAN 2.5 FP8 Image-to-Video LoRAs (NSFW Collection)

Model Description

Repository Contents

Current Structure

Expected Model Files

Hardware Requirements

Minimum Requirements

Recommended Requirements

Optimal Performance

Usage Examples

Loading LoRAs with Diffusers

Advanced LoRA Composition for Image-to-Video

Memory-Efficient Loading (FP8)

Model Specifications

Format

LoRA Configuration

Performance Characteristics

Performance Tips and Optimization

Memory Optimization

Speed Optimization

Quality Optimization

Recommended LoRA Combinations

License

Citation

Related Resources

Official Documentation

Community Resources

Related Repositories

Changelog

v1.5 (2025-10-28)

v1.4 (2025-10-28)

v1.3 (2025-10-14)

v1.2 (2025-10-14)

v1.1 (2025-10-14)

v1.0 (2025-10-13)

Future Updates

Contact and Support

Issues and Questions

Contributing

wangkanai/
wan25-fp8-i2v-loras-nsfw