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wangkanai/
wan22-fp8-i2v-loras-nsfw

Name: wan22-fp8-i2v-loras-nsfw
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⚠️ WAN 2.2 Action LoRA - Image-to-Video (Adult Content)

CONTENT WARNING: This repository contains a LoRA adapter trained on adult/NSFW content for video generation. This model is intended for adult users (18+) only and should be used responsibly in accordance with applicable laws and regulations.

Specialized LoRA (Low-Rank Adaptation) adapter for the WAN 2.2 14B image-to-video generation model, focused on specific action sequences with low-noise schedule for consistent results.

📦 Model Information

Base Model: WAN 2.2 I2V 14B (Image-to-Video)
Type: Action-Specific LoRA Adapter
Version: WAN 2.2 (enhanced generation quality vs WAN 2.1)
Precision: BF16 (Brain Floating Point 16)
Content Type: Adult/NSFW
Noise Schedule: Low-noise (consistent generation)
Camera Angle: POV (Point-of-View)
Repository Size: 293 MB

⚠️ Usage Restrictions

Age Restriction: 18+ only
Legal Compliance: Users must comply with local laws regarding adult content
Ethical Use: Not for non-consensual content generation or deepfakes
Platform Guidelines: Respect platform policies where content is shared
Content Moderation: Implement appropriate content warnings and filters

📁 Repository Contents

wan22-fp8-i2v-loras-nsfw/
└── loras/
    └── wan/
        └── wan22-action-missionary-pov-i2v-low.safetensors (293 MB)

Total Repository Size: 293 MB (single specialized I2V LoRA adapter)

🎯 LoRA Specifications

Generation Mode

I2V (Image-to-Video):

Animate existing images into video sequences
Input image guides the generation
More controlled outputs based on starting frame
Preserves character and scene consistency from input

Noise Schedule

Low-Noise Model:

More consistent and faithful reproduction
Lower variance, more predictable results
Better for realistic content
Ideal for production workflows requiring reliability

Action Category

Missionary POV Action:

Specialized motion patterns for POV perspective
First-person camera angle
Smooth, natural motion sequences
Trained for realistic movement and consistency

Technical Details

File Size: 293 MB
Rank: 16 (standard training capacity)
Format: SafeTensors (secure, efficient)
Precision: BF16 for memory efficiency

🚀 Usage Example

Image-to-Video with Action LoRA

python

from diffusers import DiffusionPipeline, AutoencoderKL
from PIL import Image
import torch

# Load base WAN 2.2 I2V model
pipe = DiffusionPipeline.from_pretrained(
    "E:/huggingface/wan22-i2v-14b-fp8",  # Adjust to your base model path
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Load WAN 2.2 VAE
pipe.vae = AutoencoderKL.from_single_file(
    "E:/huggingface/wan22-vae/wan22-vae.safetensors"
)

# Load missionary POV I2V action LoRA (low-noise for consistent generation)
pipe.load_lora_weights(
    "E:/huggingface/wan22-fp8-i2v-loras-nsfw/loras/wan/wan22-action-missionary-pov-i2v-low.safetensors"
)

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

# Generate video from image
prompt = "POV perspective, smooth movement, natural motion, cinematic quality, realistic lighting"
video = pipe(
    prompt=prompt,
    image=input_image,
    num_inference_steps=50,
    guidance_scale=7.5,
    num_frames=24
).frames

# Save video
from diffusers.utils import export_to_video
export_to_video(video, "output_missionary_pov_i2v.mp4", fps=8)

Advanced Configuration

python

# Optimal settings for low-noise I2V generation
video = pipe(
    prompt="POV perspective, smooth natural movement, realistic, high quality",
    image=input_image,
    num_inference_steps=50,      # 40-60 steps optimal
    guidance_scale=7.5,           # 7.0-8.5 for controlled motion
    num_frames=24,                # 16-32 frames supported
    height=720,                   # 720p optimized
    width=1280
).frames

# Memory optimization for lower VRAM
pipe.enable_model_cpu_offload()
video = pipe(prompt, image=input_image, num_frames=16).frames

⚙️ Technical Specifications

LoRA Architecture

Precision: BF16 for memory efficiency and numerical stability
Base Compatibility: Designed for WAN 2.2 I2V 14B architecture
Training Method: Action-specific motion patterns with low-noise schedule
Rank: 16 (293 MB standard capacity)
Format: SafeTensors (secure, efficient loading)

WAN 2.2 Improvements vs WAN 2.1

Enhanced temporal consistency and motion quality
Improved prompt adherence and control
Better handling of complex scenes
More stable generation with low-noise schedules
Superior character consistency in I2V mode

Low-Noise Schedule Characteristics

Advantages:

Realistic, photorealistic content generation
Consistent, predictable results across generations
Production workflows requiring reliability
Excellent image-to-video animation fidelity
Preserves input image characteristics

Best Use Cases:

Animating existing artwork or photos
Production content requiring consistency
Realistic human motion sequences
POV perspective animations
Professional adult content creation

💻 Hardware Requirements

Minimum Requirements

GPU: NVIDIA RTX 3060 (12GB VRAM) or equivalent
RAM: 16GB system RAM
Storage: 293 MB for LoRA + 14GB for WAN 2.2 I2V FP8 base model + 1.4GB for VAE
Precision: BF16 support (Ampere architecture or newer)

Recommended (High-Quality I2V)

GPU: NVIDIA RTX 3090 (24GB VRAM) or RTX 4070 Ti (16GB VRAM)
RAM: 32GB system RAM
Storage: 20GB for complete WAN 2.2 I2V ecosystem
Base Model: WAN 2.2 I2V FP8 (14GB) or FP16 (27GB)

High-End (Maximum Quality)

GPU: NVIDIA RTX 4090 (24GB VRAM) or A100 (40GB VRAM)
RAM: 64GB system RAM
Resolution: Optimized for 720p and 1080p high-quality output
Base Model: WAN 2.2 I2V FP16 (27GB) for best quality

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+

bash

# 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 (24 frames, 720p)

GPU Model	Steps	Time (seconds)	VRAM Usage
RTX 4090 (24GB)	50	~25s	~17GB
RTX 3090 (24GB)	50	~35s	~18GB
RTX 4070 Ti (16GB)	50	~40s	~15GB (with offload)
RTX 3060 (12GB)	50	~60s	~11GB (with offload)

Note: Actual performance varies based on prompt complexity, base model precision (FP8/FP16), input image resolution, and system configuration.

🎨 Prompting Tips

Effective Prompts for I2V Action LoRA

POV Perspective:

"POV perspective", "first-person view", "subjective camera"
"POV angle", "first-person perspective", "viewer's perspective"

Motion Quality:

"smooth movement", "fluid motion", "natural transitions"
"realistic motion", "natural movement", "smooth animation"

Quality Modifiers:

"high quality", "detailed", "professional", "cinematic"
"realistic", "photorealistic", "cinematic style"
"720p quality", "HD quality", "high definition"

Lighting and Atmosphere:

"cinematic lighting", "natural lighting", "soft lighting"
"realistic lighting", "professional cinematography"
"warm tones", "natural ambiance"

Example Prompts

"POV perspective, smooth natural movement, cinematic lighting, high quality, realistic, 720p"

"First-person view, fluid motion, natural lighting, detailed, photorealistic, HD quality"

"POV angle, realistic movement, soft lighting, cinematic quality, professional"

"Subjective camera, smooth animation, natural lighting, high detail, realistic style"

Optimization Tips for Low-Noise I2V

For Best Consistency:

Focus on technical quality keywords: "realistic", "photorealistic", "detailed"
Specify lighting precisely: "natural lighting", "soft lighting", "realistic lighting"
Emphasize smoothness: "smooth", "consistent", "stable", "natural"
Use "POV perspective" to activate trained camera angle

For Best Motion:

Combine motion quality with realism: "smooth natural movement"
Specify frame transitions: "fluid motion", "natural transitions"
Add cinematography terms: "professional cinematography", "cinematic quality"

🔧 Troubleshooting

Out of Memory (OOM) Errors

python

# Solution 1: Enable CPU offloading
pipe.enable_model_cpu_offload()

# Solution 2: Use FP8 base models instead of FP16
# FP8 I2V models are 14GB vs 27GB FP16

# Solution 3: Reduce frames
video = pipe(prompt, image=input_image, num_frames=16)  # Instead of 24

# Solution 4: Lower resolution for testing
video = pipe(prompt, image=input_image, height=480, width=854)

# Solution 5: Sequential CPU offload for extreme constraints
pipe.enable_sequential_cpu_offload()

Poor Motion Quality

Adjust inference steps: 40-60 steps optimal for WAN 2.2 action LoRAs
Tune CFG scale: 7.0-8.5 range works best for action sequences
Base model quality: FP16 base models produce better results than FP8
Input image quality: Higher quality input images produce better animations
Frame count: 24-32 frames provide smoother motion than 16 frames

Inconsistent Character Appearance

Low-noise advantage: This LoRA uses low-noise schedule for maximum consistency
Input image quality: Ensure input image is clear and high-resolution
Prompt alignment: Match prompts to trained POV perspective
Guidance scale: Higher guidance (7.5-8.5) for more controlled generation
Base model: FP16 provides better consistency than FP8/quantized models

Action Not Matching Expectations

LoRA specialization: This LoRA is trained for missionary POV action specifically
Prompt specificity: Use "POV perspective" and "smooth movement" keywords
Input composition: Ensure input image composition supports POV perspective
Frame count: 24+ frames recommended for full action sequences
Inference steps: Increase to 50-60 steps for better motion coherence

📝 Model Card

Property	Value
Model Type	LoRA Adapter for Video Diffusion (I2V)
Architecture	Low-Rank Adaptation (LoRA)
Training Method	Action-Specific Motion Patterns (Missionary POV)
Precision	BF16
Content Type	Adult/NSFW (18+)
Base Model	WAN 2.2 I2V 14B
Generation Mode	I2V (image-to-video)
Noise Variant	Low-noise (consistent generation)
Camera Angle	POV (Point-of-View)
Resolution Support	480p, 720p, 1080p optimized
File Size	293 MB
Format	SafeTensors
License	See WAN license terms
Intended Use	Adult content I2V generation with POV action
Age Restriction	18+ only
Languages	Prompt: English (primary)

📄 License

This LoRA adapter is subject to WAN model license terms. Additional restrictions:

Age Verification: Must implement age verification for end users
Legal Compliance: Users responsible for compliance with local laws
Ethical Use: Prohibited uses include non-consensual content, deepfakes, exploitation
Distribution: Distribute only with appropriate content warnings
Commercial Use: Check WAN license for commercial restrictions

⚖️ Ethical Guidelines

Prohibited Uses

❌ Non-consensual content generation
❌ Deepfakes or identity theft
❌ Content featuring minors
❌ Exploitation or harassment materials
❌ Violation of platform terms of service

Recommended Practices

✅ Implement age verification systems
✅ Use content warnings and NSFW tags
✅ Respect intellectual property and likeness rights
✅ Implement content moderation
✅ Provide opt-out mechanisms
✅ Label AI-generated content clearly

🙏 Acknowledgments

WAN Development Team for the exceptional WAN 2.2 I2V 14B model
Community contributors for responsible testing and feedback
Hugging Face for hosting infrastructure with content policies

📚 Related Resources

WAN 2.2 I2V Base Model: wan22-fp8, wan22-fp16 (I2V base models)
WAN 2.2 VAE: Required for video decoding (1.4GB)
WAN 2.2 Camera LoRAs: wan22-camera-* (SFW camera control v2 LoRAs)
WAN 2.1 NSFW LoRAs: wan21-loras-nsfw (older generation action LoRAs)
Diffusers Documentation: https://huggingface.co/docs/diffusers
WAN Official Documentation: Check Hugging Face for WAN 2.2 official pages

📧 Support

For questions or issues:

Technical issues: Open issue in this repository
Ethical concerns: Report to platform moderators
Base model questions: Refer to WAN official documentation

🔄 Version History

Current Version (v1.4)

Accurate documentation for single I2V LoRA model
Updated file structure and size information
Enhanced usage examples with absolute paths
Improved troubleshooting section
Comprehensive hardware requirements

Summary

This repository contains a specialized action LoRA adapter for WAN 2.2 I2V 14B model:

Size: 293 MB (single I2V action adapter)
Content Type: Adult/NSFW (18+ only)
Generation Mode: I2V (Image-to-Video)
Noise Schedule: Low-noise (consistent, realistic generation)
Camera Angle: POV (Point-of-View)
Action Type: Missionary POV
Resolution: 480p, 720p, 1080p optimized
Use Case: Consistent POV action video generation from input images
Requirements: WAN 2.2 I2V 14B base model + WAN 2.2 VAE

Content Warning: This model is trained on adult content and is intended for responsible adult use only. Users must comply with applicable laws, implement appropriate safeguards, and use ethically.

Technical Note: This is a specialized LoRA adapter that modifies the base WAN 2.2 I2V model to generate specific POV action sequences with low-noise schedule for consistent results. It requires the WAN 2.2 I2V base model and VAE to function.

I2V Advantage: Image-to-video generation provides superior character consistency and composition control compared to text-to-video, making it ideal for production workflows requiring reliable outputs.

Last Updated: October 2025 README Version: v1.4 Repository Size: 293 MB (single I2V action LoRA) Content Rating: Adult/NSFW (18+) Primary Use Case: POV action video generation from images with WAN 2.2 I2V 14B model

wangkanai/wan22-fp8-i2v-loras-nsfw

⚠️ WAN 2.2 Action LoRA - Image-to-Video (Adult Content)

📦 Model Information

⚠️ Usage Restrictions

📁 Repository Contents

🎯 LoRA Specifications

Generation Mode

Noise Schedule

Action Category

Technical Details

🚀 Usage Example

Image-to-Video with Action LoRA

Advanced Configuration

⚙️ Technical Specifications

LoRA Architecture

WAN 2.2 Improvements vs WAN 2.1

Low-Noise Schedule Characteristics

💻 Hardware Requirements

Minimum Requirements

Recommended (High-Quality I2V)

High-End (Maximum Quality)

Software Requirements

📊 Performance Benchmarks

I2V Generation Speed (24 frames, 720p)

🎨 Prompting Tips

Effective Prompts for I2V Action LoRA

Example Prompts

Optimization Tips for Low-Noise I2V

🔧 Troubleshooting

Out of Memory (OOM) Errors

Poor Motion Quality

Inconsistent Character Appearance

Action Not Matching Expectations

📝 Model Card

📄 License

⚖️ Ethical Guidelines

Prohibited Uses

Recommended Practices

🙏 Acknowledgments

📚 Related Resources

📧 Support

🔄 Version History

Current Version (v1.4)

Summary

wangkanai/
wan22-fp8-i2v-loras-nsfw