wan22-fp8-i2v-loras-nsfw

<!-- README Version: v1.4 -->

⚠️ 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

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

# 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+

# 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)

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

# 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

📄 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.

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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