wan22-fp8-t2v-loras-nsfw

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

⚠️ WAN 2.2 Action LoRAs (Adult Content)

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

Specialized LoRA (Low-Rank Adaptation) adapters for the WAN 2.2 14B text-to-video and image-to-video generation models, focused on specific action sequences with camera angle and noise schedule variations.

📦 Model Information

• Base Models: WAN 2.2 T2V 14B, WAN 2.2 I2V 14B
• Type: Action-Specific LoRA Adapters
• Version: WAN 2.2 (enhanced generation quality vs WAN 2.1)
• Precision: BF16 (Brain Floating Point 16)
• Content Type: Adult/NSFW
• Total Models: 7 specialized adapters
• Repository Size: ~2.6GB

⚠️ 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-t2v-loras-nsfw/
└── loras/
    └── wan/
        ├── wan22-action-doggystyle-t2v-14b-high.safetensors (586MB)
        ├── wan22-action-doggystyle-t2v-14b-low.safetensors (586MB)
        ├── wan22-action-missionary-pov-t2v-high.safetensors (293MB)
        ├── wan22-action-missionary-pov-t2v-low.safetensors (293MB)
        ├── wan22-action-orgasm-t2v-14b-high.safetensors (293MB)
        ├── wan22-action-orgasm-t2v-14b-low.safetensors (293MB)
        └── wan22-style-typeofnipples-t2v.safetensors (293MB)

Total Repository Size: 2.6GB across 7 specialized LoRA adapters

🎯 LoRA Categories

Generation Mode Variants

T2V (Text-to-Video):

• Generate videos directly from text prompts
• No input image required
• More creative freedom in scene composition
• 7 T2V LoRAs available (6 action + 1 style)

Noise Schedule Variants

High-Noise Models:

• More creative and diverse outputs
• Higher variance between generations
• Better for stylized or artistic content
• File naming: -high or -14b-high

Low-Noise Models:

• More consistent and faithful reproduction
• Lower variance, more predictable results
• Better for realistic content
• File naming: -low or -14b-low

Action Categories

Action-Specific LoRAs:

• Specialized motion patterns and sequences
• Trained on specific actions and camera angles
• POV (point-of-view) and standard angle variants
• 3 different action types + 1 style LoRA

File Size Variants

• 293MB: Standard action LoRAs with rank-16 training
• 586MB: Enhanced action LoRAs with higher rank training (more capacity and detail)

🚀 Usage Examples

Text-to-Video (T2V) with Action LoRA

from diffusers import DiffusionPipeline, AutoencoderKL
import torch

# Load base WAN 2.2 T2V model
pipe = DiffusionPipeline.from_pretrained(
    "path/to/wan22-t2v-14b",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Load WAN 2.2 VAE
pipe.vae = AutoencoderKL.from_single_file(
    "path/to/wan22-vae.safetensors"
)

# Load action-specific T2V LoRA (high-noise for creative generation)
pipe.load_lora_weights(
    "E:/huggingface/wan22-fp8-loras-nsfw/loras/wan/wan22-action-missionary-pov-t2v-high.safetensors"
)

# Generate video from text
prompt = "POV perspective, smooth camera movement, cinematic lighting, high quality"
video = pipe(
    prompt=prompt,
    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_t2v_action.mp4", fps=8)

Image-to-Video (I2V) 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(
    "path/to/wan22-i2v-14b",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Load WAN 2.2 VAE
pipe.vae = AutoencoderKL.from_single_file(
    "path/to/wan22-vae.safetensors"
)

# Note: This repository contains T2V LoRAs only
# For I2V generation, you would load a T2V LoRA with the I2V base model
# Example with T2V LoRA on I2V model:
pipe.load_lora_weights(
    "E:/huggingface/wan22-fp8-t2v-loras-nsfw/loras/wan/wan22-action-missionary-pov-t2v-low.safetensors"
)

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

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

# Save video
export_to_video(video, "output_i2v_action.mp4", fps=8)

Switching Between Noise Schedules

# Define base path
LORA_PATH = "E:/huggingface/wan22-fp8-t2v-loras-nsfw/loras/wan"

# Available T2V action LoRAs with noise variants
actions = {
    "doggystyle_high": "wan22-action-doggystyle-t2v-14b-high.safetensors",
    "doggystyle_low": "wan22-action-doggystyle-t2v-14b-low.safetensors",
    "missionary_pov_high": "wan22-action-missionary-pov-t2v-high.safetensors",
    "missionary_pov_low": "wan22-action-missionary-pov-t2v-low.safetensors",
    "orgasm_high": "wan22-action-orgasm-t2v-14b-high.safetensors",
    "orgasm_low": "wan22-action-orgasm-t2v-14b-low.safetensors",
}

# Load high-noise variant for creative generation
pipe.load_lora_weights(f"{LORA_PATH}/{actions['missionary_pov_high']}")

# Or load low-noise variant for consistent results
pipe.load_lora_weights(f"{LORA_PATH}/{actions['missionary_pov_low']}")

Using Style LoRA

# Load style-specific T2V LoRA
pipe.load_lora_weights(
    "E:/huggingface/wan22-fp8-t2v-loras-nsfw/loras/wan/wan22-style-typeofnipples-t2v.safetensors"
)

# Generate with style modifications
video = pipe(
    prompt="cinematic lighting, high quality, detailed",
    num_inference_steps=50,
    guidance_scale=7.5,
    num_frames=24
).frames

⚙️ Technical Details

LoRA Architecture

• Precision: BF16 for memory efficiency and numerical stability
• Base Compatibility: Designed for WAN 2.2 T2V/I2V 14B architecture
• Training Method: Action-specific motion patterns with noise schedule variants
• Rank: 16 (standard 293MB) or higher rank (586MB enhanced models)

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 across different noise schedules
• Compatible with advanced camera control LoRAs (v2)

Noise Schedule Selection

When to Use High-Noise:

• Creative or artistic interpretations
• More variety in outputs
• Stylized content generation
• Experimentation and exploration

When to Use Low-Noise:

• Realistic, photorealistic content
• Consistent, predictable results
• Production workflows requiring reliability
• Image-to-video animation fidelity

T2V vs I2V Selection

T2V Advantages:

• No input image required
• More creative freedom in composition
• Generate entirely novel scenes
• Better for text-driven generation

I2V Advantages:

• Control starting composition with input image
• More consistent character/scene appearance
• Animate existing artwork or photos
• Better for specific visual requirements

💻 Hardware Requirements

Minimum Requirements

• GPU: NVIDIA RTX 3060 (12GB VRAM) or equivalent
• RAM: 16GB system RAM
• Storage: 2.6GB for LoRAs + base model space (14GB FP8 or 27GB FP16)
• Precision: BF16 support (Ampere architecture or newer)

Recommended (T2V High-Quality)

• GPU: NVIDIA RTX 3090 (24GB VRAM) or RTX 4070 Ti (16GB)
• RAM: 32GB system RAM
• Storage: 50GB+ for full WAN 2.2 ecosystem
• Base Model: WAN 2.2 FP8 (14GB) or FP16 (27GB)

High-End (Maximum Quality)

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

T2V Generation Speed (RTX 4090, 24 frames, 720p)

Note: Actual performance varies based on prompt complexity, GPU model, base model precision (FP8/FP16), and system configuration. T2V LoRAs can also be used with I2V base models for image-to-video generation.

🎨 Prompting Tips

Effective Prompts for Action LoRAs

Camera and Movement:

• POV variants: "POV perspective", "first-person view", "subjective camera"
• Standard angles: "frontal view", "side angle", "dynamic perspective"
• Motion quality: "smooth movement", "fluid motion", "natural transitions"

Quality Modifiers:

• "high quality", "detailed", "professional", "cinematic"
• Resolution: "720p quality", "HD quality", "high definition"
• Style: "realistic", "photorealistic", "cinematic style"

Lighting and Atmosphere:

• "cinematic lighting", "soft lighting", "dramatic shadows"
• "warm tones", "natural lighting", "professional cinematography"

Example Prompts:

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

"Frontal view, dynamic motion, professional lighting, detailed, photorealistic, HD quality"

"First-person POV, fluid movement, soft lighting, cinematic quality, high detail"

Prompt Optimization by Noise Schedule

High-Noise Prompts (Creative):

• Include style keywords: "artistic", "stylized", "creative"
• Emphasize mood and atmosphere
• Allow for interpretation: "dramatic", "emotional"

Low-Noise Prompts (Realistic):

• Focus on technical quality: "realistic", "photorealistic", "detailed"
• Specific lighting: "natural lighting", "studio lighting"
• Emphasize consistency: "smooth", "consistent", "stable"

🔧 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 models are 14GB vs 27GB FP16

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

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

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

Poor Motion Quality

• Try different noise schedules: High vs low noise can significantly affect results
• Use enhanced LoRAs: 586MB models have more capacity than 293MB
• Adjust 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/GGUF

Inconsistent Actions

• Match LoRA to mode: Use T2V LoRAs with T2V models, I2V with I2V
• Frame count: Some actions work better with 24+ frames
• Prompt alignment: Ensure prompts match LoRA's trained patterns
• Noise schedule: Low-noise models provide more consistency
• Guidance scale: Higher guidance (7.5-8.5) for more controlled actions

T2V LoRA Usage with Different Base Models

• With T2V base model: Standard text-to-video generation from prompts
• With I2V base model: T2V LoRAs can be used with I2V models for image-to-video generation
• Camera control: POV LoRAs work better with consistent camera angles
• Style consistency: Low-noise models provide most consistent appearance

📝 Model Card

📄 License

These LoRA adapters are 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 T2V/I2V 14B models
• Community contributors for responsible testing and feedback
• Hugging Face for hosting infrastructure with content policies

📚 Related Resources

• WAN 2.2 Base Models: wan22-fp16, wan22-fp8 (T2V and 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

📧 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

Comparison with WAN 2.1 NSFW LoRAs

WAN 2.2 Advantages

• Better temporal consistency and motion quality
• Improved prompt adherence
• T2V and I2V variants available
• High/low noise schedule options
• Enhanced camera control compatibility
• Better integration with WAN 2.2 ecosystem

WAN 2.1 NSFW LoRAs

• See wan21-loras-nsfw for WAN 2.1-specific action LoRAs
• 480p/720p I2V focus
• Single noise schedule per model
• Compatible with WAN 2.1 camera control (v1)

Summary

This repository contains 7 specialized T2V LoRA adapters for WAN 2.2 14B models:

• Total Size: ~2.6GB (7 T2V action/style-specific adapters)
• Content Type: Adult/NSFW (18+ only)
• Generation Modes: T2V LoRAs (6 action + 1 style), compatible with both T2V and I2V base models
• Noise Variants: High-noise (creative) and low-noise (consistent)
• File Sizes: 293MB (standard) and 586MB (enhanced capacity)
• Resolution: 480p and 720p optimized
• Use Cases: Specialized action sequences with noise schedule control
• Requirements: WAN 2.2 T2V 14B or I2V 14B base model + VAE

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

Technical Note: These are specialized T2V LoRA adapters that modify the base WAN 2.2 T2V/I2V models to generate specific action sequences with controllable noise schedules. They require the WAN 2.2 base models and VAE to function.

WAN 2.2 Features: Enhanced quality and consistency compared to WAN 2.1, with support for both text-to-video and image-to-video generation modes, plus flexible noise schedule control for creative or realistic outputs.

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Last Updated: October 2025 Repository Version: 1.3 Total Size: ~2.6GB (7 T2V LoRAs: 6 action with high/low noise variants + 1 style) Content Rating: Adult/NSFW (18+) Primary Use Case: Specialized action and style video generation for adult content with WAN 2.2 models