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wangkanai/
wan21-fp8-loras-nsfw

Name: wan21-fp8-loras-nsfw
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⚠️ WAN 2.1 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 2.1 14B image-to-video generation model, focused on specific action sequences and camera angles.

📦 Model Information

Base Model: WAN 2.1 I2V 14B
Type: Action-Specific LoRA Adapters
Version: v1 and v2 variants
Precision: BF16 (Brain Floating Point 16)
Content Type: Adult/NSFW
Total Models: 8 specialized adapters
Repository Size: 3.2GB

⚠️ 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

wan21-fp8-loras-nsfw/
└── loras/
    └── wan/
        ├── wan21-action-bounce-v1.safetensors (343MB)
        ├── wan21-action-doggypov-v2.safetensors (514MB)
        ├── wan21-action-doggystylefromfront-t2v.safetensors (293MB)
        ├── wan21-action-front-doggy-plow-v2.safetensors (514MB)
        ├── wan21-action-missionary-pov-i2v-v2.safetensors (343MB)
        ├── wan21-action-missionary-pov-v2.safetensors (293MB)
        ├── wan21-action-orgasm-i2v-720p.safetensors (293MB)
        └── wan21-action-orgasm-v2-t2v-14b.safetensors (585MB)

Total Repository Size: 3.2GB (8 action-specific LoRA adapters)

🎯 LoRA Categories

Camera Angle Adapters

POV (Point of View): First-person perspective variations
Front: Frontal camera angles with specific framing
Dynamic: Movement-following camera tracking

Action-Specific Adapters

v1: Initial training version with standard settings
v2: Improved training with enhanced quality and motion consistency

Resolution Variants

T2V: Text-to-video optimized (standard resolution)
I2V: Image-to-video optimized (480p/720p)
720p: Higher resolution variants for improved detail

🚀 Usage Examples

Basic I2V Usage with Action LoRA

python

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

# Load base WAN 2.1 I2V model
pipe = DiffusionPipeline.from_pretrained(
    "path/to/wan21-i2v-14b",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Load WAN 2.1 VAE
pipe.vae = AutoencoderKL.from_single_file(
    "path/to/wan21-vae.safetensors"
)

# Load action-specific LoRA with absolute path
pipe.load_lora_weights(
    "E:/huggingface/wan21-fp8-loras-nsfw/loras/wan/wan21-action-missionary-pov-i2v-v2.safetensors"
)

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

# Generate video with action-specific motion
prompt = "smooth camera movement, cinematic lighting, high quality"
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_action.mp4", fps=8)

Switching Between Action LoRAs

python

# Define base path
LORA_PATH = "E:/huggingface/wan21-fp8-loras-nsfw/loras/wan"

# Available action LoRAs
actions = {
    "bounce_v1": "wan21-action-bounce-v1.safetensors",
    "doggy_pov_v2": "wan21-action-doggypov-v2.safetensors",
    "doggy_front_t2v": "wan21-action-doggystylefromfront-t2v.safetensors",
    "front_doggy_plow_v2": "wan21-action-front-doggy-plow-v2.safetensors",
    "missionary_pov_i2v_v2": "wan21-action-missionary-pov-i2v-v2.safetensors",
    "missionary_pov_v2": "wan21-action-missionary-pov-v2.safetensors",
    "orgasm_i2v_720p": "wan21-action-orgasm-i2v-720p.safetensors",
    "orgasm_v2_t2v": "wan21-action-orgasm-v2-t2v-14b.safetensors",
}

# Load specific action
selected_action = "missionary_pov_v2"
pipe.load_lora_weights(f"{LORA_PATH}/{actions[selected_action]}")

T2V Usage (Text-to-Video)

python

# Load T2V action LoRA
pipe.load_lora_weights(
    "E:/huggingface/wan21-fp8-loras-nsfw/loras/wan/wan21-action-orgasm-v2-t2v-14b.safetensors"
)

# Generate from text prompt
prompt = "cinematic scene with dynamic movement, professional lighting, high detail"
video = pipe(
    prompt=prompt,
    num_inference_steps=50,
    guidance_scale=7.5,
    num_frames=24,
    height=720,
    width=1280
).frames

⚙️ Technical Specifications

Model Architecture

Type: LoRA (Low-Rank Adaptation) adapters
Precision: BF16 (Brain Floating Point 16-bit)
Format: SafeTensors (.safetensors)
Base Compatibility: WAN 2.1 I2V 14B architecture
Training Method: Action-specific motion patterns and camera movements

LoRA Variants

v1 LoRAs (Initial Generation):

File sizes: 293-343MB
Standard motion patterns
Baseline quality and consistency

v2 LoRAs (Enhanced Generation):

File sizes: 293-586MB
Improved motion consistency and temporal coherence
Better training datasets and methodology
Recommended for production use

Resolution Support

I2V 720p Models:

Input: 1280x720 images
Output: High-resolution video sequences (720p)
Higher VRAM requirements (20GB+)
Best visual quality

Standard I2V Models:

Input: 480p or 720p images
Output: Matching resolution video
Balanced quality and performance (16-17GB VRAM)

T2V Models:

No input image required
Resolution specified in generation parameters
More flexible but requires better prompting

💻 Hardware Requirements

Minimum Requirements

GPU: NVIDIA RTX 3060 (12GB VRAM) or AMD equivalent
RAM: 16GB system RAM
Storage: 4GB for LoRAs + base model space (~50GB total)
GPU Architecture: Ampere or newer (BF16 support required)

Recommended Configuration

GPU: NVIDIA RTX 3090 (24GB VRAM) or RTX 4070 Ti (16GB)
RAM: 32GB system RAM
Storage: 100GB SSD (for full WAN 2.1 ecosystem)
Operating System: Linux or Windows 10/11

High-End (720p Generation)

GPU: NVIDIA RTX 4090 (24GB VRAM) or A100 (40GB)
RAM: 64GB system RAM
Storage: 200GB+ NVMe SSD
Optimized for: 720p high-quality output at 50+ inference steps

Software Requirements

Python: 3.9+ (3.10 or 3.11 recommended)
PyTorch: 2.0+ with CUDA 11.8 or 12.1
Diffusers: 0.25.0 or newer
Transformers: 4.36.0 or newer
CUDA Toolkit: 11.8+ or 12.1+

Installation

bash

# Install PyTorch with CUDA support
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# Install diffusion libraries
pip install diffusers transformers accelerate safetensors

# Install image/video utilities
pip install pillow opencv-python imageio

📊 Performance Benchmarks

I2V Generation Speed (NVIDIA RTX 4090, 24 frames)

LoRA Model	Resolution	Steps	Generation Time	VRAM Usage
v1 (343MB)	480p	50	~25s	~16GB
v2 (514MB)	480p	50	~28s	~17GB
720p (293MB)	720p	50	~35s	~20GB
T2V v2 (586MB)	720p	50	~40s	~22GB

Hardware Comparison (720p, 50 steps, 24 frames)

GPU Model	VRAM	Generation Time	Notes
RTX 4090	24GB	~35s	Optimal performance
RTX 3090	24GB	~45s	Excellent performance
RTX 4070 Ti	16GB	~55s	Good with optimization
RTX 3060	12GB	N/A	Requires CPU offloading

Note: Actual performance varies based on prompt complexity, frame count, and system configuration.

🎨 Prompting Tips

Effective Prompts for Action LoRAs

Camera and Movement Descriptors:

Camera angles: "POV perspective", "frontal view", "tracking camera", "dynamic angle"
Motion quality: "smooth movement", "dynamic motion", "fluid transitions", "steady camera"
Lighting: "cinematic lighting", "soft lighting", "dramatic shadows", "natural lighting"

Quality Modifiers:

Resolution: "high quality", "detailed", "HD quality", "720p quality"
Style: "realistic", "photorealistic", "cinematic style", "professional"
Atmosphere: "cinematic", "dramatic", "intimate", "intense"

Example Prompts by LoRA Type:

# POV LoRAs (missionary-pov, doggy-pov)
"POV perspective, smooth camera movement, cinematic lighting, high quality, realistic"

# Frontal LoRAs (front-doggy-plow, doggystylefromfront)
"Frontal view, dynamic motion, professional lighting, detailed composition, 720p quality"

# 720p LoRAs (orgasm-i2v-720p)
"Tracking camera, fluid motion, soft lighting, high detail, photorealistic, HD quality"

# T2V LoRAs (orgasm-v2-t2v-14b, doggystylefromfront-t2v)
"Cinematic scene, dynamic movement, professional lighting, realistic, high detail, 720p"

Action-Specific Guidance

POV LoRAs: Emphasize first-person perspective and immersive camera work
Frontal LoRAs: Focus on frontal framing, composition, and balanced lighting
720p LoRAs: Include high-resolution quality modifiers and detail keywords
T2V LoRAs: Provide more detailed scene descriptions (no input image guidance)
Bounce LoRAs: Mention rhythm, movement dynamics, and motion fluidity

🔧 Troubleshooting

Out of Memory (OOM) Errors

python

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

# Solution 2: Reduce frame count
video = pipe(prompt, num_frames=16, image=input_image)  # Instead of 24

# Solution 3: Lower resolution
video = pipe(prompt, height=480, width=854, image=input_image)

# Solution 4: Sequential CPU offload (for extreme constraints)
pipe.enable_sequential_cpu_offload()

# Solution 5: Use attention slicing
pipe.enable_attention_slicing(slice_size="auto")

Poor Motion Quality

Causes and Solutions:

Use v2 LoRAs instead of v1 for better temporal consistency
Increase inference steps to 40-60 (vs 20-30 for distilled models)
Adjust CFG scale to 7.0-8.5 range for action LoRAs
Ensure high-quality input images (for I2V models)
Match frame count to action complexity (24+ frames for complex actions)

Inconsistent Action Output

Optimization Strategies:

LoRA Selection: Choose action-specific LoRA matching your use case
Prompt Alignment: Ensure prompts match LoRA's trained patterns
Guidance Scale: Use higher guidance (7.5-8.5) for controlled actions
Input Quality: Higher resolution and quality inputs produce better results
Frame Count: Some actions require 24+ frames for smooth completion

LoRA Selection Guide

Use Case	Recommended LoRA	Notes
First-person perspective	`-pov-.safetensors`	Best for immersive POV shots
High-resolution output	`*-720p.safetensors`	Requires 20GB+ VRAM
Text-only generation	`-t2v-.safetensors`	No input image needed
Best quality/consistency	`*-v2.safetensors`	Enhanced training, recommended
Limited VRAM	`*-v1.safetensors`	Smaller models, lower memory

📝 Model Card

Property	Value
Model Type	LoRA Adapters for Video Diffusion
Architecture	Low-Rank Adaptation (LoRA)
Training Method	Action-Specific Motion Patterns
Precision	BF16 (Brain Floating Point 16)
Format	SafeTensors (.safetensors)
Content Type	Adult/NSFW (18+ only)
Base Model	WAN 2.1 I2V 14B
Variants	v1 (standard), v2 (enhanced)
Resolution Support	480p, 720p
Total Size	3.2GB (8 adapters)
License	WAN model license (see below)
Intended Use	Adult content video generation with specific actions/angles
Age Restriction	18+ years only
Supported Languages	English prompts (primary)

📄 License

These LoRA adapters are subject to the WAN model license terms with additional restrictions:

Required Safeguards

Age Verification: Must implement age verification for end users (18+)
Legal Compliance: Users responsible for compliance with local laws and regulations
Content Warnings: All distributions must include appropriate NSFW warnings
Platform Policies: Respect hosting platform content policies and terms of service

Prohibited Uses

Non-consensual content generation or deepfakes
Content featuring or depicting minors
Identity theft, impersonation, or exploitation
Harassment, abuse, or harmful content creation
Violation of individuals' rights or platform terms

Commercial Use

Check WAN base model license for commercial usage restrictions
Implement appropriate content moderation systems
Maintain age verification and legal compliance infrastructure

⚖️ Ethical Guidelines

Prohibited Uses

❌ Non-consensual content generation or deepfakes
❌ Content featuring minors in any form
❌ Identity theft, impersonation, or exploitative materials
❌ Harassment, abuse, or harmful content
❌ Violation of platform terms of service or community guidelines

Recommended Best Practices

✅ Implement robust age verification systems (18+)
✅ Use clear content warnings and NSFW tags
✅ Respect intellectual property, privacy, and likeness rights
✅ Implement content moderation and reporting mechanisms
✅ Provide user control and opt-out mechanisms
✅ Follow platform-specific content policies
✅ Maintain transparency about AI-generated content

🙏 Acknowledgments

WAN Development Team for the base WAN 2.1 I2V 14B model architecture
Community Contributors for responsible testing, feedback, and ethical guidance
Hugging Face for hosting infrastructure with content policy enforcement
Open-source Community for diffusers, transformers, and safetensors libraries

📚 Related Resources

Related Model Repositories

WAN 2.1 Base Models: wan21-fp16, wan21-fp8 (I2V base models)
WAN 2.1 VAE: Required for video decoding and encoding
WAN 2.1 Camera LoRAs: wan21-camera-* (SFW camera control LoRAs)
WAN 2.2 Models: wan22-fp16, wan22-fp8 (newer generation models)

Documentation and Tutorials

Diffusers Documentation: https://huggingface.co/docs/diffusers
WAN Official Documentation: Check base model repository for guides
LoRA Training Guide: https://huggingface.co/docs/peft/conceptual_guides/lora

Community Resources

Hugging Face Forums: Community support and discussions
GitHub Issues: Technical support and bug reports
Model Cards: Detailed specifications and usage examples

📧 Support and Contact

Technical Support

Repository Issues: Open an issue in this repository for technical problems
Base Model Questions: Refer to WAN official documentation and repositories
Library Issues: Check diffusers/transformers GitHub repositories

Ethical Concerns

Content Violations: Report to platform moderators or administrators
Legal Issues: Consult with legal counsel regarding compliance
Safety Concerns: Follow platform reporting procedures

Feature Requests

Community feedback and feature requests are welcome
Consider contributing improvements or additional LoRAs
Share responsible usage patterns and best practices

📋 Summary

This repository contains 8 specialized action LoRA adapters for the WAN 2.1 I2V 14B video generation model:

Key Specifications

Total Size: 3.2GB (8 action-specific adapters)
Content Type: Adult/NSFW (18+ only)
Variants: v1 (standard) and v2 (enhanced)
Resolution: 480p and 720p support
Precision: BF16 (Brain Floating Point 16-bit)
Format: SafeTensors (.safetensors)

Use Cases

Specialized action sequences with controlled motion patterns
Camera angle control (POV, frontal, tracking)
Text-to-video and image-to-video generation
High-resolution video synthesis (up to 720p)

Requirements

WAN 2.1 I2V 14B base model (required)
WAN 2.1 VAE (required for video encoding/decoding)
12-24GB VRAM (depending on resolution and settings)
BF16-capable GPU (NVIDIA Ampere/Ada or newer)

Content Warning

These models are trained on adult content and intended for responsible adult use only (18+). Users must:

Comply with applicable laws and regulations
Implement appropriate age verification and safeguards
Use ethically and respect individual rights
Follow platform content policies and guidelines

Technical Note

These are specialized LoRA adapters that modify the base WAN 2.1 I2V model behavior to generate specific action sequences and camera movements. They require the WAN 2.1 base model and VAE to function and cannot be used independently.

Last Updated: October 2025 Repository Version: 1.4 Total Size: 3.2GB (8 action LoRAs with v1/v2 variants) Content Rating: Adult/NSFW (18+ only) Primary Use Case: Specialized action and camera angle video generation for adult content License: WAN model license with additional ethical use restrictions

wangkanai/wan21-fp8-loras-nsfw

⚠️ WAN 2.1 Action LoRAs (Adult Content)

📦 Model Information

⚠️ Usage Restrictions

📁 Repository Contents

🎯 LoRA Categories

Camera Angle Adapters

Action-Specific Adapters

Resolution Variants

🚀 Usage Examples

Basic I2V Usage with Action LoRA

Switching Between Action LoRAs

T2V Usage (Text-to-Video)

⚙️ Technical Specifications

Model Architecture

LoRA Variants

Resolution Support

💻 Hardware Requirements

Minimum Requirements

Recommended Configuration

High-End (720p Generation)

Software Requirements

Installation

📊 Performance Benchmarks

I2V Generation Speed (NVIDIA RTX 4090, 24 frames)

Hardware Comparison (720p, 50 steps, 24 frames)

🎨 Prompting Tips

Effective Prompts for Action LoRAs

Action-Specific Guidance

🔧 Troubleshooting

Out of Memory (OOM) Errors

Poor Motion Quality

Inconsistent Action Output

LoRA Selection Guide

📝 Model Card

📄 License

Required Safeguards

Prohibited Uses

Commercial Use

⚖️ Ethical Guidelines

Prohibited Uses

Recommended Best Practices

🙏 Acknowledgments

📚 Related Resources

Related Model Repositories

Documentation and Tutorials

Community Resources

📧 Support and Contact

Technical Support

Ethical Concerns

Feature Requests

📋 Summary

Key Specifications

Use Cases

Requirements

Content Warning

Technical Note

wangkanai/
wan21-fp8-loras-nsfw