Updated YAML frontmatter to use license: apache-2.0 as specified in requirements
Removed unnecessary license_name field (not required for standard licenses)
Streamlined tags to essential discovery keywords per requirements
Removed base_model and base_model_relation fields (not applicable for LoRA collections)
Maintained all comprehensive documentation and usage examples

v1.3 (2025-10-14)

Updated version consistency (v1.3 throughout document)
Verified YAML frontmatter compliance with Hugging Face standards
Confirmed directory structure analysis (empty repository awaiting models)
Maintained comprehensive documentation for future LoRA additions

v1.2 (2025-10-14)

Fixed YAML frontmatter positioning to meet Hugging Face standards
YAML frontmatter now starts at line 1 as required
Moved version comment to proper position after YAML section
Ensured full compliance with Hugging Face model card metadata requirements

v1.1 (2025-10-13)

Enhanced repository organization documentation
Added comprehensive LoRA training specifications
Expanded performance optimization guidelines
Improved multi-LoRA blending examples
Added detailed prompt engineering best practices
Updated hardware requirements with more granular specifications
Added troubleshooting section for common issues
Clarified precision compatibility across FP16/FP8 base models

v1.0 (2025-10-13)

Initial repository structure and README
Basic LoRA usage documentation
Integration examples with FLUX.1-dev base models

Model Description

This repository contains a collection of LoRA (Low-Rank Adaptation) adapters for FLUX.1-dev models, focused on specialized content generation. LoRA adapters provide efficient fine-tuning by adding small trainable parameters to the base model, enabling style variations, character customization, and domain-specific generation without modifying the original model weights.

Key Capabilities:

Efficient fine-tuning with minimal storage footprint (typically 10-500MB per LoRA)
Compatible with FLUX.1-dev base models (FP16, FP8, and quantized variants)
Stackable adapters for combining multiple styles/concepts
Fast loading and switching between different LoRAs
Preserves base model quality while adding specialized capabilities

Repository Contents

Directory Structure

flux-dev-loras-nsfw/
├── loras/
│   └── flux/
│       └── [LoRA model files will be stored here]
└── README.md (this file)

Model Files

Current Status: Repository structure initialized, awaiting model files.

Expected File Types:

.safetensors - LoRA adapter weights (recommended format)
.json - LoRA configuration metadata
.txt - Trigger words and usage instructions

Typical LoRA Sizes:

Small LoRAs: 10-50 MB (style adapters)
Medium LoRAs: 50-200 MB (character/concept adapters)
Large LoRAs: 200-500 MB (complex multi-concept adapters)

Total Repository Size

Current: 22 KB (empty structure) Expected: Varies based on LoRA collection (typically 100MB - 5GB total)

Hardware Requirements

For LoRA Usage with FLUX.1-dev

Minimum Requirements:

GPU: NVIDIA RTX 3060 (12GB VRAM) or equivalent
RAM: 16 GB system memory
Storage: 500 MB - 10 GB (depending on collection size)
VRAM Usage: Base model (11-13GB) + LoRA overhead (100-500MB)

Recommended Setup:

GPU: NVIDIA RTX 4090 (24GB VRAM) or A100
RAM: 32 GB system memory
Storage: 10-50 GB for comprehensive collection
VRAM: 16-20GB for comfortable multi-LoRA usage

LoRA-Specific Benefits:

Much lower VRAM overhead than full model fine-tunes
Can load/unload LoRAs dynamically without restarting
Multiple LoRAs can be combined with weighted blending

Usage Examples

Basic LoRA Loading (Diffusers)

python

from diffusers import FluxPipeline
import torch

# Load base FLUX.1-dev model
pipe = FluxPipeline.from_pretrained(
    "E:\\huggingface\\flux-dev-fp16",  # Your local FLUX.1-dev path
    torch_dtype=torch.float16
)
pipe.to("cuda")

# Load LoRA adapter
pipe.load_lora_weights(
    "E:\\huggingface\\flux-dev-loras-nsfw\\loras\\flux",
    weight_name="your_lora.safetensors",
    adapter_name="custom_style"
)

# Generate with LoRA
prompt = "your prompt with trigger words"
image = pipe(
    prompt=prompt,
    num_inference_steps=30,
    guidance_scale=7.5,
    height=1024,
    width=1024
).images[0]

image.save("output.png")

Multi-LoRA Blending

python

# Load multiple LoRAs
pipe.load_lora_weights(
    "E:\\huggingface\\flux-dev-loras-nsfw\\loras\\flux",
    weight_name="style_lora.safetensors",
    adapter_name="style"
)
pipe.load_lora_weights(
    "E:\\huggingface\\flux-dev-loras-nsfw\\loras\\flux",
    weight_name="character_lora.safetensors",
    adapter_name="character"
)

# Set adapter weights for blending
pipe.set_adapters(["style", "character"], adapter_weights=[0.8, 0.6])

# Generate with combined LoRAs
image = pipe(prompt="your combined prompt").images[0]

Dynamic LoRA Switching

python

# Unload current LoRA
pipe.unload_lora_weights()

# Load different LoRA
pipe.load_lora_weights(
    "E:\\huggingface\\flux-dev-loras-nsfw\\loras\\flux",
    weight_name="different_lora.safetensors"
)

# Generate with new style
image = pipe(prompt="new prompt").images[0]

Using with ComfyUI

1. Place LoRA files in: ComfyUI/models/loras/
2. In ComfyUI workflow:
   - Add "Load LoRA" node
   - Connect to your FLUX.1-dev model
   - Set strength: 0.6-1.0 (adjust for desired effect)
   - Add trigger words to your prompt

Model Specifications

LoRA Architecture

Format: SafeTensors (recommended for security and efficiency)

Rank: Varies by LoRA (typical range: 4-128)

Low rank (4-32): Lightweight style adapters
Medium rank (32-64): Balanced quality/size
High rank (64-128): Maximum quality, larger files

Precision Options:

FP16: Standard precision for most use cases
FP32: Higher precision for professional workflows
Quantized: Experimental lower-precision variants

Base Model Compatibility:

FLUX.1-dev (primary)
FLUX.1-schnell (compatible with adjustments)
Works with FP16, FP8, and quantized base models

Training Specifications

LoRAs in this collection are typically trained with:

Training steps: 500-5000 (varies by complexity)
Learning rate: 1e-4 to 1e-5
Batch size: 1-4
Base model: FLUX.1-dev
Dataset: Specialized domain-specific images

Performance Tips

LoRA Loading Optimization

Fast Switching:

python

# Keep base model loaded, only swap LoRAs
pipe.unload_lora_weights()  # Fast
pipe.load_lora_weights(...)  # Fast
# Much faster than reloading entire base model

Memory Management:

Load only needed LoRAs to minimize VRAM usage
Use pipe.unload_lora_weights() when switching styles
Consider LoRA weight caching for frequently used adapters

Weight Adjustment:

Start with LoRA strength 0.7-1.0
Lower weights (0.3-0.6) for subtle effects
Higher weights (1.0-1.5) for strong style enforcement
Test different weights to find optimal balance

Multi-LoRA Best Practices

Combining LoRAs:

Limit to 2-3 LoRAs simultaneously for stability
Adjust individual weights to balance effects
Test combinations individually before stacking
Monitor VRAM usage when loading multiple adapters

Prompt Engineering with LoRAs:

Include trigger words specific to each LoRA
Place important trigger words early in prompt
Use emphasis syntax: (trigger word:1.2) for stronger effect
Avoid conflicting concepts between multiple LoRAs

Advanced Prompt Engineering

Trigger Word Strategies:

python

# Basic trigger word usage
prompt = "anime style, portrait of a character"  # Generic

# Optimized with LoRA trigger words
prompt = "anime_style_v2, detailed portrait, character design"  # LoRA-specific

Weight and Emphasis Techniques:

Standard: "beautiful landscape"
Emphasized: "(beautiful:1.3) landscape"
De-emphasized: "(background:0.7) focus on subject"
LoRA trigger: "(anime_style:1.2) detailed character"

Multi-LoRA Prompt Structure:

[Style LoRA trigger words] [Subject description] [Character LoRA trigger words] [Scene details]

Example:
"cinematic_lighting, fantasy_style, portrait of warrior, heroic_pose, detailed armor, misty forest background"

Negative Prompt Optimization:

python

negative_prompt = """
low quality, blurry, distorted, deformed, artifacts,
watermark, signature, text, bad anatomy, overexposed
"""
# Use negative prompts to avoid common LoRA artifacts

Context Length Management:

FLUX.1 supports up to 512 tokens in prompt
Prioritize important concepts at the beginning
Use concise, descriptive language
Avoid redundant or conflicting terms

Generation Quality

Optimal Settings:

Steps: 20-35 (FLUX.1-dev), 4-8 (FLUX.1-schnell)
Guidance Scale: 3.5-7.5 (lower for creative freedom)
Resolution: 1024x1024 native, up to 2048x2048 with VRAM
LoRA Strength: 0.7-1.0 for most use cases

Quality Troubleshooting:

Over-fitting: Reduce LoRA strength to 0.5-0.7
Weak effect: Increase strength or add trigger words
Artifacts: Lower inference steps or reduce guidance scale
VRAM errors: Reduce resolution or unload unused LoRAs

Common Issues and Solutions

Issue: LoRA not affecting output

Solution: Verify trigger words are included in prompt
Check LoRA strength is set to 0.7+
Ensure LoRA file is compatible with base model version
Try increasing adapter weight in multi-LoRA scenarios

Issue: Out of Memory (OOM) errors

Solution: Unload unused LoRAs with pipe.unload_lora_weights()
Reduce base model to FP8 precision
Lower generation resolution (e.g., 1024x1024 → 768x768)
Limit simultaneous LoRAs to 1-2 adapters
Enable CPU offloading: pipe.enable_model_cpu_offload()

Issue: Generation artifacts or distortion

Solution: Lower LoRA strength to 0.5-0.7
Reduce guidance scale to 3.5-5.0
Increase inference steps to 30-40
Check for conflicting trigger words between multiple LoRAs

Issue: Slow generation with LoRA

Solution: LoRA adds minimal overhead; check base model optimization
Ensure CUDA is properly installed and utilized
Use FP8 base model for faster inference
Consider torch.compile() for PyTorch 2.0+
Cache LoRA weights for frequently used adapters

Issue: LoRA file won't load

Solution: Verify file format is .safetensors (preferred)
Check file path uses correct absolute path format
Ensure LoRA was trained for FLUX.1-dev architecture
Try loading without adapter_name parameter
Check diffusers library version (0.30.0+ recommended)

Integration with FLUX.1 Base Models

Compatible Base Model Paths

This LoRA collection works with local FLUX.1-dev installations:

python

# FP16 precision (highest quality)
base_model = "E:\\huggingface\\flux-dev-fp16"

# FP8 precision (memory efficient)
base_model = "E:\\huggingface\\flux-dev-fp8"

# Load base model + LoRA
pipe = FluxPipeline.from_pretrained(base_model, torch_dtype=torch.float16)
pipe.load_lora_weights("E:\\huggingface\\flux-dev-loras-nsfw\\loras\\flux", ...)

Precision Considerations

FP16 Base Model:

Best quality and LoRA effect fidelity
Recommended for production use
Requires ~11-13GB VRAM

FP8 Base Model:

Memory efficient (~6-8GB VRAM)
Slight quality reduction
LoRA effects may be slightly less pronounced
Good for experimentation and iteration

Workflow Optimization

Development Workflow:

python

# 1. Initial setup - load base model once
pipe = FluxPipeline.from_pretrained(
    "E:\\huggingface\\flux-dev-fp16",
    torch_dtype=torch.float16
).to("cuda")

# 2. Iterative LoRA testing
for lora_file in ["style1.safetensors", "style2.safetensors", "style3.safetensors"]:
    pipe.unload_lora_weights()  # Fast cleanup
    pipe.load_lora_weights(
        "E:\\huggingface\\flux-dev-loras-nsfw\\loras\\flux",
        weight_name=lora_file
    )
    image = pipe(prompt).images[0]
    image.save(f"output_{lora_file}.png")

Batch Generation Pipeline:

python

import itertools

# Test multiple LoRAs with multiple prompts
loras = ["anime_v1.safetensors", "realistic_v2.safetensors"]
prompts = ["portrait of character", "landscape scene", "action pose"]
strengths = [0.6, 0.8, 1.0]

for lora, prompt, strength in itertools.product(loras, prompts, strengths):
    pipe.unload_lora_weights()
    pipe.load_lora_weights(
        "E:\\huggingface\\flux-dev-loras-nsfw\\loras\\flux",
        weight_name=lora
    )
    pipe.set_adapters([lora.split('.')[0]], adapter_weights=[strength])

    image = pipe(prompt, num_inference_steps=28).images[0]
    image.save(f"batch_{lora}_{strength}_{prompt[:10]}.png")

Production Inference Optimization:

python

# Enable torch.compile for PyTorch 2.0+
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)

# Enable attention slicing for lower VRAM
pipe.enable_attention_slicing()

# Use VAE tiling for high-res generation
pipe.vae.enable_tiling()

# Generate with optimizations
image = pipe(
    prompt=prompt,
    num_inference_steps=28,
    guidance_scale=3.5,
    generator=torch.manual_seed(42)  # Reproducible results
).images[0]

License

LoRA Collection License: Varies by individual LoRA (check metadata)

Base Model License: FLUX.1 Community License (non-commercial use)

See: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
Commercial use requires license from Black Forest Labs

Usage Restrictions:

Respect individual LoRA creator licenses
NSFW content: Ensure compliance with local laws and platform policies
Attribution required for derivative works
No redistribution without permission from LoRA creators

Citation

FLUX.1 Base Model

bibtex

@article{flux2024,
  title={FLUX.1: A New Generation of Diffusion Models},
  author={Black Forest Labs},
  journal={Technical Report},
  year={2024},
  url={https://blackforestlabs.ai/flux-1}
}

LoRA Methodology

bibtex

@article{hu2021lora,
  title={LoRA: Low-Rank Adaptation of Large Language Models},
  author={Hu, Edward J and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu},
  journal={arXiv preprint arXiv:2106.09685},
  year={2021}
}

Resources and Links

Official FLUX.1 Resources

Model Card: https://huggingface.co/black-forest-labs/FLUX.1-dev
Official Website: https://blackforestlabs.ai
GitHub: https://github.com/black-forest-labs/flux

LoRA Training and Usage

Diffusers LoRA Guide: https://huggingface.co/docs/diffusers/training/lora
LoRA Training Tutorial: https://huggingface.co/blog/lora
ComfyUI LoRA Docs: https://github.com/comfyanonymous/ComfyUI

Community and Support

Hugging Face Discussions: https://huggingface.co/black-forest-labs/FLUX.1-dev/discussions
FLUX.1 Discord: https://discord.gg/flux-community
Reddit: r/StableDiffusion, r/LocalLLaMA

LoRA Compatibility Matrix

Supported Base Models

Base Model	Precision	VRAM Required	LoRA Compatibility	Performance
FLUX.1-dev	FP16	11-13 GB	✅ Full	Best quality
FLUX.1-dev	FP8	6-8 GB	✅ Full	Good quality
FLUX.1-schnell	FP16	11-13 GB	⚠️ Partial	Fast inference
FLUX.1-schnell	FP8	6-8 GB	⚠️ Partial	Very fast

Notes:

LoRAs trained on FLUX.1-dev work best with dev models
Schnell compatibility requires testing (different distillation)
Quantized models (GGUF) have experimental LoRA support

Library Version Requirements

Minimum Requirements:

diffusers >= 0.30.0
transformers >= 4.44.0
torch >= 2.0.0
safetensors >= 0.4.0
accelerate >= 0.23.0

Recommended for Best Performance:

diffusers >= 0.31.0
transformers >= 4.45.0
torch >= 2.4.0 (with CUDA 12.1+)
xformers >= 0.0.22 (optional, for memory efficiency)

Installation Command:

pip install diffusers>=0.30.0 transformers>=4.44.0 torch>=2.0.0 safetensors accelerate

LoRA Format Compatibility

Supported Formats:

✅ .safetensors - Primary format (recommended)
✅ .bin - PyTorch format (legacy, less secure)
⚠️ .pt - Legacy format (test before use)
❌ .ckpt - Not supported (Stable Diffusion format)

Format Conversion:

python

from safetensors.torch import save_file, load_file

# Convert .bin to .safetensors
state_dict = torch.load("lora.bin", map_location="cpu")
save_file(state_dict, "lora.safetensors")

Repository Maintenance

Adding New LoRAs

When adding new LoRA files to this repository:

File Placement: Place .safetensors files in loras/flux/
Naming Convention: Use descriptive names (e.g., anime_style_v2.safetensors)
Documentation: Create accompanying .txt file with trigger words and settings
Metadata: Include .json config if available
Update README: Document file size, rank, and recommended usage settings

Organization Best Practices

Subdirectory Structure (for large collections):

loras/flux/
├── characters/    # Character-specific LoRAs
├── styles/        # Artistic style adapters
├── concepts/      # Concept/theme LoRAs
└── quality/       # Quality enhancement LoRAs

File Naming:

Include version: style_name_v1.safetensors
Indicate rank: character_r64.safetensors (if relevant)
Use lowercase with underscores

Repository Statistics

Current Status: Repository initialized, awaiting model files

Directory Structure:

Total directories: 2 (loras/, loras/flux/)
Model files: 0 (awaiting LoRA collection)
Documentation files: 1 (README.md)

Disk Usage:

Current: 22 KB
Expected with collection: 100 MB - 5 GB (varies by LoRA count)

README Version: v1.5 Last Updated: 2025-10-28 Maintained by: Local AI Model Collection Repository Path: E:\huggingface\flux-dev-loras-nsfw

wangkanai/flux-dev-loras-nsfw

FLUX.1 LoRA Collection - NSFW Models

Changelog

v1.5 (2025-10-28)

v1.4 (2025-10-28)

v1.3 (2025-10-14)

v1.2 (2025-10-14)

v1.1 (2025-10-13)

v1.0 (2025-10-13)

Model Description

Repository Contents

Directory Structure

Model Files

Total Repository Size

Hardware Requirements

For LoRA Usage with FLUX.1-dev

Usage Examples

Basic LoRA Loading (Diffusers)

Multi-LoRA Blending

Dynamic LoRA Switching

Using with ComfyUI

Model Specifications

LoRA Architecture

Training Specifications

Performance Tips

LoRA Loading Optimization

Multi-LoRA Best Practices

Advanced Prompt Engineering

Generation Quality

Common Issues and Solutions

Integration with FLUX.1 Base Models

Compatible Base Model Paths

Precision Considerations

Workflow Optimization

License

Citation

FLUX.1 Base Model

LoRA Methodology

Resources and Links

Official FLUX.1 Resources

LoRA Training and Usage

Community and Support

LoRA Compatibility Matrix

Supported Base Models

Library Version Requirements

LoRA Format Compatibility

Repository Maintenance

Adding New LoRAs

Organization Best Practices

Repository Statistics

wangkanai/
flux-dev-loras-nsfw