wangkanai / flux-dev-loras

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license: apache-2.0 library_name: diffusers pipeline_tag: text-to-image tags: - flux - lora - text-to-image - image-generation - adapter - flux-dev - low-rank-adaptation base_model: black-forest-labs/FLUX.1-dev base_model_relation: adapter

FLUX.1-dev LoRA Collection

A curated collection of Low-Rank Adaptation (LoRA) models for FLUX.1-dev, enabling lightweight fine-tuning and style adaptation for text-to-image generation.

Model Description

This repository serves as an organized storage for FLUX.1-dev LoRA adapters. LoRAs are lightweight model adaptations that modify the behavior of the base FLUX.1-dev model without requiring full model retraining. They enable:

• Style Transfer : Apply artistic styles and aesthetic transformations
• Concept Learning : Teach the model specific subjects, characters, or objects
• Quality Enhancement : Improve specific aspects like detail, lighting, or composition
• Domain Adaptation : Specialize the model for specific use cases (e.g., architecture, portraits, landscapes)

LoRAs are significantly smaller than full models (typically 10-500MB vs 20GB+), making them efficient for storage, sharing, and experimentation.

Repository Contents

flux-dev-loras/
├── README.md (8.6KB)
└── loras/
    └── flux/
        └── (LoRA .safetensors files will be stored here)

Current Status : Repository structure initialized, ready for LoRA model storage.

Typical LoRA File Sizes :

• Small LoRAs (rank 4-16): 10-50 MB
• Medium LoRAs (rank 32-64): 50-200 MB
• Large LoRAs (rank 128+): 200-500 MB

Total Repository Size : ~9 KB (empty, ready for population)

Hardware Requirements

LoRA models add minimal overhead to base FLUX.1-dev requirements:

Minimum Requirements

• VRAM : 12GB (base FLUX.1-dev requirement)
• RAM : 16GB system memory
• Disk Space : Variable depending on LoRA collection size
  • Base model: ~24GB (FP16) or ~12GB (FP8)
  • Per LoRA: 10-500MB typically
• GPU : NVIDIA RTX 3060 (12GB) or better

Recommended Requirements

• VRAM : 24GB (RTX 4090, RTX A5000)
• RAM : 32GB system memory
• Disk Space : 50-100GB for extensive LoRA collection
• GPU : NVIDIA RTX 4090 for fastest inference

Performance Notes

• LoRAs add minimal computational overhead (<5% typically)
• Multiple LoRAs can be stacked (with performance trade-offs)
• FP8 base models are compatible with FP16 LoRAs

Usage Examples

Basic LoRA Loading with Diffusers

from diffusers import FluxPipeline
import torch

# Load base FLUX.1-dev model
pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.bfloat16
).to("cuda")

# Load LoRA adapter (example path - adjust to your actual LoRA file)
pipe.load_lora_weights("E:/huggingface/flux-dev-loras/loras/flux/your-lora-name.safetensors")

# Generate image with LoRA applied
prompt = "a beautiful landscape in the style of the LoRA"
image = pipe(
    prompt=prompt,
    num_inference_steps=50,
    guidance_scale=7.5,
    height=1024,
    width=1024
).images[0]

image.save("output.png")

Multiple LoRA Stacking

from diffusers import FluxPipeline
import torch

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.bfloat16
).to("cuda")

# Load multiple LoRAs with different strengths
pipe.load_lora_weights(
    "E:/huggingface/flux-dev-loras/loras/flux/style-lora.safetensors",
    adapter_name="style"
)
pipe.load_lora_weights(
    "E:/huggingface/flux-dev-loras/loras/flux/detail-lora.safetensors",
    adapter_name="detail"
)

# Set adapter weights
pipe.set_adapters(["style", "detail"], adapter_weights=[0.8, 0.5])

# Generate with combined LoRA effects
image = pipe(
    prompt="a detailed portrait with artistic style",
    num_inference_steps=50
).images[0]

image.save("combined_output.png")

Dynamic LoRA Weight Adjustment

from diffusers import FluxPipeline
import torch

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.bfloat16
).to("cuda")

pipe.load_lora_weights(
    "E:/huggingface/flux-dev-loras/loras/flux/artistic-style.safetensors"
)

# Generate with different LoRA strengths
for strength in [0.3, 0.6, 1.0]:
    pipe.fuse_lora(lora_scale=strength)

    image = pipe(
        prompt="a mountain landscape",
        num_inference_steps=50
    ).images[0]

    image.save(f"output_strength_{strength}.png")

    # Unfuse before changing strength
    pipe.unfuse_lora()

ComfyUI Integration

LoRAs in this directory can be used directly in ComfyUI:

1. Automatic Detection : Place LoRAs in ComfyUI's models/loras/ directory, or create a symlink:

   mklink /D "ComfyUI\models\loras\flux-dev-loras" "E:\huggingface\flux-dev-loras\loras\flux"
   

2. Load in Workflow : Use the "Load LoRA" node with FLUX.1-dev checkpoint

3. Adjust Strength : Use the strength parameter (0.0-1.0) to control LoRA influence

Model Specifications

Base Model Compatibility

• Model : FLUX.1-dev by Black Forest Labs
• Architecture : Latent diffusion transformer
• Compatible Precisions : FP16, BF16, FP8 (E4M3)

LoRA Format

• Format : SafeTensors (.safetensors)
• Typical Ranks : 4, 8, 16, 32, 64, 128
• Training Method : Low-Rank Adaptation (LoRA)

Supported Libraries

• diffusers (≥0.30.0 recommended)
• ComfyUI
• InvokeAI
• Automatic1111 (with FLUX support)

Finding and Adding LoRAs

Recommended Sources

• Hugging Face Hub : https://huggingface.co/models?pipeline_tag=text-to-image&other=flux&other=lora
• CivitAI : https://civitai.com/ (filter for FLUX.1-dev LoRAs)
• Replicate : Community-trained FLUX LoRAs

Download Process

# Example: Download LoRA from Hugging Face
cd E:\huggingface\flux-dev-loras\loras\flux
huggingface-cli download username/lora-repo --local-dir .

Organization Tips

• Use descriptive filenames: style-artistic-painting.safetensors
• Group by category: style/ , character/ , concept/ , quality/
• Include metadata files ( .json ) with training details when available

Performance Tips and Optimization

Memory Optimization

• Use FP8 Base Model : Load FLUX.1-dev in FP8 to save ~12GB VRAM
• Sequential Loading : Load/unload LoRAs as needed instead of keeping all loaded
• CPU Offload : Use enable_model_cpu_offload() for VRAM-constrained systems

pipe.enable_model_cpu_offload()

Quality Optimization

• LoRA Strength Tuning : Start with 0.7-0.8 strength, adjust based on results
• Inference Steps : LoRAs work well with 30-50 steps (same as base model)
• Guidance Scale : Use 7.0-8.0 for balanced results with LoRAs

Training Your Own LoRAs

• Recommended Tools : Kohya_ss, SimpleTuner, ai-toolkit
• Dataset Size : 10-50 high-quality images for concept learning
• Rank Selection : Rank 16-32 for most use cases, higher for complex styles
• Training Steps : 1000-5000 depending on complexity and dataset size

License

LoRA Models : Individual LoRAs may have different licenses. Check each LoRA's source repository for specific licensing terms.

Base Model License : FLUX.1-dev uses the Black Forest Labs FLUX.1-dev Community License

• Commercial use allowed with restrictions
• See: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md

Repository Structure : Apache 2.0 (this organizational structure)

Citation

If you use FLUX.1-dev LoRAs in your work, please cite the base model:

@software{flux1_dev,
  author = {Black Forest Labs},
  title = {FLUX.1-dev},
  year = {2024},
  url = {https://huggingface.co/black-forest-labs/FLUX.1-dev}
}

For specific LoRAs, cite the original creators from their respective repositories.

Resources and Links

Official FLUX Resources

• Base Model: https://huggingface.co/black-forest-labs/FLUX.1-dev
• Black Forest Labs: https://blackforestlabs.ai/
• FLUX Documentation: https://github.com/black-forest-labs/flux

LoRA Training Resources

• Kohya_ss Trainer: https://github.com/bmaltais/kohya_ss
• SimpleTuner: https://github.com/bghira/SimpleTuner
• ai-toolkit: https://github.com/ostris/ai-toolkit

Community and Support

• Hugging Face Diffusers Docs: https://huggingface.co/docs/diffusers
• FLUX Discord Communities
• r/StableDiffusion (Reddit)

Model Discovery

• Hugging Face FLUX LoRAs: https://huggingface.co/models?other=flux&other=lora
• CivitAI FLUX Section: https://civitai.com/models?modelType=LORA&baseModel=FLUX.1%20D

Changelog

v1.1 (2025-10-13)

• Updated version metadata to v1.1
• Enhanced tag metadata with low-rank-adaptation
• Improved hardware requirements formatting with subsections
• Added changelog section for version tracking
• Updated repository status and last modified date

v1.0 (Initial Release)

• Initial repository structure and documentation
• Comprehensive usage examples for diffusers and ComfyUI
• Performance optimization guidelines
• LoRA training and discovery resources

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Repository Status : Initialized and ready for LoRA collection Last Updated : 2025-10-13 Maintained By : Local collection for FLUX.1-dev experimentation