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 (10.7KB)
└── 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: ~14 KB (structure initialized, ready for LoRA 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 or RTX 5090 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:

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"

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

v1.4 (2025-10-28)

Updated hardware recommendations with RTX 5090 reference
Refreshed repository size information (14 KB)
Updated last modified date to current (2025-10-28)
Verified all YAML frontmatter compliance with HuggingFace standards
Confirmed repository structure and organization remain current

v1.3 (2024-10-14)

CRITICAL FIX: Moved version header AFTER YAML frontmatter (HuggingFace requirement)
Verified YAML frontmatter is first content in file
Confirmed proper YAML structure with three-dash delimiters
All metadata fields validated against HuggingFace standards

v1.2 (2024-10-14)

Updated version metadata to v1.2
Verified repository structure and file organization
Updated repository size information
Confirmed YAML frontmatter compliance with HuggingFace standards

v1.1 (2024-10-13)

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

v1.0 (Initial Release)

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

Repository Status: Initialized and ready for LoRA collection Last Updated: 2025-10-28 Maintained By: Local collection for FLUX.1-dev experimentation

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