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README.md

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+<!-- README Version: v1.1 -->
+---
+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
+
+```python
+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
+
+```python
+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
+
+```python
+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:
+   ```bash
+   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
+```bash
+# 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
+
+```python
+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:
+
+```bibtex
+@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
+
+---
+
+**Repository Status**: Initialized and ready for LoRA collection
+**Last Updated**: 2025-10-13
+**Maintained By**: Local collection for FLUX.1-dev experimentation