Flux C4 C Design Lo R A

Introduction

The Flux-C4C-Design-LoRA is a text-to-image model designed for generating artistic and design-oriented images using diffusion-based techniques. It is implemented using LoRA (Low-Rank Adaptation) technology, which allows efficient fine-tuning of pre-trained models for specific tasks.

Architecture

The model is built on the base model "black-forest-labs/FLUX.1-dev" and utilizes LoRA for efficient parameter adaptation. It employs a diffusion pipeline for generating images based on text prompts. The model is optimized with AdamW and uses a constant learning rate scheduler, with specific configurations for noise offset and multires noise.

Training

The model was trained using 14 images with specific image processing parameters. The training involved 15 epochs, with the model being saved every epoch. The recommended dimensions for generating images are 1280x832 (3:2 aspect ratio) as the best option, and 1024x1024 (1:1 aspect ratio) as the default.

Guide: Running Locally

1. Setup Environment: Ensure you have a suitable environment for running the model, preferably with a CUDA-enabled GPU for optimal performance.

2. Install Dependencies: Install the necessary libraries, such as PyTorch, and ensure compatibility with your hardware.

3. Load the Model:

   import torch
   from pipelines import DiffusionPipeline
   
   base_model = "black-forest-labs/FLUX.1-dev"
   pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
   
   lora_repo = "strangerzonehf/Flux-C4C-Design-LoRA"
   trigger_word = "Smiley C4C"
   pipe.load_lora_weights(lora_repo)
   
   device = torch.device("cuda")
   pipe.to(device)
   

4. Generate Images: Use the trigger word "Smiley C4C" to initiate image generation with the model.

5. Cloud GPUs: Consider using cloud GPU services like AWS, Google Cloud, or Azure for resource-intensive tasks.

License

The model is released under the CreativeML OpenRAIL-M license, allowing for open usage with adherence to specified terms.