Flux Y W L Realism Lo R A

Introduction

The Flux-YWL-Realism-LoRA model, developed by STRANGERZONEHF, is a text-to-image model based on the diffusion technique. It uses LoRA (Low-Rank Adaptation) for generating realistic images from textual prompts. The model is currently in the training phase and may still contain artifacts.

Architecture

The Flux-YWL-Realism-LoRA model is built on the base model "black-forest-labs/FLUX.1-dev". It incorporates specific parameters like:

• Learning Rate Scheduler: Constant
• Optimizer: AdamW
• Network Dimensions: 64
• Network Alpha: 32
• Noise Offset: 0.03
• Multires Noise Discount: 0.1
• Multires Noise Iterations: 10
• Repeat & Steps: 26 & 3100
• Epoch: 23

The best image processing dimensions are 768 x 1024 and 1024 x 1024.

Training

The model was trained using 22 high-resolution images. It employs a constant learning rate scheduler with specific noise and optimization parameters to enhance its training efficiency. The model is not finalized yet and may not perform optimally in all scenarios.

Guide: Running Locally

Basic Steps

1. Installation: Ensure you have PyTorch installed with GPU support. Install the required pipelines library.

   pip install torch torchvision
   

2. Setup: Import necessary modules and load the base 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-YWL-Realism-LoRA"
   trigger_word = "ylw realism"  
   pipe.load_lora_weights(lora_repo)
   
   device = torch.device("cuda")
   pipe.to(device)
   

3. Trigger: Use the trigger word "ylw realism" to initiate image generation.

Cloud GPUs

Consider using cloud GPU services from AWS, Google Cloud, or Azure for efficient processing and reduced local resource consumption.

License

The model is licensed under the CreativeML OpenRAIL-M license, which outlines permissions and usage rights for the model.