Seamless Pattern Design Flux Lo R A

Introduction

The Seamless-Pattern-Design-Flux-LoRA model by prithivMLmods is a text-to-image model designed for generating vibrant and playful seamless pattern designs. It employs a diffusion-lora framework to facilitate creative digital illustrations.

Architecture

This model utilizes the Diffusers library and is based on the black-forest-labs/FLUX.1-dev base model. The architecture is designed to handle seamless pattern generation with specific focus on artistic elements such as texture, color contrast, and whimsical style.

Training

The model is trained using the AdamW optimizer with a constant learning rate scheduler. Key parameters include:

• Network Dim: 64
• Network Alpha: 32
• Repeat & Steps: 25 & 3K
• Epochs: 15
• Multires Noise Discount: 0.1

A total of 17 high-resolution images were used for training, focusing on natural language and English labeling.

Guide: Running Locally

1. Requirements: Ensure you have a CUDA-compatible device and Python environment configured with necessary libraries such as torch and Diffusers.

2. Setup:

   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 = "prithivMLmods/Seamless-Pattern-Design-Flux-LoRA"
   trigger_word = "Seamless Pattern Design"  
   pipe.load_lora_weights(lora_repo)
   
   device = torch.device("cuda")
   pipe.to(device)
   

3. Running: Utilize the trigger word "Seamless Pattern Design" to initiate image generation.

4. Cloud GPUs: Consider using cloud GPU services like AWS, GCP, or Azure for more efficient processing power, especially for larger or more complex image generation tasks.

License

The model is released under the creativeml-openrail-m license, allowing for open and creative use while ensuring compliance with specific terms.