imtiyaz

Introduction

The Imtiyaz model is a text-to-image generation tool that leverages the capabilities of the Hugging Face Diffusers library. This model is designed to create high-quality, realistic, or stylized images based on textual prompts. It is optimized to work with various prompts to generate images with specific styles and settings.

Architecture

The model is based on the FLUX.1-dev architecture, developed by Black Forest Labs, and incorporates LoRA (Low-Rank Adaptation) techniques for efficient fine-tuning. The model utilizes a pre-trained backbone for image generation tasks, and its functionality is enhanced through additional LoRA weights.

Training

The Imtiyaz model was trained using the Replicate platform, specifically with the flux-dev-lora-trainer. This setup allows the model to adapt to specific styles and prompts, enhancing its image generation capabilities. The training process involves fine-tuning the base model with additional LoRA weights to achieve the desired image outputs.

Guide: Running Locally

To run the Imtiyaz model locally, follow these steps:

1. Set Up Environment: Ensure you have Python and the required libraries installed, including diffusers and torch.

2. Install Diffusers Library:

   pip install diffusers
   

3. Load the Model:

   from diffusers import AutoPipelineForText2Image
   import torch
   
   pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
   pipeline.load_lora_weights('imtiyaz0/imtiyaz', weight_name='lora.safetensors')
   

4. Generate Images:

   image = pipeline('your prompt').images[0]
   

   Replace 'your prompt' with your desired text prompt.

5. Hardware Requirements: For optimal performance, especially with larger models, consider using a cloud GPU service such as AWS EC2, Google Cloud, or Azure.

License

The Imtiyaz model is distributed under the Flux-1-dev-non-commercial-license. For more details, refer to the license documentation.