H S Tcolor Klimbim_ Rank256 Flux Lo R A_ By Silver Age Poets

Introduction

The HSTcolorKlimbim_Rank256FluxLoRA_BySilverAgePoets is a text-to-image model developed by AlekseyCalvin. It leverages a Rank 256 LoRA adapter to enhance the generation of images with an artistic, colorized look, influenced by Klimbim's historical photograph colorizations. The model is built on the FLUX-family base models and is compatible with the Diffusers library.

Architecture

This model uses a Rank 256 adapter, capable of affecting up to approximately 1.4 billion parameters within a typical FLUX-family model's 12 billion parameters. It operates as a Diffusion Transformer (DiT) LoRA without text-encoder training weights. The model generates images by applying a style inspired by early 20th-century color photography, with a focus on detailed textures and interplay of light and shadow.

Training

The model was trained on a collection of high-quality images selected from Klimbim's colorized historical photographs. These images were used to influence the model's ability to generate artistic analog color photos with a detailed, historical appearance.

Guide: Running Locally

To run the model locally, follow these steps:

1. Install the Diffusers Library: Ensure you have the Diffusers library installed. This can be done using pip:

   pip install diffusers
   

2. Run the Model: Use the following Python code to run 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('AlekseyCalvin/HSTcolorKlimbim_Rank256FluxLoRA_BySilverAgePoets')
   image = pipeline('your prompt').images[0]
   

3. Consider Cloud GPUs: For better performance, especially with large models, consider using cloud-based GPU services such as AWS, Google Cloud, or Azure.

License

The model is released under the Apache 2.0 License, allowing for both personal and commercial use, modification, and distribution, provided that the license terms are met.