How to setup Text to Image generation wish Diffusion models (Flux)

Stable Diffusion is the technology to produce images from a text description. this technology forms the basis of the FLUX models. This guide explains how you can setup text to image generation in Ozeki AI studio using a FLUX model.

Recommended hardware:

To use image generation it is recommended to use a GPU.

Download the required models from:

https://huggingface.co/second-state/FLUX.1-dev-GGUF/tree/main

Save the files to:

Flux model: C:\AIModels\flux1-dev-Q5_1.gguff
ClipModelPath: C:\AIModels\clip_l.safetensors
T5xxlModelPath: C:\AIModels\t5xxl_fp16.safetensors
AEModelPath: C:\AIModels\ae.safetensors

Background information

When using Flux for text-to-image generation, each model plays a unique role in creating high-quality images:

CLIP Model: This model is used to encode the text input, capturing key features and semantic information. It helps the system understand the context and details of the text prompt.

T5XXL Model: This large language model processes the text input to expand or refine the descriptions, providing richer semantic information. It enhances the text understanding, making the generated images more accurate and detailed

AE Model (Autoencoder): The AE model, often referred to as a VAE (Variational Autoencoder), is used for generating the actual images It takes the processed text information and converts it into a visual representation

Flux Model: The Flux model integrates and orchestrates all these models, ensuring they work together seamlessly.

By combining these models, Flux can create more comprehensive and accurate text-to-image representations. It ensures that the generated images closely match the text descriptions while maintaining a balance between creativity and adherence to the prompts.