Kameshr/flux-pipeline

LoRA-Based Text-to-Image Diffusion Model

This model is a LoRA-based text-to-image diffusion model with quantization and is specifically optimized for environments with 16 GB RAM like Google Colab. It uses LoRA for lightweight fine-tuning and quantization to reduce memory demands.

Model Overview

• Model Type: Text-to-Image Diffusion
• Optimization: LoRA + Quantization
• Precision: Half-precision (float16) with 4-bit quantization to reduce memory footprint.
• Memory Requirements: Designed for 16 GB RAM with CPU offloading capabilities.

Key Features

• LoRA (Low-Rank Adaptation): Allows efficient fine-tuning without large memory overhead.
• 4-bit Quantization: Reduces memory usage while maintaining model quality.
• CPU Offloading: Enables stable performance within memory constraints by offloading parts of the model to the CPU.

Usage Instructions

• Environment: Use in Google Colab (16 GB RAM recommended).
• Inference: Run text-to-image generation using a simple text prompt.
• Memory Management: To prevent memory issues, utilize CPU offloading and periodically clear the cache.

This model setup is optimized for straightforward, memory-efficient inference on Colab. Ideal for users working in constrained environments.

Colab Notebook for Reference

To get started with the model, you can refer to this Colab Notebook for a full guide and hands-on demonstration.