---
library_name: diffusers
license: mit
---
## 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](https://colab.research.google.com/drive/1m4gd-wSpZtByu5m0ebZorajnprghln2X?usp=sharing) for a full guide and hands-on demonstration.