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Kameshr/
flux-pipeline

Name: flux-pipeline
Availability: InStock

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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.

Kameshr/flux-pipeline