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library_name: diffusers |
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license: mit |
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## LoRA-Based Text-to-Image Diffusion Model |
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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. |
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### Model Overview |
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- **Model Type**: Text-to-Image Diffusion |
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- **Optimization**: LoRA + Quantization |
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- **Precision**: Half-precision (float16) with 4-bit quantization to reduce memory footprint. |
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- **Memory Requirements**: Designed for 16 GB RAM with CPU offloading capabilities. |
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### Key Features |
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- **LoRA (Low-Rank Adaptation)**: Allows efficient fine-tuning without large memory overhead. |
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- **4-bit Quantization**: Reduces memory usage while maintaining model quality. |
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- **CPU Offloading**: Enables stable performance within memory constraints by offloading parts of the model to the CPU. |
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### Usage Instructions |
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- **Environment**: Use in Google Colab (16 GB RAM recommended). |
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- **Inference**: Run text-to-image generation using a simple text prompt. |
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- **Memory Management**: To prevent memory issues, utilize CPU offloading and periodically clear the cache. |
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This model setup is optimized for straightforward, memory-efficient inference on Colab. Ideal for users working in constrained environments. |
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### Colab Notebook for Reference |
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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. |
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