thu-nics/MixDQ
[ECCV24] MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization
This project helps anyone working with Text-to-Image AI models like Stable Diffusion to generate images more efficiently. You provide a text prompt, and the model quickly produces an image, using significantly less memory and computing power. This is ideal for researchers, AI artists, or developers who need to run these advanced models on less powerful hardware or speed up their creative workflows.
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Use this if you want to generate high-quality images from text prompts using diffusion models, but need to reduce the memory footprint and increase the speed of the generation process, especially on consumer-grade GPUs.
Not ideal if you are looking for a completely new text-to-image model or if you require absolute peak visual fidelity without any concern for computational efficiency.
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Python
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Last pushed
Nov 27, 2024
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