mapo-t2i/mapo
Official codebase for Margin-aware Preference Optimization for Aligning Diffusion Models without Reference (MaPO).
This project helps machine learning engineers fine-tune existing text-to-image diffusion models to generate images that align better with human preferences. You input a pre-trained diffusion model and a dataset of image pairs with human preference labels. The output is a refined diffusion model capable of producing higher-quality, more preferred images.
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Use this if you are a machine learning engineer working with large diffusion models and want to improve their image generation quality based on explicit human feedback, without needing a reference image for comparison.
Not ideal if you don't have access to substantial GPU resources (like an H100 with 40GB VRAM) or are looking for a pre-packaged, low-code solution for image generation.
Stars
82
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9
Language
Python
License
Apache-2.0
Category
Last pushed
Jun 11, 2024
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