dongzhuoyao/self-guided-diffusion-models
An official implementation of CVPR 2023 "Self-Guided Diffusion Models"
This project offers a way to generate new images that align with specific conditions, whether those conditions are broad categories (like "animals") or highly detailed instructions (like "a cat with a striped tail in a living room"). It takes existing image datasets and, through a process of 'self-guidance', learns to create novel images that fit precise descriptions or structural layouts. Image generation researchers and practitioners who work with generative AI models for creating visually coherent content would find this useful.
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Use this if you need fine-grained control over the characteristics of generated images, from overall themes to specific object placements and pixel-level features.
Not ideal if you are looking for a simple, out-of-the-box solution for casual image generation without needing to delve into model training and configuration.
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Language
Python
License
MIT
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Last pushed
Jun 02, 2023
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