pmh9960/GCDP
Official PyTorch implementation of "Learning to Generate Semantic Layouts for Higher Text-Image Correspondence in Text-to-Image Synthesis." (ICCV 2023)
This project helps graphic designers, content creators, or researchers who need to generate realistic images from text descriptions, especially when working with specialized datasets like urban scenes or celebrity faces. It takes a text prompt and generates both a high-quality image and a corresponding 'semantic layout' (like a mask showing where objects are). This ensures the generated image accurately matches the text description, even with limited training data.
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Use this if you need to generate highly accurate images from text descriptions within specific domains where large text-image datasets are not available, such as for architectural visualizations or character design.
Not ideal if you are working with generic image generation where web-scale datasets are abundant, as its primary benefit is in improving correspondence for niche domains.
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Python
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Last pushed
Nov 02, 2023
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