atfortes/Awesome-Controllable-Diffusion
Papers and resources on Controllable Generation using Diffusion Models, including ControlNet, DreamBooth, IP-Adapter.
This resource curates a collection of academic papers and materials focused on advanced techniques for controlling AI image generation. It provides insights into how users can precisely guide AI models to create images that match specific layouts, styles, or compositions, moving beyond simple text prompts. Researchers and practitioners in computer vision and generative AI would use this to stay updated on the latest methods for fine-grained image control.
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Use this if you are a researcher or advanced practitioner looking for academic papers and code implementations on how to exert more control over AI-generated images, such as specifying object placement, artistic styles, or even generating 3D models.
Not ideal if you are looking for a beginner's guide to AI image generation or a user-friendly tool to create images without understanding the underlying technical concepts.
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