atfortes/Awesome-Controllable-Diffusion
A curated list of 70+ research papers on adding conditional controls to diffusion models for text-to-image generation.
Not currently ranked — collecting fresh signals.
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This repository aggregates academic papers and resources on controllable generation using diffusion models. It covers techniques such as ControlNet, DreamBooth, IP-Adapter, T2I-Adapter, and related personalization methods for stable diffusion. The list is organized by year (2023–2025) and serves as a reference for researchers and practitioners working on conditional image synthesis.
Frequently asked
- What is atfortes/Awesome-Controllable-Diffusion?
- A curated list of 70+ research papers on adding conditional controls to diffusion models for text-to-image generation.
- Is Awesome-Controllable-Diffusion open source?
- Yes — atfortes/Awesome-Controllable-Diffusion is open source, released under the MIT license.
- How popular is Awesome-Controllable-Diffusion?
- atfortes/Awesome-Controllable-Diffusion has 505 stars on GitHub.
- Where can I find Awesome-Controllable-Diffusion?
- atfortes/Awesome-Controllable-Diffusion is on GitHub at https://github.com/atfortes/Awesome-Controllable-Diffusion.