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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A collection of resources on controllable generation with text-to-image diffusion models.
| Date | Stars |
|---|---|
| 2026-07-24 | 1111 |
| 2026-07-25 | 1111 |
| 2026-07-28 | 1111 |
| 2026-07-30 | 1111 |
| 2026-07-31 | 1110 |
| 2026-08-06 | 1110 |
Today
— stars today
This week
-1 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<!-- # <p align=center>`awesome gan-inversion`</p> -->
[](https://github.com/sindresorhus/awesome)
[](https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity)
[](http://makeapullrequest.com)
[](https://arxiv.org/abs/2403.04279)
<br />
<p align="center">
<h1 align="center">Awesome Controllable T2I Diffusion Models</h1>
</p>
<br />
We are focusing on how to **Control** text-to-image diffusion models with **Novel Conditions**.
For more detailed information, please refer to our survey paper: [Controllable Generation with Text-to-Image Diffusion Models: A Survey](https://arxiv.org/abs/2403.04279)
<p align="center">
<img src="assets/count.png" alt="img" width="49%" />
<img src="assets/controllable_generation.png" alt="img" width="49%" />
</p>
## 💖 Citation
**If you find value in our survey paper or curated collection, please consider citing our work and starring our repo to support us.**
```text
@article{cao2024controllable,
title={Controllable Generation with Text-to-Image Diffusion Models: A Survey},
author={Pu Cao and Feng Zhou and Qing Song and Lu Yang},
journal={arXiv preprint arXiv:2403.04279},
year={2024}
}
```
## 🎁 How to contribute to this repository?
Since the following content is generated based on our database, please provide the following information in the **issue** to help us fill in the database to add new papers (please do not submit a PR directly).
```text
1. Paper title
2. arXiv ID (if any)
3. Publication status (if any)
```
## 🌈 Contents
- [Generation with Specific Condition](#Generation-with-Specific-Condition)
- [Personalization](#Personalization)
- [Subject-Driven Generation](#Subject-Driven-Generation)
- [Person-Driven Generation](#Person-Driven-Generation)
- [Style-Driven Generation](#Style-Driven-Generation)
- [Interaction-Driven Generation](#Interaction-Driven-Generation)
- [Image-Driven Generation](#Image-Driven-Generation)
- [Distribution-Driven Generation](#Distribution-Driven-Generation)
- [Spatial Control](#Spatial-Control)
- [Advanced Text-Conditioned Generation](#Advanced-Text-Conditioned-Generation)
- [In-Context Generation](#In-Context-Generation)
- [Brain-Guided Generation](#Brain-Guided-Generation)
- [Sound-Guided Generation](#Sound-Guided-Generation)
- [Text Rendering](#Text-Rendering)
- [Generation with Multiple Conditions](#Generation-with-Multiple-Conditions)
- [Joint Training](#Joint-Training)
- [Continual Learning](#Continual-Learning)
- [Weight Fusion](#Weight-Fusion)
- [Attention-based Integration](#Attention-based-Integration)
- [Guidance Composition](#Guidance-Composition)
- [Universal Controllable Generation](#Universal-Controllable-Generation)
- [Universal Conditional Score Prediction](#Universal-Conditional-Score-Prediction)
- [Universal Condition-Guided Score Estimation](#Universal-Condition-Guided-Score-Estimation)
<!-- start -->
## 🚀Generation with Specific Condition
### 🍇Personalization
#### 🍉Subject-Driven Generation
**DreamBlend: Advancing Personalized Fine-tuning of Text-to-Image Diffusion Models.**<br>
*Shwetha Ram, Tal Neiman, Qianli Feng, Andrew Stuart, Son Tran, Trishul Chilimbi.*<br>
arXiv 2024. [[PDF](https://arxiv.org/abs/2411.19390)]
**MagicTailor: Component-Controllable Personalization in Text-to-Image Diffusion Models.**<br>
*Donghao Zhou, Jiancheng Huang, Jinbin Bai, Jiaze Wang, Hao Chen, Guangyong Chen, Xiaowei Hu, Pheng-Ann Heng.*<br>
arXiv 2024. [[PDF](https://arxiv.org/abs/2410.13370)]
**PartCraft: Crafting Creative Objects by Parts.**<br>
*Kam Woh Ng, Xiatian Zhu, Yi-Zhe Song, Tao Xiang.*<br>
ECCV 2024. [[PDF](https://arxiv.org/aExcerpt of 75,015 characters
Read on GitHub
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:22b24d7e317ce60b, topic:diffusion-models, topic:text-to-image, desc:text-to-image
matched fp:22b24d7e317ce60b, topic:awesome, topic:awesome-list, desc:collection of resources