|
--- |
|
license: apache-2.0 |
|
--- |
|
|
|
# IterComp(ICLR 2025) |
|
|
|
Official Repository of the paper: *[IterComp](https://arxiv.org/abs/2410.07171)*. |
|
<p align="left"> |
|
<a href='https://arxiv.org/abs/2410.07171'> |
|
<img src='https://img.shields.io/badge/Arxiv-2410.07171-A42C25?style=flat&logo=arXiv&logoColor=A42C25'></a> |
|
<a href='https://github.com/YangLing0818/IterComp'> |
|
<img src='https://img.shields.io/badge/GitHub-Code-black?style=flat&logo=github&logoColor=white'></a> |
|
</p> |
|
|
|
<img src="./itercomp.png" style="zoom:50%;" /> |
|
|
|
## News🔥🔥🔥 |
|
|
|
**[2025.02]** We open-source three composition-aware reward models in [HuggingFace Repo](https://huggingface.co/comin/IterComp/tree/main/reward_models), which can be used for preference learning and as **new image generation evaluators**. |
|
|
|
**[2025.02]** We enhance IterComp-RPG with LLMs that possess the strongest reasoning capabilities, including [**DeepSeek-R1**](https://github.com/deepseek-ai/DeepSeek-R1), [**OpenAI o3-mini**](https://openai.com/index/openai-o3-mini/), and [**OpenAI o1**](https://openai.com/index/learning-to-reason-with-llms/) to achieve outstanding compositional image generation under complex prompts. |
|
|
|
**[2025.01]** IterComp is accepted by ICLR 2025!!! |
|
|
|
**[2024.10]** Checkpoints of base diffusion model are publicly available on [HuggingFace Repo](https://huggingface.co/comin/IterComp). |
|
|
|
**[2024.10]** Our main code of IterComp is released. |
|
|
|
## Introduction |
|
|
|
IterComp is one of the new State-of-the-Art compositional generation methods. In this repository, we release the model training from [SDXL Base 1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) . |
|
|
|
## Text-to-Image Usage |
|
|
|
```python |
|
from diffusers import DiffusionPipeline |
|
import torch |
|
|
|
pipe = DiffusionPipeline.from_pretrained("comin/IterComp", torch_dtype=torch.float16, use_safetensors=True) |
|
pipe.to("cuda") |
|
# if using torch < 2.0 |
|
# pipe.enable_xformers_memory_efficient_attention() |
|
|
|
prompt = "An astronaut riding a green horse" |
|
image = pipe(prompt=prompt).images[0] |
|
image.save("output.png") |
|
``` |
|
|
|
IterComp can **serve as a powerful backbone for various compositional generation methods**, such as [RPG](https://github.com/YangLing0818/RPG-DiffusionMaster) and [Omost](https://github.com/lllyasviel/Omost). We recommend integrating IterComp into these approaches to achieve more advanced compositional generation results. |
|
|
|
## Citation |
|
|
|
``` |
|
@article{zhang2024itercomp, |
|
title={IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation}, |
|
author={Zhang, Xinchen and Yang, Ling and Li, Guohao and Cai, Yaqi and Xie, Jiake and Tang, Yong and Yang, Yujiu and Wang, Mengdi and Cui, Bin}, |
|
journal={arXiv preprint arXiv:2410.07171}, |
|
year={2024} |
|
} |
|
``` |
|
|
|
## |
|
|
