Instructions to use comin/IterComp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use comin/IterComp with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("comin/IterComp", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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README.md
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## News🔥🔥🔥
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* IterComp
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## Introduction
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## News🔥🔥🔥
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**[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**.
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**[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.
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**[2025.01]** IterComp is accepted by ICLR 2025!!!
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**[2024.10]** Checkpoints of base diffusion model are publicly available on [HuggingFace Repo](https://huggingface.co/comin/IterComp).
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**[2024.10]** Our main code of IterComp is released.
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## Introduction
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