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
- Draw Things
- DiffusionBee
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license: apache-2.0
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# IterComp
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Official Repository of the paper: *[IterComp](https://arxiv.org/abs/2410.07171)*.
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<p align="left">
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## News🔥🔥🔥
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* Oct.9, 2024. Our checkpoints are publicly available on [HuggingFace Repo](https://huggingface.co/comin/IterComp).
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## Introduction
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license: apache-2.0
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# IterComp(ICLR 2025)
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Official Repository of the paper: *[IterComp](https://arxiv.org/abs/2410.07171)*.
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<p align="left">
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## News🔥🔥🔥
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* IterComp is accepted by ICLR 2025!!
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* Oct.9, 2024. Our checkpoints are publicly available on [HuggingFace Repo](https://huggingface.co/comin/IterComp).
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## Introduction
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