Instructions to use InstantX/InstantIR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use InstantX/InstantIR with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("InstantX/InstantIR", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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<a href='https://arxiv.org/abs/2410.06551'><img src='https://img.shields.io/badge/paper-arXiv-b31b1b.svg' style="height: 24px;"></a>
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<a href='https://jy-joy.github.io/InstantIR'><img src='https://img.shields.io/badge/project-Website-green' style="height: 24px;"></a>
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<a href='https://github.com/
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> **InstantIR** is a novel single-image restoration model designed to resurrect your damaged images, delivering extrem-quality yet realistic details. You can further boost **InstantIR** performance with additional text prompts, even achieve customized editing!
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<a href='https://arxiv.org/abs/2410.06551'><img src='https://img.shields.io/badge/paper-arXiv-b31b1b.svg' style="height: 24px;"></a>
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<a href='https://jy-joy.github.io/InstantIR'><img src='https://img.shields.io/badge/project-Website-green' style="height: 24px;"></a>
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<a href='https://github.com/instantX-research/InstantIR'><img src='https://img.shields.io/badge/code-Github-informational' style="height: 24px;"></a>
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> **InstantIR** is a novel single-image restoration model designed to resurrect your damaged images, delivering extrem-quality yet realistic details. You can further boost **InstantIR** performance with additional text prompts, even achieve customized editing!
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