Instructions to use showlab/OmniConsistency with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use showlab/OmniConsistency 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("showlab/OmniConsistency", 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
add pipeline tag for better discoverability
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by linoyts HF Staff - opened
README.md
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license: mit
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base_model:
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- black-forest-labs/FLUX.1-dev
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---
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**OmniConsistency: Learning Style-Agnostic
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<br>
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[Show Lab](https://sites.google.com/view/showlab), National University of Singapore
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<br>
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<img src='./figure/teaser.png' width='100%' />
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## Installation
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license: mit
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base_model:
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- black-forest-labs/FLUX.1-dev
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pipeline_tag: image-to-image
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---
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**OmniConsistency: Learning Style-Agnostic
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<br>
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[Show Lab](https://sites.google.com/view/showlab), National University of Singapore
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<br>
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[[official code]](https://github.com/showlab/OmniConsistency)
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<img src='./figure/teaser.png' width='100%' />
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## Installation
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