Instructions to use FQiao/GenStereo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FQiao/GenStereo 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("FQiao/GenStereo", 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 `library_name: diffusers` to metadata
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by nielsr HF Staff - opened
README.md
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license: mit
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pipeline_tag: image-to-image
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# [ICCV 2025] GenStereo: Towards Open-World Generation of Stereo Images and Unsupervised Matching
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This repository contains the model presented in [GenStereo: Towards Open-World Generation of Stereo Images and Unsupervised Matching](https://huggingface.co/papers/2503.12720). The models are finetuned on Stable Diffusion 1.5, for SD v2.1, you can find [here](https://huggingface.co/FQiao/GenStereo-sd2.1).
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# [ICCV 2025] GenStereo: Towards Open-World Generation of Stereo Images and Unsupervised Matching
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This repository contains the model presented in [GenStereo: Towards Open-World Generation of Stereo Images and Unsupervised Matching](https://huggingface.co/papers/2503.12720). The models are finetuned on Stable Diffusion 1.5, for SD v2.1, you can find [here](https://huggingface.co/FQiao/GenStereo-sd2.1).
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