Instructions to use aggr8/PixEdit-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use aggr8/PixEdit-v1 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("aggr8/PixEdit-v1", 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
Improve model card
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by nielsr HF Staff - opened
README.md
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datasets:
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- AILab-CVC/SEED-Data-Edit-Part2-3
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- McGill-NLP/AURORA
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---
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```text
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@misc{goswami2024grapegenerateplaneditframeworkcompositional,
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title={GraPE: A Generate-Plan-Edit Framework for Compositional T2I Synthesis},
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url={https://arxiv.org/abs/2412.06089},
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}
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```
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base_model:
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- PixArt-alpha/PixArt-Sigma
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datasets:
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- AILab-CVC/SEED-Data-Edit-Part2-3
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- McGill-NLP/AURORA
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license: cc-by-4.0
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pipeline_tag: image-to-image
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library_name: diffusers
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```text
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@misc{goswami2024grapegenerateplaneditframeworkcompositional,
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title={GraPE: A Generate-Plan-Edit Framework for Compositional T2I Synthesis},
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url={https://arxiv.org/abs/2412.06089},
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}
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Project page: https://dair-iitd.github.io/GraPE/
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Code: https://github.com/dair-iitd/PixEdit
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```
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