Instructions to use RiverZ/ICEdit-normal-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use RiverZ/ICEdit-normal-lora 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("RiverZ/ICEdit-normal-lora", 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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README.md
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license: other
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license_name: no-commercial
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license_link: https://github.com/River-Zhang/ICEdit/blob/main/LICENSE
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---
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license: other
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license_name: no-commercial
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license_link: https://github.com/River-Zhang/ICEdit/blob/main/LICENSE
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datasets:
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- osunlp/MagicBrush
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- TIGER-Lab/OmniEdit-Filtered-1.2M
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language:
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- en
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base_model:
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- black-forest-labs/FLUX.1-Fill-dev
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pipeline_tag: image-to-image
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library_name: diffusers
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---
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This checkpoint is identical to the one hosted at https://huggingface.co/RiverZ/normal-lora. We've simply given it a different name to make it more discoverable and easier for you to find during your searches.
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