Instructions to use zino36/FLUX.1-dev-Controlnet-Inpainting-Beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zino36/FLUX.1-dev-Controlnet-Inpainting-Beta 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("zino36/FLUX.1-dev-Controlnet-Inpainting-Beta", 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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This repository hosts an improved Inpainting ControlNet checkpoint for the [alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha](https://huggingface.co/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha) model, developed by the AlimamaCreative Team.
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## Key Enhancements
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Our latest inpainting model brings significant improvements compared to the previous version:
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This repository hosts an improved Inpainting ControlNet checkpoint for the [alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha](https://huggingface.co/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha) model, developed by the AlimamaCreative Team.
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## 🔥News!
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- **[2024.10.16]** We release a 8-step FLUX.1 distilled lora [alimama-creative/FLUX.1-Turbo-Alpha](https://huggingface.co/alimama-creative/FLUX.1-Turbo-Alpha), which is compatible with our Inpainting Controlnet.
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## Key Enhancements
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Our latest inpainting model brings significant improvements compared to the previous version:
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