Instructions to use jasperai/Flux.1-dev-Controlnet-Upscaler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jasperai/Flux.1-dev-Controlnet-Upscaler 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("jasperai/Flux.1-dev-Controlnet-Upscaler", 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

- Xet hash:
- 05535d43a0235e257d29d11786ab8d6a9497d3f7722272d741115de16de89ea4
- Size of remote file:
- 8.07 MB
- SHA256:
- 6d6627f382b9c4e5b1340c4eb9c5bf5c75385a97c8ac5a73c243059fca33dffd
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