Image-to-Image
Diffusers
Safetensors
ControlNet
super-resolution
upscaler
text-to-image
stable-diffusion
lora
fluxpipeline
Instructions to use R1000/Flux.1-dev-Controlnet-Upscaler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use R1000/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("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("R1000/Flux.1-dev-Controlnet-Upscaler") 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
- Local Apps
- Draw Things
Update README.md
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README.md
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@@ -6,7 +6,7 @@ license: other
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license_name: flux-1-dev-non-commercial-license
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license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
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pipeline_tag: image-to-image
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inference:
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tags:
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- ControlNet
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- super-resolution
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license_name: flux-1-dev-non-commercial-license
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license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
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pipeline_tag: image-to-image
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inference: True
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tags:
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- ControlNet
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- super-resolution
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