Instructions to use InstantX/FLUX.1-dev-Controlnet-Canny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use InstantX/FLUX.1-dev-Controlnet-Canny with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("InstantX/FLUX.1-dev-Controlnet-Canny", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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README.md
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# FLUX.1-dev Controlnet
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<img src="./images/image_demo.jpg" width = "800" />
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<img src="./images/image_demo_weight.png" width = "800" />
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# FLUX.1-dev Controlnet
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We have completed the training of the first version.
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The training was conducted with a total pixel count of `1024*1024` at multi-scale.
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We trained for 30k steps using a batch size of 8*8.
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<img src="./images/image_demo.jpg" width = "800" />
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<img src="./images/image_demo_weight.png" width = "800" />
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