Text-to-Image
Diffusers
Safetensors
English
Text-to-Image
ControlNet
Diffusers
Flux.1-dev
image-generation
Stable Diffusion
Instructions to use Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", 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
- Local Apps
- Draw Things
- DiffusionBee
Update README.md (#3)
Browse files- Update README.md (4c305dcf156961467fa575d55e02cff2ab678d43)
Co-authored-by: Seungyong Lee <RyanL22@users.noreply.huggingface.co>
README.md
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@@ -70,7 +70,7 @@ control_mode_depth = 2
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control_image_canny = load_image("https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro/resolve/main/assets/canny.jpg")
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control_mode_canny = 0
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width, height =
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image = pipe(
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prompt,
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control_image_canny = load_image("https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro/resolve/main/assets/canny.jpg")
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control_mode_canny = 0
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width, height = control_image_depth.size
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image = pipe(
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prompt,
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