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
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README.md
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@@ -54,7 +54,7 @@ from diffusers.utils import load_image
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from diffusers import FluxControlNetPipeline, FluxControlNetModel, FluxMultiControlNetModel
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base_model = 'black-forest-labs/FLUX.1-dev'
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controlnet_model_union = '
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controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union, torch_dtype=torch.bfloat16)
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controlnet = FluxMultiControlNetModel([controlnet_union]) # we always recommend loading via FluxMultiControlNetModel
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```python
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from diffusers import FluxControlNetModel, FluxMultiControlNetModel
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controlnet_model_union = '
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controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union, torch_dtype=torch.bfloat16)
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controlnet_model_depth = '
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controlnet_depth = FluxControlNetModel.from_pretrained(controlnet_model_depth, torch_dtype=torch.bfloat16)
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controlnet = FluxMultiControlNetModel([controlnet_union, controlnet_depth])
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from diffusers import FluxControlNetPipeline, FluxControlNetModel, FluxMultiControlNetModel
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base_model = 'black-forest-labs/FLUX.1-dev'
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controlnet_model_union = 'Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro'
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controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union, torch_dtype=torch.bfloat16)
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controlnet = FluxMultiControlNetModel([controlnet_union]) # we always recommend loading via FluxMultiControlNetModel
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```python
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from diffusers import FluxControlNetModel, FluxMultiControlNetModel
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controlnet_model_union = 'Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro'
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controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union, torch_dtype=torch.bfloat16)
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controlnet_model_depth = 'Shakker-Labs/FLUX.1-dev-Controlnet-Depth'
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controlnet_depth = FluxControlNetModel.from_pretrained(controlnet_model_depth, torch_dtype=torch.bfloat16)
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controlnet = FluxMultiControlNetModel([controlnet_union, controlnet_depth])
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