Instructions to use limingcv/reward_controlnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use limingcv/reward_controlnet with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("limingcv/reward_controlnet", 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
Update the depth model path
Browse files
model.py
CHANGED
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@@ -20,7 +20,7 @@ CONTROLNET_MODEL_IDS = {
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'scribble': 'lllyasviel/control_v11p_sd15_scribble',
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'softedge': 'lllyasviel/control_v11p_sd15_softedge',
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'segmentation': 'lllyasviel/control_v11p_sd15_seg',
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'depth': 'lllyasviel/
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'NormalBae': 'lllyasviel/control_v11p_sd15_normalbae',
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'lineart': 'lllyasviel/control_v11p_sd15_lineart',
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'lineart_anime': 'lllyasviel/control_v11p_sd15s2_lineart_anime',
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'scribble': 'lllyasviel/control_v11p_sd15_scribble',
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'softedge': 'lllyasviel/control_v11p_sd15_softedge',
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'segmentation': 'lllyasviel/control_v11p_sd15_seg',
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+
'depth': 'lllyasviel/control_v11f1p_sd15_depth',
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'NormalBae': 'lllyasviel/control_v11p_sd15_normalbae',
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'lineart': 'lllyasviel/control_v11p_sd15_lineart',
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'lineart_anime': 'lllyasviel/control_v11p_sd15s2_lineart_anime',
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