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
Ming Li commited on
Commit ·
e96df59
1
Parent(s): 9a76fba
update requirements
Browse files- requirements.txt +1 -1
requirements.txt
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@@ -7,7 +7,7 @@ gradio_client==0.6.1
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huggingface-hub==0.21.2
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mediapipe==0.10.1
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opencv-python-headless==4.8.0.74
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safetensors==0.
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torch==2.0.1
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torchvision==0.15.2
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transformers==4.38.1
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huggingface-hub==0.21.2
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mediapipe==0.10.1
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opencv-python-headless==4.8.0.74
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safetensors==0.4.1
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torch==2.0.1
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torchvision==0.15.2
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transformers==4.38.1
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