Instructions to use tensorart/SD3.5M-Controlnet-Canny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tensorart/SD3.5M-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("tensorart/SD3.5M-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
- Local Apps Settings
- Draw Things
- DiffusionBee
Update README.md
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README.md
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@@ -40,6 +40,7 @@ image = pipe(
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negative_prompt=negative_prompt,
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control_image=control_image,
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guidance_scale=4.5,
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).images[0]
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image.save('image.jpg')
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```
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num_inference_steps=8,
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negative_prompt=negative_prompt,
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control_image=control_image,
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-
guidance_scale=1.5
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).images[0]
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image.save('image.jpg')
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```
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negative_prompt=negative_prompt,
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control_image=control_image,
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guidance_scale=4.5,
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controlnet_conditioning_scale=0.8
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).images[0]
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image.save('image.jpg')
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```
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num_inference_steps=8,
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negative_prompt=negative_prompt,
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control_image=control_image,
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+
guidance_scale=1.5,
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controlnet_conditioning_scale=0.8
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).images[0]
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image.save('image.jpg')
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```
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