Instructions to use InstantX/SD3-Controlnet-Canny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use InstantX/SD3-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("InstantX/SD3-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
Update README.md
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# SD3 Controlnet
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## Limitation
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Due to the fact that only 1024*1024 pixel resolution was used during the training phase,
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the inference performs best at this size, with other sizes yielding suboptimal results.
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We will initiate multi-resolution training in the future, and at that time, we will open-source the new weights.
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license: apache-2.0
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# SD3 Controlnet
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## Limitation
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Due to the fact that only 1024*1024 pixel resolution was used during the training phase,
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the inference performs best at this size, with other sizes yielding suboptimal results.
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We will initiate multi-resolution training in the future, and at that time, we will open-source the new weights.
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