Instructions to use DionTimmer/controlnet_qrcode-control_v1p_sd15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DionTimmer/controlnet_qrcode-control_v1p_sd15 with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("DionTimmer/controlnet_qrcode-control_v1p_sd15") pipe = StableDiffusionControlNetPipeline.from_pretrained( "fill-in-base-model", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
Update README.md
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by Loop16 - opened
README.md
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license: openrail++
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language:
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- en
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library_name: diffusers
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pipeline_tag: image-to-image
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---
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init_image = resize_for_condition_image(init_image, 768)
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generator = torch.manual_seed(123121231)
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image = pipe(prompt="a bilboard in NYC with a qrcode",
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negative_prompt="ugly, disfigured, low quality, blurry
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image=init_image,
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control_image=condition_image,
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width=768,
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license: openrail++
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language:
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- en
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- es
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library_name: diffusers
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pipeline_tag: image-to-image
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---
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init_image = resize_for_condition_image(init_image, 768)
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generator = torch.manual_seed(123121231)
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image = pipe(prompt="a bilboard in NYC with a qrcode",
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negative_prompt="ugly, disfigured, low quality, blurry",
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image=init_image,
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control_image=condition_image,
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width=768,
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