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@@ -108,3 +108,14 @@ gen_images = pipe(
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  gen_images.save('out_canny.png')
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  ```
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  <a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_canny.png"><img width="480" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_canny.png"/></a>
 
 
 
 
 
 
 
 
 
 
 
 
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  gen_images.save('out_canny.png')
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  ```
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  <a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_canny.png"><img width="480" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_canny.png"/></a>
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+ ### Training
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+ Our training script was built on top of the official training script that we provide [here](https://github.com/huggingface/diffusers/blob/main/examples/t2i_adapter/README_sdxl.md).
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+ The model is trained on 3M high-resolution image-text pairs from LAION-Aesthetics V2 with
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+ - Training steps: 20000
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+ - Batch size: Data parallel with a single gpu batch size of `16` for a total batch size of `256`.
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+ - Learning rate: Constant learning rate of `1e-5`.
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+ - Mixed precision: fp16