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@@ -21,9 +21,8 @@ how you can load this into a diffusers-based notebook like [Doohickey](https://g
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  from huggingface_hub import hf_hub_download
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  stable_inversion = "user/my-stable-inversion" #@param {type:"string"}
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- if len(stable_inversion)>1:
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- g = hf_hub_download(repo_id=stable_inversion, filename="token_embeddings.pt")
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- text_encoder.text_model.embeddings.token_embedding.weight = torch.load(g)
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  ```
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  it was trained on 1024 images matching the 'genshin_impact' tag on safebooru, epochs 1 and 2 had the model being fed the full captions, epoch 3 had 50% of the tags in the caption, and epoch 4 had 25% of the tags in the caption. Learning rate was 1e-3 and the loss curve looked like this ![](https://pbs.twimg.com/media/FdsdivkWIBQYmZd?format=jpg&name=small)
 
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  from huggingface_hub import hf_hub_download
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  stable_inversion = "user/my-stable-inversion" #@param {type:"string"}
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+ inversion_path = hf_hub_download(repo_id=stable_inversion, filename="token_embeddings.pt")
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+ text_encoder.text_model.embeddings.token_embedding.weight = torch.load(inversion_path)
 
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  ```
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  it was trained on 1024 images matching the 'genshin_impact' tag on safebooru, epochs 1 and 2 had the model being fed the full captions, epoch 3 had 50% of the tags in the caption, and epoch 4 had 25% of the tags in the caption. Learning rate was 1e-3 and the loss curve looked like this ![](https://pbs.twimg.com/media/FdsdivkWIBQYmZd?format=jpg&name=small)