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@@ -40,7 +40,7 @@ This model is still in its infancy and it's meant to be constantly updated and t
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  - #### Stable:
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  - No stable version available right now.
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  - #### Beta:
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- - [vae.2410000.pt](https://huggingface.co/Sygil/Sygil-Muse/blob/main/vae.2410000.pt): Trained from scratch for 2.41M steps
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  - [maskgit.1206000.p](https://huggingface.co/Sygil/Sygil-Muse/blob/main/maskgit.1206000.pt): Maskgit trained from the VAE for 1.20M steps
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  Note: Checkpoints under the Beta section are updated daily or at least 3-4 times a week. This is usually the equivalent of 1-2 training session,
@@ -64,7 +64,7 @@ The model was trained on the following dataset:
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  - **Resolution/Image Size**: First trained at a resolution of 64x64 and then increased to 512x512. Check the notes down below for more details on this.
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  - **Dimension:** 256
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  - **vq_codebook_size:** 256
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- - **Total Training Steps:** 2,410,000
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  Note: On Muse we can change the image_size or resolution at any time without having to train the model from scratch again, this allows us to first train the model at low resolution using the same `dim` and `vq_codebook_size` to train faster and then we can increase the `image_size` and use a higher resolution once the model has trained enough.
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  - #### Stable:
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  - No stable version available right now.
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  - #### Beta:
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+ - [vae.2670000.pt](https://huggingface.co/Sygil/Sygil-Muse/blob/main/vae.2670000.pt): Trained from scratch for 2.67M steps
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  - [maskgit.1206000.p](https://huggingface.co/Sygil/Sygil-Muse/blob/main/maskgit.1206000.pt): Maskgit trained from the VAE for 1.20M steps
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  Note: Checkpoints under the Beta section are updated daily or at least 3-4 times a week. This is usually the equivalent of 1-2 training session,
 
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  - **Resolution/Image Size**: First trained at a resolution of 64x64 and then increased to 512x512. Check the notes down below for more details on this.
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  - **Dimension:** 256
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  - **vq_codebook_size:** 256
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+ - **Total Training Steps:** 2,670,000
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  Note: On Muse we can change the image_size or resolution at any time without having to train the model from scratch again, this allows us to first train the model at low resolution using the same `dim` and `vq_codebook_size` to train faster and then we can increase the `image_size` and use a higher resolution once the model has trained enough.
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