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authorXavier <xiaozhisheng950@gmail.com>2022-09-06 09:40:06 -0700
committerGitHub <noreply@github.com>2022-09-06 09:40:06 -0700
commit1318e0795a2319d351711f318cce952df96e6de1 (patch)
tree7e8eb523bb086b2588d3de88cd73fcf6a1815cd3
parentUpdate README.md (diff)
Update README.md
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@@ -33,7 +33,8 @@ python main.py --base configs/stable-diffusion/v1-finetune_unfrozen.yaml
--class_word <xxx>
```
-Detailed configuration can be found in configs/stable-diffusion/v1-finetune_unfrozen.yaml. In particular, the default learning rate is ```1.0e-6``` as I found the ```1.0e-5``` in the Dreambooth paper leads to poor editability. The parameter ```reg_weight``` corresponds to the weight of regularization in the Dreambooth paper, and the default is set to ```1.0```.
+Detailed configuration can be found in ```configs/stable-diffusion/v1-finetune_unfrozen.yaml```. In particular, the default learning rate is ```1.0e-6``` as I found the ```1.0e-5``` in the Dreambooth paper leads to poor editability. The parameter ```reg_weight``` corresponds to the weight of regularization in the Dreambooth paper, and the default is set to ```1.0```.
+Dreambooth requires a placeholder word ```[V]```, called identifier, as in the paper. This identifier needs to be a relatively rare tokens in the vocabulary. The original paper approaches this by using a rare word in T5-XXL tokenizer. For simplicity, here I just use a random word ```sks``` and hard coded it.. If you want to change that, simply make a change in [this file](https://github.com/XavierXiao/Dreambooth-Stable-Diffusion/blob/main/ldm/data/personalized.py#L10).
### Generation