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author | Xavier <xiaozhisheng950@gmail.com> | 2022-09-06 10:41:11 -0700 |
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committer | GitHub <noreply@github.com> | 2022-09-06 10:41:11 -0700 |
commit | 725245bcca28937c4bdcc5b476cce53f580a19e6 (patch) | |
tree | bdef767f593ad6fc21dd2ce755a392d2c96e1ed4 | |
parent | Update README.md (diff) |
Update README.md
-rw-r--r-- | README.md | 2 |
1 files changed, 1 insertions, 1 deletions
@@ -9,7 +9,7 @@ The implementation makes minimum changes over the official codebase of Textual I ## Usage ### Preparation -To fine-tune a stable diffusion model, you need to obtain the pre-trained stable diffusion models following their [instructions](https://github.com/CompVis/stable-diffusion#stable-diffusion-v1). Weights can be downloads on [HuggingFace](https://huggingface.co/CompVis). You can decide which version of checkpoint to use, but I use ```sd-v1-4-full-ema.ckpt```. +To fine-tune a stable diffusion model, you need to obtain the pre-trained stable diffusion models following their [instructions](https://github.com/CompVis/stable-diffusion#stable-diffusion-v1). Weights can be downloaded on [HuggingFace](https://huggingface.co/CompVis). You can decide which version of checkpoint to use, but I use ```sd-v1-4-full-ema.ckpt```. We also need to create a set of images for regularization, as the fine-tuning algorithm of Dreambooth requires that. Details of the algorithm can be found in the paper. The text prompt can be ```photo of a <class>```, where ```<class>``` is a word that describes the class of your object, such as ```dog```. The command is |