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author | Xavier <xiaozhisheng950@gmail.com> | 2022-09-06 00:35:02 -0700 |
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committer | GitHub <noreply@github.com> | 2022-09-06 00:35:02 -0700 |
commit | dfff8609ea43ae7ca33bf146d33bab04f5649bc1 (patch) | |
tree | a5cfe63bcc848a183a7323a2db50e67dc035b30b | |
parent | Update README.md (diff) |
Update README.md
-rw-r--r-- | README.md | 16 |
1 files changed, 14 insertions, 2 deletions
@@ -11,14 +11,26 @@ The implementation makes minimum changes over the official codebase of Textual I ### 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```. -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 ```phito of a xxx```, where ```xxx``` is a word that describes the class of your object, such as ```dog```. The command is +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 xxx```, where ```xxx``` is a word that describes the class of your object, such as ```dog```. The command is ``` -python scripts/stable_txt2img.py --ddim_eta 0.0 --n_samples 8 --n_iter 1 --scale 10.0 --ddim_steps 50 --ckpt /path/to/original/stable-diffusion/sd-v1-4-full-ema.ckpt --prompt "a photo of a xxx" +python scripts/stable_txt2img.py --ddim_eta 0.0 --n_samples 8 --n_iter 1 --scale 10.0 --ddim_steps 50 --ckpt /path/to/original/stable-diffusion/sd-v1-4-full-ema.ckpt --prompt "a photo of a <xxx>" ``` I generate 8 images for regularization. After that, save the generated images (separately, one image per ```.png``` file) at ```/root/to/regularization/images```. ### Training +Training can be done by running the following command + +``` +python main.py --base configs/stable-diffusion/v1-finetune_unfrozen.yaml + -t + --actual_resume /path/to/original/stable-diffusion/sd-v1-4-full-ema.ckpt + -n <job name> + --gpus 0, + --data_root /root/to/training/images + --reg_data_root /root/to/regularization/images + --class_word <xxx> +``` ### Generation |