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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -10,7 +10,7 @@ The implementation makes minimum changes over the official codebase of Textual I
## Usage

### Preparation
First set-up the ```ldm``` enviroment following the instruction from textual inversion repo, or the original Stable Diffusion repo.
First set-up the ```ldm``` environment following the instruction from textual inversion repo, or the original Stable Diffusion repo.

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```.

Expand All @@ -23,7 +23,7 @@ python scripts/stable_txt2img.py --ddim_eta 0.0 --n_samples 8 --n_iter 1 --scale
I generate 8 images for regularization, but more regularization images may lead to stronger regularization and better editability. After that, save the generated images (separately, one image per ```.png``` file) at ```/root/to/regularization/images```.

**Updates on 9/9**
We should definitely use more images for regularization. Please try 100 or 200, to better align with the original paper. To acomodate this, I shorten the "repeat" of reg dataset in the [config file](https://github.com/XavierXiao/Dreambooth-Stable-Diffusion/blob/main/configs/stable-diffusion/v1-finetune_unfrozen.yaml#L96).
We should definitely use more images for regularization. Please try 100 or 200, to better align with the original paper. To accommodate this, I shorten the "repeat" of reg dataset in the [config file](https://github.com/XavierXiao/Dreambooth-Stable-Diffusion/blob/main/configs/stable-diffusion/v1-finetune_unfrozen.yaml#L96).

For some cases, if the generated regularization images are highly unrealistic (happens when you want to generate "man" or "woman"), you can find a diverse set of images (of man/woman) online, and use them as regularization images.

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