From 928cf6063d44f8191bc2c497c8db73bf2a586ada Mon Sep 17 00:00:00 2001 From: Amit Moryossef Date: Mon, 26 Sep 2022 10:33:05 +0300 Subject: [PATCH] docs(readme): add installation commands --- README.md | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 0b6a8424..31797488 100644 --- a/README.md +++ b/README.md @@ -12,11 +12,22 @@ The implementation makes minimum changes over the official codebase of Textual I ### Preparation First set-up the ```ldm``` enviroment following the instruction from textual inversion repo, or the original Stable Diffusion repo. +```bash +git clone https://github.com/XavierXiao/Dreambooth-Stable-Diffusion.git +cd Dreambooth-Stable-Diffusion +conda env create --file environment.yaml +``` + 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```. +```bash +git lfs install +git clone https://huggingface.co/CompVis/stable-diffusion-v-1-4-original +``` + 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. Note that in the original paper, the regularization images seem to be generated on-the-fly. However, here I generated a set of regularization images before the training. The text prompt for generating regularization images can be ```photo of a ```, where `````` is a word that describes the class of your object, such as ```dog```. The command is -``` +```bash 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 " ``` @@ -30,7 +41,7 @@ For some cases, if the generated regularization images are highly unrealistic (h ### Training Training can be done by running the following command -``` +```bash 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 @@ -50,7 +61,7 @@ Training will be run for 800 steps, and two checkpoints will be saved at ```./lo ### Generation After training, personalized samples can be obtained by running the command -``` +```bash python scripts/stable_txt2img.py --ddim_eta 0.0 --n_samples 8 --n_iter 1