make the execution work

.dockerignore

@@ -0,0 +1,4 @@
+./venv
+./danbooru-aesthetic
+./logs
+*.ckpt

.gitignore

@@ -40,6 +40,7 @@ lib64/
 parts/
 sdist/
 var/
+venv/
 wheels/
 share/python-wheels/
 *.egg-info/
@@ -54,4 +55,4 @@ MANIFEST
 /src/
 
 #Obsidian
-.obsidian/
+.obsidian/

Dockerfile

@@ -0,0 +1,10 @@
+FROM pytorch/pytorch:latest
+
+RUN apt update && \
+    apt install -y git curl unzip vim && \
+    pip install git+https://github.com/derfred/lightning.git@waifu-1.6.0#egg=pytorch-lightning
+RUN mkdir /waifu
+COPY . /waifu/
+WORKDIR /waifu
+RUN grep -v pytorch-lightning requirements.txt > requirements-waifu.txt && \
+    pip install -r requirements-waifu.txt

docs/en/training/executing.md

@@ -1,13 +1,51 @@
-# 1. Executing
+# 3. Executing
 
-## Installation
+There are two modes of executing the training:
+1. Using docker image. This is the fastest way to get started.
+2. Using system python install. Allows more customization.
+
+Note: You will need to provide the initial checkpoint for resuming the training. This must be a version with the full EMA. Otherwise you will get this error:
+```
+RuntimeError: Error(s) in loading state_dict for LatentDiffusion:
+  Missing key(s) in state_dict: "model_ema.diffusion_modeltime_embed0weight", "model_ema.diffusion_modeltime_embed0bias".... (Many lines of similar outputs)
+```
+
+## 1. Using docker image
+
+An image is provided at `ghcr.io/derfred/waifu-diffusion`. Execute it using by adjusting the NUM_GPU variable:
+```
+docker run -it -e NUM_GPU=x ghcr.io/derfred/waifu-diffusion
+```
+
+Next you will want to download the starting checkpoint into the file `model.ckpt` and copy the training data in the directory `/waifu/danbooru-aesthetic`.
+
+Finally execute the training using:
+```
+sh train.sh -t -n "aesthetic" --resume_from_checkpoint model.ckpt --base ./configs/stable-diffusion/v1-finetune-4gpu.yaml --no-test --seed 25 --scale_lr False --data_root "./danbooru-aesthetic"
+```
+
+## 2. system python install
 
 First install the dependencies:
 ```bash
 pip install -r requirements.txt
 ```
 
-## Executing
+Next you will want to download the starting checkpoint into the file `model.ckpt` and copy the training data in the directory `/waifu/danbooru-aesthetic`.
-```bash
+
-sh train.sh
+Also you will need to edit the configuration in `./configs/stable-diffusion/v1-finetune-4gpu.yaml`. In the `data` section (around line 70) change the `batch_size` and `num_workers` to the number of GPUs you are using:
 ```
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 4
+    num_workers: 4
+    wrap: false
+```
+
+Finally execute the training using the following command. You need to adjust the `--gpu` parameter according to your GPU settings.
+```bash
+sh train.sh -t -n "aesthetic" --resume_from_checkpoint model.ckpt --base ./configs/stable-diffusion/v1-finetune-4gpu.yaml --no-test --seed 25 --scale_lr False --data_root "./danbooru-aesthetic" --gpu=0,1,2,3,
+```
+
+In case you get an error stating `KeyError: 'Trying to restore optimizer state but checkpoint contains only the model. This is probably due to ModelCheckpoint.save_weights_only being set to True.'` follow these instructions: https://discord.com/channels/930499730843250783/953132470528798811/1018668937052962908

requirements.txt

@@ -1,10 +1,10 @@
-numpy==1.19.2
+numpy==1.21.6
 albumentations==0.4.3
-opencv-python==4.1.2.30
+opencv-python
 pudb==2019.2
 imageio==2.9.0
 imageio-ffmpeg==0.4.2
-pytorch-lightning==1.6.5
+pytorch-lightning==1.6.0
 omegaconf==2.1.1
 test-tube>=0.7.5
 streamlit>=0.73.1

train.sh

@@ -1 +1,15 @@
-python3 main.py --train --resume model.ckpt --base ./configs/stable-diffusion/v1-finetune-4gpu.yaml --no-test --seed 25 --scale_lr False --gpus 0,1,2,3
+#!/bin/bash
+
+ARGS=""
+if [ ! -z "$NUM_GPU" ]; then
+  ARGS="--gpu="
+  for i in $(seq 0 $((NUM_GPU-1)))
+  do
+    ARGS="$ARGS$i,"
+  done
+
+  sed -i "s/batch_size: 4/batch_size: $NUM_GPU/g" ./configs/stable-diffusion/v1-finetune-4gpu.yaml
+  sed -i "s/num_workers: 4/num_workers: $NUM_GPU/g" ./configs/stable-diffusion/v1-finetune-4gpu.yaml
+fi
+
+python3 main.py $ARGS "$@"