Stable Diffusion Dreambooth (Finetuning)

Introduction

Stable diffusion has revolutionalized text2image models by producing high quality images based on a prompt. Dreambooth is a approach for personalization of text-to-image diffusion models. With images as input subject, we can fine-tune a pretrained text-to-image model

Although the dreambooth paper used Imagen to finetune the pre-trained model since both the Imagen model and Dreambooth code are closed source, several opensource projects have emerged using stable diffusion.

Dreambooth makes stable-diffusion even more powered with the ability to generate realistic looking pictures of humans, animals or any other object by just training them on 20-30 images.

In this example tutorial, we will be fine-tuning a pretrained stable diffusion using images of a human and generating images of him drinking coffee.

To get started, you need to install the Bacalhau client, see more information here

Prerequisite

Quick Test

The following command generates the following:

Subject: SBF
Prompt: a photo of SBF without hair

bacalhau docker run \
 --gpu 1 \
 --timeout 3600 \
 --wait-timeout-secs 3600 \
  -i QmRKnvqvpFzLjEoeeNNGHtc7H8fCn9TvNWHFnbBHkK8Mhy  \
  jsacex/dreambooth:full \
  -- bash finetune.sh /inputs /outputs "a photo of sbf man" "a photo of man" 3000 "/man" "/model"

Inference

bacalhau docker run \
 --gpu 1 \
  -v QmUEJPr5pfV6tRzWQuNSSb3wdcN6tRQS5tdk3dYSCJ55Xs:/SBF.ckpt \
   jsacex/stable-diffusion-ckpt \
   -- conda run --no-capture-output -n ldm python scripts/txt2img.py --prompt "a photo of sbf without hair" --plms --ckpt ../SBF.ckpt --skip_grid --n_samples 1 --skip_grid --outdir ../outputs 

Output:

Setting up Docker Container

info

You can skip this section entirely and directly go to running a job on bacalhau

Building this container requires you to have a supported GPU which needs to have 16gb+ of memory, since it can be resource intensive.

We will create a Dockerfile and add the desired configuration to the file. These commands specify how the image will be built, and what extra requirements will be included.

FROM pytorch/pytorch:1.12.1-cuda11.3-cudnn8-devel
WORKDIR /
# Install requirements
# RUN git clone https://github.com/TheLastBen/diffusers
RUN apt update && apt install wget git unzip -y
RUN wget -q https://gist.githubusercontent.com/js-ts/28684a7e6217214ec944a9224584e9af/raw/d7492bc8f36700b75d51e3346259d1a466b99a40/train_dreambooth.py
RUN wget -q https://github.com/TheLastBen/diffusers/raw/main/scripts/convert_diffusers_to_original_stable_diffusion.py
# RUN cp /content/convert_diffusers_to_original_stable_diffusion.py /content/diffusers/scripts/convert_diffusers_to_original_stable_diffusion.py
RUN pip install -qq git+https://github.com/TheLastBen/diffusers
RUN pip install -q accelerate==0.12.0 transformers ftfy bitsandbytes gradio natsort
# Install xformers
RUN pip install -q https://github.com/metrolobo/xformers_wheels/releases/download/1d31a3ac_various_6/xformers-0.0.14.dev0-cp37-cp37m-linux_x86_64.whl

RUN wget 'https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/Regularization/Women' -O woman.zip

RUN wget 'https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/Regularization/Men' -O man.zip

RUN wget 'https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/Regularization/Mix' -O mix.zip

RUN unzip -j woman.zip -d woman

RUN unzip -j man.zip -d man

RUN unzip -j mix.zip -d mix

This container is using the pytorch/pytorch:1.12.1-cuda11.3-cudnn8-devel image and the working directory is set. Next, we add our custom code and pull the dependent repositories.

python clear_mem.py

accelerate launch train_dreambooth.py \
    --image_captions_filename \
   --train_text_encoder \
    --save_n_steps=$(expr $5 / 6) \
    --stop_text_encoder_training=$(expr $5 + 100) \
       --class_data_dir="$6" \
  --pretrained_model_name_or_path=${7:=/model} \
--tokenizer_name=${7:=/model}/tokenizer/ \
    --instance_data_dir="$1" \
    --output_dir="$2" \
    --instance_prompt="$3" \
   --class_prompt="$4" \
    --seed=96576 \
    --resolution=512 \
    --mixed_precision="fp16" \
    --train_batch_size=1 \
    --gradient_accumulation_steps=1 \
    --use_8bit_adam \
    --learning_rate=2e-6 \
    --lr_scheduler="polynomial" \
    --center_crop \
    --lr_warmup_steps=0 \
    --max_train_steps=$5

echo  Convert weights to ckpt
python convert_diffusers_to_original_stable_diffusion.py --model_path $2  --checkpoint_path $2/model.ckpt --half
echo model saved at $2/model.ckpt

The shell script is there to make things much simpler, since the command to train the model needs many parameters to pass and later convert the model weights to checkpoint, you can edit this script and add in your own parameters

Downloading the models

To download the models and run a test job in the Docker file, copy the following:

FROM pytorch/pytorch:1.12.1-cuda11.3-cudnn8-devel
WORKDIR /
# Install requirements
# RUN git clone https://github.com/TheLastBen/diffusers
RUN apt update && apt install wget git unzip -y
RUN wget -q https://gist.githubusercontent.com/js-ts/28684a7e6217214ec944a9224584e9af/raw/d7492bc8f36700b75d51e3346259d1a466b99a40/train_dreambooth.py
RUN wget -q https://github.com/TheLastBen/diffusers/raw/main/scripts/convert_diffusers_to_original_stable_diffusion.py
# RUN cp /content/convert_diffusers_to_original_stable_diffusion.py /content/diffusers/scripts/convert_diffusers_to_original_stable_diffusion.py
RUN pip install -qq git+https://github.com/TheLastBen/diffusers
RUN pip install -q accelerate==0.12.0 transformers ftfy bitsandbytes gradio natsort
# Install xformers
RUN pip install -q https://github.com/metrolobo/xformers_wheels/releases/download/1d31a3ac_various_6/xformers-0.0.14.dev0-cp37-cp37m-linux_x86_64.whl
# You need to accept the model license before downloading or using the Stable Diffusion weights. Please, visit the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5), read the license and tick the checkbox if you agree. You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work.
# https://huggingface.co/settings/tokens
RUN mkdir -p ~/.huggingface
RUN echo -n "<your-hugging-face-token>" > ~/.huggingface/token
# copy the test dataset from a local file
# COPY jfk /jfk

# Download and extract the test dataset
RUN wget https://github.com/js-ts/test-images/raw/main/jfk.zip
RUN unzip -j jfk.zip -d jfk
RUN mkdir model
RUN wget 'https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/Regularization/Women' -O woman.zip
RUN wget 'https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/Regularization/Men' -O man.zip
RUN wget 'https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/Regularization/Mix' -O mix.zip
RUN unzip -j woman.zip -d woman
RUN unzip -j man.zip -d man
RUN unzip -j mix.zip -d mix

RUN  accelerate launch train_dreambooth.py \
    --image_captions_filename \
   --train_text_encoder \
    --save_starting_step=5\
    --stop_text_encoder_training=31 \
       --class_data_dir=/man \
    --save_n_steps=5 \
  --pretrained_model_name_or_path="CompVis/stable-diffusion-v1-4" \
    --instance_data_dir="/jfk" \
    --output_dir="/model" \
    --instance_prompt="a photo of jfk man" \
   --class_prompt="a photo of man" \
    --instance_prompt="" \
    --seed=96576 \
    --resolution=512 \
    --mixed_precision="fp16" \
    --train_batch_size=1 \
    --gradient_accumulation_steps=1 \
    --use_8bit_adam \
    --learning_rate=2e-6 \
    --lr_scheduler="polynomial" \
    --center_crop \
    --lr_warmup_steps=0 \
    --max_train_steps=30

COPY finetune.sh /finetune.sh

RUN wget -q https://gist.githubusercontent.com/js-ts/624fecc3fff807d4948688cb28993a94/raw/fd69ac084debe26a815485c1f363b8a45566f1ba/clear_mem.py
# Removing your token
RUN rm -rf  ~/.huggingface/token

finetune.sh

python clear_mem.py

accelerate launch train_dreambooth.py \
    --image_captions_filename \
   --train_text_encoder \
    --save_n_steps=$(expr $5 / 6) \
    --stop_text_encoder_training=$(expr $5 + 100) \
       --class_data_dir="$6" \
  --pretrained_model_name_or_path=${7:=/model} \
--tokenizer_name=${7:=/model}/tokenizer/ \
    --instance_data_dir="$1" \
    --output_dir="$2" \
    --instance_prompt="$3" \
   --class_prompt="$4" \
    --seed=96576 \
    --resolution=512 \
    --mixed_precision="fp16" \
    --train_batch_size=1 \
    --gradient_accumulation_steps=1 \
    --use_8bit_adam \
    --learning_rate=2e-6 \
    --lr_scheduler="polynomial" \
    --center_crop \
    --lr_warmup_steps=0 \
    --max_train_steps=$5

echo  Convert weights to ckpt
python convert_diffusers_to_original_stable_diffusion.py --model_path $2  --checkpoint_path $2/model.ckpt --half
echo model saved at $2/model.ckpt

Build the Docker container

We will run docker build command to build the container;

docker build -t <hub-user>/<repo-name>:<tag> .

Before running the command replace;

hub-user with your docker hub username, If you don’t have a docker hub account follow these instructions to create docker account, and use the username of the account you created
repo-name with the name of the container, you can name it anything you want
tag this is not required but you can use the latest tag

Now you can push this repository to the registry designated by its name or tag.

docker push <hub-user>/<repo-name>:<tag>

Create the Subject Dataset

The optimal dataset size is between 20-30 images. You can choose the images of the subject in different positions, full body images, half body, pictures of the face etc.

Only the subject should appear in the image so you can crop the image to just fit the subject. Make sure that the images are of 512x512 size and are named in the following pattern since the subject name is David Aronchick we name the images in the following pattern

David Aronchick.jpg, David Aronchick (2).jpg ... David Aronchick (n).jpg

You can view the Subject Image dataset of David Aronchick for refernce

After the Subject dataset is created we upload it to IPFS

Uploading the Subject Images to IPFS

In this case we will be using NFT.Storage (Recommneded Option) to upload files and directories with NFTUp

To upload your dataset using NFTup just drag and drop your directory it will upload it to IPFS

After the checkpoint file has been uploaded copy its CID bafybeidqbuphwkqwgrobv2vakwsh3l6b4q2mx7xspgh4l7lhulhc3dfa7a

Approaches to run a Bacalhau Job on a Finetuned Model

Since there are a lot of combinations that you can try, processing of finetuned model can take almost 1hr+ to complete. Here are some few approaches that you can try based upon your requirements

Case0 If the subject is of class male

bacalhau docker run \
--gpu 1 \
--timeout 3600 \
--wait-timeout-secs 3600 \
--timeout 3600 \
--wait-timeout-secs 3600 \
-i <CID-OF-THE-SUBJECT> \
 jsacex/dreambooth:full \
-- bash finetune.sh /inputs /outputs "a photo of <name-of-the-subject> man" "a photo of man" 3000 "/man" "/model"

Structure of the command

No of GPUs --gpu 1
CID of the Subject Images -i bafybeidqbuphwkqwgrobv2vakwsh3l6b4q2mx7xspgh4l7lhulhc3dfa7a
Name of our Image jsacex/dreambooth:latest

-- bash finetune.sh /inputs /outputs "a photo of aronchick man" "a photo of man" 3000 "/man"

Path to the subject Images /inputs
Path to save the generated outputs /outputs
Subject name along with class "a photo of < name of the subject > < class >" -> "a photo of aronchick man"
Name of the class "a photo of < class >" -> "a photo of man"

Number of iterations is 3000 this number should be no of subject images x 100. So if there are 30 Images it would be 3000, it takes around 32Mins on a v100 for 3000 Iterations but you can increase/decrease the number based on your requirements

Path to our class Images /man

Here is our command with our parameters replaced

%%bash --out job_id
bacalhau docker run \
--gpu 1 \
--timeout 3600 \
--wait-timeout-secs 3600 \
--timeout 3600 \
--wait-timeout-secs 3600 \
-i bafybeidqbuphwkqwgrobv2vakwsh3l6b4q2mx7xspgh4l7lhulhc3dfa7a \
--wait \
--id-only \
  jsacex/dreambooth:full \
-- bash finetune.sh /inputs /outputs "a photo of aronchick man" "a photo of man" 3000 "/man" "/model"

If your subject fits the above class but has a different name you just need to replace the input CID and the subject name which in this case is SBF

 bacalhau docker run \
 --gpu 1 \
-i QmRKnvqvpFzLjEoeeNNGHtc7H8fCn9TvNWHFnbBHkK8Mhy \
  jsacex/dreambooth:full \
-- bash finetune.sh /inputs /outputs "a photo of sbf man" "a photo of man" 3000 "/man"  "/model"

Case1 If the subject is of class female

Use the /woman class images

bacalhau docker run \
--gpu 1 \
--timeout 3600 \
--wait-timeout-secs 3600 \
-i <CID-OF-THE-SUBJECT> \
  jsacex/dreambooth:full \
-- bash finetune.sh /inputs /outputs "a photo of <name-of-the-subject> woman" "a photo of woman" 3000 "/woman"  "/model"

Case2 If the subject is of class mix

provide your own regularization images or use the mix class

Use the /mix class images if the class of the subject is mix

bacalhau docker run \
--gpu 1 \
--timeout 3600 \
--wait-timeout-secs 3600 \
-i <CID-OF-THE-SUBJECT> \
  jsacex/dreambooth:full \
-- bash finetune.sh /inputs /outputs "a photo of <name-of-the-subject> mix" "a photo of mix" 3000 "/mix"  "/model"

Case3 If you want to a different tokenizer, model and a different shell script with custom parameters

You can upload the model to IPFS and then create a gist and mount the model and script to the lightweight container

bacalhau docker run \
--gpu 1 \
--timeout 3600 \
--wait-timeout-secs 3600 \
-v bafybeidqbuphwkqwgrobv2vakwsh3l6b4q2mx7xspgh4l7lhulhc3dfa7a:/aronchick \
-v <CID-OF-THE-MODEL>:/model 
-u https://gist.githubusercontent.com/js-ts/54b270a36aa3c35fdc270640680b3bd4/raw/7d8e8fa47bc3811ef63772f7fc7f4360aa9d51a8/finetune.sh
--wait \
--id-only \
  jsacex/dreambooth:lite \
-- bash /inputs/finetune.sh /aronchick /outputs "a photo of aronchick man" "a photo of man" 3000 "/man" "/model"

When a job is sumbitted, Bacalhau prints out the related job_id. We store that in an environment variable so that we can reuse it later on.

Checking the State of your Jobs

Job status: You can check the status of the job using bacalhau list.

%%bash
bacalhau list --id-filter ${JOB_ID} --wide

[92;100m CREATED           [0m[92;100m ID                                   [0m[92;100m JOB                                                                                                                 [0m[92;100m STATE     [0m[92;100m VERIFIED [0m[92;100m PUBLISHED                                            [0m
[97;40m 22-11-14-07:16:17 [0m[97;40m 9fcd210b-bc8a-4f38-bf5e-5ac39e0b8be4 [0m[97;40m Docker jsacex/dreambooth:latest bash finetune.sh /inputs /outputs a photo of aronchick man a photo of man 3000 /man [0m[97;40m Completed [0m[97;40m          [0m[97;40m /ipfs/QmcmD7M7pYLP8QgwjqpbP4dojRLiLuEBdhevuCD9kFmbdV [0m

When it says Published or Completed, that means the job is done, and we can get the results.

Job information: You can find out more information about your job by using bacalhau describe.

%%bash
bacalhau describe ${JOB_ID}

In the next steps, we will be doing inference on the finetuned model

Inference on the Fine Tuned Model

info

Refer https://docs.bacalhau.org/examples/model-inference/Stable-Diffusion-CKPT-Inference on details of how to build a SD inference container

Bacalhau currently doesn't support mounting subpaths of the CID, so instead of just mounting the model.ckpt file we need to mount the whole output CID which is of 6.4GB, which might results in errors like FAILED TO COPY /inputs. So you have to manually copy the CID of the model.ckpt which is of 2GB

To get the CID of the model.ckpt file go to https://gateway.ipfs.io/ipfs/< YOUR-OUTPUT-CID >/outputs/

https://gateway.ipfs.io/ipfs/QmcmD7M7pYLP8QgwjqpbP4dojRLiLuEBdhevuCD9kFmbdV/outputs/

If you use the Brave browser

ipfs://QmdpsqZn9BZx9XxzCsyPcJyS7yfYacmQXZxHzcuYwzmtGg/outputs

Using IPFS CLI

ipfs ls QmdpsqZn9BZx9XxzCsyPcJyS7yfYacmQXZxHzcuYwzmtGg/outputs

Copy the link of model.ckpt highlighted in the box https://gateway.ipfs.io/ipfs/QmdpsqZn9BZx9XxzCsyPcJyS7yfYacmQXZxHzcuYwzmtGg?filename=model.ckpt

Extract the CID portion of the link and copy it

Run the Bacalhau Job on the Fine-Tuned Model

To run a Bacalhau Job on the fine-tuned model, we will use the bacalhau docker run command.

%%bash --out job_id
bacalhau docker run \
--gpu 1 \
--timeout 3600 \
--wait-timeout-secs 3600 \
--wait \
--id-only \
-i QmdpsqZn9BZx9XxzCsyPcJyS7yfYacmQXZxHzcuYwzmtGg \
jsacex/stable-diffusion-ckpt \
-- conda run --no-capture-output -n ldm python scripts/txt2img.py --prompt "a photo of aronchick drinking coffee" --plms --ckpt ../inputs/model.ckpt --skip_grid --n_samples 1 --skip_grid --outdir ../outputs 

If you are facing difficulties using the above method you can mount the whole output CID

bacalhau docker run \
--gpu 1 \
--timeout 3600 \
--wait-timeout-secs 3600 \
--wait \
--id-only \
-i QmcmD7M7pYLP8QgwjqpbP4dojRLiLuEBdhevuCD9kFmbdV \
jsacex/stable-diffusion-ckpt \
-- conda run --no-capture-output -n ldm python scripts/txt2img.py --prompt "a photo of aronchick drinking coffee" --plms --ckpt ../inputs/outputs/model.ckpt --skip_grid --n_samples 1 --skip_grid --outdir ../outputs 

When a job is sumbitted, Bacalhau prints out the related job_id. We store that in an environment variable so that we can reuse it later on.

Checking the State of your Jobs

Job status: You can check the status of the job using bacalhau list.

%%bash
bacalhau list --id-filter ${JOB_ID} --wide

[92;100m CREATED           [0m[92;100m ID                                   [0m[92;100m JOB                                                                                                                                                                                                                                                   [0m[92;100m STATE     [0m[92;100m VERIFIED [0m[92;100m PUBLISHED                                            [0m
[97;40m 22-11-14-08:44:56 [0m[97;40m 3c5e4faa-ab2e-4576-bd61-e31140493196 [0m[97;40m Docker jsacex/stable-diffusion-ckpt conda run --no-capture-output -n ldm python scripts/txt2img.py --prompt a photo of aronchick drinking coffee --plms --ckpt ../inputs/outputs/model.ckpt --skip_grid --n_samples 1 --skip_grid --outdir ../outputs [0m[97;40m Completed [0m[97;40m          [0m[97;40m /ipfs/Qmep1mvj6RkyWa5Hptz6JhpAXAZziPDtStEopJjBYav6MM [0m

When it says Published or Completed, that means the job is done, and we can get the results.

Job information: You can find out more information about your job by using bacalhau describe.

%%bash
bacalhau describe ${JOB_ID}

Job download: You can download your job results directly by using bacalhau get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory and downloaded our job output to be stored in that directory.

%%bash
rm -rf results && mkdir -p results
bacalhau get $JOB_ID --output-dir results

Fetching results of job '3c5e4faa-ab2e-4576-bd61-e31140493196'...
Results for job '3c5e4faa-ab2e-4576-bd61-e31140493196' have been written to...
results


2022/11/14 09:29:52 failed to sufficiently increase receive buffer size (was: 208 kiB, wanted: 2048 kiB, got: 416 kiB). See https://github.com/lucas-clemente/quic-go/wiki/UDP-Receive-Buffer-Size for details.

After the download has finished you should see the following contents in results directory

Viewing your Job Output

Each job creates 3 subfolders: the combined_results,per_shard files, and the raw directory. To view the file, run the following command:

%%bash ls results/combined_results/outputs

Display Image

import IPython.display as display
display.Image("results/combined_results/outputs/samples/00001.png")

png

Stable Diffusion Dreambooth (Finetuning)

Introduction​

Prerequisite​

Quick Test​

Inference​

Setting up Docker Container​

Downloading the models​

Build the Docker container​

Create the Subject Dataset​

Uploading the Subject Images to IPFS​

Approaches to run a Bacalhau Job on a Finetuned Model​

Case0 If the subject is of class male​

Case1 If the subject is of class female​

Case2 If the subject is of class mix​

Case3 If you want to a different tokenizer, model and a different shell script with custom parameters​

Checking the State of your Jobs​

Inference on the Fine Tuned Model​

Run the Bacalhau Job on the Fine-Tuned Model​

Checking the State of your Jobs​

Viewing your Job Output​

Display Image​

Introduction

Prerequisite

Quick Test

Inference

Setting up Docker Container

Downloading the models

Build the Docker container

Create the Subject Dataset

Uploading the Subject Images to IPFS

Approaches to run a Bacalhau Job on a Finetuned Model

Case0 If the subject is of class male

Case1 If the subject is of class female

Case2 If the subject is of class mix

Case3 If you want to a different tokenizer, model and a different shell script with custom parameters

Checking the State of your Jobs

Inference on the Fine Tuned Model

Run the Bacalhau Job on the Fine-Tuned Model

Checking the State of your Jobs

Viewing your Job Output

Display Image