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# DreamBooth

[DreamBooth](https://arxiv.org/abs/2208.12242)๋Š” ํ•œ ์ฃผ์ œ์— ๋Œ€ํ•œ ์ ์€ ์ด๋ฏธ์ง€(3~5๊ฐœ)๋งŒ์œผ๋กœ๋„ stable diffusion๊ณผ ๊ฐ™์ด text-to-image ๋ชจ๋ธ์„ ๊ฐœ์ธํ™”ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์ž…๋‹ˆ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋ชจ๋ธ์€ ๋‹ค์–‘ํ•œ ์žฅ๋ฉด, ํฌ์ฆˆ ๋ฐ ์žฅ๋ฉด(๋ทฐ)์—์„œ ํ”ผ์‚ฌ์ฒด์— ๋Œ€ํ•ด ๋งฅ๋ฝํ™”(contextualized)๋œ ์ด๋ฏธ์ง€๋ฅผ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

![ํ”„๋กœ์ ํŠธ ๋ธ”๋กœ๊ทธ์—์„œ์˜ DreamBooth ์˜ˆ์‹œ](https://dreambooth.github.io/DreamBooth_files/teaser_static.jpg)
<small>์—์„œ์˜ Dreambooth ์˜ˆ์‹œ <a href="https://dreambooth.github.io">project's blog.</a></small>


์ด ๊ฐ€์ด๋“œ๋Š” ๋‹ค์–‘ํ•œ GPU, Flax ์‚ฌ์–‘์— ๋Œ€ํ•ด [`CompVis/stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) ๋ชจ๋ธ๋กœ DreamBooth๋ฅผ ํŒŒ์ธํŠœ๋‹ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ๋ณด์—ฌ์ค๋‹ˆ๋‹ค. ๋” ๊นŠ์ด ํŒŒ๊ณ ๋“ค์–ด ์ž‘๋™ ๋ฐฉ์‹์„ ํ™•์ธํ•˜๋Š” ๋ฐ ๊ด€์‹ฌ์ด ์žˆ๋Š” ๊ฒฝ์šฐ, ์ด ๊ฐ€์ด๋“œ์— ์‚ฌ์šฉ๋œ DreamBooth์˜ ๋ชจ๋“  ํ•™์Šต ์Šคํฌ๋ฆฝํŠธ๋ฅผ [์—ฌ๊ธฐ](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth)์—์„œ ์ฐพ์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

์Šคํฌ๋ฆฝํŠธ๋ฅผ ์‹คํ–‰ํ•˜๊ธฐ ์ „์— ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์˜ ํ•™์Šต์— ํ•„์š”ํ•œ dependencies๋ฅผ ์„ค์น˜ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. ๋˜ํ•œ `main` GitHub ๋ธŒ๋žœ์น˜์—์„œ ๐Ÿงจ Diffusers๋ฅผ ์„ค์น˜ํ•˜๋Š” ๊ฒƒ์ด ์ข‹์Šต๋‹ˆ๋‹ค.

```bash
pip install git+https://github.com/huggingface/diffusers
pip install -U -r diffusers/examples/dreambooth/requirements.txt
```

xFormers๋Š” ํ•™์Šต์— ํ•„์š”ํ•œ ์š”๊ตฌ ์‚ฌํ•ญ์€ ์•„๋‹ˆ์ง€๋งŒ, ๊ฐ€๋Šฅํ•˜๋ฉด [์„ค์น˜](../optimization/xformers)ํ•˜๋Š” ๊ฒƒ์ด ์ข‹์Šต๋‹ˆ๋‹ค. ํ•™์Šต ์†๋„๋ฅผ ๋†’์ด๊ณ  ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰์„ ์ค„์ผ ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์ž…๋‹ˆ๋‹ค.

๋ชจ๋“  dependencies์„ ์„ค์ •ํ•œ ํ›„ ๋‹ค์Œ์„ ์‚ฌ์šฉํ•˜์—ฌ [๐Ÿค— Accelerate](https://github.com/huggingface/accelerate/) ํ™˜๊ฒฝ์„ ๋‹ค์Œ๊ณผ ๊ฐ™์ด ์ดˆ๊ธฐํ™”ํ•ฉ๋‹ˆ๋‹ค:

```bash
accelerate config
```

๋ณ„๋„ ์„ค์ • ์—†์ด ๊ธฐ๋ณธ ๐Ÿค— Accelerate ํ™˜๊ฒฝ์„ ์„ค์น˜ํ•˜๋ ค๋ฉด ๋‹ค์Œ์„ ์‹คํ–‰ํ•ฉ๋‹ˆ๋‹ค:

```bash
accelerate config default
```

๋˜๋Š” ํ˜„์žฌ ํ™˜๊ฒฝ์ด ๋…ธํŠธ๋ถ๊ณผ ๊ฐ™์€ ๋Œ€ํ™”ํ˜• ์…ธ์„ ์ง€์›ํ•˜์ง€ ์•Š๋Š” ๊ฒฝ์šฐ ๋‹ค์Œ์„ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค:

```py
from accelerate.utils import write_basic_config

write_basic_config()
```

## ํŒŒ์ธํŠœ๋‹

<Tip warning={true}>

DreamBooth ํŒŒ์ธํŠœ๋‹์€ ํ•˜์ดํผํŒŒ๋ผ๋ฏธํ„ฐ์— ๋งค์šฐ ๋ฏผ๊ฐํ•˜๊ณ  ๊ณผ์ ํ•ฉ๋˜๊ธฐ ์‰ฝ์Šต๋‹ˆ๋‹ค. ์ ์ ˆํ•œ ํ•˜์ดํผํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์„ ํƒํ•˜๋Š” ๋ฐ ๋„์›€์ด ๋˜๋„๋ก ๋‹ค์–‘ํ•œ ๊ถŒ์žฅ ์„ค์ •์ด ํฌํ•จ๋œ [์‹ฌ์ธต ๋ถ„์„](https://huggingface.co/blog/dreambooth)์„ ์‚ดํŽด๋ณด๋Š” ๊ฒƒ์ด ์ข‹์Šต๋‹ˆ๋‹ค.

</Tip>

<frameworkcontent>
<pt>
[๋ช‡ ์žฅ์˜ ๊ฐ•์•„์ง€ ์ด๋ฏธ์ง€๋“ค](https://drive.google.com/drive/folders/1BO_dyz-p65qhBRRMRA4TbZ8qW4rB99JZ)๋กœ DreamBooth๋ฅผ ์‹œ๋„ํ•ด๋ด…์‹œ๋‹ค. 
์ด๋ฅผ ๋‹ค์šด๋กœ๋“œํ•ด ๋””๋ ‰ํ„ฐ๋ฆฌ์— ์ €์žฅํ•œ ๋‹ค์Œ `INSTANCE_DIR` ํ™˜๊ฒฝ ๋ณ€์ˆ˜๋ฅผ ํ•ด๋‹น ๊ฒฝ๋กœ๋กœ ์„ค์ •ํ•ฉ๋‹ˆ๋‹ค:


```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path_to_training_images"
export OUTPUT_DIR="path_to_saved_model"
```

๊ทธ๋Ÿฐ ๋‹ค์Œ, ๋‹ค์Œ ๋ช…๋ น์„ ์‚ฌ์šฉํ•˜์—ฌ ํ•™์Šต ์Šคํฌ๋ฆฝํŠธ๋ฅผ ์‹คํ–‰ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค (์ „์ฒด ํ•™์Šต ์Šคํฌ๋ฆฝํŠธ๋Š” [์—ฌ๊ธฐ](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py)์—์„œ ์ฐพ์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค):

```bash
accelerate launch train_dreambooth.py \
  --pretrained_model_name_or_path=$MODEL_NAME  \
  --instance_data_dir=$INSTANCE_DIR \
  --output_dir=$OUTPUT_DIR \
  --instance_prompt="a photo of sks dog" \
  --resolution=512 \
  --train_batch_size=1 \
  --gradient_accumulation_steps=1 \
  --learning_rate=5e-6 \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --max_train_steps=400
```
</pt>
<jax>

TPU์— ์•ก์„ธ์Šคํ•  ์ˆ˜ ์žˆ๊ฑฐ๋‚˜ ๋” ๋น ๋ฅด๊ฒŒ ํ›ˆ๋ จํ•˜๊ณ  ์‹ถ๋‹ค๋ฉด [Flax ํ•™์Šต ์Šคํฌ๋ฆฝํŠธ](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_flax.py)๋ฅผ ์‚ฌ์šฉํ•ด ๋ณผ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. Flax ํ•™์Šต ์Šคํฌ๋ฆฝํŠธ๋Š” gradient checkpointing ๋˜๋Š” gradient accumulation์„ ์ง€์›ํ•˜์ง€ ์•Š์œผ๋ฏ€๋กœ, ๋ฉ”๋ชจ๋ฆฌ๊ฐ€ 30GB ์ด์ƒ์ธ GPU๊ฐ€ ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค.

์Šคํฌ๋ฆฝํŠธ๋ฅผ ์‹คํ–‰ํ•˜๊ธฐ ์ „์— ์š”๊ตฌ ์‚ฌํ•ญ์ด ์„ค์น˜๋˜์–ด ์žˆ๋Š”์ง€ ํ™•์ธํ•˜์‹ญ์‹œ์˜ค.

```bash
pip install -U -r requirements.txt
```

๊ทธ๋Ÿฌ๋ฉด ๋‹ค์Œ ๋ช…๋ น์–ด๋กœ ํ•™์Šต ์Šคํฌ๋ฆฝํŠธ๋ฅผ ์‹คํ–‰์‹œํ‚ฌ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค:

```bash
export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
export INSTANCE_DIR="path-to-instance-images"
export OUTPUT_DIR="path-to-save-model"

python train_dreambooth_flax.py \
  --pretrained_model_name_or_path=$MODEL_NAME  \
  --instance_data_dir=$INSTANCE_DIR \
  --output_dir=$OUTPUT_DIR \
  --instance_prompt="a photo of sks dog" \
  --resolution=512 \
  --train_batch_size=1 \
  --learning_rate=5e-6 \
  --max_train_steps=400
```
</jax>
</frameworkcontent>

### Prior-preserving(์‚ฌ์ „ ๋ณด์กด) loss๋ฅผ ์‚ฌ์šฉํ•œ ํŒŒ์ธํŠœ๋‹

๊ณผ์ ํ•ฉ๊ณผ language drift๋ฅผ ๋ฐฉ์ง€ํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์ „ ๋ณด์กด์ด ์‚ฌ์šฉ๋ฉ๋‹ˆ๋‹ค(๊ด€์‹ฌ์ด ์žˆ๋Š” ๊ฒฝ์šฐ [๋…ผ๋ฌธ](https://arxiv.org/abs/2208.12242)์„ ์ฐธ์กฐํ•˜์„ธ์š”).  ์‚ฌ์ „ ๋ณด์กด์„ ์œ„ํ•ด ๋™์ผํ•œ ํด๋ž˜์Šค์˜ ๋‹ค๋ฅธ ์ด๋ฏธ์ง€๋ฅผ ํ•™์Šต ํ”„๋กœ์„ธ์Šค์˜ ์ผ๋ถ€๋กœ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค. ์ข‹์€ ์ ์€ Stable Diffusion ๋ชจ๋ธ ์ž์ฒด๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ด๋Ÿฌํ•œ ์ด๋ฏธ์ง€๋ฅผ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค! ํ•™์Šต ์Šคํฌ๋ฆฝํŠธ๋Š” ์ƒ์„ฑ๋œ ์ด๋ฏธ์ง€๋ฅผ ์šฐ๋ฆฌ๊ฐ€ ์ง€์ •ํ•œ ๋กœ์ปฌ ๊ฒฝ๋กœ์— ์ €์žฅํ•ฉ๋‹ˆ๋‹ค.

์ €์ž๋“ค์— ๋”ฐ๋ฅด๋ฉด ์‚ฌ์ „ ๋ณด์กด์„ ์œ„ํ•ด `num_epochs * num_samples`๊ฐœ์˜ ์ด๋ฏธ์ง€๋ฅผ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์ด ์ข‹์Šต๋‹ˆ๋‹ค. 200-300๊ฐœ์—์„œ ๋Œ€๋ถ€๋ถ„ ์ž˜ ์ž‘๋™ํ•ฉ๋‹ˆ๋‹ค.

<frameworkcontent>
<pt>
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path_to_training_images"
export CLASS_DIR="path_to_class_images"
export OUTPUT_DIR="path_to_saved_model"

accelerate launch train_dreambooth.py \
  --pretrained_model_name_or_path=$MODEL_NAME  \
  --instance_data_dir=$INSTANCE_DIR \
  --class_data_dir=$CLASS_DIR \
  --output_dir=$OUTPUT_DIR \
  --with_prior_preservation --prior_loss_weight=1.0 \
  --instance_prompt="a photo of sks dog" \
  --class_prompt="a photo of dog" \
  --resolution=512 \
  --train_batch_size=1 \
  --gradient_accumulation_steps=1 \
  --learning_rate=5e-6 \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --num_class_images=200 \
  --max_train_steps=800
```
</pt>
<jax>
```bash
export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"

python train_dreambooth_flax.py \
  --pretrained_model_name_or_path=$MODEL_NAME  \
  --instance_data_dir=$INSTANCE_DIR \
  --class_data_dir=$CLASS_DIR \
  --output_dir=$OUTPUT_DIR \
  --with_prior_preservation --prior_loss_weight=1.0 \
  --instance_prompt="a photo of sks dog" \
  --class_prompt="a photo of dog" \
  --resolution=512 \
  --train_batch_size=1 \
  --learning_rate=5e-6 \
  --num_class_images=200 \
  --max_train_steps=800
```
</jax>
</frameworkcontent>

## ํ…์ŠคํŠธ ์ธ์ฝ”๋”์™€ and UNet๋กœ ํŒŒ์ธํŠœ๋‹ํ•˜๊ธฐ

ํ•ด๋‹น ์Šคํฌ๋ฆฝํŠธ๋ฅผ ์‚ฌ์šฉํ•˜๋ฉด `unet`๊ณผ ํ•จ๊ป˜ `text_encoder`๋ฅผ ํŒŒ์ธํŠœ๋‹ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์‹คํ—˜์—์„œ(์ž์„ธํ•œ ๋‚ด์šฉ์€ [๐Ÿงจ Diffusers๋ฅผ ์‚ฌ์šฉํ•ด DreamBooth๋กœ Stable Diffusion ํ•™์Šตํ•˜๊ธฐ](https://huggingface.co/blog/dreambooth) ๊ฒŒ์‹œ๋ฌผ์„ ํ™•์ธํ•˜์„ธ์š”), ํŠนํžˆ ์–ผ๊ตด ์ด๋ฏธ์ง€๋ฅผ ์ƒ์„ฑํ•  ๋•Œ ํ›จ์”ฌ ๋” ๋‚˜์€ ๊ฒฐ๊ณผ๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

<Tip warning={true}>

ํ…์ŠคํŠธ ์ธ์ฝ”๋”๋ฅผ ํ•™์Šต์‹œํ‚ค๋ ค๋ฉด ์ถ”๊ฐ€ ๋ฉ”๋ชจ๋ฆฌ๊ฐ€ ํ•„์š”ํ•ด 16GB GPU๋กœ๋Š” ๋™์ž‘ํ•˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค. ์ด ์˜ต์…˜์„ ์‚ฌ์šฉํ•˜๋ ค๋ฉด ์ตœ์†Œ 24GB VRAM์ด ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค.

</Tip>

`--train_text_encoder` ์ธ์ˆ˜๋ฅผ ํ•™์Šต ์Šคํฌ๋ฆฝํŠธ์— ์ „๋‹ฌํ•˜์—ฌ `text_encoder` ๋ฐ `unet`์„ ํŒŒ์ธํŠœ๋‹ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค:

<frameworkcontent>
<pt>
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path_to_training_images"
export CLASS_DIR="path_to_class_images"
export OUTPUT_DIR="path_to_saved_model"

accelerate launch train_dreambooth.py \
  --pretrained_model_name_or_path=$MODEL_NAME  \
  --train_text_encoder \
  --instance_data_dir=$INSTANCE_DIR \
  --class_data_dir=$CLASS_DIR \
  --output_dir=$OUTPUT_DIR \
  --with_prior_preservation --prior_loss_weight=1.0 \
  --instance_prompt="a photo of sks dog" \
  --class_prompt="a photo of dog" \
  --resolution=512 \
  --train_batch_size=1 \
  --use_8bit_adam
  --gradient_checkpointing \
  --learning_rate=2e-6 \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --num_class_images=200 \
  --max_train_steps=800
```
</pt>
<jax>
```bash
export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"

python train_dreambooth_flax.py \
  --pretrained_model_name_or_path=$MODEL_NAME  \
  --train_text_encoder \
  --instance_data_dir=$INSTANCE_DIR \
  --class_data_dir=$CLASS_DIR \
  --output_dir=$OUTPUT_DIR \
  --with_prior_preservation --prior_loss_weight=1.0 \
  --instance_prompt="a photo of sks dog" \
  --class_prompt="a photo of dog" \
  --resolution=512 \
  --train_batch_size=1 \
  --learning_rate=2e-6 \
  --num_class_images=200 \
  --max_train_steps=800
```
</jax>
</frameworkcontent>

## LoRA๋กœ ํŒŒ์ธํŠœ๋‹ํ•˜๊ธฐ

DreamBooth์—์„œ ๋Œ€๊ทœ๋ชจ ๋ชจ๋ธ์˜ ํ•™์Šต์„ ๊ฐ€์†ํ™”ํ•˜๊ธฐ ์œ„ํ•œ ํŒŒ์ธํŠœ๋‹ ๊ธฐ์ˆ ์ธ LoRA(Low-Rank Adaptation of Large Language Models)๋ฅผ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ž์„ธํ•œ ๋‚ด์šฉ์€ [LoRA ํ•™์Šต](training/lora#dreambooth) ๊ฐ€์ด๋“œ๋ฅผ ์ฐธ์กฐํ•˜์„ธ์š”.

### ํ•™์Šต ์ค‘ ์ฒดํฌํฌ์ธํŠธ ์ €์žฅํ•˜๊ธฐ

Dreambooth๋กœ ํ›ˆ๋ จํ•˜๋Š” ๋™์•ˆ ๊ณผ์ ํ•ฉํ•˜๊ธฐ ์‰ฌ์šฐ๋ฏ€๋กœ, ๋•Œ๋•Œ๋กœ ํ•™์Šต ์ค‘์— ์ •๊ธฐ์ ์ธ ์ฒดํฌํฌ์ธํŠธ๋ฅผ ์ €์žฅํ•˜๋Š” ๊ฒƒ์ด ์œ ์šฉํ•ฉ๋‹ˆ๋‹ค. ์ค‘๊ฐ„ ์ฒดํฌํฌ์ธํŠธ ์ค‘ ํ•˜๋‚˜๊ฐ€ ์ตœ์ข… ๋ชจ๋ธ๋ณด๋‹ค ๋” ์ž˜ ์ž‘๋™ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค! ์ฒดํฌํฌ์ธํŠธ ์ €์žฅ ๊ธฐ๋Šฅ์„ ํ™œ์„ฑํ™”ํ•˜๋ ค๋ฉด ํ•™์Šต ์Šคํฌ๋ฆฝํŠธ์— ๋‹ค์Œ ์ธ์ˆ˜๋ฅผ ์ „๋‹ฌํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค:

```bash
  --checkpointing_steps=500
```

์ด๋ ‡๊ฒŒ ํ•˜๋ฉด `output_dir`์˜ ํ•˜์œ„ ํด๋”์— ์ „์ฒด ํ•™์Šต ์ƒํƒœ๊ฐ€ ์ €์žฅ๋ฉ๋‹ˆ๋‹ค. ํ•˜์œ„ ํด๋” ์ด๋ฆ„์€ ์ ‘๋‘์‚ฌ `checkpoint-`๋กœ ์‹œ์ž‘ํ•˜๊ณ  ์ง€๊ธˆ๊นŒ์ง€ ์ˆ˜ํ–‰๋œ step ์ˆ˜์ž…๋‹ˆ๋‹ค. ์˜ˆ์‹œ๋กœ `checkpoint-1500`์€ 1500 ํ•™์Šต step ํ›„์— ์ €์žฅ๋œ ์ฒดํฌํฌ์ธํŠธ์ž…๋‹ˆ๋‹ค.

#### ์ €์žฅ๋œ ์ฒดํฌํฌ์ธํŠธ์—์„œ ํ›ˆ๋ จ ์žฌ๊ฐœํ•˜๊ธฐ

์ €์žฅ๋œ ์ฒดํฌํฌ์ธํŠธ์—์„œ ํ›ˆ๋ จ์„ ์žฌ๊ฐœํ•˜๋ ค๋ฉด, `--resume_from_checkpoint` ์ธ์ˆ˜๋ฅผ ์ „๋‹ฌํ•œ ๋‹ค์Œ ์‚ฌ์šฉํ•  ์ฒดํฌํฌ์ธํŠธ์˜ ์ด๋ฆ„์„ ์ง€์ •ํ•˜๋ฉด ๋ฉ๋‹ˆ๋‹ค. ํŠน์ˆ˜ ๋ฌธ์ž์—ด `"latest"`๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ €์žฅ๋œ ๋งˆ์ง€๋ง‰ ์ฒดํฌํฌ์ธํŠธ(์ฆ‰, step ์ˆ˜๊ฐ€ ๊ฐ€์žฅ ๋งŽ์€ ์ฒดํฌํฌ์ธํŠธ)์—์„œ ์žฌ๊ฐœํ•  ์ˆ˜๋„ ์žˆ์Šต๋‹ˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด ๋‹ค์Œ์€ 1500 step ํ›„์— ์ €์žฅ๋œ ์ฒดํฌํฌ์ธํŠธ์—์„œ๋ถ€ํ„ฐ ํ•™์Šต์„ ์žฌ๊ฐœํ•ฉ๋‹ˆ๋‹ค:

```bash
  --resume_from_checkpoint="checkpoint-1500"
```

์›ํ•˜๋Š” ๊ฒฝ์šฐ ์ผ๋ถ€ ํ•˜์ดํผํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์กฐ์ •ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

#### ์ €์žฅ๋œ ์ฒดํฌํฌ์ธํŠธ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ถ”๋ก  ์ˆ˜ํ–‰ํ•˜๊ธฐ

์ €์žฅ๋œ ์ฒดํฌํฌ์ธํŠธ๋Š” ํ›ˆ๋ จ ์žฌ๊ฐœ์— ์ ํ•ฉํ•œ ํ˜•์‹์œผ๋กœ ์ €์žฅ๋ฉ๋‹ˆ๋‹ค. ์—ฌ๊ธฐ์—๋Š” ๋ชจ๋ธ ๊ฐ€์ค‘์น˜๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์˜ตํ‹ฐ๋งˆ์ด์ €, ๋ฐ์ดํ„ฐ ๋กœ๋” ๋ฐ ํ•™์Šต๋ฅ ์˜ ์ƒํƒœ๋„ ํฌํ•จ๋ฉ๋‹ˆ๋‹ค.

**`"accelerate>=0.16.0"`**์ด ์„ค์น˜๋œ ๊ฒฝ์šฐ ๋‹ค์Œ ์ฝ”๋“œ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ค‘๊ฐ„ ์ฒดํฌํฌ์ธํŠธ์—์„œ ์ถ”๋ก ์„ ์‹คํ–‰ํ•ฉ๋‹ˆ๋‹ค.

```python
from diffusers import DiffusionPipeline, UNet2DConditionModel
from transformers import CLIPTextModel
import torch

# ํ•™์Šต์— ์‚ฌ์šฉ๋œ ๊ฒƒ๊ณผ ๋™์ผํ•œ ์ธ์ˆ˜(model, revision)๋กœ ํŒŒ์ดํ”„๋ผ์ธ์„ ๋ถˆ๋Ÿฌ์˜ต๋‹ˆ๋‹ค.
model_id = "CompVis/stable-diffusion-v1-4"

unet = UNet2DConditionModel.from_pretrained("/sddata/dreambooth/daruma-v2-1/checkpoint-100/unet")

# `args.train_text_encoder`๋กœ ํ•™์Šตํ•œ ๊ฒฝ์šฐ๋ฉด ํ…์ŠคํŠธ ์ธ์ฝ”๋”๋ฅผ ๊ผญ ๋ถˆ๋Ÿฌ์˜ค์„ธ์š”
text_encoder = CLIPTextModel.from_pretrained("/sddata/dreambooth/daruma-v2-1/checkpoint-100/text_encoder")

pipeline = DiffusionPipeline.from_pretrained(model_id, unet=unet, text_encoder=text_encoder, dtype=torch.float16)
pipeline.to("cuda")

# ์ถ”๋ก ์„ ์ˆ˜ํ–‰ํ•˜๊ฑฐ๋‚˜ ์ €์žฅํ•˜๊ฑฐ๋‚˜, ํ—ˆ๋ธŒ์— ํ‘ธ์‹œํ•ฉ๋‹ˆ๋‹ค.
pipeline.save_pretrained("dreambooth-pipeline")
```

If you have **`"accelerate<0.16.0"`** installed, you need to convert it to an inference pipeline first:

```python
from accelerate import Accelerator
from diffusers import DiffusionPipeline

# ํ•™์Šต์— ์‚ฌ์šฉ๋œ ๊ฒƒ๊ณผ ๋™์ผํ•œ ์ธ์ˆ˜(model, revision)๋กœ ํŒŒ์ดํ”„๋ผ์ธ์„ ๋ถˆ๋Ÿฌ์˜ต๋‹ˆ๋‹ค.
model_id = "CompVis/stable-diffusion-v1-4"
pipeline = DiffusionPipeline.from_pretrained(model_id)

accelerator = Accelerator()

# ์ดˆ๊ธฐ ํ•™์Šต์— `--train_text_encoder`๊ฐ€ ์‚ฌ์šฉ๋œ ๊ฒฝ์šฐ text_encoder๋ฅผ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค.
unet, text_encoder = accelerator.prepare(pipeline.unet, pipeline.text_encoder)

# ์ฒดํฌํฌ์ธํŠธ ๊ฒฝ๋กœ๋กœ๋ถ€ํ„ฐ ์ƒํƒœ๋ฅผ ๋ณต์›ํ•ฉ๋‹ˆ๋‹ค. ์—ฌ๊ธฐ์„œ๋Š” ์ ˆ๋Œ€ ๊ฒฝ๋กœ๋ฅผ ์‚ฌ์šฉํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.
accelerator.load_state("/sddata/dreambooth/daruma-v2-1/checkpoint-100")

# unwrapped ๋ชจ๋ธ๋กœ ํŒŒ์ดํ”„๋ผ์ธ์„ ๋‹ค์‹œ ๋นŒ๋“œํ•ฉ๋‹ˆ๋‹ค.(.unet and .text_encoder๋กœ์˜ ํ• ๋‹น๋„ ์ž‘๋™ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค)
pipeline = DiffusionPipeline.from_pretrained(
    model_id,
    unet=accelerator.unwrap_model(unet),
    text_encoder=accelerator.unwrap_model(text_encoder),
)

# ์ถ”๋ก ์„ ์ˆ˜ํ–‰ํ•˜๊ฑฐ๋‚˜ ์ €์žฅํ•˜๊ฑฐ๋‚˜, ํ—ˆ๋ธŒ์— ํ‘ธ์‹œํ•ฉ๋‹ˆ๋‹ค.
pipeline.save_pretrained("dreambooth-pipeline")
```

## ๊ฐ GPU ์šฉ๋Ÿ‰์—์„œ์˜ ์ตœ์ ํ™”

ํ•˜๋“œ์›จ์–ด์— ๋”ฐ๋ผ 16GB์—์„œ 8GB๊นŒ์ง€ GPU์—์„œ DreamBooth๋ฅผ ์ตœ์ ํ™”ํ•˜๋Š” ๋ช‡ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•์ด ์žˆ์Šต๋‹ˆ๋‹ค!

### xFormers

[xFormers](https://github.com/facebookresearch/xformers)๋Š” Transformers๋ฅผ ์ตœ์ ํ™”ํ•˜๊ธฐ ์œ„ํ•œ toolbox์ด๋ฉฐ, ๐Ÿงจ Diffusers์—์„œ ์‚ฌ์šฉ๋˜๋Š”[memory-efficient attention](https://facebookresearch.github.io/xformers/components/ops.html#module-xformers.ops)  ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ํฌํ•จํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. [xFormers๋ฅผ ์„ค์น˜](./optimization/xformers)ํ•œ ๋‹ค์Œ ํ•™์Šต ์Šคํฌ๋ฆฝํŠธ์— ๋‹ค์Œ ์ธ์ˆ˜๋ฅผ ์ถ”๊ฐ€ํ•ฉ๋‹ˆ๋‹ค:

```bash
  --enable_xformers_memory_efficient_attention
```

xFormers๋Š” Flax์—์„œ ์‚ฌ์šฉํ•  ์ˆ˜ ์—†์Šต๋‹ˆ๋‹ค.

### ๊ทธ๋ž˜๋””์–ธํŠธ ์—†์Œ์œผ๋กœ ์„ค์ •

๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰์„ ์ค„์ผ ์ˆ˜ ์žˆ๋Š” ๋˜ ๋‹ค๋ฅธ ๋ฐฉ๋ฒ•์€ [๊ธฐ์šธ๊ธฐ ์„ค์ •](https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html)์„ 0 ๋Œ€์‹  `None`์œผ๋กœ ํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋กœ ์ธํ•ด ํŠน์ • ๋™์ž‘์ด ๋ณ€๊ฒฝ๋  ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ ๋ฌธ์ œ๊ฐ€ ๋ฐœ์ƒํ•˜๋ฉด ์ด ์ธ์ˆ˜๋ฅผ ์ œ๊ฑฐํ•ด ๋ณด์‹ญ์‹œ์˜ค. ํ•™์Šต ์Šคํฌ๋ฆฝํŠธ์— ๋‹ค์Œ ์ธ์ˆ˜๋ฅผ ์ถ”๊ฐ€ํ•˜์—ฌ ๊ทธ๋ž˜๋””์–ธํŠธ๋ฅผ `None`์œผ๋กœ ์„ค์ •ํ•ฉ๋‹ˆ๋‹ค.

```bash
  --set_grads_to_none
```

### 16GB GPU

Gradient checkpointing๊ณผ [bitsandbytes](https://github.com/TimDettmers/bitsandbytes)์˜ 8๋น„ํŠธ ์˜ตํ‹ฐ๋งˆ์ด์ €์˜ ๋„์›€์œผ๋กœ, 16GB GPU์—์„œ dreambooth๋ฅผ ํ›ˆ๋ จํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. bitsandbytes๊ฐ€ ์„ค์น˜๋˜์–ด ์žˆ๋Š”์ง€ ํ™•์ธํ•˜์„ธ์š”:

```bash
pip install bitsandbytes
```

๊ทธ ๋‹ค์Œ, ํ•™์Šต ์Šคํฌ๋ฆฝํŠธ์— `--use_8bit_adam` ์˜ต์…˜์„ ๋ช…์‹œํ•ฉ๋‹ˆ๋‹ค:

```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path_to_training_images"
export CLASS_DIR="path_to_class_images"
export OUTPUT_DIR="path_to_saved_model"

accelerate launch train_dreambooth.py \
  --pretrained_model_name_or_path=$MODEL_NAME  \
  --instance_data_dir=$INSTANCE_DIR \
  --class_data_dir=$CLASS_DIR \
  --output_dir=$OUTPUT_DIR \
  --with_prior_preservation --prior_loss_weight=1.0 \
  --instance_prompt="a photo of sks dog" \
  --class_prompt="a photo of dog" \
  --resolution=512 \
  --train_batch_size=1 \
  --gradient_accumulation_steps=2 --gradient_checkpointing \
  --use_8bit_adam \
  --learning_rate=5e-6 \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --num_class_images=200 \
  --max_train_steps=800
```

### 12GB GPU

12GB GPU์—์„œ DreamBooth๋ฅผ ์‹คํ–‰ํ•˜๋ ค๋ฉด gradient checkpointing, 8๋น„ํŠธ ์˜ตํ‹ฐ๋งˆ์ด์ €, xFormers๋ฅผ ํ™œ์„ฑํ™”ํ•˜๊ณ  ๊ทธ๋ž˜๋””์–ธํŠธ๋ฅผ `None`์œผ๋กœ ์„ค์ •ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.

```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"

accelerate launch train_dreambooth.py \
  --pretrained_model_name_or_path=$MODEL_NAME  \
  --instance_data_dir=$INSTANCE_DIR \
  --class_data_dir=$CLASS_DIR \
  --output_dir=$OUTPUT_DIR \
  --with_prior_preservation --prior_loss_weight=1.0 \
  --instance_prompt="a photo of sks dog" \
  --class_prompt="a photo of dog" \
  --resolution=512 \
  --train_batch_size=1 \
  --gradient_accumulation_steps=1 --gradient_checkpointing \
  --use_8bit_adam \
  --enable_xformers_memory_efficient_attention \
  --set_grads_to_none \
  --learning_rate=2e-6 \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --num_class_images=200 \
  --max_train_steps=800
```

### 8GB GPU์—์„œ ํ•™์Šตํ•˜๊ธฐ

8GB GPU์— ๋Œ€ํ•ด์„œ๋Š” [DeepSpeed](https://www.deepspeed.ai/)๋ฅผ ์‚ฌ์šฉํ•ด ์ผ๋ถ€ ํ…์„œ๋ฅผ VRAM์—์„œ CPU ๋˜๋Š” NVME๋กœ ์˜คํ”„๋กœ๋“œํ•˜์—ฌ ๋” ์ ์€ GPU ๋ฉ”๋ชจ๋ฆฌ๋กœ ํ•™์Šตํ•  ์ˆ˜๋„ ์žˆ์Šต๋‹ˆ๋‹ค.

๐Ÿค— Accelerate ํ™˜๊ฒฝ์„ ๊ตฌ์„ฑํ•˜๋ ค๋ฉด ๋‹ค์Œ ๋ช…๋ น์„ ์‹คํ–‰ํ•˜์„ธ์š”:

```bash
accelerate config
```

ํ™˜๊ฒฝ ๊ตฌ์„ฑ ์ค‘์— DeepSpeed๋ฅผ ์‚ฌ์šฉํ•  ๊ฒƒ์„ ํ™•์ธํ•˜์„ธ์š”.
๊ทธ๋Ÿฌ๋ฉด DeepSpeed stage 2, fp16 ํ˜ผํ•ฉ ์ •๋ฐ€๋„๋ฅผ ๊ฒฐํ•ฉํ•˜๊ณ  ๋ชจ๋ธ ๋งค๊ฐœ๋ณ€์ˆ˜์™€ ์˜ตํ‹ฐ๋งˆ์ด์ € ์ƒํƒœ๋ฅผ ๋ชจ๋‘ CPU๋กœ ์˜คํ”„๋กœ๋“œํ•˜๋ฉด 8GB VRAM ๋ฏธ๋งŒ์—์„œ ํ•™์Šตํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. 
๋‹จ์ ์€ ๋” ๋งŽ์€ ์‹œ์Šคํ…œ RAM(์•ฝ 25GB)์ด ํ•„์š”ํ•˜๋‹ค๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ถ”๊ฐ€ ๊ตฌ์„ฑ ์˜ต์…˜์€ [DeepSpeed ๋ฌธ์„œ](https://huggingface.co/docs/accelerate/usage_guides/deepspeed)๋ฅผ ์ฐธ์กฐํ•˜์„ธ์š”.

๋˜ํ•œ ๊ธฐ๋ณธ Adam ์˜ตํ‹ฐ๋งˆ์ด์ €๋ฅผ DeepSpeed์˜ ์ตœ์ ํ™”๋œ Adam ๋ฒ„์ „์œผ๋กœ ๋ณ€๊ฒฝํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.
์ด๋Š” ์ƒ๋‹นํ•œ ์†๋„ ํ–ฅ์ƒ์„ ์œ„ํ•œ Adam์ธ [`deepspeed.ops.adam.DeepSpeedCPUAdam`](https://deepspeed.readthedocs.io/en/latest/optimizers.html#adam-cpu)์ž…๋‹ˆ๋‹ค. 
`DeepSpeedCPUAdam`์„ ํ™œ์„ฑํ™”ํ•˜๋ ค๋ฉด ์‹œ์Šคํ…œ์˜ CUDA toolchain ๋ฒ„์ „์ด PyTorch์™€ ํ•จ๊ป˜ ์„ค์น˜๋œ ๊ฒƒ๊ณผ ๋™์ผํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.

8๋น„ํŠธ ์˜ตํ‹ฐ๋งˆ์ด์ €๋Š” ํ˜„์žฌ DeepSpeed์™€ ํ˜ธํ™˜๋˜์ง€ ์•Š๋Š” ๊ฒƒ ๊ฐ™์Šต๋‹ˆ๋‹ค.

๋‹ค์Œ ๋ช…๋ น์œผ๋กœ ํ•™์Šต์„ ์‹œ์ž‘ํ•ฉ๋‹ˆ๋‹ค:

```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path_to_training_images"
export CLASS_DIR="path_to_class_images"
export OUTPUT_DIR="path_to_saved_model"

accelerate launch train_dreambooth.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --instance_data_dir=$INSTANCE_DIR \
  --class_data_dir=$CLASS_DIR \
  --output_dir=$OUTPUT_DIR \
  --with_prior_preservation --prior_loss_weight=1.0 \
  --instance_prompt="a photo of sks dog" \
  --class_prompt="a photo of dog" \
  --resolution=512 \
  --train_batch_size=1 \
  --sample_batch_size=1 \
  --gradient_accumulation_steps=1 --gradient_checkpointing \
  --learning_rate=5e-6 \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --num_class_images=200 \
  --max_train_steps=800 \
  --mixed_precision=fp16
```

## ์ถ”๋ก 

๋ชจ๋ธ์„ ํ•™์Šตํ•œ ํ›„์—๋Š”, ๋ชจ๋ธ์ด ์ €์žฅ๋œ ๊ฒฝ๋กœ๋ฅผ ์ง€์ •ํ•ด [`StableDiffusionPipeline`]๋กœ ์ถ”๋ก ์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ํ”„๋กฌํ”„ํŠธ์— ํ•™์Šต์— ์‚ฌ์šฉ๋œ ํŠน์ˆ˜ `์‹๋ณ„์ž`(์ด์ „ ์˜ˆ์‹œ์˜ `sks`)๊ฐ€ ํฌํ•จ๋˜์–ด ์žˆ๋Š”์ง€ ํ™•์ธํ•˜์„ธ์š”.

**`"accelerate>=0.16.0"`**์ด ์„ค์น˜๋˜์–ด ์žˆ๋Š” ๊ฒฝ์šฐ ๋‹ค์Œ ์ฝ”๋“œ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ค‘๊ฐ„ ์ฒดํฌํฌ์ธํŠธ์—์„œ ์ถ”๋ก ์„ ์‹คํ–‰ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค:

```python
from diffusers import StableDiffusionPipeline
import torch

model_id = "path_to_saved_model"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")

prompt = "A photo of sks dog in a bucket"
image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")
```

[์ €์žฅ๋œ ํ•™์Šต ์ฒดํฌํฌ์ธํŠธ](#inference-from-a-saved-checkpoint)์—์„œ๋„ ์ถ”๋ก ์„ ์‹คํ–‰ํ•  ์ˆ˜๋„ ์žˆ์Šต๋‹ˆ๋‹ค.