diffusers/examples/text_to_image/README.md

# Stable Diffusion text-to-image fine-tuning

The `train_text_to_image.py` script shows how to fine-tune stable diffusion model on your own dataset.

___Note___:

___This script is experimental. The script fine-tunes the whole model and often times the model overifits and runs into issues like catastrophic forgetting. It's recommended to try different hyperparamters to get the best result on your dataset.___


## Running locally 
### Installing the dependencies

Before running the scripts, make sure to install the library's training dependencies:

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

And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:

```bash
accelerate config
```

### Pokemon example

You need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-4`, so you'll need to visit [its card](https://huggingface.co/CompVis/stable-diffusion-v1-4), 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. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).

Run the following command to authenticate your token

```bash
huggingface-cli login
```

If you have already cloned the repo, then you won't need to go through these steps.

<br>

#### Hardware
With `gradient_checkpointing` and `mixed_precision` it should be possible to fine tune the model on a single 24GB GPU. For higher `batch_size` and faster training it's better to use GPUs with >30GB memory.

```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export dataset_name="lambdalabs/pokemon-blip-captions"

accelerate launch train_text_to_image.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --dataset_name=$dataset_name \
  --use_ema \
  --resolution=512 --center_crop --random_flip \
  --train_batch_size=1 \
  --gradient_accumulation_steps=4 \
  --gradient_checkpointing \
  --mixed_precision="fp16" \
  --max_train_steps=15000 \
  --learning_rate=1e-05 \
  --max_grad_norm=1 \
  --lr_scheduler="constant" --lr_warmup_steps=0 \
  --output_dir="sd-pokemon-model" 
```

Or use the Flax implementation if you need a speedup

```bash
export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
export dataset_name="lambdalabs/pokemon-blip-captions"

python train_text_to_image_flax.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --dataset_name=$dataset_name \
  --resolution=512 --center_crop --random_flip \
  --train_batch_size=1 \
  --mixed_precision="fp16" \
  --max_train_steps=15000 \
  --learning_rate=1e-05 \
  --max_grad_norm=1 \
  --output_dir="sd-pokemon-model" 
```


To run on your own training files prepare the dataset according to the format required by `datasets`, you can find the instructions for how to do that in this [document](https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder-with-metadata).
If you wish to use custom loading logic, you should modify the script, we have left pointers for that in the training script.

```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export TRAIN_DIR="path_to_your_dataset"

accelerate launch train_text_to_image.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$TRAIN_DIR \
  --use_ema \
  --resolution=512 --center_crop --random_flip \
  --train_batch_size=1 \
  --gradient_accumulation_steps=4 \
  --gradient_checkpointing \
  --mixed_precision="fp16" \
  --max_train_steps=15000 \
  --learning_rate=1e-05 \
  --max_grad_norm=1 \
  --lr_scheduler="constant" --lr_warmup_steps=0 \
  --output_dir="sd-pokemon-model"
```

Or use the Flax implementation if you need a speedup

```bash
export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
export TRAIN_DIR="path_to_your_dataset"

python train_text_to_image_flax.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$TRAIN_DIR \
  --resolution=512 --center_crop --random_flip \
  --train_batch_size=1 \
  --mixed_precision="fp16" \
  --max_train_steps=15000 \
  --learning_rate=1e-05 \
  --max_grad_norm=1 \
  --output_dir="sd-pokemon-model"
```

Once the training is finished the model will be saved in the `output_dir` specified in the command. In this example it's `sd-pokemon-model`. To load the fine-tuned model for inference just pass that path to `StableDiffusionPipeline`


```python
from diffusers import StableDiffusionPipeline

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

image = pipe(prompt="yoda").images[0]
image.save("yoda-pokemon.png")
```