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## Textual Inversion fine-tuning example for SDXL
```sh
export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
export DATA_DIR="./cat"
accelerate launch textual_inversion_sdxl.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$DATA_DIR \
--learnable_property="object" \
--placeholder_token="<cat-toy>" \
--initializer_token="toy" \
--mixed_precision="bf16" \
--resolution=768 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--max_train_steps=500 \
--learning_rate=5.0e-04 \
--scale_lr \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--save_as_full_pipeline \
--output_dir="./textual_inversion_cat_sdxl"
```
Training of both text encoders is supported.
### Inference Example
Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionXLPipeline`.
Make sure to include the `placeholder_token` in your prompt.
```python
from diffusers import StableDiffusionXLPipeline
import torch
model_id = "./textual_inversion_cat_sdxl"
pipe = StableDiffusionXLPipeline.from_pretrained(model_id,torch_dtype=torch.float16).to("cuda")
prompt = "A <cat-toy> backpack"
image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("cat-backpack.png")
image = pipe(prompt="", prompt_2=prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("cat-backpack-prompt_2.png")
```
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