Once you're happy with the changes to your script or if you're okay with the default configuration, you're ready to launch the training script! 🚀  
This guide uses the [fusing/instructpix2pix-1000-samples](https://huggingface.co/datasets/fusing/instructpix2pix-1000-samples) dataset, which is a smaller version of the [original dataset](https://huggingface.co/datasets/timbrooks/instructpix2pix-clip-filtered). You can also create and use your own dataset if you'd like (see the [Create a dataset for training](create_dataset) guide).  
Set the `MODEL_NAME` environment variable to the name of the model (can be a model id on the Hub or a path to a local model), and the `DATASET_ID` to the name of the dataset on the Hub. The script creates and saves all the components (feature extractor, scheduler, text encoder, UNet, etc.) to a subfolder in your repository.  
<Tip>  
For better results, try longer training runs with a larger dataset. We've only tested this training script on a smaller-scale dataset.  
<br>  
To monitor training progress with Weights and Biases, add the `--report_to=wandb` parameter to the training command and specify a validation image with `--val_image_url` and a validation prompt with `--validation_prompt`. This can be really useful for debugging the model.  
</Tip>  
If you’re training on more than one GPU, add the `--multi_gpu` parameter to the `accelerate launch` command.  
```bash
accelerate launch --mixed_precision="fp16" train_instruct_pix2pix.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--dataset_name=$DATASET_ID \
--enable_xformers_memory_efficient_attention \
--resolution=256 \
--random_flip \
--train_batch_size=4 \
--gradient_accumulation_steps=4 \
--gradient_checkpointing \
--max_train_steps=15000 \
--checkpointing_steps=5000 \
--checkpoints_total_limit=1 \
--learning_rate=5e-05 \
--max_grad_norm=1 \
--lr_warmup_steps=0 \
--conditioning_dropout_prob=0.05 \
--mixed_precision=fp16 \
--seed=42 \
--push_to_hub
```  
After training is finished, you can use your new InstructPix2Pix for inference:  
```py
import PIL
import requests
import torch
from diffusers import StableDiffusionInstructPix2PixPipeline
from diffusers.utils import load_image

pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained("your_cool_model", torch_dtype=torch.float16).to("cuda")
generator = torch.Generator("cuda").manual_seed(0)

image = load_image("https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/test_pix2pix_4.png")
prompt = "add some ducks to the lake"
num_inference_steps = 20
image_guidance_scale = 1.5
guidance_scale = 10

edited_image = pipeline(
prompt,
image=image,
num_inference_steps=num_inference_steps,
image_guidance_scale=image_guidance_scale,
guidance_scale=guidance_scale,
generator=generator,
).images[0]
edited_image.save("edited_image.png")
```  
You should experiment with different `num_inference_steps`, `image_guidance_scale`, and `guidance_scale` values to see how they affect inference speed and quality. The guidance scale parameters are especially impactful because they control how much the original image and edit instructions affect the edited image.
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/instructpix2pix.md