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InstructPix2Pix SDXL training example
This is based on the original InstructPix2Pix training example.
Stable Diffusion XL (or SDXL) is the latest image generation model that is tailored towards more photorealistic outputs with more detailed imagery and composition compared to previous SD models. It leverages a three times larger UNet backbone. The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder.
The train_instruct_pix2pix_sdxl.py script shows how to implement the training procedure and adapt it for Stable Diffusion XL.
Disclaimer: Even though train_instruct_pix2pix_sdxl.py implements the InstructPix2Pix
training procedure while being faithful to the original implementation we have only tested it on a small-scale dataset. This can impact the end results. For better results, we recommend longer training runs with a larger dataset. Here you can find a large dataset for InstructPix2Pix training.
Running locally with PyTorch
Installing the dependencies
Refer to the original InstructPix2Pix training example for installing the dependencies.
You will also need to get access of SDXL by filling the form.
Toy example
As mentioned before, we'll use a small toy dataset for training. The dataset is a smaller version of the original dataset used in the InstructPix2Pix paper.
Configure environment variables such as the dataset identifier and the Stable Diffusion checkpoint:
export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
export DATASET_ID="fusing/instructpix2pix-1000-samples"
Now, we can launch training:
accelerate launch train_instruct_pix2pix_sdxl.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 \
--seed=42 \
--push_to_hub
Additionally, we support performing validation inference to monitor training progress
with Weights and Biases. You can enable this feature with report_to="wandb":
accelerate launch train_instruct_pix2pix_sdxl.py \
--pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0 \
--dataset_name=$DATASET_ID \
--use_ema \
--enable_xformers_memory_efficient_attention \
--resolution=512 --random_flip \
--train_batch_size=4 --gradient_accumulation_steps=4 --gradient_checkpointing \
--max_train_steps=15000 \
--checkpointing_steps=5000 --checkpoints_total_limit=1 \
--learning_rate=5e-05 --lr_warmup_steps=0 \
--conditioning_dropout_prob=0.05 \
--seed=42 \
--val_image_url_or_path="https://datasets-server.huggingface.co/assets/fusing/instructpix2pix-1000-samples/--/fusing--instructpix2pix-1000-samples/train/23/input_image/image.jpg" \
--validation_prompt="make it in japan" \
--report_to=wandb \
--push_to_hub
We recommend this type of validation as it can be useful for model debugging. Note that you need wandb installed to use this. You can install wandb by running pip install wandb.
Here, you can find an example training run that includes some validation samples and the training hyperparameters.
Note: In the original paper, the authors observed that even when the model is trained with an image resolution of 256x256, it generalizes well to bigger resolutions such as 512x512. This is likely because of the larger dataset they used during training.
Training with multiple GPUs
accelerate allows for seamless multi-GPU training. Follow the instructions here
for running distributed training with accelerate. Here is an example command:
accelerate launch --mixed_precision="fp16" --multi_gpu train_instruct_pix2pix_sdxl.py \
--pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0 \
--dataset_name=$DATASET_ID \
--use_ema \
--enable_xformers_memory_efficient_attention \
--resolution=512 --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 --lr_warmup_steps=0 \
--conditioning_dropout_prob=0.05 \
--seed=42 \
--val_image_url_or_path="https://datasets-server.huggingface.co/assets/fusing/instructpix2pix-1000-samples/--/fusing--instructpix2pix-1000-samples/train/23/input_image/image.jpg" \
--validation_prompt="make it in japan" \
--report_to=wandb \
--push_to_hub
Inference
Once training is complete, we can perform inference:
import PIL
import requests
import torch
from diffusers import StableDiffusionXLInstructPix2PixPipeline
model_id = "your_model_id" # <- replace this
pipe = StableDiffusionXLInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
generator = torch.Generator("cuda").manual_seed(0)
url = "https://datasets-server.huggingface.co/assets/fusing/instructpix2pix-1000-samples/--/fusing--instructpix2pix-1000-samples/train/23/input_image/image.jpg"
def download_image(url):
image = PIL.Image.open(requests.get(url, stream=True).raw)
image = PIL.ImageOps.exif_transpose(image)
image = image.convert("RGB")
return image
image = download_image(url)
prompt = "make it Japan"
num_inference_steps = 20
image_guidance_scale = 1.5
guidance_scale = 10
edited_image = pipe(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")
We encourage you to play with the following three parameters to control speed and quality during performance:
num_inference_stepsimage_guidance_scaleguidance_scale
Particularly, image_guidance_scale and guidance_scale can have a profound impact
on the generated ("edited") image (see here for an example).
If you're looking for some interesting ways to use the InstructPix2Pix training methodology, we welcome you to check out this blog post: Instruction-tuning Stable Diffusion with InstructPix2Pix.
Compare between SD and SDXL
We aim to understand the differences resulting from the use of SD-1.5 and SDXL-0.9 as pretrained models. To achieve this, we trained on the small toy dataset using both of these pretrained models. The training script is as follows:
export MODEL_NAME="runwayml/stable-diffusion-v1-5" or "stabilityai/stable-diffusion-xl-base-0.9"
export DATASET_ID="fusing/instructpix2pix-1000-samples"
accelerate launch train_instruct_pix2pix.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--dataset_name=$DATASET_ID \
--use_ema \
--enable_xformers_memory_efficient_attention \
--resolution=512 --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 --lr_warmup_steps=0 \
--conditioning_dropout_prob=0.05 \
--seed=42 \
--val_image_url="https://datasets-server.huggingface.co/assets/fusing/instructpix2pix-1000-samples/--/fusing--instructpix2pix-1000-samples/train/23/input_image/image.jpg" \
--validation_prompt="make it in Japan" \
--report_to=wandb \
--push_to_hub
We discovered that compared to training with SD-1.5 as the pretrained model, SDXL-0.9 results in a lower training loss value (SD-1.5 yields 0.0599, SDXL scores 0.0254). Moreover, from a visual perspective, the results obtained using SDXL demonstrated fewer artifacts and a richer detail. Notably, SDXL starts to preserve the structure of the original image earlier on.
The following two GIFs provide intuitive visual results. We observed, for each step, what kind of results could be achieved using the image
