Diffusers documentation

Custom Diffusion training example

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Custom Diffusion training example

Custom Diffusion is a method to customize text-to-image models like Stable Diffusion given just a few (4~5) images of a subject. The train_custom_diffusion.py script shows how to implement the training procedure and adapt it for stable diffusion.

This training example was contributed by Nupur Kumari (one of the authors of Custom Diffusion).

Running locally with PyTorch

Installing the dependencies

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

Important

To make sure you can successfully run the latest versions of the example scripts, we highly recommend installing from source and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:

git clone https://github.com/huggingface/diffusers
cd diffusers
pip install -e .

Then cd into the example folder

cd examples/custom_diffusion

Now run

pip install -r requirements.txt
pip install clip-retrieval

And initialize an 🤗Accelerate environment with:

accelerate config

Or for a default accelerate configuration without answering questions about your environment

accelerate config default

Or if your environment doesn’t support an interactive shell e.g. a notebook

from accelerate.utils import write_basic_config

write_basic_config()

Cat example 😺

Now let’s get our dataset. Download dataset from here and unzip it. To use your own dataset, take a look at the Create a dataset for training guide.

We also collect 200 real images using clip-retrieval which are combined with the target images in the training dataset as a regularization. This prevents overfitting to the the given target image. The following flags enable the regularization with_prior_preservation, real_prior with prior_loss_weight=1.. The class_prompt should be the category name same as target image. The collected real images are with text captions similar to the class_prompt. The retrieved image are saved in class_data_dir. You can disable real_prior to use generated images as regularization. To collect the real images use this command first before training.

pip install clip-retrieval
python retrieve.py --class_prompt cat --class_data_dir real_reg/samples_cat --num_class_images 200

Note: Change the resolution to 768 if you are using the stable-diffusion-2 768x768 model.

The script creates and saves model checkpoints and a pytorch_custom_diffusion_weights.bin file in your repository.

export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export OUTPUT_DIR="path-to-save-model"
export INSTANCE_DIR="./data/cat"

accelerate launch train_custom_diffusion.py \
  --pretrained_model_name_or_path=$MODEL_NAME  \
  --instance_data_dir=$INSTANCE_DIR \
  --output_dir=$OUTPUT_DIR \
  --class_data_dir=./real_reg/samples_cat/ \
  --with_prior_preservation --real_prior --prior_loss_weight=1.0 \
  --class_prompt="cat" --num_class_images=200 \
  --instance_prompt="photo of a <new1> cat"  \
  --resolution=512  \
  --train_batch_size=2  \
  --learning_rate=1e-5  \
  --lr_warmup_steps=0 \
  --max_train_steps=250 \
  --scale_lr --hflip  \
  --modifier_token "<new1>" \
  --push_to_hub

Use --enable_xformers_memory_efficient_attention for faster training with lower VRAM requirement (16GB per GPU). Follow this guide for installation instructions.

To track your experiments using Weights and Biases (wandb) and to save intermediate results (whcih we HIGHLY recommend), follow these steps:

Install wandb: pip install wandb.
Authorize: wandb login.
Then specify a validation_prompt and set report_to to wandb while launching training. You can also configure the following related arguments:
- num_validation_images
- validation_steps

Here is an example command:

accelerate launch train_custom_diffusion.py \
  --pretrained_model_name_or_path=$MODEL_NAME  \
  --instance_data_dir=$INSTANCE_DIR \
  --output_dir=$OUTPUT_DIR \
  --class_data_dir=./real_reg/samples_cat/ \
  --with_prior_preservation --real_prior --prior_loss_weight=1.0 \
  --class_prompt="cat" --num_class_images=200 \
  --instance_prompt="photo of a <new1> cat"  \
  --resolution=512  \
  --train_batch_size=2  \
  --learning_rate=1e-5  \
  --lr_warmup_steps=0 \
  --max_train_steps=250 \
  --scale_lr --hflip  \
  --modifier_token "<new1>" \
  --validation_prompt="<new1> cat sitting in a bucket" \
  --report_to="wandb" \
  --push_to_hub

Here is an example Weights and Biases page where you can check out the intermediate results along with other training details.

If you specify --push_to_hub, the learned parameters will be pushed to a repository on the Hugging Face Hub. Here is an example repository.

Training on multiple concepts 🐱🪵

Provide a json file with the info about each concept, similar to this.

To collect the real images run this command for each concept in the json file.

pip install clip-retrieval
python retrieve.py --class_prompt {} --class_data_dir {} --num_class_images 200

And then we’re ready to start training!

export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export OUTPUT_DIR="path-to-save-model"

accelerate launch train_custom_diffusion.py \
  --pretrained_model_name_or_path=$MODEL_NAME  \
  --output_dir=$OUTPUT_DIR \
  --concepts_list=./concept_list.json \
  --with_prior_preservation --real_prior --prior_loss_weight=1.0 \
  --resolution=512  \
  --train_batch_size=2  \
  --learning_rate=1e-5  \
  --lr_warmup_steps=0 \
  --max_train_steps=500 \
  --num_class_images=200 \
  --scale_lr --hflip  \
  --modifier_token "<new1>+<new2>" \
  --push_to_hub

Here is an example Weights and Biases page where you can check out the intermediate results along with other training details.

Training on human faces

For fine-tuning on human faces we found the following configuration to work better: learning_rate=5e-6, max_train_steps=1000 to 2000, and freeze_model=crossattn with at least 15-20 images.

To collect the real images use this command first before training.

pip install clip-retrieval
python retrieve.py --class_prompt person --class_data_dir real_reg/samples_person --num_class_images 200

Then start training!

export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export OUTPUT_DIR="path-to-save-model"
export INSTANCE_DIR="path-to-images"

accelerate launch train_custom_diffusion.py \
  --pretrained_model_name_or_path=$MODEL_NAME  \
  --instance_data_dir=$INSTANCE_DIR \
  --output_dir=$OUTPUT_DIR \
  --class_data_dir=./real_reg/samples_person/ \
  --with_prior_preservation --real_prior --prior_loss_weight=1.0 \
  --class_prompt="person" --num_class_images=200 \
  --instance_prompt="photo of a <new1> person"  \
  --resolution=512  \
  --train_batch_size=2  \
  --learning_rate=5e-6  \
  --lr_warmup_steps=0 \
  --max_train_steps=1000 \
  --scale_lr --hflip --noaug \
  --freeze_model crossattn \
  --modifier_token "<new1>" \
  --enable_xformers_memory_efficient_attention \
  --push_to_hub

Inference

Once you have trained a model using the above command, you can run inference using the below command. Make sure to include the modifier token (e.g. \<new1> in above example) in your prompt.

import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16).to("cuda")
pipe.unet.load_attn_procs("path-to-save-model", weight_name="pytorch_custom_diffusion_weights.bin")
pipe.load_textual_inversion("path-to-save-model", weight_name="<new1>.bin")

image = pipe(
    "<new1> cat sitting in a bucket",
    num_inference_steps=100,
    guidance_scale=6.0,
    eta=1.0,
).images[0]
image.save("cat.png")

It’s possible to directly load these parameters from a Hub repository:

import torch
from huggingface_hub.repocard import RepoCard
from diffusers import DiffusionPipeline

model_id = "sayakpaul/custom-diffusion-cat"
card = RepoCard.load(model_id)
base_model_id = card.data.to_dict()["base_model"]

pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16).to("cuda")
pipe.unet.load_attn_procs(model_id, weight_name="pytorch_custom_diffusion_weights.bin")
pipe.load_textual_inversion(model_id, weight_name="<new1>.bin")

image = pipe(
    "<new1> cat sitting in a bucket",
    num_inference_steps=100,
    guidance_scale=6.0,
    eta=1.0,
).images[0]
image.save("cat.png")

Here is an example of performing inference with multiple concepts:

import torch
from huggingface_hub.repocard import RepoCard
from diffusers import DiffusionPipeline

model_id = "sayakpaul/custom-diffusion-cat-wooden-pot"
card = RepoCard.load(model_id)
base_model_id = card.data.to_dict()["base_model"]

pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16).to("cuda")
pipe.unet.load_attn_procs(model_id, weight_name="pytorch_custom_diffusion_weights.bin")
pipe.load_textual_inversion(model_id, weight_name="<new1>.bin")
pipe.load_textual_inversion(model_id, weight_name="<new2>.bin")

image = pipe(
    "the <new1> cat sculpture in the style of a <new2> wooden pot",
    num_inference_steps=100,
    guidance_scale=6.0,
    eta=1.0,
).images[0]
image.save("multi-subject.png")

Here, cat and wooden pot refer to the multiple concepts.

Inference from a training checkpoint

You can also perform inference from one of the complete checkpoint saved during the training process, if you used the --checkpointing_steps argument.

TODO.

Set grads to none

To save even more memory, pass the --set_grads_to_none argument to the script. This will set grads to None instead of zero. However, be aware that it changes certain behaviors, so if you start experiencing any problems, remove this argument.

More info: https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html

Experimental results

You can refer to our webpage that discusses our experiments in detail.

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