Advanced diffusion training examples

Train Dreambooth LoRA with Stable Diffusion XL

💡 This example follows the techniques and recommended practices covered in the blog post: LoRA training scripts of the world, unite!. Make sure to check it out before starting 🤗

DreamBooth is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject.

LoRA - Low-Rank Adaption of Large Language Models, was first introduced by Microsoft in LoRA: Low-Rank Adaptation of Large Language Models by Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen In a nutshell, LoRA allows to adapt pretrained models by adding pairs of rank-decomposition matrices to existing weights and only training those newly added weights. This has a couple of advantages:

Previous pretrained weights are kept frozen so that the model is not prone to catastrophic forgetting
Rank-decomposition matrices have significantly fewer parameters than the original model, which means that trained LoRA weights are easily portable.
LoRA attention layers allow to control to which extent the model is adapted towards new training images via a scale parameter. cloneofsimo was the first to try out LoRA training for Stable Diffusion in the popular lora GitHub repository.

The train_dreambooth_lora_sdxl_advanced.py script shows how to implement dreambooth-LoRA, combining the training process shown in train_dreambooth_lora_sdxl.py, with advanced features and techniques, inspired and built upon contributions by Nataniel Ruiz: Dreambooth, Rinon Gal: Textual Inversion, Ron Mokady: Pivotal Tuning, Simo Ryu: cog-sdxl, Kohya: sd-scripts, The Last Ben: fast-stable-diffusion ❤️

💡If this is your first time training a Dreambooth LoRA, congrats!🥳 You might want to familiarize yourself more with the techniques: Dreambooth blog, Using LoRA for Efficient Stable Diffusion Fine-Tuning blog

📚 Read more about the advanced features and best practices in this community derived blog post: LoRA training scripts of the world, unite!

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 in the examples/advanced_diffusion_training folder and run

pip install -r requirements.txt

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()

When running accelerate config, if we specify torch compile mode to True there can be dramatic speedups. Note also that we use PEFT library as backend for LoRA training, make sure to have peft>=0.6.0 installed in your environment.

Pivotal Tuning

Training with text encoder(s)

Alongside the UNet, LoRA fine-tuning of the text encoders is also supported. In addition to the text encoder optimization available with train_dreambooth_lora_sdxl_advanced.py, in the advanced script pivotal tuning is also supported. pivotal tuning combines Textual Inversion with regular diffusion fine-tuning - we insert new tokens into the text encoders of the model, instead of reusing existing ones. We then optimize the newly-inserted token embeddings to represent the new concept.

To do so, just specify --train_text_encoder_ti while launching training (for regular text encoder optimizations, use --train_text_encoder). Please keep the following points in mind:

SDXL has two text encoders. So, we fine-tune both using LoRA.
When not fine-tuning the text encoders, we ALWAYS precompute the text embeddings to save memory.

3D icon example

Now let's get our dataset. For this example we will use some cool images of 3d rendered icons: https://huggingface.co/datasets/linoyts/3d_icon.

Let's first download it locally:

from huggingface_hub import snapshot_download

local_dir = "./3d_icon"
snapshot_download(
    "LinoyTsaban/3d_icon",
    local_dir=local_dir, repo_type="dataset",
    ignore_patterns=".gitattributes",
)

Let's review some of the advanced features we're going to be using for this example:

custom captions: To use custom captioning, first ensure that you have the datasets library installed, otherwise you can install it by

pip install datasets

Now we'll simply specify the name of the dataset and caption column (in this case it's "prompt")

--dataset_name=./3d_icon
--caption_column=prompt

You can also load a dataset straight from by specifying it's name in dataset_name. Look here for more info on creating/loadin your own caption dataset.

optimizer: for this example, we'll use prodigy - an adaptive optimizer
pivotal tuning
min SNR gamma

Now, we can launch training:

export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
export DATASET_NAME="./3d_icon"
export OUTPUT_DIR="3d-icon-SDXL-LoRA"
export VAE_PATH="madebyollin/sdxl-vae-fp16-fix"

accelerate launch train_dreambooth_lora_sdxl_advanced.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --pretrained_vae_model_name_or_path=$VAE_PATH \
  --dataset_name=$DATASET_NAME \
  --instance_prompt="3d icon in the style of TOK" \
  --validation_prompt="a TOK icon of an astronaut riding a horse, in the style of TOK" \
  --output_dir=$OUTPUT_DIR \
  --caption_column="prompt" \
  --mixed_precision="bf16" \
  --resolution=1024 \
  --train_batch_size=3 \
  --repeats=1 \
  --report_to="wandb"\
  --gradient_accumulation_steps=1 \
  --gradient_checkpointing \
  --learning_rate=1.0 \
  --text_encoder_lr=1.0 \
  --optimizer="prodigy"\
  --train_text_encoder_ti\
  --train_text_encoder_ti_frac=0.5\
  --snr_gamma=5.0 \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --rank=8 \
  --max_train_steps=1000 \
  --checkpointing_steps=2000 \
  --seed="0" \
  --push_to_hub

To better track our training experiments, we're using the following flags in the command above:

report_to="wandb will ensure the training runs are tracked on Weights and Biases. To use it, be sure to install wandb with pip install wandb.
validation_prompt and validation_epochs to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected.

Our experiments were conducted on a single 40GB A100 GPU.

Inference

Once training is done, we can perform inference like so:

starting with loading the unet lora weights

import torch
from huggingface_hub import hf_hub_download, upload_file
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL
from safetensors.torch import load_file

username = "linoyts"
repo_id = f"{username}/3d-icon-SDXL-LoRA"

pipe = DiffusionPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0",
        torch_dtype=torch.float16,
        variant="fp16",
).to("cuda")


pipe.load_lora_weights(repo_id, weight_name="pytorch_lora_weights.safetensors")

now we load the pivotal tuning embeddings

text_encoders = [pipe.text_encoder, pipe.text_encoder_2]
tokenizers = [pipe.tokenizer, pipe.tokenizer_2]

embedding_path = hf_hub_download(repo_id=repo_id, filename="3d-icon-SDXL-LoRA_emb.safetensors", repo_type="model")

state_dict = load_file(embedding_path)
# load embeddings of text_encoder 1 (CLIP ViT-L/14)
pipe.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
# load embeddings of text_encoder 2 (CLIP ViT-G/14)
pipe.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)

let's generate images

instance_token = "<s0><s1>"
prompt = f"a {instance_token} icon of an orange llama eating ramen, in the style of {instance_token}"

image = pipe(prompt=prompt, num_inference_steps=25, cross_attention_kwargs={"scale": 1.0}).images[0]
image.save("llama.png")

Comfy UI / AUTOMATIC1111 Inference

The new script fully supports textual inversion loading with Comfy UI and AUTOMATIC1111 formats!

AUTOMATIC1111 / SD.Next
In AUTOMATIC1111/SD.Next we will load a LoRA and a textual embedding at the same time.

LoRA: Besides the diffusers format, the script will also train a WebUI compatible LoRA. It is generated as {your_lora_name}.safetensors. You can then include it in your models/Lora directory.
Embedding: the embedding is the same for diffusers and WebUI. You can download your {lora_name}_emb.safetensors file from a trained model, and include it in your embeddings directory.

You can then run inference by prompting a y2k_emb webpage about the movie Mean Girls <lora:y2k:0.9>. You can use the y2k_emb token normally, including increasing its weight by doing (y2k_emb:1.2).

ComfyUI
In ComfyUI we will load a LoRA and a textual embedding at the same time. - LoRA: Besides the diffusers format, the script will also train a ComfyUI compatible LoRA. It is generated as `{your_lora_name}.safetensors`. You can then include it in your `models/Lora` directory. Then you will load the LoRALoader node and hook that up with your model and CLIP. Official guide for loading LoRAs - Embedding: the embedding is the same for diffusers and WebUI. You can download your `{lora_name}_emb.safetensors` file from a trained model, and include it in your `models/embeddings` directory and use it in your prompts like `embedding:y2k_emb`. Official guide for loading embeddings.

Specifying a better VAE

SDXL's VAE is known to suffer from numerical instability issues. This is why we also expose a CLI argument namely --pretrained_vae_model_name_or_path that lets you specify the location of a better VAE (such as this one).

DoRA training

The advanced script supports DoRA training too!

Proposed in DoRA: Weight-Decomposed Low-Rank Adaptation, DoRA is very similar to LoRA, except it decomposes the pre-trained weight into two components, magnitude and direction and employs LoRA for directional updates to efficiently minimize the number of trainable parameters. The authors found that by using DoRA, both the learning capacity and training stability of LoRA are enhanced without any additional overhead during inference.

💡DoRA training is still experimental and is likely to require different hyperparameter values to perform best compared to a LoRA. Specifically, we've noticed 2 differences to take into account your training:

LoRA seem to converge faster than DoRA (so a set of parameters that may lead to overfitting when training a LoRA may be working well for a DoRA)

DoRA quality superior to LoRA especially in lower ranks the difference in quality of DoRA of rank 8 and LoRA of rank 8 appears to be more significant than when training ranks of 32 or 64 for example. This is also aligned with some of the quantitative analysis shown in the paper.

Usage

To use DoRA you need to install peft from main:

pip install git+https://github.com/huggingface/peft.git

Enable DoRA training by adding this flag

--use_dora

Inference The inference is the same as if you train a regular LoRA 🤗

Conducting EDM-style training

It's now possible to perform EDM-style training as proposed in Elucidating the Design Space of Diffusion-Based Generative Models.

simply set:

+  --do_edm_style_training \

Other SDXL-like models that use the EDM formulation, such as playgroundai/playground-v2.5-1024px-aesthetic, can also be DreamBooth'd with the script. Below is an example command:

accelerate launch train_dreambooth_lora_sdxl_advanced.py \
  --pretrained_model_name_or_path="playgroundai/playground-v2.5-1024px-aesthetic"  \
  --dataset_name="linoyts/3d_icon" \
  --instance_prompt="3d icon in the style of TOK" \
  --validation_prompt="a TOK icon of an astronaut riding a horse, in the style of TOK" \
  --output_dir="3d-icon-SDXL-LoRA" \
  --do_edm_style_training \
  --caption_column="prompt" \
  --mixed_precision="bf16" \
  --resolution=1024 \
  --train_batch_size=3 \
  --repeats=1 \
  --report_to="wandb"\
  --gradient_accumulation_steps=1 \
  --gradient_checkpointing \
  --learning_rate=1.0 \
  --text_encoder_lr=1.0 \
  --optimizer="prodigy"\
  --train_text_encoder_ti\
  --train_text_encoder_ti_frac=0.5\
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --rank=8 \
  --max_train_steps=1000 \
  --checkpointing_steps=2000 \
  --seed="0" \
  --push_to_hub

Min-SNR gamma is not supported with the EDM-style training yet. When training with the PlaygroundAI model, it's recommended to not pass any "variant".

B-LoRA training

The advanced script now supports B-LoRA training too!

Proposed in Implicit Style-Content Separation using B-LoRA, B-LoRA is a method that leverages LoRA to implicitly separate the style and content components of a single image. It was shown that learning the LoRA weights of two specific blocks (referred to as B-LoRAs) achieves style-content separation that cannot be achieved by training each B-LoRA independently. Once trained, the two B-LoRAs can be used as independent components to allow various image stylization tasks

Usage Enable B-LoRA training by adding this flag

--use_blora

You can train a B-LoRA with as little as 1 image, and 1000 steps. Try this default configuration as a start:

!accelerate launch train_dreambooth_b-lora_sdxl.py \
 --pretrained_model_name_or_path="stabilityai/stable-diffusion-xl-base-1.0" \
 --instance_data_dir="linoyts/B-LoRA_teddy_bear" \
 --output_dir="B-LoRA_teddy_bear" \
 --instance_prompt="a [v18]" \
 --resolution=1024 \
 --rank=64 \
 --train_batch_size=1 \
 --learning_rate=5e-5 \
 --lr_scheduler="constant" \
 --lr_warmup_steps=0 \
 --max_train_steps=1000 \
 --checkpointing_steps=2000 \
 --seed="0" \
 --gradient_checkpointing \
 --mixed_precision="fp16"

Inference The inference is a bit different:

we need load specific unet layers (as opposed to a regular LoRA/DoRA)
the trained layers we load, changes based on our objective (e.g. style/content)

import torch
from diffusers import StableDiffusionXLPipeline, AutoencoderKL

# taken & modified from B-LoRA repo - https://github.com/yardenfren1996/B-LoRA/blob/main/blora_utils.py
def is_belong_to_blocks(key, blocks):
    try:
        for g in blocks:
            if g in key:
                return True
        return False
    except Exception as e:
        raise type(e)(f'failed to is_belong_to_block, due to: {e}')

def lora_lora_unet_blocks(lora_path, alpha, target_blocks):
  state_dict, _ = pipeline.lora_state_dict(lora_path)
  filtered_state_dict = {k: v * alpha for k, v in state_dict.items() if is_belong_to_blocks(k, target_blocks)}
  return filtered_state_dict

vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipeline = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    vae=vae,
    torch_dtype=torch.float16,
).to("cuda")

# pick a blora for content/style (you can also set one to None)
content_B_lora_path  = "lora-library/B-LoRA-teddybear"
style_B_lora_path= "lora-library/B-LoRA-pen_sketch"


content_B_LoRA = lora_lora_unet_blocks(content_B_lora_path,alpha=1,target_blocks=["unet.up_blocks.0.attentions.0"])
style_B_LoRA = lora_lora_unet_blocks(style_B_lora_path,alpha=1.1,target_blocks=["unet.up_blocks.0.attentions.1"])
combined_lora = {**content_B_LoRA, **style_B_LoRA}

# Load both loras
pipeline.load_lora_into_unet(combined_lora, None, pipeline.unet)

#generate
prompt = "a [v18] in [v30] style"
pipeline(prompt, num_images_per_prompt=4).images

LoRA training of Targeted U-net Blocks

The advanced script now supports custom choice of U-net blocks to train during Dreambooth LoRA tuning.

This feature is still experimental

Recently, works like B-LoRA showed the potential advantages of learning the LoRA weights of specific U-net blocks, not only in speed & memory, but also in reducing the amount of needed data, improving style manipulation and overcoming overfitting issues. In light of this, we're introducing a new feature to the advanced script to allow for configurable U-net learned blocks.

Usage Configure LoRA learned U-net blocks adding a lora_unet_blocks flag, with a comma seperated string specifying the targeted blocks. e.g:

--lora_unet_blocks="unet.up_blocks.0.attentions.0,unet.up_blocks.0.attentions.1"

if you specify both --use_blora and --lora_unet_blocks, values given in --lora_unet_blocks will be ignored. When enabling --use_blora, targeted U-net blocks are automatically set to be "unet.up_blocks.0.attentions.0,unet.up_blocks.0.attentions.1" as discussed in the paper. If you wish to experiment with different blocks, specify --lora_unet_blocks only.

Inference Inference is the same as for B-LoRAs, except the input targeted blocks should be modified based on your training configuration.

import torch
from diffusers import StableDiffusionXLPipeline, AutoencoderKL

# taken & modified from B-LoRA repo - https://github.com/yardenfren1996/B-LoRA/blob/main/blora_utils.py
def is_belong_to_blocks(key, blocks):
    try:
        for g in blocks:
            if g in key:
                return True
        return False
    except Exception as e:
        raise type(e)(f'failed to is_belong_to_block, due to: {e}')

def lora_lora_unet_blocks(lora_path, alpha, target_blocks):
  state_dict, _ = pipeline.lora_state_dict(lora_path)
  filtered_state_dict = {k: v * alpha for k, v in state_dict.items() if is_belong_to_blocks(k, target_blocks)}
  return filtered_state_dict

vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipeline = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    vae=vae,
    torch_dtype=torch.float16,
).to("cuda")

lora_path  = "lora-library/B-LoRA-pen_sketch"

state_dict = lora_lora_unet_blocks(content_B_lora_path,alpha=1,target_blocks=["unet.up_blocks.0.attentions.0"])

# Load trained lora layers into the unet
pipeline.load_lora_into_unet(state_dict, None, pipeline.unet)

#generate
prompt = "a dog in [v30] style"
pipeline(prompt, num_images_per_prompt=4).images

Tips and Tricks

Check out these recommended practices

Running on Colab Notebook

Check out this notebook. to train using the advanced features (including pivotal tuning), and this notebook to train on a free colab, using some of the advanced features (excluding pivotal tuning)