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Unverified Commit 600ef8a4 authored by Sayak Paul's avatar Sayak Paul Committed by GitHub
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Allow SD3 DreamBooth LoRA fine-tuning on a free-tier Colab (#8762) 



* add experimental scripts to train SD3 transformer lora on colab

* add readme

* add colab

* Apply suggestions from code review
Co-authored-by: default avatarSteven Liu <59462357+stevhliu@users.noreply.github.com>

* fix link in the notebook.

---------
Co-authored-by: default avatarSteven Liu <59462357+stevhliu@users.noreply.github.com>
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examples/research_projects/sd3_lora_colab/README.md 0 → 100644
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# Running Stable Diffusion 3 DreamBooth LoRA training under 16GB
This is an **EDUCATIONAL** project that provides utilities for DreamBooth LoRA training for [Stable Diffusion 3 (SD3)](ttps://huggingface.co/papers/2403.03206) under 16GB GPU VRAM. This means you can successfully try out this project using a [free-tier Colab Notebook](./sd3_dreambooth_lora_16gb.ipynb) instance. 🤗
> [!NOTE]
> SD3 is gated, so you need to make sure you agree to [share your contact info](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers) to access the model before using it with Diffusers. Once you have access, you need to log in so your system knows you’re authorized. Use the command below to log in:
```bash
huggingface-cli login
```
This will also allow us to push the trained model parameters to the Hugging Face Hub platform.
For setup, inference code, and details on how to run the code, please follow the Colab Notebook provided above.
## How
We make use of several techniques to make this possible:
* Compute the embeddings from the instance prompt and serialize them for later reuse. This is implemented in the [`compute_embeddings.py`](./compute_embeddings.py) script. We use an 8bit (as introduced in [`LLM.int8()`](https://arxiv.org/abs/2208.07339)) T5 to reduce memory requirements to ~10.5GB.
* In the `train_dreambooth_sd3_lora_miniature.py` script, we make use of:
* 8bit Adam for optimization through the `bitsandbytes` library.
* Gradient checkpointing and gradient accumulation.
* FP16 precision.
* Flash attention through `F.scaled_dot_product_attention()`.
Computing the text embeddings is arguably the most memory-intensive part in the pipeline as SD3 employs three text encoders. If we run them in FP32, it will take about 20GB of VRAM. With FP16, we are down to 12GB.
## Gotchas
This project is educational. It exists to showcase the possibility of fine-tuning a big diffusion system on consumer GPUs. But additional components might have to be added to obtain state-of-the-art performance. Below are some commonly known gotchas that users should be aware of:
* Training of text encoders is purposefully disabled.
* Techniques such as prior-preservation is unsupported.
* Custom instance captions for instance images are unsupported, but this should be relatively easy to integrate.
Hopefully, this project gives you a template to extend it further to suit your needs.
\ No newline at end of file
examples/research_projects/sd3_lora_colab/compute_embeddings.py 0 → 100644
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#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import glob
import hashlib
import pandas as pd
import torch
from transformers import T5EncoderModel
from diffusers import StableDiffusion3Pipeline
PROMPT = "a photo of sks dog"
MAX_SEQ_LENGTH = 77
LOCAL_DATA_DIR = "dog"
OUTPUT_PATH = "sample_embeddings.parquet"
def bytes_to_giga_bytes(bytes):
return bytes / 1024 / 1024 / 1024
def generate_image_hash(image_path):
with open(image_path, "rb") as f:
img_data = f.read()
return hashlib.sha256(img_data).hexdigest()
def load_sd3_pipeline():
id = "stabilityai/stable-diffusion-3-medium-diffusers"
text_encoder = T5EncoderModel.from_pretrained(id, subfolder="text_encoder_3", load_in_8bit=True, device_map="auto")
pipeline = StableDiffusion3Pipeline.from_pretrained(
id, text_encoder_3=text_encoder, transformer=None, vae=None, device_map="balanced"
)
return pipeline
@torch.no_grad()
def compute_embeddings(pipeline, prompt, max_sequence_length):
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = pipeline.encode_prompt(prompt=prompt, prompt_2=None, prompt_3=None, max_sequence_length=max_sequence_length)
print(
f"{prompt_embeds.shape=}, {negative_prompt_embeds.shape=}, {pooled_prompt_embeds.shape=}, {negative_pooled_prompt_embeds.shape}"
)
max_memory = bytes_to_giga_bytes(torch.cuda.max_memory_allocated())
print(f"Max memory allocated: {max_memory:.3f} GB")
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
def run(args):
pipeline = load_sd3_pipeline()
prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = compute_embeddings(
pipeline, args.prompt, args.max_sequence_length
)
# Assumes that the images within `args.local_image_dir` have a JPEG extension. Change
# as needed.
image_paths = glob.glob(f"{args.local_data_dir}/*.jpeg")
data = []
for image_path in image_paths:
img_hash = generate_image_hash(image_path)
data.append(
(img_hash, prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds)
)
# Create a DataFrame
embedding_cols = [
"prompt_embeds",
"negative_prompt_embeds",
"pooled_prompt_embeds",
"negative_pooled_prompt_embeds",
]
df = pd.DataFrame(
data,
columns=["image_hash"] + embedding_cols,
)
# Convert embedding lists to arrays (for proper storage in parquet)
for col in embedding_cols:
df[col] = df[col].apply(lambda x: x.cpu().numpy().flatten().tolist())
# Save the dataframe to a parquet file
df.to_parquet(args.output_path)
print(f"Data successfully serialized to {args.output_path}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--prompt", type=str, default=PROMPT, help="The instance prompt.")
parser.add_argument(
"--max_sequence_length",
type=int,
default=MAX_SEQ_LENGTH,
help="Maximum sequence length to use for computing the embeddings. The more the higher computational costs.",
)
parser.add_argument(
"--local_data_dir", type=str, default=LOCAL_DATA_DIR, help="Path to the directory containing instance images."
)
parser.add_argument("--output_path", type=str, default=OUTPUT_PATH, help="Path to serialize the parquet file.")
args = parser.parse_args()
run(args)
examples/research_projects/sd3_lora_colab/sd3_dreambooth_lora_16gb.ipynb 0 → 100644
View file @ 600ef8a4
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "a6xLZDgOajbd"
},
"source": [
"# Running Stable Diffusion 3 (SD3) DreamBooth LoRA training under 16GB GPU VRAM"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0jPZpMTwafua"
},
"source": [
"## Install Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lIYdn1woOS1n",
"outputId": "6d4a6332-d1f5-46e2-ad2b-c9e51b9f279a"
},
"outputs": [],
"source": [
"!pip install -q -U git+https://github.com/huggingface/diffusers\n",
"!pip install -q -U \\\n",
" transformers \\\n",
" accelerate \\\n",
" wandb \\\n",
" bitsandbytes \\\n",
" peft"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5qUNciw6aov2"
},
"source": [
"As SD3 is gated, before using it with diffusers you first need to go to the [Stable Diffusion 3 Medium Hugging Face page](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Bpk5FleeK1NR",
"outputId": "54d8e774-514e-46fe-b9a7-0185e0bcf211"
},
"outputs": [],
"source": [
"!huggingface-cli login"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tcF7gl4FasJV"
},
"source": [
"## Clone `diffusers`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QgSOJYglJKiM",
"outputId": "be51f30f-8848-4a79-ae91-c4fb89c244ba"
},
"outputs": [],
"source": [
"!git clone https://github.com/huggingface/diffusers\n",
"%cd diffusers/examples/research_projects/sd3_lora_colab"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "X9dBawr6ayRY"
},
"source": [
"## Download instance data images"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 351,
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},
"id": "La1rBYWFNjEP",
"outputId": "e8567843-193e-4653-86b8-be26390700df"
},
"outputs": [],
"source": [
"from huggingface_hub import snapshot_download\n",
"\n",
"local_dir = \"./dog\"\n",
"snapshot_download(\n",
" \"diffusers/dog-example\",\n",
" local_dir=local_dir, repo_type=\"dataset\",\n",
" ignore_patterns=\".gitattributes\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "hbsIzdjbOzgi"
},
"outputs": [],
"source": [
"!rm -rf dog/.huggingface"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "88sOTn2ga07q"
},
"source": [
"## Compute embeddings\n",
"\n",
"Here we are using the default instance prompt \"a photo of sks dog\". But you can configure this. Refer to the `compute_embeddings.py` script for details on other supported arguments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ha6hPLpHLM8c",
"outputId": "82843eb0-473e-4d6b-d11d-1f79bcd1d11a"
},
"outputs": [],
"source": [
"!python compute_embeddings.py"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "10iMo-RUa_yv"
},
"source": [
"## Clear memory"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-YltRmPgMuNa"
},
"outputs": [],
"source": [
"import torch\n",
"import gc\n",
"\n",
"\n",
"def flush():\n",
" torch.cuda.empty_cache()\n",
" gc.collect()\n",
"\n",
"flush()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UO5oEtOJbBS9"
},
"source": [
"## Train!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HuJ6hdm2M4Aw",
"outputId": "0b2d8ca3-c65f-4bb4-af9a-809b77116510"
},
"outputs": [],
"source": [
"!accelerate launch train_dreambooth_lora_sd3_miniature.py \\\n",
" --pretrained_model_name_or_path=\"stabilityai/stable-diffusion-3-medium-diffusers\" \\\n",
" --instance_data_dir=\"dog\" \\\n",
" --data_df_path=\"sample_embeddings.parquet\" \\\n",
" --output_dir=\"trained-sd3-lora-miniature\" \\\n",
" --mixed_precision=\"fp16\" \\\n",
" --instance_prompt=\"a photo of sks dog\" \\\n",
" --resolution=1024 \\\n",
" --train_batch_size=1 \\\n",
" --gradient_accumulation_steps=4 --gradient_checkpointing \\\n",
" --use_8bit_adam \\\n",
" --learning_rate=1e-4 \\\n",
" --report_to=\"wandb\" \\\n",
" --lr_scheduler=\"constant\" \\\n",
" --lr_warmup_steps=0 \\\n",
" --max_train_steps=500 \\\n",
" --seed=\"0\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "itS-dsJ0gjy3"
},
"source": [
"Training will take about an hour to complete depending on the length of your dataset."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BpOuL7S1bI6j"
},
"source": [
"## Inference"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "clfMv4jKfQzb"
},
"outputs": [],
"source": [
"flush()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "np03SXHkbKpG"
},
"outputs": [],
"source": [
"from diffusers import DiffusionPipeline\n",
"import torch\n",
"\n",
"pipeline = DiffusionPipeline.from_pretrained(\n",
" \"stabilityai/stable-diffusion-3-medium-diffusers\",\n",
" torch_dtype=torch.float16\n",
")\n",
"lora_output_path = \"trained-sd3-lora-miniature\"\n",
"pipeline.load_lora_weights(\"trained-sd3-lora-miniature\")\n",
"\n",
"pipeline.enable_sequential_cpu_offload()\n",
"\n",
"image = pipeline(\"a photo of sks dog in a bucket\").images[0]\n",
"image.save(\"bucket_dog.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HDfrY2opjGjD"
},
"source": [
"Note that inference will be very slow in this case because we're loading and unloading individual components of the models and that introduces significant data movement overhead. Refer to [this resource](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more memory optimization related techniques."
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
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examples/research_projects/sd3_lora_colab/train_dreambooth_lora_sd3_miniature.py 0 → 100644
View file @ 600ef8a4
#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import copy
import gc
import hashlib
import logging
import math
import os
import random
import shutil
from contextlib import nullcontext
from pathlib import Path
import numpy as np
import pandas as pd
import torch
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder
from peft import LoraConfig, set_peft_model_state_dict
from peft.utils import get_peft_model_state_dict
from PIL import Image
from PIL.ImageOps import exif_transpose
from torch.utils.data import Dataset
from torchvision import transforms
from torchvision.transforms.functional import crop
from tqdm.auto import tqdm
import diffusers
from diffusers import (
AutoencoderKL,
FlowMatchEulerDiscreteScheduler,
SD3Transformer2DModel,
StableDiffusion3Pipeline,
)
from diffusers.optimization import get_scheduler
from diffusers.training_utils import (
cast_training_params,
compute_density_for_timestep_sampling,
compute_loss_weighting_for_sd3,
)
from diffusers.utils import (
check_min_version,
convert_unet_state_dict_to_peft,
is_wandb_available,
)
from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
from diffusers.utils.torch_utils import is_compiled_module
if is_wandb_available():
import wandb
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.30.0.dev0")
logger = get_logger(__name__)
def save_model_card(
repo_id: str,
images=None,
base_model: str = None,
train_text_encoder=False,
instance_prompt=None,
validation_prompt=None,
repo_folder=None,
):
widget_dict = []
if images is not None:
for i, image in enumerate(images):
image.save(os.path.join(repo_folder, f"image_{i}.png"))
widget_dict.append(
{"text": validation_prompt if validation_prompt else " ", "output": {"url": f"image_{i}.png"}}
)
model_description = f"""
# SD3 DreamBooth LoRA - {repo_id}
<Gallery />
## Model description
These are {repo_id} DreamBooth weights for {base_model}.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: {train_text_encoder}.
## Trigger words
You should use {instance_prompt} to trigger the image generation.
## Download model
[Download]({repo_id}/tree/main) them in the Files & versions tab.
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/LICENSE).
"""
model_card = load_or_create_model_card(
repo_id_or_path=repo_id,
from_training=True,
license="openrail++",
base_model=base_model,
prompt=instance_prompt,
model_description=model_description,
widget=widget_dict,
)
tags = [
"text-to-image",
"diffusers-training",
"diffusers",
"lora",
"sd3",
"sd3-diffusers",
"template:sd-lora",
]
model_card = populate_model_card(model_card, tags=tags)
model_card.save(os.path.join(repo_folder, "README.md"))
def log_validation(
pipeline,
args,
accelerator,
pipeline_args,
epoch,
is_final_validation=False,
):
logger.info(
f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
f" {args.validation_prompt}."
)
pipeline.enable_model_cpu_offload()
pipeline.set_progress_bar_config(disable=True)
# run inference
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
# autocast_ctx = torch.autocast(accelerator.device.type) if not is_final_validation else nullcontext()
autocast_ctx = nullcontext()
with autocast_ctx:
images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]
for tracker in accelerator.trackers:
phase_name = "test" if is_final_validation else "validation"
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images(phase_name, np_images, epoch, dataformats="NHWC")
if tracker.name == "wandb":
tracker.log(
{
phase_name: [
wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images)
]
}
)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
return images
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--variant",
type=str,
default=None,
help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
)
parser.add_argument(
"--instance_data_dir",
type=str,
default=None,
help=("A folder containing the training data. "),
)
parser.add_argument(
"--data_df_path",
type=str,
default=None,
help=("Path to the parquet file serialized with compute_embeddings.py."),
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument(
"--instance_prompt",
type=str,
default=None,
required=True,
help="The prompt with identifier specifying the instance, e.g. 'photo of a TOK dog', 'in the style of TOK'",
)
parser.add_argument(
"--max_sequence_length",
type=int,
default=77,
help="Maximum sequence length to use with with the T5 text encoder",
)
parser.add_argument(
"--validation_prompt",
type=str,
default=None,
help="A prompt that is used during validation to verify that the model is learning.",
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images that should be generated during validation with `validation_prompt`.",
)
parser.add_argument(
"--validation_epochs",
type=int,
default=50,
help=(
"Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`."
),
)
parser.add_argument(
"--rank",
type=int,
default=4,
help=("The dimension of the LoRA update matrices."),
)
parser.add_argument(
"--output_dir",
type=str,
default="sd3-dreambooth-lora",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop",
default=False,
action="store_true",
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument(
"--random_flip",
action="store_true",
help="whether to randomly flip images horizontally",
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--lr_num_cycles",
type=int,
default=1,
help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
)
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument(
"--weighting_scheme",
type=str,
default="logit_normal",
choices=["sigma_sqrt", "logit_normal", "mode", "cosmap"],
)
parser.add_argument(
"--logit_mean", type=float, default=0.0, help="mean to use when using the `'logit_normal'` weighting scheme."
)
parser.add_argument(
"--logit_std", type=float, default=1.0, help="std to use when using the `'logit_normal'` weighting scheme."
)
parser.add_argument(
"--mode_scale",
type=float,
default=1.29,
help="Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme`.",
)
parser.add_argument(
"--optimizer",
type=str,
default="AdamW",
help=('The optimizer type to use. Choose between ["AdamW"]'),
)
parser.add_argument(
"--use_8bit_adam",
action="store_true",
help="Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW",
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-04, help="Weight decay to use for unet params")
parser.add_argument(
"--adam_epsilon",
type=float,
default=1e-08,
help="Epsilon value for the Adam optimizer.",
)
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--prior_generation_precision",
type=str,
default=None,
choices=["no", "fp32", "fp16", "bf16"],
help=(
"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
if args.instance_data_dir is None:
raise ValueError("Specify `instance_data_dir`.")
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
return args
class DreamBoothDataset(Dataset):
"""
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
It pre-processes the images.
"""
def __init__(
self,
data_df_path,
instance_data_root,
instance_prompt,
size=1024,
center_crop=False,
):
# Logistics
self.size = size
self.center_crop = center_crop
self.instance_prompt = instance_prompt
self.instance_data_root = Path(instance_data_root)
if not self.instance_data_root.exists():
raise ValueError("Instance images root doesn't exists.")
# Load images.
instance_images = [Image.open(path) for path in list(Path(instance_data_root).iterdir())]
image_hashes = [self.generate_image_hash(path) for path in list(Path(instance_data_root).iterdir())]
self.instance_images = instance_images
self.image_hashes = image_hashes
# Image transformations
self.pixel_values = self.apply_image_transformations(
instance_images=instance_images, size=size, center_crop=center_crop
)
# Map hashes to embeddings.
self.data_dict = self.map_image_hash_embedding(data_df_path=data_df_path)
self.num_instance_images = len(instance_images)
self._length = self.num_instance_images
def __len__(self):
return self._length
def __getitem__(self, index):
example = {}
instance_image = self.pixel_values[index % self.num_instance_images]
image_hash = self.image_hashes[index % self.num_instance_images]
prompt_embeds, pooled_prompt_embeds = self.data_dict[image_hash]
example["instance_images"] = instance_image
example["prompt_embeds"] = prompt_embeds
example["pooled_prompt_embeds"] = pooled_prompt_embeds
return example
def apply_image_transformations(self, instance_images, size, center_crop):
pixel_values = []
train_resize = transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR)
train_crop = transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size)
train_flip = transforms.RandomHorizontalFlip(p=1.0)
train_transforms = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
for image in instance_images:
image = exif_transpose(image)
if not image.mode == "RGB":
image = image.convert("RGB")
image = train_resize(image)
if args.random_flip and random.random() < 0.5:
# flip
image = train_flip(image)
if args.center_crop:
y1 = max(0, int(round((image.height - args.resolution) / 2.0)))
x1 = max(0, int(round((image.width - args.resolution) / 2.0)))
image = train_crop(image)
else:
y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))
image = crop(image, y1, x1, h, w)
image = train_transforms(image)
pixel_values.append(image)
return pixel_values
def convert_to_torch_tensor(self, embeddings: list):
prompt_embeds = embeddings[0]
pooled_prompt_embeds = embeddings[1]
prompt_embeds = np.array(prompt_embeds).reshape(154, 4096)
pooled_prompt_embeds = np.array(pooled_prompt_embeds).reshape(2048)
return torch.from_numpy(prompt_embeds), torch.from_numpy(pooled_prompt_embeds)
def map_image_hash_embedding(self, data_df_path):
hashes_df = pd.read_parquet(data_df_path)
data_dict = {}
for i, row in hashes_df.iterrows():
embeddings = [row["prompt_embeds"], row["pooled_prompt_embeds"]]
prompt_embeds, pooled_prompt_embeds = self.convert_to_torch_tensor(embeddings=embeddings)
data_dict.update({row["image_hash"]: (prompt_embeds, pooled_prompt_embeds)})
return data_dict
def generate_image_hash(self, image_path):
with open(image_path, "rb") as f:
img_data = f.read()
return hashlib.sha256(img_data).hexdigest()
def collate_fn(examples):
pixel_values = [example["instance_images"] for example in examples]
prompt_embeds = [example["prompt_embeds"] for example in examples]
pooled_prompt_embeds = [example["pooled_prompt_embeds"] for example in examples]
pixel_values = torch.stack(pixel_values)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
prompt_embeds = torch.stack(prompt_embeds)
pooled_prompt_embeds = torch.stack(pooled_prompt_embeds)
batch = {
"pixel_values": pixel_values,
"prompt_embeds": prompt_embeds,
"pooled_prompt_embeds": pooled_prompt_embeds,
}
return batch
def main(args):
if args.report_to == "wandb" and args.hub_token is not None:
raise ValueError(
"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
" Please use `huggingface-cli login` to authenticate with the Hub."
)
if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
# due to pytorch#99272, MPS does not yet support bfloat16.
raise ValueError(
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
)
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
kwargs_handlers=[kwargs],
)
# Disable AMP for MPS.
if torch.backends.mps.is_available():
accelerator.native_amp = False
if args.report_to == "wandb":
if not is_wandb_available():
raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name,
exist_ok=True,
).repo_id
# Load scheduler and models
noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
args.pretrained_model_name_or_path, subfolder="scheduler"
)
noise_scheduler_copy = copy.deepcopy(noise_scheduler)
vae = AutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="vae",
revision=args.revision,
variant=args.variant,
)
transformer = SD3Transformer2DModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="transformer", revision=args.revision, variant=args.variant
)
transformer.requires_grad_(False)
vae.requires_grad_(False)
# For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora transformer) to half-precision
# as these weights are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
if torch.backends.mps.is_available() and weight_dtype == torch.bfloat16:
# due to pytorch#99272, MPS does not yet support bfloat16.
raise ValueError(
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
)
vae.to(accelerator.device, dtype=torch.float32)
transformer.to(accelerator.device, dtype=weight_dtype)
if args.gradient_checkpointing:
transformer.enable_gradient_checkpointing()
# now we will add new LoRA weights to the attention layers
transformer_lora_config = LoraConfig(
r=args.rank,
lora_alpha=args.rank,
init_lora_weights="gaussian",
target_modules=["to_k", "to_q", "to_v", "to_out.0"],
)
transformer.add_adapter(transformer_lora_config)
def unwrap_model(model):
model = accelerator.unwrap_model(model)
model = model._orig_mod if is_compiled_module(model) else model
return model
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
transformer_lora_layers_to_save = None
for model in models:
if isinstance(model, type(unwrap_model(transformer))):
transformer_lora_layers_to_save = get_peft_model_state_dict(model)
else:
raise ValueError(f"unexpected save model: {model.__class__}")
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
StableDiffusion3Pipeline.save_lora_weights(
output_dir,
transformer_lora_layers=transformer_lora_layers_to_save,
)
def load_model_hook(models, input_dir):
transformer_ = None
while len(models) > 0:
model = models.pop()
if isinstance(model, type(unwrap_model(transformer))):
transformer_ = model
else:
raise ValueError(f"unexpected save model: {model.__class__}")
lora_state_dict = StableDiffusion3Pipeline.lora_state_dict(input_dir)
transformer_state_dict = {
f'{k.replace("transformer.", "")}': v for k, v in lora_state_dict.items() if k.startswith("unet.")
}
transformer_state_dict = convert_unet_state_dict_to_peft(transformer_state_dict)
incompatible_keys = set_peft_model_state_dict(transformer_, transformer_state_dict, adapter_name="default")
if incompatible_keys is not None:
# check only for unexpected keys
unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
if unexpected_keys:
logger.warning(
f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
f" {unexpected_keys}. "
)
# Make sure the trainable params are in float32. This is again needed since the base models
# are in `weight_dtype`. More details:
# https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804
if args.mixed_precision == "fp16":
models = [transformer_]
# only upcast trainable parameters (LoRA) into fp32
cast_training_params(models)
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if args.allow_tf32 and torch.cuda.is_available():
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Make sure the trainable params are in float32.
if args.mixed_precision == "fp16":
models = [transformer]
# only upcast trainable parameters (LoRA) into fp32
cast_training_params(models, dtype=torch.float32)
# Optimization parameters
transformer_lora_parameters = list(filter(lambda p: p.requires_grad, transformer.parameters()))
transformer_parameters_with_lr = {"params": transformer_lora_parameters, "lr": args.learning_rate}
params_to_optimize = [transformer_parameters_with_lr]
# Optimizer creation
if not args.optimizer.lower() == "adamw":
logger.warning(
f"Unsupported choice of optimizer: {args.optimizer}. Supported optimizers include [adamW]."
"Defaulting to adamW"
)
args.optimizer = "adamw"
if args.use_8bit_adam and not args.optimizer.lower() == "adamw":
logger.warning(
f"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was "
f"set to {args.optimizer.lower()}"
)
if args.optimizer.lower() == "adamw":
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
optimizer = optimizer_class(
params_to_optimize,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Dataset and DataLoaders creation:
train_dataset = DreamBoothDataset(
data_df_path=args.data_df_path,
instance_data_root=args.instance_data_dir,
instance_prompt=args.instance_prompt,
size=args.resolution,
center_crop=args.center_crop,
)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
collate_fn=lambda examples: collate_fn(examples),
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
num_cycles=args.lr_num_cycles,
power=args.lr_power,
)
# Prepare everything with our `accelerator`.
transformer, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
transformer, optimizer, train_dataloader, lr_scheduler
)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
tracker_name = "dreambooth-sd3-lora-miniature"
accelerator.init_trackers(tracker_name, config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the mos recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
else:
initial_global_step = 0
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
sigmas = noise_scheduler_copy.sigmas.to(device=accelerator.device, dtype=dtype)
schedule_timesteps = noise_scheduler_copy.timesteps.to(accelerator.device)
timesteps = timesteps.to(accelerator.device)
step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
sigma = sigmas[step_indices].flatten()
while len(sigma.shape) < n_dim:
sigma = sigma.unsqueeze(-1)
return sigma
for epoch in range(first_epoch, args.num_train_epochs):
transformer.train()
for step, batch in enumerate(train_dataloader):
models_to_accumulate = [transformer]
with accelerator.accumulate(models_to_accumulate):
pixel_values = batch["pixel_values"].to(dtype=vae.dtype)
# Convert images to latent space
model_input = vae.encode(pixel_values).latent_dist.sample()
model_input = model_input * vae.config.scaling_factor
model_input = model_input.to(dtype=weight_dtype)
# Sample noise that we'll add to the latents
noise = torch.randn_like(model_input)
bsz = model_input.shape[0]
# Sample a random timestep for each image
# for weighting schemes where we sample timesteps non-uniformly
u = compute_density_for_timestep_sampling(
weighting_scheme=args.weighting_scheme,
batch_size=bsz,
logit_mean=args.logit_mean,
logit_std=args.logit_std,
mode_scale=args.mode_scale,
)
indices = (u * noise_scheduler_copy.config.num_train_timesteps).long()
timesteps = noise_scheduler_copy.timesteps[indices].to(device=model_input.device)
# Add noise according to flow matching.
sigmas = get_sigmas(timesteps, n_dim=model_input.ndim, dtype=model_input.dtype)
noisy_model_input = sigmas * noise + (1.0 - sigmas) * model_input
# Predict the noise residual
prompt_embeds, pooled_prompt_embeds = batch["prompt_embeds"], batch["pooled_prompt_embeds"]
prompt_embeds = prompt_embeds.to(device=accelerator.device, dtype=weight_dtype)
pooled_prompt_embeds = pooled_prompt_embeds.to(device=accelerator.device, dtype=weight_dtype)
model_pred = transformer(
hidden_states=noisy_model_input,
timestep=timesteps,
encoder_hidden_states=prompt_embeds,
pooled_projections=pooled_prompt_embeds,
return_dict=False,
)[0]
# Follow: Section 5 of https://arxiv.org/abs/2206.00364.
# Preconditioning of the model outputs.
model_pred = model_pred * (-sigmas) + noisy_model_input
# these weighting schemes use a uniform timestep sampling
# and instead post-weight the loss
weighting = compute_loss_weighting_for_sd3(weighting_scheme=args.weighting_scheme, sigmas=sigmas)
# flow matching loss
target = model_input
# Compute regular loss.
loss = torch.mean(
(weighting.float() * (model_pred.float() - target.float()) ** 2).reshape(target.shape[0], -1),
1,
)
loss = loss.mean()
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = transformer_lora_parameters
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if accelerator.is_main_process:
if global_step % args.checkpointing_steps == 0:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
if accelerator.is_main_process:
if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
pipeline = StableDiffusion3Pipeline.from_pretrained(
args.pretrained_model_name_or_path,
vae=vae,
transformer=accelerator.unwrap_model(transformer),
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
pipeline_args = {"prompt": args.validation_prompt}
images = log_validation(
pipeline=pipeline,
args=args,
accelerator=accelerator,
pipeline_args=pipeline_args,
epoch=epoch,
)
torch.cuda.empty_cache()
gc.collect()
# Save the lora layers
accelerator.wait_for_everyone()
if accelerator.is_main_process:
transformer = unwrap_model(transformer)
transformer = transformer.to(torch.float32)
transformer_lora_layers = get_peft_model_state_dict(transformer)
StableDiffusion3Pipeline.save_lora_weights(
save_directory=args.output_dir,
transformer_lora_layers=transformer_lora_layers,
)
# Final inference
# Load previous pipeline
pipeline = StableDiffusion3Pipeline.from_pretrained(
args.pretrained_model_name_or_path,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
# load attention processors
pipeline.load_lora_weights(args.output_dir)
# run inference
images = []
if args.validation_prompt and args.num_validation_images > 0:
pipeline_args = {"prompt": args.validation_prompt}
images = log_validation(
pipeline=pipeline,
args=args,
accelerator=accelerator,
pipeline_args=pipeline_args,
epoch=epoch,
is_final_validation=True,
)
if args.push_to_hub:
save_model_card(
repo_id,
images=images,
base_model=args.pretrained_model_name_or_path,
instance_prompt=args.instance_prompt,
validation_prompt=args.validation_prompt,
repo_folder=args.output_dir,
)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
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