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refactor: Update SDXL-LoRA inference pipeline to load multiple adapter weights
ebbf256
import os | |
from config import Config | |
import shutil | |
import random | |
import math | |
import numpy as np | |
import torch | |
import torch.nn.functional as F | |
import transformers | |
from accelerate import Accelerator | |
from accelerate.logging import get_logger | |
from accelerate.utils import ( | |
DistributedDataParallelKwargs, | |
ProjectConfiguration, | |
set_seed, | |
) | |
from datasets import load_dataset | |
from huggingface_hub import create_repo, upload_folder | |
from packaging import version | |
from peft import LoraConfig, set_peft_model_state_dict | |
from peft.utils import get_peft_model_state_dict | |
from torchvision import transforms | |
from torchvision.transforms.functional import crop | |
from tqdm.auto import tqdm | |
from transformers import AutoTokenizer, PretrainedConfig | |
import diffusers | |
logger = get_logger(__name__) | |
from diffusers import ( | |
AutoencoderKL, | |
DDPMScheduler, | |
StableDiffusionXLPipeline, | |
UNet2DConditionModel, | |
) | |
from diffusers.loaders import LoraLoaderMixin | |
from diffusers.optimization import get_scheduler | |
from diffusers.training_utils import ( | |
_set_state_dict_into_text_encoder, | |
cast_training_params, | |
compute_snr, | |
) | |
from diffusers.utils import ( | |
convert_state_dict_to_diffusers, | |
convert_unet_state_dict_to_peft, | |
is_wandb_available, | |
is_xformers_available, | |
) | |
from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card | |
from diffusers.utils.torch_utils import is_compiled_module | |
logger = get_logger(__name__) | |
def save_model_card( | |
repo_id: str, | |
images: list = None, | |
base_model: str = None, | |
dataset_name: str = None, | |
train_text_encoder: bool = False, | |
repo_folder: str = None, | |
vae_path: str = None, | |
): | |
img_str = "" | |
if images is not None: | |
for i, image in enumerate(images): | |
image.save(os.path.join(repo_folder, f"image_{i}.png")) | |
img_str += f"![img_{i}](./image_{i}.png)\n" | |
img_str = "" # Declare the img_str variable | |
model_description = "SDXL Product Images" | |
model_card = load_or_create_model_card( | |
repo_id_or_path=repo_id, | |
from_training=True, | |
license="creativeml-openrail-m", | |
base_model=base_model, | |
model_description=model_description, | |
inference=True, | |
) | |
tags = [ | |
"stable-diffusion-xl", | |
"stable-diffusion-xl-diffusers", | |
"text-to-image", | |
"diffusers", | |
"diffusers-training", | |
] | |
model_card = populate_model_card(model_card, tags=tags) | |
model_card.save(os.path.join(repo_folder, "README.md")) | |
def import_model_class_from_model_name_or_path( | |
pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder" | |
): | |
text_encoder_config = PretrainedConfig.from_pretrained( | |
pretrained_model_name_or_path, subfolder=subfolder, revision=revision | |
) | |
model_class = text_encoder_config.architectures[0] | |
if model_class == "CLIPTextModel": | |
from transformers import CLIPTextModel | |
return CLIPTextModel | |
elif model_class == "CLIPTextModelWithProjection": | |
from transformers import CLIPTextModelWithProjection | |
return CLIPTextModelWithProjection | |
else: | |
raise ValueError(f"{model_class} is not supported.") | |
def tokenize_prompt(tokenizer, prompt): | |
text_inputs = tokenizer( | |
prompt, | |
padding="max_length", | |
max_length=tokenizer.model_max_length, | |
truncation=True, | |
return_tensors="pt", | |
) | |
text_input_ids = text_inputs.input_ids | |
return text_input_ids | |
def encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None): | |
prompt_embeds_list = [] | |
for i, text_encoder in enumerate(text_encoders): | |
if tokenizers is not None: | |
tokenizer = tokenizers[i] | |
text_input_ids = tokenize_prompt(tokenizer, prompt) | |
else: | |
assert text_input_ids_list is not None | |
text_input_ids = text_input_ids_list[i] | |
prompt_embeds = text_encoder( | |
text_input_ids.to(text_encoder.device), | |
output_hidden_states=True, | |
return_dict=False, | |
) | |
# We are only ALWAYS interested in the pooled output of the final text encoder | |
pooled_prompt_embeds = prompt_embeds[0] | |
prompt_embeds = prompt_embeds[-1][-2] | |
bs_embed, seq_len, _ = prompt_embeds.shape | |
prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1) | |
prompt_embeds_list.append(prompt_embeds) | |
prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) | |
pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1) | |
return prompt_embeds, pooled_prompt_embeds | |
def main(): | |
config = Config() | |
from pathlib import Path | |
from contextlib import nullcontext | |
if config.report_to == "wandb" and config.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." | |
) | |
logging_dir = Path(config.output_dir, config.logging_dir) | |
if torch.backends.mps.is_available() and config.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." | |
) | |
accelerator_project_config = ProjectConfiguration( | |
project_dir=config.output_dir, logging_dir=logging_dir | |
) | |
kwargs = DistributedDataParallelKwargs(find_unused_parameters=True) | |
accelerator = Accelerator( | |
gradient_accumulation_steps=config.gradient_accumulation_steps, | |
mixed_precision=config.mixed_precision, | |
log_with=config.report_to, | |
project_config=accelerator_project_config, | |
kwargs_handlers=[kwargs], | |
) | |
import logging | |
if config.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." | |
) | |
import wandb | |
# 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, | |
) | |
from datasets import utils as datasets_utils | |
logger.info(accelerator.state, main_process_only=False) | |
if accelerator.is_local_main_process: | |
datasets_utils.logging.set_verbosity_warning() | |
transformers.utils.logging.set_verbosity_warning() | |
diffusers.utils.logging.set_verbosity_info() | |
else: | |
datasets_utils.logging.set_verbosity_error() | |
transformers.utils.logging.set_verbosity_error() | |
diffusers.utils.logging.set_verbosity_error() | |
# If passed along, set the training seed now. | |
if config.seed is not None: | |
set_seed(config.seed) | |
# Handle the repository creation | |
if accelerator.is_main_process: | |
if config.output_dir is not None: | |
os.makedirs(config.output_dir, exist_ok=True) | |
if config.push_to_hub: | |
repo_id = create_repo( | |
repo_id=config.hub_model_id or Path(config.output_dir).name, | |
exist_ok=True, | |
token=config.hub_token, | |
).repo_id | |
# Load the tokenizers | |
tokenizer_one = AutoTokenizer.from_pretrained( | |
config.pretrained_model_name_or_path, | |
subfolder="tokenizer", | |
revision=config.revision, | |
use_fast=False, | |
) | |
tokenizer_two = AutoTokenizer.from_pretrained( | |
config.pretrained_model_name_or_path, | |
subfolder="tokenizer_2", | |
revision=config.revision, | |
use_fast=False, | |
) | |
# import correct text encoder classes | |
text_encoder_cls_one = import_model_class_from_model_name_or_path( | |
config.pretrained_model_name_or_path, config.revision | |
) | |
text_encoder_cls_two = import_model_class_from_model_name_or_path( | |
config.pretrained_model_name_or_path, | |
config.revision, | |
subfolder="text_encoder_2", | |
) | |
# Load scheduler and models | |
noise_scheduler = DDPMScheduler.from_pretrained( | |
config.pretrained_model_name_or_path, subfolder="scheduler" | |
) | |
text_encoder_one = text_encoder_cls_one.from_pretrained( | |
config.pretrained_model_name_or_path, | |
subfolder="text_encoder", | |
revision=config.revision, | |
variant=config.variant, | |
) | |
text_encoder_two = text_encoder_cls_two.from_pretrained( | |
config.pretrained_model_name_or_path, | |
subfolder="text_encoder_2", | |
revision=config.revision, | |
variant=config.variant, | |
) | |
vae_path = ( | |
config.pretrained_model_name_or_path | |
if config.pretrained_vae_model_name_or_path is None | |
else config.pretrained_vae_model_name_or_path | |
) | |
vae = AutoencoderKL.from_pretrained( | |
vae_path, | |
subfolder="vae" if config.pretrained_vae_model_name_or_path is None else None, | |
revision=config.revision, | |
variant=config.variant, | |
) | |
unet = UNet2DConditionModel.from_pretrained( | |
config.pretrained_model_name_or_path, | |
subfolder="unet", | |
revision=config.revision, | |
variant=config.variant, | |
) | |
# We only train the additional adapter LoRA layers | |
vae.requires_grad_(False) | |
text_encoder_one.requires_grad_(False) | |
text_encoder_two.requires_grad_(False) | |
unet.requires_grad_(False) | |
# For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) 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 | |
# Move unet, vae and text_encoder to device and cast to weight_dtype | |
# The VAE is in float32 to avoid NaN losses. | |
unet.to(accelerator.device, dtype=weight_dtype) | |
if config.pretrained_vae_model_name_or_path is None: | |
vae.to(accelerator.device, dtype=torch.float32) | |
else: | |
vae.to(accelerator.device, dtype=weight_dtype) | |
text_encoder_one.to(accelerator.device, dtype=weight_dtype) | |
text_encoder_two.to(accelerator.device, dtype=weight_dtype) | |
if config.enable_xformers_memory_efficient_attention: | |
if is_xformers_available(): | |
import xformers | |
xformers_version = version.parse(xformers.__version__) | |
if xformers_version == version.parse("0.0.16"): | |
logger.warning( | |
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details." | |
) | |
unet.enable_xformers_memory_efficient_attention() | |
else: | |
raise ValueError( | |
"xformers is not available. Make sure it is installed correctly" | |
) | |
# now we will add new LoRA weights to the attention layers | |
# Set correct lora layers | |
unet_lora_config = LoraConfig( | |
r=config.rank, | |
lora_alpha=config.rank, | |
init_lora_weights="gaussian", | |
target_modules=["to_k", "to_q", "to_v", "to_out.0"], | |
) | |
unet.add_adapter(unet_lora_config) | |
# The text encoder comes from 🤗 transformers, we will also attach adapters to it. | |
if config.train_text_encoder: | |
# ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16 | |
text_lora_config = LoraConfig( | |
r=config.rank, | |
lora_alpha=config.rank, | |
init_lora_weights="gaussian", | |
target_modules=["q_proj", "k_proj", "v_proj", "out_proj"], | |
) | |
text_encoder_one.add_adapter(text_lora_config) | |
text_encoder_two.add_adapter(text_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: | |
# there are only two options here. Either are just the unet attn processor layers | |
# or there are the unet and text encoder attn layers | |
unet_lora_layers_to_save = None | |
text_encoder_one_lora_layers_to_save = None | |
text_encoder_two_lora_layers_to_save = None | |
for model in models: | |
if isinstance(unwrap_model(model), type(unwrap_model(unet))): | |
unet_lora_layers_to_save = convert_state_dict_to_diffusers( | |
get_peft_model_state_dict(model) | |
) | |
elif isinstance( | |
unwrap_model(model), type(unwrap_model(text_encoder_one)) | |
): | |
text_encoder_one_lora_layers_to_save = ( | |
convert_state_dict_to_diffusers( | |
get_peft_model_state_dict(model) | |
) | |
) | |
elif isinstance( | |
unwrap_model(model), type(unwrap_model(text_encoder_two)) | |
): | |
text_encoder_two_lora_layers_to_save = ( | |
convert_state_dict_to_diffusers( | |
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 | |
if weights: | |
weights.pop() | |
StableDiffusionXLPipeline.save_lora_weights( | |
output_dir, | |
unet_lora_layers=unet_lora_layers_to_save, | |
text_encoder_lora_layers=text_encoder_one_lora_layers_to_save, | |
text_encoder_2_lora_layers=text_encoder_two_lora_layers_to_save, | |
) | |
def load_model_hook(models, input_dir): | |
unet_ = None | |
text_encoder_one_ = None | |
text_encoder_two_ = None | |
while len(models) > 0: | |
model = models.pop() | |
if isinstance(model, type(unwrap_model(unet))): | |
unet_ = model | |
elif isinstance(model, type(unwrap_model(text_encoder_one))): | |
text_encoder_one_ = model | |
elif isinstance(model, type(unwrap_model(text_encoder_two))): | |
text_encoder_two_ = model | |
else: | |
raise ValueError(f"unexpected save model: {model.__class__}") | |
lora_state_dict, _ = LoraLoaderMixin.lora_state_dict(input_dir) | |
unet_state_dict = { | |
f'{k.replace("unet.", "")}': v | |
for k, v in lora_state_dict.items() | |
if k.startswith("unet.") | |
} | |
unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict) | |
incompatible_keys = set_peft_model_state_dict( | |
unet_, unet_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}. " | |
) | |
if config.train_text_encoder: | |
_set_state_dict_into_text_encoder( | |
lora_state_dict, prefix="text_encoder.", text_encoder=text_encoder_one_ | |
) | |
_set_state_dict_into_text_encoder( | |
lora_state_dict, | |
prefix="text_encoder_2.", | |
text_encoder=text_encoder_two_, | |
) | |
# 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 config.mixed_precision == "fp16": | |
models = [unet_] | |
if config.train_text_encoder: | |
models.extend([text_encoder_one_, text_encoder_two_]) | |
cast_training_params(models, dtype=torch.float32) | |
accelerator.register_save_state_pre_hook(save_model_hook) | |
accelerator.register_load_state_pre_hook(load_model_hook) | |
if config.gradient_checkpointing: | |
unet.enable_gradient_checkpointing() | |
if config.train_text_encoder: | |
text_encoder_one.gradient_checkpointing_enable() | |
text_encoder_two.gradient_checkpointing_enable() | |
# Enable TF32 for faster training on Ampere GPUs, | |
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices | |
if config.allow_tf32: | |
torch.backends.cuda.matmul.allow_tf32 = True | |
if config.scale_lr: | |
config.learning_rate = ( | |
config.learning_rate | |
* config.gradient_accumulation_steps | |
* config.train_batch_size | |
* accelerator.num_processes | |
) | |
# Make sure the trainable params are in float32. | |
if config.mixed_precision == "fp16": | |
models = [unet] | |
if config.train_text_encoder: | |
models.extend([text_encoder_one, text_encoder_two]) | |
cast_training_params(models, dtype=torch.float32) | |
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs | |
if config.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 creation | |
params_to_optimize = list(filter(lambda p: p.requires_grad, unet.parameters())) | |
if config.train_text_encoder: | |
params_to_optimize = ( | |
params_to_optimize | |
+ list(filter(lambda p: p.requires_grad, text_encoder_one.parameters())) | |
+ list(filter(lambda p: p.requires_grad, text_encoder_two.parameters())) | |
) | |
optimizer = optimizer_class( | |
params_to_optimize, | |
lr=config.learning_rate, | |
betas=(config.adam_beta1, config.adam_beta2), | |
weight_decay=config.adam_weight_decay, | |
eps=config.adam_epsilon, | |
) | |
# Get the datasets: you can either provide your own training and evaluation files (see below) | |
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub). | |
# In distributed training, the load_dataset function guarantees that only one local process can concurrently | |
# download the dataset. | |
if config.dataset_name is not None: | |
# Downloading and loading a dataset from the hub. | |
dataset = load_dataset( | |
config.dataset_name, | |
config.dataset_config_name, | |
cache_dir=config.cache_dir, | |
data_dir=config.train_data_dir, | |
) | |
else: | |
data_files = {} | |
if config.train_data_dir is not None: | |
data_files["test"] = os.path.join(config.train_data_dir, "**") | |
dataset = load_dataset( | |
"imagefolder", | |
data_files=data_files, | |
cache_dir=config.cache_dir, | |
) | |
# See more about loading custom images at | |
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder | |
# Preprocessing the datasets. | |
# We need to tokenize inputs and targets. | |
column_names = dataset["test"].column_names | |
# 6. Get the column names for input/target. | |
DATASET_NAME_MAPPING = { | |
"lambdalabs/pokemon-blip-captions": ("image", "text"), | |
} | |
dataset_columns = DATASET_NAME_MAPPING.get(config.dataset_name, None) | |
if config.image_column is None: | |
image_column = ( | |
dataset_columns[0] if dataset_columns is not None else column_names[0] | |
) | |
else: | |
image_column = config.image_column | |
if image_column not in column_names: | |
raise ValueError( | |
f"--image_column' value '{config.image_column}' needs to be one of: {', '.join(column_names)}" | |
) | |
if config.caption_column is None: | |
caption_column = ( | |
dataset_columns[1] if dataset_columns is not None else column_names[1] | |
) | |
else: | |
caption_column = config.caption_column | |
if caption_column not in column_names: | |
raise ValueError( | |
f"--caption_column' value '{config.caption_column}' needs to be one of: {', '.join(column_names)}" | |
) | |
# Preprocessing the datasets. | |
# We need to tokenize input captions and transform the images. | |
def tokenize_captions(examples, is_train=True): | |
captions = [] | |
for caption in examples[caption_column]: | |
if isinstance(caption, str): | |
captions.append(caption) | |
elif isinstance(caption, (list, np.ndarray)): | |
# take a random caption if there are multiple | |
captions.append(random.choice(caption) if is_train else caption[0]) | |
else: | |
raise ValueError( | |
f"Caption column `{caption_column}` should contain either strings or lists of strings." | |
) | |
tokens_one = tokenize_prompt(tokenizer_one, captions) | |
tokens_two = tokenize_prompt(tokenizer_two, captions) | |
return tokens_one, tokens_two | |
# Preprocessing the datasets. | |
train_resize = transforms.Resize( | |
config.resolution, interpolation=transforms.InterpolationMode.BILINEAR | |
) | |
train_crop = ( | |
transforms.CenterCrop(config.resolution) | |
if config.center_crop | |
else transforms.RandomCrop(config.resolution) | |
) | |
train_flip = transforms.RandomHorizontalFlip(p=1.0) | |
train_transforms = transforms.Compose( | |
[ | |
transforms.ToTensor(), | |
transforms.Normalize([0.5], [0.5]), | |
] | |
) | |
def preprocess_train(examples): | |
images = [image.convert("RGB") for image in examples[image_column]] | |
# image aug | |
original_sizes = [] | |
all_images = [] | |
crop_top_lefts = [] | |
for image in images: | |
original_sizes.append((image.height, image.width)) | |
image = train_resize(image) | |
if config.random_flip and random.random() < 0.5: | |
# flip | |
image = train_flip(image) | |
if config.center_crop: | |
y1 = max(0, int(round((image.height - config.resolution) / 2.0))) | |
x1 = max(0, int(round((image.width - config.resolution) / 2.0))) | |
image = train_crop(image) | |
else: | |
y1, x1, h, w = train_crop.get_params( | |
image, (config.resolution, config.resolution) | |
) | |
image = crop(image, y1, x1, h, w) | |
crop_top_left = (y1, x1) | |
crop_top_lefts.append(crop_top_left) | |
image = train_transforms(image) | |
all_images.append(image) | |
examples["original_sizes"] = original_sizes | |
examples["crop_top_lefts"] = crop_top_lefts | |
examples["pixel_values"] = all_images | |
tokens_one, tokens_two = tokenize_captions(examples) | |
examples["input_ids_one"] = tokens_one | |
examples["input_ids_two"] = tokens_two | |
if config.debug_loss: | |
fnames = [ | |
os.path.basename(image.filename) | |
for image in examples[image_column] | |
if image.filename | |
] | |
if fnames: | |
examples["filenames"] = fnames | |
return examples | |
with accelerator.main_process_first(): | |
if config.max_train_samples is not None: | |
dataset["test"] = ( | |
dataset["test"] | |
.shuffle(seed=config.seed) | |
.select(range(config.max_train_samples)) | |
) | |
# Set the training transforms | |
train_dataset = dataset["test"].with_transform( | |
preprocess_train, output_all_columns=True | |
) | |
def collate_fn(examples): | |
pixel_values = torch.stack([example["pixel_values"] for example in examples]) | |
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() | |
original_sizes = [example["original_sizes"] for example in examples] | |
crop_top_lefts = [example["crop_top_lefts"] for example in examples] | |
input_ids_one = torch.stack([example["input_ids_one"] for example in examples]) | |
input_ids_two = torch.stack([example["input_ids_two"] for example in examples]) | |
result = { | |
"pixel_values": pixel_values, | |
"input_ids_one": input_ids_one, | |
"input_ids_two": input_ids_two, | |
"original_sizes": original_sizes, | |
"crop_top_lefts": crop_top_lefts, | |
} | |
filenames = [ | |
example["filenames"] for example in examples if "filenames" in example | |
] | |
if filenames: | |
result["filenames"] = filenames | |
return result | |
# DataLoaders creation: | |
train_dataloader = torch.utils.data.DataLoader( | |
train_dataset, | |
shuffle=True, | |
collate_fn=collate_fn, | |
batch_size=config.train_batch_size, | |
num_workers=config.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) / config.gradient_accumulation_steps | |
) | |
if config.max_train_steps is None: | |
config.max_train_steps = config.num_train_epochs * num_update_steps_per_epoch | |
overrode_max_train_steps = True | |
lr_scheduler = get_scheduler( | |
config.lr_scheduler, | |
optimizer=optimizer, | |
num_warmup_steps=config.lr_warmup_steps * config.gradient_accumulation_steps, | |
num_training_steps=config.max_train_steps * config.gradient_accumulation_steps, | |
) | |
# Prepare everything with our `accelerator`. | |
if config.train_text_encoder: | |
( | |
unet, | |
text_encoder_one, | |
text_encoder_two, | |
optimizer, | |
train_dataloader, | |
lr_scheduler, | |
) = accelerator.prepare( | |
unet, | |
text_encoder_one, | |
text_encoder_two, | |
optimizer, | |
train_dataloader, | |
lr_scheduler, | |
) | |
else: | |
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( | |
unet, 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) / config.gradient_accumulation_steps | |
) | |
if overrode_max_train_steps: | |
config.max_train_steps = config.num_train_epochs * num_update_steps_per_epoch | |
# Afterwards we recalculate our number of training epochs | |
config.num_train_epochs = math.ceil( | |
config.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: | |
accelerator.init_trackers("text2image-fine-tune", config=vars(config)) | |
# Train! | |
total_batch_size = ( | |
config.train_batch_size | |
* accelerator.num_processes | |
* config.gradient_accumulation_steps | |
) | |
logger.info("***** Running training *****") | |
logger.info(f" Num examples = {len(train_dataset)}") | |
logger.info(f" Num Epochs = {config.num_train_epochs}") | |
logger.info(f" Instantaneous batch size per device = {config.train_batch_size}") | |
logger.info( | |
f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}" | |
) | |
logger.info(f" Gradient Accumulation steps = {config.gradient_accumulation_steps}") | |
logger.info(f" Total optimization steps = {config.max_train_steps}") | |
global_step = 0 | |
first_epoch = 0 | |
# Potentially load in the weights and states from a previous save | |
if config.resume_from_checkpoint: | |
if config.resume_from_checkpoint != "latest": | |
path = os.path.basename(config.resume_from_checkpoint) | |
else: | |
# Get the most recent checkpoint | |
dirs = os.listdir(config.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 '{config.resume_from_checkpoint}' does not exist. Starting a new training run." | |
) | |
config.resume_from_checkpoint = None | |
initial_global_step = 0 | |
else: | |
accelerator.print(f"Resuming from checkpoint {path}") | |
accelerator.load_state(os.path.join(config.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, config.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, | |
) | |
for epoch in range(first_epoch, config.num_train_epochs): | |
unet.train() | |
if config.train_text_encoder: | |
text_encoder_one.train() | |
text_encoder_two.train() | |
train_loss = 0.0 | |
for step, batch in enumerate(train_dataloader): | |
with accelerator.accumulate(unet): | |
# Convert images to latent space | |
if config.pretrained_vae_model_name_or_path is not None: | |
pixel_values = batch["pixel_values"].to(dtype=weight_dtype) | |
else: | |
pixel_values = batch["pixel_values"] | |
model_input = vae.encode(pixel_values).latent_dist.sample() | |
model_input = model_input * vae.config.scaling_factor | |
if config.pretrained_vae_model_name_or_path is None: | |
model_input = model_input.to(weight_dtype) | |
# Sample noise that we'll add to the latents | |
noise = torch.randn_like(model_input) | |
if config.noise_offset: | |
# https://www.crosslabs.org//blog/diffusion-with-offset-noise | |
noise += config.noise_offset * torch.randn( | |
(model_input.shape[0], model_input.shape[1], 1, 1), | |
device=model_input.device, | |
) | |
bsz = model_input.shape[0] | |
# Sample a random timestep for each image | |
timesteps = torch.randint( | |
0, | |
noise_scheduler.config.num_train_timesteps, | |
(bsz,), | |
device=model_input.device, | |
) | |
timesteps = timesteps.long() | |
# Add noise to the model input according to the noise magnitude at each timestep | |
# (this is the forward diffusion process) | |
noisy_model_input = noise_scheduler.add_noise( | |
model_input, noise, timesteps | |
) | |
# time ids | |
def compute_time_ids(original_size, crops_coords_top_left): | |
# Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids | |
target_size = (config.resolution, config.resolution) | |
add_time_ids = list( | |
original_size + crops_coords_top_left + target_size | |
) | |
add_time_ids = torch.tensor([add_time_ids]) | |
add_time_ids = add_time_ids.to( | |
accelerator.device, dtype=weight_dtype | |
) | |
return add_time_ids | |
add_time_ids = torch.cat( | |
[ | |
compute_time_ids(s, c) | |
for s, c in zip( | |
batch["original_sizes"], batch["crop_top_lefts"] | |
) | |
] | |
) | |
# Predict the noise residual | |
unet_added_conditions = {"time_ids": add_time_ids} | |
prompt_embeds, pooled_prompt_embeds = encode_prompt( | |
text_encoders=[text_encoder_one, text_encoder_two], | |
tokenizers=None, | |
prompt=None, | |
text_input_ids_list=[ | |
batch["input_ids_one"], | |
batch["input_ids_two"], | |
], | |
) | |
unet_added_conditions.update({"text_embeds": pooled_prompt_embeds}) | |
model_pred = unet( | |
noisy_model_input, | |
timesteps, | |
prompt_embeds, | |
added_cond_kwargs=unet_added_conditions, | |
return_dict=False, | |
)[0] | |
# Get the target for loss depending on the prediction type | |
if config.prediction_type is not None: | |
# set prediction_type of scheduler if defined | |
noise_scheduler.register_to_config( | |
prediction_type=config.prediction_type | |
) | |
if noise_scheduler.config.prediction_type == "epsilon": | |
target = noise | |
elif noise_scheduler.config.prediction_type == "v_prediction": | |
target = noise_scheduler.get_velocity(model_input, noise, timesteps) | |
else: | |
raise ValueError( | |
f"Unknown prediction type {noise_scheduler.config.prediction_type}" | |
) | |
if config.snr_gamma is None: | |
loss = F.mse_loss( | |
model_pred.float(), target.float(), reduction="mean" | |
) | |
else: | |
# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556. | |
# Since we predict the noise instead of x_0, the original formulation is slightly changed. | |
# This is discussed in Section 4.2 of the same paper. | |
snr = compute_snr(noise_scheduler, timesteps) | |
mse_loss_weights = torch.stack( | |
[snr, config.snr_gamma * torch.ones_like(timesteps)], dim=1 | |
).min(dim=1)[0] | |
if noise_scheduler.config.prediction_type == "epsilon": | |
mse_loss_weights = mse_loss_weights / snr | |
elif noise_scheduler.config.prediction_type == "v_prediction": | |
mse_loss_weights = mse_loss_weights / (snr + 1) | |
loss = F.mse_loss( | |
model_pred.float(), target.float(), reduction="none" | |
) | |
loss = ( | |
loss.mean(dim=list(range(1, len(loss.shape)))) | |
* mse_loss_weights | |
) | |
loss = loss.mean() | |
if config.debug_loss and "filenames" in batch: | |
for fname in batch["filenames"]: | |
accelerator.log({"loss_for_" + fname: loss}, step=global_step) | |
# Gather the losses across all processes for logging (if we use distributed training). | |
avg_loss = accelerator.gather( | |
loss.repeat(config.train_batch_size) | |
).mean() | |
train_loss += avg_loss.item() / config.gradient_accumulation_steps | |
# Backpropagate | |
accelerator.backward(loss) | |
if accelerator.sync_gradients: | |
accelerator.clip_grad_norm_( | |
params_to_optimize, config.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 | |
accelerator.log({"train_loss": train_loss}, step=global_step) | |
train_loss = 0.0 | |
if accelerator.is_main_process: | |
if global_step % config.checkpointing_steps == 0: | |
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit` | |
if config.checkpoints_total_limit is not None: | |
checkpoints = os.listdir(config.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) >= config.checkpoints_total_limit: | |
num_to_remove = ( | |
len(checkpoints) | |
- config.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( | |
config.output_dir, removing_checkpoint | |
) | |
shutil.rmtree(removing_checkpoint) | |
save_path = os.path.join( | |
config.output_dir, f"checkpoint-{global_step}" | |
) | |
accelerator.save_state(save_path) | |
logger.info(f"Saved state to {save_path}") | |
logs = { | |
"step_loss": loss.detach().item(), | |
"lr": lr_scheduler.get_last_lr()[0], | |
} | |
progress_bar.set_postfix(**logs) | |
if global_step >= config.max_train_steps: | |
break | |
if accelerator.is_main_process: | |
if ( | |
config.validation_prompt is not None | |
and epoch % config.validation_epochs == 0 | |
): | |
logger.info( | |
f"Running validation... \n Generating {config.num_validation_images} images with prompt:" | |
f" {config.validation_prompt}." | |
) | |
# create pipeline | |
pipeline = StableDiffusionXLPipeline.from_pretrained( | |
config.pretrained_model_name_or_path, | |
vae=vae, | |
text_encoder=unwrap_model(text_encoder_one), | |
text_encoder_2=unwrap_model(text_encoder_two), | |
unet=unwrap_model(unet), | |
revision=config.revision, | |
variant=config.variant, | |
torch_dtype=weight_dtype, | |
) | |
pipeline = pipeline.to(accelerator.device) | |
pipeline.set_progress_bar_config(disable=True) | |
# run inference | |
generator = ( | |
torch.Generator(device=accelerator.device).manual_seed(config.seed) | |
if config.seed | |
else None | |
) | |
pipeline_args = {"prompt": config.validation_prompt} | |
if torch.backends.mps.is_available(): | |
autocast_ctx = nullcontext() | |
else: | |
autocast_ctx = torch.autocast(accelerator.device.type) | |
with autocast_ctx: | |
images = [ | |
pipeline(**pipeline_args, generator=generator).images[0] | |
for _ in range(config.num_validation_images) | |
] | |
for tracker in accelerator.trackers: | |
if tracker.name == "tensorboard": | |
np_images = np.stack([np.asarray(img) for img in images]) | |
tracker.writer.add_images( | |
"validation", np_images, epoch, dataformats="NHWC" | |
) | |
if tracker.name == "wandb": | |
tracker.log( | |
{ | |
"validation": [ | |
wandb.Image( | |
image, | |
caption=f"{i}: {config.validation_prompt}", | |
) | |
for i, image in enumerate(images) | |
] | |
} | |
) | |
del pipeline | |
torch.cuda.empty_cache() | |
# Save the lora layers | |
accelerator.wait_for_everyone() | |
if accelerator.is_main_process: | |
unet = unwrap_model(unet) | |
unet_lora_state_dict = convert_state_dict_to_diffusers( | |
get_peft_model_state_dict(unet) | |
) | |
if config.train_text_encoder: | |
text_encoder_one = unwrap_model(text_encoder_one) | |
text_encoder_two = unwrap_model(text_encoder_two) | |
text_encoder_lora_layers = convert_state_dict_to_diffusers( | |
get_peft_model_state_dict(text_encoder_one) | |
) | |
text_encoder_2_lora_layers = convert_state_dict_to_diffusers( | |
get_peft_model_state_dict(text_encoder_two) | |
) | |
else: | |
text_encoder_lora_layers = None | |
text_encoder_2_lora_layers = None | |
StableDiffusionXLPipeline.save_lora_weights( | |
save_directory=config.output_dir, | |
unet_lora_layers=unet_lora_state_dict, | |
text_encoder_lora_layers=text_encoder_lora_layers, | |
text_encoder_2_lora_layers=text_encoder_2_lora_layers, | |
) | |
del unet | |
del text_encoder_one | |
del text_encoder_two | |
del text_encoder_lora_layers | |
del text_encoder_2_lora_layers | |
torch.cuda.empty_cache() | |
# Final inference | |
# Make sure vae.dtype is consistent with the unet.dtype | |
if config.mixed_precision == "fp16": | |
vae.to(weight_dtype) | |
# Load previous pipeline | |
pipeline = StableDiffusionXLPipeline.from_pretrained( | |
config.pretrained_model_name_or_path, | |
vae=vae, | |
revision=config.revision, | |
variant=config.variant, | |
torch_dtype=weight_dtype, | |
) | |
pipeline = pipeline.to(accelerator.device) | |
# load attention processors | |
pipeline.load_lora_weights(config.output_dir) | |
# run inference | |
images = [] | |
if config.validation_prompt and config.num_validation_images > 0: | |
generator = ( | |
torch.Generator(device=accelerator.device).manual_seed(config.seed) | |
if config.seed | |
else None | |
) | |
images = [ | |
pipeline( | |
config.validation_prompt, | |
num_inference_steps=25, | |
generator=generator, | |
).images[0] | |
for _ in range(config.num_validation_images) | |
] | |
for tracker in accelerator.trackers: | |
if tracker.name == "tensorboard": | |
np_images = np.stack([np.asarray(img) for img in images]) | |
tracker.writer.add_images( | |
"test", np_images, epoch, dataformats="NHWC" | |
) | |
if tracker.name == "wandb": | |
tracker.log( | |
{ | |
"test": [ | |
wandb.Image( | |
image, caption=f"{i}: {config.validation_prompt}" | |
) | |
for i, image in enumerate(images) | |
] | |
} | |
) | |
if config.push_to_hub: | |
save_model_card( | |
repo_id, | |
images=images, | |
base_model=config.pretrained_model_name_or_path, | |
dataset_name=config.dataset_name, | |
train_text_encoder=config.train_text_encoder, | |
repo_folder=config.output_dir, | |
vae_path=config.pretrained_vae_model_name_or_path, | |
) | |
upload_folder( | |
repo_id=repo_id, | |
folder_path=config.output_dir, | |
commit_message="End of training", | |
ignore_patterns=["step_*", "epoch_*"], | |
) | |
accelerator.end_training() | |
if __name__ == "__main__": | |
main() | |