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train_diffusion_orpo_sdxl_lora_wds.py
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train_diffusion_orpo_sdxl_lora_wds.py
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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
import argparse
import contextlib
import logging
import math
import os
import random
import shutil
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
import wandb
import webdataset as wds
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
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
from diffusers import (
AutoencoderKL,
DDPMScheduler,
DiffusionPipeline,
UNet2DConditionModel,
)
from diffusers.loaders import StableDiffusionXLLoraLoaderMixin
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, convert_state_dict_to_diffusers, convert_unet_state_dict_to_peft
from diffusers.utils.import_utils import is_xformers_available
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.25.0.dev0")
logger = get_logger(__name__)
VALIDATION_PROMPTS = [
"portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography",
"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
"A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece",
]
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 log_validation(args, unet, vae, accelerator, weight_dtype, epoch, is_final_validation=False):
logger.info(f"Running validation... \n Generating images with prompts:\n" f" {VALIDATION_PROMPTS}.")
if is_final_validation:
if args.mixed_precision == "fp16":
vae.to(weight_dtype)
# create pipeline
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
vae=vae,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
if not is_final_validation:
pipeline.unet = accelerator.unwrap_model(unet)
else:
pipeline.load_lora_weights(args.output_dir, weight_name="pytorch_lora_weights.safetensors")
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
# run inference
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
images = []
context = contextlib.nullcontext() if is_final_validation else torch.cuda.amp.autocast()
guidance_scale = 5.0
num_inference_steps = 25
for prompt in VALIDATION_PROMPTS:
with context:
image = pipeline(
prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=generator
).images[0]
images.append(image)
tracker_key = "test" if is_final_validation else "validation"
for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images(tracker_key, np_images, epoch, dataformats="NHWC")
if tracker.name == "wandb":
tracker.log(
{
tracker_key: [
wandb.Image(image, caption=f"{i}: {VALIDATION_PROMPTS[i]}") for i, image in enumerate(images)
]
}
)
# Also log images without the LoRA params for comparison.
if is_final_validation:
pipeline.disable_lora()
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
no_lora_images = [
pipeline(
prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=generator
).images[0]
for prompt in VALIDATION_PROMPTS
]
for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in no_lora_images])
tracker.writer.add_images("test_without_lora", np_images, epoch, dataformats="NHWC")
if tracker.name == "wandb":
tracker.log(
{
"test_without_lora": [
wandb.Image(image, caption=f"{i}: {VALIDATION_PROMPTS[i]}")
for i, image in enumerate(no_lora_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(
"--pretrained_vae_model_name_or_path",
type=str,
default=None,
help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--dataset_path",
type=str,
default="pipe:aws s3 cp s3://diffusion-preference-opt/{00000..00644}.tar -",
)
parser.add_argument(
"--num_train_examples",
type=int,
default=1001352,
)
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(
"--run_validation",
default=False,
action="store_true",
help="Whether to run validation inference in between training and also after training. Helps to track progress.",
)
parser.add_argument(
"--validation_steps",
type=int,
default=200,
help="Run validation every X steps.",
)
parser.add_argument(
"--output_dir",
type=str,
default="diffusion-orpo-lora-sdxl",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=1024,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--vae_encode_batch_size",
type=int,
default=8,
help="Batch size to use for VAE encoding of the images for efficient processing.",
)
parser.add_argument(
"--no_hflip",
action="store_true",
help="whether to randomly flip images horizontally",
)
parser.add_argument(
"--random_crop",
default=False,
action="store_true",
help=(
"Whether to random crop the input images to the resolution. If not set, the images will be center-cropped."
),
)
parser.add_argument("--global_batch_size", type=int, default=64, help="Total batch size.")
parser.add_argument(
"--per_gpu_batch_size", type=int, default=8, help="Number of samples in a batch for a single GPU."
)
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(
"--beta_orpo",
type=float,
default=0.1,
help="ORPO contribution factor.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-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(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
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-2, help="Weight decay to use.")
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")
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
parser.add_argument(
"--rank",
type=int,
default=4,
help=("The dimension of the LoRA update matrices."),
)
parser.add_argument(
"--tracker_name",
type=str,
default="diffusion-orpo-lora-sdxl",
help=("The name of the tracker to report results to."),
)
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
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
def tokenize_captions(tokenizers, sample):
tokens_one = tokenizers[0](
sample["original_prompt"],
truncation=True,
padding="max_length",
max_length=tokenizers[0].model_max_length,
return_tensors="pt",
).input_ids
tokens_two = tokenizers[1](
sample["original_prompt"],
truncation=True,
padding="max_length",
max_length=tokenizers[1].model_max_length,
return_tensors="pt",
).input_ids
return tokens_one, tokens_two
@torch.no_grad()
def encode_prompt(text_encoders, text_input_ids_list):
prompt_embeds_list = []
for i, text_encoder in enumerate(text_encoders):
text_input_ids = text_input_ids_list[i]
prompt_embeds = text_encoder(
text_input_ids.to(text_encoder.device),
output_hidden_states=True,
)
# We are only ALWAYS interested in the pooled output of the final text encoder
pooled_prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.hidden_states[-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 get_dataset(args):
dataset = (
wds.WebDataset(args.dataset_path, resampled=True, handler=wds.warn_and_continue)
.shuffle(690, handler=wds.warn_and_continue)
.decode("pil", handler=wds.warn_and_continue)
.rename(
original_prompt="original_prompt.txt",
jpg_0="jpg_0.jpg",
jpg_1="jpg_1.jpg",
label_0="label_0.txt",
label_1="label_1.txt",
handler=wds.warn_and_continue,
)
)
return dataset
def get_loader(args, tokenizer_one, tokenizer_two):
# 1,001,352
num_batches = math.ceil(args.num_train_examples / args.global_batch_size)
num_worker_batches = math.ceil(
args.num_train_examples / (args.global_batch_size * args.dataloader_num_workers)
) # per dataloader worker
num_batches = num_worker_batches * args.dataloader_num_workers
num_samples = num_batches * args.global_batch_size
dataset = get_dataset(args)
train_resize = transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR)
train_crop = transforms.RandomCrop(args.resolution) if args.random_crop else transforms.CenterCrop(args.resolution)
train_flip = transforms.RandomHorizontalFlip(p=1.0)
to_tensor = transforms.ToTensor()
normalize = transforms.Normalize([0.5], [0.5])
def preprocess_images(sample):
jpg_0_image = sample["jpg_0"]
original_size = (jpg_0_image.height, jpg_0_image.width)
crop_top_left = []
jpg_1_image = sample["jpg_1"]
# Need to bring down the image to the same resolution.
# This seems like the simplest reasonable approach.
# "::-1" because PIL resize takes (width, height).
jpg_1_image = jpg_1_image.resize(original_size[::-1])
# We randomize selection and rejection.
label_0 = sample["label_0"]
if sample["label_0"] == 0.5:
if random.random() < 0.5:
label_0 = 0
else:
label_0 = 1
# Double on channel dim, jpg_y then jpg_w
if label_0 == 0:
pixel_values = torch.cat([to_tensor(image) for image in [jpg_1_image, jpg_0_image]])
else:
pixel_values = torch.cat([to_tensor(image) for image in [jpg_0_image, jpg_1_image]])
# Resize.
combined_im = train_resize(pixel_values)
# Flipping.
if not args.no_hflip and random.random() < 0.5:
combined_im = train_flip(combined_im)
# Cropping.
if not args.random_crop:
y1 = max(0, int(round((combined_im.shape[1] - args.resolution) / 2.0)))
x1 = max(0, int(round((combined_im.shape[2] - args.resolution) / 2.0)))
combined_im = train_crop(combined_im)
else:
y1, x1, h, w = train_crop.get_params(combined_im, (args.resolution, args.resolution))
combined_im = crop(combined_im, y1, x1, h, w)
crop_top_left = (y1, x1)
combined_im = normalize(combined_im)
tokens_one, tokens_two = tokenize_captions([tokenizer_one, tokenizer_two], sample)
return {
"pixel_values": combined_im,
"original_size": original_size,
"crop_top_left": crop_top_left,
"tokens_one": tokens_one,
"tokens_two": tokens_two,
}
def collate_fn(samples):
pixel_values = torch.stack([sample["pixel_values"] for sample in samples])
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
original_sizes = [example["original_size"] for example in samples]
crop_top_lefts = [example["crop_top_left"] for example in samples]
input_ids_one = torch.stack([example["tokens_one"] for example in samples])
input_ids_two = torch.stack([example["tokens_two"] for example in samples])
return {
"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,
}
dataset = dataset.map(preprocess_images, handler=wds.warn_and_continue)
dataset = dataset.batched(args.per_gpu_batch_size, partial=False, collation_fn=collate_fn)
dataset = dataset.with_epoch(num_worker_batches)
dataloader = wds.WebLoader(
dataset,
batch_size=None,
shuffle=False,
num_workers=args.dataloader_num_workers,
pin_memory=True,
persistent_workers=True,
)
# add meta-data to dataloader instance for convenience
dataloader.num_batches = num_batches
dataloader.num_samples = num_samples
return dataloader
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."
)
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
# Disable AMP for MPS.
if torch.backends.mps.is_available():
accelerator.native_amp = False
# 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, token=args.hub_token
).repo_id
# Load the tokenizers
tokenizer_one = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
tokenizer_two = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer_2",
revision=args.revision,
use_fast=False,
)
# import correct text encoder classes
text_encoder_cls_one = import_model_class_from_model_name_or_path(
args.pretrained_model_name_or_path, args.revision
)
text_encoder_cls_two = import_model_class_from_model_name_or_path(
args.pretrained_model_name_or_path, args.revision, subfolder="text_encoder_2"
)
# Load scheduler and models
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder_one = text_encoder_cls_one.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
)
text_encoder_two = text_encoder_cls_two.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
)
vae_path = (
args.pretrained_model_name_or_path
if args.pretrained_vae_model_name_or_path is None
else args.pretrained_vae_model_name_or_path
)
vae = AutoencoderKL.from_pretrained(
vae_path,
subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
revision=args.revision,
variant=args.variant,
)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.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 and text_encoders to device and cast to weight_dtype
unet.to(accelerator.device, dtype=weight_dtype)
text_encoder_one.to(accelerator.device, dtype=weight_dtype)
text_encoder_two.to(accelerator.device, dtype=weight_dtype)
# The VAE is always in float32 to avoid NaN losses.
vae.to(accelerator.device, dtype=torch.float32)
# Set up LoRA.
unet_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"],
)
# Add adapter and make sure the trainable params are in float32.
unet.add_adapter(unet_lora_config)
if args.mixed_precision == "fp16":
for param in unet.parameters():
# only upcast trainable parameters (LoRA) into fp32
if param.requires_grad:
param.data = param.to(torch.float32)
if args.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")
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
# 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 atten layers
unet_lora_layers_to_save = None
for model in models:
if isinstance(model, type(accelerator.unwrap_model(unet))):
unet_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
weights.pop()
StableDiffusionXLLoraLoaderMixin.save_lora_weights(
output_dir,
unet_lora_layers=unet_lora_layers_to_save,
text_encoder_lora_layers=None,
text_encoder_2_lora_layers=None,
)
def load_model_hook(models, input_dir):
unet_ = None
while len(models) > 0:
model = models.pop()
if isinstance(model, type(accelerator.unwrap_model(unet))):
unet_ = model
else:
raise ValueError(f"unexpected save model: {model.__class__}")
lora_state_dict, network_alphas = StableDiffusionXLLoraLoaderMixin.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}. "
)
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:
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rat * args.gradient_accumulation_steps * args.per_gpu_batch_size * accelerator.num_processes
)
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
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 creation
params_to_optimize = list(filter(lambda p: p.requires_grad, unet.parameters()))
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Dataset and DataLoaders creation:
args.global_batch_size = args.per_gpu_batch_size * accelerator.num_processes
train_dataloader = get_loader(args, tokenizer_one=tokenizer_one, tokenizer_two=tokenizer_two)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / 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,
)
unet, optimizer, lr_scheduler = accelerator.prepare(unet, optimizer, 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(train_dataloader.num_batches / 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:
accelerator.init_trackers(args.tracker_name, config=vars(args))
# Train!
total_batch_size = args.per_gpu_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {train_dataloader.num_samples}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.per_gpu_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,
)
unet.train()
for epoch in range(first_epoch, args.num_train_epochs):
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(unet):
# (batch_size, 2*channels, h, w) -> (2*batch_size, channels, h, w)
pixel_values = batch["pixel_values"].to(dtype=vae.dtype, device=accelerator.device, non_blocking=True)
feed_pixel_values = torch.cat(pixel_values.chunk(2, dim=1))
latents = []
for i in range(0, feed_pixel_values.shape[0], args.vae_encode_batch_size):
latents.append(
vae.encode(feed_pixel_values[i : i + args.vae_encode_batch_size]).latent_dist.sample()
)
latents = torch.cat(latents, dim=0)
latents = latents * vae.config.scaling_factor
if args.pretrained_vae_model_name_or_path is None:
latents = latents.to(weight_dtype)
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents).chunk(2)[0].repeat(2, 1, 1, 1)
# Sample a random timestep for each image
bsz = latents.shape[0] // 2
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device, dtype=torch.long
).repeat(2)
# 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(latents, noise, timesteps)
# time ids
def compute_time_ids(original_size, crops_coords_top_left):
# Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids
target_size = (args.resolution, args.resolution)
add_time_ids = list(tuple(original_size) + tuple(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"])]
).repeat(2, 1)
# Get the text embedding for conditioning
prompt_embeds, pooled_prompt_embeds = encode_prompt(
[text_encoder_one, text_encoder_two], [batch["input_ids_one"], batch["input_ids_two"]]
)
prompt_embeds = prompt_embeds.repeat(2, 1, 1)
pooled_prompt_embeds = pooled_prompt_embeds.repeat(2, 1)
# Predict the noise residual
model_pred = unet(
noisy_model_input,
timesteps,
prompt_embeds,
added_cond_kwargs={"time_ids": add_time_ids, "text_embeds": pooled_prompt_embeds},
).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
# ODDS ratio loss.
# In the diffusion formulation, we're assuming that the MSE loss
# approximates the logp.
model_losses = F.mse_loss(model_pred.float(), target.float(), reduction="none")
model_losses = model_losses.mean(dim=list(range(1, len(model_losses.shape))))
model_losses_w, model_losses_l = model_losses.chunk(2)
log_odds = model_losses_w - model_losses_l
# Ratio loss.
ratio = F.logsigmoid(log_odds)
ratio_losses = args.beta_orpo * ratio