From 189c6170098cd987f0d66f7929542d8cf46ab037 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=92=8B=E7=A1=95?= Date: Tue, 29 Oct 2024 17:30:15 +0800 Subject: [PATCH] NPU implementation for FLUX --- examples/dreambooth/t.py | 2046 -------------------------------------- 1 file changed, 2046 deletions(-) delete mode 100644 examples/dreambooth/t.py diff --git a/examples/dreambooth/t.py b/examples/dreambooth/t.py deleted file mode 100644 index ab0e6d0a546a..000000000000 --- a/examples/dreambooth/t.py +++ /dev/null @@ -1,2046 +0,0 @@ -#!/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 copy -import gc -import itertools -import logging -import math -import os -import random -import shutil -import warnings -from contextlib import nullcontext -from pathlib import Path - -import numpy as np -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 huggingface_hub.utils import insecure_hashlib -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 -from transformers import ( - CLIPTextModelWithProjection, - CLIPTokenizer, - PretrainedConfig, - T5EncoderModel, - T5TokenizerFast, -) - -import diffusers -from diffusers import ( - AutoencoderKL, - FlowMatchEulerDiscreteScheduler, - FluxPipeline, - FluxTransformer2DModel, -) -from diffusers.optimization import get_scheduler -from diffusers.training_utils import ( - compute_density_for_timestep_sampling, - compute_loss_weighting_for_sd3, -) -from diffusers.utils import ( - check_min_version, - is_wandb_available, -) -from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card -from diffusers.utils.import_utils import is_torch_npu_available -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("0.31.0.dev0") - -logger = get_logger(__name__) - -if is_torch_npu_available(): - import torch_npu - - torch.npu.config.allow_internal_format = False - torch.npu.set_compile_mode(jit_compile=False) - - -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""" -# Flux [dev] DreamBooth - {repo_id} - - - -## Model description - -These are {repo_id} DreamBooth weights for {base_model}. - -The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md). - -Was the text encoder fine-tuned? {train_text_encoder}. - -## Trigger words - -You should use `{instance_prompt}` to trigger the image generation. - -## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) - -```py -from diffusers import AutoPipelineForText2Image -import torch -pipeline = AutoPipelineForText2Image.from_pretrained('{repo_id}', torch_dtype=torch.bfloat16).to('cuda') -image = pipeline('{validation_prompt if validation_prompt else instance_prompt}').images[0] -``` - -## License - -Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). -""" - model_card = load_or_create_model_card( - repo_id_or_path=repo_id, - from_training=True, - license="other", - base_model=base_model, - prompt=instance_prompt, - model_description=model_description, - widget=widget_dict, - ) - tags = [ - "text-to-image", - "diffusers-training", - "diffusers", - "flux", - "flux-diffusers", - "template:sd-lora", - ] - - model_card = populate_model_card(model_card, tags=tags) - model_card.save(os.path.join(repo_folder, "README.md")) - - -def load_text_encoders(class_one, class_two): - text_encoder_one = class_one.from_pretrained( - args.pretrained_model_name_or_path, - subfolder="text_encoder", - revision=args.revision, - variant=args.variant, - ) - text_encoder_two = class_two.from_pretrained( - args.pretrained_model_name_or_path, - subfolder="text_encoder_2", - revision=args.revision, - variant=args.variant, - ) - return text_encoder_one, text_encoder_two - - -def log_validation( - pipeline, - args, - accelerator, - pipeline_args, - epoch, - torch_dtype, - is_final_validation=False, -): - logger.info( - f"Running validation... \n Generating {args.num_validation_images} images with prompt:" - f" {args.validation_prompt}." - ) - pipeline = pipeline.to(accelerator.device, dtype=torch_dtype) - 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() - elif is_torch_npu_available(): - torch_npu.npu.empty_cache() - - return images - - -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 == "T5EncoderModel": - from transformers import T5EncoderModel - - return T5EncoderModel - else: - raise ValueError(f"{model_class} is not supported.") - - -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( - "--dataset_name", - type=str, - default=None, - help=( - "The name of the Dataset (from the HuggingFace hub) containing the training data of instance images (could be your own, possibly private," - " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem," - " or to a folder containing files that 🤗 Datasets can understand." - ), - ) - parser.add_argument( - "--dataset_config_name", - type=str, - default=None, - help="The config of the Dataset, leave as None if there's only one config.", - ) - parser.add_argument( - "--instance_data_dir", - type=str, - default=None, - help=("A folder containing the training data. "), - ) - - parser.add_argument( - "--cache_dir", - type=str, - default=None, - help="The directory where the downloaded models and datasets will be stored.", - ) - - parser.add_argument( - "--image_column", - type=str, - default="image", - help="The column of the dataset containing the target image. By " - "default, the standard Image Dataset maps out 'file_name' " - "to 'image'.", - ) - parser.add_argument( - "--caption_column", - type=str, - default=None, - help="The column of the dataset containing the instance prompt for each image", - ) - - parser.add_argument( - "--repeats", - type=int, - default=1, - help="How many times to repeat the training data.", - ) - - parser.add_argument( - "--class_data_dir", - type=str, - default=None, - required=False, - help="A folder containing the training data of class images.", - ) - 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( - "--class_prompt", - type=str, - default=None, - help="The prompt to specify images in the same class as provided instance images.", - ) - 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( - "--with_prior_preservation", - default=False, - action="store_true", - help="Flag to add prior preservation loss.", - ) - parser.add_argument( - "--prior_loss_weight", - type=float, - default=1.0, - help="The weight of prior preservation loss.", - ) - parser.add_argument( - "--num_class_images", - type=int, - default=100, - help=( - "Minimal class images for prior preservation loss. If there are not enough images already present in" - " class_data_dir, additional images will be sampled with class_prompt." - ), - ) - parser.add_argument( - "--output_dir", - type=str, - default="flux-dreambooth", - 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_text_encoder", - action="store_true", - help="Whether to train the text encoder. If set, the text encoder should be float32 precision.", - ) - parser.add_argument( - "--train_batch_size", - type=int, - default=4, - help="Batch size (per device) for the training dataloader.", - ) - parser.add_argument( - "--sample_batch_size", - type=int, - default=4, - help="Batch size (per device) for sampling images.", - ) - 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( - "--guidance_scale", - type=float, - default=3.5, - help="the FLUX.1 dev variant is a guidance distilled model", - ) - - parser.add_argument( - "--text_encoder_lr", - type=float, - default=5e-6, - help="Text encoder learning rate 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="none", - choices=["sigma_sqrt", "logit_normal", "mode", "cosmap", "none"], - help=( - 'We default to the "none" weighting scheme for uniform sampling and uniform loss' - ), - ) - 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", "prodigy"]'), - ) - - 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 and Prodigy optimizers.", - ) - parser.add_argument( - "--adam_beta2", - type=float, - default=0.999, - help="The beta2 parameter for the Adam and Prodigy optimizers.", - ) - parser.add_argument( - "--prodigy_beta3", - type=float, - default=None, - help="coefficients for computing the Prodigy stepsize using running averages. If set to None, " - "uses the value of square root of beta2. Ignored if optimizer is adamW", - ) - parser.add_argument( - "--prodigy_decouple", - type=bool, - default=True, - help="Use AdamW style decoupled weight decay", - ) - parser.add_argument( - "--adam_weight_decay", - type=float, - default=1e-04, - help="Weight decay to use for unet params", - ) - parser.add_argument( - "--adam_weight_decay_text_encoder", - type=float, - default=1e-03, - help="Weight decay to use for text_encoder", - ) - - parser.add_argument( - "--adam_epsilon", - type=float, - default=1e-08, - help="Epsilon value for the Adam optimizer and Prodigy optimizers.", - ) - - parser.add_argument( - "--prodigy_use_bias_correction", - type=bool, - default=True, - help="Turn on Adam's bias correction. True by default. Ignored if optimizer is adamW", - ) - parser.add_argument( - "--prodigy_safeguard_warmup", - type=bool, - default=True, - help="Remove lr from the denominator of D estimate to avoid issues during warm-up stage. True by default. " - "Ignored if optimizer is adamW", - ) - 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.dataset_name is None and args.instance_data_dir is None: - raise ValueError("Specify either `--dataset_name` or `--instance_data_dir`") - - if args.dataset_name is not None and args.instance_data_dir is not None: - raise ValueError( - "Specify only one of `--dataset_name` or `--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 - - if args.with_prior_preservation: - if args.class_data_dir is None: - raise ValueError("You must specify a data directory for class images.") - if args.class_prompt is None: - raise ValueError("You must specify prompt for class images.") - else: - # logger is not available yet - if args.class_data_dir is not None: - warnings.warn( - "You need not use --class_data_dir without --with_prior_preservation." - ) - if args.class_prompt is not None: - warnings.warn( - "You need not use --class_prompt without --with_prior_preservation." - ) - - 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, - instance_data_root, - instance_prompt, - class_prompt, - class_data_root=None, - class_num=None, - size=1024, - repeats=1, - center_crop=False, - ): - self.size = size - self.center_crop = center_crop - - self.instance_prompt = instance_prompt - self.custom_instance_prompts = None - self.class_prompt = class_prompt - - # if --dataset_name is provided or a metadata jsonl file is provided in the local --instance_data directory, - # we load the training data using load_dataset - if args.dataset_name is not None: - try: - from datasets import load_dataset - except ImportError: - raise ImportError( - "You are trying to load your data using the datasets library. If you wish to train using custom " - "captions please install the datasets library: `pip install datasets`. If you wish to load a " - "local folder containing images only, specify --instance_data_dir instead." - ) - # Downloading and loading a dataset from the hub. - # See more about loading custom images at - # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script - dataset = load_dataset( - args.dataset_name, - args.dataset_config_name, - cache_dir=args.cache_dir, - ) - # Preprocessing the datasets. - column_names = dataset["train"].column_names - - # 6. Get the column names for input/target. - if args.image_column is None: - image_column = column_names[0] - logger.info(f"image column defaulting to {image_column}") - else: - image_column = args.image_column - if image_column not in column_names: - raise ValueError( - f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}" - ) - instance_images = dataset["train"][image_column] - - if args.caption_column is None: - logger.info( - "No caption column provided, defaulting to instance_prompt for all images. If your dataset " - "contains captions/prompts for the images, make sure to specify the " - "column as --caption_column" - ) - self.custom_instance_prompts = None - else: - if args.caption_column not in column_names: - raise ValueError( - f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}" - ) - custom_instance_prompts = dataset["train"][args.caption_column] - # create final list of captions according to --repeats - self.custom_instance_prompts = [] - for caption in custom_instance_prompts: - self.custom_instance_prompts.extend( - itertools.repeat(caption, repeats) - ) - else: - self.instance_data_root = Path(instance_data_root) - if not self.instance_data_root.exists(): - raise ValueError("Instance images root doesn't exists.") - - instance_images = [ - Image.open(path) for path in list(Path(instance_data_root).iterdir()) - ] - self.custom_instance_prompts = None - - self.instance_images = [] - for img in instance_images: - self.instance_images.extend(itertools.repeat(img, repeats)) - - self.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 self.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) - self.pixel_values.append(image) - - self.num_instance_images = len(self.instance_images) - self._length = self.num_instance_images - - if class_data_root is not None: - self.class_data_root = Path(class_data_root) - self.class_data_root.mkdir(parents=True, exist_ok=True) - self.class_images_path = list(self.class_data_root.iterdir()) - if class_num is not None: - self.num_class_images = min(len(self.class_images_path), class_num) - else: - self.num_class_images = len(self.class_images_path) - self._length = max(self.num_class_images, self.num_instance_images) - else: - self.class_data_root = None - - self.image_transforms = transforms.Compose( - [ - transforms.Resize( - size, interpolation=transforms.InterpolationMode.BILINEAR - ), - ( - transforms.CenterCrop(size) - if center_crop - else transforms.RandomCrop(size) - ), - transforms.ToTensor(), - transforms.Normalize([0.5], [0.5]), - ] - ) - - def __len__(self): - return self._length - - def __getitem__(self, index): - example = {} - instance_image = self.pixel_values[index % self.num_instance_images] - example["instance_images"] = instance_image - - if self.custom_instance_prompts: - caption = self.custom_instance_prompts[index % self.num_instance_images] - if caption: - example["instance_prompt"] = caption - else: - example["instance_prompt"] = self.instance_prompt - - else: # custom prompts were provided, but length does not match size of image dataset - example["instance_prompt"] = self.instance_prompt - - if self.class_data_root: - class_image = Image.open( - self.class_images_path[index % self.num_class_images] - ) - class_image = exif_transpose(class_image) - - if not class_image.mode == "RGB": - class_image = class_image.convert("RGB") - example["class_images"] = self.image_transforms(class_image) - example["class_prompt"] = self.class_prompt - - return example - - -def collate_fn(examples, with_prior_preservation=False): - pixel_values = [example["instance_images"] for example in examples] - prompts = [example["instance_prompt"] for example in examples] - - # Concat class and instance examples for prior preservation. - # We do this to avoid doing two forward passes. - if with_prior_preservation: - pixel_values += [example["class_images"] for example in examples] - prompts += [example["class_prompt"] for example in examples] - - pixel_values = torch.stack(pixel_values) - pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() - - batch = {"pixel_values": pixel_values, "prompts": prompts} - return batch - - -class PromptDataset(Dataset): - "A simple dataset to prepare the prompts to generate class images on multiple GPUs." - - def __init__(self, prompt, num_samples): - self.prompt = prompt - self.num_samples = num_samples - - def __len__(self): - return self.num_samples - - def __getitem__(self, index): - example = {} - example["prompt"] = self.prompt - example["index"] = index - return example - - -def tokenize_prompt(tokenizer, prompt, max_sequence_length): - text_inputs = tokenizer( - prompt, - padding="max_length", - max_length=max_sequence_length, - truncation=True, - return_length=False, - return_overflowing_tokens=False, - return_tensors="pt", - ) - text_input_ids = text_inputs.input_ids - return text_input_ids - - -def _encode_prompt_with_t5( - text_encoder, - tokenizer, - max_sequence_length=512, - prompt=None, - num_images_per_prompt=1, - device=None, - text_input_ids=None, -): - prompt = [prompt] if isinstance(prompt, str) else prompt - batch_size = len(prompt) - - if tokenizer is not None: - text_inputs = tokenizer( - prompt, - padding="max_length", - max_length=max_sequence_length, - truncation=True, - return_length=False, - return_overflowing_tokens=False, - return_tensors="pt", - ) - text_input_ids = text_inputs.input_ids - else: - if text_input_ids is None: - raise ValueError( - "text_input_ids must be provided when the tokenizer is not specified" - ) - - prompt_embeds = text_encoder(text_input_ids.to(device))[0] - - dtype = text_encoder.dtype - prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) - - _, seq_len, _ = prompt_embeds.shape - - # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method - prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) - prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) - - return prompt_embeds - - -def _encode_prompt_with_clip( - text_encoder, - tokenizer, - prompt: str, - device=None, - text_input_ids=None, - num_images_per_prompt: int = 1, -): - prompt = [prompt] if isinstance(prompt, str) else prompt - batch_size = len(prompt) - - if tokenizer is not None: - text_inputs = tokenizer( - prompt, - padding="max_length", - max_length=77, - truncation=True, - return_overflowing_tokens=False, - return_length=False, - return_tensors="pt", - ) - - text_input_ids = text_inputs.input_ids - else: - if text_input_ids is None: - raise ValueError( - "text_input_ids must be provided when the tokenizer is not specified" - ) - - prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=False) - - # Use pooled output of CLIPTextModel - prompt_embeds = prompt_embeds.pooler_output - prompt_embeds = prompt_embeds.to(dtype=text_encoder.dtype, device=device) - - # duplicate text embeddings for each generation per prompt, using mps friendly method - prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) - prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, -1) - - return prompt_embeds - - -def encode_prompt( - text_encoders, - tokenizers, - prompt: str, - max_sequence_length, - device=None, - num_images_per_prompt: int = 1, - text_input_ids_list=None, -): - prompt = [prompt] if isinstance(prompt, str) else prompt - batch_size = len(prompt) - dtype = text_encoders[0].dtype - device = device if device is not None else text_encoders[1].device - pooled_prompt_embeds = _encode_prompt_with_clip( - text_encoder=text_encoders[0], - tokenizer=tokenizers[0], - prompt=prompt, - device=device, - num_images_per_prompt=num_images_per_prompt, - text_input_ids=text_input_ids_list[0] if text_input_ids_list else None, - ) - - prompt_embeds = _encode_prompt_with_t5( - text_encoder=text_encoders[1], - tokenizer=tokenizers[1], - max_sequence_length=max_sequence_length, - prompt=prompt, - num_images_per_prompt=num_images_per_prompt, - device=device, - text_input_ids=text_input_ids_list[1] if text_input_ids_list else None, - ) - - text_ids = torch.zeros(batch_size, prompt_embeds.shape[1], 3).to( - device=device, dtype=dtype - ) - text_ids = text_ids.repeat(num_images_per_prompt, 1, 1) - - return prompt_embeds, pooled_prompt_embeds, text_ids - - -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) - - # Generate class images if prior preservation is enabled. - if args.with_prior_preservation: - class_images_dir = Path(args.class_data_dir) - if not class_images_dir.exists(): - class_images_dir.mkdir(parents=True) - cur_class_images = len(list(class_images_dir.iterdir())) - - if cur_class_images < args.num_class_images: - has_supported_fp16_accelerator = ( - torch.cuda.is_available() - or torch.backends.mps.is_available() - or is_torch_npu_available() - ) - torch_dtype = ( - torch.float16 if has_supported_fp16_accelerator else torch.float32 - ) - if args.prior_generation_precision == "fp32": - torch_dtype = torch.float32 - elif args.prior_generation_precision == "fp16": - torch_dtype = torch.float16 - elif args.prior_generation_precision == "bf16": - torch_dtype = torch.bfloat16 - pipeline = FluxPipeline.from_pretrained( - args.pretrained_model_name_or_path, - torch_dtype=torch_dtype, - revision=args.revision, - variant=args.variant, - ) - pipeline.set_progress_bar_config(disable=True) - - num_new_images = args.num_class_images - cur_class_images - logger.info(f"Number of class images to sample: {num_new_images}.") - - sample_dataset = PromptDataset(args.class_prompt, num_new_images) - sample_dataloader = torch.utils.data.DataLoader( - sample_dataset, batch_size=args.sample_batch_size - ) - - sample_dataloader = accelerator.prepare(sample_dataloader) - pipeline.to(accelerator.device) - - for example in tqdm( - sample_dataloader, - desc="Generating class images", - disable=not accelerator.is_local_main_process, - ): - images = pipeline(example["prompt"]).images - - for i, image in enumerate(images): - hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest() - image_filename = ( - class_images_dir - / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg" - ) - image.save(image_filename) - - del pipeline - if torch.cuda.is_available(): - torch.cuda.empty_cache() - elif is_torch_npu_available(): - torch_npu.npu.empty_cache() - - # 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 the tokenizers - tokenizer_one = CLIPTokenizer.from_pretrained( - args.pretrained_model_name_or_path, - subfolder="tokenizer", - revision=args.revision, - ) - tokenizer_two = T5TokenizerFast.from_pretrained( - args.pretrained_model_name_or_path, - subfolder="tokenizer_2", - revision=args.revision, - ) - - # 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 = FlowMatchEulerDiscreteScheduler.from_pretrained( - args.pretrained_model_name_or_path, subfolder="scheduler" - ) - noise_scheduler_copy = copy.deepcopy(noise_scheduler) - text_encoder_one, text_encoder_two = load_text_encoders( - text_encoder_cls_one, text_encoder_cls_two - ) - vae = AutoencoderKL.from_pretrained( - args.pretrained_model_name_or_path, - subfolder="vae", - revision=args.revision, - variant=args.variant, - ) - transformer = FluxTransformer2DModel.from_pretrained( - args.pretrained_model_name_or_path, - subfolder="transformer", - revision=args.revision, - variant=args.variant, - ) - - transformer.requires_grad_(True) - vae.requires_grad_(False) - if args.train_text_encoder: - text_encoder_one.requires_grad_(True) - text_encoder_two.requires_grad_(False) - else: - text_encoder_one.requires_grad_(False) - text_encoder_two.requires_grad_(False) - - # For mixed precision training we cast all non-trainable weights (vae, text_encoder and 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=weight_dtype) - if not args.train_text_encoder: - text_encoder_one.to(accelerator.device, dtype=weight_dtype) - text_encoder_two.to(accelerator.device, dtype=weight_dtype) - - if args.gradient_checkpointing: - transformer.enable_gradient_checkpointing() - if args.train_text_encoder: - text_encoder_one.gradient_checkpointing_enable() - - 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: - for i, model in enumerate(models): - if isinstance(unwrap_model(model), FluxTransformer2DModel): - unwrap_model(model).save_pretrained( - os.path.join(output_dir, "transformer") - ) - elif isinstance( - unwrap_model(model), (CLIPTextModelWithProjection, T5EncoderModel) - ): - if isinstance(unwrap_model(model), CLIPTextModelWithProjection): - unwrap_model(model).save_pretrained( - os.path.join(output_dir, "text_encoder") - ) - else: - unwrap_model(model).save_pretrained( - os.path.join(output_dir, "text_encoder_2") - ) - else: - raise ValueError(f"Wrong model supplied: {type(model)=}.") - - # make sure to pop weight so that corresponding model is not saved again - weights.pop() - - def load_model_hook(models, input_dir): - for _ in range(len(models)): - # pop models so that they are not loaded again - model = models.pop() - - # load diffusers style into model - if isinstance(unwrap_model(model), FluxTransformer2DModel): - load_model = FluxTransformer2DModel.from_pretrained( - input_dir, subfolder="transformer" - ) - model.register_to_config(**load_model.config) - - model.load_state_dict(load_model.state_dict()) - elif isinstance( - unwrap_model(model), (CLIPTextModelWithProjection, T5EncoderModel) - ): - try: - load_model = CLIPTextModelWithProjection.from_pretrained( - input_dir, subfolder="text_encoder" - ) - model(**load_model.config) - model.load_state_dict(load_model.state_dict()) - except Exception: - try: - load_model = T5EncoderModel.from_pretrained( - input_dir, subfolder="text_encoder_2" - ) - model(**load_model.config) - model.load_state_dict(load_model.state_dict()) - except Exception: - raise ValueError( - f"Couldn't load the model of type: ({type(model)})." - ) - else: - raise ValueError(f"Unsupported model found: {type(model)=}") - - del load_model - - 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 - ) - - # Optimization parameters - transformer_parameters_with_lr = { - "params": transformer.parameters(), - "lr": args.learning_rate, - } - if args.train_text_encoder: - # different learning rate for text encoder and unet - text_parameters_one_with_lr = { - "params": text_encoder_one.parameters(), - "weight_decay": args.adam_weight_decay_text_encoder, - "lr": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate, - } - params_to_optimize = [ - transformer_parameters_with_lr, - text_parameters_one_with_lr, - ] - else: - params_to_optimize = [transformer_parameters_with_lr] - - # Optimizer creation - if not (args.optimizer.lower() == "prodigy" or args.optimizer.lower() == "adamw"): - logger.warning( - f"Unsupported choice of optimizer: {args.optimizer}.Supported optimizers include [adamW, prodigy]." - "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, - ) - - if args.optimizer.lower() == "prodigy": - try: - import prodigyopt - except ImportError: - raise ImportError( - "To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`" - ) - - optimizer_class = prodigyopt.Prodigy - - if args.learning_rate <= 0.1: - logger.warning( - "Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0" - ) - if args.train_text_encoder and args.text_encoder_lr: - logger.warning( - f"Learning rates were provided both for the transformer and the text encoder- e.g. text_encoder_lr:" - f" {args.text_encoder_lr} and learning_rate: {args.learning_rate}. " - f"When using prodigy only learning_rate is used as the initial learning rate." - ) - # changes the learning rate of text_encoder_parameters_one and text_encoder_parameters_two to be - # --learning_rate - params_to_optimize[1]["lr"] = args.learning_rate - params_to_optimize[2]["lr"] = args.learning_rate - - optimizer = optimizer_class( - params_to_optimize, - lr=args.learning_rate, - betas=(args.adam_beta1, args.adam_beta2), - beta3=args.prodigy_beta3, - weight_decay=args.adam_weight_decay, - eps=args.adam_epsilon, - decouple=args.prodigy_decouple, - use_bias_correction=args.prodigy_use_bias_correction, - safeguard_warmup=args.prodigy_safeguard_warmup, - ) - - # Dataset and DataLoaders creation: - train_dataset = DreamBoothDataset( - instance_data_root=args.instance_data_dir, - instance_prompt=args.instance_prompt, - class_prompt=args.class_prompt, - class_data_root=args.class_data_dir if args.with_prior_preservation else None, - class_num=args.num_class_images, - size=args.resolution, - repeats=args.repeats, - 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, args.with_prior_preservation), - num_workers=args.dataloader_num_workers, - ) - - if not args.train_text_encoder: - tokenizers = [tokenizer_one, tokenizer_two] - text_encoders = [text_encoder_one, text_encoder_two] - - def compute_text_embeddings(prompt, text_encoders, tokenizers): - with torch.no_grad(): - prompt_embeds, pooled_prompt_embeds, text_ids = encode_prompt( - text_encoders, tokenizers, prompt, args.max_sequence_length - ) - prompt_embeds = prompt_embeds.to(accelerator.device) - pooled_prompt_embeds = pooled_prompt_embeds.to(accelerator.device) - text_ids = text_ids.to(accelerator.device) - return prompt_embeds, pooled_prompt_embeds, text_ids - - # If no type of tuning is done on the text_encoder and custom instance prompts are NOT - # provided (i.e. the --instance_prompt is used for all images), we encode the instance prompt once to avoid - # the redundant encoding. - if not args.train_text_encoder and not train_dataset.custom_instance_prompts: - ( - instance_prompt_hidden_states, - instance_pooled_prompt_embeds, - instance_text_ids, - ) = compute_text_embeddings(args.instance_prompt, text_encoders, tokenizers) - - # Handle class prompt for prior-preservation. - if args.with_prior_preservation: - if not args.train_text_encoder: - class_prompt_hidden_states, class_pooled_prompt_embeds, class_text_ids = ( - compute_text_embeddings(args.class_prompt, text_encoders, tokenizers) - ) - - # Clear the memory here - if not args.train_text_encoder and not train_dataset.custom_instance_prompts: - del tokenizers, text_encoders - # Explicitly delete the objects as well, otherwise only the lists are deleted and the original references remain, preventing garbage collection - del text_encoder_one, text_encoder_two - gc.collect() - if torch.cuda.is_available(): - torch.cuda.empty_cache() - elif is_torch_npu_available(): - torch_npu.npu.empty_cache() - - # If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images), - # pack the statically computed variables appropriately here. This is so that we don't - # have to pass them to the dataloader. - - if not train_dataset.custom_instance_prompts: - if not args.train_text_encoder: - prompt_embeds = instance_prompt_hidden_states - pooled_prompt_embeds = instance_pooled_prompt_embeds - text_ids = instance_text_ids - if args.with_prior_preservation: - prompt_embeds = torch.cat( - [prompt_embeds, class_prompt_hidden_states], dim=0 - ) - pooled_prompt_embeds = torch.cat( - [pooled_prompt_embeds, class_pooled_prompt_embeds], dim=0 - ) - text_ids = torch.cat([text_ids, class_text_ids], dim=0) - # if we're optimizing the text encoder (both if instance prompt is used for all images or custom prompts) we need to tokenize and encode the - # batch prompts on all training steps - else: - tokens_one = tokenize_prompt( - tokenizer_one, args.instance_prompt, max_sequence_length=77 - ) - tokens_two = tokenize_prompt( - tokenizer_two, args.instance_prompt, max_sequence_length=512 - ) - if args.with_prior_preservation: - class_tokens_one = tokenize_prompt( - tokenizer_one, args.class_prompt, max_sequence_length=77 - ) - class_tokens_two = tokenize_prompt( - tokenizer_two, args.class_prompt, max_sequence_length=512 - ) - tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0) - tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0) - - # 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`. - if args.train_text_encoder: - ( - transformer, - text_encoder_one, - optimizer, - train_dataloader, - lr_scheduler, - ) = accelerator.prepare( - transformer, - text_encoder_one, - optimizer, - train_dataloader, - lr_scheduler, - ) - else: - 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-flux-dev-lora" - 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() - if args.train_text_encoder: - text_encoder_one.train() - - for step, batch in enumerate(train_dataloader): - models_to_accumulate = [transformer] - if args.train_text_encoder: - models_to_accumulate.extend([text_encoder_one]) - with accelerator.accumulate(models_to_accumulate): - pixel_values = batch["pixel_values"].to(dtype=vae.dtype) - prompts = batch["prompts"] - - # encode batch prompts when custom prompts are provided for each image - - if train_dataset.custom_instance_prompts: - if not args.train_text_encoder: - prompt_embeds, pooled_prompt_embeds, text_ids = ( - compute_text_embeddings(prompts, text_encoders, tokenizers) - ) - else: - tokens_one = tokenize_prompt( - tokenizer_one, prompts, max_sequence_length=77 - ) - tokens_two = tokenize_prompt( - tokenizer_two, - prompts, - max_sequence_length=args.max_sequence_length, - ) - prompt_embeds, pooled_prompt_embeds, text_ids = encode_prompt( - text_encoders=[text_encoder_one, text_encoder_two], - tokenizers=[None, None], - text_input_ids_list=[tokens_one, tokens_two], - max_sequence_length=args.max_sequence_length, - prompt=prompts, - ) - else: - if args.train_text_encoder: - prompt_embeds, pooled_prompt_embeds, text_ids = encode_prompt( - text_encoders=[text_encoder_one, text_encoder_two], - tokenizers=[None, None], - text_input_ids_list=[tokens_one, tokens_two], - max_sequence_length=args.max_sequence_length, - prompt=args.instance_prompt, - ) - - # Convert images to latent space - model_input = vae.encode(pixel_values).latent_dist.sample() - model_input = ( - model_input - vae.config.shift_factor - ) * vae.config.scaling_factor - model_input = model_input.to(dtype=weight_dtype) - - vae_scale_factor = 2 ** (len(vae.config.block_out_channels)) - - latent_image_ids = FluxPipeline._prepare_latent_image_ids( - model_input.shape[0], - model_input.shape[2], - model_input.shape[3], - accelerator.device, - 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. - # zt = (1 - texp) * x + texp * z1 - sigmas = get_sigmas( - timesteps, n_dim=model_input.ndim, dtype=model_input.dtype - ) - noisy_model_input = (1.0 - sigmas) * model_input + sigmas * noise - - packed_noisy_model_input = FluxPipeline._pack_latents( - noisy_model_input, - batch_size=model_input.shape[0], - num_channels_latents=model_input.shape[1], - height=model_input.shape[2], - width=model_input.shape[3], - ) - - # handle guidance - if transformer.config.guidance_embeds: - guidance = torch.tensor( - [args.guidance_scale], device=accelerator.device - ) - guidance = guidance.expand(model_input.shape[0]) - else: - guidance = None - - # Predict the noise residual - model_pred = transformer( - hidden_states=packed_noisy_model_input, - # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing) - timestep=timesteps / 1000, - guidance=guidance, - pooled_projections=pooled_prompt_embeds, - encoder_hidden_states=prompt_embeds, - txt_ids=text_ids, - img_ids=latent_image_ids, - return_dict=False, - )[0] - # upscaling height & width as discussed in https://github.com/huggingface/diffusers/pull/9257#discussion_r1731108042 - model_pred = FluxPipeline._unpack_latents( - model_pred, - height=int(model_input.shape[2] * vae_scale_factor / 2), - width=int(model_input.shape[3] * vae_scale_factor / 2), - vae_scale_factor=vae_scale_factor, - ) - - # 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 = noise - model_input - - if args.with_prior_preservation: - # Chunk the noise and model_pred into two parts and compute the loss on each part separately. - model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0) - target, target_prior = torch.chunk(target, 2, dim=0) - - # Compute prior loss - prior_loss = torch.mean( - ( - weighting.float() - * (model_pred_prior.float() - target_prior.float()) ** 2 - ).reshape(target_prior.shape[0], -1), - 1, - ) - prior_loss = prior_loss.mean() - - # Compute regular loss. - loss = torch.mean( - ( - weighting.float() * (model_pred.float() - target.float()) ** 2 - ).reshape(target.shape[0], -1), - 1, - ) - loss = loss.mean() - - if args.with_prior_preservation: - # Add the prior loss to the instance loss. - loss = loss + args.prior_loss_weight * prior_loss - - accelerator.backward(loss) - if accelerator.sync_gradients: - params_to_clip = ( - itertools.chain( - transformer.parameters(), text_encoder_one.parameters() - ) - if args.train_text_encoder - else transformer.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 - ): - # create pipeline - if not args.train_text_encoder: - text_encoder_one, text_encoder_two = load_text_encoders( - text_encoder_cls_one, text_encoder_cls_two - ) - else: # even when training the text encoder we're only training text encoder one - 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, - ) - pipeline = FluxPipeline.from_pretrained( - args.pretrained_model_name_or_path, - vae=vae, - text_encoder=accelerator.unwrap_model(text_encoder_one), - text_encoder_2=accelerator.unwrap_model(text_encoder_two), - 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_dtype=weight_dtype, - ) - if not args.train_text_encoder: - del text_encoder_one, text_encoder_two - if torch.cuda.is_available(): - torch.cuda.empty_cache() - elif is_torch_npu_available(): - torch_npu.npu.empty_cache() - gc.collect() - - # Save the lora layers - accelerator.wait_for_everyone() - if accelerator.is_main_process: - transformer = unwrap_model(transformer) - - if args.train_text_encoder: - text_encoder_one = unwrap_model(text_encoder_one) - pipeline = FluxPipeline.from_pretrained( - args.pretrained_model_name_or_path, - transformer=transformer, - text_encoder=text_encoder_one, - ) - else: - pipeline = FluxPipeline.from_pretrained( - args.pretrained_model_name_or_path, transformer=transformer - ) - - # save the pipeline - pipeline.save_pretrained(args.output_dir) - - # Final inference - # Load previous pipeline - pipeline = FluxPipeline.from_pretrained( - args.output_dir, - revision=args.revision, - variant=args.variant, - torch_dtype=weight_dtype, - ) - - # 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, - torch_dtype=weight_dtype, - ) - - if args.push_to_hub: - save_model_card( - repo_id, - images=images, - base_model=args.pretrained_model_name_or_path, - train_text_encoder=args.train_text_encoder, - 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)