From 2349e42defb16a3d946fb901947ea2c65af45809 Mon Sep 17 00:00:00 2001 From: Ray Wang Date: Fri, 12 Apr 2024 10:14:51 +0800 Subject: [PATCH 1/2] init train ella --- train_ella.py | 1193 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1193 insertions(+) create mode 100644 train_ella.py diff --git a/train_ella.py b/train_ella.py new file mode 100644 index 0000000..7af5b3d --- /dev/null +++ b/train_ella.py @@ -0,0 +1,1193 @@ +#!/usr/bin/env python +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import logging +import math +import os +import random +import shutil +from contextlib import nullcontext + +import accelerate +import datasets +import diffusers +import numpy as np +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +import transformers +from accelerate import Accelerator +from accelerate.logging import get_logger +from accelerate.state import AcceleratorState +from accelerate.utils import ProjectConfiguration, set_seed +from datasets import load_dataset +from diffusers import ( + AutoencoderKL, + DDPMScheduler, + StableDiffusionPipeline, + UNet2DConditionModel, +) +from diffusers.optimization import get_scheduler +from diffusers.training_utils import EMAModel, compute_snr +from diffusers.utils import deprecate, is_wandb_available +from diffusers.utils.import_utils import is_xformers_available +from diffusers.utils.torch_utils import is_compiled_module +from packaging import version +from torchvision import transforms +from tqdm.auto import tqdm +from transformers import CLIPTextModel, CLIPTokenizer +from transformers.utils import ContextManagers + +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("0.28.0.dev0") + +logger = get_logger(__name__, log_level="INFO") + +DATASET_NAME_MAPPING = { + "lambdalabs/pokemon-blip-captions": ("image", "text"), +} + + +def log_validation( + vae, text_encoder, tokenizer, unet, args, accelerator, weight_dtype, epoch +): + logger.info("Running validation... ") + + pipeline = StableDiffusionPipeline.from_pretrained( + args.pretrained_model_name_or_path, + vae=accelerator.unwrap_model(vae), + text_encoder=accelerator.unwrap_model(text_encoder), + tokenizer=tokenizer, + unet=accelerator.unwrap_model(unet), + safety_checker=None, + revision=args.revision, + variant=args.variant, + torch_dtype=weight_dtype, + ) + pipeline = pipeline.to(accelerator.device) + pipeline.set_progress_bar_config(disable=True) + + if args.enable_xformers_memory_efficient_attention: + pipeline.enable_xformers_memory_efficient_attention() + + generator = ( + None + if args.seed is None + else torch.Generator(device=accelerator.device).manual_seed(args.seed) + ) + + images = [] + for i in range(len(args.validation_prompts)): + autocast_ctx = ( + nullcontext() + if torch.backends.mps.is_available() + else torch.autocast(accelerator.device.type) + ) + + with autocast_ctx: + image = pipeline( + args.validation_prompts[i], num_inference_steps=20, generator=generator + ).images[0] + + images.append(image) + + for tracker in accelerator.trackers: + if tracker.name == "tensorboard": + np_images = np.stack([np.asarray(img) for img in images]) + tracker.writer.add_images( + "validation", np_images, epoch, dataformats="NHWC" + ) + elif tracker.name == "wandb": + tracker.log( + { + "validation": [ + wandb.Image(image, caption=f"{i}: {args.validation_prompts[i]}") + for i, image in enumerate(images) + ] + } + ) + else: + logger.warning(f"image logging not implemented for {tracker.name}") + + del pipeline + torch.cuda.empty_cache() + + return images + + +def parse_args(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--input_perturbation", + type=float, + default=0, + help="The scale of input perturbation. Recommended 0.1.", + ) + 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) to train on (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( + "--train_data_dir", + type=str, + default=None, + help=( + "A folder containing the training data. Folder contents must follow the structure described in" + " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file" + " must exist to provide the captions for the images. Ignored if `dataset_name` is specified." + ), + ) + parser.add_argument( + "--image_column", + type=str, + default="image", + help="The column of the dataset containing an image.", + ) + parser.add_argument( + "--caption_column", + type=str, + default="text", + help="The column of the dataset containing a caption or a list of captions.", + ) + parser.add_argument( + "--max_train_samples", + type=int, + default=None, + help=( + "For debugging purposes or quicker training, truncate the number of training examples to this " + "value if set." + ), + ) + parser.add_argument( + "--validation_prompts", + type=str, + default=None, + nargs="+", + help=( + "A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`." + ), + ) + parser.add_argument( + "--output_dir", + type=str, + default="sd-model-finetuned", + 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=512, + help=( + "The resolution for input images, all the images in the train/validation dataset will be resized to this" + " resolution" + ), + ) + parser.add_argument( + "--center_crop", + default=False, + action="store_true", + help=( + "Whether to center crop the input images to the resolution. If not set, the images will be randomly" + " cropped. The images will be resized to the resolution first before cropping." + ), + ) + parser.add_argument( + "--random_flip", + action="store_true", + help="whether to randomly flip images horizontally", + ) + parser.add_argument( + "--train_batch_size", + type=int, + default=16, + help="Batch size (per device) for the training dataloader.", + ) + parser.add_argument("--num_train_epochs", type=int, default=100) + 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( + "--gradient_accumulation_steps", + type=int, + default=1, + help="Number of updates steps to accumulate before performing a backward/update pass.", + ) + parser.add_argument( + "--gradient_checkpointing", + action="store_true", + help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=1e-4, + help="Initial learning rate (after the potential warmup period) to use.", + ) + parser.add_argument( + "--scale_lr", + action="store_true", + default=False, + help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", + ) + parser.add_argument( + "--lr_scheduler", + type=str, + default="constant", + help=( + 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' + ' "constant", "constant_with_warmup"]' + ), + ) + parser.add_argument( + "--lr_warmup_steps", + type=int, + default=500, + help="Number of steps for the warmup in the lr scheduler.", + ) + parser.add_argument( + "--snr_gamma", + type=float, + default=None, + help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. " + "More details here: https://arxiv.org/abs/2303.09556.", + ) + parser.add_argument( + "--use_8bit_adam", + action="store_true", + help="Whether or not to use 8-bit Adam from bitsandbytes.", + ) + 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( + "--use_ema", action="store_true", help="Whether to use EMA model." + ) + parser.add_argument( + "--non_ema_revision", + type=str, + default=None, + required=False, + help=( + "Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or" + " remote repository specified with --pretrained_model_name_or_path." + ), + ) + 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( + "--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( + "--hub_token", + type=str, + default=None, + help="The token to use to push to the Model Hub.", + ) + parser.add_argument( + "--prediction_type", + type=str, + default=None, + help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.", + ) + 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( + "--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( + "--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( + "--local_rank", + type=int, + default=-1, + help="For distributed training: local_rank", + ) + parser.add_argument( + "--checkpointing_steps", + type=int, + default=500, + help=( + "Save a checkpoint of the training state every X updates. These checkpoints are only 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( + "--enable_xformers_memory_efficient_attention", + action="store_true", + help="Whether or not to use xformers.", + ) + parser.add_argument( + "--noise_offset", type=float, default=0, help="The scale of noise offset." + ) + parser.add_argument( + "--validation_epochs", + type=int, + default=5, + help="Run validation every X epochs.", + ) + parser.add_argument( + "--tracker_project_name", + type=str, + default="text2image-fine-tune", + help=( + "The `project_name` argument passed to Accelerator.init_trackers for" + " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator" + ), + ) + parser.add_argument( + "--with_clip", + action="store_true", + help="Whether or not to use CLIP during train ELLA.", + ) + + 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 + + # Sanity checks + if args.dataset_name is None and args.train_data_dir is None: + raise ValueError("Need either a dataset name or a training folder.") + + # default to using the same revision for the non-ema model if not specified + if args.non_ema_revision is None: + args.non_ema_revision = args.revision + + return args + + +def main(): + args = parse_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 args.non_ema_revision is not None: + deprecate( + "non_ema_revision!=None", + "0.15.0", + message=( + "Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to" + " use `--variant=non_ema` instead." + ), + ) + logging_dir = os.path.join(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: + datasets.utils.logging.set_verbosity_warning() + transformers.utils.logging.set_verbosity_warning() + diffusers.utils.logging.set_verbosity_info() + else: + datasets.utils.logging.set_verbosity_error() + transformers.utils.logging.set_verbosity_error() + diffusers.utils.logging.set_verbosity_error() + + # If passed along, set the training seed now. + if 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) + + # Load scheduler, tokenizer and models. + noise_scheduler = DDPMScheduler.from_pretrained( + args.pretrained_model_name_or_path, subfolder="scheduler" + ) + + tokenizer = CLIPTokenizer.from_pretrained( + args.pretrained_model_name_or_path, + subfolder="tokenizer", + revision=args.revision, + ) + + def deepspeed_zero_init_disabled_context_manager(): + """ + returns either a context list that includes one that will disable zero.Init or an empty context list + """ + deepspeed_plugin = ( + AcceleratorState().deepspeed_plugin + if accelerate.state.is_initialized() + else None + ) + if deepspeed_plugin is None: + return [] + + return [deepspeed_plugin.zero3_init_context_manager(enable=False)] + + # Currently Accelerate doesn't know how to handle multiple models under Deepspeed ZeRO stage 3. + # For this to work properly all models must be run through `accelerate.prepare`. But accelerate + # will try to assign the same optimizer with the same weights to all models during + # `deepspeed.initialize`, which of course doesn't work. + # + # For now the following workaround will partially support Deepspeed ZeRO-3, by excluding the 2 + # frozen models from being partitioned during `zero.Init` which gets called during + # `from_pretrained` So CLIPTextModel and AutoencoderKL will not enjoy the parameter sharding + # across multiple gpus and only UNet2DConditionModel will get ZeRO sharded. + with ContextManagers(deepspeed_zero_init_disabled_context_manager()): + text_encoder = CLIPTextModel.from_pretrained( + args.pretrained_model_name_or_path, + subfolder="text_encoder", + revision=args.revision, + variant=args.variant, + ) + vae = AutoencoderKL.from_pretrained( + args.pretrained_model_name_or_path, + subfolder="vae", + revision=args.revision, + variant=args.variant, + ) + + unet = UNet2DConditionModel.from_pretrained( + args.pretrained_model_name_or_path, + subfolder="unet", + revision=args.non_ema_revision, + ) + + # Freeze vae and text_encoder and set unet to trainable + vae.requires_grad_(False) + text_encoder.requires_grad_(False) + unet.train() + + # Create EMA for the unet. + if args.use_ema: + ema_unet = UNet2DConditionModel.from_pretrained( + args.pretrained_model_name_or_path, + subfolder="unet", + revision=args.revision, + variant=args.variant, + ) + ema_unet = EMAModel( + ema_unet.parameters(), + model_cls=UNet2DConditionModel, + model_config=ema_unet.config, + ) + + 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" + ) + + # `accelerate` 0.16.0 will have better support for customized saving + if version.parse(accelerate.__version__) >= version.parse("0.16.0"): + # 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: + if args.use_ema: + ema_unet.save_pretrained(os.path.join(output_dir, "unet_ema")) + + for _i, model in enumerate(models): + model.save_pretrained(os.path.join(output_dir, "unet")) + + # make sure to pop weight so that corresponding model is not saved again + weights.pop() + + def load_model_hook(models, input_dir): + if args.use_ema: + load_model = EMAModel.from_pretrained( + os.path.join(input_dir, "unet_ema"), UNet2DConditionModel + ) + ema_unet.load_state_dict(load_model.state_dict()) + ema_unet.to(accelerator.device) + del load_model + + for _ in range(len(models)): + # pop models so that they are not loaded again + model = models.pop() + + # load diffusers style into model + load_model = UNet2DConditionModel.from_pretrained( + input_dir, subfolder="unet" + ) + model.register_to_config(**load_model.config) + + model.load_state_dict(load_model.state_dict()) + del load_model + + accelerator.register_save_state_pre_hook(save_model_hook) + accelerator.register_load_state_pre_hook(load_model_hook) + + if args.gradient_checkpointing: + unet.enable_gradient_checkpointing() + + # 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_rate + * args.gradient_accumulation_steps + * args.train_batch_size + * accelerator.num_processes + ) + + # Initialize the optimizer + if args.use_8bit_adam: + try: + import bitsandbytes as bnb + except ImportError: + raise ImportError( + "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`" + ) + + optimizer_cls = bnb.optim.AdamW8bit + else: + optimizer_cls = torch.optim.AdamW + + optimizer = optimizer_cls( + unet.parameters(), + lr=args.learning_rate, + betas=(args.adam_beta1, args.adam_beta2), + weight_decay=args.adam_weight_decay, + eps=args.adam_epsilon, + ) + + # Get the datasets: you can either provide your own training and evaluation files (see below) + # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub). + + # In distributed training, the load_dataset function guarantees that only one local process can concurrently + # download the dataset. + if args.dataset_name is not None: + # Downloading and loading a dataset from the hub. + dataset = load_dataset( + args.dataset_name, + args.dataset_config_name, + cache_dir=args.cache_dir, + data_dir=args.train_data_dir, + ) + else: + data_files = {} + if args.train_data_dir is not None: + data_files["train"] = os.path.join(args.train_data_dir, "**") + dataset = load_dataset( + "imagefolder", + data_files=data_files, + cache_dir=args.cache_dir, + ) + # See more about loading custom images at + # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder + + # Preprocessing the datasets. + # We need to tokenize inputs and targets. + column_names = dataset["train"].column_names + + # 6. Get the column names for input/target. + dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None) + if args.image_column is None: + image_column = ( + dataset_columns[0] if dataset_columns is not None else column_names[0] + ) + else: + image_column = args.image_column + if image_column not in column_names: + raise ValueError( + f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}" + ) + if args.caption_column is None: + caption_column = ( + dataset_columns[1] if dataset_columns is not None else column_names[1] + ) + else: + caption_column = args.caption_column + if caption_column not in column_names: + raise ValueError( + f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}" + ) + + # Preprocessing the datasets. + # We need to tokenize input captions and transform the images. + def tokenize_captions(examples, is_train=True): + captions = [] + for caption in examples[caption_column]: + if isinstance(caption, str): + captions.append(caption) + elif isinstance(caption, (list, np.ndarray)): + # take a random caption if there are multiple + captions.append(random.choice(caption) if is_train else caption[0]) + else: + raise ValueError( + f"Caption column `{caption_column}` should contain either strings or lists of strings." + ) + inputs = tokenizer( + captions, + max_length=tokenizer.model_max_length, + padding="max_length", + truncation=True, + return_tensors="pt", + ) + return inputs.input_ids + + # Preprocessing the datasets. + train_transforms = transforms.Compose( + [ + transforms.Resize( + args.resolution, interpolation=transforms.InterpolationMode.BILINEAR + ), + ( + transforms.CenterCrop(args.resolution) + if args.center_crop + else transforms.RandomCrop(args.resolution) + ), + ( + transforms.RandomHorizontalFlip() + if args.random_flip + else transforms.Lambda(lambda x: x) + ), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + ] + ) + + def preprocess_train(examples): + images = [image.convert("RGB") for image in examples[image_column]] + examples["pixel_values"] = [train_transforms(image) for image in images] + examples["input_ids"] = tokenize_captions(examples) + return examples + + with accelerator.main_process_first(): + if args.max_train_samples is not None: + dataset["train"] = ( + dataset["train"] + .shuffle(seed=args.seed) + .select(range(args.max_train_samples)) + ) + # Set the training transforms + train_dataset = dataset["train"].with_transform(preprocess_train) + + def collate_fn(examples): + pixel_values = torch.stack([example["pixel_values"] for example in examples]) + pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() + input_ids = torch.stack([example["input_ids"] for example in examples]) + return {"pixel_values": pixel_values, "input_ids": input_ids} + + # DataLoaders creation: + train_dataloader = torch.utils.data.DataLoader( + train_dataset, + shuffle=True, + collate_fn=collate_fn, + batch_size=args.train_batch_size, + num_workers=args.dataloader_num_workers, + ) + + # Scheduler and math around the number of training steps. + overrode_max_train_steps = False + num_update_steps_per_epoch = math.ceil( + len(train_dataloader) / args.gradient_accumulation_steps + ) + if args.max_train_steps is None: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + overrode_max_train_steps = True + + lr_scheduler = get_scheduler( + args.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes, + num_training_steps=args.max_train_steps * accelerator.num_processes, + ) + + # Prepare everything with our `accelerator`. + unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + unet, optimizer, train_dataloader, lr_scheduler + ) + + if args.use_ema: + ema_unet.to(accelerator.device) + + # 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 + args.mixed_precision = accelerator.mixed_precision + elif accelerator.mixed_precision == "bf16": + weight_dtype = torch.bfloat16 + args.mixed_precision = accelerator.mixed_precision + + # Move text_encode and vae to gpu and cast to weight_dtype + text_encoder.to(accelerator.device, dtype=weight_dtype) + vae.to(accelerator.device, dtype=weight_dtype) + + # 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_config = dict(vars(args)) + tracker_config.pop("validation_prompts") + accelerator.init_trackers(args.tracker_project_name, tracker_config) + + # Function for unwrapping if model was compiled with `torch.compile`. + def unwrap_model(model): + model = accelerator.unwrap_model(model) + model = model._orig_mod if is_compiled_module(model) else model + return model + + # 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 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 most 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, + ) + + for epoch in range(first_epoch, args.num_train_epochs): + train_loss = 0.0 + for _step, batch in enumerate(train_dataloader): + with accelerator.accumulate(unet): + # Convert images to latent space + latents = vae.encode( + batch["pixel_values"].to(weight_dtype) + ).latent_dist.sample() + latents = latents * vae.config.scaling_factor + + # Sample noise that we'll add to the latents + noise = torch.randn_like(latents) + if args.noise_offset: + # https://www.crosslabs.org//blog/diffusion-with-offset-noise + noise += args.noise_offset * torch.randn( + (latents.shape[0], latents.shape[1], 1, 1), + device=latents.device, + ) + if args.input_perturbation: + new_noise = noise + args.input_perturbation * torch.randn_like( + noise + ) + bsz = latents.shape[0] + # Sample a random timestep for each image + timesteps = torch.randint( + 0, + noise_scheduler.config.num_train_timesteps, + (bsz,), + device=latents.device, + ) + timesteps = timesteps.long() + + # Add noise to the latents according to the noise magnitude at each timestep + # (this is the forward diffusion process) + if args.input_perturbation: + noisy_latents = noise_scheduler.add_noise( + latents, new_noise, timesteps + ) + else: + noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) + + # Get the text embedding for conditioning + encoder_hidden_states = text_encoder( + batch["input_ids"], return_dict=False + )[0] + + # Get the target for loss depending on the prediction type + if args.prediction_type is not None: + # set prediction_type of scheduler if defined + noise_scheduler.register_to_config( + prediction_type=args.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}" + ) + + # Predict the noise residual and compute loss + model_pred = unet( + noisy_latents, timesteps, encoder_hidden_states, return_dict=False + )[0] + + if args.snr_gamma is None: + loss = F.mse_loss( + model_pred.float(), target.float(), reduction="mean" + ) + else: + # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556. + # Since we predict the noise instead of x_0, the original formulation is slightly changed. + # This is discussed in Section 4.2 of the same paper. + snr = compute_snr(noise_scheduler, timesteps) + mse_loss_weights = torch.stack( + [snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1 + ).min(dim=1)[0] + if noise_scheduler.config.prediction_type == "epsilon": + mse_loss_weights = mse_loss_weights / snr + elif noise_scheduler.config.prediction_type == "v_prediction": + mse_loss_weights = mse_loss_weights / (snr + 1) + + loss = F.mse_loss( + model_pred.float(), target.float(), reduction="none" + ) + loss = ( + loss.mean(dim=list(range(1, len(loss.shape)))) + * mse_loss_weights + ) + loss = loss.mean() + + # Gather the losses across all processes for logging (if we use distributed training). + avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean() + train_loss += avg_loss.item() / args.gradient_accumulation_steps + + # Backpropagate + accelerator.backward(loss) + if accelerator.sync_gradients: + accelerator.clip_grad_norm_(unet.parameters(), 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: + if args.use_ema: + ema_unet.step(unet.parameters()) + progress_bar.update(1) + global_step += 1 + accelerator.log({"train_loss": train_loss}, step=global_step) + train_loss = 0.0 + + if global_step % args.checkpointing_steps == 0: + if accelerator.is_main_process: + # _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 = { + "step_loss": loss.detach().item(), + "lr": lr_scheduler.get_last_lr()[0], + } + progress_bar.set_postfix(**logs) + + if global_step >= args.max_train_steps: + break + + if accelerator.is_main_process: + if ( + args.validation_prompts is not None + and epoch % args.validation_epochs == 0 + ): + if args.use_ema: + # Store the UNet parameters temporarily and load the EMA parameters to perform inference. + ema_unet.store(unet.parameters()) + ema_unet.copy_to(unet.parameters()) + log_validation( + vae, + text_encoder, + tokenizer, + unet, + args, + accelerator, + weight_dtype, + global_step, + ) + if args.use_ema: + # Switch back to the original UNet parameters. + ema_unet.restore(unet.parameters()) + + # Create the pipeline using the trained modules and save it. + accelerator.wait_for_everyone() + if accelerator.is_main_process: + unet = unwrap_model(unet) + if args.use_ema: + ema_unet.copy_to(unet.parameters()) + + pipeline = StableDiffusionPipeline.from_pretrained( + args.pretrained_model_name_or_path, + text_encoder=text_encoder, + vae=vae, + unet=unet, + revision=args.revision, + variant=args.variant, + ) + pipeline.save_pretrained(args.output_dir) + + # Run a final round of inference. + images = [] + if args.validation_prompts is not None: + logger.info("Running inference for collecting generated images...") + pipeline = pipeline.to(accelerator.device) + pipeline.torch_dtype = weight_dtype + pipeline.set_progress_bar_config(disable=True) + + if args.enable_xformers_memory_efficient_attention: + pipeline.enable_xformers_memory_efficient_attention() + + if args.seed is None: + generator = None + else: + generator = torch.Generator(device=accelerator.device).manual_seed( + args.seed + ) + + for i in range(len(args.validation_prompts)): + with torch.autocast("cuda"): + image = pipeline( + args.validation_prompts[i], + num_inference_steps=20, + generator=generator, + ).images[0] + images.append(image) + + accelerator.end_training() + + +if __name__ == "__main__": + main() From 01c3eebfef345e69872f0f2e41c8dd0e280b63a9 Mon Sep 17 00:00:00 2001 From: Ray Wang Date: Sun, 14 Apr 2024 21:20:09 +0800 Subject: [PATCH 2/2] train text to image lora with lora --- train_text_to_image_lora_with_ella.py | 1052 +++++++++++++++++++++++++ 1 file changed, 1052 insertions(+) create mode 100644 train_text_to_image_lora_with_ella.py diff --git a/train_text_to_image_lora_with_ella.py b/train_text_to_image_lora_with_ella.py new file mode 100644 index 0000000..93174e6 --- /dev/null +++ b/train_text_to_image_lora_with_ella.py @@ -0,0 +1,1052 @@ +#!/usr/bin/env python +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Fine-tuning script for Stable Diffusion for text2image with support for LoRA.""" + +import argparse +import logging +import math +import os +import random +import shutil +from contextlib import nullcontext +from pathlib import Path +from typing import Optional + +import datasets +import diffusers +import numpy as np +import safetensors.torch +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +import transformers +from accelerate import Accelerator +from accelerate.logging import get_logger +from accelerate.utils import ProjectConfiguration, set_seed +from datasets import load_dataset +from diffusers import ( + AutoencoderKL, + DDPMScheduler, + DiffusionPipeline, + StableDiffusionPipeline, + UNet2DConditionModel, +) +from diffusers.optimization import get_scheduler +from diffusers.training_utils import cast_training_params, compute_snr +from diffusers.utils import convert_state_dict_to_diffusers, is_wandb_available +from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card +from diffusers.utils.import_utils import is_xformers_available +from diffusers.utils.torch_utils import is_compiled_module +from huggingface_hub import create_repo, upload_folder +from packaging import version +from peft import LoraConfig +from peft.utils import get_peft_model_state_dict +from torchvision import transforms +from tqdm.auto import tqdm + +from model import ELLA, T5TextEmbedder + +# Will error if the minimal version of diffusers is not installed. Remove at your own risks. +# check_min_version("0.28.0.dev0") + + +def load_ella(filename, device, dtype): + ella = ELLA() + safetensors.torch.load_model(ella, filename, strict=True) + ella.to(device, dtype=dtype) + return ella + + +logger = get_logger(__name__, log_level="INFO") + + +def save_model_card( + repo_id: str, + images: Optional[list] = None, + base_model: Optional[str] = None, + dataset_name: Optional[str] = None, + repo_folder: Optional[str] = None, +): + img_str = "" + if images is not None: + for i, image in enumerate(images): + image.save(os.path.join(repo_folder, f"image_{i}.png")) + img_str += f"![img_{i}](./image_{i}.png)\n" + + model_description = f""" +# LoRA text2image fine-tuning - {repo_id} +These are LoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \n +{img_str} +""" + + model_card = load_or_create_model_card( + repo_id_or_path=repo_id, + from_training=True, + license="creativeml-openrail-m", + base_model=base_model, + model_description=model_description, + inference=True, + ) + + tags = [ + "stable-diffusion", + "stable-diffusion-diffusers", + "text-to-image", + "diffusers", + "diffusers-training", + "lora", + ] + model_card = populate_model_card(model_card, tags=tags) + + model_card.save(os.path.join(repo_folder, "README.md")) + + +def parse_args(): + 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_ella", + type=str, + default=None, + required=True, + help="Path to pretrained ELLA model", + ) + 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) to train on (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( + "--train_data_dir", + type=str, + default=None, + help=( + "A folder containing the training data. Folder contents must follow the structure described in" + " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file" + " must exist to provide the captions for the images. Ignored if `dataset_name` is specified." + ), + ) + parser.add_argument( + "--image_column", + type=str, + default="image", + help="The column of the dataset containing an image.", + ) + parser.add_argument( + "--caption_column", + type=str, + default="text", + help="The column of the dataset containing a caption or a list of captions.", + ) + parser.add_argument( + "--validation_prompt", + type=str, + default=None, + help="A prompt that is sampled during training for inference.", + ) + 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=1, + help=( + "Run fine-tuning validation every X epochs. The validation process consists of running the prompt" + " `args.validation_prompt` multiple times: `args.num_validation_images`." + ), + ) + parser.add_argument( + "--max_train_samples", + type=int, + default=None, + help=( + "For debugging purposes or quicker training, truncate the number of training examples to this " + "value if set." + ), + ) + parser.add_argument( + "--output_dir", + type=str, + default="sd-model-finetuned-lora", + 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=512, + help=( + "The resolution for input images, all the images in the train/validation dataset will be resized to this" + " resolution" + ), + ) + parser.add_argument( + "--center_crop", + default=False, + action="store_true", + help=( + "Whether to center crop the input images to the resolution. If not set, the images will be randomly" + " cropped. The images will be resized to the resolution first before cropping." + ), + ) + parser.add_argument( + "--random_flip", + action="store_true", + help="whether to randomly flip images horizontally", + ) + parser.add_argument( + "--train_batch_size", + type=int, + default=16, + help="Batch size (per device) for the training dataloader.", + ) + parser.add_argument("--num_train_epochs", type=int, default=100) + 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( + "--gradient_accumulation_steps", + type=int, + default=1, + help="Number of updates steps to accumulate before performing a backward/update pass.", + ) + parser.add_argument( + "--gradient_checkpointing", + action="store_true", + help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=1e-4, + help="Initial learning rate (after the potential warmup period) to use.", + ) + parser.add_argument( + "--scale_lr", + action="store_true", + default=False, + help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", + ) + parser.add_argument( + "--lr_scheduler", + type=str, + default="constant", + help=( + 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' + ' "constant", "constant_with_warmup"]' + ), + ) + parser.add_argument( + "--lr_warmup_steps", + type=int, + default=500, + help="Number of steps for the warmup in the lr scheduler.", + ) + parser.add_argument( + "--snr_gamma", + type=float, + default=None, + help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. " + "More details here: https://arxiv.org/abs/2303.09556.", + ) + parser.add_argument( + "--use_8bit_adam", + action="store_true", + help="Whether or not to use 8-bit Adam from bitsandbytes.", + ) + 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( + "--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( + "--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( + "--prediction_type", + type=str, + default=None, + help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.", + ) + 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( + "--mixed_precision", + type=str, + default=None, + choices=["no", "fp16"], + help=( + "Whether to use mixed precision. Choose between fp16" + "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( + "--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( + "--local_rank", + type=int, + default=-1, + help="For distributed training: local_rank", + ) + parser.add_argument( + "--checkpointing_steps", + type=int, + default=500, + help=( + "Save a checkpoint of the training state every X updates. These checkpoints are only 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( + "--enable_xformers_memory_efficient_attention", + action="store_true", + help="Whether or not to use xformers.", + ) + parser.add_argument( + "--noise_offset", type=float, default=0, help="The scale of noise offset." + ) + parser.add_argument( + "--rank", + type=int, + default=4, + help=("The dimension of the LoRA update matrices."), + ) + + 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 + + # Sanity checks + if args.dataset_name is None and args.train_data_dir is None: + raise ValueError("Need either a dataset name or a training folder.") + + return args + + +DATASET_NAME_MAPPING = { + "lambdalabs/pokemon-blip-captions": ("image", "text"), +} + + +def main(): + args = parse_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 + + 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." + ) + import wandb + + # Make one log on every process with the configuration for debugging. + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + level=logging.INFO, + ) + logger.info(accelerator.state, main_process_only=False) + if accelerator.is_local_main_process: + datasets.utils.logging.set_verbosity_warning() + transformers.utils.logging.set_verbosity_warning() + diffusers.utils.logging.set_verbosity_info() + else: + datasets.utils.logging.set_verbosity_error() + transformers.utils.logging.set_verbosity_error() + diffusers.utils.logging.set_verbosity_error() + + # If passed along, set the training seed now. + if 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 scheduler, tokenizer and models. + noise_scheduler = DDPMScheduler.from_pretrained( + args.pretrained_model_name_or_path, subfolder="scheduler" + ) + # tokenizer = CLIPTokenizer.from_pretrained( + # args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision + # ) + # text_encoder = CLIPTextModel.from_pretrained( + # args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision + # ) + t5_text_embedder = T5TextEmbedder( + pretrained_path="google/flan-t5-xl", max_length=128 + ) + ella = load_ella(args.pretrained_ella, device="cuda", dtype=torch.float16) + + vae = AutoencoderKL.from_pretrained( + args.pretrained_model_name_or_path, + subfolder="vae", + revision=args.revision, + variant=args.variant, + ) + unet = UNet2DConditionModel.from_pretrained( + args.pretrained_model_name_or_path, + subfolder="unet", + revision=args.revision, + variant=args.variant, + ) + # freeze parameters of models to save more memory + unet.requires_grad_(False) + vae.requires_grad_(False) + t5_text_embedder.requires_grad_(False) + ella.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 + + # Freeze the unet parameters before adding adapters + for param in unet.parameters(): + param.requires_grad_(False) + + 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"], + ) + + # Move unet, vae and text_encoder to device and cast to weight_dtype + unet.to(accelerator.device, dtype=weight_dtype) + vae.to(accelerator.device, dtype=weight_dtype) + t5_text_embedder.to( + accelerator.device, dtype=weight_dtype + ) # ella is trained with fp16 T5 + ella.to(accelerator.device, dtype=weight_dtype) + + # Add adapter and make sure the trainable params are in float32. + unet.add_adapter(unet_lora_config) + if args.mixed_precision == "fp16": + # only upcast trainable parameters (LoRA) into fp32 + cast_training_params(unet, dtype=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" + ) + + lora_layers = filter(lambda p: p.requires_grad, unet.parameters()) + + if args.gradient_checkpointing: + unet.enable_gradient_checkpointing() + + # 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_rate + * args.gradient_accumulation_steps + * args.train_batch_size + * accelerator.num_processes + ) + + # Initialize the optimizer + if args.use_8bit_adam: + try: + import bitsandbytes as bnb + except ImportError: + raise ImportError( + "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`" + ) + + optimizer_cls = bnb.optim.AdamW8bit + else: + optimizer_cls = torch.optim.AdamW + + optimizer = optimizer_cls( + lora_layers, + lr=args.learning_rate, + betas=(args.adam_beta1, args.adam_beta2), + weight_decay=args.adam_weight_decay, + eps=args.adam_epsilon, + ) + + # Get the datasets: you can either provide your own training and evaluation files (see below) + # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub). + + # In distributed training, the load_dataset function guarantees that only one local process can concurrently + # download the dataset. + if args.dataset_name is not None: + # Downloading and loading a dataset from the hub. + dataset = load_dataset( + args.dataset_name, + args.dataset_config_name, + cache_dir=args.cache_dir, + data_dir=args.train_data_dir, + ) + else: + data_files = {} + if args.train_data_dir is not None: + data_files["train"] = os.path.join(args.train_data_dir, "**") + dataset = load_dataset( + "imagefolder", + data_files=data_files, + cache_dir=args.cache_dir, + ) + # See more about loading custom images at + # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder + + # Preprocessing the datasets. + # We need to tokenize inputs and targets. + column_names = dataset["train"].column_names + + # 6. Get the column names for input/target. + dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None) + if args.image_column is None: + image_column = ( + dataset_columns[0] if dataset_columns is not None else column_names[0] + ) + else: + image_column = args.image_column + if image_column not in column_names: + raise ValueError( + f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}" + ) + if args.caption_column is None: + caption_column = ( + dataset_columns[1] if dataset_columns is not None else column_names[1] + ) + else: + caption_column = args.caption_column + if caption_column not in column_names: + raise ValueError( + f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}" + ) + + # Preprocessing the datasets. + # We need to tokenize input captions and transform the images. + def tokenize_captions(examples, is_train=True): + captions = [] + for caption in examples[caption_column]: + if isinstance(caption, str): + captions.append(caption) + elif isinstance(caption, (list, np.ndarray)): + # take a random caption if there are multiple + captions.append(random.choice(caption) if is_train else caption[0]) + else: + raise ValueError( + f"Caption column `{caption_column}` should contain either strings or lists of strings." + ) + # inputs = tokenizer( + # captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt" + # ) + return captions + + # Preprocessing the datasets. + train_transforms = transforms.Compose( + [ + transforms.Resize( + args.resolution, interpolation=transforms.InterpolationMode.BILINEAR + ), + ( + transforms.CenterCrop(args.resolution) + if args.center_crop + else transforms.RandomCrop(args.resolution) + ), + ( + transforms.RandomHorizontalFlip() + if args.random_flip + else transforms.Lambda(lambda x: x) + ), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + ] + ) + + def unwrap_model(model): + model = accelerator.unwrap_model(model) + model = model._orig_mod if is_compiled_module(model) else model + return model + + def preprocess_train(examples): + images = [image.convert("RGB") for image in examples[image_column]] + examples["pixel_values"] = [train_transforms(image) for image in images] + examples["captions"] = tokenize_captions(examples) + return examples + + with accelerator.main_process_first(): + if args.max_train_samples is not None: + dataset["train"] = ( + dataset["train"] + .shuffle(seed=args.seed) + .select(range(args.max_train_samples)) + ) + # Set the training transforms + train_dataset = dataset["train"].with_transform(preprocess_train) + + def collate_fn(examples): + pixel_values = torch.stack([example["pixel_values"] for example in examples]) + pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() + # input_ids = torch.stack([example["input_ids"] for example in examples]) + captions = [example["captions"] for example in examples] + return {"pixel_values": pixel_values, "captions": captions} + + # DataLoaders creation: + train_dataloader = torch.utils.data.DataLoader( + train_dataset, + shuffle=True, + collate_fn=collate_fn, + batch_size=args.train_batch_size, + num_workers=args.dataloader_num_workers, + ) + + # Scheduler and math around the number of training steps. + overrode_max_train_steps = False + num_update_steps_per_epoch = math.ceil( + len(train_dataloader) / args.gradient_accumulation_steps + ) + if args.max_train_steps is None: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + overrode_max_train_steps = True + + lr_scheduler = get_scheduler( + args.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes, + num_training_steps=args.max_train_steps * accelerator.num_processes, + ) + + # Prepare everything with our `accelerator`. + unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + unet, optimizer, train_dataloader, lr_scheduler + ) + + # We need to recalculate our total training steps as the size of the training dataloader may have changed. + num_update_steps_per_epoch = math.ceil( + len(train_dataloader) / 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("text2image-fine-tune", 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 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 most 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, + ) + + for epoch in range(first_epoch, args.num_train_epochs): + unet.train() + train_loss = 0.0 + for _step, batch in enumerate(train_dataloader): + with accelerator.accumulate(unet): + # Convert images to latent space + latents = vae.encode( + batch["pixel_values"].to(dtype=weight_dtype) + ).latent_dist.sample() + latents = latents * vae.config.scaling_factor + + # Sample noise that we'll add to the latents + noise = torch.randn_like(latents) + if args.noise_offset: + # https://www.crosslabs.org//blog/diffusion-with-offset-noise + noise += args.noise_offset * torch.randn( + (latents.shape[0], latents.shape[1], 1, 1), + device=latents.device, + ) + + bsz = latents.shape[0] + # Sample a random timestep for each image + timesteps = torch.randint( + 0, + noise_scheduler.config.num_train_timesteps, + (bsz,), + device=latents.device, + ) + timesteps = timesteps.long() + + # Add noise to the latents according to the noise magnitude at each timestep + # (this is the forward diffusion process) + noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) + + # Get the text embedding for conditioning + encoder_hidden_states = ella( + t5_text_embedder(batch["captions"]), timesteps + ) + + # Get the target for loss depending on the prediction type + if args.prediction_type is not None: + # set prediction_type of scheduler if defined + noise_scheduler.register_to_config( + prediction_type=args.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}" + ) + + # Predict the noise residual and compute loss + model_pred = unet( + noisy_latents, timesteps, encoder_hidden_states, return_dict=False + )[0] + + if args.snr_gamma is None: + loss = F.mse_loss( + model_pred.float(), target.float(), reduction="mean" + ) + else: + # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556. + # Since we predict the noise instead of x_0, the original formulation is slightly changed. + # This is discussed in Section 4.2 of the same paper. + snr = compute_snr(noise_scheduler, timesteps) + mse_loss_weights = torch.stack( + [snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1 + ).min(dim=1)[0] + if noise_scheduler.config.prediction_type == "epsilon": + mse_loss_weights = mse_loss_weights / snr + elif noise_scheduler.config.prediction_type == "v_prediction": + mse_loss_weights = mse_loss_weights / (snr + 1) + + loss = F.mse_loss( + model_pred.float(), target.float(), reduction="none" + ) + loss = ( + loss.mean(dim=list(range(1, len(loss.shape)))) + * mse_loss_weights + ) + loss = loss.mean() + + # Gather the losses across all processes for logging (if we use distributed training). + avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean() + train_loss += avg_loss.item() / args.gradient_accumulation_steps + + # Backpropagate + accelerator.backward(loss) + if accelerator.sync_gradients: + params_to_clip = lora_layers + 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 + accelerator.log({"train_loss": train_loss}, step=global_step) + train_loss = 0.0 + + if global_step % args.checkpointing_steps == 0: + if accelerator.is_main_process: + # _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) + + unwrapped_unet = unwrap_model(unet) + unet_lora_state_dict = convert_state_dict_to_diffusers( + get_peft_model_state_dict(unwrapped_unet) + ) + + StableDiffusionPipeline.save_lora_weights( + save_directory=save_path, + unet_lora_layers=unet_lora_state_dict, + safe_serialization=True, + ) + + logger.info(f"Saved state to {save_path}") + + logs = { + "step_loss": loss.detach().item(), + "lr": lr_scheduler.get_last_lr()[0], + } + progress_bar.set_postfix(**logs) + + if global_step >= args.max_train_steps: + break + + # Save the lora layers + accelerator.wait_for_everyone() + if accelerator.is_main_process: + unet = unet.to(torch.float32) + + unwrapped_unet = unwrap_model(unet) + unet_lora_state_dict = convert_state_dict_to_diffusers( + get_peft_model_state_dict(unwrapped_unet) + ) + StableDiffusionPipeline.save_lora_weights( + save_directory=args.output_dir, + unet_lora_layers=unet_lora_state_dict, + safe_serialization=True, + ) + accelerator.end_training() + + +if __name__ == "__main__": + main()