import argparse import hashlib import itertools import json import logging import math import uuid import warnings from os import environ, listdir, makedirs from os.path import basename, join from pathlib import Path from typing import List import datasets 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.utils import ProjectConfiguration, set_seed from huggingface_hub import create_repo, upload_folder from PIL import Image from torch import dtype from torch.nn import Module from torch.utils.data import Dataset from torchvision import transforms from tqdm.auto import tqdm from transformers import AutoTokenizer, PretrainedConfig import diffusers from diffusers import ( AutoencoderKL, DDPMScheduler, DiffusionPipeline, DPMSolverMultistepScheduler, UNet2DConditionModel, ) from diffusers.optimization import get_scheduler from diffusers.utils import check_min_version, is_wandb_available from diffusers.utils.import_utils import is_xformers_available 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("0.13.0.dev0") logger = get_logger(__name__) def log_validation_images_to_tracker( images: List[np.array], label: str, validation_prompt: str, accelerator: Accelerator, epoch: int ): logger.info(f"Logging images to tracker for validation prompt: {validation_prompt}.") for tracker in accelerator.trackers: if tracker.name == "tensorboard": np_images = np.stack([np.asarray(img) for img in images]) tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC") if tracker.name == "wandb": tracker.log( { "validation": [ wandb.Image(image, caption=f"{label}_{epoch}_{i}: {validation_prompt}") for i, image in enumerate(images) ] } ) # TODO: Add `prompt_embeds` and `negative_prompt_embeds` parameters to the function when `pre_compute_text_embeddings` # argument is implemented. def generate_validation_images( text_encoder: Module, tokenizer: Module, unet: Module, vae: Module, arguments: argparse.Namespace, accelerator: Accelerator, weight_dtype: dtype, ): logger.info("Running validation images.") pipeline_args = {} if text_encoder is not None: pipeline_args["text_encoder"] = accelerator.unwrap_model(text_encoder) if vae is not None: pipeline_args["vae"] = vae # create pipeline (note: unet and vae are loaded again in float32) pipeline = DiffusionPipeline.from_pretrained( arguments.pretrained_model_name_or_path, tokenizer=tokenizer, unet=accelerator.unwrap_model(unet), revision=arguments.revision, torch_dtype=weight_dtype, **pipeline_args, ) # We train on the simplified learning objective. If we were previously predicting a variance, we need the # scheduler to ignore it scheduler_args = {} if "variance_type" in pipeline.scheduler.config: variance_type = pipeline.scheduler.config.variance_type if variance_type in ["learned", "learned_range"]: variance_type = "fixed_small" scheduler_args["variance_type"] = variance_type pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args) pipeline = pipeline.to(accelerator.device) pipeline.set_progress_bar_config(disable=True) generator = ( None if arguments.seed is None else torch.Generator(device=accelerator.device).manual_seed(arguments.seed) ) images_sets = [] for vp, nvi, vnp, vis, vgs in zip( arguments.validation_prompt, arguments.validation_number_images, arguments.validation_negative_prompt, arguments.validation_inference_steps, arguments.validation_guidance_scale, ): images = [] if vp is not None: logger.info( f"Generating {nvi} images with prompt: '{vp}', negative prompt: '{vnp}', inference steps: {vis}, " f"guidance scale: {vgs}." ) pipeline_args = {"prompt": vp, "negative_prompt": vnp, "num_inference_steps": vis, "guidance_scale": vgs} # run inference # TODO: it would be good to measure whether it's faster to run inference on all images at once, one at a # time or in small batches for _ in range(nvi): with torch.autocast("cuda"): image = pipeline(**pipeline_args, num_images_per_prompt=1, generator=generator).images[0] images.append(image) images_sets.append(images) del pipeline if torch.cuda.is_available(): torch.cuda.empty_cache() return images_sets def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str): text_encoder_config = PretrainedConfig.from_pretrained( pretrained_model_name_or_path, subfolder="text_encoder", revision=revision, ) model_class = text_encoder_config.architectures[0] if model_class == "CLIPTextModel": from transformers import CLIPTextModel return CLIPTextModel elif model_class == "RobertaSeriesModelWithTransformation": from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation return RobertaSeriesModelWithTransformation 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( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--instance_data_dir", type=str, default=None, required=False, help="A folder containing the training data of instance images.", ) 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=False, help="The prompt with identifier specifying the instance", ) 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( "--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="text-inversion-model", 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("--train_text_encoder", action="store_true", help="Whether to train the text encoder") 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. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`." " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state" " for more docs" ), ) 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=5e-6, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--scale_lr", action="store_true", default=False, help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", ) parser.add_argument( "--lr_scheduler", type=str, default="constant", help=( 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' ' "constant", "constant_with_warmup"]' ), ) parser.add_argument( "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument( "--lr_num_cycles", type=int, default=1, help="Number of hard resets of the lr in cosine_with_restarts scheduler.", ) parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.") parser.add_argument( "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." ) parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") parser.add_argument( "--hub_model_id", type=str, default=None, help="The name of the repository to keep in sync with the local `output_dir`.", ) parser.add_argument( "--logging_dir", type=str, default="logs", help=( "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." ), ) parser.add_argument( "--allow_tf32", action="store_true", help=( "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" ), ) parser.add_argument( "--report_to", type=str, default="tensorboard", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' ), ) parser.add_argument( "--validation_steps", type=int, default=None, help=( "Run validation every X steps. Validation consists of running the prompt(s) `validation_prompt` " "multiple times (`validation_number_images`) and logging the images." ), ) parser.add_argument( "--validation_prompt", type=str, default=None, help="A prompt that is used during validation to verify that the model is learning. You can use commas to " "define multiple negative prompts. This parameter can be defined also within the file given by " "`concepts_list` parameter in the respective subject.", ) parser.add_argument( "--validation_number_images", type=int, default=4, help="Number of images that should be generated during validation with the validation parameters given. This " "can be defined within the file given by `concepts_list` parameter in the respective subject.", ) parser.add_argument( "--validation_negative_prompt", type=str, default=None, help="A negative prompt that is used during validation to verify that the model is learning. You can use commas" " to define multiple negative prompts, each one corresponding to a validation prompt. This parameter can " "be defined also within the file given by `concepts_list` parameter in the respective subject.", ) parser.add_argument( "--validation_inference_steps", type=int, default=25, help="Number of inference steps (denoising steps) to run during validation. This can be defined within the " "file given by `concepts_list` parameter in the respective subject.", ) parser.add_argument( "--validation_guidance_scale", type=float, default=7.5, help="To control how much the image generation process follows the text prompt. This can be defined within the " "file given by `concepts_list` parameter in the respective subject.", ) parser.add_argument( "--mixed_precision", type=str, default=None, choices=["no", "fp16", "bf16"], help=( "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." ), ) parser.add_argument( "--prior_generation_precision", type=str, default=None, choices=["no", "fp32", "fp16", "bf16"], help=( "Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" " 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32." ), ) parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") parser.add_argument( "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers." ) parser.add_argument( "--set_grads_to_none", action="store_true", help=( "Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain" " behaviors, so disable this argument if it causes any problems. More info:" " https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html" ), ) parser.add_argument( "--concepts_list", type=str, default=None, help="Path to json file containing a list of multiple concepts, will overwrite parameters like instance_prompt," " class_prompt, etc.", ) if input_args: args = parser.parse_args(input_args) else: args = parser.parse_args() if not args.concepts_list and (not args.instance_data_dir or not args.instance_prompt): raise ValueError( "You must specify either instance parameters (data directory, prompt, etc.) or use " "the `concept_list` parameter and specify them within the file." ) if args.concepts_list: if args.instance_prompt: raise ValueError("If you are using `concepts_list` parameter, define the instance prompt within the file.") if args.instance_data_dir: raise ValueError( "If you are using `concepts_list` parameter, define the instance data directory within the file." ) if args.validation_steps and (args.validation_prompt or args.validation_negative_prompt): raise ValueError( "If you are using `concepts_list` parameter, define validation parameters for " "each subject within the file:\n - `validation_prompt`." "\n - `validation_negative_prompt`.\n - `validation_guidance_scale`." "\n - `validation_number_images`.\n - `validation_prompt`." "\n - `validation_inference_steps`.\nThe `validation_steps` parameter is the only one " "that needs to be defined outside the file." ) env_local_rank = int(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 not args.concepts_list: if not args.class_data_dir: raise ValueError("You must specify a data directory for class images.") if not args.class_prompt: raise ValueError("You must specify prompt for class images.") else: if args.class_data_dir: raise ValueError( "If you are using `concepts_list` parameter, define the class data directory within the file." ) if args.class_prompt: raise ValueError( "If you are using `concepts_list` parameter, define the class prompt within the file." ) else: # logger is not available yet if not args.class_data_dir: warnings.warn( "Ignoring `class_data_dir` parameter, you need to use it together with `with_prior_preservation`." ) if not args.class_prompt: warnings.warn( "Ignoring `class_prompt` parameter, you need to use it together with `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 and then tokenizes prompts. """ def __init__( self, instance_data_root, instance_prompt, tokenizer, class_data_root=None, class_prompt=None, size=512, center_crop=False, ): self.size = size self.center_crop = center_crop self.tokenizer = tokenizer self.instance_data_root = [] self.instance_images_path = [] self.num_instance_images = [] self.instance_prompt = [] self.class_data_root = [] if class_data_root is not None else None self.class_images_path = [] self.num_class_images = [] self.class_prompt = [] self._length = 0 for i in range(len(instance_data_root)): self.instance_data_root.append(Path(instance_data_root[i])) if not self.instance_data_root[i].exists(): raise ValueError("Instance images root doesn't exists.") self.instance_images_path.append(list(Path(instance_data_root[i]).iterdir())) self.num_instance_images.append(len(self.instance_images_path[i])) self.instance_prompt.append(instance_prompt[i]) self._length += self.num_instance_images[i] if class_data_root is not None: self.class_data_root.append(Path(class_data_root[i])) self.class_data_root[i].mkdir(parents=True, exist_ok=True) self.class_images_path.append(list(self.class_data_root[i].iterdir())) self.num_class_images.append(len(self.class_images_path)) if self.num_class_images[i] > self.num_instance_images[i]: self._length -= self.num_instance_images[i] self._length += self.num_class_images[i] self.class_prompt.append(class_prompt[i]) 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 = {} for i in range(len(self.instance_images_path)): instance_image = Image.open(self.instance_images_path[i][index % self.num_instance_images[i]]) if not instance_image.mode == "RGB": instance_image = instance_image.convert("RGB") example[f"instance_images_{i}"] = self.image_transforms(instance_image) example[f"instance_prompt_ids_{i}"] = self.tokenizer( self.instance_prompt[i], truncation=True, padding="max_length", max_length=self.tokenizer.model_max_length, return_tensors="pt", ).input_ids if self.class_data_root: for i in range(len(self.class_data_root)): class_image = Image.open(self.class_images_path[i][index % self.num_class_images[i]]) if not class_image.mode == "RGB": class_image = class_image.convert("RGB") example[f"class_images_{i}"] = self.image_transforms(class_image) example[f"class_prompt_ids_{i}"] = self.tokenizer( self.class_prompt[i], truncation=True, padding="max_length", max_length=self.tokenizer.model_max_length, return_tensors="pt", ).input_ids return example def collate_fn(num_instances, examples, with_prior_preservation=False): input_ids = [] pixel_values = [] for i in range(num_instances): input_ids += [example[f"instance_prompt_ids_{i}"] for example in examples] pixel_values += [example[f"instance_images_{i}"] 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: for i in range(num_instances): input_ids += [example[f"class_prompt_ids_{i}"] for example in examples] pixel_values += [example[f"class_images_{i}"] for example in examples] pixel_values = torch.stack(pixel_values) pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() input_ids = torch.cat(input_ids, dim=0) batch = { "input_ids": input_ids, "pixel_values": pixel_values, } 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 main(args): logging_dir = Path(args.output_dir, args.logging_dir) accelerator_project_config = ProjectConfiguration( total_limit=args.checkpoints_total_limit, 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, ) 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.") # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models. # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate. if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1: raise ValueError( "Gradient accumulation is not supported when training the text encoder in distributed training. " "Please set gradient_accumulation_steps to 1. This feature will be supported in the future." ) instance_data_dir = [] instance_prompt = [] class_data_dir = [] if args.with_prior_preservation else None class_prompt = [] if args.with_prior_preservation else None if args.concepts_list: with open(args.concepts_list, "r") as f: concepts_list = json.load(f) if args.validation_steps: args.validation_prompt = [] args.validation_number_images = [] args.validation_negative_prompt = [] args.validation_inference_steps = [] args.validation_guidance_scale = [] for concept in concepts_list: instance_data_dir.append(concept["instance_data_dir"]) instance_prompt.append(concept["instance_prompt"]) if args.with_prior_preservation: try: class_data_dir.append(concept["class_data_dir"]) class_prompt.append(concept["class_prompt"]) except KeyError: raise KeyError( "`class_data_dir` or `class_prompt` not found in concepts_list while using " "`with_prior_preservation`." ) else: if "class_data_dir" in concept: warnings.warn( "Ignoring `class_data_dir` key, to use it you need to enable `with_prior_preservation`." ) if "class_prompt" in concept: warnings.warn( "Ignoring `class_prompt` key, to use it you need to enable `with_prior_preservation`." ) if args.validation_steps: args.validation_prompt.append(concept.get("validation_prompt", None)) args.validation_number_images.append(concept.get("validation_number_images", 4)) args.validation_negative_prompt.append(concept.get("validation_negative_prompt", None)) args.validation_inference_steps.append(concept.get("validation_inference_steps", 25)) args.validation_guidance_scale.append(concept.get("validation_guidance_scale", 7.5)) else: # Parse instance and class inputs, and double check that lengths match instance_data_dir = args.instance_data_dir.split(",") instance_prompt = args.instance_prompt.split(",") assert all( x == len(instance_data_dir) for x in [len(instance_data_dir), len(instance_prompt)] ), "Instance data dir and prompt inputs are not of the same length." if args.with_prior_preservation: class_data_dir = args.class_data_dir.split(",") class_prompt = args.class_prompt.split(",") assert all( x == len(instance_data_dir) for x in [len(instance_data_dir), len(instance_prompt), len(class_data_dir), len(class_prompt)] ), "Instance & class data dir or prompt inputs are not of the same length." if args.validation_steps: validation_prompts = args.validation_prompt.split(",") num_of_validation_prompts = len(validation_prompts) args.validation_prompt = validation_prompts args.validation_number_images = [args.validation_number_images] * num_of_validation_prompts negative_validation_prompts = [None] * num_of_validation_prompts if args.validation_negative_prompt: negative_validation_prompts = args.validation_negative_prompt.split(",") while len(negative_validation_prompts) < num_of_validation_prompts: negative_validation_prompts.append(None) args.validation_negative_prompt = negative_validation_prompts assert num_of_validation_prompts == len( negative_validation_prompts ), "The length of negative prompts for validation is greater than the number of validation prompts." args.validation_inference_steps = [args.validation_inference_steps] * num_of_validation_prompts args.validation_guidance_scale = [args.validation_guidance_scale] * num_of_validation_prompts # 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) # Generate class images if prior preservation is enabled. if args.with_prior_preservation: for i in range(len(class_data_dir)): class_images_dir = Path(class_data_dir[i]) 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: torch_dtype = torch.float16 if accelerator.device.type == "cuda" 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 = DiffusionPipeline.from_pretrained( args.pretrained_model_name_or_path, torch_dtype=torch_dtype, safety_checker=None, revision=args.revision, ) 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(class_prompt[i], 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 ii, image in enumerate(images): hash_image = hashlib.sha1(image.tobytes()).hexdigest() image_filename = ( class_images_dir / f"{example['index'][ii] + cur_class_images}-{hash_image}.jpg" ) image.save(image_filename) # Clean up the memory deleting one-time-use variables. del pipeline del sample_dataloader del sample_dataset if torch.cuda.is_available(): torch.cuda.empty_cache() # Handle the repository creation if accelerator.is_main_process: if args.output_dir is not None: makedirs(args.output_dir, exist_ok=True) if args.push_to_hub: repo_id = create_repo( repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token ).repo_id # Load the tokenizer tokenizer = None if args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False) elif args.pretrained_model_name_or_path: tokenizer = AutoTokenizer.from_pretrained( args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, use_fast=False, ) # import correct text encoder class text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision) # Load scheduler and models noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") text_encoder = text_encoder_cls.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision ) vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision) unet = UNet2DConditionModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision ) vae.requires_grad_(False) if not args.train_text_encoder: text_encoder.requires_grad_(False) if args.enable_xformers_memory_efficient_attention: if is_xformers_available(): unet.enable_xformers_memory_efficient_attention() else: raise ValueError("xformers is not available. Make sure it is installed correctly") if args.gradient_checkpointing: unet.enable_gradient_checkpointing() if args.train_text_encoder: text_encoder.gradient_checkpointing_enable() # Enable TF32 for faster training on Ampere GPUs, # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices if 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 ) # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs if args.use_8bit_adam: try: import bitsandbytes as bnb except ImportError: raise ImportError( "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`." ) optimizer_class = bnb.optim.AdamW8bit else: optimizer_class = torch.optim.AdamW # Optimizer creation params_to_optimize = ( itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters() ) optimizer = optimizer_class( params_to_optimize, lr=args.learning_rate, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, eps=args.adam_epsilon, ) # Dataset and DataLoaders creation: train_dataset = DreamBoothDataset( instance_data_root=instance_data_dir, instance_prompt=instance_prompt, class_data_root=class_data_dir, class_prompt=class_prompt, tokenizer=tokenizer, size=args.resolution, center_crop=args.center_crop, ) train_dataloader = torch.utils.data.DataLoader( train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=lambda examples: collate_fn(len(instance_data_dir), examples, args.with_prior_preservation), num_workers=1, ) # 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: unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( unet, text_encoder, optimizer, train_dataloader, lr_scheduler ) else: unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( unet, optimizer, train_dataloader, lr_scheduler ) # For mixed precision training we cast the text_encoder and vae weights to half-precision # as these models are only used for inference, keeping weights in full precision is not required. weight_dtype = torch.float32 if accelerator.mixed_precision == "fp16": weight_dtype = torch.float16 elif accelerator.mixed_precision == "bf16": weight_dtype = torch.bfloat16 # Move vae and text_encoder to device and cast to weight_dtype vae.to(accelerator.device, dtype=weight_dtype) if not args.train_text_encoder: text_encoder.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 initialize automatically on the main process. if accelerator.is_main_process: accelerator.init_trackers("dreambooth", 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 = basename(args.resume_from_checkpoint) else: # Get the mos recent checkpoint dirs = 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 else: accelerator.print(f"Resuming from checkpoint {path}") accelerator.load_state(join(args.output_dir, path)) global_step = int(path.split("-")[1]) resume_global_step = global_step * args.gradient_accumulation_steps first_epoch = global_step // num_update_steps_per_epoch resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps) # Only show the progress bar once on each machine. progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process) progress_bar.set_description("Steps") for epoch in range(first_epoch, args.num_train_epochs): unet.train() if args.train_text_encoder: text_encoder.train() for step, batch in enumerate(train_dataloader): # Skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step: if step % args.gradient_accumulation_steps == 0: progress_bar.update(1) continue 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) bsz = latents.shape[0] # Sample a random timestep for each image time_steps = torch.randint( 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device ) time_steps = time_steps.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, time_steps) # Get the text embedding for conditioning encoder_hidden_states = text_encoder(batch["input_ids"])[0] # Predict the noise residual model_pred = unet(noisy_latents, time_steps, encoder_hidden_states).sample # Get the target for loss depending on the prediction type if noise_scheduler.config.prediction_type == "epsilon": target = noise elif noise_scheduler.config.prediction_type == "v_prediction": target = noise_scheduler.get_velocity(latents, noise, time_steps) else: raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") 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 instance loss loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") # Compute prior loss prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean") # Add the prior loss to the instance loss. loss = loss + args.prior_loss_weight * prior_loss else: loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") accelerator.backward(loss) if accelerator.sync_gradients: params_to_clip = ( itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters() ) accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) optimizer.step() lr_scheduler.step() optimizer.zero_grad(set_to_none=args.set_grads_to_none) # 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: save_path = join(args.output_dir, f"checkpoint-{global_step}") accelerator.save_state(save_path) logger.info(f"Saved state to {save_path}") if ( args.validation_steps and any(args.validation_prompt) and global_step % args.validation_steps == 0 ): images_set = generate_validation_images( text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype ) for images, validation_prompt in zip(images_set, args.validation_prompt): if len(images) > 0: label = str(uuid.uuid1())[:8] # generate an id for different set of images log_validation_images_to_tracker( images, label, validation_prompt, accelerator, global_step ) 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 # Create the pipeline using the trained modules and save it. accelerator.wait_for_everyone() if accelerator.is_main_process: pipeline = DiffusionPipeline.from_pretrained( args.pretrained_model_name_or_path, unet=accelerator.unwrap_model(unet), text_encoder=accelerator.unwrap_model(text_encoder), revision=args.revision, ) pipeline.save_pretrained(args.output_dir) if args.push_to_hub: 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)