#!/usr/bin/env python # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and import argparse import gc import itertools import logging import math import os import shutil import warnings from pathlib import Path 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 ( DistributedDataParallelKwargs, ProjectConfiguration, set_seed, ) from huggingface_hub import create_repo, upload_folder from huggingface_hub.utils import insecure_hashlib from packaging import version from PIL import Image from PIL.ImageOps import exif_transpose 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, DPMSolverMultistepScheduler, StableDiffusionXLPipeline, UNet2DConditionModel, ) from diffusers.loaders import LoraLoaderMixin from diffusers.models.lora import LoRALinearLayer from diffusers.optimization import get_scheduler from diffusers.training_utils import compute_snr, unet_lora_state_dict from diffusers.utils import check_min_version, is_wandb_available from diffusers.utils.import_utils import is_xformers_available # Will error if the minimal version of diffusers is not installed. Remove at your own risks. check_min_version("0.24.0.dev0") logger = get_logger(__name__) # TODO: This function should be removed once training scripts are rewritten in PEFT def text_encoder_lora_state_dict(text_encoder): state_dict = {} def text_encoder_attn_modules(text_encoder): from transformers import CLIPTextModel, CLIPTextModelWithProjection attn_modules = [] if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)): for i, layer in enumerate(text_encoder.text_model.encoder.layers): name = f"text_model.encoder.layers.{i}.self_attn" mod = layer.self_attn attn_modules.append((name, mod)) return attn_modules for name, module in text_encoder_attn_modules(text_encoder): for k, v in module.q_proj.lora_linear_layer.state_dict().items(): state_dict[f"{name}.q_proj.lora_linear_layer.{k}"] = v for k, v in module.k_proj.lora_linear_layer.state_dict().items(): state_dict[f"{name}.k_proj.lora_linear_layer.{k}"] = v for k, v in module.v_proj.lora_linear_layer.state_dict().items(): state_dict[f"{name}.v_proj.lora_linear_layer.{k}"] = v for k, v in module.out_proj.lora_linear_layer.state_dict().items(): state_dict[f"{name}.out_proj.lora_linear_layer.{k}"] = v return state_dict def save_model_card( repo_id: str, images=None, base_model=str, train_text_encoder=False, instance_prompt=str, validation_prompt=str, repo_folder=None, vae_path=None, ): img_str = "widget:\n" if images else "" for i, image in enumerate(images): image.save(os.path.join(repo_folder, f"image_{i}.png")) img_str += f""" - text: '{validation_prompt if validation_prompt else ' '}' output: url: "image_{i}.png" """ yaml = f""" --- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora {img_str} base_model: {base_model} instance_prompt: {instance_prompt} license: openrail++ --- """ model_card = f""" # SDXL LoRA DreamBooth - {repo_id} ## Model description These are {repo_id} LoRA adaption weights for {base_model}. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: {train_text_encoder}. Special VAE used for training: {vae_path}. ## Trigger words You should use {instance_prompt} to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download]({repo_id}/tree/main) them in the Files & versions tab. """ with open(os.path.join(repo_folder, "README.md"), "w") as f: f.write(yaml + model_card) def import_model_class_from_model_name_or_path( pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder" ): text_encoder_config = PretrainedConfig.from_pretrained( pretrained_model_name_or_path, subfolder=subfolder, revision=revision ) model_class = text_encoder_config.architectures[0] if model_class == "CLIPTextModel": from transformers import CLIPTextModel return CLIPTextModel elif model_class == "CLIPTextModelWithProjection": from transformers import CLIPTextModelWithProjection return CLIPTextModelWithProjection else: raise ValueError(f"{model_class} is not supported.") def parse_args(input_args=None): parser = argparse.ArgumentParser(description="Simple example of a training script.") parser.add_argument( "--pretrained_model_name_or_path", type=str, default=None, required=True, help="Path to pretrained model or model identifier from huggingface.co/models.", ) parser.add_argument( "--pretrained_vae_model_name_or_path", type=str, default=None, help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.", ) parser.add_argument( "--revision", type=str, default=None, required=False, help="Revision of pretrained model identifier from huggingface.co/models.", ) parser.add_argument( "--dataset_name", type=str, default=None, help=( "The name of the Dataset (from the HuggingFace hub) containing the training data of instance images (could be your own, possibly private," " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem," " or to a folder containing files that 🤗 Datasets can understand." ), ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The config of the Dataset, leave as None if there's only one config.", ) parser.add_argument( "--instance_data_dir", type=str, default=None, help=("A folder containing the training data. "), ) parser.add_argument( "--cache_dir", type=str, default=None, help="The directory where the downloaded models and datasets will be stored.", ) parser.add_argument( "--image_column", type=str, default="image", help="The column of the dataset containing the target image. By " "default, the standard Image Dataset maps out 'file_name' " "to 'image'.", ) parser.add_argument( "--caption_column", type=str, default=None, help="The column of the dataset containing the instance prompt for each image", ) parser.add_argument( "--repeats", type=int, default=1, help="How many times to repeat the training data.", ) parser.add_argument( "--class_data_dir", type=str, default=None, required=False, help="A folder containing the training data of class images.", ) parser.add_argument( "--instance_prompt", type=str, default=None, required=True, help="The prompt with identifier specifying the instance, e.g. 'photo of a TOK dog', 'in the style of TOK'", ) parser.add_argument( "--class_prompt", type=str, default=None, help="The prompt to specify images in the same class as provided instance images.", ) parser.add_argument( "--validation_prompt", type=str, default=None, help="A prompt that is used during validation to verify that the model is learning.", ) parser.add_argument( "--num_validation_images", type=int, default=4, help="Number of images that should be generated during validation with `validation_prompt`.", ) parser.add_argument( "--validation_epochs", type=int, default=50, help=( "Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt" " `args.validation_prompt` multiple times: `args.num_validation_images`." ), ) parser.add_argument( "--with_prior_preservation", default=False, action="store_true", help="Flag to add prior preservation loss.", ) parser.add_argument( "--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.", ) parser.add_argument( "--num_class_images", type=int, default=100, help=( "Minimal class images for prior preservation loss. If there are not enough images already present in" " class_data_dir, additional images will be sampled with class_prompt." ), ) parser.add_argument( "--output_dir", type=str, default="lora-dreambooth-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=1024, help=( "The resolution for input images, all the images in the train/validation dataset will be resized to this" " resolution" ), ) parser.add_argument( "--crops_coords_top_left_h", type=int, default=0, help=( "Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet." ), ) parser.add_argument( "--crops_coords_top_left_w", type=int, default=0, help=( "Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet." ), ) parser.add_argument( "--center_crop", default=True, 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. If set, the text encoder should be float32 precision.", ) parser.add_argument( "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images.", ) parser.add_argument("--num_train_epochs", type=int, default=1) parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--checkpointing_steps", type=int, default=500, help=( "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final" " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming" " training using `--resume_from_checkpoint`." ), ) parser.add_argument( "--checkpoints_total_limit", type=int, default=None, help=("Max number of checkpoints to store."), ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help=( "Whether training should be resumed from a previous checkpoint. Use a path saved by" ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' ), ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--gradient_checkpointing", action="store_true", help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", ) parser.add_argument( "--learning_rate", type=float, default=1e-4, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--text_encoder_lr", type=float, default=5e-6, help="Text encoder learning rate to use.", ) parser.add_argument( "--scale_lr", action="store_true", default=False, help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", ) parser.add_argument( "--lr_scheduler", type=str, default="constant", help=( 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' ' "constant", "constant_with_warmup"]' ), ) parser.add_argument( "--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( "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler.", ) parser.add_argument( "--lr_num_cycles", type=int, default=1, help="Number of hard resets of the lr in cosine_with_restarts scheduler.", ) parser.add_argument( "--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.", ) parser.add_argument( "--dataloader_num_workers", type=int, default=0, help=( "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process." ), ) parser.add_argument( "--optimizer", type=str, default="AdamW", help=('The optimizer type to use. Choose between ["AdamW", "prodigy"]'), ) parser.add_argument( "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW", ) parser.add_argument( "--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam and Prodigy optimizers.", ) parser.add_argument( "--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam and Prodigy optimizers.", ) parser.add_argument( "--prodigy_beta3", type=float, default=None, help="coefficients for computing the Prodidy stepsize using running averages. If set to None, " "uses the value of square root of beta2. Ignored if optimizer is adamW", ) parser.add_argument( "--prodigy_decouple", type=bool, default=True, help="Use AdamW style decoupled weight decay", ) parser.add_argument( "--adam_weight_decay", type=float, default=1e-04, help="Weight decay to use for unet params", ) parser.add_argument( "--adam_weight_decay_text_encoder", type=float, default=1e-03, help="Weight decay to use for text_encoder", ) parser.add_argument( "--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer and Prodigy optimizers.", ) parser.add_argument( "--prodigy_use_bias_correction", type=bool, default=True, help="Turn on Adam's bias correction. True by default. Ignored if optimizer is adamW", ) parser.add_argument( "--prodigy_safeguard_warmup", type=bool, default=True, help="Remove lr from the denominator of D estimate to avoid issues during warm-up stage. True by default. " "Ignored if optimizer is adamW", ) parser.add_argument( "--max_grad_norm", default=1.0, type=float, help="Max gradient norm." ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.", ) parser.add_argument( "--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.", ) parser.add_argument( "--hub_model_id", type=str, default=None, help="The name of the repository to keep in sync with the local `output_dir`.", ) parser.add_argument( "--logging_dir", type=str, default="logs", help=( "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." ), ) parser.add_argument( "--allow_tf32", action="store_true", help=( "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" ), ) parser.add_argument( "--report_to", type=str, default="tensorboard", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' ), ) parser.add_argument( "--mixed_precision", type=str, default=None, choices=["no", "fp16", "bf16"], help=( "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." ), ) parser.add_argument( "--prior_generation_precision", type=str, default=None, choices=["no", "fp32", "fp16", "bf16"], help=( "Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" " 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32." ), ) parser.add_argument( "--local_rank", type=int, default=-1, help="For distributed training: local_rank", ) parser.add_argument( "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers.", ) parser.add_argument( "--rank", type=int, default=4, help=("The dimension of the LoRA update matrices."), ) if input_args is not None: args = parser.parse_args(input_args) else: args = parser.parse_args() if args.dataset_name is None and args.instance_data_dir is None: raise ValueError("Specify either `--dataset_name` or `--instance_data_dir`") if args.dataset_name is not None and args.instance_data_dir is not None: raise ValueError( "Specify only one of `--dataset_name` or `--instance_data_dir`" ) env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) if env_local_rank != -1 and env_local_rank != args.local_rank: args.local_rank = env_local_rank if args.with_prior_preservation: if args.class_data_dir is None: raise ValueError("You must specify a data directory for class images.") if args.class_prompt is None: raise ValueError("You must specify prompt for class images.") else: # logger is not available yet if args.class_data_dir is not None: warnings.warn( "You need not use --class_data_dir without --with_prior_preservation." ) if args.class_prompt is not None: warnings.warn( "You need not use --class_prompt without --with_prior_preservation." ) return args class DreamBoothDataset(Dataset): """ A dataset to prepare the instance and class images with the prompts for fine-tuning the model. It pre-processes the images. """ def __init__( self, instance_data_root, instance_prompt, class_prompt, class_data_root=None, class_num=None, size=1024, repeats=1, center_crop=False, ): self.size = size self.center_crop = center_crop self.instance_prompt = instance_prompt self.custom_instance_prompts = None self.class_prompt = class_prompt # if --dataset_name is provided or a metadata jsonl file is provided in the local --instance_data directory, # we load the training data using load_dataset if args.dataset_name is not None: try: from datasets import load_dataset except ImportError: raise ImportError( "You are trying to load your data using the datasets library. If you wish to train using custom " "captions please install the datasets library: `pip install datasets`. If you wish to load a " "local folder containing images only, specify --instance_data_dir instead." ) # Downloading and loading a dataset from the hub. # See more about loading custom images at # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script dataset = load_dataset( args.dataset_name, args.dataset_config_name, cache_dir=args.cache_dir, ) # Preprocessing the datasets. column_names = dataset["train"].column_names # 6. Get the column names for input/target. if args.image_column is None: image_column = column_names[0] logger.info(f"image column defaulting to {image_column}") else: image_column = args.image_column if image_column not in column_names: raise ValueError( f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}" ) instance_images = dataset["train"][image_column] if args.caption_column is None: logger.info( "No caption column provided, defaulting to instance_prompt for all images. If your dataset " "contains captions/prompts for the images, make sure to specify the " "column as --caption_column" ) self.custom_instance_prompts = None else: if args.caption_column not in column_names: raise ValueError( f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}" ) custom_instance_prompts = dataset["train"][args.caption_column] # create final list of captions according to --repeats self.custom_instance_prompts = [] for caption in custom_instance_prompts: self.custom_instance_prompts.extend( itertools.repeat(caption, repeats) ) else: self.instance_data_root = Path(instance_data_root) if not self.instance_data_root.exists(): raise ValueError("Instance images root doesn't exists.") instance_images = [ Image.open(path) for path in list(Path(instance_data_root).iterdir()) ] self.custom_instance_prompts = None self.instance_images = [] for img in instance_images: self.instance_images.extend(itertools.repeat(img, repeats)) self.num_instance_images = len(self.instance_images) self._length = self.num_instance_images if class_data_root is not None: self.class_data_root = Path(class_data_root) self.class_data_root.mkdir(parents=True, exist_ok=True) self.class_images_path = list(self.class_data_root.iterdir()) if class_num is not None: self.num_class_images = min(len(self.class_images_path), class_num) else: self.num_class_images = len(self.class_images_path) self._length = max(self.num_class_images, self.num_instance_images) else: self.class_data_root = None self.image_transforms = transforms.Compose( [ transforms.Resize( size, interpolation=transforms.InterpolationMode.BILINEAR ), # transforms.CenterCrop(size) # if center_crop # else transforms.RandomCrop(size), transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ] ) def __len__(self): return self._length def __getitem__(self, index): example = {} instance_image = self.instance_images[index % self.num_instance_images] # instance_image = exif_transpose(instance_image) if not instance_image.mode == "RGB": instance_image = instance_image.convert("RGB") example["instance_images"] = self.image_transforms(instance_image) if self.custom_instance_prompts: caption = self.custom_instance_prompts[index % self.num_instance_images] if caption: example["instance_prompt"] = caption else: example["instance_prompt"] = self.instance_prompt else: # costum prompts were provided, but length does not match size of image dataset example["instance_prompt"] = self.instance_prompt if self.class_data_root: class_image = Image.open( self.class_images_path[index % self.num_class_images] ) class_image = exif_transpose(class_image) if not class_image.mode == "RGB": class_image = class_image.convert("RGB") example["class_images"] = self.image_transforms(class_image) example["class_prompt"] = self.class_prompt return example def collate_fn(examples, with_prior_preservation=False): pixel_values = [example["instance_images"] for example in examples] prompts = [example["instance_prompt"] for example in examples] # Concat class and instance examples for prior preservation. # We do this to avoid doing two forward passes. if with_prior_preservation: pixel_values += [example["class_images"] for example in examples] prompts += [example["class_prompt"] for example in examples] pixel_values = torch.stack(pixel_values) pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() batch = {"pixel_values": pixel_values, "prompts": prompts} return batch class PromptDataset(Dataset): "A simple dataset to prepare the prompts to generate class images on multiple GPUs." def __init__(self, prompt, num_samples): self.prompt = prompt self.num_samples = num_samples def __len__(self): return self.num_samples def __getitem__(self, index): example = {} example["prompt"] = self.prompt example["index"] = index return example def tokenize_prompt(tokenizer, prompt): text_inputs = tokenizer( prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids return text_input_ids # Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt def encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None): prompt_embeds_list = [] for i, text_encoder in enumerate(text_encoders): if tokenizers is not None: tokenizer = tokenizers[i] text_input_ids = tokenize_prompt(tokenizer, prompt) else: assert text_input_ids_list is not None text_input_ids = text_input_ids_list[i] prompt_embeds = text_encoder( text_input_ids.to(text_encoder.device), output_hidden_states=True, ) # We are only ALWAYS interested in the pooled output of the final text encoder pooled_prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.hidden_states[-2] bs_embed, seq_len, _ = prompt_embeds.shape prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1) prompt_embeds_list.append(prompt_embeds) prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1) return prompt_embeds, pooled_prompt_embeds def is_belong_to_groups(key: str, groups: list) -> bool: try: for g in groups: if key.startswith(g): return True return False except Exception as e: raise type(e)(f'failed to is_belong_to_groups, due to: {e}') def filter_lora_layers(lora_state_dict: dict, groups: list) -> dict: try: return {k: v for k, v in lora_state_dict.items() if is_belong_to_groups(k, groups)} except Exception as e: raise type(e)(f'failed to filter_lora_layers, due to: {e}') def main(args): logging_dir = Path(args.output_dir, args.logging_dir) accelerator_project_config = ProjectConfiguration( project_dir=args.output_dir, logging_dir=logging_dir ) kwargs = DistributedDataParallelKwargs(find_unused_parameters=True) accelerator = Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision, log_with=args.report_to, project_config=accelerator_project_config, kwargs_handlers=[kwargs], ) 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: transformers.utils.logging.set_verbosity_warning() diffusers.utils.logging.set_verbosity_info() else: transformers.utils.logging.set_verbosity_error() diffusers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Generate class images if prior preservation is enabled. if args.with_prior_preservation: class_images_dir = Path(args.class_data_dir) if not class_images_dir.exists(): class_images_dir.mkdir(parents=True) cur_class_images = len(list(class_images_dir.iterdir())) if cur_class_images < args.num_class_images: 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 = StableDiffusionXLPipeline.from_pretrained( args.pretrained_model_name_or_path, torch_dtype=torch_dtype, 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(args.class_prompt, num_new_images) sample_dataloader = torch.utils.data.DataLoader( sample_dataset, batch_size=args.sample_batch_size ) sample_dataloader = accelerator.prepare(sample_dataloader) pipeline.to(accelerator.device) for example in tqdm( sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process, ): images = pipeline(example["prompt"]).images for i, image in enumerate(images): hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest() image_filename = ( class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg" ) image.save(image_filename) del pipeline if torch.cuda.is_available(): torch.cuda.empty_cache() # Handle the repository creation if accelerator.is_main_process: if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) if args.push_to_hub: repo_id = create_repo( repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token, ).repo_id # Load the tokenizers tokenizer_one = AutoTokenizer.from_pretrained( args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, use_fast=False, ) tokenizer_two = AutoTokenizer.from_pretrained( args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision, use_fast=False, ) # import correct text encoder classes text_encoder_cls_one = import_model_class_from_model_name_or_path( args.pretrained_model_name_or_path, args.revision ) text_encoder_cls_two = import_model_class_from_model_name_or_path( args.pretrained_model_name_or_path, args.revision, subfolder="text_encoder_2" ) # Load scheduler and models noise_scheduler = DDPMScheduler.from_pretrained( args.pretrained_model_name_or_path, subfolder="scheduler" ) text_encoder_one = text_encoder_cls_one.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, ) text_encoder_two = text_encoder_cls_two.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, ) vae_path = ( args.pretrained_model_name_or_path if args.pretrained_vae_model_name_or_path is None else args.pretrained_vae_model_name_or_path ) vae = AutoencoderKL.from_pretrained( vae_path, subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None, revision=args.revision, ) unet = UNet2DConditionModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision ) # We only train the additional adapter LoRA layers vae.requires_grad_(False) text_encoder_one.requires_grad_(False) text_encoder_two.requires_grad_(False) unet.requires_grad_(False) # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision # as these weights are only used for inference, keeping weights in full precision is not required. weight_dtype = torch.float32 if accelerator.mixed_precision == "fp16": weight_dtype = torch.float16 elif accelerator.mixed_precision == "bf16": weight_dtype = torch.bfloat16 # Move unet, vae and text_encoder to device and cast to weight_dtype unet.to(accelerator.device, dtype=weight_dtype) # The VAE is always in float32 to avoid NaN losses. vae.to(accelerator.device, dtype=torch.float32) text_encoder_one.to(accelerator.device, dtype=weight_dtype) text_encoder_two.to(accelerator.device, dtype=weight_dtype) 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.warn( "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, " "please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details." ) unet.enable_xformers_memory_efficient_attention() else: raise ValueError( "xformers is not available. Make sure it is installed correctly" ) if args.gradient_checkpointing: unet.enable_gradient_checkpointing() if args.train_text_encoder: text_encoder_one.gradient_checkpointing_enable() text_encoder_two.gradient_checkpointing_enable() BLORA_BLOCKS = ['up_blocks.0.attentions.0', 'up_blocks.0.attentions.1'] # now we will add new LoRA weights to the attention layers # Set correct lora layers unet_lora_parameters = [] for attn_processor_name, attn_processor in unet.attn_processors.items(): # Parse the attention module. if not is_belong_to_groups(attn_processor_name, BLORA_BLOCKS): continue attn_module = unet for n in attn_processor_name.split(".")[:-1]: attn_module = getattr(attn_module, n) # Set the `lora_layer` attribute of the attention-related matrices. attn_module.to_q.set_lora_layer( LoRALinearLayer( in_features=attn_module.to_q.in_features, out_features=attn_module.to_q.out_features, rank=args.rank, ) ) attn_module.to_k.set_lora_layer( LoRALinearLayer( in_features=attn_module.to_k.in_features, out_features=attn_module.to_k.out_features, rank=args.rank, ) ) attn_module.to_v.set_lora_layer( LoRALinearLayer( in_features=attn_module.to_v.in_features, out_features=attn_module.to_v.out_features, rank=args.rank, ) ) attn_module.to_out[0].set_lora_layer( LoRALinearLayer( in_features=attn_module.to_out[0].in_features, out_features=attn_module.to_out[0].out_features, rank=args.rank, ) ) # Accumulate the LoRA params to optimize. unet_lora_parameters.extend(attn_module.to_q.lora_layer.parameters()) unet_lora_parameters.extend(attn_module.to_k.lora_layer.parameters()) unet_lora_parameters.extend(attn_module.to_v.lora_layer.parameters()) unet_lora_parameters.extend(attn_module.to_out[0].lora_layer.parameters()) # The text encoder comes from 🤗 transformers, so we cannot directly modify it. # So, instead, we monkey-patch the forward calls of its attention-blocks. if args.train_text_encoder: # ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16 text_lora_parameters_one = LoraLoaderMixin._modify_text_encoder( text_encoder_one, dtype=torch.float32, rank=args.rank ) text_lora_parameters_two = LoraLoaderMixin._modify_text_encoder( text_encoder_two, dtype=torch.float32, rank=args.rank ) # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format def save_model_hook(models, weights, output_dir): if accelerator.is_main_process: # there are only two options here. Either are just the unet attn processor layers # or there are the unet and text encoder atten layers unet_lora_layers_to_save = None text_encoder_one_lora_layers_to_save = None text_encoder_two_lora_layers_to_save = None for model in models: if isinstance(model, type(accelerator.unwrap_model(unet))): unet_lora_layers_to_save = unet_lora_state_dict(model) elif isinstance( model, type(accelerator.unwrap_model(text_encoder_one)) ): text_encoder_one_lora_layers_to_save = text_encoder_lora_state_dict( model ) elif isinstance( model, type(accelerator.unwrap_model(text_encoder_two)) ): text_encoder_two_lora_layers_to_save = text_encoder_lora_state_dict( model ) else: raise ValueError(f"unexpected save model: {model.__class__}") # make sure to pop weight so that corresponding model is not saved again weights.pop() StableDiffusionXLPipeline.save_lora_weights( output_dir, unet_lora_layers=unet_lora_layers_to_save, text_encoder_lora_layers=text_encoder_one_lora_layers_to_save, text_encoder_2_lora_layers=text_encoder_two_lora_layers_to_save, ) def load_model_hook(models, input_dir): unet_ = None text_encoder_one_ = None text_encoder_two_ = None while len(models) > 0: model = models.pop() if isinstance(model, type(accelerator.unwrap_model(unet))): unet_ = model elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))): text_encoder_one_ = model elif isinstance(model, type(accelerator.unwrap_model(text_encoder_two))): text_encoder_two_ = model else: raise ValueError(f"unexpected save model: {model.__class__}") lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir) LoraLoaderMixin.load_lora_into_unet( lora_state_dict, network_alphas=network_alphas, unet=unet_ ) text_encoder_state_dict = { k: v for k, v in lora_state_dict.items() if "text_encoder." in k } LoraLoaderMixin.load_lora_into_text_encoder( text_encoder_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_one_, ) text_encoder_2_state_dict = { k: v for k, v in lora_state_dict.items() if "text_encoder_2." in k } LoraLoaderMixin.load_lora_into_text_encoder( text_encoder_2_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_two_, ) accelerator.register_save_state_pre_hook(save_model_hook) accelerator.register_load_state_pre_hook(load_model_hook) # Enable TF32 for faster training on Ampere GPUs, # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices if args.allow_tf32: torch.backends.cuda.matmul.allow_tf32 = True if args.scale_lr: args.learning_rate = ( args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes ) # Optimization parameters unet_lora_parameters_with_lr = { "params": unet_lora_parameters, "lr": args.learning_rate, } if args.train_text_encoder: # different learning rate for text encoder and unet text_lora_parameters_one_with_lr = { "params": text_lora_parameters_one, "weight_decay": args.adam_weight_decay_text_encoder, "lr": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate, } text_lora_parameters_two_with_lr = { "params": text_lora_parameters_two, "weight_decay": args.adam_weight_decay_text_encoder, "lr": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate, } params_to_optimize = [ unet_lora_parameters_with_lr, text_lora_parameters_one_with_lr, text_lora_parameters_two_with_lr, ] else: params_to_optimize = [unet_lora_parameters_with_lr] # Optimizer creation if not (args.optimizer.lower() == "prodigy" or args.optimizer.lower() == "adamw"): logger.warn( f"Unsupported choice of optimizer: {args.optimizer}.Supported optimizers include [adamW, prodigy]." "Defaulting to adamW" ) args.optimizer = "adamw" if args.use_8bit_adam and not args.optimizer.lower() == "adamw": logger.warn( f"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was " f"set to {args.optimizer.lower()}" ) if args.optimizer.lower() == "adamw": if args.use_8bit_adam: try: import bitsandbytes as bnb except ImportError: raise ImportError( "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`." ) optimizer_class = bnb.optim.AdamW8bit else: optimizer_class = torch.optim.AdamW optimizer = optimizer_class( params_to_optimize, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, eps=args.adam_epsilon, ) if args.optimizer.lower() == "prodigy": try: import prodigyopt except ImportError: raise ImportError( "To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`" ) optimizer_class = prodigyopt.Prodigy if args.learning_rate <= 0.1: logger.warn( "Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0" ) if args.train_text_encoder and args.text_encoder_lr: logger.warn( f"Learning rates were provided both for the unet and the text encoder- e.g. text_encoder_lr:" f" {args.text_encoder_lr} and learning_rate: {args.learning_rate}. " f"When using prodigy only learning_rate is used as the initial learning rate." ) # changes the learning rate of text_encoder_parameters_one and text_encoder_parameters_two to be # --learning_rate params_to_optimize[1]["lr"] = args.learning_rate params_to_optimize[2]["lr"] = args.learning_rate optimizer = optimizer_class( params_to_optimize, lr=args.learning_rate, betas=(args.adam_beta1, args.adam_beta2), beta3=args.prodigy_beta3, weight_decay=args.adam_weight_decay, eps=args.adam_epsilon, decouple=args.prodigy_decouple, use_bias_correction=args.prodigy_use_bias_correction, safeguard_warmup=args.prodigy_safeguard_warmup, ) # Dataset and DataLoaders creation: train_dataset = DreamBoothDataset( instance_data_root=args.instance_data_dir, instance_prompt=args.instance_prompt, class_prompt=args.class_prompt, class_data_root=args.class_data_dir if args.with_prior_preservation else None, class_num=args.num_class_images, size=args.resolution, repeats=args.repeats, center_crop=args.center_crop, ) train_dataloader = torch.utils.data.DataLoader( train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation), num_workers=args.dataloader_num_workers, ) # Computes additional embeddings/ids required by the SDXL UNet. # regular text embeddings (when `train_text_encoder` is not True) # pooled text embeddings # time ids def compute_time_ids(): # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids original_size = (args.resolution, args.resolution) target_size = (args.resolution, args.resolution) crops_coords_top_left = ( args.crops_coords_top_left_h, args.crops_coords_top_left_w, ) add_time_ids = list(original_size + crops_coords_top_left + target_size) add_time_ids = torch.tensor([add_time_ids]) add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype) return add_time_ids if not args.train_text_encoder: tokenizers = [tokenizer_one, tokenizer_two] text_encoders = [text_encoder_one, text_encoder_two] def compute_text_embeddings(prompt, text_encoders, tokenizers): with torch.no_grad(): prompt_embeds, pooled_prompt_embeds = encode_prompt( text_encoders, tokenizers, prompt ) prompt_embeds = prompt_embeds.to(accelerator.device) pooled_prompt_embeds = pooled_prompt_embeds.to(accelerator.device) return prompt_embeds, pooled_prompt_embeds # Handle instance prompt. instance_time_ids = compute_time_ids() # If no type of tuning is done on the text_encoder and custom instance prompts are NOT # provided (i.e. the --instance_prompt is used for all images), we encode the instance prompt once to avoid # the redundant encoding. if not args.train_text_encoder and not train_dataset.custom_instance_prompts: ( instance_prompt_hidden_states, instance_pooled_prompt_embeds, ) = compute_text_embeddings(args.instance_prompt, text_encoders, tokenizers) # Handle class prompt for prior-preservation. if args.with_prior_preservation: class_time_ids = compute_time_ids() if not args.train_text_encoder: ( class_prompt_hidden_states, class_pooled_prompt_embeds, ) = compute_text_embeddings(args.class_prompt, text_encoders, tokenizers) # Clear the memory here if not args.train_text_encoder and not train_dataset.custom_instance_prompts: del tokenizers, text_encoders gc.collect() torch.cuda.empty_cache() # If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images), # pack the statically computed variables appropriately here. This is so that we don't # have to pass them to the dataloader. add_time_ids = instance_time_ids if args.with_prior_preservation: add_time_ids = torch.cat([add_time_ids, class_time_ids], dim=0) if not train_dataset.custom_instance_prompts: if not args.train_text_encoder: prompt_embeds = instance_prompt_hidden_states unet_add_text_embeds = instance_pooled_prompt_embeds if args.with_prior_preservation: prompt_embeds = torch.cat( [prompt_embeds, class_prompt_hidden_states], dim=0 ) unet_add_text_embeds = torch.cat( [unet_add_text_embeds, class_pooled_prompt_embeds], dim=0 ) # if we're optmizing the text encoder (both if instance prompt is used for all images or custom prompts) we need to tokenize and encode the # batch prompts on all training steps else: tokens_one = tokenize_prompt(tokenizer_one, args.instance_prompt) tokens_two = tokenize_prompt(tokenizer_two, args.instance_prompt) if args.with_prior_preservation: class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt) class_tokens_two = tokenize_prompt(tokenizer_two, args.class_prompt) tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0) tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil( len(train_dataloader) / args.gradient_accumulation_steps ) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( args.lr_scheduler, optimizer=optimizer, num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes, num_training_steps=args.max_train_steps * accelerator.num_processes, num_cycles=args.lr_num_cycles, power=args.lr_power, ) # Prepare everything with our `accelerator`. if args.train_text_encoder: ( unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler, ) = accelerator.prepare( unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler, ) else: unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( unet, optimizer, train_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil( len(train_dataloader) / 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("dreambooth-lora-sd-xl", config=vars(args)) # Train! total_batch_size = ( args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps ) logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num batches each epoch = {len(train_dataloader)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") logger.info( f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}" ) logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") global_step = 0 first_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint != "latest": path = os.path.basename(args.resume_from_checkpoint) else: # Get the mos recent checkpoint dirs = os.listdir(args.output_dir) dirs = [d for d in dirs if d.startswith("checkpoint")] dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) path = dirs[-1] if len(dirs) > 0 else None if path is None: accelerator.print( f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run." ) args.resume_from_checkpoint = None initial_global_step = 0 else: accelerator.print(f"Resuming from checkpoint {path}") accelerator.load_state(os.path.join(args.output_dir, path)) global_step = int(path.split("-")[1]) initial_global_step = global_step first_epoch = global_step // num_update_steps_per_epoch else: initial_global_step = 0 progress_bar = tqdm( range(0, args.max_train_steps), initial=initial_global_step, desc="Steps", # Only show the progress bar once on each machine. disable=not accelerator.is_local_main_process, ) for epoch in range(first_epoch, args.num_train_epochs): unet.train() if args.train_text_encoder: text_encoder_one.train() text_encoder_two.train() # set top parameter requires_grad = True for gradient checkpointing works text_encoder_one.text_model.embeddings.requires_grad_(True) text_encoder_two.text_model.embeddings.requires_grad_(True) for step, batch in enumerate(train_dataloader): with accelerator.accumulate(unet): pixel_values = batch["pixel_values"].to(dtype=vae.dtype) prompts = batch["prompts"] # encode batch prompts when custom prompts are provided for each image - if train_dataset.custom_instance_prompts: if not args.train_text_encoder: prompt_embeds, unet_add_text_embeds = compute_text_embeddings( prompts, text_encoders, tokenizers ) else: tokens_one = tokenize_prompt(tokenizer_one, prompts) tokens_two = tokenize_prompt(tokenizer_two, prompts) # Convert images to latent space model_input = vae.encode(pixel_values).latent_dist.sample() model_input = model_input * vae.config.scaling_factor if args.pretrained_vae_model_name_or_path is None: model_input = model_input.to(weight_dtype) # Sample noise that we'll add to the latents noise = torch.randn_like(model_input) bsz = model_input.shape[0] # Sample a random timestep for each image timesteps = torch.randint( 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device, ) timesteps = timesteps.long() # Add noise to the model input according to the noise magnitude at each timestep # (this is the forward diffusion process) noisy_model_input = noise_scheduler.add_noise( model_input, noise, timesteps ) # Calculate the elements to repeat depending on the use of prior-preservation and custom captions. if not train_dataset.custom_instance_prompts: elems_to_repeat_text_embeds = ( bsz // 2 if args.with_prior_preservation else bsz ) elems_to_repeat_time_ids = ( bsz // 2 if args.with_prior_preservation else bsz ) else: elems_to_repeat_text_embeds = 1 elems_to_repeat_time_ids = ( bsz // 2 if args.with_prior_preservation else bsz ) # Predict the noise residual if not args.train_text_encoder: unet_added_conditions = { "time_ids": add_time_ids.repeat(elems_to_repeat_time_ids, 1), "text_embeds": unet_add_text_embeds.repeat( elems_to_repeat_text_embeds, 1 ), } prompt_embeds_input = prompt_embeds.repeat( elems_to_repeat_text_embeds, 1, 1 ) model_pred = unet( noisy_model_input, timesteps, prompt_embeds_input, added_cond_kwargs=unet_added_conditions, ).sample else: unet_added_conditions = { "time_ids": add_time_ids.repeat(elems_to_repeat_time_ids, 1) } prompt_embeds, pooled_prompt_embeds = encode_prompt( text_encoders=[text_encoder_one, text_encoder_two], tokenizers=None, prompt=None, text_input_ids_list=[tokens_one, tokens_two], ) unet_added_conditions.update( { "text_embeds": pooled_prompt_embeds.repeat( elems_to_repeat_text_embeds, 1 ) } ) prompt_embeds_input = prompt_embeds.repeat( elems_to_repeat_text_embeds, 1, 1 ) model_pred = unet( noisy_model_input, timesteps, prompt_embeds_input, added_cond_kwargs=unet_added_conditions, ).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(model_input, noise, timesteps) 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 prior loss prior_loss = F.mse_loss( model_pred_prior.float(), target_prior.float(), reduction="mean" ) 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) base_weight = ( torch.stack( [snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1 ).min(dim=1)[0] / snr ) if noise_scheduler.config.prediction_type == "v_prediction": # Velocity objective needs to be floored to an SNR weight of one. mse_loss_weights = base_weight + 1 else: # Epsilon and sample both use the same loss weights. mse_loss_weights = base_weight loss = F.mse_loss( model_pred.float(), target.float(), reduction="none" ) loss = ( loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights ) loss = loss.mean() if args.with_prior_preservation: # Add the prior loss to the instance loss. loss = loss + args.prior_loss_weight * prior_loss accelerator.backward(loss) if accelerator.sync_gradients: params_to_clip = ( itertools.chain( unet_lora_parameters, text_lora_parameters_one, text_lora_parameters_two, ) if args.train_text_encoder else unet_lora_parameters ) accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) global_step += 1 if accelerator.is_main_process: if global_step % args.checkpointing_steps == 0: # _before_ saving state, check if this save would set us over the `checkpoints_total_limit` if args.checkpoints_total_limit is not None: checkpoints = os.listdir(args.output_dir) checkpoints = [ d for d in checkpoints if d.startswith("checkpoint") ] checkpoints = sorted( checkpoints, key=lambda x: int(x.split("-")[1]) ) # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints if len(checkpoints) >= args.checkpoints_total_limit: num_to_remove = ( len(checkpoints) - args.checkpoints_total_limit + 1 ) removing_checkpoints = checkpoints[0:num_to_remove] logger.info( f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints" ) logger.info( f"removing checkpoints: {', '.join(removing_checkpoints)}" ) for removing_checkpoint in removing_checkpoints: removing_checkpoint = os.path.join( args.output_dir, removing_checkpoint ) shutil.rmtree(removing_checkpoint) save_path = os.path.join( args.output_dir, f"checkpoint-{global_step}" ) accelerator.save_state(save_path) logger.info(f"Saved state to {save_path}") logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} progress_bar.set_postfix(**logs) accelerator.log(logs, step=global_step) if global_step >= args.max_train_steps: break if accelerator.is_main_process: if ( args.validation_prompt is not None and epoch % args.validation_epochs == 0 ): logger.info( f"Running validation... \n Generating {args.num_validation_images} images with prompt:" f" {args.validation_prompt}." ) # create pipeline if not args.train_text_encoder: text_encoder_one = text_encoder_cls_one.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, ) text_encoder_two = text_encoder_cls_two.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, ) pipeline = StableDiffusionXLPipeline.from_pretrained( args.pretrained_model_name_or_path, vae=vae, text_encoder=accelerator.unwrap_model(text_encoder_one), text_encoder_2=accelerator.unwrap_model(text_encoder_two), unet=accelerator.unwrap_model(unet), revision=args.revision, torch_dtype=weight_dtype, ) # 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) # run inference generator = ( torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None ) pipeline_args = {"prompt": args.validation_prompt, 'num_images_per_prompt': args.num_validation_images} images = pipeline(**pipeline_args, generator=generator).images for tracker in accelerator.trackers: if tracker.name == "tensorboard": np_images = np.stack([np.asarray(img) for img in images]) tracker.writer.add_images( "validation", np_images, epoch, dataformats="NHWC" ) if tracker.name == "wandb": tracker.log( { "validation": [ wandb.Image( image, caption=f"{i}: {args.validation_prompt}" ) for i, image in enumerate(images) ] } ) del pipeline torch.cuda.empty_cache() # Save the lora layers accelerator.wait_for_everyone() if accelerator.is_main_process: unet = accelerator.unwrap_model(unet) unet = unet.to(torch.float32) unet_lora_layers = unet_lora_state_dict(unet) if args.train_text_encoder: text_encoder_one = accelerator.unwrap_model(text_encoder_one) text_encoder_lora_layers = text_encoder_lora_state_dict( text_encoder_one.to(torch.float32) ) text_encoder_two = accelerator.unwrap_model(text_encoder_two) text_encoder_2_lora_layers = text_encoder_lora_state_dict( text_encoder_two.to(torch.float32) ) else: text_encoder_lora_layers = None text_encoder_2_lora_layers = None StableDiffusionXLPipeline.save_lora_weights( save_directory=args.output_dir, unet_lora_layers=unet_lora_layers, text_encoder_lora_layers=text_encoder_lora_layers, text_encoder_2_lora_layers=text_encoder_2_lora_layers, ) # remove unuse models for save GPU memory unet = unet.cpu() text_encoder_one = text_encoder_one.cpu() text_encoder_two = text_encoder_two.cpu() del unet, text_encoder_one, text_encoder_two del optimizer if args.train_text_encoder: del text_encoder_lora_layers, text_encoder_2_lora_layers # Final inference # Load previous pipeline vae = AutoencoderKL.from_pretrained( vae_path, subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None, revision=args.revision, torch_dtype=weight_dtype, ) pipeline = StableDiffusionXLPipeline.from_pretrained( args.pretrained_model_name_or_path, vae=vae, revision=args.revision, torch_dtype=weight_dtype, ) # 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 ) # load attention processors pipeline.load_lora_weights(args.output_dir) # run inference images = [] if args.validation_prompt and args.num_validation_images > 0: pipeline = pipeline.to(accelerator.device) generator = ( torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None ) images = pipeline(args.validation_prompt, num_images_per_prompt=args.num_validation_images, generator=generator).images for tracker in accelerator.trackers: if tracker.name == "tensorboard": np_images = np.stack([np.asarray(img) for img in images]) tracker.writer.add_images( "test", np_images, epoch, dataformats="NHWC" ) if tracker.name == "wandb": tracker.log( { "test": [ wandb.Image( image, caption=f"{i}: {args.validation_prompt}" ) for i, image in enumerate(images) ] } ) if args.push_to_hub: save_model_card( repo_id, images=images, base_model=args.pretrained_model_name_or_path, train_text_encoder=args.train_text_encoder, instance_prompt=args.instance_prompt, validation_prompt=args.validation_prompt, repo_folder=args.output_dir, vae_path=args.pretrained_vae_model_name_or_path, ) 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)