diff --git a/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py b/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py new file mode 100644 index 0000000000..f032634a11 --- /dev/null +++ b/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py @@ -0,0 +1,1968 @@ +#!/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 hashlib +import itertools +import logging +import math +import os +import shutil +import warnings +from pathlib import Path +from typing import List, Optional + +import numpy as np +import torch +import torch.nn.functional as F + +# imports of the TokenEmbeddingsHandler class +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 packaging import version +from PIL import Image +from PIL.ImageOps import exif_transpose +from safetensors.torch import save_file +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, text_encoder_lora_state_dict +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__) + + +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 +widget: +{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( + "--token_abstraction", + default="TOK", + help="identifier specifying the instance(or instances) as used in instance_prompt, validation prompt, " + "captions - e.g. TOK", + ) + + parser.add_argument( + "--num_new_tokens_per_abstraction", + default=2, + help="number of new tokens inserted to the tokenizers per token_abstraction value when " + "--train_text_encoder_ti = True. By default, each --token_abstraction (e.g. TOK) is mapped to 2 new " + "tokens - ", + ) + + 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=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. 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( + "--train_text_encoder_ti", + action="store_true", + help=("Whether to use textual inversion"), + ) + + parser.add_argument( + "--train_text_encoder_ti_frac", + type=float, + default=0.5, + help=("The percentage of epochs to perform textual inversion"), + ) + + parser.add_argument( + "--train_text_encoder_frac", + type=float, + default=0.5, + help=("The percentage of epochs to perform text encoder tuning"), + ) + + 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`") + + if args.train_text_encoder and args.train_text_encoder_ti: + raise ValueError( + "Specify only one of `--train_text_encoder` or `--train_text_encoder_ti. " + "For full LoRA text encoder training check --train_text_encoder, for textual " + "inversion training check `--train_text_encoder_ti`" + ) + + if args.train_text_encoder_ti: + if isinstance(args.token_abstraction, str): + args.token_abstraction = [args.token_abstraction] + elif isinstance(args.token_abstraction, List): + args.token_abstraction = args.token_abstraction + else: + raise ValueError( + f"Unsupported type for --args.token_abstraction: {type(args.token_abstraction)}. " + f"Supported types are: str (for a single instance identifier) or List[str] (for multiple concepts)" + ) + + 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 + + +# Taken from https://github.com/replicate/cog-sdxl/blob/main/dataset_and_utils.py +class TokenEmbeddingsHandler: + def __init__(self, text_encoders, tokenizers): + self.text_encoders = text_encoders + self.tokenizers = tokenizers + + self.train_ids: Optional[torch.Tensor] = None + self.inserting_toks: Optional[List[str]] = None + self.embeddings_settings = {} + + def initialize_new_tokens(self, inserting_toks: List[str]): + idx = 0 + for tokenizer, text_encoder in zip(self.tokenizers, self.text_encoders): + assert isinstance(inserting_toks, list), "inserting_toks should be a list of strings." + assert all( + isinstance(tok, str) for tok in inserting_toks + ), "All elements in inserting_toks should be strings." + + self.inserting_toks = inserting_toks + special_tokens_dict = {"additional_special_tokens": self.inserting_toks} + tokenizer.add_special_tokens(special_tokens_dict) + text_encoder.resize_token_embeddings(len(tokenizer)) + + self.train_ids = tokenizer.convert_tokens_to_ids(self.inserting_toks) + + # random initialization of new tokens + std_token_embedding = text_encoder.text_model.embeddings.token_embedding.weight.data.std() + + print(f"{idx} text encodedr's std_token_embedding: {std_token_embedding}") + + text_encoder.text_model.embeddings.token_embedding.weight.data[self.train_ids] = ( + torch.randn(len(self.train_ids), text_encoder.text_model.config.hidden_size) + .to(device=self.device) + .to(dtype=self.dtype) + * std_token_embedding + ) + self.embeddings_settings[ + f"original_embeddings_{idx}" + ] = text_encoder.text_model.embeddings.token_embedding.weight.data.clone() + self.embeddings_settings[f"std_token_embedding_{idx}"] = std_token_embedding + + inu = torch.ones((len(tokenizer),), dtype=torch.bool) + inu[self.train_ids] = False + + self.embeddings_settings[f"index_no_updates_{idx}"] = inu + + print(self.embeddings_settings[f"index_no_updates_{idx}"].shape) + + idx += 1 + + def save_embeddings(self, file_path: str): + assert self.train_ids is not None, "Initialize new tokens before saving embeddings." + tensors = {} + for idx, text_encoder in enumerate(self.text_encoders): + assert text_encoder.text_model.embeddings.token_embedding.weight.data.shape[0] == len( + self.tokenizers[0] + ), "Tokenizers should be the same." + new_token_embeddings = text_encoder.text_model.embeddings.token_embedding.weight.data[self.train_ids] + tensors[f"text_encoders_{idx}"] = new_token_embeddings + + save_file(tensors, file_path) + + @property + def dtype(self): + return self.text_encoders[0].dtype + + @property + def device(self): + return self.text_encoders[0].device + + # def _load_embeddings(self, loaded_embeddings, tokenizer, text_encoder): + # # Assuming new tokens are of the format + # self.inserting_toks = [f"" for i in range(loaded_embeddings.shape[0])] + # special_tokens_dict = {"additional_special_tokens": self.inserting_toks} + # tokenizer.add_special_tokens(special_tokens_dict) + # text_encoder.resize_token_embeddings(len(tokenizer)) + # + # self.train_ids = tokenizer.convert_tokens_to_ids(self.inserting_toks) + # assert self.train_ids is not None, "New tokens could not be converted to IDs." + # text_encoder.text_model.embeddings.token_embedding.weight.data[ + # self.train_ids + # ] = loaded_embeddings.to(device=self.device).to(dtype=self.dtype) + + @torch.no_grad() + def retract_embeddings(self): + for idx, text_encoder in enumerate(self.text_encoders): + index_no_updates = self.embeddings_settings[f"index_no_updates_{idx}"] + text_encoder.text_model.embeddings.token_embedding.weight.data[index_no_updates] = ( + self.embeddings_settings[f"original_embeddings_{idx}"][index_no_updates] + .to(device=text_encoder.device) + .to(dtype=text_encoder.dtype) + ) + + # for the parts that were updated, we need to normalize them + # to have the same std as before + std_token_embedding = self.embeddings_settings[f"std_token_embedding_{idx}"] + + index_updates = ~index_no_updates + new_embeddings = text_encoder.text_model.embeddings.token_embedding.weight.data[index_updates] + off_ratio = std_token_embedding / new_embeddings.std() + + new_embeddings = new_embeddings * (off_ratio**0.1) + text_encoder.text_model.embeddings.token_embedding.weight.data[index_updates] = new_embeddings + + # def load_embeddings(self, file_path: str): + # with safe_open(file_path, framework="pt", device=self.device.type) as f: + # for idx in range(len(self.text_encoders)): + # text_encoder = self.text_encoders[idx] + # tokenizer = self.tokenizers[idx] + # + # loaded_embeddings = f.get_tensor(f"text_encoders_{idx}") + # self._load_embeddings(loaded_embeddings, tokenizer, text_encoder) + + +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, + token_abstraction_dict=None, # token mapping for textual inversion + 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 + self.token_abstraction_dict = token_abstraction_dict + + # 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: + if args.train_text_encoder_ti: + # replace instances of --token_abstraction in caption with the new tokens: "" etc. + for token_abs, token_replacement in self.token_abstraction_dict.items(): + caption = caption.replace(token_abs, "".join(token_replacement)) + 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, add_special_tokens=False): + text_inputs = tokenizer( + prompt, + padding="max_length", + max_length=tokenizer.model_max_length, + truncation=True, + add_special_tokens=add_special_tokens, + 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 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 = 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 + ) + + if args.train_text_encoder_ti: + token_abstraction_dict = {} + token_idx = 0 + for i, token in enumerate(args.token_abstraction): + token_abstraction_dict[token] = [ + f"" for j in range(args.num_new_tokens_per_abstraction) + ] + token_idx += args.num_new_tokens_per_abstraction - 1 + + # replace instances of --token_abstraction in --instance_prompt with the new tokens: "" etc. + for token_abs, token_replacement in token_abstraction_dict.items(): + args.instance_prompt = args.instance_prompt.replace(token_abs, "".join(token_replacement)) + if args.with_prior_preservation: + args.class_prompt = args.class_prompt.replace(token_abs, "".join(token_replacement)) + + # initialize the new tokens for textual inversion + embedding_handler = TokenEmbeddingsHandler( + [text_encoder_one, text_encoder_two], [tokenizer_one, tokenizer_two] + ) + inserting_toks = [] + for new_tok in token_abstraction_dict.values(): + inserting_toks.extend(new_tok) + embedding_handler.initialize_new_tokens(inserting_toks=inserting_toks) + + # 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() + + # 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. + 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 + ) + + # if we use textual inversion, we freeze all parameters except for the token embeddings + # in text encoder + elif args.train_text_encoder_ti: + text_lora_parameters_one = [] + for name, param in text_encoder_one.named_parameters(): + if "token_embedding" in name: + param.requires_grad = True + text_lora_parameters_one.append(param) + else: + param.requires_grad = False + text_lora_parameters_two = [] + for name, param in text_encoder_two.named_parameters(): + if "token_embedding" in name: + param.requires_grad = True + text_lora_parameters_two.append(param) + else: + param.requires_grad = False + + # 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 + ) + + # If neither --train_text_encoder nor --train_text_encoder_ti, text_encoders remain frozen during training + freeze_text_encoder = not (args.train_text_encoder or args.train_text_encoder_ti) + + # Optimization parameters + unet_lora_parameters_with_lr = {"params": unet_lora_parameters, "lr": args.learning_rate} + if not freeze_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, + token_abstraction_dict=token_abstraction_dict if args.train_text_encoder_ti 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 freeze_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 freeze_text_encoder: + class_prompt_hidden_states, class_pooled_prompt_embeds = compute_text_embeddings( + args.class_prompt, text_encoders, tokenizers + ) + + # Clear the memory here + if freeze_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 --train_text_encoder_ti we need add_special_tokens to be True fo textual inversion + add_special_tokens = True if args.train_text_encoder_ti else False + + if not train_dataset.custom_instance_prompts: + if freeze_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, add_special_tokens) + tokens_two = tokenize_prompt(tokenizer_two, args.instance_prompt, add_special_tokens) + if args.with_prior_preservation: + class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt, add_special_tokens) + class_tokens_two = tokenize_prompt(tokenizer_two, args.class_prompt, add_special_tokens) + 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 not freeze_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, + ) + + if args.train_text_encoder: + num_train_epochs_text_encoder = int(args.train_text_encoder_frac * args.num_train_epochs) + elif args.train_text_encoder_ti: # args.train_text_encoder_ti + num_train_epochs_text_encoder = int(args.train_text_encoder_ti_frac * args.num_train_epochs) + + for epoch in range(first_epoch, args.num_train_epochs): + # if performing any kind of optimization of text_encoder params + if args.train_text_encoder or args.train_text_encoder_ti: + if epoch == num_train_epochs_text_encoder: + print("PIVOT HALFWAY", epoch) + # stopping optimization of text_encoder params + params_to_optimize = params_to_optimize[:1] + # reinitializing the optimizer to optimize only on unet params + if args.optimizer.lower() == "prodigy": + 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, + ) + else: # AdamW or 8-bit-AdamW + optimizer = optimizer_class( + params_to_optimize, + betas=(args.adam_beta1, args.adam_beta2), + weight_decay=args.adam_weight_decay, + eps=args.adam_epsilon, + ) + else: + # still optimizng the text encoder + text_encoder_one.train() + text_encoder_two.train() + # set top parameter requires_grad = True for gradient checkpointing works + if args.train_text_encoder: + text_encoder_one.text_model.embeddings.requires_grad_(True) + text_encoder_two.text_model.embeddings.requires_grad_(True) + + unet.train() + for step, batch in enumerate(train_dataloader): + with accelerator.accumulate(unet): + pixel_values = batch["pixel_values"].to(dtype=vae.dtype) + prompts = batch["prompts"] + print(prompts) + # encode batch prompts when custom prompts are provided for each image - + if train_dataset.custom_instance_prompts: + if freeze_text_encoder: + prompt_embeds, unet_add_text_embeds = compute_text_embeddings( + prompts, text_encoders, tokenizers + ) + + else: + tokens_one = tokenize_prompt(tokenizer_one, prompts, add_special_tokens) + tokens_two = tokenize_prompt(tokenizer_two, prompts, add_special_tokens) + + # 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 freeze_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 or args.train_text_encoder_ti) + else unet_lora_parameters + ) + accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + + # every step, we reset the embeddings to the original embeddings. + if args.train_text_encoder_ti: + for idx, text_encoder in enumerate(text_encoders): + embedding_handler.retract_embeddings() + + # 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} + + with torch.cuda.amp.autocast(): + images = [ + pipeline(**pipeline_args, generator=generator).images[0] + for _ in range(args.num_validation_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, + ) + + # 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_inference_steps=25, generator=generator).images[0] + for _ in range(args.num_validation_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: + if args.train_text_encoder_ti: + embedding_handler.save_embeddings( + f"{args.output_dir}/embeddings.safetensors", + ) + 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)