import argparse import itertools import logging import math import os import shutil import warnings from pathlib import Path from typing import List, Optional import json 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 peft import LoraConfig from peft.utils import get_peft_model_state_dict 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.optimization import get_scheduler from diffusers.training_utils import compute_snr from diffusers.utils import ( convert_state_dict_to_diffusers, is_wandb_available, ) logger = get_logger(__name__) 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.") # pretrained model config parser.add_argument("--pretrained_model_name_or_path", type=str, default="stabilityai/stable-diffusion-xl-base-1.0",) parser.add_argument("--pretrained_vae_model_name_or_path", type=str, default="madebyollin/sdxl-vae-fp16-fix") parser.add_argument("--revision", type=str, default=None) parser.add_argument("--variant", type=str, default=None) # data config parser.add_argument("--config_dir", type=str, default="") parser.add_argument("--config_name", type=str, default="") # validation config 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=0, help="Number of images that should be generated during validation with `validation_prompt`.",) parser.add_argument("--validation_epochs", type=int, default=50000) # use prior preservation 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.") # save config parser.add_argument("--output_dir", type=str, default="outdir", help="The output directory where the model predictions and checkpoints will be written.",) parser.add_argument("--checkpointing_steps", type=int, default=500, help="Save a checkpoint of the training state every X updates") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") # dataloader config 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") 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_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader.") 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("--num_train_epochs", type=int, default=1) parser.add_argument("--max_train_steps", type=int, default=1000, help="Total number of training steps to perform. If provided, overrides num_train_epochs.",) 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.") # train config parser.add_argument("--dcoloss_beta", type=float, default=1000, help="Sigloss value for DCO loss, use -1 if do not using dco loss") parser.add_argument("--train_text_encoder_ti", action="store_true", help=("Whether to use textual inversion"),) 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.",) # optimizer config parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.",) parser.add_argument("--gradient_checkpointing", action="store_true", help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",) parser.add_argument("--learning_rate", type=float, default=5e-5, 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=0, 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.") # optimizer config 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("--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=None, 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("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") # save config 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="fp16", 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("--local_rank", type=int, default=-1, help="For distributed training: local_rank") parser.add_argument("--rank", type=int, default=32, help=("The dimension of the LoRA update matrices."),) parser.add_argument("--offset_noise", type=float, default=0.0) if input_args is not None: args = parser.parse_args(input_args) else: args = parser.parse_args() env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) if env_local_rank != -1 and env_local_rank != args.local_rank: args.local_rank = env_local_rank 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 = None self.inserting_tokens = None self.embeddings_settings = {} def initialize_new_tokens(self, inserting_tokens, initializer_tokens): idx = 0 for tokenizer, text_encoder in zip(self.tokenizers, self.text_encoders): assert isinstance(inserting_tokens, list), "inserting_tokens should be a list of strings." assert all( isinstance(tok, str) for tok in inserting_tokens ), "All elements in inserting_tokens should be strings." self.inserting_tokens = inserting_tokens special_tokens_dict = {"additional_special_tokens": self.inserting_tokens} tokenizer.add_special_tokens(special_tokens_dict) text_encoder.resize_token_embeddings(len(tokenizer)) self.train_ids = tokenizer.convert_tokens_to_ids(self.inserting_tokens) std_token_embedding = text_encoder.text_model.embeddings.token_embedding.weight.data.std() self.embeddings_settings[f"std_token_embedding_{idx}"] = std_token_embedding print(f"{idx} text encodedr's std_token_embedding: {std_token_embedding}") embeddings = [] embeddings_norm = [] for initializer_token in initializer_tokens: if initializer_token == "": emb = torch.randn(1, text_encoder.text_model.config.hidden_size).to(device=self.device).to(dtype=self.dtype) * std_token_embedding embeddings.append(emb) embeddings_norm.append(std_token_embedding) else: initializer_token_id = tokenizer.encode(initializer_token, add_special_tokens=False) emb = text_encoder.text_model.embeddings.token_embedding.weight.data[initializer_token_id] embeddings.append(emb) embeddings_norm.append(emb.norm().item()) embeddings = torch.cat(embeddings, dim=0) text_encoder.text_model.embeddings.token_embedding.weight.data[self.train_ids] = embeddings embeddings_norm = torch.tensor(embeddings_norm).unsqueeze(1) self.embeddings_settings[f"token_embedding_norm_{idx}"] = embeddings_norm self.embeddings_settings[ f"original_embeddings_{idx}" ] = text_encoder.text_model.embeddings.token_embedding.weight.data.clone() inu = torch.ones((len(tokenizer),), dtype=torch.bool) inu[self.train_ids] = False self.embeddings_settings[f"index_no_updates_{idx}"] = inu idx += 1 def save_embeddings(self, file_path: str): assert self.train_ids is not None, "Initialize new tokens before saving embeddings." tensors = {} # text_encoder_0 - CLIP ViT-L/14, text_encoder_1 - CLIP ViT-G/14 idx_to_text_encoder_name = {0: "clip_l", 1: "clip_g"} 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[idx_to_text_encoder_name[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 @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) ) index_updates = ~index_no_updates new_embeddings = text_encoder.text_model.embeddings.token_embedding.weight.data[index_updates] new_embeddings = F.normalize(new_embeddings, dim=-1) * self.embeddings_settings[f"token_embedding_norm_{idx}"].view(-1, 1).to(device=text_encoder.device) text_encoder.text_model.embeddings.token_embedding.weight.data[index_updates] = new_embeddings.to(device=text_encoder.device).to(dtype=text_encoder.dtype) class TrainDataset(Dataset): def __init__(self, args): self.size = args.resolution self.center_crop = args.center_crop self.config_dir = args.config_dir self.config_name = args.config_name self.train_with_dco_loss = (args.dcoloss_beta > 0.) self.train_text_encoder_ti = args.train_text_encoder_ti self.with_prior_preservation = args.with_prior_preservation with open(self.config_dir, 'r') as data_config: data_cfg = json.load(data_config)[self.config_name] self.instance_images = [Image.open(path) for path in data_cfg["images"]] self.instance_prompts = [prompt for prompt in data_cfg["prompts"]] if self.train_text_encoder_ti and self.train_with_dco_loss: self.base_prompts = [prompt for prompt in data_cfg["base_prompts"]] self.num_instance_images = len(self.instance_images) self._length = self.num_instance_images if self.with_prior_preservation: self.num_class_images = args.num_class_images class_dir = data_cfg["class_images_dir"] self.class_images = [Image.open(class_dir+f"/{i}.png") for i in range(self.num_class_images)] self.class_prompts = [prompt for prompt in data_cfg["class_prompts"]] self._length = max(self.num_class_images, self.num_instance_images) self.image_transforms = transforms.Compose( [ transforms.Resize(self.size, interpolation=transforms.InterpolationMode.BILINEAR), transforms.CenterCrop(self.size) if self.center_crop else transforms.RandomCrop(self.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) prompt = self.instance_prompts[index % self.num_instance_images] example["instance_prompt"] = prompt if self.train_text_encoder_ti and self.train_with_dco_loss: base_prompt = self.base_prompts[index % self.num_instance_images] example["base_prompt"] = base_prompt if self.with_prior_preservation: class_image = self.class_images[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, args): pixel_values = [example["instance_images"] for example in examples] prompts = [example["instance_prompt"] for example in examples] if args.train_text_encoder_ti and (args.dcoloss_beta > 0.): base_prompts = [example["base_prompt"] for example in examples] if args.with_prior_preservation: pixel_values += [example["class_images"] for example in examples] prompts += [example["class_prompt"] for example in examples] if args.train_text_encoder_ti and (args.dcoloss_beta > 0.0): base_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} if args.train_text_encoder_ti and (args.dcoloss_beta > 0.0): batch.update({"base_prompts": base_prompts}) return batch 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 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) # Handle the repository creation if accelerator.is_main_process: if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) # Load the tokenizers tokenizer_one = AutoTokenizer.from_pretrained( args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, variant=args.variant, use_fast=False, ) tokenizer_two = AutoTokenizer.from_pretrained( args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision, variant=args.variant, 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, variant=args.variant ) text_encoder_two = text_encoder_cls_two.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant ) 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, variant=args.variant, ) vae_scaling_factor = vae.config.scaling_factor unet = UNet2DConditionModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant ) if args.train_text_encoder_ti: with open(args.config_dir, 'r') as data_config: data_cfg = json.load(data_config)[args.config_name] inserting_tokens = data_cfg["inserting_tokens"] initializer_tokens = data_cfg["initializer_tokens"] logger.info(f"List of token identifiers: {inserting_tokens}") # initialize the new tokens for textual inversion embedding_handler = TokenEmbeddingsHandler( [text_encoder_one, text_encoder_two], [tokenizer_one, tokenizer_two] ) embedding_handler.initialize_new_tokens( inserting_tokens=inserting_tokens, initializer_tokens=initializer_tokens ) # 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.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 unet_lora_config = LoraConfig( r=args.rank, lora_alpha=args.rank, init_lora_weights="gaussian", target_modules=["to_k", "to_q", "to_v", "to_out.0"], ) unet.add_adapter(unet_lora_config) # 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: text_lora_config = LoraConfig( r=args.rank, lora_alpha=args.rank, init_lora_weights="gaussian", target_modules=["q_proj", "k_proj", "v_proj", "out_proj"], ) text_encoder_one.add_adapter(text_lora_config) text_encoder_two.add_adapter(text_lora_config) # if we use textual inversion, we freeze all parameters except for the token embeddings elif args.train_text_encoder_ti: text_lora_parameters_one = [] for name, param in text_encoder_one.named_parameters(): if "token_embedding" in name: # ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16 param = param.to(dtype=torch.float32) 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: # ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16 param = param.to(dtype=torch.float32) param.requires_grad = True text_lora_parameters_two.append(param) else: param.requires_grad = False # Make sure the trainable params are in float32. if args.mixed_precision == "fp16": models = [unet] if args.train_text_encoder: models.extend([text_encoder_one, text_encoder_two]) for model in models: for param in model.parameters(): # only upcast trainable parameters (LoRA) into fp32 if param.requires_grad: param.data = param.to(torch.float32) # 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 = convert_state_dict_to_diffusers(get_peft_model_state_dict(model)) elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))): if args.train_text_encoder: text_encoder_one_lora_layers_to_save = convert_state_dict_to_diffusers( get_peft_model_state_dict(model) ) elif isinstance(model, type(accelerator.unwrap_model(text_encoder_two))): if args.train_text_encoder: text_encoder_two_lora_layers_to_save = convert_state_dict_to_diffusers( get_peft_model_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, ) if args.train_text_encoder_ti: embedding_handler.save_embeddings(f"{output_dir}/learned_embeds.safetensors") 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 ) unet_lora_parameters = list(filter(lambda p: p.requires_grad, unet.parameters())) if args.train_text_encoder: text_lora_parameters_one = list(filter(lambda p: p.requires_grad, text_encoder_one.parameters())) text_lora_parameters_two = list(filter(lambda p: p.requires_grad, text_encoder_two.parameters())) # 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 if args.adam_weight_decay_text_encoder else args.adam_weight_decay, "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 if args.adam_weight_decay_text_encoder else args.adam_weight_decay, "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 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, ) # Dataset and DataLoaders creation: train_dataset = TrainDataset(args) train_dataloader = torch.utils.data.DataLoader( train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=lambda examples: collate_fn(examples, args), num_workers=args.dataloader_num_workers, ) # Computes additional embeddings/ids required by the SDXL UNet. 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 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() add_time_ids = instance_time_ids if args.with_prior_preservation: class_time_ids = compute_time_ids() add_time_ids = torch.cat([add_time_ids, class_time_ids], 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("fine-tune sdxl", 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): # if performing any kind of optimization of text_encoder params if args.train_text_encoder or args.train_text_encoder_ti: 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): prompts = batch["prompts"] if args.train_text_encoder_ti and (args.dcoloss_beta > 0.0): base_prompts = batch["base_prompts"] base_prompt_embeds, base_add_embeds = compute_text_embeddings( base_prompts, text_encoders, tokenizers ) # 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) tokens_two = tokenize_prompt(tokenizer_two, prompts) pixel_values = batch["pixel_values"].to(dtype=vae.dtype) model_input = vae.encode(pixel_values).latent_dist.sample() model_input = model_input * vae_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) noise = noise + args.offset_noise * torch.randn(model_input.shape[0], model_input.shape[1], 1, 1, device=model_input.device) 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 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. 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 if args.dcoloss_beta > 0.0: with torch.no_grad(): cross_attention_kwargs = {"scale": 0.0} refer_pred = unet( noisy_model_input, timesteps, prompt_embeds_input, added_cond_kwargs=unet_added_conditions, cross_attention_kwargs=cross_attention_kwargs, ).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 if args.dcoloss_beta > 0.0: base_prompts = batch["base_prompts"] with torch.no_grad(): base_prompt_embeds, base_add_embeds = compute_text_embeddings( base_prompts, text_encoders, tokenizers ) cross_attention_kwargs = {"scale": 0.0} base_added_conditions = {"time_ids": add_time_ids, "text_embeds": base_add_embeds} refer_pred = unet( noisy_model_input, timesteps, base_prompt_embeds, added_cond_kwargs=base_added_conditions, cross_attention_kwargs=cross_attention_kwargs ).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) prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean") if args.snr_gamma is None: if args.dcoloss_beta > 0.0: loss_model = F.mse_loss(model_pred.float(), target.float(), reduction="mean") loss_refer = F.mse_loss(refer_pred.float(), target.float(), reduction="mean") diff = loss_model - loss_refer inside_term = -1 * args.dcoloss_beta * diff loss = -1 * torch.nn.LogSigmoid()(inside_term) else: 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. if args.with_prior_preservation: # if we're using prior preservation, we calc snr for instance loss only - # and hence only need timesteps corresponding to instance images snr_timesteps, _ = torch.chunk(timesteps, 2, dim=0) else: snr_timesteps = timesteps snr = compute_snr(noise_scheduler, snr_timesteps) base_weight = ( torch.stack([snr, args.snr_gamma * torch.ones_like(snr_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 if args.dcoloss_beta > 0.0: loss_model = F.mse_loss(model_pred.float(), target.float(), reduction="none") loss_model = loss_model.mean(dim=list(range(1, len(loss_model.shape)))) * mse_loss_weights loss_model = loss_model.mean() loss_refer = F.mse_loss(refer_pred.float(), target.float(), reduction="none") loss_refer = loss_refer.mean(dim=list(range(1, len(loss_refer.shape)))) * mse_loss_weights loss_refer = loss_refer.mean() diff = loss_model - loss_refer inside_term = -1 * args.dcoloss_beta * diff loss = -1 * torch.nn.LogSigmoid()(inside_term) else: 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: 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 freeze_text_encoder: text_encoder_one = text_encoder_cls_one.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant, ) text_encoder_two = text_encoder_cls_two.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant, ) pipeline = 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, variant=args.variant, 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 = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet)) if args.train_text_encoder: text_encoder_one = accelerator.unwrap_model(text_encoder_one) text_encoder_lora_layers = convert_state_dict_to_diffusers( get_peft_model_state_dict(text_encoder_one.to(torch.float32)) ) text_encoder_two = accelerator.unwrap_model(text_encoder_two) text_encoder_2_lora_layers = convert_state_dict_to_diffusers( get_peft_model_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, ) images = [] if args.validation_prompt and args.num_validation_images > 0: # 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, variant=args.variant, torch_dtype=weight_dtype, ) pipeline = StableDiffusionXLPipeline.from_pretrained( args.pretrained_model_name_or_path, vae=vae, revision=args.revision, variant=args.variant, 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 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.train_text_encoder_ti: embedding_handler.save_embeddings( f"{args.output_dir}/learned_embeds.safetensors", ) accelerator.end_training() if __name__ == "__main__": args = parse_args() main(args)