# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from contextlib import nullcontext import argparse import copy import logging import math import os import shutil from pathlib import Path from datasets import load_dataset import torch import torch.nn.functional as F from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration, set_seed from peft import LoraConfig, PeftModel, get_peft_model from PIL import Image from PIL.ImageOps import exif_transpose from torch.utils.data import DataLoader, Dataset, default_collate from torchvision import transforms from transformers import ( CLIPTextModelWithProjection, CLIPTokenizer, ) import diffusers.optimization from diffusers import AmusedPipeline, AmusedScheduler, EMAModel, UVit2DModel, VQModel from diffusers.utils import is_wandb_available if is_wandb_available(): import wandb logger = get_logger(__name__, log_level="INFO") def parse_args(): parser = argparse.ArgumentParser() parser.add_argument( "--pretrained_model_name_or_path", type=str, default=None, required=True, help="Path to pretrained model or model identifier from huggingface.co/models.", ) parser.add_argument( "--revision", type=str, default=None, required=False, help="Revision of pretrained model identifier from huggingface.co/models.", ) parser.add_argument( "--variant", type=str, default=None, help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16", ) parser.add_argument( "--instance_data_dataset", type=str, default=None, required=False, help="A Hugging Face dataset containing the training images", ) parser.add_argument( "--instance_data_dir", type=str, default=None, required=False, help="A folder containing the training data of instance images.", ) parser.add_argument( "--instance_data_image", type=str, default=None, required=False, help="A single training image" ) parser.add_argument( "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." ) 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( "--allow_tf32", action="store_true", help=( "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" ), ) parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.") parser.add_argument("--ema_decay", type=float, default=0.9999) parser.add_argument("--ema_update_after_step", type=int, default=0) parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") parser.add_argument( "--output_dir", type=str, default="muse_training", 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( "--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( "--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. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. " "In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference." "Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components." "See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step" "instructions." ), ) parser.add_argument( "--logging_steps", type=int, default=50, ) parser.add_argument( "--checkpoints_total_limit", type=int, default=None, help=( "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`." " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state" " for more details" ), ) 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( "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader." ) 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( "--learning_rate", type=float, default=0.0003, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--scale_lr", action="store_true", default=False, help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", ) parser.add_argument( "--lr_scheduler", type=str, default="constant", help=( 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' ' "constant", "constant_with_warmup"]' ), ) parser.add_argument( "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument( "--validation_steps", type=int, default=100, help=( "Run validation every X steps. Validation consists of running the prompt" " `args.validation_prompt` multiple times: `args.num_validation_images`" " and logging the images." ), ) parser.add_argument( "--mixed_precision", type=str, default=None, choices=["no", "fp16", "bf16"], help=( "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." ), ) parser.add_argument( "--report_to", type=str, default="tensorboard", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' ), ) parser.add_argument("--validation_prompts", type=str, nargs="*") parser.add_argument( "--resolution", type=int, default=512, help=( "The resolution for input images, all the images in the train/validation dataset will be resized to this" " resolution" ), ) parser.add_argument("--split_vae_encode", type=int, required=False, default=None) parser.add_argument("--min_masking_rate", type=float, default=0.0) parser.add_argument("--cond_dropout_prob", type=float, default=0.0) parser.add_argument("--max_grad_norm", default=None, type=float, help="Max gradient norm.", required=False) parser.add_argument("--use_lora", action="store_true", help="TODO") parser.add_argument("--lora_r", default=16, type=int) parser.add_argument("--lora_alpha", default=32, type=int) parser.add_argument("--lora_target_modules", default=["to_q", "to_k", "to_v"], type=str, nargs="+") parser.add_argument("--train_text_encoder", action="store_true") parser.add_argument("--image_key", type=str, required=False) parser.add_argument("--prompt_key", type=str, required=False) 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("--prompt_prefix", type=str, required=False, default=None) args = parser.parse_args() 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.") num_datasources = sum([x is not None for x in [args.instance_data_dir, args.instance_data_image, args.instance_data_dataset]]) if num_datasources != 1: raise ValueError("provide one and only one of `--instance_data_dir`, `--instance_data_image`, or `--instance_data_dataset`") if args.instance_data_dir is not None: if not os.path.exists(args.instance_data_dir): raise ValueError(f"Does not exist: `--args.instance_data_dir` {args.instance_data_dir}") if args.instance_data_image is not None: if not os.path.exists(args.instance_data_image): raise ValueError(f"Does not exist: `--args.instance_data_image` {args.instance_data_image}") if args.instance_data_dataset is not None and (args.image_key is None or args.prompt_key is None): raise ValueError("`--instance_data_dataset` requires setting `--image_key` and `--prompt_key`") return args class InstanceDataRootDataset(Dataset): def __init__( self, instance_data_root, tokenizer, size=512, ): self.size = size self.tokenizer = tokenizer self.instance_images_path = list(Path(instance_data_root).iterdir()) def __len__(self): return len(self.instance_images_path) def __getitem__(self, index): image_path = self.instance_images_path[index % len(self.instance_images_path)] instance_image = Image.open(image_path) rv = process_image(instance_image, self.size) prompt = os.path.splitext(os.path.basename(image_path))[0] rv["prompt_input_ids"] = tokenize_prompt(self.tokenizer, prompt)[0] return rv class InstanceDataImageDataset(Dataset): def __init__( self, instance_data_image, train_batch_size, size=512, ): self.value = process_image(Image.open(instance_data_image), size) self.train_batch_size = train_batch_size def __len__(self): # Needed so a full batch of the data can be returned. Otherwise will return # batches of size 1 return self.train_batch_size def __getitem__(self, index): return self.value class HuggingFaceDataset(Dataset): def __init__( self, hf_dataset, tokenizer, image_key, prompt_key, prompt_prefix=None, size=512, ): self.size = size self.image_key = image_key self.prompt_key = prompt_key self.tokenizer = tokenizer self.hf_dataset = hf_dataset self.prompt_prefix = prompt_prefix def __len__(self): return len(self.hf_dataset) def __getitem__(self, index): item = self.hf_dataset[index] rv = process_image(item[self.image_key], self.size) prompt = item[self.prompt_key] if self.prompt_prefix is not None: prompt = self.prompt_prefix + prompt rv["prompt_input_ids"] = tokenize_prompt(self.tokenizer, prompt)[0] return rv def process_image(image, size): image = exif_transpose(image) if not image.mode == "RGB": image = image.convert("RGB") orig_height = image.height orig_width = image.width image = transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR)(image) c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(size, size)) image = transforms.functional.crop(image, c_top, c_left, size, size) image = transforms.ToTensor()(image) micro_conds = torch.tensor( [ orig_width, orig_height, c_top, c_left, 6.0 ], ) return {"image": image, "micro_conds": micro_conds} @torch.no_grad() def tokenize_prompt(tokenizer, prompt): return tokenizer( prompt, truncation=True, padding="max_length", max_length=77, return_tensors="pt", ).input_ids def encode_prompt(text_encoder, input_ids): outputs = text_encoder(input_ids, return_dict=True, output_hidden_states=True) encoder_hidden_states = outputs.hidden_states[-2] cond_embeds = outputs[0] return encoder_hidden_states, cond_embeds def main(args): if args.allow_tf32: torch.backends.cuda.matmul.allow_tf32 = True logging_dir = Path(args.output_dir, args.logging_dir) accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir) accelerator = Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision, log_with=args.report_to, project_config=accelerator_project_config, ) if accelerator.is_main_process: os.makedirs(args.output_dir, exist_ok=True) # 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_main_process: accelerator.init_trackers("amused", config=vars(copy.deepcopy(args))) if args.seed is not None: set_seed(args.seed) resume_from_checkpoint = args.resume_from_checkpoint if resume_from_checkpoint: if resume_from_checkpoint == "latest": # Get the most recent checkpoint dirs = os.listdir(args.output_dir) dirs = [d for d in dirs if d.startswith("checkpoint")] dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) if len(dirs) > 0: resume_from_checkpoint = os.path.join(args.output_dir, dirs[-1]) else: resume_from_checkpoint = None if resume_from_checkpoint is None: accelerator.print( f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run." ) else: accelerator.print(f"Resuming from checkpoint {resume_from_checkpoint}") # TODO - will have to fix loading if training text encoder text_encoder = CLIPTextModelWithProjection.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant ) tokenizer = CLIPTokenizer.from_pretrained( args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, variant=args.variant ) vq_model = VQModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="vqvae", revision=args.revision, variant=args.variant ) if args.train_text_encoder: text_encoder.train() text_encoder.requires_grad_(True) else: text_encoder.eval() text_encoder.requires_grad_(False) vq_model.requires_grad_(False) if args.use_lora: model = UVit2DModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="transformer", revision=args.revision, variant=args.variant ) if resume_from_checkpoint is not None: model = PeftModel.from_pretrained( model, os.path.join(resume_from_checkpoint, "transformer"), is_trainable=True ) else: lora_config = LoraConfig( r=args.lora_r, lora_alpha=args.lora_alpha, target_modules=args.lora_target_modules, ) model = get_peft_model(model, lora_config) else: if resume_from_checkpoint is not None: model = UVit2DModel.from_pretrained(resume_from_checkpoint, subfolder="transformer") else: model = UVit2DModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="transformer", revision=args.revision, variant=args.variant, ) model.train() if args.gradient_checkpointing: model.enable_gradient_checkpointing() if args.train_text_encoder: text_encoder.gradient_checkpointing_enable() if args.use_ema: if resume_from_checkpoint is not None: ema = EMAModel.from_pretrained(os.path.join(resume_from_checkpoint, "ema_model"), model_cls=UVit2DModel) else: ema = EMAModel( model.parameters(), decay=args.ema_decay, update_after_step=args.ema_update_after_step, model_cls=UVit2DModel, model_config=model.config, ) # TODO - this will save the lora weights in the peft format. We want to save in # diffusers format def save_model_hook(models, weights, output_dir): if accelerator.is_main_process: for model in models: if isinstance(model, UVit2DModel): models[0].save_pretrained(os.path.join(output_dir, "transformer")) elif isinstance(model, CLIPTextModelWithProjection): models[0].save_pretrained(os.path.join(output_dir, "text_encoder")) weights.pop() if args.use_ema: ema.save_pretrained(os.path.join(output_dir, "ema_model")) def load_model_hook(models, input_dir): # All models are initially instantiated from the checkpoint and so # don't have to be loaded in the accelerate hook for _ in range(len(models)): models.pop() accelerator.register_load_state_pre_hook(load_model_hook) accelerator.register_save_state_pre_hook(save_model_hook) if args.scale_lr: args.learning_rate = ( args.learning_rate * args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps ) if args.use_8bit_adam: try: import bitsandbytes as bnb except ImportError: raise ImportError( "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`" ) optimizer_cls = bnb.optim.AdamW8bit else: optimizer_cls = torch.optim.AdamW # no decay on bias and layernorm and embedding no_decay = ["bias", "layer_norm.weight", "mlm_ln.weight", "embeddings.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.adam_weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] # TODO - does not actually take text encoder parameters optimizer = optimizer_cls( optimizer_grouped_parameters, lr=args.learning_rate, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, eps=args.adam_epsilon, ) logger.info("Creating dataloaders and lr_scheduler") total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps if args.instance_data_dir is not None: dataset = InstanceDataRootDataset( instance_data_root=args.instance_data_dir, tokenizer=tokenizer, size=args.resolution, ) elif args.instance_data_image is not None: dataset = InstanceDataImageDataset( instance_data_image=args.instance_data_image, train_batch_size=args.train_batch_size, size=args.resolution, ) elif args.instance_data_dataset is not None: dataset = HuggingFaceDataset( hf_dataset=load_dataset(args.instance_data_dataset, split="train"), tokenizer=tokenizer, image_key=args.image_key, prompt_key=args.prompt_key, prompt_prefix=args.prompt_prefix, size=args.resolution, ) else: assert False train_dataloader = DataLoader( dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers, collate_fn=default_collate, ) train_dataloader.num_batches = len(train_dataloader) lr_scheduler = diffusers.optimization.get_scheduler( args.lr_scheduler, optimizer=optimizer, num_training_steps=args.max_train_steps*accelerator.num_processes, num_warmup_steps=args.lr_warmup_steps*accelerator.num_processes, ) logger.info("Preparing model, optimizer and dataloaders") if args.train_text_encoder: model, optimizer, lr_scheduler, train_dataloader, text_encoder = accelerator.prepare( model, optimizer, lr_scheduler, train_dataloader, text_encoder ) else: model, optimizer, lr_scheduler, train_dataloader = accelerator.prepare( model, optimizer, lr_scheduler, train_dataloader ) train_dataloader.num_batches = len(train_dataloader) weight_dtype = torch.float32 if accelerator.mixed_precision == "fp16": weight_dtype = torch.float16 elif accelerator.mixed_precision == "bf16": weight_dtype = torch.bfloat16 if not args.train_text_encoder: text_encoder.to(device=accelerator.device, dtype=weight_dtype) vq_model.to(device=accelerator.device) if args.use_ema: ema.to(accelerator.device) with nullcontext() if args.train_text_encoder else torch.no_grad(): empty_embeds, empty_clip_embeds = encode_prompt(text_encoder, tokenize_prompt(tokenizer, "").to(text_encoder.device, non_blocking=True)) # There is a single image, we can just pre-encode the single prompt if args.instance_data_image is not None: prompt = os.path.splitext(os.path.basename(args.instance_data_image))[0] encoder_hidden_states, cond_embeds = encode_prompt(text_encoder, tokenize_prompt(tokenizer, prompt).to(text_encoder.device, non_blocking=True)) encoder_hidden_states = encoder_hidden_states.repeat(args.train_batch_size, 1, 1) cond_embeds = cond_embeds.repeat(args.train_batch_size, 1) # 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(train_dataloader.num_batches / args.gradient_accumulation_steps) # Afterwards we recalculate our number of training epochs. # Note: We are not doing epoch based training here, but just using this for book keeping and being able to # reuse the same training loop with other datasets/loaders. num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Train! logger.info("***** Running training *****") logger.info(f" Num training steps = {args.max_train_steps}") 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}") if resume_from_checkpoint is None: global_step = 0 first_epoch = 0 else: accelerator.load_state(resume_from_checkpoint) global_step = int(os.path.basename(resume_from_checkpoint).split("-")[1]) first_epoch = global_step // num_update_steps_per_epoch # As stated above, we are not doing epoch based training here, but just using this for book keeping and being able to # reuse the same training loop with other datasets/loaders. for epoch in range(first_epoch, num_train_epochs): for batch in train_dataloader: with torch.no_grad(): micro_conds = batch["micro_conds"].to(accelerator.device, non_blocking=True) pixel_values = batch["image"].to(accelerator.device, non_blocking=True) batch_size = pixel_values.shape[0] split_batch_size = args.split_vae_encode if args.split_vae_encode is not None else batch_size num_splits = math.ceil(batch_size / split_batch_size) image_tokens = [] for i in range(num_splits): start_idx = i * split_batch_size end_idx = min((i + 1) * split_batch_size, batch_size) bs = pixel_values.shape[0] image_tokens.append( vq_model.quantize(vq_model.encode(pixel_values[start_idx:end_idx]).latents)[2][2].reshape( bs, -1 ) ) image_tokens = torch.cat(image_tokens, dim=0) batch_size, seq_len = image_tokens.shape timesteps = torch.rand(batch_size, device=image_tokens.device) mask_prob = torch.cos(timesteps * math.pi * 0.5) mask_prob = mask_prob.clip(args.min_masking_rate) num_token_masked = (seq_len * mask_prob).round().clamp(min=1) batch_randperm = torch.rand(batch_size, seq_len, device=image_tokens.device).argsort(dim=-1) mask = batch_randperm < num_token_masked.unsqueeze(-1) mask_id = accelerator.unwrap_model(model).config.vocab_size - 1 input_ids = torch.where(mask, mask_id, image_tokens) labels = torch.where(mask, image_tokens, -100) if args.cond_dropout_prob > 0.0: assert encoder_hidden_states is not None batch_size = encoder_hidden_states.shape[0] mask = ( torch.zeros((batch_size, 1, 1), device=encoder_hidden_states.device).float().uniform_(0, 1) < args.cond_dropout_prob ) empty_embeds_ = empty_embeds.expand(batch_size, -1, -1) encoder_hidden_states = torch.where( (encoder_hidden_states * mask).bool(), encoder_hidden_states, empty_embeds_ ) empty_clip_embeds_ = empty_clip_embeds.expand(batch_size, -1) cond_embeds = torch.where((cond_embeds * mask.squeeze(-1)).bool(), cond_embeds, empty_clip_embeds_) bs = input_ids.shape[0] vae_scale_factor = 2 ** (len(vq_model.config.block_out_channels) - 1) resolution = args.resolution // vae_scale_factor input_ids = input_ids.reshape(bs, resolution, resolution) if "prompt_input_ids" in batch: with nullcontext() if args.train_text_encoder else torch.no_grad(): encoder_hidden_states, cond_embeds = encode_prompt(text_encoder, batch["prompt_input_ids"].to(accelerator.device, non_blocking=True)) # Train Step with accelerator.accumulate(model): codebook_size = accelerator.unwrap_model(model).config.codebook_size logits = ( model( input_ids=input_ids, encoder_hidden_states=encoder_hidden_states, micro_conds=micro_conds, pooled_text_emb=cond_embeds, ) .reshape(bs, codebook_size, -1) .permute(0, 2, 1) .reshape(-1, codebook_size) ) loss = F.cross_entropy( logits, labels.view(-1), ignore_index=-100, reduction="mean", ) # Gather the losses across all processes for logging (if we use distributed training). avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean() avg_masking_rate = accelerator.gather(mask_prob.repeat(args.train_batch_size)).mean() accelerator.backward(loss) if args.max_grad_norm is not None and accelerator.sync_gradients: accelerator.clip_grad_norm_(model.parameters(), args.max_grad_norm) optimizer.step() lr_scheduler.step() optimizer.zero_grad(set_to_none=True) # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: if args.use_ema: ema.step(model.parameters()) if (global_step + 1) % args.logging_steps == 0: logs = { "step_loss": avg_loss.item(), "lr": lr_scheduler.get_last_lr()[0], "avg_masking_rate": avg_masking_rate.item(), } accelerator.log(logs, step=global_step + 1) logger.info( f"Step: {global_step + 1} " f"Loss: {avg_loss.item():0.4f} " f"LR: {lr_scheduler.get_last_lr()[0]:0.6f}" ) if (global_step + 1) % args.checkpointing_steps == 0: save_checkpoint(args, accelerator, global_step + 1) if (global_step + 1) % args.validation_steps == 0 and accelerator.is_main_process: if args.use_ema: ema.store(model.parameters()) ema.copy_to(model.parameters()) with torch.no_grad(): logger.info("Generating images...") model.eval() if args.train_text_encoder: text_encoder.eval() scheduler = AmusedScheduler.from_pretrained( args.pretrained_model_name_or_path, subfolder="scheduler", revision=args.revision, variant=args.variant, ) pipe = AmusedPipeline( transformer=accelerator.unwrap_model(model), tokenizer=tokenizer, text_encoder=text_encoder, vqvae=vq_model, scheduler=scheduler, ) pil_images = pipe(prompt=args.validation_prompts).images wandb_images = [ wandb.Image(image, caption=args.validation_prompts[i]) for i, image in enumerate(pil_images) ] wandb.log({"generated_images": wandb_images}, step=global_step + 1) model.train() if args.train_text_encoder: text_encoder.train() if args.use_ema: ema.restore(model.parameters()) global_step += 1 # Stop training if max steps is reached if global_step >= args.max_train_steps: break # End for accelerator.wait_for_everyone() # Evaluate and save checkpoint at the end of training save_checkpoint(args, accelerator, global_step) # Save the final trained checkpoint if accelerator.is_main_process: model = accelerator.unwrap_model(model) if args.use_ema: ema.copy_to(model.parameters()) model.save_pretrained(args.output_dir) accelerator.end_training() def save_checkpoint(args, accelerator, global_step): output_dir = args.output_dir # _before_ saving state, check if this save would set us over the `checkpoints_total_limit` if accelerator.is_main_process and args.checkpoints_total_limit is not None: checkpoints = os.listdir(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(output_dir, removing_checkpoint) shutil.rmtree(removing_checkpoint) save_path = Path(output_dir) / f"checkpoint-{global_step}" accelerator.save_state(save_path) logger.info(f"Saved state to {save_path}") if __name__ == "__main__": main(parse_args())