From 5d2abb1749b5d2f2f22ad603b5c2bf9182864520 Mon Sep 17 00:00:00 2001 From: Volpeon Date: Tue, 27 Sep 2022 10:03:12 +0200 Subject: More cleanup --- main.py | 850 ---------------------------------------------------------------- 1 file changed, 850 deletions(-) delete mode 100644 main.py (limited to 'main.py') diff --git a/main.py b/main.py deleted file mode 100644 index 51b64c1..0000000 --- a/main.py +++ /dev/null @@ -1,850 +0,0 @@ -import argparse -import itertools -import math -import os -import random -import datetime -from pathlib import Path -from typing import Optional - -import numpy as np -import torch -import torch.nn.functional as F -import torch.utils.checkpoint - -from accelerate import Accelerator -from accelerate.logging import get_logger -from accelerate.utils import LoggerType, set_seed -from diffusers import AutoencoderKL, DDPMScheduler, LMSDiscreteScheduler, StableDiffusionPipeline, UNet2DConditionModel -from diffusers.optimization import get_scheduler -from pipelines.stable_diffusion.no_check import NoCheck -from PIL import Image -from tqdm.auto import tqdm -from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer -from slugify import slugify -import json -import os - -from data import CSVDataModule - -logger = get_logger(__name__) - - -def parse_args(): - parser = argparse.ArgumentParser( - description="Simple example of a training script." - ) - parser.add_argument( - "--pretrained_model_name_or_path", - type=str, - default=None, - help="Path to pretrained model or model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--tokenizer_name", - type=str, - default=None, - help="Pretrained tokenizer name or path if not the same as model_name", - ) - parser.add_argument( - "--train_data_dir", - type=str, - default=None, - help="A folder containing the training data." - ) - parser.add_argument( - "--placeholder_token", - type=str, - default=None, - help="A token to use as a placeholder for the concept.", - ) - parser.add_argument( - "--initializer_token", - type=str, - default=None, - help="A token to use as initializer word." - ) - parser.add_argument( - "--vectors_per_token", - type=int, - default=1, - help="Vectors per token." - ) - parser.add_argument( - "--repeats", - type=int, - default=100, - help="How many times to repeat the training data.") - parser.add_argument( - "--output_dir", - type=str, - default="text-inversion-model", - help="The output directory where the model predictions and checkpoints will be written.", - ) - parser.add_argument( - "--seed", - type=int, - default=None, - help="A seed for reproducible training.") - parser.add_argument( - "--resolution", - type=int, - default=512, - help=( - "The resolution for input images, all the images in the train/validation dataset will be resized to this" - " resolution" - ), - ) - parser.add_argument( - "--center_crop", - action="store_true", - help="Whether to center crop images before resizing to resolution" - ) - parser.add_argument( - "--num_train_epochs", - type=int, - default=100) - parser.add_argument( - "--max_train_steps", - type=int, - default=5000, - help="Total number of training steps to perform. If provided, overrides num_train_epochs.", - ) - 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( - "--scale_lr", - action="store_true", - default=True, - 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( - "--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( - "--mixed_precision", - type=str, - default="no", - 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." - ), - ) - parser.add_argument( - "--local_rank", - type=int, - default=-1, - help="For distributed training: local_rank" - ) - parser.add_argument( - "--checkpoint_frequency", - type=int, - default=500, - help="How often to save a checkpoint and sample image", - ) - parser.add_argument( - "--sample_image_size", - type=int, - default=512, - help="Size of sample images", - ) - parser.add_argument( - "--stable_sample_batches", - type=int, - default=1, - help="Number of fixed seed sample batches to generate per checkpoint", - ) - parser.add_argument( - "--random_sample_batches", - type=int, - default=1, - help="Number of random seed sample batches to generate per checkpoint", - ) - parser.add_argument( - "--sample_batch_size", - type=int, - default=1, - help="Number of samples to generate per batch", - ) - parser.add_argument( - "--train_batch_size", - type=int, - default=1, - help="Batch size (per device) for the training dataloader." - ) - parser.add_argument( - "--sample_steps", - type=int, - default=50, - help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.", - ) - parser.add_argument( - "--resume_from", - type=str, - default=None, - help="Path to a directory to resume training from (ie, logs/token_name/2022-09-22T23-36-27)" - ) - parser.add_argument( - "--resume_checkpoint", - type=str, - default=None, - help="Path to a specific checkpoint to resume training from (ie, logs/token_name/2022-09-22T23-36-27/checkpoints/something.bin)." - ) - parser.add_argument( - "--config", - type=str, - default=None, - help="Path to a JSON configuration file containing arguments for invoking this script. If resume_from is given, its resume.json takes priority over this." - ) - - args = parser.parse_args() - if args.resume_from is not None: - with open(f"{args.resume_from}/resume.json", 'rt') as f: - args = parser.parse_args( - namespace=argparse.Namespace(**json.load(f)["args"])) - elif args.config is not None: - with open(args.config, 'rt') as f: - args = parser.parse_args( - namespace=argparse.Namespace(**json.load(f)["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 - - if args.train_data_dir is None: - raise ValueError("You must specify --train_data_dir") - - if args.pretrained_model_name_or_path is None: - raise ValueError("You must specify --pretrained_model_name_or_path") - - if args.placeholder_token is None: - raise ValueError("You must specify --placeholder_token") - - if args.initializer_token is None: - raise ValueError("You must specify --initializer_token") - - if args.output_dir is None: - raise ValueError("You must specify --output_dir") - - return args - - -def freeze_params(params): - for param in params: - param.requires_grad = False - - -def save_resume_file(basepath, args, extra={}): - info = {"args": vars(args)} - info["args"].update(extra) - with open(f"{basepath}/resume.json", "w") as f: - json.dump(info, f, indent=4) - - -def make_grid(images, rows, cols): - w, h = images[0].size - grid = Image.new('RGB', size=(cols*w, rows*h)) - for i, image in enumerate(images): - grid.paste(image, box=(i % cols*w, i//cols*h)) - return grid - - -class Checkpointer: - def __init__( - self, - datamodule, - accelerator, - vae, - unet, - tokenizer, - placeholder_token, - placeholder_token_id, - output_dir, - sample_image_size, - random_sample_batches, - sample_batch_size, - stable_sample_batches, - seed - ): - self.datamodule = datamodule - self.accelerator = accelerator - self.vae = vae - self.unet = unet - self.tokenizer = tokenizer - self.placeholder_token = placeholder_token - self.placeholder_token_id = placeholder_token_id - self.output_dir = output_dir - self.sample_image_size = sample_image_size - self.seed = seed - self.random_sample_batches = random_sample_batches - self.sample_batch_size = sample_batch_size - self.stable_sample_batches = stable_sample_batches - - @torch.no_grad() - def checkpoint(self, step, postfix, text_encoder, save_samples=True, path=None): - print("Saving checkpoint for step %d..." % step) - with self.accelerator.autocast(): - if path is None: - checkpoints_path = f"{self.output_dir}/checkpoints" - os.makedirs(checkpoints_path, exist_ok=True) - - unwrapped = self.accelerator.unwrap_model(text_encoder) - - # Save a checkpoint - learned_embeds = unwrapped.get_input_embeddings().weight[self.placeholder_token_id] - learned_embeds_dict = {self.placeholder_token: learned_embeds.detach().cpu()} - - filename = f"%s_%d_%s.bin" % (slugify(self.placeholder_token), step, postfix) - if path is not None: - torch.save(learned_embeds_dict, path) - else: - torch.save(learned_embeds_dict, f"{checkpoints_path}/{filename}") - torch.save(learned_embeds_dict, f"{checkpoints_path}/last.bin") - del unwrapped - del learned_embeds - - @torch.no_grad() - def save_samples(self, mode, step, text_encoder, height, width, guidance_scale, eta, num_inference_steps): - samples_path = f"{self.output_dir}/samples/{mode}" - os.makedirs(samples_path, exist_ok=True) - checker = NoCheck() - - unwrapped = self.accelerator.unwrap_model(text_encoder) - # Save a sample image - pipeline = StableDiffusionPipeline( - text_encoder=unwrapped, - vae=self.vae, - unet=self.unet, - tokenizer=self.tokenizer, - scheduler=LMSDiscreteScheduler( - beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear" - ), - safety_checker=NoCheck(), - feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"), - ).to(self.accelerator.device) - pipeline.enable_attention_slicing() - - data = { - "training": self.datamodule.train_dataloader(), - "validation": self.datamodule.val_dataloader(), - }[mode] - - if mode == "validation" and self.stable_sample_batches > 0 and step > 0: - stable_latents = torch.randn( - (self.sample_batch_size, pipeline.unet.in_channels, height // 8, width // 8), - device=pipeline.device, - generator=torch.Generator(device=pipeline.device).manual_seed(self.seed), - ) - - all_samples = [] - filename = f"stable_step_%d.png" % (step) - - data_enum = enumerate(data) - - # Generate and save stable samples - for i in range(0, self.stable_sample_batches): - prompt = [prompt for i, batch in data_enum for j, prompt in enumerate( - batch["prompt"]) if i * data.batch_size + j < self.sample_batch_size] - - with self.accelerator.autocast(): - samples = pipeline( - prompt=prompt, - height=self.sample_image_size, - latents=stable_latents[:len(prompt)], - width=self.sample_image_size, - guidance_scale=guidance_scale, - eta=eta, - num_inference_steps=num_inference_steps, - output_type='pil' - )["sample"] - - all_samples += samples - del samples - - image_grid = make_grid(all_samples, self.stable_sample_batches, self.sample_batch_size) - image_grid.save(f"{samples_path}/{filename}") - - del all_samples - del image_grid - del stable_latents - - all_samples = [] - filename = f"step_%d.png" % (step) - - data_enum = enumerate(data) - - # Generate and save random samples - for i in range(0, self.random_sample_batches): - prompt = [prompt for i, batch in data_enum for j, prompt in enumerate( - batch["prompt"]) if i * data.batch_size + j < self.sample_batch_size] - - with self.accelerator.autocast(): - samples = pipeline( - prompt=prompt, - height=self.sample_image_size, - width=self.sample_image_size, - guidance_scale=guidance_scale, - eta=eta, - num_inference_steps=num_inference_steps, - output_type='pil' - )["sample"] - - all_samples += samples - del samples - - image_grid = make_grid(all_samples, self.random_sample_batches, self.sample_batch_size) - image_grid.save(f"{samples_path}/{filename}") - - del all_samples - del image_grid - - del checker - del unwrapped - del pipeline - torch.cuda.empty_cache() - - -class ImageToLatents(): - def __init__(self, vae): - self.vae = vae - self.encoded_pixel_values_cache = {} - - @torch.no_grad() - def __call__(self, batch): - key = "|".join(batch["key"]) - if self.encoded_pixel_values_cache.get(key, None) is None: - self.encoded_pixel_values_cache[key] = self.vae.encode(batch["pixel_values"]).latent_dist - latents = self.encoded_pixel_values_cache[key].sample().detach().half() * 0.18215 - return latents - - -def main(): - args = parse_args() - - global_step_offset = 0 - if args.resume_from is not None: - basepath = f"{args.resume_from}" - print("Resuming state from %s" % args.resume_from) - with open(f"{basepath}/resume.json", 'r') as f: - state = json.load(f) - global_step_offset = state["args"].get("global_step", 0) - - print("We've trained %d steps so far" % global_step_offset) - else: - now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") - basepath = f"{args.output_dir}/{slugify(args.placeholder_token)}/{now}" - os.makedirs(basepath, exist_ok=True) - - accelerator = Accelerator( - log_with=LoggerType.TENSORBOARD, - logging_dir=f"{basepath}", - gradient_accumulation_steps=args.gradient_accumulation_steps, - mixed_precision=args.mixed_precision - ) - - # If passed along, set the training seed now. - if args.seed is not None: - set_seed(args.seed) - - # Load the tokenizer and add the placeholder token as a additional special token - if args.tokenizer_name: - tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name) - elif args.pretrained_model_name_or_path: - tokenizer = CLIPTokenizer.from_pretrained( - args.pretrained_model_name_or_path + '/tokenizer' - ) - - # Add the placeholder token in tokenizer - num_added_tokens = tokenizer.add_tokens(args.placeholder_token) - if num_added_tokens == 0: - raise ValueError( - f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different" - " `placeholder_token` that is not already in the tokenizer." - ) - - # Convert the initializer_token, placeholder_token to ids - initializer_token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False) - # Check if initializer_token is a single token or a sequence of tokens - if args.vectors_per_token % len(initializer_token_ids) != 0: - raise ValueError( - f"vectors_per_token ({args.vectors_per_token}) must be divisible by initializer token ({len(initializer_token_ids)}).") - - initializer_token_ids = torch.tensor(initializer_token_ids) - placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token) - - # Load models and create wrapper for stable diffusion - text_encoder = CLIPTextModel.from_pretrained( - args.pretrained_model_name_or_path + '/text_encoder', - ) - vae = AutoencoderKL.from_pretrained( - args.pretrained_model_name_or_path + '/vae', - ) - unet = UNet2DConditionModel.from_pretrained( - args.pretrained_model_name_or_path + '/unet', - ) - - if args.gradient_checkpointing: - unet.enable_gradient_checkpointing() - - slice_size = unet.config.attention_head_dim // 2 - unet.set_attention_slice(slice_size) - - # Resize the token embeddings as we are adding new special tokens to the tokenizer - text_encoder.resize_token_embeddings(len(tokenizer)) - - # Initialise the newly added placeholder token with the embeddings of the initializer token - token_embeds = text_encoder.get_input_embeddings().weight.data - - initializer_token_embeddings = text_encoder.get_input_embeddings()(initializer_token_ids) - - if args.resume_checkpoint is not None: - token_embeds[placeholder_token_id] = torch.load(args.resume_checkpoint)[ - args.placeholder_token] - else: - token_embeds[placeholder_token_id] = initializer_token_embeddings - - # Freeze vae and unet - freeze_params(vae.parameters()) - freeze_params(unet.parameters()) - # Freeze all parameters except for the token embeddings in text encoder - params_to_freeze = itertools.chain( - text_encoder.text_model.encoder.parameters(), - text_encoder.text_model.final_layer_norm.parameters(), - text_encoder.text_model.embeddings.position_embedding.parameters(), - ) - freeze_params(params_to_freeze) - - if args.scale_lr: - args.learning_rate = ( - args.learning_rate * args.gradient_accumulation_steps * - args.train_batch_size * accelerator.num_processes - ) - - # Initialize the optimizer - optimizer = torch.optim.AdamW( - text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings - lr=args.learning_rate, - betas=(args.adam_beta1, args.adam_beta2), - weight_decay=args.adam_weight_decay, - eps=args.adam_epsilon, - ) - - # TODO (patil-suraj): laod scheduler using args - noise_scheduler = DDPMScheduler( - beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, tensor_format="pt" - ) - - datamodule = CSVDataModule( - data_root=args.train_data_dir, batch_size=args.train_batch_size, tokenizer=tokenizer, - size=args.resolution, placeholder_token=args.placeholder_token, repeats=args.repeats, - center_crop=args.center_crop) - - datamodule.prepare_data() - datamodule.setup() - - train_dataloader = datamodule.train_dataloader() - val_dataloader = datamodule.val_dataloader() - - checkpointer = Checkpointer( - datamodule=datamodule, - accelerator=accelerator, - vae=vae, - unet=unet, - tokenizer=tokenizer, - placeholder_token=args.placeholder_token, - placeholder_token_id=placeholder_token_id, - output_dir=basepath, - sample_image_size=args.sample_image_size, - sample_batch_size=args.sample_batch_size, - random_sample_batches=args.random_sample_batches, - stable_sample_batches=args.stable_sample_batches, - seed=args.seed - ) - - # Scheduler and math around the number of training steps. - overrode_max_train_steps = False - num_update_steps_per_epoch = math.ceil( - (len(train_dataloader) + len(val_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 * args.gradient_accumulation_steps, - num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, - ) - - text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare( - text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler - ) - - # Move vae and unet to device - vae.to(accelerator.device) - unet.to(accelerator.device) - - # Keep vae and unet in eval mode as we don't train these - vae.eval() - unet.eval() - - # 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) + len(val_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("textual_inversion", 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 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}") - # Only show the progress bar once on each machine. - - global_step = 0 - min_val_loss = np.inf - - imageToLatents = ImageToLatents(vae) - - checkpointer.save_samples( - "validation", - 0, - text_encoder, - args.resolution, args.resolution, 7.5, 0.0, args.sample_steps) - - progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) - progress_bar.set_description("Global steps") - - local_progress_bar = tqdm(range(num_update_steps_per_epoch), disable=not accelerator.is_local_main_process) - local_progress_bar.set_description("Steps") - - try: - for epoch in range(args.num_train_epochs): - local_progress_bar.reset() - - text_encoder.train() - train_loss = 0.0 - - for step, batch in enumerate(train_dataloader): - with accelerator.accumulate(text_encoder): - with accelerator.autocast(): - # Convert images to latent space - latents = imageToLatents(batch) - - # Sample noise that we'll add to the latents - noise = torch.randn(latents.shape).to(latents.device) - bsz = latents.shape[0] - # Sample a random timestep for each image - timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, - (bsz,), device=latents.device).long() - - # Add noise to the latents according to the noise magnitude at each timestep - # (this is the forward diffusion process) - noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) - - # Get the text embedding for conditioning - encoder_hidden_states = text_encoder(batch["input_ids"])[0] - - # Predict the noise residual - noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample - - loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean() - - accelerator.backward(loss) - - # Zero out the gradients for all token embeddings except the newly added - # embeddings for the concept, as we only want to optimize the concept embeddings - if accelerator.num_processes > 1: - grads = text_encoder.module.get_input_embeddings().weight.grad - else: - grads = text_encoder.get_input_embeddings().weight.grad - # Get the index for tokens that we want to zero the grads for - index_grads_to_zero = torch.arange(len(tokenizer)) != placeholder_token_id - grads.data[index_grads_to_zero, :] = grads.data[index_grads_to_zero, :].fill_(0) - - optimizer.step() - if not accelerator.optimizer_step_was_skipped: - lr_scheduler.step() - optimizer.zero_grad() - - loss = loss.detach().item() - train_loss += loss - - # Checks if the accelerator has performed an optimization step behind the scenes - if accelerator.sync_gradients: - progress_bar.update(1) - local_progress_bar.update(1) - - global_step += 1 - - if global_step % args.checkpoint_frequency == 0 and global_step > 0 and accelerator.is_main_process: - progress_bar.clear() - local_progress_bar.clear() - - checkpointer.checkpoint(global_step + global_step_offset, "training", text_encoder) - save_resume_file(basepath, args, { - "global_step": global_step + global_step_offset, - "resume_checkpoint": f"{basepath}/checkpoints/last.bin" - }) - checkpointer.save_samples( - "training", - global_step + global_step_offset, - text_encoder, - args.resolution, args.resolution, 7.5, 0.0, args.sample_steps) - - logs = {"mode": "training", "loss": loss, "lr": lr_scheduler.get_last_lr()[0]} - local_progress_bar.set_postfix(**logs) - - if global_step >= args.max_train_steps: - break - - train_loss /= len(train_dataloader) - - text_encoder.eval() - val_loss = 0.0 - - for step, batch in enumerate(val_dataloader): - with torch.no_grad(), accelerator.autocast(): - latents = imageToLatents(batch) - - noise = torch.randn(latents.shape).to(latents.device) - bsz = latents.shape[0] - timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, - (bsz,), device=latents.device).long() - - noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) - - encoder_hidden_states = text_encoder(batch["input_ids"])[0] - - noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample - - noise_pred, noise = accelerator.gather_for_metrics((noise_pred, noise)) - - loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean() - - loss = loss.detach().item() - val_loss += loss - - if accelerator.sync_gradients: - progress_bar.update(1) - local_progress_bar.update(1) - - logs = {"mode": "validation", "loss": loss} - local_progress_bar.set_postfix(**logs) - - val_loss /= len(val_dataloader) - - accelerator.log({"train/loss": train_loss, "val/loss": val_loss}, step=global_step) - - progress_bar.clear() - local_progress_bar.clear() - - if min_val_loss > val_loss: - accelerator.print(f"Validation loss reached new minimum: {min_val_loss:.2e} -> {val_loss:.2e}") - checkpointer.checkpoint(global_step + global_step_offset, "milestone", text_encoder) - min_val_loss = val_loss - - checkpointer.save_samples( - "validation", - global_step + global_step_offset, - text_encoder, - args.resolution, args.resolution, 7.5, 0.0, args.sample_steps) - - accelerator.wait_for_everyone() - - # Create the pipeline using using the trained modules and save it. - if accelerator.is_main_process: - print("Finished! Saving final checkpoint and resume state.") - checkpointer.checkpoint( - global_step + global_step_offset, - "end", - text_encoder, - path=f"{basepath}/learned_embeds.bin" - ) - - save_resume_file(basepath, args, { - "global_step": global_step + global_step_offset, - "resume_checkpoint": f"{basepath}/checkpoints/last.bin" - }) - - accelerator.end_training() - - except KeyboardInterrupt: - if accelerator.is_main_process: - print("Interrupted, saving checkpoint and resume state...") - checkpointer.checkpoint(global_step + global_step_offset, "end", text_encoder) - save_resume_file(basepath, args, { - "global_step": global_step + global_step_offset, - "resume_checkpoint": f"{basepath}/checkpoints/last.bin" - }) - accelerator.end_training() - quit() - - -if __name__ == "__main__": - main() -- cgit v1.2.3-70-g09d2