From 68540b27849564994d921968a36faa9b997e626d Mon Sep 17 00:00:00 2001 From: Volpeon Date: Wed, 21 Dec 2022 09:17:25 +0100 Subject: Moved common training code into separate module --- train_dreambooth.py | 126 +++++++--------------------------------------------- 1 file changed, 16 insertions(+), 110 deletions(-) (limited to 'train_dreambooth.py') diff --git a/train_dreambooth.py b/train_dreambooth.py index 0f8fece..9749c62 100644 --- a/train_dreambooth.py +++ b/train_dreambooth.py @@ -16,7 +16,6 @@ from accelerate.utils import LoggerType, set_seed from diffusers import AutoencoderKL, DDPMScheduler, DPMSolverMultistepScheduler, UNet2DConditionModel from diffusers.optimization import get_scheduler, get_cosine_with_hard_restarts_schedule_with_warmup from diffusers.training_utils import EMAModel -from PIL import Image from tqdm.auto import tqdm from transformers import CLIPTextModel, CLIPTokenizer from slugify import slugify @@ -25,6 +24,7 @@ from common import load_text_embeddings from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion from data.csv import CSVDataModule from training.optimization import get_one_cycle_schedule +from training.util import AverageMeter, CheckpointerBase, freeze_params, save_args from models.clip.prompt import PromptProcessor logger = get_logger(__name__) @@ -385,41 +385,7 @@ def parse_args(): return args -def save_args(basepath: Path, args, extra={}): - info = {"args": vars(args)} - info["args"].update(extra) - with open(basepath.joinpath("args.json"), "w") as f: - json.dump(info, f, indent=4) - - -def freeze_params(params): - for param in params: - param.requires_grad = False - - -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 AverageMeter: - def __init__(self, name=None): - self.name = name - self.reset() - - def reset(self): - self.sum = self.count = self.avg = 0 - - def update(self, val, n=1): - self.sum += val * n - self.count += n - self.avg = self.sum / self.count - - -class Checkpointer: +class Checkpointer(CheckpointerBase): def __init__( self, datamodule, @@ -437,9 +403,20 @@ class Checkpointer: sample_image_size, sample_batches, sample_batch_size, - seed + seed, ): - self.datamodule = datamodule + super().__init__( + datamodule=datamodule, + output_dir=output_dir, + instance_identifier=instance_identifier, + placeholder_token=placeholder_token, + placeholder_token_id=placeholder_token_id, + sample_image_size=sample_image_size, + seed=seed or torch.random.seed(), + sample_batches=sample_batches, + sample_batch_size=sample_batch_size + ) + self.accelerator = accelerator self.vae = vae self.unet = unet @@ -447,14 +424,6 @@ class Checkpointer: self.tokenizer = tokenizer self.text_encoder = text_encoder self.scheduler = scheduler - self.output_dir = output_dir - self.instance_identifier = instance_identifier - self.placeholder_token = placeholder_token - self.placeholder_token_id = placeholder_token_id - self.sample_image_size = sample_image_size - self.seed = seed or torch.random.seed() - self.sample_batches = sample_batches - self.sample_batch_size = sample_batch_size @torch.no_grad() def save_model(self): @@ -481,8 +450,6 @@ class Checkpointer: @torch.no_grad() def save_samples(self, step, num_inference_steps, guidance_scale=7.5, eta=0.0): - samples_path = Path(self.output_dir).joinpath("samples") - unet = self.ema_unet.averaged_model if self.ema_unet is not None else self.accelerator.unwrap_model(self.unet) text_encoder = self.accelerator.unwrap_model(self.text_encoder) @@ -495,72 +462,11 @@ class Checkpointer: ).to(self.accelerator.device) pipeline.set_progress_bar_config(dynamic_ncols=True) - train_data = self.datamodule.train_dataloader() - val_data = self.datamodule.val_dataloader() - - generator = torch.Generator(device=pipeline.device).manual_seed(self.seed) - stable_latents = torch.randn( - (self.sample_batch_size, pipeline.unet.in_channels, self.sample_image_size // 8, self.sample_image_size // 8), - device=pipeline.device, - generator=generator, - ) - - with torch.autocast("cuda"), torch.inference_mode(): - for pool, data, latents in [("stable", val_data, stable_latents), ("val", val_data, None), ("train", train_data, None)]: - all_samples = [] - file_path = samples_path.joinpath(pool, f"step_{step}.jpg") - file_path.parent.mkdir(parents=True, exist_ok=True) - - data_enum = enumerate(data) - - batches = [ - batch - for j, batch in data_enum - if j * data.batch_size < self.sample_batch_size * self.sample_batches - ] - prompts = [ - prompt.format(identifier=self.instance_identifier) - for batch in batches - for prompt in batch["prompts"] - ] - nprompts = [ - prompt - for batch in batches - for prompt in batch["nprompts"] - ] - - for i in range(self.sample_batches): - prompt = prompts[i * self.sample_batch_size:(i + 1) * self.sample_batch_size] - nprompt = nprompts[i * self.sample_batch_size:(i + 1) * self.sample_batch_size] - - samples = pipeline( - prompt=prompt, - negative_prompt=nprompt, - height=self.sample_image_size, - width=self.sample_image_size, - image=latents[:len(prompt)] if latents is not None else None, - generator=generator if latents is not None else None, - guidance_scale=guidance_scale, - eta=eta, - num_inference_steps=num_inference_steps, - output_type='pil' - ).images - - all_samples += samples - - del samples - - image_grid = make_grid(all_samples, self.sample_batches, self.sample_batch_size) - image_grid.save(file_path, quality=85) - - del all_samples - del image_grid + super().save_samples(pipeline, step, num_inference_steps, guidance_scale, eta) del unet del text_encoder del pipeline - del generator - del stable_latents if torch.cuda.is_available(): torch.cuda.empty_cache() -- cgit v1.2.3-54-g00ecf