More cleanup

1 files changed, 0 insertions, 850 deletions

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()