mcti.py

import argparse
import logging
import math
import os
import random
import shutil
import warnings
from pathlib import Path

import numpy as np
import PIL
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder

from packaging import version
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
from transformers import CLIPProcessor, CLIPModel

import diffusers
from diffusers import (
    AutoencoderKL,
    DDPMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    StableDiffusionPipeline,
    UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available


if is_wandb_available():
    import wandb

if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
    PIL_INTERPOLATION = {
        "linear": PIL.Image.Resampling.BILINEAR,
        "bilinear": PIL.Image.Resampling.BILINEAR,
        "bicubic": PIL.Image.Resampling.BICUBIC,
        "lanczos": PIL.Image.Resampling.LANCZOS,
        "nearest": PIL.Image.Resampling.NEAREST,
    }
else:
    PIL_INTERPOLATION = {
        "linear": PIL.Image.LINEAR,
        "bilinear": PIL.Image.BILINEAR,
        "bicubic": PIL.Image.BICUBIC,
        "lanczos": PIL.Image.LANCZOS,
        "nearest": PIL.Image.NEAREST,
    }
# ------------------------------------------------------------------------------

check_min_version("0.19.0.dev0")

logger = get_logger(__name__)


def save_model_card(repo_id: str, images=None, base_model=str, repo_folder=None):
    img_str = ""
    for i, image in enumerate(images):
        image.save(os.path.join(repo_folder, f"image_{i}.png"))
        img_str += f"![img_{i}](./image_{i}.png)\n"

    yaml = f"""
---
license: creativeml-openrail-m
base_model: {base_model}
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
inference: true
---
    """
    model_card = f"""
# Textual inversion text2image fine-tuning - {repo_id}
These are textual inversion adaption weights for {base_model}. You can find some example images in the following. \n
{img_str}
"""
    with open(os.path.join(repo_folder, "README.md"), "w") as f:
        f.write(yaml + model_card)


def log_validation(text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype, epoch):
    logger.info(
        f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
        f" {args.validation_prompt}."
    )
    pipeline = DiffusionPipeline.from_pretrained(
        args.pretrained_model_name_or_path,
        text_encoder=accelerator.unwrap_model(text_encoder),
        tokenizer=tokenizer,
        unet=unet,
        vae=vae,
        safety_checker=None,
        revision=args.revision,
        torch_dtype=weight_dtype,
    )
    pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
    pipeline = pipeline.to(accelerator.device)
    pipeline.set_progress_bar_config(disable=True)

    generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)
    images = []
    for _ in range(args.num_validation_images):
        with torch.autocast("cuda"):
            image = pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
        images.append(image)

    for tracker in accelerator.trackers:
        if tracker.name == "tensorboard":
            np_images = np.stack([np.asarray(img) for img in images])
            tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
        if tracker.name == "wandb":
            tracker.log(
                {
                    "validation": [
                        wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images)
                    ]
                }
            )

    del pipeline
    torch.cuda.empty_cache()
    return images


def save_progress(text_encoder, placeholder_token_ids, accelerator, args, save_path):
    learned_embeds = (
        accelerator.unwrap_model(text_encoder)
        .get_input_embeddings()
        .weight[min(placeholder_token_ids) : max(placeholder_token_ids) + 1]
    )
    learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}
    torch.save(learned_embeds_dict, save_path)


def parse_args():
    parser = argparse.ArgumentParser(description="Simple example of a training script.")
    parser.add_argument(
        "--save_steps",
        type=int,
        default=10,
        help="Save learned_embeds.bin every X updates steps.",
    )
    parser.add_argument(
        "--save_as_full_pipeline",
        action="store_true",
        help="Save the complete stable diffusion pipeline.",
    )
    parser.add_argument(
        "--num_vectors",
        type=int,
        default=1,
        help="How many textual inversion vectors shall be used to learn the concept.",
    )
    parser.add_argument(
        "--pretrained_model_name_or_path",
        type=str,
        default="runwayml/stable-diffusion-v1-5",
        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(
        "--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="data/flowers/images_class_n_shots/5/1", 
        help="A folder containing the training data."
    )
    parser.add_argument(
        "--class_label", type=int, 
        default=0, 
    )

    parser.add_argument(
        "--placeholder_token",
        type=str,
        default="<cat-toy>",
        help="A token to use as a placeholder for the concept.",
    )
    parser.add_argument(
        "--initializer_token", type=str, 
        default="flower", 
        help="A token to use as initializer word."
    )
    parser.add_argument("--learnable_property", type=str, 
                        default="object", 
                        help="Choose between 'object' and 'style'")
    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="output/cat_cls/pet_cat1",
        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("--cls_warm_up_step", type=int, default=0, \
                        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(
        "--train_batch_size", type=int, 
        default=1, 
        help="Batch size (per device) for the training dataloader."
    )
    parser.add_argument("--num_train_epochs", type=int, default=100)
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=500,
        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=4,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--gradient_checkpointing",
        action="store_true",
        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=5e-4,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument(
        "--scale_lr",
        action="store_true",
        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
    )
    parser.add_argument('--no-scale_lr', dest='scale_lr', action='store_false')
    parser.set_defaults(scale_lr=True)
    
    parser.add_argument(
        "--proto_feat",
        action="store_true",
    )
    parser.add_argument('--no-proto_feat', dest='proto_feat', action='store_false')
    parser.set_defaults(proto_feat=False)

    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=0, 
        help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument(
        "--lr_num_cycles",
        type=int,
        default=1,
        help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
    )
    parser.add_argument(
        "--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("--alpha", type=float, default=1.0, )
    parser.add_argument("--gamma", type=float, default=0.1, )
    parser.add_argument("--gamma_end", type=float, default=0.0001, help="The beta1 parameter for the Adam optimizer.")

    parser.add_argument("--logit_scale", type=float, default=10.0, )
    parser.add_argument(
        "--learn_logit",
        action="store_true",
        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
    )
    parser.add_argument('--no-learn_logit', dest='scale_lr', action='store_false')
    parser.set_defaults(learn_logit=True)

    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("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")

    parser.add_argument("--dataset_name", type=str, default="flowers", \
        choices=["flowers", "imagenet","foods", "pets", "aircrafts", "cars", "ucf","eurosat", "dtd", "caltech", "stl", "cifar", "person"])
    parser.add_argument("--project_name", type=str, default="TI_cls", )
    parser.add_argument("--group_name", type=str, default="5_shot", )
    parser.add_argument("--run_name", type=str, default="1",)
    parser.add_argument(
        "--hub_model_id",
        type=str,
        default=None,
        help="The name of the repository to keep in sync with the local `output_dir`.",
    )
    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(
        "--mixed_precision",
        type=str,
        default="fp16",
        choices=["no", "fp16", "bf16"],
        help=(
            "Whether to use mixed precision. Choose"
            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
            "and an Nvidia Ampere GPU."
        ),
    )
    parser.add_argument(
        "--allow_tf32",
        action="store_true",
        help=(
            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
        ),
    )
    parser.add_argument(
        "--report_to",
        type=str,
        default="tensorboard",
        help=(
            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
        ),
    )
    parser.add_argument(
        "--validation_prompt",
        type=str,
        default=None,
        help="A prompt that is used during validation to verify that the model is learning.",
    )
    parser.add_argument(
        "--num_validation_images",
        type=int,
        default=4,
        help="Number of images that should be generated during validation with `validation_prompt`.",
    )
    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(
        "--validation_epochs",
        type=int,
        default=None,
        help=(
            "Deprecated in favor of validation_steps. Run validation every X epochs. Validation consists of running the prompt"
            " `args.validation_prompt` multiple times: `args.num_validation_images`"
            " and logging the images."
        ),
    )
    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
    parser.add_argument(
        "--checkpointing_steps",
        type=int,
        default=500,
        help=(
            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
            " training using `--resume_from_checkpoint`."
        ),
    )
    parser.add_argument(
        "--checkpoints_total_limit",
        type=int,
        default=None,
        help=("Max number of checkpoints to store."),
    )
    parser.add_argument(
        "--resume_from_checkpoint",
        type=str,
        default=None,
        help=(
            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
        ),
    )
    parser.add_argument(
        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
    )

    parser.add_argument("--concept_category", type=str, default='flowers_5_shots',)
    parser.add_argument("--feat_dir", type=str, default="feature", )

    args = parser.parse_args()
    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
    if env_local_rank != -1 and env_local_rank != args.local_rank:
        args.local_rank = env_local_rank

    if args.train_data_dir is None:
        raise ValueError("You must specify a train data directory.")

    return args


imagenet_templates_small = [
    "a photo of a {}",
    "a rendering of a {}",
    "a cropped photo of the {}",
    "the photo of a {}",
    "a photo of a clean {}",
    "a photo of a dirty {}",
    "a dark photo of the {}",
    "a photo of my {}",
    "a photo of the cool {}",
    "a close-up photo of a {}",
    "a bright photo of the {}",
    "a cropped photo of a {}",
    "a photo of the {}",
    "a good photo of the {}",
    "a photo of one {}",
    "a close-up photo of the {}",
    "a rendition of the {}",
    "a photo of the clean {}",
    "a rendition of a {}",
    "a photo of a nice {}",
    "a good photo of a {}",
    "a photo of the nice {}",
    "a photo of the small {}",
    "a photo of the weird {}",
    "a photo of the large {}",
    "a photo of a cool {}",
    "a photo of a small {}",
]

imagenet_style_templates_small = [
    "a painting in the style of {}",
    "a rendering in the style of {}",
    "a cropped painting in the style of {}",
    "the painting in the style of {}",
    "a clean painting in the style of {}",
    "a dirty painting in the style of {}",
    "a dark painting in the style of {}",
    "a picture in the style of {}",
    "a cool painting in the style of {}",
    "a close-up painting in the style of {}",
    "a bright painting in the style of {}",
    "a cropped painting in the style of {}",
    "a good painting in the style of {}",
    "a close-up painting in the style of {}",
    "a rendition in the style of {}",
    "a nice painting in the style of {}",
    "a small painting in the style of {}",
    "a weird painting in the style of {}",
    "a large painting in the style of {}",
]


class TextualInversionDataset(Dataset):
    def __init__(
        self,
        data_root,
        tokenizer,
        learnable_property="object", 
        size=512,
        repeats=100,
        interpolation="bicubic",
        flip_p=0.5,
        set="train",
        placeholder_token="*",
        center_crop=False,
    ):
        self.data_root = data_root
        self.tokenizer = tokenizer
        self.learnable_property = learnable_property
        self.size = size
        self.placeholder_token = placeholder_token
        self.center_crop = center_crop
        self.flip_p = flip_p

        self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]

        self.num_images = len(self.image_paths)
        self._length = self.num_images

        if set == "train":
            self._length = self.num_images * repeats

        self.interpolation = {
            "linear": PIL_INTERPOLATION["linear"],
            "bilinear": PIL_INTERPOLATION["bilinear"],
            "bicubic": PIL_INTERPOLATION["bicubic"],
            "lanczos": PIL_INTERPOLATION["lanczos"],
        }[interpolation]

        self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
        self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)

    def __len__(self):
        return self._length

    def __getitem__(self, i):
        example = {}
        image = Image.open(self.image_paths[i % self.num_images])

        if not image.mode == "RGB":
            image = image.convert("RGB")

        placeholder_string = self.placeholder_token
        text = random.choice(self.templates).format(placeholder_string)

        example["input_ids"] = self.tokenizer(
            text,
            padding="max_length",
            truncation=True,
            max_length=self.tokenizer.model_max_length,
            return_tensors="pt",
        ).input_ids[0]

        img = np.array(image).astype(np.uint8)

        if self.center_crop:
            crop = min(img.shape[0], img.shape[1])
            (
                h,
                w,
            ) = (
                img.shape[0],
                img.shape[1],
            )
            img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]

        image = Image.fromarray(img)
        image = image.resize((self.size, self.size), resample=self.interpolation)

        image = self.flip_transform(image)
        image = np.array(image).astype(np.uint8)
        image = (image / 127.5 - 1.0).astype(np.float32)

        example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
        return example


def main():
    args = parse_args()

    feature_dir = os.path.join(args.feat_dir, args.concept_category)
    dataset_name= args.dataset_name
    if dataset_name == 'pets':
        concept_name_list = sorted(os.listdir(feature_dir))
    elif dataset_name == 'flowers':
        concept_name_list = list(range(1,103))
    elif dataset_name in ['foods', 'ucf']:
        concept_name_list = list(range(0,101))
    elif dataset_name in ['aircrafts', ]:
        concept_name_list = list(range(0,100))
    elif dataset_name == 'cars':
        concept_name_list = list(range(0,196))
    elif dataset_name == 'eurosat':
        concept_name_list = list(range(0,10))
    elif dataset_name == 'dtd':
        concept_name_list = list(range(0,47))
    elif dataset_name == 'caltech':
        concept_name_list = list(range(0,102))
    elif dataset_name in ['stl', 'cifar']:
        concept_name_list = list(range(0,10))
    elif dataset_name in ["person"]:
        concept_name_list = list(range(0,20))
    elif dataset_name in ["imagenet"]:
        concept_name_list = list(range(0,1000))
    else:
        raise NotImplementedError

    print(concept_name_list)

    image_embed_list=[]
    for index in range(len(concept_name_list)):
        curr_img_embed_list=[]
        concept_name = str(concept_name_list[index])
        concept_feat_dir = os.path.join(feature_dir,concept_name)
        concept_feat_list=os.listdir(concept_feat_dir)
        for concept_feat_name in concept_feat_list:
            curr_img_embed_list.append(torch.load(os.path.join(concept_feat_dir,concept_feat_name)))

        if not args.proto_feat:
            image_embed_list.append(curr_img_embed_list)
        else:
            image_embed_list.append(torch.cat(curr_img_embed_list).mean(dim=0).reshape(1,1,-1))

    gamma_a = -math.log(args.gamma_end) + math.log(args.gamma)
    gamma_b = -math.log(args.gamma) / gamma_a

    logging_dir = os.path.join(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 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.")

    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(accelerator.state, main_process_only=False)
    if accelerator.is_local_main_process:
        transformers.utils.logging.set_verbosity_warning()
        diffusers.utils.logging.set_verbosity_info()
    else:
        transformers.utils.logging.set_verbosity_error()
        diffusers.utils.logging.set_verbosity_error()

    if args.seed is not None:
        set_seed(args.seed)

    if args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)
    if accelerator.is_main_process:
        if args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)

        if args.push_to_hub:
            repo_id = create_repo(
                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
            ).repo_id

    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, subfolder="tokenizer")

    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
    text_encoder = CLIPTextModel.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
    )
    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
    unet = UNet2DConditionModel.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
    )

    clip_id="openai/clip-vit-large-patch14"
    doble_model_text_projection = CLIPModel.from_pretrained(clip_id).text_projection.to(accelerator.device)

    placeholder_tokens = [args.placeholder_token]

    if args.num_vectors < 1:
        raise ValueError(f"--num_vectors has to be larger or equal to 1, but is {args.num_vectors}")

    additional_tokens = []
    for i in range(1, args.num_vectors):
        additional_tokens.append(f"{args.placeholder_token}_{i}")
    placeholder_tokens += additional_tokens

    num_added_tokens = tokenizer.add_tokens(placeholder_tokens)
    if num_added_tokens != args.num_vectors:
        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."
        )

    token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
    if len(token_ids) > 1:
        raise ValueError("The initializer token must be a single token.")

    initializer_token_id = token_ids[0]
    placeholder_token_ids = tokenizer.convert_tokens_to_ids(placeholder_tokens)

    text_encoder.resize_token_embeddings(len(tokenizer))

    token_embeds = text_encoder.get_input_embeddings().weight.data
    with torch.no_grad():
        for token_id in placeholder_token_ids:
            token_embeds[token_id] = token_embeds[initializer_token_id].clone()

    vae.requires_grad_(False)
    unet.requires_grad_(False)
    text_encoder.text_model.encoder.requires_grad_(False)
    text_encoder.text_model.final_layer_norm.requires_grad_(False)
    text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)

    if args.gradient_checkpointing:
        unet.train()
        text_encoder.gradient_checkpointing_enable()
        unet.enable_gradient_checkpointing()

    if args.enable_xformers_memory_efficient_attention:
        if is_xformers_available():
            import xformers

            xformers_version = version.parse(xformers.__version__)
            if xformers_version == version.parse("0.0.16"):
                logger.warn(
                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
                )
            unet.enable_xformers_memory_efficient_attention()
        else:
            raise ValueError("xformers is not available. Make sure it is installed correctly")

    if args.allow_tf32:
        torch.backends.cuda.matmul.allow_tf32 = True

    if args.scale_lr:
        args.learning_rate = (
            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
        )

    weight_dtype = torch.float32
    if accelerator.mixed_precision == "fp16":
        weight_dtype = torch.float16
    elif accelerator.mixed_precision == "bf16":
        weight_dtype = torch.bfloat16

    optimizer = torch.optim.AdamW(
        text_encoder.get_input_embeddings().parameters(),  
        lr=args.learning_rate,
        betas=(args.adam_beta1, args.adam_beta2),
        weight_decay=args.adam_weight_decay,
        eps=args.adam_epsilon,
    )
    train_dataset = TextualInversionDataset(
        data_root=args.train_data_dir,
        tokenizer=tokenizer,
        size=args.resolution,
        placeholder_token=args.placeholder_token,
        repeats=args.repeats,
        learnable_property=args.learnable_property,
        center_crop=args.center_crop,
        set="train",
    )
    train_dataloader = torch.utils.data.DataLoader(
        train_dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers
    )
    if args.validation_epochs is not None:
        warnings.warn(
            f"FutureWarning: You are doing logging with validation_epochs={args.validation_epochs}."
            " Deprecated validation_epochs in favor of `validation_steps`"
            f"Setting `args.validation_steps` to {args.validation_epochs * len(train_dataset)}",
            FutureWarning,
            stacklevel=2,
        )
        args.validation_steps = args.validation_epochs * len(train_dataset)

    overrode_max_train_steps = False
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if args.max_train_steps is None:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
        overrode_max_train_steps = True

    lr_scheduler = get_scheduler(
        args.lr_scheduler,
        optimizer=optimizer,
        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
        num_cycles=args.lr_num_cycles * args.gradient_accumulation_steps,
    )

    text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        text_encoder, optimizer, train_dataloader, lr_scheduler
    )

    unet.to(accelerator.device, dtype=weight_dtype)
    vae.to(accelerator.device, dtype=weight_dtype)

    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if overrode_max_train_steps:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

    if accelerator.is_main_process:
        accelerator.init_trackers(args.project_name, \
                                  config=vars(args), \
                                  init_kwargs={"wandb":{"name":args.run_name}})

    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(train_dataset)}")
    logger.info(f"  Num Epochs = {args.num_train_epochs}")
    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
    logger.info(f"  Total optimization steps = {args.max_train_steps}")
    global_step = 0
    first_epoch = 0
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint != "latest":
            path = os.path.basename(args.resume_from_checkpoint)
        else:
            dirs = os.listdir(args.output_dir)
            dirs = [d for d in dirs if d.startswith("checkpoint")]
            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
            path = dirs[-1] if len(dirs) > 0 else None

        if path is None:
            accelerator.print(
                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
            )
            args.resume_from_checkpoint = None
        else:
            accelerator.print(f"Resuming from checkpoint {path}")
            accelerator.load_state(os.path.join(args.output_dir, path))
            global_step = int(path.split("-")[1])

            resume_global_step = global_step * args.gradient_accumulation_steps
            first_epoch = global_step // num_update_steps_per_epoch
            resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)

    progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
    progress_bar.set_description("Steps")

    orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.clone()

    for epoch in range(first_epoch, args.num_train_epochs):
        text_encoder.train()
        for step, batch in enumerate(train_dataloader):
            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
                if step % args.gradient_accumulation_steps == 0:
                    progress_bar.update(1)
                continue

            with accelerator.accumulate(text_encoder):
                latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample().detach()
                latents = latents * vae.config.scaling_factor

                noise = torch.randn_like(latents)
                bsz = latents.shape[0]
                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
                timesteps = timesteps.long()

                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)

                text_outputs = text_encoder(batch["input_ids"])
                text_pooled_output = text_outputs.pooler_output 
                text_embeds = doble_model_text_projection(text_pooled_output)
                selected_elements = [random.choice(sublist) for sublist in image_embed_list]
                image_embeds = torch.cat(selected_elements).to(dtype=weight_dtype).to(accelerator.device)

                image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True)
                image_embeds = image_embeds.to(dtype=weight_dtype)
                text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)
                text_embeds = text_embeds.to(dtype=weight_dtype)
                
                _logit_scale = args.logit_scale

                logits_per_text = torch.matmul(image_embeds, text_embeds.t()) * _logit_scale
                logits_per_image = logits_per_text.t()
                targets=torch.tensor([args.class_label,]).to(accelerator.device)

                cls_loss = F.cross_entropy(logits_per_image, targets)
                
                encoder_hidden_states = text_outputs[0].to(dtype=weight_dtype)
                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample

                if noise_scheduler.config.prediction_type == "epsilon":
                    target = noise
                elif noise_scheduler.config.prediction_type == "v_prediction":
                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
                else:
                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")

                mse_loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
                gamma = args.gamma
                loss = args.alpha*mse_loss + gamma * cls_loss

                accelerator.backward(loss)

                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

                index_no_updates = torch.ones((len(tokenizer),), dtype=torch.bool)
                index_no_updates[min(placeholder_token_ids) : max(placeholder_token_ids) + 1] = False

                with torch.no_grad():
                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
                        index_no_updates
                    ] = orig_embeds_params[index_no_updates]

            if accelerator.sync_gradients:
                images = []
                progress_bar.update(1)
                global_step += 1
                if global_step % args.save_steps == 0:
                    save_path = os.path.join(args.output_dir, f"learned_embeds-steps-{global_step}.bin")
                    save_progress(text_encoder, placeholder_token_ids, accelerator, args, save_path)

                if accelerator.is_main_process:
                    if global_step % args.checkpointing_steps == 0:
                        if args.checkpoints_total_limit is not None:
                            checkpoints = os.listdir(args.output_dir)
                            checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
                            checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))

                            if len(checkpoints) >= args.checkpoints_total_limit:
                                num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
                                removing_checkpoints = checkpoints[0:num_to_remove]

                                logger.info(
                                    f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
                                )
                                logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")

                                for removing_checkpoint in removing_checkpoints:
                                    removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
                                    shutil.rmtree(removing_checkpoint)

                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")

                    if args.validation_prompt is not None and global_step % args.validation_steps == 0:
                        images = log_validation(
                            text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype, epoch
                        )

            logs = {
                    "loss": loss.detach().item(), 
                    "mse_loss": mse_loss.detach().item(), 
                    "cls_loss": cls_loss.detach().item(), 
                    "gamma": gamma, 
                    "lr": lr_scheduler.get_last_lr()[0],
                    }
            progress_bar.set_postfix(**logs)
            accelerator.log(logs, step=global_step)

            if global_step >= args.max_train_steps:
                break
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        if args.push_to_hub and not args.save_as_full_pipeline:
            logger.warn("Enabling full model saving because --push_to_hub=True was specified.")
            save_full_model = True
        else:
            save_full_model = args.save_as_full_pipeline
        if save_full_model:
            pipeline = StableDiffusionPipeline.from_pretrained(
                args.pretrained_model_name_or_path,
                text_encoder=accelerator.unwrap_model(text_encoder),
                vae=vae,
                unet=unet,
                tokenizer=tokenizer,
            )
            pipeline.save_pretrained(args.output_dir)
        save_path = os.path.join(args.output_dir, "learned_embeds.bin")
        save_progress(text_encoder, placeholder_token_ids, accelerator, args, save_path)

        if args.push_to_hub:
            save_model_card(
                repo_id,
                images=images,
                base_model=args.pretrained_model_name_or_path,
                repo_folder=args.output_dir,
            )
            upload_folder(
                repo_id=repo_id,
                folder_path=args.output_dir,
                commit_message="End of training",
                ignore_patterns=["step_*", "epoch_*"],
            )

    accelerator.end_training()


if __name__ == "__main__":
    main()