Update train.py

train.py

@@ -1,1370 +1,106 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Fine-tuning script for Stable Diffusion XL for text2image."""
-
-import argparse
-import functools
-import gc
-import logging
-import math
-import os
-import random
-import shutil
-from contextlib import nullcontext
-from pathlib import Path
-
-import accelerate
-import datasets
-import numpy as np
+# %%
 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 DistributedType, ProjectConfiguration, set_seed
-from datasets import concatenate_datasets, load_dataset
-from huggingface_hub import create_repo, upload_folder
-from packaging import version
-from torchvision import transforms
-from torchvision.transforms.functional import crop
-from tqdm.auto import tqdm
-from transformers import AutoTokenizer, PretrainedConfig
-
-import diffusers
-from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionXLPipeline, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
-from diffusers.training_utils import EMAModel, compute_snr
-from diffusers.utils import check_min_version, is_wandb_available
-from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
-from diffusers.utils.import_utils import is_torch_npu_available, is_xformers_available
-from diffusers.utils.torch_utils import is_compiled_module
-
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.36.0.dev0")
-
-logger = get_logger(__name__)
-if is_torch_npu_available():
-    import torch_npu
-
-    torch.npu.config.allow_internal_format = False
-
-DATASET_NAME_MAPPING = {
-    "lambdalabs/naruto-blip-captions": ("image", "text"),
-}
-
-
-def save_model_card(
-    repo_id: str,
-    images: list = None,
-    validation_prompt: str = None,
-    base_model: str = None,
-    dataset_name: str = None,
-    repo_folder: str = None,
-    vae_path: str = None,
-):
-    img_str = ""
-    if images is not None:
-        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"
-
-    model_description = f"""
-# Text-to-image finetuning - {repo_id}
-
-This pipeline was finetuned from **{base_model}** on the **{dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompt: {validation_prompt}: \n
-{img_str}
-
-Special VAE used for training: {vae_path}.
-"""
-
-    model_card = load_or_create_model_card(
-        repo_id_or_path=repo_id,
-        from_training=True,
-        license="creativeml-openrail-m",
-        base_model=base_model,
-        model_description=model_description,
-        inference=True,
-    )
-
-    tags = [
-        "stable-diffusion-xl",
-        "stable-diffusion-xl-diffusers",
-        "text-to-image",
-        "diffusers-training",
-        "diffusers",
-    ]
-    model_card = populate_model_card(model_card, tags=tags)
-
-    model_card.save(os.path.join(repo_folder, "README.md"))
-
-
-def import_model_class_from_model_name_or_path(
-    pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
-):
-    text_encoder_config = PretrainedConfig.from_pretrained(
-        pretrained_model_name_or_path, subfolder=subfolder, revision=revision
-    )
-    model_class = text_encoder_config.architectures[0]
-
-    if model_class == "CLIPTextModel":
-        from transformers import CLIPTextModel
-
-        return CLIPTextModel
-    elif model_class == "CLIPTextModelWithProjection":
-        from transformers import CLIPTextModelWithProjection
-
-        return CLIPTextModelWithProjection
-    else:
-        raise ValueError(f"{model_class} is not supported.")
-
-
-def parse_args(input_args=None):
-    parser = argparse.ArgumentParser(description="Simple example of a training script.")
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        default=None,
-        required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--pretrained_vae_model_name_or_path",
-        type=str,
-        default=None,
-        help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.",
-    )
-    parser.add_argument(
-        "--revision",
-        type=str,
-        default=None,
-        required=False,
-        help="Revision of pretrained model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--variant",
-        type=str,
-        default=None,
-        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
-    )
-    parser.add_argument(
-        "--dataset_name",
-        type=str,
-        default=None,
-        help=(
-            "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
-            " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
-            " or to a folder containing files that 🤗 Datasets can understand."
-        ),
-    )
-    parser.add_argument(
-        "--dataset_config_name",
-        type=str,
-        default=None,
-        help="The config of the Dataset, leave as None if there's only one config.",
-    )
-    parser.add_argument(
-        "--train_data_dir",
-        type=str,
-        default=None,
-        help=(
-            "A folder containing the training data. Folder contents must follow the structure described in"
-            " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
-            " must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
-        ),
-    )
-    parser.add_argument(
-        "--image_column", type=str, default="image", help="The column of the dataset containing an image."
-    )
-    parser.add_argument(
-        "--caption_column",
-        type=str,
-        default="text",
-        help="The column of the dataset containing a caption or a list of captions.",
-    )
-    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_epochs",
-        type=int,
-        default=1,
-        help=(
-            "Run fine-tuning validation every X epochs. The validation process consists of running the prompt"
-            " `args.validation_prompt` multiple times: `args.num_validation_images`."
-        ),
-    )
-    parser.add_argument(
-        "--max_train_samples",
-        type=int,
-        default=None,
-        help=(
-            "For debugging purposes or quicker training, truncate the number of training examples to this "
-            "value if set."
-        ),
-    )
-    parser.add_argument(
-        "--proportion_empty_prompts",
-        type=float,
-        default=0,
-        help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="sdxl-model-finetuned",
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument(
-        "--cache_dir",
-        type=str,
-        default=None,
-        help="The directory where the downloaded models and datasets will be stored.",
-    )
-    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=1024,
-        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",
-        default=False,
-        action="store_true",
-        help=(
-            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
-            " cropped. The images will be resized to the resolution first before cropping."
-        ),
-    )
-    parser.add_argument(
-        "--random_flip",
-        action="store_true",
-        help="whether to randomly flip images horizontally",
-    )
-    parser.add_argument(
-        "--train_batch_size", type=int, default=16, 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=None,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument(
-        "--checkpointing_steps",
-        type=int,
-        default=500,
-        help=(
-            "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
-            " checkpoints in case they are better than the last checkpoint, and are also 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(
-        "--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=False,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument(
-        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
-    )
-    parser.add_argument(
-        "--timestep_bias_strategy",
-        type=str,
-        default="none",
-        choices=["earlier", "later", "range", "none"],
-        help=(
-            "The timestep bias strategy, which may help direct the model toward learning low or high frequency details."
-            " Choices: ['earlier', 'later', 'range', 'none']."
-            " The default is 'none', which means no bias is applied, and training proceeds normally."
-            " The value of 'later' will increase the frequency of the model's final training timesteps."
-        ),
-    )
-    parser.add_argument(
-        "--timestep_bias_multiplier",
-        type=float,
-        default=1.0,
-        help=(
-            "The multiplier for the bias. Defaults to 1.0, which means no bias is applied."
-            " A value of 2.0 will double the weight of the bias, and a value of 0.5 will halve it."
-        ),
-    )
-    parser.add_argument(
-        "--timestep_bias_begin",
-        type=int,
-        default=0,
-        help=(
-            "When using `--timestep_bias_strategy=range`, the beginning (inclusive) timestep to bias."
-            " Defaults to zero, which equates to having no specific bias."
-        ),
-    )
-    parser.add_argument(
-        "--timestep_bias_end",
-        type=int,
-        default=1000,
-        help=(
-            "When using `--timestep_bias_strategy=range`, the final timestep (inclusive) to bias."
-            " Defaults to 1000, which is the number of timesteps that Stable Diffusion is trained on."
-        ),
-    )
-    parser.add_argument(
-        "--timestep_bias_portion",
-        type=float,
-        default=0.25,
-        help=(
-            "The portion of timesteps to bias. Defaults to 0.25, which 25% of timesteps will be biased."
-            " A value of 0.5 will bias one half of the timesteps. The value provided for `--timestep_bias_strategy` determines"
-            " whether the biased portions are in the earlier or later timesteps."
-        ),
-    )
-    parser.add_argument(
-        "--snr_gamma",
-        type=float,
-        default=None,
-        help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
-        "More details here: https://huggingface.co/papers/2303.09556.",
-    )
-    parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
-    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(
-        "--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(
-        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
-    )
-    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("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    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(
-        "--prediction_type",
-        type=str,
-        default=None,
-        help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.",
-    )
-    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(
-        "--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(
-        "--mixed_precision",
-        type=str,
-        default=None,
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
-            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
-            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
-        ),
-    )
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-    parser.add_argument(
-        "--enable_npu_flash_attention", action="store_true", help="Whether or not to use npu flash attention."
-    )
-    parser.add_argument(
-        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
-    )
-    parser.add_argument("--noise_offset", type=float, default=0, help="The scale of noise offset.")
-    parser.add_argument(
-        "--image_interpolation_mode",
-        type=str,
-        default="lanczos",
-        choices=[
-            f.lower() for f in dir(transforms.InterpolationMode) if not f.startswith("__") and not f.endswith("__")
-        ],
-        help="The image interpolation method to use for resizing images.",
-    )
-
-    if input_args is not None:
-        args = parser.parse_args(input_args)
-    else:
-        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
-
-    # Sanity checks
-    if args.dataset_name is None and args.train_data_dir is None:
-        raise ValueError("Need either a dataset name or a training folder.")
-    if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
-        raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
-
-    return args
-
-
-# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt
-def encode_prompt(batch, text_encoders, tokenizers, proportion_empty_prompts, caption_column, is_train=True):
-    prompt_embeds_list = []
-    prompt_batch = batch[caption_column]
-
-    captions = []
-    for caption in prompt_batch:
-        if random.random() < proportion_empty_prompts:
-            captions.append("")
-        elif isinstance(caption, str):
-            captions.append(caption)
-        elif isinstance(caption, (list, np.ndarray)):
-            # take a random caption if there are multiple
-            captions.append(random.choice(caption) if is_train else caption[0])
-
-    with torch.no_grad():
-        for tokenizer, text_encoder in zip(tokenizers, text_encoders):
-            text_inputs = tokenizer(
-                captions,
-                padding="max_length",
-                max_length=tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            prompt_embeds = text_encoder(
-                text_input_ids.to(text_encoder.device),
-                output_hidden_states=True,
-                return_dict=False,
-            )
-
-            # We are only ALWAYS interested in the pooled output of the final text encoder
-            pooled_prompt_embeds = prompt_embeds[0]
-            prompt_embeds = prompt_embeds[-1][-2]
-            bs_embed, seq_len, _ = prompt_embeds.shape
-            prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
-            prompt_embeds_list.append(prompt_embeds)
-
-    prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
-    pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)
-    return {"prompt_embeds": prompt_embeds.cpu(), "pooled_prompt_embeds": pooled_prompt_embeds.cpu()}
-
-
-def compute_vae_encodings(batch, vae):
-    images = batch.pop("pixel_values")
-    pixel_values = torch.stack(list(images))
-    pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-    pixel_values = pixel_values.to(vae.device, dtype=vae.dtype)
-
-    with torch.no_grad():
-        model_input = vae.encode(pixel_values).latent_dist.sample()
-    model_input = model_input * vae.config.scaling_factor
-
-    # There might have slightly performance improvement
-    # by changing model_input.cpu() to accelerator.gather(model_input)
-    return {"model_input": model_input.cpu()}
-
-
-def generate_timestep_weights(args, num_timesteps):
-    weights = torch.ones(num_timesteps)
-
-    # Determine the indices to bias
-    num_to_bias = int(args.timestep_bias_portion * num_timesteps)
-
-    if args.timestep_bias_strategy == "later":
-        bias_indices = slice(-num_to_bias, None)
-    elif args.timestep_bias_strategy == "earlier":
-        bias_indices = slice(0, num_to_bias)
-    elif args.timestep_bias_strategy == "range":
-        # Out of the possible 1000 timesteps, we might want to focus on eg. 200-500.
-        range_begin = args.timestep_bias_begin
-        range_end = args.timestep_bias_end
-        if range_begin < 0:
-            raise ValueError(
-                "When using the range strategy for timestep bias, you must provide a beginning timestep greater or equal to zero."
-            )
-        if range_end > num_timesteps:
-            raise ValueError(
-                "When using the range strategy for timestep bias, you must provide an ending timestep smaller than the number of timesteps."
-            )
-        bias_indices = slice(range_begin, range_end)
-    else:  # 'none' or any other string
-        return weights
-    if args.timestep_bias_multiplier <= 0:
-        return ValueError(
-            "The parameter --timestep_bias_multiplier is not intended to be used to disable the training of specific timesteps."
-            " If it was intended to disable timestep bias, use `--timestep_bias_strategy none` instead."
-            " A timestep bias multiplier less than or equal to 0 is not allowed."
-        )
-
-    # Apply the bias
-    weights[bias_indices] *= args.timestep_bias_multiplier
+from transformers import AutoProcessor, AutoModelForVision2Seq, BitsAndBytesConfig
 
-    # Normalize
-    weights /= weights.sum()
 
-    return weights
-
-
-def main(args):
-    if args.report_to == "wandb" and args.hub_token is not None:
-        raise ValueError(
-            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
-        )
-
-    logging_dir = Path(args.output_dir, args.logging_dir)
-
-    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
-
-    if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
-        # due to pytorch#99272, MPS does not yet support bfloat16.
-        raise ValueError(
-            "Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
-        )
-
-    accelerator = Accelerator(
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        mixed_precision=args.mixed_precision,
-        log_with=args.report_to,
-        project_config=accelerator_project_config,
-    )
-
-    # Disable AMP for MPS.
-    if torch.backends.mps.is_available():
-        accelerator.native_amp = False
-
-    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.")
-        import wandb
-
-    # Make one log on every process with the configuration for debugging.
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    logger.info(accelerator.state, main_process_only=False)
-    if accelerator.is_local_main_process:
-        datasets.utils.logging.set_verbosity_warning()
-        transformers.utils.logging.set_verbosity_warning()
-        diffusers.utils.logging.set_verbosity_info()
-    else:
-        datasets.utils.logging.set_verbosity_error()
-        transformers.utils.logging.set_verbosity_error()
-        diffusers.utils.logging.set_verbosity_error()
-
-    # If passed along, set the training seed now.
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    # Handle the repository creation
-    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
-
-    # Load the tokenizers
-    tokenizer_one = AutoTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="tokenizer",
-        revision=args.revision,
-        use_fast=False,
-    )
-    tokenizer_two = AutoTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="tokenizer_2",
-        revision=args.revision,
-        use_fast=False,
-    )
-
-    # import correct text encoder classes
-    text_encoder_cls_one = import_model_class_from_model_name_or_path(
-        args.pretrained_model_name_or_path, args.revision
-    )
-    text_encoder_cls_two = import_model_class_from_model_name_or_path(
-        args.pretrained_model_name_or_path, args.revision, subfolder="text_encoder_2"
-    )
-
-    # Load scheduler and models
-    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
-    # Check for terminal SNR in combination with SNR Gamma
-    text_encoder_one = text_encoder_cls_one.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
-    )
-    text_encoder_two = text_encoder_cls_two.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
-    )
-    vae_path = (
-        args.pretrained_model_name_or_path
-        if args.pretrained_vae_model_name_or_path is None
-        else args.pretrained_vae_model_name_or_path
-    )
-    vae = AutoencoderKL.from_pretrained(
-        vae_path,
-        subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
-        revision=args.revision,
-        variant=args.variant,
+def load_model(model_name="datalab-to/chandra", device_id=0):
+    bnb_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_compute_dtype=torch.bfloat16,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_use_double_quant=True,
     )
-    unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
-    )
-
-    # Freeze vae and text encoders.
-    vae.requires_grad_(False)
-    text_encoder_one.requires_grad_(False)
-    text_encoder_two.requires_grad_(False)
-    # Set unet as trainable.
-    unet.train()
-
-    # For mixed precision training we cast all non-trainable weights to half-precision
-    # as these weights are only used for inference, keeping weights in full precision is not required.
-    weight_dtype = torch.float32
-    if accelerator.mixed_precision == "fp16":
-        weight_dtype = torch.float16
-    elif accelerator.mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-
-    # Move unet, vae and text_encoder to device and cast to weight_dtype
-    # The VAE is in float32 to avoid NaN losses.
-    vae.to(accelerator.device, dtype=torch.float32)
-    text_encoder_one.to(accelerator.device, dtype=weight_dtype)
-    text_encoder_two.to(accelerator.device, dtype=weight_dtype)
-
-    # Create EMA for the unet.
-    if args.use_ema:
-        ema_unet = UNet2DConditionModel.from_pretrained(
-            args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
-        )
-        ema_unet = EMAModel(ema_unet.parameters(), model_cls=UNet2DConditionModel, model_config=ema_unet.config)
-    if args.enable_npu_flash_attention:
-        if is_torch_npu_available():
-            logger.info("npu flash attention enabled.")
-            unet.enable_npu_flash_attention()
-        else:
-            raise ValueError("npu flash attention requires torch_npu extensions and is supported only on npu devices.")
-    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.warning(
-                    "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")
-
-    # `accelerate` 0.16.0 will have better support for customized saving
-    if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
-        # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
-        def save_model_hook(models, weights, output_dir):
-            if accelerator.is_main_process:
-                if args.use_ema:
-                    ema_unet.save_pretrained(os.path.join(output_dir, "unet_ema"))
-
-                for i, model in enumerate(models):
-                    model.save_pretrained(os.path.join(output_dir, "unet"))
-
-                    # make sure to pop weight so that corresponding model is not saved again
-                    if weights:
-                        weights.pop()
-
-        def load_model_hook(models, input_dir):
-            if args.use_ema:
-                load_model = EMAModel.from_pretrained(os.path.join(input_dir, "unet_ema"), UNet2DConditionModel)
-                ema_unet.load_state_dict(load_model.state_dict())
-                ema_unet.to(accelerator.device)
-                del load_model
-
-            for _ in range(len(models)):
-                # pop models so that they are not loaded again
-                model = models.pop()
-
-                # load diffusers style into model
-                load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
-                model.register_to_config(**load_model.config)
 
-                model.load_state_dict(load_model.state_dict())
-                del load_model
+    processor = AutoProcessor.from_pretrained(model_name)
 
-        accelerator.register_save_state_pre_hook(save_model_hook)
-        accelerator.register_load_state_pre_hook(load_model_hook)
-
-    if args.gradient_checkpointing:
-        unet.enable_gradient_checkpointing()
-
-    # Enable TF32 for faster training on Ampere GPUs,
-    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
-    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
-        )
-
-    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
-    if args.use_8bit_adam:
-        try:
-            import bitsandbytes as bnb
-        except ImportError:
-            raise ImportError(
-                "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
-            )
-
-        optimizer_class = bnb.optim.AdamW8bit
-    else:
-        optimizer_class = torch.optim.AdamW
-
-    # Optimizer creation
-    params_to_optimize = unet.parameters()
-    optimizer = optimizer_class(
-        params_to_optimize,
-        lr=args.learning_rate,
-        betas=(args.adam_beta1, args.adam_beta2),
-        weight_decay=args.adam_weight_decay,
-        eps=args.adam_epsilon,
+    model = AutoModelForVision2Seq.from_pretrained(
+        model_name,
+        quantization_config=bnb_config,
+        dtype=torch.bfloat16,
+        device_map={"": device_id},
     )
 
-    # Get the datasets: you can either provide your own training and evaluation files (see below)
-    # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
-
-    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
-    # download the dataset.
-    if args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        dataset = load_dataset(
-            args.dataset_name, args.dataset_config_name, cache_dir=args.cache_dir, data_dir=args.train_data_dir
-        )
-    else:
-        data_files = {}
-        if args.train_data_dir is not None:
-            data_files["train"] = os.path.join(args.train_data_dir, "**")
-        dataset = load_dataset(
-            "imagefolder",
-            data_files=data_files,
-            cache_dir=args.cache_dir,
-        )
-        # See more about loading custom images at
-        # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
-
-    # Preprocessing the datasets.
-    # We need to tokenize inputs and targets.
-    column_names = dataset["train"].column_names
-
-    # 6. Get the column names for input/target.
-    dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)
-    if args.image_column is None:
-        image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
-    else:
-        image_column = args.image_column
-        if image_column not in column_names:
-            raise ValueError(
-                f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
-            )
-    if args.caption_column is None:
-        caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
-    else:
-        caption_column = args.caption_column
-        if caption_column not in column_names:
-            raise ValueError(
-                f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
-            )
-
-    # Preprocessing the datasets.
-    interpolation = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper(), None)
-    if interpolation is None:
-        raise ValueError(f"Unsupported interpolation mode {interpolation=}.")
-    train_resize = transforms.Resize(args.resolution, interpolation=interpolation)
-    train_crop = transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution)
-    train_flip = transforms.RandomHorizontalFlip(p=1.0)
-    train_transforms = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])
-
-    def preprocess_train(examples):
-        images = [image.convert("RGB") for image in examples[image_column]]
-        # image aug
-        original_sizes = []
-        all_images = []
-        crop_top_lefts = []
-        for image in images:
-            original_sizes.append((image.height, image.width))
-            image = train_resize(image)
-            if args.random_flip and random.random() < 0.5:
-                # flip
-                image = train_flip(image)
-            if args.center_crop:
-                y1 = max(0, int(round((image.height - args.resolution) / 2.0)))
-                x1 = max(0, int(round((image.width - args.resolution) / 2.0)))
-                image = train_crop(image)
-            else:
-                y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))
-                image = crop(image, y1, x1, h, w)
-            crop_top_left = (y1, x1)
-            crop_top_lefts.append(crop_top_left)
-            image = train_transforms(image)
-            all_images.append(image)
-
-        examples["original_sizes"] = original_sizes
-        examples["crop_top_lefts"] = crop_top_lefts
-        examples["pixel_values"] = all_images
-        return examples
+    return processor, model
 
-    with accelerator.main_process_first():
-        if args.max_train_samples is not None:
-            dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
-        # Set the training transforms
-        train_dataset = dataset["train"].with_transform(preprocess_train)
+# %%
+def caption_batch(batch, processor, model):
+    images = batch["image"]
 
-    # Let's first compute all the embeddings so that we can free up the text encoders
-    # from memory. We will pre-compute the VAE encodings too.
-    text_encoders = [text_encoder_one, text_encoder_two]
-    tokenizers = [tokenizer_one, tokenizer_two]
-    compute_embeddings_fn = functools.partial(
-        encode_prompt,
-        text_encoders=text_encoders,
-        tokenizers=tokenizers,
-        proportion_empty_prompts=args.proportion_empty_prompts,
-        caption_column=args.caption_column,
-    )
-    compute_vae_encodings_fn = functools.partial(compute_vae_encodings, vae=vae)
-    with accelerator.main_process_first():
-        from datasets.fingerprint import Hasher
-
-        # fingerprint used by the cache for the other processes to load the result
-        # details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401
-        new_fingerprint = Hasher.hash(args)
-        new_fingerprint_for_vae = Hasher.hash((vae_path, args))
-        train_dataset_with_embeddings = train_dataset.map(
-            compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint
-        )
-        train_dataset_with_vae = train_dataset.map(
-            compute_vae_encodings_fn,
-            batched=True,
-            batch_size=args.train_batch_size,
-            new_fingerprint=new_fingerprint_for_vae,
-        )
-        precomputed_dataset = concatenate_datasets(
-            [train_dataset_with_embeddings, train_dataset_with_vae.remove_columns(["image", "text"])], axis=1
-        )
-        precomputed_dataset = precomputed_dataset.with_transform(preprocess_train)
-
-    del compute_vae_encodings_fn, compute_embeddings_fn, text_encoder_one, text_encoder_two
-    del text_encoders, tokenizers, vae
-    gc.collect()
-    if is_torch_npu_available():
-        torch_npu.npu.empty_cache()
-    elif torch.cuda.is_available():
-        torch.cuda.empty_cache()
-
-    def collate_fn(examples):
-        model_input = torch.stack([torch.tensor(example["model_input"]) for example in examples])
-        original_sizes = [example["original_sizes"] for example in examples]
-        crop_top_lefts = [example["crop_top_lefts"] for example in examples]
-        prompt_embeds = torch.stack([torch.tensor(example["prompt_embeds"]) for example in examples])
-        pooled_prompt_embeds = torch.stack([torch.tensor(example["pooled_prompt_embeds"]) for example in examples])
-
-        return {
-            "model_input": model_input,
-            "prompt_embeds": prompt_embeds,
-            "pooled_prompt_embeds": pooled_prompt_embeds,
-            "original_sizes": original_sizes,
-            "crop_top_lefts": crop_top_lefts,
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": image},
+                {
+                    "type": "text",
+                    "text": "Describe the image, and skip mentioning that it's illustrated or from anime.",
+                },
+            ],
         }
+        for image in images
+    ]
 
-    # DataLoaders creation:
-    train_dataloader = torch.utils.data.DataLoader(
-        precomputed_dataset,
-        shuffle=True,
-        collate_fn=collate_fn,
-        batch_size=args.train_batch_size,
-        num_workers=args.dataloader_num_workers,
-    )
-
-    # Scheduler and math around the number of training steps.
-    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,
-    )
+    inputs = processor.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_dict=True,
+        return_tensors="pt",
+    ).to(model.device)
 
-    # Prepare everything with our `accelerator`.
-    unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        unet, optimizer, train_dataloader, lr_scheduler
-    )
+    with torch.no_grad():
+        generated = model.generate(**inputs)
 
-    if args.use_ema:
-        ema_unet.to(accelerator.device)
+    decoded = processor.batch_decode(generated)
+    captions = [d.split("<|im_start|>assistant\n")[-1] for d in decoded]
 
-    # 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) / 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)
+    return {"image": images, "text": captions}
 
-    # 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("text2image-fine-tune-sdxl", config=vars(args))
+# %%
+import datasets
+from datasets import Dataset
+from typing import cast
+from concurrent.futures import ThreadPoolExecutor
 
-    # Function for unwrapping if torch.compile() was used in accelerate.
-    def unwrap_model(model):
-        model = accelerator.unwrap_model(model)
-        model = model._orig_mod if is_compiled_module(model) else model
-        return model
 
-    if torch.backends.mps.is_available() or "playground" in args.pretrained_model_name_or_path:
-        autocast_ctx = nullcontext()
-    else:
-        autocast_ctx = torch.autocast(accelerator.device.type)
+input_dataset = "none-yet/anime-captions"
+output_dataset = "nroggendorff/anime-captions"
 
-    # Train!
-    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+loaded = datasets.load_dataset(input_dataset, split="train")
 
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(precomputed_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 isinstance(loaded, datasets.DatasetDict):
+    ds = cast(Dataset, loaded["train"])
+else:
+    ds = cast(Dataset, loaded)
 
-    # Potentially load in the weights and states from a previous save
-    if args.resume_from_checkpoint:
-        if args.resume_from_checkpoint != "latest":
-            path = os.path.basename(args.resume_from_checkpoint)
-        else:
-            # Get the most recent checkpoint
-            dirs = os.listdir(args.output_dir)
-            dirs = [d for d in dirs if d.startswith("checkpoint")]
-            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
-            path = dirs[-1] if len(dirs) > 0 else None
+num_gpus = torch.cuda.device_count()
+models = [load_model(device_id=i) for i in range(num_gpus)]
 
-        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
-            initial_global_step = 0
-        else:
-            accelerator.print(f"Resuming from checkpoint {path}")
-            accelerator.load_state(os.path.join(args.output_dir, path))
-            global_step = int(path.split("-")[1])
+batch_size = 8
+shard_size = len(ds) // num_gpus
 
-            initial_global_step = global_step
-            first_epoch = global_step // num_update_steps_per_epoch
 
-    else:
-        initial_global_step = 0
+def process_shard(shard_idx, processor, model):
+    start = shard_idx * shard_size
+    end = start + shard_size if shard_idx < num_gpus - 1 else len(ds)
+    shard = ds.select(range(start, end))
 
-    progress_bar = tqdm(
-        range(0, args.max_train_steps),
-        initial=initial_global_step,
-        desc="Steps",
-        # Only show the progress bar once on each machine.
-        disable=not accelerator.is_local_main_process,
+    return shard.map(
+        lambda batch: caption_batch(batch, processor, model),
+        batched=True,
+        batch_size=batch_size,
+        remove_columns=shard.column_names,
     )
 
-    for epoch in range(first_epoch, args.num_train_epochs):
-        train_loss = 0.0
-        for step, batch in enumerate(train_dataloader):
-            with accelerator.accumulate(unet):
-                # Sample noise that we'll add to the latents
-                model_input = batch["model_input"].to(accelerator.device)
-                noise = torch.randn_like(model_input)
-                if args.noise_offset:
-                    # https://www.crosslabs.org//blog/diffusion-with-offset-noise
-                    noise += args.noise_offset * torch.randn(
-                        (model_input.shape[0], model_input.shape[1], 1, 1), device=model_input.device
-                    )
-
-                bsz = model_input.shape[0]
-                if args.timestep_bias_strategy == "none":
-                    # Sample a random timestep for each image without bias.
-                    timesteps = torch.randint(
-                        0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device
-                    )
-                else:
-                    # Sample a random timestep for each image, potentially biased by the timestep weights.
-                    # Biasing the timestep weights allows us to spend less time training irrelevant timesteps.
-                    weights = generate_timestep_weights(args, noise_scheduler.config.num_train_timesteps).to(
-                        model_input.device
-                    )
-                    timesteps = torch.multinomial(weights, bsz, replacement=True).long()
-
-                # Add noise to the model input according to the noise magnitude at each timestep
-                # (this is the forward diffusion process)
-                noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps).to(dtype=weight_dtype)
-
-                # time ids
-                def compute_time_ids(original_size, crops_coords_top_left):
-                    # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids
-                    target_size = (args.resolution, args.resolution)
-                    add_time_ids = list(original_size + crops_coords_top_left + target_size)
-                    add_time_ids = torch.tensor([add_time_ids], device=accelerator.device, dtype=weight_dtype)
-                    return add_time_ids
-
-                add_time_ids = torch.cat(
-                    [compute_time_ids(s, c) for s, c in zip(batch["original_sizes"], batch["crop_top_lefts"])]
-                )
-
-                # Predict the noise residual
-                unet_added_conditions = {"time_ids": add_time_ids}
-                prompt_embeds = batch["prompt_embeds"].to(accelerator.device, dtype=weight_dtype)
-                pooled_prompt_embeds = batch["pooled_prompt_embeds"].to(accelerator.device)
-                unet_added_conditions.update({"text_embeds": pooled_prompt_embeds})
-                model_pred = unet(
-                    noisy_model_input,
-                    timesteps,
-                    prompt_embeds,
-                    added_cond_kwargs=unet_added_conditions,
-                    return_dict=False,
-                )[0]
-
-                # Get the target for loss depending on the prediction type
-                if args.prediction_type is not None:
-                    # set prediction_type of scheduler if defined
-                    noise_scheduler.register_to_config(prediction_type=args.prediction_type)
-
-                if noise_scheduler.config.prediction_type == "epsilon":
-                    target = noise
-                elif noise_scheduler.config.prediction_type == "v_prediction":
-                    target = noise_scheduler.get_velocity(model_input, noise, timesteps)
-                elif noise_scheduler.config.prediction_type == "sample":
-                    # We set the target to latents here, but the model_pred will return the noise sample prediction.
-                    target = model_input
-                    # We will have to subtract the noise residual from the prediction to get the target sample.
-                    model_pred = model_pred - noise
-                else:
-                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
-                if args.snr_gamma is None:
-                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-                else:
-                    # Compute loss-weights as per Section 3.4 of https://huggingface.co/papers/2303.09556.
-                    # Since we predict the noise instead of x_0, the original formulation is slightly changed.
-                    # This is discussed in Section 4.2 of the same paper.
-                    snr = compute_snr(noise_scheduler, timesteps)
-                    mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(
-                        dim=1
-                    )[0]
-                    if noise_scheduler.config.prediction_type == "epsilon":
-                        mse_loss_weights = mse_loss_weights / snr
-                    elif noise_scheduler.config.prediction_type == "v_prediction":
-                        mse_loss_weights = mse_loss_weights / (snr + 1)
-
-                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
-                    loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
-                    loss = loss.mean()
-
-                # Gather the losses across all processes for logging (if we use distributed training).
-                avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
-                train_loss += avg_loss.item() / args.gradient_accumulation_steps
-
-                # Backpropagate
-                accelerator.backward(loss)
-                if accelerator.sync_gradients:
-                    params_to_clip = unet.parameters()
-                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
-                optimizer.step()
-                lr_scheduler.step()
-                optimizer.zero_grad()
 
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            if accelerator.sync_gradients:
-                if args.use_ema:
-                    ema_unet.step(unet.parameters())
-                progress_bar.update(1)
-                global_step += 1
-                accelerator.log({"train_loss": train_loss}, step=global_step)
-                train_loss = 0.0
-
-                # DeepSpeed requires saving weights on every device; saving weights only on the main process would cause issues.
-                if accelerator.distributed_type == DistributedType.DEEPSPEED or accelerator.is_main_process:
-                    if global_step % args.checkpointing_steps == 0:
-                        # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
-                        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]))
-
-                            # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
-                            if len(checkpoints) >= args.checkpoints_total_limit:
-                                num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
-                                removing_checkpoints = checkpoints[0:num_to_remove]
-
-                                logger.info(
-                                    f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
-                                )
-                                logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
-
-                                for removing_checkpoint in removing_checkpoints:
-                                    removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
-                                    shutil.rmtree(removing_checkpoint)
-
-                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
-                        accelerator.save_state(save_path)
-                        logger.info(f"Saved state to {save_path}")
-
-            logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
-            progress_bar.set_postfix(**logs)
-
-            if global_step >= args.max_train_steps:
-                break
-
-        if accelerator.is_main_process:
-            if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
-                logger.info(
-                    f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
-                    f" {args.validation_prompt}."
-                )
-                if args.use_ema:
-                    # Store the UNet parameters temporarily and load the EMA parameters to perform inference.
-                    ema_unet.store(unet.parameters())
-                    ema_unet.copy_to(unet.parameters())
-
-                # create pipeline
-                vae = AutoencoderKL.from_pretrained(
-                    vae_path,
-                    subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
-                    revision=args.revision,
-                    variant=args.variant,
-                )
-                pipeline = StableDiffusionXLPipeline.from_pretrained(
-                    args.pretrained_model_name_or_path,
-                    vae=vae,
-                    unet=accelerator.unwrap_model(unet),
-                    revision=args.revision,
-                    variant=args.variant,
-                    torch_dtype=weight_dtype,
-                )
-                if args.prediction_type is not None:
-                    scheduler_args = {"prediction_type": args.prediction_type}
-                    pipeline.scheduler = pipeline.scheduler.from_config(pipeline.scheduler.config, **scheduler_args)
-
-                pipeline = pipeline.to(accelerator.device)
-                pipeline.set_progress_bar_config(disable=True)
-
-                # run inference
-                generator = (
-                    torch.Generator(device=accelerator.device).manual_seed(args.seed)
-                    if args.seed is not None
-                    else None
-                )
-                pipeline_args = {"prompt": args.validation_prompt}
-
-                with autocast_ctx:
-                    images = [
-                        pipeline(**pipeline_args, generator=generator, num_inference_steps=25).images[0]
-                        for _ in range(args.num_validation_images)
-                    ]
-
-                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
-                if is_torch_npu_available():
-                    torch_npu.npu.empty_cache()
-                elif torch.cuda.is_available():
-                    torch.cuda.empty_cache()
-
-                if args.use_ema:
-                    # Switch back to the original UNet parameters.
-                    ema_unet.restore(unet.parameters())
-
-    accelerator.wait_for_everyone()
-    if accelerator.is_main_process:
-        unet = unwrap_model(unet)
-        if args.use_ema:
-            ema_unet.copy_to(unet.parameters())
-
-        # Serialize pipeline.
-        vae = AutoencoderKL.from_pretrained(
-            vae_path,
-            subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
-            revision=args.revision,
-            variant=args.variant,
-            torch_dtype=weight_dtype,
-        )
-        pipeline = StableDiffusionXLPipeline.from_pretrained(
-            args.pretrained_model_name_or_path,
-            unet=unet,
-            vae=vae,
-            revision=args.revision,
-            variant=args.variant,
-            torch_dtype=weight_dtype,
-        )
-        if args.prediction_type is not None:
-            scheduler_args = {"prediction_type": args.prediction_type}
-            pipeline.scheduler = pipeline.scheduler.from_config(pipeline.scheduler.config, **scheduler_args)
-        pipeline.save_pretrained(args.output_dir)
-
-        # run inference
-        images = []
-        if args.validation_prompt and args.num_validation_images > 0:
-            pipeline = pipeline.to(accelerator.device)
-            generator = (
-                torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed is not None else None
-            )
-
-            with autocast_ctx:
-                images = [
-                    pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
-                    for _ in range(args.num_validation_images)
-                ]
-
-            for tracker in accelerator.trackers:
-                if tracker.name == "tensorboard":
-                    np_images = np.stack([np.asarray(img) for img in images])
-                    tracker.writer.add_images("test", np_images, epoch, dataformats="NHWC")
-                if tracker.name == "wandb":
-                    tracker.log(
-                        {
-                            "test": [
-                                wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
-                                for i, image in enumerate(images)
-                            ]
-                        }
-                    )
-
-        if args.push_to_hub:
-            save_model_card(
-                repo_id=repo_id,
-                images=images,
-                validation_prompt=args.validation_prompt,
-                base_model=args.pretrained_model_name_or_path,
-                dataset_name=args.dataset_name,
-                repo_folder=args.output_dir,
-                vae_path=args.pretrained_vae_model_name_or_path,
-            )
-            upload_folder(
-                repo_id=repo_id,
-                folder_path=args.output_dir,
-                commit_message="End of training",
-                ignore_patterns=["step_*", "epoch_*"],
-            )
-
-    accelerator.end_training()
+with ThreadPoolExecutor(max_workers=num_gpus) as executor:
+    futures = [
+        executor.submit(process_shard, i, proc, model)
+        for i, (proc, model) in enumerate(models)
+    ]
+    shards = [f.result() for f in futures]
 
+ds = datasets.concatenate_datasets(shards)
 
-if __name__ == "__main__":
-    args = parse_args()
-    main(args)
+# %%
+ds.push_to_hub(output_dataset, create_pr=True)