train_text_to_image_gligen_sam.py

#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 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

import argparse
import logging
import math
import os
import shutil
from pathlib import Path
import accelerate
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import torch.utils.data
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.state import AcceleratorState
from accelerate.utils import ProjectConfiguration, set_seed

from huggingface_hub import create_repo, upload_folder
from packaging import version
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
from transformers.utils import ContextManagers

import diffusers
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionGLIGENPipeline, UNet2DConditionModel
from diffusers.optimization import get_scheduler
from diffusers.training_utils import EMAModel
from diffusers.utils import check_min_version, deprecate, is_wandb_available, make_image_grid
from diffusers.utils.import_utils import is_xformers_available


from utils.parser import load_args
from dataset.sam_dataset import SAMDataset


if is_wandb_available():
    import wandb

# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.20.0")

logger = get_logger(__name__, log_level="INFO")


# def save_model_card(
#     args,
#     repo_id: str,
#     images=None,
#     repo_folder=None,
# ):
#     img_str = ""
#     if len(images) > 0:
#         image_grid = make_image_grid(images, 1, len(args.validation_prompts))
#         image_grid.save(os.path.join(repo_folder, "val_imgs_grid.png"))
#         img_str += "![val_imgs_grid](./val_imgs_grid.png)\n"

#     yaml = f"""
# ---
# license: creativeml-openrail-m
# base_model: {args.pretrained_model_name_or_path}
# datasets:
# - {args.dataset_name}
# tags:
# - stable-diffusion
# - stable-diffusion-diffusers
# - text-to-image
# - diffusers
# inference: true
# ---
#     """
#     model_card = f"""
# # Text-to-image finetuning - {repo_id}

# This pipeline was finetuned from **{args.pretrained_model_name_or_path}** on the **{args.dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: {args.validation_prompts}: \n
# {img_str}

# ## Pipeline usage

# You can use the pipeline like so:

# ```python
# from diffusers import DiffusionPipeline
# import torch

# pipeline = DiffusionPipeline.from_pretrained("{repo_id}", torch_dtype=torch.float16)
# prompt = "{args.validation_prompts[0]}"
# image = pipeline(prompt).images[0]
# image.save("my_image.png")
# ```

# ## Training info

# These are the key hyperparameters used during training:

# * Epochs: {args.num_train_epochs}
# * Learning rate: {args.learning_rate}
# * Batch size: {args.train_batch_size}
# * Gradient accumulation steps: {args.gradient_accumulation_steps}
# * Image resolution: {args.resolution}
# * Mixed-precision: {args.mixed_precision}

# """
#     wandb_info = ""
#     if is_wandb_available():
#         wandb_run_url = None
#         if wandb.run is not None:
#             wandb_run_url = wandb.run.url

#     if wandb_run_url is not None:
#         wandb_info = f"""
# More information on all the CLI arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).
# """

#     model_card += wandb_info

#     with open(os.path.join(repo_folder, "README.md"), "w") as f:
#         f.write(yaml + model_card)


def log_validation(vae, text_encoder, tokenizer, unet, args, accelerator, weight_dtype, epoch):
    logger.info("Running validation... ")

    pipeline = StableDiffusionGLIGENPipeline.from_pretrained(
        args.pretrained_model_name_or_path,
        vae=accelerator.unwrap_model(vae),
        text_encoder=accelerator.unwrap_model(text_encoder),
        tokenizer=tokenizer,
        unet=accelerator.unwrap_model(unet),
        safety_checker=None,
        revision=args.revision,
        torch_dtype=weight_dtype,
    )
    pipeline = pipeline.to(accelerator.device)
    pipeline.set_progress_bar_config(disable=True)

    if args.enable_xformers_memory_efficient_attention:
        pipeline.enable_xformers_memory_efficient_attention()

    if args.seed is None:
        generator = None
    else:
        generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)

    # The validation prompts should contain specified objects, irrespective of using semantic grounding or not!
    args.validation_prompts = ["An image of grassland with a dog."]
    
    # Each rectangular box is defined as a List[float] of 4 elements [xmin, ymin, xmax, ymax] 
    # where each value is between [0,1].
    gligen_phrases = ["a dog"]
    gligen_boxes = [[0.1, 0.6, 0.3, 0.8]]
    
    images = []
    for i in range(len(args.validation_prompts)):
        with torch.autocast("cuda"):
            image = pipeline(
                args.validation_prompts[i], num_inference_steps=50, generator=generator, gligen_phrases=gligen_phrases, gligen_boxes=gligen_boxes, gligen_scheduled_sampling_beta=1, height=args.resolution, width=args.resolution
            ).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")
        elif tracker.name == "wandb":
            tracker.log(
                {
                    "validation": [
                        wandb.Image(image, caption=f"{i}: {args.validation_prompts[i]}")
                        for i, image in enumerate(images)
                    ]
                }
            )
        else:
            logger.warn(f"image logging not implemented for {tracker.name}")

    del pipeline
    torch.cuda.empty_cache()

    return images


def parse_args():
    parser = argparse.ArgumentParser(description="Simple example of a training script.")
    parser.add_argument(
        "--input_perturbation", type=float, default=0, help="The scale of input perturbation. Recommended 0.1."
    )
    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(
        "--revision",
        type=str,
        default=None,
        required=False,
        help="Revision of pretrained model identifier from huggingface.co/models.",
    )
    parser.add_argument(
        "--override_unet_weights",
        type=str,
        default=None,
        help="The path to weights to override the pretrained weights in UNet.",
    )
    parser.add_argument(
        "--data_path",
        type=str,
        default="./data",
        help="Data path.",
    )
    # 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('--config', metavar='C', type=str, nargs='?',
                        help='path to config', default='./dataset/sam_boxtext2img.yaml')
    # From detectron2
    parser.add_argument(
        "--opts",
        help="Modify config options using the command-line 'KEY VALUE' pairs",
        default=[],
        nargs=argparse.REMAINDER,
    )
    # parser.add_argument(
    #     "--train_data_config",
    #     type=str,
    #     default="./dataset/alldata_boxtext2img.yaml",
    #     help=(
    #         "Dataset configuration path."
    #     ),
    # )
    # 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(
    #     "--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(
    #     "--validation_prompts",
    #     type=str,
    #     default=None,
    #     nargs="+",
    #     help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."),
    # )
    parser.add_argument(
        "--output_dir",
        type=str,
        default="sd-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.")
    # resolution is used in inference (we read latents directly in 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(
    #     "--truncate_pad_boxes",
    #     type=int,
    #     default=32,
    #     help=(
    #         "Truncate or pad to reach this number of boxes"
    #     ),
    # )
    
    # 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(
        "--no_caption_only",
        action="store_true",
        help="whether to drop the caption when the boxes are dropped",
    )
    parser.add_argument(
        "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
    )
    # `num_train_epochs` is not supported (since we use iterable as dataset)
    # 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(
        "--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(
        "--no_semantic_grounding",
        action="store_true",
        help="If enabled, do not pass semantic information into grounding.",
    )
    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(
        "--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://arxiv.org/abs/2303.09556.",
    )
    parser.add_argument(
        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
    )
    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("--use_ema", action="store_true", help="Whether to use EMA model.")
    parser.add_argument(
        "--non_ema_revision",
        type=str,
        default=None,
        required=False,
        help=(
            "Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or"
            " remote repository specified with --pretrained_model_name_or_path."
        ),
    )
    parser.add_argument(
        "--dataloader_num_workers",
        type=int,
        default=4,
        help=(
            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
        ),
    )
    parser.add_argument("--prob_use_caption", type=float, default=0.5, help="The prob of keeping caption.")
    parser.add_argument("--prob_use_boxes", type=float, default=0.9, help="The prob of keeping boxes.")
    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.prediciton_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(
        "--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(
        "--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("--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("--enable_flash_attention", action="store_true", help="Enable flash attention.")
    parser.add_argument("--noise_offset", type=float, default=0, help="The scale of noise offset.")
    # parser.add_argument(
    #     "--validation_epochs",
    #     type=int,
    #     default=5,
    #     help="Run validation every X epochs.",
    # )
    parser.add_argument(
        "--validation_steps",
        type=int,
        default=10,
        help="Run validation every X steps.",
    )
    parser.add_argument(
        "--tracker_project_name",
        type=str,
        default="text2image-fine-tune",
        help=(
            "The `project_name` argument passed to Accelerator.init_trackers for"
            " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
        ),
    )

    args = parser.parse_args()

    config_path = args.config
    print(f"Loading config from {config_path}")

    config = load_args(config_path, cli_opts=args.opts)
    print(str(config))
 
    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.")

    # default to using the same revision for the non-ema model if not specified
    if args.non_ema_revision is None:
        args.non_ema_revision = args.revision

    return args, config


def main():
    args, config = parse_args()

    if args.non_ema_revision is not None:
        deprecate(
            "non_ema_revision!=None",
            "0.15.0",
            message=(
                "Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to"
                " use `--variant=non_ema` instead."
            ),
        )
    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,
        # we don't process the dataloader so we don't need to specify
        # dispatch_batches=False,
        # split_batches=False
    )

    # 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)

    if args.enable_flash_attention:
        print("Flash attention enabled")
        
        from flash_attn import flash_attn_func
        
        torch_scaled_dot_product_attention = F.scaled_dot_product_attention
        
        def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False):
            # torch convention: B, num heads, seq len, C
            # print(f"Using flash attention, query: {query.shape}, key: {key.shape}, value: {value.shape}")
            assert attn_mask is None, attn_mask
            if query.shape[-1] > 256:
                return torch_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal)
            return torch.permute(flash_attn_func(torch.permute(query, [0, 2, 1, 3]), torch.permute(key, [0, 2, 1, 3]), torch.permute(value, [0, 2, 1, 3]), dropout_p=dropout_p, causal=is_causal), [0, 2, 1, 3])

        F.scaled_dot_product_attention = scaled_dot_product_attention
        
        # Use memory efficient attention as a fallback
        torch.backends.cuda.enable_flash_sdp(False)
        torch.backends.cuda.enable_mem_efficient_sdp(True)
        torch.backends.cuda.enable_math_sdp(False)
    else:
        print("Flash attention is not enabled. Using mem-efficient attention.")
        torch.backends.cuda.enable_flash_sdp(False)
        torch.backends.cuda.enable_mem_efficient_sdp(True)
        torch.backends.cuda.enable_math_sdp(False)

    # 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 scheduler, tokenizer and models.
    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
    tokenizer = CLIPTokenizer.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
    )

    def deepspeed_zero_init_disabled_context_manager():
        """
        returns either a context list that includes one that will disable zero.Init or an empty context list
        """
        deepspeed_plugin = AcceleratorState().deepspeed_plugin if accelerate.state.is_initialized() else None
        if deepspeed_plugin is None:
            return []

        return [deepspeed_plugin.zero3_init_context_manager(enable=False)]

    # Currently Accelerate doesn't know how to handle multiple models under Deepspeed ZeRO stage 3.
    # For this to work properly all models must be run through `accelerate.prepare`. But accelerate
    # will try to assign the same optimizer with the same weights to all models during
    # `deepspeed.initialize`, which of course doesn't work.
    #
    # For now the following workaround will partially support Deepspeed ZeRO-3, by excluding the 2
    # frozen models from being partitioned during `zero.Init` which gets called during
    # `from_pretrained` So CLIPTextModel and AutoencoderKL will not enjoy the parameter sharding
    # across multiple gpus and only UNet2DConditionModel will get ZeRO sharded.
    with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
        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_additional_kwargs = dict(attention_type="gated", low_cpu_mem_usage=False, device_map=None)

    unet: UNet2DConditionModel = UNet2DConditionModel.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="unet", revision=args.non_ema_revision, **unet_additional_kwargs
    )
    if args.override_unet_weights:
        logger.info(f"Overriding UNet weights with {args.override_unet_weights}")
        mismatches = unet.load_state_dict(torch.load(args.override_unet_weights, map_location="cpu"), strict=False)

        assert len(mismatches.unexpected_keys) == 0, f"There are unexpected keys: {mismatches.unexpected_keys}"
        assert all(["fuser" in key or "position_net" in key for key in mismatches.missing_keys]), f"There are missing keys that are not fuser or position_net: {[key for key in mismatches.missing_keys if not ('fuser' in key or 'position_net' in key)]}"

    logger.info(f"{unet}")

    # Freeze vae and text_encoder
    vae.requires_grad_(False)
    text_encoder.requires_grad_(False)
    for name, param in unet.named_parameters():
        if "position_net" in name or ".fuser" in name:
            param.requires_grad_(True)
            logger.info(f"Has grad: {name}")
        else:
            param.requires_grad_(False)
            logger.info(f"No grad: {name}")

    # 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, **unet_additional_kwargs
        )
        ema_unet = EMAModel(ema_unet.parameters(), model_cls=UNet2DConditionModel, model_config=ema_unet.config)

    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")


    def compute_snr(timesteps):
        """
        Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
        """
        alphas_cumprod = noise_scheduler.alphas_cumprod
        sqrt_alphas_cumprod = alphas_cumprod**0.5
        sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5

        # Expand the tensors.
        # Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
        sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
        while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
            sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
        alpha = sqrt_alphas_cumprod.expand(timesteps.shape)

        sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
        while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
            sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
        sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)

        # Compute SNR.
        snr = (alpha / sigma) ** 2
        return snr

    # `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 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
                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 i 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

        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
        )

    # Initialize the optimizer
    # if args.use_8bit_adam:
    #     try:
    #         import bitsandbytes as bnb
    #     except ImportError:
    #         raise ImportError(
    #             "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
    #         )

    #     optimizer_cls = bnb.optim.AdamW8bit
    # else:
    #     optimizer_cls = torch.optim.AdamW
    
    optimizer_cls = torch.optim.AdamW

    optimizer = optimizer_cls(
        unet.parameters(),
        lr=args.learning_rate,
        betas=(args.adam_beta1, args.adam_beta2),
        weight_decay=args.adam_weight_decay,
        eps=args.adam_epsilon,
    )

    # 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)}"
    #         )

    def transform(data_item):
        caption = data_item['caption']
        inputs = tokenizer(
            caption, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
        )
        data_item['input_ids'] = inputs.input_ids[0]
        return data_item

    # SAMDataset repeats infinitely
    ddp_rank = accelerator.process_index
    num_ddp_processes = accelerator.num_processes
    train_dataset = SAMDataset(data_path=args.data_path, train_shards=config.train_shards, prob_use_caption=args.prob_use_caption, prob_use_boxes=args.prob_use_boxes, box_confidence_th=0.25, batch_size=args.train_batch_size, transform=transform, shard_shuffle_seed=None, ddp_rank=ddp_rank, num_ddp_processes=num_ddp_processes, no_caption_only=args.no_caption_only)

    # DataLoaders creation:

    def collate_fn(examples):
        # batch size is set to 1 (we handle batching in the dataset)
        examples = examples[0]
        collated_examples = {}
        
        for key in ['latents', 'input_ids', 'boxes', 'text_masks']:
            collated_examples[key] = torch.stack([example[key] for example in examples])
            
        for key in ['latents', 'boxes', 'text_masks']:
            collated_examples[key] = collated_examples[key].float()
        
        collated_examples['latents'] = collated_examples['latents'].to(memory_format=torch.contiguous_format)

        for key in ['id', 'box_phrases', 'caption']:
            collated_examples[key] = [example[key] for example in examples]

        return collated_examples

    # DataLoaders creation:
    train_dataloader = torch.utils.data.DataLoader(
        train_dataset,
        # iterable dataset does not support shuffling (shuffling is implemented in the dataset)
        shuffle=False,
        # shuffle=True,
        collate_fn=collate_fn,
        batch_size=1,
        # batch_size=args.train_batch_size,
        num_workers=args.dataloader_num_workers,
        pin_memory=True
    )

    # 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 * accelerator.num_processes,
        num_training_steps=args.max_train_steps * accelerator.num_processes,
    )

    # Prepare everything with our `accelerator`.
    unet_config = unet.config
    # unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
    #     unet, optimizer, train_dataloader, lr_scheduler
    # )

    # we manually manage the train_dataloader (otherwise it will use `IterableDatasetShard`, which we do not need)
    unet, optimizer, lr_scheduler = accelerator.prepare(
        unet, optimizer, lr_scheduler
    )

    if args.use_ema:
        ema_unet.to(accelerator.device)

    # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) 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
        args.mixed_precision = accelerator.mixed_precision
    elif accelerator.mixed_precision == "bf16":
        weight_dtype = torch.bfloat16
        args.mixed_precision = accelerator.mixed_precision

    # Move text_encode and vae to gpu and cast to weight_dtype
    text_encoder.to(accelerator.device, dtype=weight_dtype)
    vae.to(accelerator.device, dtype=weight_dtype)

    # 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)

    # 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:
        tracker_config = dict(vars(args))
        # tracker_config.pop("validation_prompts")
        accelerator.init_trackers(args.tracker_project_name, tracker_config, init_kwargs={"wandb":{"name": os.path.basename(args.output_dir)}})

        # Log code with wandb
        wandb.run.log_code(".")

    # Train!
    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

    # 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

        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)
            first_epoch = 0
            resume_step = resume_global_step

    # Only show the progress bar once on each machine.
    # progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
    # We skip steps so we should always from step 0.
    progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
    progress_bar.set_description("Steps")

    # The dataset is repeating (so we only use 1 epoch).
    args.num_train_epochs = 1
    for epoch in range(first_epoch, args.num_train_epochs):
        unet.train()
        train_loss = 0.0
        for step, batch in enumerate(train_dataloader):
            # Skip steps until we reach the resumed step
            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
            # gligen data preprocessing
            boxes_batch = batch['boxes'].to(accelerator.device, dtype=weight_dtype)
            masks_batch = batch['text_masks'].to(accelerator.device, dtype=weight_dtype)
            text_embeddings_batch = []
            for gligen_phrase in batch['box_phrases']:
                # 32 should correspond to max_boxes_per_image
                text_embeddings = torch.zeros(
                    32, unet_config.cross_attention_dim, device=accelerator.device, dtype=text_encoder.dtype
                )
                N = len(gligen_phrase)
                if N > 0:
                    # prepare batched input to the PositionNet (boxes, phrases, mask)
                    # Get tokens for phrases from pre-trained CLIPTokenizer
                    tokenizer_inputs = tokenizer(gligen_phrase, padding=True, return_tensors="pt").to(accelerator.device)
                    text_embeddings[:N] = text_encoder(**tokenizer_inputs).pooler_output
                text_embeddings_batch.append(text_embeddings)
            text_embeddings_batch = torch.stack(text_embeddings_batch, dim=0)

            with accelerator.accumulate(unet):
                # Convert images to latent space
                # latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
                # latents = latents * vae.config.scaling_factor

                # process input ids and latents
                input_ids = batch['input_ids'].to(accelerator.device)
                latents = batch["latents"].to(accelerator.device)

                # Sample noise that we'll add to the latents
                noise = torch.randn_like(latents)
                if args.noise_offset:
                    # https://www.crosslabs.org//blog/diffusion-with-offset-noise
                    noise += args.noise_offset * torch.randn(
                        (latents.shape[0], latents.shape[1], 1, 1), device=latents.device
                    )
                if args.input_perturbation:
                    new_noise = noise + args.input_perturbation * torch.randn_like(noise)
                bsz = latents.shape[0]
                # Sample a random timestep for each image
                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
                timesteps = timesteps.long()

                # Add noise to the latents according to the noise magnitude at each timestep
                # (this is the forward diffusion process)
                if args.input_perturbation:
                    noisy_latents = noise_scheduler.add_noise(latents, new_noise, timesteps)
                else:
                    noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)

                # Get the text embedding for conditioning
                encoder_hidden_states = text_encoder(input_ids)[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(latents, noise, timesteps)
                else:
                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")

                cross_attention_kwargs = {
                    "gligen": {"boxes": boxes_batch, "positive_embeddings": text_embeddings_batch, "masks": masks_batch}
                }
                # Predict the noise residual and compute loss
                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs).sample

                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://arxiv.org/abs/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(timesteps)
                    mse_loss_weights = (
                        torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
                    )
                    # We first calculate the original loss. Then we mean over the non-batch dimensions and
                    # rebalance the sample-wise losses with their respective loss weights.
                    # Finally, we take the mean of the rebalanced loss.
                    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:
                    accelerator.clip_grad_norm_(unet.parameters(), 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

                if global_step % args.checkpointing_steps == 0:
                    if accelerator.is_main_process:
                        # _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}")

                # Validation was originally out of the for loop and evaluated per `args.validation_epochs` epochs.
                # if args.validation_prompts is not None and epoch % args.validation_epochs == 0:
                # if epoch % args.validation_epochs == 0:
                if global_step % args.validation_steps == 0:
                    if accelerator.is_main_process:
                        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())
                        log_validation(
                            vae,
                            text_encoder,
                            tokenizer,
                            unet,
                            args,
                            accelerator,
                            weight_dtype,
                            global_step,
                        )
                        if args.use_ema:
                            # Switch back to the original UNet parameters.
                            ema_unet.restore(unet.parameters())


            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

    # Create the pipeline using the trained modules and save it.
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        unet = accelerator.unwrap_model(unet)
        if args.use_ema:
            ema_unet.copy_to(unet.parameters())

        pipeline = StableDiffusionGLIGENPipeline.from_pretrained(
            args.pretrained_model_name_or_path,
            text_encoder=text_encoder,
            vae=vae,
            unet=unet,
            revision=args.revision,
        )
        pipeline.save_pretrained(args.output_dir)

        # Run a final round of inference.
        # images = []
        # if args.validation_prompts is not None:
        #     logger.info("Running inference for collecting generated images...")
        #     pipeline = pipeline.to(accelerator.device)
        #     pipeline.torch_dtype = weight_dtype
        #     pipeline.set_progress_bar_config(disable=True)

        #     if args.enable_xformers_memory_efficient_attention:
        #         pipeline.enable_xformers_memory_efficient_attention()

        #     if args.seed is None:
        #         generator = None
        #     else:
        #         generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)

        #     for i in range(len(args.validation_prompts)):
        #         with torch.autocast("cuda"):
        #             image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]
        #         images.append(image)

        if args.push_to_hub:
            # save_model_card(args, repo_id, images, 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()