train_controlnet.py

#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
# Hacked together by / Copyright 2024 Genius Patrick @ MindSpore Team.
#
# 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 random
import shutil
from pathlib import Path

import datasets
import numpy as np
import yaml
from datasets import disable_caching, load_dataset
from PIL import Image
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig

import mindspore as ms
from mindspore import mint, nn
from mindspore.amp import StaticLossScaler
from mindspore.dataset import GeneratorDataset, transforms, vision

from mindone.diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDPMScheduler,
    StableDiffusionControlNetPipeline,
    UNet2DConditionModel,
    UniPCMultistepScheduler,
)
from mindone.diffusers.optimization import get_scheduler
from mindone.diffusers.training_utils import AttrJitWrapper, TrainStep, init_distributed_device, is_master, set_seed

logger = logging.getLogger(__name__)


def log_validation(pipeline, args, trackers, logging_dir, step, is_final_validation=False):
    logger.info("Running validation... ")

    if args.seed is None:
        generator = None
    else:
        generator = np.random.Generator(np.random.PCG64(seed=args.seed))

    if len(args.validation_image) == len(args.validation_prompt):
        validation_images = args.validation_image
        validation_prompts = args.validation_prompt
    elif len(args.validation_image) == 1:
        validation_images = args.validation_image * len(args.validation_prompt)
        validation_prompts = args.validation_prompt
    elif len(args.validation_prompt) == 1:
        validation_images = args.validation_image
        validation_prompts = args.validation_prompt * len(args.validation_image)
    else:
        raise ValueError(
            "number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`"
        )

    image_logs = []
    for validation_prompt, validation_image in zip(validation_prompts, validation_images):
        validation_image = Image.open(validation_image).convert("RGB")
        validation_image = validation_image.resize((args.resolution, args.resolution))
        images = []
        for _ in range(args.num_validation_images):
            image = pipeline(validation_prompt, validation_image, num_inference_steps=20, generator=generator)[0][0]
            images.append(image)
        image_logs.append(
            {"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt}
        )

    def get_valid_filename(name):
        import re

        s = str(name).strip().replace(" ", "_")
        s = re.sub(r"(?u)[^-\w.]", "", s)
        if s in {"", ".", ".."}:
            raise ValueError(f"Cannot get valid filename from '{name}'")
        return s

    tracker_key = "test" if is_final_validation else "validation"
    if is_master(args):
        validation_logging_dir = os.path.join(logging_dir, tracker_key, f"step{step}")
        os.makedirs(validation_logging_dir, exist_ok=True)
        for log in image_logs:
            images = log["images"]
            validation_prompt = log["validation_prompt"]
            validation_image = log["validation_image"]
            validation_logging_dir_sub = os.path.join(validation_logging_dir, get_valid_filename(validation_prompt))
            os.makedirs(validation_logging_dir_sub, exist_ok=True)
            validation_image.save(os.path.join(validation_logging_dir_sub, "validation_image.jpg"))
            for idx, image in enumerate(images):
                image.save(os.path.join(validation_logging_dir_sub, f"{idx:04d}.jpg"))
    for tracker_name, tracker_writer in trackers.items():
        if tracker_name == "tensorboard":
            for log in image_logs:
                images = log["images"]
                validation_prompt = log["validation_prompt"]
                validation_image = log["validation_image"]
                formatted_images = []
                formatted_images.append(np.asarray(validation_image))
                for image in images:
                    formatted_images.append(np.asarray(image))
                formatted_images = np.stack(formatted_images)
                tracker_writer.add_images(
                    get_valid_filename(validation_prompt), formatted_images, step, dataformats="NHWC"
                )
        else:
            logger.warning(f"image logging not implemented for {tracker_name}")

    logger.info("Validation done.")
    return image_logs


def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
    text_encoder_config = PretrainedConfig.from_pretrained(
        pretrained_model_name_or_path,
        subfolder="text_encoder",
        revision=revision,
    )
    model_class = text_encoder_config.architectures[0]

    if model_class == "CLIPTextModel":
        from mindone.transformers import CLIPTextModel

        return CLIPTextModel
    elif model_class == "RobertaSeriesModelWithTransformation":
        raise NotImplementedError("RobertaSeriesModelWithTransformation has not yet implemented.")
    else:
        raise ValueError(f"{model_class} is not supported.")


def parse_args(input_args=None):
    parser = argparse.ArgumentParser(description="Simple example of a ControlNet 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(
        "--controlnet_model_name_or_path",
        type=str,
        default=None,
        help="Path to pretrained controlnet model or model identifier from huggingface.co/models."
        " If not specified controlnet weights are initialized from unet.",
    )
    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(
        "--tokenizer_name",
        type=str,
        default=None,
        help="Pretrained tokenizer name or path if not the same as model_name",
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default="controlnet-model",
        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=512,
        help=(
            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
            " resolution"
        ),
    )
    parser.add_argument(
        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
    )
    parser.add_argument("--num_train_epochs", type=int, default=1)
    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. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
            "In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
            "Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
            "See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
            "instructions."
        ),
    )
    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=5e-6,
        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(
        "--lr_num_cycles",
        type=int,
        default=1,
        help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
    )
    parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
    parser.add_argument(
        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
    )
    parser.add_argument(
        "--dataloader_num_workers",
        type=int,
        default=1,
        help="Number of subprocesses to use for data loading.",
    )
    parser.add_argument(
        "--enable_mindspore_data_sink",
        action="store_true",
        help=(
            "Whether or not to enable `Data Sinking` feature from MindData which boosting data "
            "fetching and transferring from host to device. For more information, see "
            "https://www.mindspore.cn/tutorials/experts/en/r2.2/optimize/execution_opt.html#data-sinking. "
            "Note: To avoid breaking the iteration logic of the training, the size of data sinking is set to 1."
        ),
    )
    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(
        "--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(
        "--allow_tf32",
        action="store_true",
        help=(
            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
        ),
    )
    parser.add_argument(
        "--report_to",
        type=str,
        default="tensorboard",
        help=(
            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
        ),
    )
    parser.add_argument(
        "--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("--distributed", default=False, action="store_true", help="Enable distributed training")
    parser.add_argument(
        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
    )
    parser.add_argument(
        "--set_grads_to_none",
        action="store_true",
        help=(
            "Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
            " behaviors, so disable this argument if it causes any problems. More info:"
            " https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
        ),
    )
    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 the target image."
    )
    parser.add_argument(
        "--conditioning_image_column",
        type=str,
        default="conditioning_image",
        help="The column of the dataset containing the controlnet conditioning 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(
        "--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(
        "--validation_prompt",
        type=str,
        default=None,
        nargs="+",
        help=(
            "A set of prompts evaluated every `--validation_steps` and logged to `--report_to`."
            " Provide either a matching number of `--validation_image`s, a single `--validation_image`"
            " to be used with all prompts, or a single prompt that will be used with all `--validation_image`s."
        ),
    )
    parser.add_argument(
        "--validation_image",
        type=str,
        default=None,
        nargs="+",
        help=(
            "A set of paths to the controlnet conditioning image be evaluated every `--validation_steps`"
            " and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a"
            " a single `--validation_prompt` to be used with all `--validation_image`s, or a single"
            " `--validation_image` that will be used with all `--validation_prompt`s."
        ),
    )
    parser.add_argument(
        "--num_validation_images",
        type=int,
        default=4,
        help="Number of images to be generated for each `--validation_image`, `--validation_prompt` pair",
    )
    parser.add_argument(
        "--validation_steps",
        type=int,
        default=100,
        help=(
            "Run validation every X steps. Validation consists of running the prompt"
            " `args.validation_prompt` multiple times: `args.num_validation_images`"
            " and logging the images."
        ),
    )
    parser.add_argument(
        "--tracker_project_name",
        type=str,
        default="train_controlnet",
        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"
        ),
    )

    if input_args is not None:
        args = parser.parse_args(input_args)
    else:
        args = parser.parse_args()

    if args.dataset_name is None and args.train_data_dir is None:
        raise ValueError("Specify either `--dataset_name` or `--train_data_dir`")

    if args.dataset_name is not None and args.train_data_dir is not None:
        raise ValueError("Specify only one of `--dataset_name` or `--train_data_dir`")

    if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
        raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")

    if args.validation_prompt is not None and args.validation_image is None:
        raise ValueError("`--validation_image` must be set if `--validation_prompt` is set")

    if args.validation_prompt is None and args.validation_image is not None:
        raise ValueError("`--validation_prompt` must be set if `--validation_image` is set")

    if (
        args.validation_image is not None
        and args.validation_prompt is not None
        and len(args.validation_image) != 1
        and len(args.validation_prompt) != 1
        and len(args.validation_image) != len(args.validation_prompt)
    ):
        raise ValueError(
            "Must provide either 1 `--validation_image`, 1 `--validation_prompt`,"
            " or the same number of `--validation_prompt`s and `--validation_image`s"
        )

    if args.resolution % 8 != 0:
        raise ValueError(
            "`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the controlnet encoder."
        )

    return args


def make_train_dataset(args, tokenizer):
    # 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).
    if args.cache_dir is None:
        disable_caching()

    # 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,
        )
    else:
        if args.train_data_dir is not None:
            dataset = load_dataset(
                args.train_data_dir,
                cache_dir=args.cache_dir,
            )
        # See more about loading custom images at
        # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script

    # Preprocessing the datasets.
    # We need to tokenize inputs and targets.
    column_names = dataset["train"].column_names

    # 6. Get the column names for input/target.
    if args.image_column is None:
        image_column = column_names[0]
        logger.info(f"image column defaulting to {image_column}")
    else:
        image_column = args.image_column
        if image_column not in column_names:
            raise ValueError(
                f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
            )

    if args.caption_column is None:
        caption_column = column_names[1]
        logger.info(f"caption column defaulting to {caption_column}")
    else:
        caption_column = args.caption_column
        if caption_column not in column_names:
            raise ValueError(
                f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
            )

    if args.conditioning_image_column is None:
        conditioning_image_column = column_names[2]
        logger.info(f"conditioning image column defaulting to {conditioning_image_column}")
    else:
        conditioning_image_column = args.conditioning_image_column
        if conditioning_image_column not in column_names:
            raise ValueError(
                f"`--conditioning_image_column` value '{args.conditioning_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"  # noqa: E501
            )

    def tokenize_captions(examples, is_train=True):
        captions = []
        for caption in examples[caption_column]:
            if random.random() < args.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])
            else:
                raise ValueError(
                    f"Caption column `{caption_column}` should contain either strings or lists of strings."
                )
        inputs = tokenizer(
            captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="np"
        )
        return inputs.input_ids

    image_transforms = transforms.Compose(
        [
            vision.Resize(args.resolution, interpolation=vision.Inter.BILINEAR),
            vision.CenterCrop(args.resolution),
            vision.ToTensor(),
            vision.Normalize([0.5], [0.5], is_hwc=False),
        ]
    )

    conditioning_image_transforms = transforms.Compose(
        [
            vision.Resize(args.resolution, interpolation=vision.Inter.BILINEAR),
            vision.CenterCrop(args.resolution),
            vision.ToTensor(),
        ]
    )

    def preprocess_train(examples):
        images = [image.convert("RGB") for image in examples[image_column]]
        images = [image_transforms(image)[0] for image in images]

        conditioning_images = [image.convert("RGB") for image in examples[conditioning_image_column]]
        conditioning_images = [conditioning_image_transforms(image)[0] for image in conditioning_images]

        examples["pixel_values"] = images
        examples["conditioning_pixel_values"] = conditioning_images
        examples["input_ids"] = tokenize_captions(examples)

        return examples

    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)

    return train_dataset


def collate_fn(examples):
    pixel_values = np.stack([example["pixel_values"] for example in examples])
    pixel_values = pixel_values.astype(np.float32)

    conditioning_pixel_values = np.stack([example["conditioning_pixel_values"] for example in examples])
    conditioning_pixel_values = conditioning_pixel_values.astype(np.float32)

    input_ids = np.stack([example["input_ids"] for example in examples])

    return pixel_values, conditioning_pixel_values, input_ids


def main():
    args = parse_args()
    ms.set_context(mode=ms.GRAPH_MODE, jit_syntax_level=ms.STRICT)
    init_distributed_device(args)  # read attr distributed, writer attrs rank/local_rank/world_size

    # tensorboard, mindinsight, wandb logging stuff into logging_dir
    logging_dir = Path(args.output_dir, args.logging_dir)

    # 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,
    )
    datasets.utils.logging.get_logger().propagate = False

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    # Handle the repository creation
    if is_master(args):
        if args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)
            os.makedirs(logging_dir, exist_ok=True)

    # Load the tokenizer
    if args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
    elif args.pretrained_model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(
            args.pretrained_model_name_or_path,
            subfolder="tokenizer",
            revision=args.revision,
            use_fast=False,
        )

    # import correct text encoder class
    text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)

    # Load scheduler and models
    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
    text_encoder = text_encoder_cls.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
    )
    vae = AutoencoderKL.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
    )
    unet = UNet2DConditionModel.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
    )
    # set sample_size of unet
    unet.register_to_config(sample_size=args.resolution // (2 ** (len(vae.config.block_out_channels) - 1)))

    if args.controlnet_model_name_or_path:
        logger.info("Loading existing controlnet weights")
        controlnet = ControlNetModel.from_pretrained(args.controlnet_model_name_or_path)
    else:
        logger.info("Initializing controlnet weights from unet")
        controlnet = ControlNetModel.from_unet(unet)

    def freeze_params(m: nn.Cell):
        for p in m.get_parameters():
            p.requires_grad = False

    freeze_params(vae)
    freeze_params(unet)
    freeze_params(text_encoder)

    # For mixed precision training we cast the text_encoder and vae weights to half-precision
    # as these models are only used for inference, keeping weights in full precision is not required.
    weight_dtype = ms.float32
    if args.mixed_precision == "fp16":
        weight_dtype = ms.float16
    elif args.mixed_precision == "bf16":
        weight_dtype = ms.bfloat16

    # Move vae, unet and text_encoder to device and cast to weight_dtype
    vae.to(dtype=weight_dtype)
    unet.to(dtype=weight_dtype)
    text_encoder.to(dtype=weight_dtype)
    controlnet.set_train(True)

    if args.enable_xformers_memory_efficient_attention:
        unet.enable_xformers_memory_efficient_attention()
        controlnet.enable_xformers_memory_efficient_attention()

    if args.gradient_checkpointing:
        controlnet.enable_gradient_checkpointing()

    train_dataset = make_train_dataset(args, tokenizer)

    class DatasetForMindData:
        def __init__(self, data):
            self.data = data

        def __getitem__(self, idx):
            idx = idx.item() if isinstance(idx, np.integer) else idx
            return self.data[idx]

        def __len__(self):
            return len(self.data)

    train_dataloader = GeneratorDataset(
        DatasetForMindData(train_dataset),
        column_names=["example"],
        shuffle=True,
        shard_id=args.rank,
        num_shards=args.world_size,
        num_parallel_workers=args.dataloader_num_workers,
    ).batch(
        batch_size=args.train_batch_size,
        per_batch_map=lambda examples, batch_info: collate_fn(examples),
        input_columns=["example"],
        output_columns=["c1", "c2", "c3"],
        num_parallel_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

    if args.scale_lr:
        args.learning_rate = (
            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * args.world_size
        )
    lr_scheduler = get_scheduler(
        args.lr_scheduler,
        args.learning_rate,
        num_warmup_steps=args.lr_warmup_steps,
        num_training_steps=args.max_train_steps,
        num_cycles=args.lr_num_cycles,
        power=args.lr_power,
    )

    # Optimizer creation
    params_to_optimize = controlnet.trainable_params()
    optimizer = nn.AdamWeightDecay(
        params_to_optimize,
        learning_rate=lr_scheduler,
        beta1=args.adam_beta1,
        beta2=args.adam_beta2,
        weight_decay=args.adam_weight_decay,
        eps=args.adam_epsilon,
    )

    # Prepare everything with our `accelerator`.
    # TODO: We will update the training methods during mixed precision training to ensure the performance and strategies during the training process.
    if args.mixed_precision and args.mixed_precision != "no":
        controlnet.to_float(weight_dtype)
        for _, cell in controlnet.cells_and_names():
            cell.to_float(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 is_master(args):
        with open(logging_dir / "hparams.yml", "w") as f:
            yaml.dump(vars(args), f, indent=4)
    trackers = dict()
    for tracker_name in args.report_to.split(","):
        if tracker_name == "tensorboard":
            from tensorboardX import SummaryWriter

            trackers[tracker_name] = SummaryWriter(str(logging_dir), write_to_disk=is_master(args))
        else:
            logger.warning(f"Tracker {tracker_name} is not implemented, omitting...")

    train_step = TrainStepForControlNet(
        vae=vae,
        text_encoder=text_encoder,
        unet=unet,
        controlnet=controlnet,
        optimizer=optimizer,
        noise_scheduler=noise_scheduler,
        weight_dtype=weight_dtype,
        length_of_dataloader=len(train_dataloader),
        args=args,
    ).set_train()

    if args.enable_mindspore_data_sink:
        sink_process = ms.data_sink(train_step, train_dataloader)
    else:
        sink_process = None

    # create pipeline
    pipeline = StableDiffusionControlNetPipeline.from_pretrained(
        args.pretrained_model_name_or_path,
        vae=vae,
        text_encoder=text_encoder,
        tokenizer=tokenizer,
        unet=unet,
        controlnet=controlnet,
        safety_checker=None,
        revision=args.revision,
        variant=args.variant,
        mindspore_dtype=weight_dtype,
    )
    pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
    pipeline.set_progress_bar_config(disable=True)

    # Train!
    total_batch_size = args.train_batch_size * args.world_size * args.gradient_accumulation_steps

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(train_dataset)}")
    logger.info(f"  Num batches each epoch = {len(train_dataloader)}")
    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:
            if is_master(args):
                logger.info(f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.")
            args.resume_from_checkpoint = None
            initial_global_step = 0
        else:
            if is_master(args):
                logger.info(f"Resuming from checkpoint {path}")
            # TODO: load optimizer & grad scaler etc. like accelerator.load_state
            input_model_file = os.path.join(args.output_dir, path, "unet/diffusion_pytorch_model.safetensors")
            ms.load_param_into_net(unet, ms.load_checkpoint(input_model_file, format="safetensors"), strict_load=True)
            global_step = int(path.split("-")[1])

            initial_global_step = global_step
            first_epoch = global_step // num_update_steps_per_epoch
    else:
        initial_global_step = 0

    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 is_master(args),
    )

    train_dataloader_iter = train_dataloader.create_tuple_iterator(num_epochs=args.num_train_epochs - first_epoch)
    for epoch in range(first_epoch, args.num_train_epochs):
        controlnet.set_train(True)
        for step, batch in (
            ((_, None) for _ in range(len(train_dataloader)))  # dummy iterator
            if args.enable_mindspore_data_sink
            else enumerate(train_dataloader_iter)
        ):
            if args.enable_mindspore_data_sink:
                loss, model_pred = sink_process()
            else:
                loss, model_pred = train_step(*batch)

            # Checks if the accelerator has performed an optimization step behind the scenes
            if train_step.sync_gradients:
                progress_bar.update(1)
                global_step += 1

                if is_master(args):
                    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}")
                        # TODO: save optimizer & grad scaler etc. like accelerator.save_state
                        os.makedirs(save_path, exist_ok=True)
                        unet.save_pretrained(os.path.join(save_path, "unet"))
                        logger.info(f"Saved state to {save_path}")

                if args.validation_prompt is not None and global_step % args.validation_steps == 0:
                    log_validation(pipeline, args, trackers, logging_dir, global_step)

            logs = {"loss": loss.numpy().item(), "lr": optimizer.get_lr().numpy().item()}
            progress_bar.set_postfix(**logs)
            for tracker_name, tracker in trackers.items():
                if tracker_name == "tensorboard":
                    tracker.add_scalars("train", logs, global_step)

            if global_step >= args.max_train_steps:
                break

    # Save the controlnet.
    if is_master(args):
        controlnet.save_pretrained(args.output_dir)

    # Run a final round of validation.
    if args.validation_prompt is not None:
        log_validation(pipeline, args, trackers, logging_dir, global_step, is_final_validation=True)

    # End of training
    for tracker_name, tracker in trackers.items():
        if tracker_name == "tensorboard":
            tracker.close()


class TrainStepForControlNet(TrainStep):
    def __init__(
        self,
        vae: nn.Cell,
        text_encoder: nn.Cell,
        unet: nn.Cell,
        controlnet: nn.Cell,
        optimizer: nn.Optimizer,
        noise_scheduler,
        weight_dtype,
        length_of_dataloader,
        args,
    ):
        super().__init__(
            controlnet,
            optimizer,
            StaticLossScaler(65536),
            args.max_grad_norm,
            args.gradient_accumulation_steps,
            gradient_accumulation_kwargs=dict(length_of_dataloader=length_of_dataloader),
        )
        self.controlnet = controlnet
        self.unet = unet
        self.vae = vae
        self.vae_scaling_factor = vae.config.scaling_factor
        self.text_encoder = text_encoder
        self.noise_scheduler = noise_scheduler
        self.noise_scheduler_num_train_timesteps = noise_scheduler.config.num_train_timesteps
        self.noise_scheduler_prediction_type = noise_scheduler.config.prediction_type
        self.weight_dtype = weight_dtype
        self.args = AttrJitWrapper(**vars(args))

    def forward(self, pixel_values, conditioning_pixel_values, input_ids):
        # Convert images to latent space
        latents = self.vae.diag_gauss_dist.sample(self.vae.encode(pixel_values.to(self.weight_dtype))[0])
        latents = latents * self.vae_scaling_factor

        # Sample noise that we'll add to the latents
        noise = mint.randn_like(latents, dtype=latents.dtype)
        bsz = latents.shape[0]
        # Sample a random timestep for each image
        timesteps = mint.randint(0, self.noise_scheduler_num_train_timesteps, (bsz,))
        timesteps = timesteps.long()

        # Add noise to the latents according to the noise magnitude at each timestep
        # (this is the forward diffusion process)
        noisy_latents = self.noise_scheduler.add_noise(latents.float(), noise.float(), timesteps).to(
            dtype=self.weight_dtype
        )

        # Get the text embedding for conditioning
        encoder_hidden_states = self.text_encoder(input_ids, return_dict=False)[0]

        controlnet_image = conditioning_pixel_values.to(dtype=self.weight_dtype)

        down_block_res_samples, mid_block_res_sample = self.controlnet(
            noisy_latents,
            timesteps,
            encoder_hidden_states=encoder_hidden_states,
            controlnet_cond=controlnet_image,
            return_dict=False,
        )

        # Predict the noise residual
        model_pred = self.unet(
            noisy_latents,
            timesteps,
            encoder_hidden_states=encoder_hidden_states,
            down_block_additional_residuals=[sample.to(dtype=self.weight_dtype) for sample in down_block_res_samples],
            mid_block_additional_residual=mid_block_res_sample.to(dtype=self.weight_dtype),
            return_dict=False,
        )[0]

        # Get the target for loss depending on the prediction type
        if self.noise_scheduler_prediction_type == "epsilon":
            target = noise
        elif self.noise_scheduler_prediction_type == "v_prediction":
            target = self.noise_scheduler.get_velocity(latents, noise, timesteps)
        else:
            raise ValueError(f"Unknown prediction type {self.noise_scheduler_prediction_type}")
        loss = mint.nn.functional.mse_loss(model_pred.float(), target.float(), reduction="mean")

        loss = self.scale_loss(loss)
        return loss, model_pred


if __name__ == "__main__":
    main()