Tiger Model/Fine-Training.py

#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 Hui Lu, Fang Dai, Siqiong Yao.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import logging
import math
import os
import random
import shutil
from pathlib import Path
from pynvml import *
import accelerate
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers123
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from datasets import load_dataset
from huggingface_hub import create_repo, upload_folder
from packaging import version
from PIL import Image
from torchvision import transforms
from tqdm.auto import tqdm
import transformers
from transformers import AutoTokenizer, PretrainedConfig
import tensorflow as tf
tf.get_logger().setLevel('ERROR')
from collections import Counter
import diffusers_Tiger
from diffusers_Tiger import (
    AutoencoderKL,
    ControlNetModel,
    DDPMScheduler,
    StableDiffusionControlNetPipeline,
    StableDiffusionControlNetInpaintPipeline,
    UNet2DConditionModel,
    UniPCMultistepScheduler,
    DDIMScheduler
)
from diffusers_Tiger.optimization import get_scheduler
from diffusers_Tiger.utils import check_min_version, is_wandb_available
from diffusers_Tiger.utils.import_utils import is_xformers_available
from diffusers_Tiger import fuse

if is_wandb_available():
    import wandb
import warnings
warnings.filterwarnings('ignore')

# Will error if the minimal version of diffusers123 is not installed. Remove at your own risks.
check_min_version("0.19.0.dev0")

logger = get_logger(__name__)


def image_grid(imgs, rows, cols):
    assert len(imgs) == rows * cols

    w, h = imgs[0].sizeelerator
    grid = Image.new("RGB", size=(cols * w, rows * h))

    for i, img in enumerate(imgs):
        grid.paste(img, box=(i % cols * w, i // cols * h))
    return grid

def make_inpaint_condition(image, image_mask):
    image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
    image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0

    assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
    image[image_mask > 0.5] = -1.0  # set as masked pixel
    image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
    image = torch.from_numpy(image)
    return image

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

    controlnet_nd = accelerator.unwrap_model(controlnet)

    pipeline = StableDiffusionControlNetInpaintPipeline.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,
        torch_dtype=weight_dtype,
    )
    pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
    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)

    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 = []
    images = []
    for validation_prompt, validation_image1 in zip(validation_prompts, validation_images):
        validation_image = Image.open(validation_image1).convert("RGB").resize((512, 512))
        mask_image = Image.open(validation_image1).convert("RGB").resize((512, 512))

        control_image = make_inpaint_condition(validation_image, mask_image)

        for _ in range(args.num_validation_images):
            with torch.autocast("cuda"):
                seed = random.randint(1,1000000)
                generator = torch.Generator(device='cuda').manual_seed(seed)
                image = pipeline(
                    validation_prompt,
                    num_inference_steps=50,
                    generator=generator,
                    eta=1.0,
                    image=validation_image,
                    mask_image=mask_image,
                    control_image=control_image,
                    guidance_scale = 7
                ).images[0]

            images.append(image)
            

        image_logs.append(
            {"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt}
        )

    for tracker in accelerator.trackers:
        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(validation_prompt, formatted_images, step, dataformats="NHWC")
        elif tracker.name == "wandb":
            formatted_images = []

            for log in image_logs:
                images = log["images"]
                validation_prompt = log["validation_prompt"]
                validation_image = log["validation_image"]

                formatted_images.append(wandb.Image(validation_image, caption="Controlnet conditioning"))

                for image in images:
                    image = wandb.Image(image, caption=validation_prompt)
                    formatted_images.append(image)

            tracker.log({"validation": formatted_images})
        else:
            logger.warn(f"image logging not implemented for {tracker.name}")

        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 transformers123 import CLIPTextModel

        return CLIPTextModel
    elif model_class == "RobertaSeriesModelWithTransformation":
        from diffusers123.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation

        return RobertaSeriesModelWithTransformation
    else:
        raise ValueError(f"{model_class} is not supported.")


def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=None):
    img_str = ""
    if image_logs is not None:
        img_str = "You can find some example images below.\n"
        for i, log in enumerate(image_logs):
            images = log["images"]
            validation_prompt = log["validation_prompt"]
            validation_image = log["validation_image"]
            validation_image.save(os.path.join(repo_folder, "image_control.png"))
            img_str += f"prompt: {validation_prompt}\n"
            images = [validation_image] + images
            image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f"images_{i}.png"))
            img_str += f"![images_{i})](./images_{i}.png)\n"

    yaml = f"""
    ---
    license: creativeml-openrail-m
    base_model: {base_model}
    tags:
    - stable-diffusion
    - stable-diffusion-diffusers
    - text-to-image
    - diffusers
    - controlnet
    inference: true
    ---
    """
    model_card = f"""
# controlnet-{repo_id}

These are controlnet weights trained on {base_model} with new type of conditioning.
{img_str}
"""
    with open(os.path.join(repo_folder, "README.md"), "w") as f:
        f.write(yaml + model_card)


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. Trainable model components should be"
            " float32 precision."
        ),
    )
    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="/export/home/daifang/Diffusion/own_code/dataset",
        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/diffusers123/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=0,
        help=(
            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
        ),
    )
    parser.add_argument("--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="no",
        choices=["no", "fp16", "bf16"],
        help=(
            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
            " 1.10.and an Nvidia Ampere GPU.  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(
        "--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_nd_column",
        type=str,
        default="condition_nd",
        help="The column of the dataset containing the controlnet conditioning image.",
    )
    parser.add_argument(
        "--conditioning_bg_column",
        type=str,
        default="condition_bg",
        help="The column of the dataset containing the controlnet conditioning image.",
    )
    parser.add_argument(
        "--caption_column_nd",
        type=str,
        default="text_nd",
        help="The column of the dataset containing a caption or a list of captions.",
    )
    parser.add_argument(
        "--caption_column_bg",
        type=str,
        default="text_nd",
        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, accelerator):
    if args.dataset_name is not None:
        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,
            )
    column_names = dataset["train"].column_names
    ##########################################################################################################################################################################
    # Get the column names for input/target.
    # target image
    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)}"
            )
    # condition nodule image
    if args.conditioning_nd_column is None:

        conditioning_nd_column = column_names[1]
        logger.info(f"conditioning image column defaulting to {conditioning_nd_column}")
    else:
        conditioning_nd_column = args.conditioning_nd_column
        if conditioning_nd_column not in column_names:
            raise ValueError(
                f"`--conditioning_nd_column` value '{args.conditioning_nd_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
            )
    # condition background image
    if args.conditioning_bg_column is None:
        conditioning_bg_column = column_names[2]
        logger.info(f"conditioning bg column defaulting to {conditioning_bg_column}")
    else:
        conditioning_bg_column = args.conditioning_bg_column
        if conditioning_bg_column not in column_names:
            raise ValueError(
                f"`--conditioning_bg_column` value '{args.conditioning_bg_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
            )
    # condition nodule text

    if args.caption_column_nd is None:
        caption_column_nd = column_names[3]
        logger.info(f"caption column defaulting to {caption_column_nd}")
    else:
        caption_column_nd = args.caption_column_nd
        if caption_column_nd not in column_names:
            raise ValueError(
                f"`--caption_column` value '{args.caption_column_nd}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
            )
    # condition backgrorund text
    if args.caption_column_bg is None:
        caption_column_bg = column_names[4]
        logger.info(f"caption column defaulting to {caption_column_bg}")
    else:
        caption_column_bg = args.caption_column_bg
        if caption_column_bg not in column_names:
            raise ValueError(
                f"`--caption_column` value '{args.caption_column_bg}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
            )
    ##########################################################################################################################################################################

    def tokenize_captions(examples, caption_column, names, 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_nd}` should contain either strings or lists of strings."
                )

        inputs = tokenizer(
            captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
        )

        def calculate_word_frequencies(phrases):
            total_counts = Counter()
            total_words = 0
            for phrase in phrases:
                words = phrase.replace(',', '').split()
                total_counts.update(words)
                total_words += len(words)
            frequencies = {word: count / total_words for word, count in total_counts.items()}
            return frequencies, total_words

        def calculate_average_frequencies(phrases, word_frequencies):
            average_frequencies = []
            for phrase in phrases:
                words = phrase.replace(',', '').split()
                total_freq = sum(word_frequencies[word] for word in words)
                avg_freq = total_freq / len(words) if words else 0
                average_frequencies.append((phrase, avg_freq))
            return average_frequencies
        if names == 'nd':
            word_frequencies, total_word_count = calculate_word_frequencies(captions)
            weight_matrix = calculate_average_frequencies(captions, word_frequencies)
            # Extract the values to replace
            values = [desc[1] for desc in weight_matrix]
            # Replace the first zero in each row with the corresponding value
            for i in range(inputs.input_ids.shape[0]):
                weight = int(values[i]*10**5)
                inputs.input_ids[i][0] = weight
        assert not torch.isnan(inputs.input_ids).any(), "inputs.input_ids contains NaN values"
        
        return inputs.input_ids

    image_transforms = transforms.Compose(
        [
            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
            transforms.CenterCrop(args.resolution),
            transforms.ToTensor(),
            transforms.Normalize([0.5], [0.5]),
        ]
    )

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

    def preprocess_train(examples):
        images = [image.convert("RGB") for image in examples[image_column]]
        images = [image_transforms(image) for image in images]
        conditioning_nd = [Image.open(image).convert("RGB") for image in examples[conditioning_nd_column]]
        conditioning_nd = [conditioning_image_transforms(image) for image in conditioning_nd]

        conditioning_bg = [Image.open(image).convert("RGB") for image in examples[conditioning_bg_column]]
        conditioning_bg = [conditioning_image_transforms(image) for image in conditioning_bg]

        examples["pixel_values"] = images
        examples["conditioning_pixel_values_nd"] = conditioning_nd
        examples["conditioning_pixel_values_bg"] = conditioning_bg
        examples["input_ids_nd"] = tokenize_captions(examples, caption_column = caption_column_nd, names = 'nd')
        examples["input_ids_bg"] = tokenize_captions(examples, caption_column = caption_column_bg, names = 'bg')

        return examples

    with accelerator.main_process_first():
        if args.max_train_samples is not None:
            dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
        # Set the training transforms
        train_dataset = dataset["train"].with_transform(preprocess_train)

    return train_dataset


def collate_fn(examples):
    pixel_values = torch.stack([example["pixel_values"] for example in examples])
    pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()

    conditioning_pixel_values_nd = torch.stack([example["conditioning_pixel_values_nd"] for example in examples])
    conditioning_pixel_values_nd = conditioning_pixel_values_nd.to(memory_format=torch.contiguous_format).float()

    conditioning_pixel_values_bg = torch.stack([example["conditioning_pixel_values_bg"] for example in examples])
    conditioning_pixel_values_bg = conditioning_pixel_values_bg.to(memory_format=torch.contiguous_format).float()

    input_ids_nd = torch.stack([example["input_ids_nd"] for example in examples])
    input_ids_bg = torch.stack([example["input_ids_bg"] for example in examples])

    return {
        "pixel_values": pixel_values,
        "conditioning_pixel_values_nd": conditioning_pixel_values_nd,
        "conditioning_pixel_values_bg": conditioning_pixel_values_bg,
        "input_ids_nd": input_ids_nd,
        "input_ids_bg": input_ids_bg,
    }


def main(args):
    logging_dir = Path(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,
    )

    # 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:
        transformers.utils.logging.set_verbosity_warning()
        diffusers_Tiger.utils.logging.set_verbosity_info()
    else:
        transformers.utils.logging.set_verbosity_error()
        diffusers_Tiger.utils.logging.set_verbosity_error()

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

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

    # 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
    )
    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
    unet = UNet2DConditionModel.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
    )

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


    if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
        def save_model_hook(models, weights, output_dir):
            weights.pop()
            model1 = models[0]
            sub_dir = "controlnet_nd"
            model1.save_pretrained(os.path.join(output_dir, sub_dir))
            

        def load_model_hook(models, input_dir):
            while len(models) > 0:
                # pop models so that they are not loaded again
                model = models.pop()

                # load diffusers123 style into model
                load_model = ControlNetModel.from_pretrained(input_dir, subfolder="controlnet")
                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)

    vae.requires_grad_(False)
    unet.requires_grad_(False)
    text_encoder.requires_grad_(False)
    controlnet_nd.requires_grad_(True).train()
    controlnet_bg.requires_grad_(True).train()

    if args.gradient_checkpointing:
        controlnet_nd.enable_gradient_checkpointing()
        controlnet_bg.enable_gradient_checkpointing()

    # Check that all trainable models are in full precision
    low_precision_error_string = (
        " Please make sure to always have all model weights in full float32 precision when starting training - even if"
        " doing mixed precision training, copy of the weights should still be float32."
    )

    if accelerator.unwrap_model(controlnet_nd).dtype != torch.float32:
        raise ValueError(
            f"Controlnet loaded as datatype {accelerator.unwrap_model(controlnet_nd).dtype}. {low_precision_error_string}"
        )
    if accelerator.unwrap_model(controlnet_bg).dtype != torch.float32:
        raise ValueError(
            f"Controlnet loaded as datatype {accelerator.unwrap_model(controlnet_bg).dtype}. {low_precision_error_string}"
        )
    # Enable TF32 for faster training on Ampere GPUs,
    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
    if args.allow_tf32:
        torch.backends.cuda.matmul.allow_tf32 = True

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

    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
    if args.use_8bit_adam:
        try:
            import bitsandbytes as bnb
        except ImportError:
            raise ImportError(
                "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
            )

        optimizer_class = bnb.optim.AdamW8bit
    else:
        optimizer_class = torch.optim.AdamW

    # Optimizer creation
    params_to_optimize_nd = controlnet_nd.parameters()
    params_to_optimize_bg = controlnet_bg.parameters()
    
    optimizer_nd = optimizer_class(
        params_to_optimize_nd,
        lr=args.learning_rate,
        betas=(args.adam_beta1, args.adam_beta2),
        weight_decay=args.adam_weight_decay,
        eps=args.adam_epsilon,
    )
    optimizer_bg = optimizer_class(
        params_to_optimize_bg,
        lr=args.learning_rate,
        betas=(args.adam_beta1, args.adam_beta2),
        weight_decay=args.adam_weight_decay,
        eps=args.adam_epsilon,
    )
    train_dataset = make_train_dataset(args, tokenizer, accelerator)

    train_dataloader = torch.utils.data.DataLoader(
        train_dataset,
        shuffle=True,
        collate_fn=collate_fn,
        batch_size=args.train_batch_size,
        num_workers=args.dataloader_num_workers,
    )

    # Scheduler and math around the number of training steps.
    overrode_max_train_steps = False
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if args.max_train_steps is None:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
        overrode_max_train_steps = True

    lr_scheduler = get_scheduler(
        args.lr_scheduler,
        optimizer=optimizer_nd,
        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
        num_training_steps=args.max_train_steps * accelerator.num_processes,
        num_cycles=args.lr_num_cycles,
        power=args.lr_power)

    # Prepare everything with our `accelerator`.
    controlnet_nd, controlnet_bg, optimizer_nd, optimizer_bg, train_dataloader, lr_scheduler = accelerator.prepare(
        controlnet_nd, controlnet_bg, optimizer_nd, optimizer_bg, train_dataloader, lr_scheduler
        )
    # 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 = torch.float32
    if accelerator.mixed_precision == "fp16":
        weight_dtype = torch.float16
    elif accelerator.mixed_precision == "bf16":
        weight_dtype = torch.bfloat16

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

        # tensorboard cannot handle list types for config
        tracker_config.pop("validation_prompt")
        tracker_config.pop("validation_image")

        accelerator.init_trackers(args.tracker_project_name, config=tracker_config)

    # 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 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:
            accelerator.print(
                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
            )
            args.resume_from_checkpoint = None
            initial_global_step = 0
        else:
            accelerator.print(f"Resuming from checkpoint {path}")
            accelerator.load_state(os.path.join(args.output_dir, path))
            global_step = int(path.split("-")[1])

            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 accelerator.is_local_main_process,
    )

    image_logs = None
    for epoch in range(first_epoch, args.num_train_epochs):
        for step, batch in enumerate(train_dataloader):
            # with accelerator.accumulate(controlnet_nd):
                # Convert images to latent space
            latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
            latents = latents * vae.config.scaling_factor
            # Sample noise that we'll add to the latents
            noise = torch.randn_like(latents)
            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)
            noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
            # Get the text embedding for conditioning

            weight_nd = batch["input_ids_nd"][:, 0]
            weight_nd = weight_nd / 10**5
            batch["input_ids_nd"][:, 0] = 49406
            encoder_hidden_states_nd = text_encoder(batch["input_ids_nd"])[0]
            encoder_hidden_states_bg = text_encoder(batch["input_ids_bg"])[0]
            controlnet_image_nd = batch["conditioning_pixel_values_nd"].to(dtype=weight_dtype)
            controlnet_image_bg = batch["conditioning_pixel_values_bg"].to(dtype=weight_dtype)
            # print(weight_nd)
            down_block_res_samples_nd, mid_block_res_sample_nd = controlnet_nd(
                                                                        noisy_latents,
                                                                        timesteps,
                                                                        encoder_hidden_states=encoder_hidden_states_nd, # text 
                                                                        controlnet_cond=controlnet_image_nd,
                                                                        return_dict=False,
                                                                        weight=weight_nd)



            down_block_res_samples_bg, mid_block_res_sample_bg = controlnet_bg(
                                                                        noisy_latents,
                                                                        timesteps,
                                                                        encoder_hidden_states=encoder_hidden_states_bg, # text 
                                                                        controlnet_cond=controlnet_image_bg,
                                                                        return_dict=False)
            # Predict the noise residual
            samples_nd_list, samples_bg_list = [], []
            for number in range(len(down_block_res_samples_nd)):
                if number > 1 :
                    sample = down_block_res_samples_nd[number]
                    samples_nd = torch.stack((down_block_res_samples_nd[number][0].to('cpu'), \
                                                        down_block_res_samples_nd[number][0].to('cpu')))
                    samples_bg = torch.stack((down_block_res_samples_bg[number][0].to('cpu'), \
                                                        down_block_res_samples_bg[number][0].to('cpu')))
                    channels = sample.shape[1]
                    model_fuse_down = fuse.AFF(channels=channels).to(device='cpu')
                    output = model_fuse_down(samples_nd, samples_bg)[0].unsqueeze(0)

                    samples_nd_list.append(output)
                    samples_bg_list.append(output)
                else:
                    samples_nd_list.append(down_block_res_samples_nd[number])
                    samples_bg_list.append(down_block_res_samples_bg[number])
            mid_block_res_sample = mid_block_res_sample_bg + mid_block_res_sample_nd
            model_pred_nd = unet(
                            noisy_latents,
                            timesteps,
                            encoder_hidden_states=encoder_hidden_states_nd.to('cuda'),
                            down_block_additional_residuals=[
                                sample.to(dtype=weight_dtype).to('cuda') for sample in samples_nd_list],
                            mid_block_additional_residual=mid_block_res_sample.to('cuda').to(dtype=weight_dtype),
                        ).sample
            model_pred_bg = unet(
                            noisy_latents,
                            timesteps,
                            encoder_hidden_states=encoder_hidden_states_bg.to('cuda'),
                            down_block_additional_residuals=[
                                sample.to(dtype=weight_dtype).to('cuda') for sample in samples_bg_list],
                            mid_block_additional_residual=mid_block_res_sample.to('cuda').to(dtype=weight_dtype),
                        ).sample
            # Get the target for loss depending on the prediction type
            if noise_scheduler.config.prediction_type == "epsilon":
                target = noise
            elif noise_scheduler.config.prediction_type == "v_prediction":  # use
                target = noise_scheduler.get_velocity(latents, noise, timesteps)
            else:
                raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
            loss_nd = F.mse_loss(model_pred_nd.to('cuda').float(), target.float(), reduction="mean")
            loss_bg = F.mse_loss(model_pred_bg.to('cuda').float(), target.float(), reduction="mean")
            optimizer_nd.zero_grad(set_to_none=args.set_grads_to_none)
            optimizer_bg.zero_grad(set_to_none=args.set_grads_to_none)
            # h0, h1 = nvmlDeviceGetHandleByIndex(0), nvmlDeviceGetHandleByIndex(1)
            # info0, info1 = nvmlDeviceGetMemoryInfo(h0), nvmlDeviceGetMemoryInfo(h1)
            # print(f'0free     : {info0.free}   1free     : {info1.free}')
            loss = loss_nd + loss_bg
            accelerator.backward(loss)
            # loss_nd.backward()
            # loss_bg.backward()
            # if accelerator.sync_gradients:
            #     params_to_clip_nd = controlnet_nd.parameters()
            #     accelerator.clip_grad_norm_(params_to_clip_nd, args.max_grad_norm)
            #     params_to_clip_bg = controlnet_bg.parameters()
            #     accelerator.clip_grad_norm_(params_to_clip_bg, args.max_grad_norm)
            optimizer_nd.step()
            
            optimizer_bg.step()

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

                if accelerator.is_main_process:
                    if global_step % args.checkpointing_steps == 0:
                        # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
                        if args.checkpoints_total_limit is not None:
                            checkpoints = os.listdir(args.output_dir)
                            checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
                            checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))

                            # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
                            if len(checkpoints) >= args.checkpoints_total_limit:
                                num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
                                removing_checkpoints = checkpoints[0:num_to_remove]

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

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

                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
                        accelerator.save_state(save_path)
                        logger.info(f"Saved state to {save_path}")

                        # if args.validation_prompt is not None :
                        #     image_logs = log_validation(
                        #         vae,
                        #         text_encoder,
                        #         tokenizer,
                        #         unet,
                        #         controlnet_nd,
                        #         controlnet_bg,
                        #         args,
                        #         accelerator,
                        #         weight_dtype,
                        #         global_step,
                        #     )

            logs = {"loss": loss.detach().item()}
            progress_bar.set_postfix(**logs)
            accelerator.log(logs, step=global_step)

            if global_step >= args.max_train_steps:
                break

    # Create the pipeline using using the trained modules and save it.
    # accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        controlnet_nd = accelerator.unwrap_model(controlnet_nd)
        controlnet_nd.save_pretrained(args.output_dir)
        controlnet_bg = accelerator.unwrap_model(controlnet_bg)
        controlnet_bg.save_pretrained(args.output_dir)

    accelerator.end_training()


if __name__ == "__main__":
    args = parse_args()
    main(args)