Delete train_controlnet_inpaint.py

train_controlnet_inpaint.py

@@ -1,1244 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2024, Yahoo Research
-#
-# 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
-import cv2
-from PIL import Image, ImageOps
-import accelerate
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-import transformers
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import 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
-from transformers import AutoTokenizer, PretrainedConfig
-
-import diffusers
-from diffusers import (
-    AutoencoderKL,
-    ControlNetModel,
-    DDPMScheduler,
-    StableDiffusionControlNetPipeline,
-    UNet2DConditionModel,
-    UniPCMultistepScheduler,
-)
-from diffusers.optimization import get_scheduler
-from diffusers.utils import check_min_version, is_wandb_available
-from diffusers.utils.import_utils import is_xformers_available
-
-
-if is_wandb_available():
-    import wandb
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.20.0.dev0")
-
-logger = get_logger(__name__)
-
-
-def image_grid(imgs, rows, cols):
-    assert len(imgs) == rows * cols
-
-    w, h = imgs[0].size
-    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 log_validation(vae, text_encoder, tokenizer, unet, controlnet, args, accelerator, weight_dtype, step):
-    logger.info("Running validation... ")
-
-    controlnet = accelerator.unwrap_model(controlnet)
-
-    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,
-        torch_dtype=weight_dtype,
-    )
-    pipeline.scheduler = UniPCMultistepScheduler.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_inpainting_images = args.validation_inpainting_image
-        validation_prompts = args.validation_prompt
-    elif len(args.validation_image) == 1:
-        validation_images = args.validation_image * len(args.validation_prompt)
-        validation_inpainting_images = args.validation_inpainting_image * len(args.validation_prompt)
-        validation_prompts = args.validation_prompt
-    elif len(args.validation_prompt) == 1:
-        validation_images = args.validation_image
-        validation_inpainting_images = args.validation_inpainting_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, validation_inpainting_image in zip(validation_prompts, validation_images, validation_inpainting_images):
-        mask = Image.open(validation_image)
-        mask = resize_with_padding(mask, (512,512))
-
-        inpainting_image  = Image.open(validation_inpainting_image).convert("RGB")
-        inpainting_image = resize_with_padding(inpainting_image, (512,512))
-
-        validation_image = Image.composite(inpainting_image, mask, mask.convert('L')).convert('RGB')
-        images = []
-        for _ in range(args.num_validation_images):
-            with torch.autocast("cuda"):
-                image = pipeline(
-                    validation_prompt, validation_image, num_inference_steps=20, generator=generator
-                ).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 transformers import CLIPTextModel
-
-        return CLIPTextModel
-    elif model_class == "RobertaSeriesModelWithTransformation":
-        from diffusers.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=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=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=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(
-        "--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(
-        "--validation_inpainting_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):
-    # Get the datasets: you can either provide your own training and evaluation files (see below)
-    # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
-
-    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
-    # download the dataset.
-    if args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        dataset = load_dataset(
-            args.dataset_name,
-            args.dataset_config_name,
-            cache_dir=args.cache_dir,
-        )
-    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)}"
-            )
-
-    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="pt"
-        )
-        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):
-        examples["pixel_values"] = examples[image_column] #images
-        examples["conditioning_pixel_values"] =  examples[conditioning_image_column] #conditioning_images
-        examples["input_ids"] = tokenize_captions(examples)
-
-        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 resize_with_padding(img, expected_size):
-    img.thumbnail((expected_size[0], expected_size[1]))
-    # print(img.size)
-    delta_width = expected_size[0] - img.size[0]
-    delta_height = expected_size[1] - img.size[1]
-    pad_width = delta_width // 2
-    pad_height = delta_height // 2
-    padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)
-    return ImageOps.expand(img, padding)
-
-def prepare_mask_and_masked_image(image, mask):
-    image = np.array(image.convert("RGB"))
-    image = image[None].transpose(0, 3, 1, 2)
-    image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-
-    mask = np.array(mask.convert("L"))
-    mask = mask.astype(np.float32) / 255.0
-    mask = mask[None, None]
-    mask[mask < 0.5] = 0
-    mask[mask >= 0.5] = 1
-    #mask = torch.from_numpy(mask)
-
-    masked_image = image * (mask < 0.5)
-
-    return mask, masked_image
-
-def collate_fn(examples):
-    pixel_values = [example["pixel_values"].convert("RGB") for example in examples]
-    conditioning_images = [example["conditioning_pixel_values"].convert("RGB") for example in examples]
-    masks = []
-    masked_images = []
-
-    # Resize and random crop images
-    for i in range(len(pixel_values)):
-        image = np.array(pixel_values[i])
-        mask = np.array(conditioning_images[i])
-        dim_min_ind = np.argmin(image.shape[0:2])
-        dim = [0, 0]
-
-        resize_len = 768.0
-        ratio = resize_len / image.shape[0:2][dim_min_ind]
-        dim[1-dim_min_ind] = int(resize_len)
-        dim[dim_min_ind] = int(ratio * image.shape[0:2][1-dim_min_ind])
-        dim = tuple(dim)
-
-        # resize image
-        image = cv2.resize(image, dim, interpolation = cv2.INTER_AREA)
-        mask = cv2.resize(mask, dim, interpolation = cv2.INTER_AREA)
-        max_x = image.shape[1] - 512
-        max_y = image.shape[0] - 512
-        x = np.random.randint(0, max_x)
-        y = np.random.randint(0, max_y)
-        image = image[y: y + 512, x: x + 512]
-        mask = mask[y: y + 512, x: x + 512]
-
-        # fix for bluish outputs
-        r = np.copy(image[:,:,0])
-        image[:,:,0] = image[:,:,2]
-        image[:,:,2] = r
-        image = Image.fromarray(image)
-        b, g, r = image.split()
-        image = Image.merge("RGB", (r, g, b))
-        pixel_values[i] = image
-        conditioning_images[i] = Image.composite(image, Image.fromarray(mask), Image.fromarray(mask).convert('L')).convert('RGB')
-
-
-    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(),
-            transforms.Normalize([0.5], [0.5])
-        ]
-    )
-
-    pixel_values = [image_transforms(image) for image in pixel_values]
-    pixel_values = torch.stack(pixel_values)
-    pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-
-    conditioning_images = [conditioning_image_transforms(image) for image in conditioning_images]
-    conditioning_pixel_values = torch.stack(conditioning_images)
-    conditioning_pixel_values = conditioning_pixel_values.to(memory_format=torch.contiguous_format).float()
-
-    input_ids = torch.stack([example["input_ids"] for example in examples])
-
- #   masks = torch.stack(masks)
- #   masked_images = torch.stack(masked_images)
-
-    return {
-        "pixel_values": pixel_values,
-        "conditioning_pixel_values": conditioning_pixel_values,
-        "input_ids": input_ids,
-  #      "masks": masks, "masked_images": masked_images
-    }
-
-#    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 = torch.stack([example["conditioning_pixel_values"] for example in examples])
-#    conditioning_pixel_values = conditioning_pixel_values.to(memory_format=torch.contiguous_format).float()
-
- #   input_ids = torch.stack([example["input_ids"] for example in examples])
-
-  #  return {
-  #      "pixel_values": pixel_values,
-  #      "conditioning_pixel_values": conditioning_pixel_values,
-  #      "input_ids": input_ids,
-  #  }
-
-
-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.utils.logging.set_verbosity_info()
-    else:
-        transformers.utils.logging.set_verbosity_error()
-        diffusers.utils.logging.set_verbosity_error()
-
-    # If passed along, set the training seed now.
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    # Handle the repository creation
-    if accelerator.is_main_process:
-        if args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-
-        if args.push_to_hub:
-            repo_id = create_repo(
-                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
-            ).repo_id
-
-    # Load the 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 = ControlNetModel.from_pretrained(args.controlnet_model_name_or_path)
-    else:
-        logger.info("Initializing controlnet weights from unet")
-        controlnet = ControlNetModel.from_unet(unet)
-
-    # `accelerate` 0.16.0 will have better support for customized saving
-    if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
-        # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
-        def save_model_hook(models, weights, output_dir):
-            i = len(weights) - 1
-
-            while len(weights) > 0:
-                weights.pop()
-                model = models[i]
-
-                sub_dir = "controlnet"
-                model.save_pretrained(os.path.join(output_dir, sub_dir))
-
-                i -= 1
-
-        def load_model_hook(models, input_dir):
-            while len(models) > 0:
-                # pop models so that they are not loaded again
-                model = models.pop()
-
-                # load diffusers 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.train()
-
-    if args.enable_xformers_memory_efficient_attention:
-        if is_xformers_available():
-            import xformers
-
-            xformers_version = version.parse(xformers.__version__)
-            if xformers_version == version.parse("0.0.16"):
-                logger.warn(
-                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
-                )
-            unet.enable_xformers_memory_efficient_attention()
-            controlnet.enable_xformers_memory_efficient_attention()
-        else:
-            raise ValueError("xformers is not available. Make sure it is installed correctly")
-
-    if args.gradient_checkpointing:
-        controlnet.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).dtype != torch.float32:
-        raise ValueError(
-            f"Controlnet loaded as datatype {accelerator.unwrap_model(controlnet).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 = controlnet.parameters()
-    optimizer = optimizer_class(
-        params_to_optimize,
-        lr=args.learning_rate,
-        betas=(args.adam_beta1, args.adam_beta2),
-        weight_decay=args.adam_weight_decay,
-        eps=args.adam_epsilon,
-    )
-
-    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,
-        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, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        controlnet, optimizer, 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")
-        tracker_config.pop("validation_inpainting_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):
-                # 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
-                encoder_hidden_states = text_encoder(batch["input_ids"])[0]
-
-                controlnet_image = batch["conditioning_pixel_values"].to(dtype=weight_dtype)
-
-                down_block_res_samples, mid_block_res_sample = controlnet(
-                    noisy_latents,
-                    timesteps,
-                    encoder_hidden_states=encoder_hidden_states,
-                    controlnet_cond=controlnet_image,
-                    return_dict=False,
-                )
-
-                # Predict the noise residual
-                model_pred = unet(
-                    noisy_latents,
-                    timesteps,
-                    encoder_hidden_states=encoder_hidden_states,
-                    down_block_additional_residuals=[
-                        sample.to(dtype=weight_dtype) for sample in down_block_res_samples
-                    ],
-                    mid_block_additional_residual=mid_block_res_sample.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":
-                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
-                else:
-                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-                loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
-                accelerator.backward(loss)
-                if accelerator.sync_gradients:
-                    params_to_clip = controlnet.parameters()
-                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
-                optimizer.step()
-                lr_scheduler.step()
-                optimizer.zero_grad(set_to_none=args.set_grads_to_none)
-
-            # 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 and global_step % args.validation_steps == 0:
-                        image_logs = log_validation(
-                            vae,
-                            text_encoder,
-                            tokenizer,
-                            unet,
-                            controlnet,
-                            args,
-                            accelerator,
-                            weight_dtype,
-                            global_step,
-                        )
-
-            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
-            progress_bar.set_postfix(**logs)
-            accelerator.log(logs, step=global_step)
-
-            if global_step >= args.max_train_steps:
-                break
-
-    # Create the pipeline using using the trained modules and save it.
-    accelerator.wait_for_everyone()
-    if accelerator.is_main_process:
-        controlnet = accelerator.unwrap_model(controlnet)
-        controlnet.save_pretrained(args.output_dir)
-
-        if args.push_to_hub:
-            save_model_card(
-                repo_id,
-                image_logs=image_logs,
-                base_model=args.pretrained_model_name_or_path,
-                repo_folder=args.output_dir,
-            )
-            upload_folder(
-                repo_id=repo_id,
-                folder_path=args.output_dir,
-                commit_message="End of training",
-                ignore_patterns=["step_*", "epoch_*"],
-            )
-
-    accelerator.end_training()
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    main(args)