train_controlnet.py

#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
import argparse
import logging
import math
import os
import random
import shutil
from pathlib import Path
from torchvision.utils import save_image
import accelerate
import numpy as np
import functools
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
import gc
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
from relighting.light_directions import get_light_dir_encoding, BACKWARD_DIR_IDS
from aim import Run
from pprint import pprint
from lpips import LPIPS
import random 

import diffusers
from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDPMScheduler,
    DDIMScheduler,
    StableDiffusionControlNetPipeline,
    StableDiffusionXLControlNetPipeline,
    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


def is_wandb_available():
    return False
# 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("0.22.0.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


@torch.no_grad()
def log_validation(vae, unet, controlnet, args, accelerator, weight_dtype, step, num_samples=None, save_grid=True, text_encoder=None, tokenizer=None):
    logger.info("Running validation... ")
    if num_samples is None:
        num_samples = args.num_validation_images

    controlnet = accelerator.unwrap_model(controlnet)
    pipeline: StableDiffusionControlNetPipeline = 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 = DDIMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler", timestep_spacing="trailing", rescale_betas_zero_snr=True)

    pipeline = pipeline.to(accelerator.device)
    # pipeline.set_progress_bar_config(disable=True)

    if not args.disable_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 = []

    val_index = 0
    if args.skip_custom:
        validation_images = []
    else:
        validation_images = [x for x in os.listdir("validation_images") if "_gt." not in x]
    
    for val_im_name in validation_images + validation_images:
        validation_prompt = ""
        if val_im_name in validation_images:
            val_image_path = "validation_images" + "/" + val_im_name
            validation_image = Image.open(val_image_path).convert("RGB")
            validation_image = validation_image.resize(image_size, Image.LANCZOS)
            if args.concat_depth_maps:
                depth_image_path = val_image_path.replace("validation_images/", "validation_depths/").replace(".png", "_pred.png")
                depth_image = Image.open(depth_image_path).convert("L")
                depth_image = depth_image.resize(image_size, Image.LANCZOS)

        else:
            val_image_path = (args.images_dir + "/" + val_im_name).replace(".jpg", ".png")
            validation_image = Image.open(val_image_path).convert("RGB")
            image_size = validation_image.size
            depth_image_path = val_image_path.replace("_exr/", "_depths/").split("_dir")[0] + "_dir_10_pred.png"
            depth_image = Image.open(depth_image_path).convert("L")

        encoder_hidden_states = None
        if args.inject_lighting_direction:
            filename = val_image_path.split("/")[-1]
            target_dir = BACKWARD_DIR_IDS[hash(filename) % len(BACKWARD_DIR_IDS)]
            if val_im_name in validation_images:
                gt_image_path = val_image_path.replace(".png", "_gt.png")
                if os.path.exists(gt_image_path):
                    gt_image = Image.open(gt_image_path).convert("RGB")
                    gt_image = gt_image.resize(image_size, Image.LANCZOS)
                else:
                    gt_image = None
            else:
                name, ext = filename.split(".")
                new_name = "_".join(name.split("_")[:-1]) + "_" + str(target_dir)
                gt_image_path = os.path.dirname(val_image_path) + "/" + new_name + "." + ext
                gt_image = Image.open(gt_image_path).convert("RGB")
                probe_path = os.path.dirname(os.path.dirname(gt_image_path)) + "/probes_exr/" + new_name + "_probe.png"
                probe_image = Image.open(probe_path).convert("RGB")
                with torch.autocast("cuda", dtype=weight_dtype):
                    probe_tensor = TF.to_tensor(probe_image).to(weight_dtype).cuda() * 2 - 1
                    probe_tokens = (controlnet.patch_projection(probe_tensor) + controlnet.patch_embeddings).flatten(2, 3).mT / math.sqrt(2)
                    text_encoded = text_encoder(INPUT_IDS.to(accelerator.device)[None])[0]
                    encoder_hidden_states = torch.cat([text_encoded, probe_tokens], dim=1)
        else:
            gt_image = None
            
        images = []

        for _ in range(num_samples):
            with torch.autocast("cuda", dtype=weight_dtype):
                controlnet_kwargs = {}
                if args.inject_lighting_direction:
                    controlnet_kwargs["timestep_cond"] = get_light_dir_encoding(target_dir)[None].cuda()
                if args.concat_depth_maps:
                    validation_input = torch.cat([
                        TF.to_tensor(validation_image).to(weight_dtype).cuda()[None],
                        TF.to_tensor(depth_image).to(weight_dtype).cuda()[None]
                    ], dim=1)
                else:
                    validation_input = validation_image
                image = pipeline(
                    prompt=validation_prompt, 
                    image=validation_input, 
                    num_inference_steps=5, 
                    generator=generator,
                    controlnet_kwargs=controlnet_kwargs, 
                    guidance_scale=args.sampling_cfg_scale
                ).images[0]

            images.append(image)

        image_logs.append(
            {"validation_image": validation_image, "images": images, "validation_prompt": f"val_{val_index}", "gt": gt_image, "custom": val_im_name in validation_images}
        )

        val_grid_path =  os.path.join(args.output_dir, val_im_name.replace(".", f"_step_{step}.") if val_im_name in validation_images else f"val_{val_index}_step{step}.png" )
        if save_grid:
            grid_input = [validation_image] + images
            if gt_image is not None:
                grid_input.append(gt_image)
            image_grid(grid_input, 1, len(grid_input)).save(val_grid_path)
        val_index += 1

    return image_logs


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

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

        return CLIPTextModel
    elif model_class == "CLIPTextModelWithProjection":
        from transformers import CLIPTextModelWithProjection

        return CLIPTextModelWithProjection
    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="stabilityai/stable-diffusion-2-1",
        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(
        "--train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader."
    )
    parser.add_argument("--num_train_epochs", type=int, default=10)
    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(
        "--sampler",
        type=str,
        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
        default="unipc"
    )
    parser.add_argument(
        "--checkpointing_steps",
        type=int,
        default=50000,
        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=1e-4,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument(
        "--scale_lr",
        action="store_true",
        default=False,
        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
    )
    parser.add_argument(
        "--lr_scheduler",
        type=str,
        default="constant",
        help=(
            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
            ' "constant", "constant_with_warmup"]'
        ),
    )
    parser.add_argument(
        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument(
        "--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=8,
        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="",
        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="bf16",
        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(
        "--disable_xformers_memory_efficient_attention", action="store_false", 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(
        "--images_dir",
        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=[""],
        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(
        "--crops_coords_top_left_h",
        type=int,
        default=0,
        help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
    )
    parser.add_argument(
        "--crops_coords_top_left_w",
        type=int,
        default=0,
        help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
    )
    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=2,
        help="Number of images to be generated for each `--validation_image`, `--validation_prompt` pair",
    )
    parser.add_argument(
        "--validation_steps",
        type=int,
        default=10_000,
        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="",
        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"
        ),
    )
    parser.add_argument(
        "--inject_lighting_direction",
        action="store_true",
    )
    parser.add_argument(
        "--concat_depth_maps",
        action="store_true",
    )
    parser.add_argument(
        "--dir_sh",
        type=int, 
        default=-1
    )
    parser.add_argument(
        "--eval_only",
        action="store_true",
    )
    parser.add_argument(
        "--force_use_aim",
        action="store_true",
    )
    parser.add_argument(
        "--skip_custom",
        action="store_true",
    )
    parser.add_argument(
        "--skip_first_val",
        action="store_true",
    )
    parser.add_argument(
        "--sampling_cfg_scale",
        type=float,
        default=1.0
    )
    parser.add_argument(
        "--dropout_rgb",
        type=float,
        default=0.0
    )

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

    args.train_batch_size //= torch.cuda.device_count() or 1
    
    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

import json
import torchvision.transforms.functional as TF
import cv2

INPUT_IDS = torch.tensor([
        49406, 49407,     0,     0,     0,     0,     0,     0,     0,     0,
    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
    0,     0,     0,     0,     0,     0,     0
] # this is a tokenized version of the empty string
)

compute_embeddings_fn = None
from PIL import UnidentifiedImageError

class MyDataset:
    def __init__(self):
        self.json = [
            json.loads(line) for line in open(f"relighting/training_pairs.jsonl", "r").read().splitlines()
        ]
    
    def __len__(self):
        return len(self.json)
    
    def __getitem__(self, i):
        try:
            "Should return a tensor in range [-1, 1]"
            name = self.json[i]["image"].replace(".jpg", ".png")
            image_path = args.images_dir + "/" + name
            image = Image.open(image_path).convert("RGB")
            probe_path = os.path.dirname(args.images_dir) + "/probes_exr/" + name.replace(".png", "_probe.png")  

            conditioning_image_path = args.images_dir + "/" + self.json[i]["conditioning_image"].replace(".jpg", ".png")
            conditioning_image = Image.open(conditioning_image_path).convert("RGB")
            target_dir = get_light_dir_encoding(int(image_path.split("_dir_")[-1].replace(".png", "")))

            result = {
                "text": "",
                "image": image,
                "target_dir": target_dir,
                "conditioning_image": conditioning_image,
                "pixel_values": TF.to_tensor(image) * 2 - 1,
                "conditioning_pixel_values": TF.to_tensor(conditioning_image),
                "input_ids": INPUT_IDS,
                **(self.xtras)
            }
            assert "_exr" in image_path #!!! update this
            depth_image = Image.open(image_path.replace("_exr/", "_depths/").split("_dir")[0] + "_dir_10_pred.png").convert("L") #!!! update this
            result["depth_image"] = depth_image
            result["depth_pixel_values"] = TF.to_tensor(depth_image)

        except UnidentifiedImageError:
            print(f"WARNING: invalid file for scene {image_path}, will return another random batch item")
            return super().__getitem__(random.choice(list(range(len(self)))))
        return result 


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()

    if args.use_probes:
        probes = torch.stack([example["probe_image"] for example in examples])
        probes = probes.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])
    target_dir = torch.stack([example["target_dir"] for example in examples])

    result = {
        "pixel_values": pixel_values,
        "conditioning_pixel_values": conditioning_pixel_values,
        "target_dir": target_dir,
    }

    if args.concat_depth_maps:
        depth_pixel_values = torch.stack([example["depth_pixel_values"] for example in examples])
        depth_pixel_values = depth_pixel_values.to(memory_format=torch.contiguous_format).float()
        result["depth_pixel_values"] = depth_pixel_values

    result["input_ids"] = input_ids

    if args.use_probes:
        result["probes"] = probes
        

    return result


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)

        with open(args.output_dir + "/args.txt", "w") as f:
            pprint(vars(args), stream=f)

        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


    # import correct text encoder classes

    noise_scheduler = DDIMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")

    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
    )
    lpips = LPIPS(net="alex")

    if args.controlnet_model_name_or_path:
        logger.info("Loading existing controlnet weights")
        controlnet = ControlNetModel.from_pretrained(args.controlnet_model_name_or_path, conditioning_channels=4 if args.concat_depth_maps else 3)
    else:
        logger.info("Initializing controlnet weights from unet")
        controlnet = ControlNetModel.from_unet(unet, conditioning_channels=4 if args.concat_depth_maps else 3)

    @torch.no_grad()
    def modify_layers(controlnet):
        if args.inject_lighting_direction:
            controlnet.time_embedding.cond_proj = torch.nn.Linear(len(get_light_dir_encoding(0)), controlnet.time_embedding.in_channels, bias=False)
    
    modify_layers(controlnet)
    # `accelerate` 0.16.0 will have better support for customized saving
    if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
        # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
        def save_model_hook(models, weights, output_dir):
            if accelerator.is_main_process:
                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", conditioning_channels=4 if args.concat_depth_maps else 3, extra_init_fn=modify_layers)
                load_model.state_dict().keys()
                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 not args.disable_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()
        unet.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 = MyDataset()

    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,
        pin_memory=True,
        prefetch_factor=2 if args.dataloader_num_workers != 0 else None,
        drop_last=True
    )

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

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

    # 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

    # Prepare everything with our `accelerator`.
    controlnet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        controlnet, optimizer, train_dataloader, lr_scheduler
    )
    
    # 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)
    lpips.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 = 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(path)
            global_step = int(path.split("/")[-1].split("-")[1])

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

    def run_val(val_dir, num_samples, save_grid=True):
        image_logs = log_validation(
            vae,
            unet,
            controlnet,
            args,
            accelerator,
            weight_dtype,
            global_step,
            num_samples,
            save_grid=save_grid
        )
        os.makedirs(val_dir)

        target_resolutions = [256, 512, 1024, 1536]
        
        psnr_scores = {res:0.0 for res in target_resolutions}
        lpips_scores = {res:0.0 for res in target_resolutions}

        for i, log in enumerate(image_logs):
            log["validation_image"].save(f"{val_dir}/{i:04d}_input.png")
            log["images"][-1].save(f"{val_dir}/{i:04d}_pred.png")
            if "gt" in log and log["gt"] is not None:
                log["gt"].save(f"{val_dir}/{i:04d}_target.png")
                total_images = 0
                for res in target_resolutions:
                    if log["images"][0].size[0] < res:
                        continue
                    for pred in log["images"]:
                        pred = pred.resize((res, res//2*3))
                        gt = log["gt"].resize((res, res//2*3))
                        psnr_scores[res] += 10 * np.log10(255**2 / np.mean((np.array(pred) - np.array(gt))**2))
                        total_images += 1
                        with torch.autocast("cuda", dtype=weight_dtype):
                            lpips_scores[res] += lpips(TF.to_tensor(pred).cuda() * 2 - 1, TF.to_tensor(gt).cuda() * 2 - 1).item()
                    psnr_scores[res] /= total_images
                    lpips_scores[res] /= total_images
        
        if IS_QUEUED_JOB: 
            for res in target_resolutions:
                run.track(psnr_scores[res], name=f'psnr_{res}x{res//2*3}', step=global_step)
                run.track(lpips_scores[res], name=f'lpips_{res}x{res//2*3}', step=global_step)
                with open(f"{args.output_dir}/scores_{res}.csv", "a") as f:
                    print(round(psnr_scores[res],3), round(lpips_scores[res], 3), file=f)

    if not args.eval_only:
        # Initialize a new run
        IS_QUEUED_JOB = (os.getenv("OAR_JOB_NAME", "") != "") or args.force_use_aim
        if accelerator.is_main_process and IS_QUEUED_JOB:
            experiment = args.output_dir.split("/")[-2]
            run = Run(experiment=experiment)

        text_encoded = None
        image_logs = None
        
        for epoch in range(first_epoch, args.num_train_epochs):
            for step, batch in tqdm(enumerate(train_dataloader), initial=initial_global_step, total=args.max_train_steps, disable=not accelerator.is_local_main_process):
                if step == 1:
                    import time 
                    start = time.time()
                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
                    if text_encoded is None: 
                        encoder_hidden_states = text_encoded = text_encoder(batch["input_ids"])[0]
                    else:
                        encoder_hidden_states = text_encoded
                    controlnet_image = batch["conditioning_pixel_values"].to(dtype=weight_dtype)
                    if args.concat_depth_maps:
                        if random.random() < args.dropout_rgb:
                            controlnet_image = controlnet_image * 0
                        controlnet_image = torch.cat([controlnet_image, batch["depth_pixel_values"].to(dtype=weight_dtype)], dim=1)
                    
                    encoder_hidden_states_controlnet = encoder_hidden_states

                    # down_block_res_samples, mid_block_res_sample = controlnet(
                    down_block_res_samples, mid_block_res_sample = controlnet(
                        noisy_latents,
                        timesteps,
                        encoder_hidden_states=encoder_hidden_states_controlnet,
                        controlnet_cond=controlnet_image,
                        return_dict=False,
                        timestep_cond=batch["target_dir"] if args.inject_lighting_direction else None
                    )

                    # 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
                    assert noise_scheduler.config.prediction_type == "v_prediction"
                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
                    
                    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:
                    if accelerator.is_main_process:
                        if global_step % args.validation_steps == 0 and not (global_step == 0 and args.skip_first_val):
                            run_val(f"{args.output_dir}/val_results/{global_step // args.validation_steps:03d}", args.num_validation_images)
                    
                    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 accelerator.is_main_process:
                    if IS_QUEUED_JOB:
                        run.track(loss.detach().item(), name='loss', step=global_step)
                        run.track(loss.detach().item(), name='loss_per_image', step=global_step*args.train_batch_size)
                        run.track(loss.detach().item(), name='loss_per_pixel', step=global_step*args.train_batch_size * batch["pixel_values"].shape[-1] * batch["pixel_values"].shape[-2])
                
                if global_step >= args.max_train_steps:
                    print("time elapsed", time.time() - start)
                    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()
    print(args)
    main(args)