repo_description: [TIP2026] Official codes of CCSRv2 and CCSRv1: Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-Resolution
repo_topics: arbitrary-steps-image-super-resolution ccsr diffusion
code: #!/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 ADD.models.discriminator import ProjectedDiscriminator
  
  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 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,
      DDPMScheduler,
      StableDiffusionControlNetPipeline,
      UniPCMultistepScheduler,
  )
  from models.controlnet import ControlNetModel
  from models.unet_2d_condition import UNet2DConditionModel
  # from models.losses import LPIPSWithDiscriminator
  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
  from accelerate import DistributedDataParallelKwargs
  
  from dataloaders.paired_dataset_txt import PairedCaptionDataset
  
  
  from ADD.models.vit import vit_large, vit_small
  import ADD.utils.util_net as util_net
  
  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("0.21.0.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_prompts = args.validation_prompt
      elif len(args.validation_image) == 1:
          validation_images = args.validation_image * len(args.validation_prompt)
          validation_prompts = args.validation_prompt
      elif len(args.validation_prompt) == 1:
          validation_images = args.validation_image
          validation_prompts = args.validation_prompt * len(args.validation_image)
      else:
          raise ValueError(
              "number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`"
          )
  
      image_logs = []
  
      for validation_prompt, validation_image in zip(validation_prompts, validation_images):
          validation_image = Image.open(validation_image).convert("RGB")
  
          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('--dataset_root_folders', type=str, default="")
      parser.add_argument("--is_module", action="store_true")
      parser.add_argument("--t_max", type=float, default=0.6666)
      parser.add_argument("--t_min", type=float, default=0.5)
      parser.add_argument("--num_inference_steps", type=int, default=1)
      parser.add_argument("--start_timesteps", type=int, default=999)
  
      parser.add_argument("--lambda_l2", type=float, default=1.0)
      parser.add_argument("--lambda_lpips", type=float, default=1.0)
      parser.add_argument("--lambda_disc", type=float, default=0.05)
      parser.add_argument("--lambda_disc_train", type=float, default=0.5)
      parser.add_argument("--begin_disc", type=float, default=100)
  
      parser.add_argument(
          "--is_start_lr",
          type=bool,
          default=True,
          help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
      )
      parser.add_argument(
          "--vae_model_name_or_path",
          type=str,
          default='',
          help="Path to pretrained vae model."
          " If not specified vae weights are initialized from pre-trained model.",
      )
      parser.add_argument(
          "--pretrained_model_name_or_path",
          type=str,
          default="",
          help="Path to pretrained model or model identifier from huggingface.co/models.",
      )
      parser.add_argument(
          "--controlnet_model_name_or_path",
          type=str,
          default='',
          help="Path to pretrained controlnet model."
          " If not specified controlnet weights are initialized from unet.",
      )
      parser.add_argument(
          "--output_dir",
          type=str,
          default="./experiments/test",
          help="The output directory where the model predictions and checkpoints will be written.",
      )
      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=1000)
      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(
          "--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(
          "--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=0, help="A seed for reproducible training.")
      
      
      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-5,
          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="fp16",
          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(
          "--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(
          "--validation_image",
          type=str,
          default=[""],
          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=100,
          help="Number of images to be generated for each `--validation_image`, `--validation_prompt` pair",
      )
      parser.add_argument(
          "--validation_steps",
          type=int,
          default=1,
          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_ccsr_stage2",
          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.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 previous_timestep(timestep):
      if noise_scheduler.custom_timesteps:
          index = (noise_scheduler.timesteps == timestep).nonzero(as_tuple=True)[0][0]
          if index == noise_scheduler.timesteps.shape[0] - 1:
              prev_t = torch.tensor(-1)
          else:
              prev_t = noise_scheduler.timesteps[index + 1]
      else:
          num_inference_steps = (
              noise_scheduler.num_inference_steps if noise_scheduler.num_inference_steps else noise_scheduler.config.num_train_timesteps
          )
          prev_t = timestep - noise_scheduler.config.num_train_timesteps // num_inference_steps
  
      return prev_t
  
  def predict_start_from_noise(sample, t, model_output):
      t = t.to(noise_scheduler.alphas_cumprod.device)
      prev_t = previous_timestep(t)
  
      # 1. compute alphas, betas
      alpha_prod_t = noise_scheduler.alphas_cumprod[t].to(sample.device)
      alpha_prod_t_prev = noise_scheduler.alphas_cumprod[prev_t] if prev_t >= 0 else noise_scheduler.one
      alpha_prod_t_prev = alpha_prod_t_prev.to(sample.device)
      beta_prod_t = 1 - alpha_prod_t
      beta_prod_t_prev = 1 - alpha_prod_t_prev
      current_alpha_t = alpha_prod_t / alpha_prod_t_prev
      current_beta_t = 1 - current_alpha_t
  
      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      if noise_scheduler.config.prediction_type == "epsilon":
          pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
      elif noise_scheduler.config.prediction_type == "sample":
          pred_original_sample = model_output
      elif noise_scheduler.config.prediction_type == "v_prediction":
          pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
      else:
          raise ValueError(
              f"prediction_type given as {noise_scheduler.config.prediction_type} must be one of `epsilon`, `sample` or"
              " `v_prediction`  for the DDPMScheduler."
          )
  
      return pred_original_sample
  
  # def main(args):
  args = parse_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 smodels
  noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
  noise_scheduler.set_timesteps(args.num_inference_steps)
  
  text_encoder = text_encoder_cls.from_pretrained(
      args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
  )
  unet = UNet2DConditionModel.from_pretrained(
      args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
  )
  
  # Load VAE model
  if args.vae_model_name_or_path:
      logger.info("Loading existing vae weights")
      vae = AutoencoderKL.from_pretrained(args.vae_model_name_or_path, subfolder="vae", revision=args.revision)
  else:
      logger.info("Loading pretrained vae weights")
      vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
  
  # Load Controlnet model
  if args.controlnet_model_name_or_path:
      logger.info("Loading existing controlnet weights")
      controlnet = ControlNetModel.from_pretrained(args.controlnet_model_name_or_path,  subfolder="controlnet")
  else:
      logger.info("Initializing controlnet weights from unet")
      controlnet = ControlNetModel.from_unet(unet, use_vae_encode_condition=True)
      
  # # Load discriminator model
  # discriminatornet = LPIPSWithDiscriminator(disc_start=1.0, kl_weight=0, perceptual_weight=1.0, disc_weight=0.5, disc_factor=1.0)
  
  # Load discriminator model
  discriminatornet = ProjectedDiscriminator(c_dim=384).train()
  criterion_GAN = torch.nn.BCEWithLogitsLoss()
  # 实例化提取cls_lr的特征网络
  model_fea = vit_small(patch_size=14, img_size=518, block_chunks=0, init_values=1.0)
  util_net.reload_model(model_fea, torch.load('preset/models/dino/dinov2_vits14_pretrain.pth'))
  model_fea.requires_grad_(False)
  
  # load lpips model
  import lpips
  net_lpips = lpips.LPIPS(net='vgg').cuda()
  
  # `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
          assert len(models) == 2 and len(weights) == 2
          for i, model in enumerate(models):
              if i==0:
                  sub_dir = 'vae'
                  model.save_pretrained(os.path.join(output_dir, sub_dir))
              # make sure to pop weight so that corresponding model is not saved again
              weights.pop()
  
      def load_model_hook(models, input_dir):
          assert len(models) == 2
          for i in range(len(models)):
              # pop models so that they are not loaded again
              model = models.pop()
  
              # load diffusers style into model
              if not isinstance(model, UNet2DConditionModel):
                  load_model = ControlNetModel.from_pretrained(input_dir, subfolder="controlnet") # , low_cpu_mem_usage=False, ignore_mismatched_sizes=True
              else:
                  load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet") # , low_cpu_mem_usage=False, ignore_mismatched_sizes=True
  
              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.requires_grad_(False)
  discriminatornet.train()
  vae.train()
  
  # unlease vae decoder for training
  for name, params in vae.named_parameters():
      if 'decoder' in name:
          params.requires_grad = True
  
  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:
      vae.enable_gradient_checkpointing()
      discriminatornet.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(vae).dtype != torch.float32:
      raise ValueError(
          f"vae loaded as datatype {accelerator.unwrap_model(vae).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 = list(vae.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,
  )
      
  params_to_optimize_disc = list(discriminatornet.parameters())
  optimizer_disc = optimizer_class(
      params_to_optimize_disc,
      lr=args.learning_rate,
      betas=(0.9, 0.999),
      weight_decay=args.adam_weight_decay,
      eps=args.adam_epsilon,
  )
  
  train_dataset = PairedCaptionDataset(root_folders=args.dataset_root_folders,
                                      tokenizer=tokenizer,
                                      gt_ratio=0) # let lr is gt
  
  train_dataloader = torch.utils.data.DataLoader(
      train_dataset,
      num_workers=args.dataloader_num_workers,
      batch_size=args.train_batch_size,
      shuffle=False
  )
  
  # 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 controlnet, unet and text_encoder to device and cast to weight_dtype
  controlnet.to(accelerator.device, dtype=weight_dtype)
  text_encoder.to(accelerator.device, dtype=weight_dtype)
  unet.to(accelerator.device, dtype=weight_dtype)
  model_fea.to(accelerator.device, dtype=weight_dtype)
  
  # 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,
  )
  
  lr_scheduler_disc = get_scheduler(
      args.lr_scheduler,
      optimizer=optimizer_disc,
      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`.
  vae, discriminatornet, optimizer, optimizer_disc, train_dataloader, lr_scheduler, lr_scheduler_disc = accelerator.prepare(
      vae, discriminatornet, optimizer, optimizer_disc, train_dataloader, lr_scheduler, lr_scheduler_disc
  )
  
  # 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 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,
  )
  
  for epoch in range(first_epoch, args.num_train_epochs):
      for step, batch in enumerate(train_dataloader):
          l_acc = [vae, discriminatornet]
          with accelerator.accumulate(*l_acc):
              with torch.no_grad():
                  total_time_steps = noise_scheduler.timesteps
                  num_time_steps = len(total_time_steps)
                  if num_time_steps != 1:
                      timesteps_loop = total_time_steps[-round(num_time_steps*args.t_max):]
                      timesteps_loop = timesteps_loop[:-round(num_time_steps*args.t_min)]
                      t_max = timesteps_loop[0]
                      t_min = timesteps_loop[-1]
  
                  pixel_values = batch["pixel_values"].to(accelerator.device)
                  if args.is_module:
                      latents_gt = vae.module.encode(pixel_values).latent_dist.sample()
                      latents_gt = latents_gt * vae.module.config.scaling_factor # Convert images to latent space
                  else:
                      latents_gt = vae.encode(pixel_values).latent_dist.sample()
                      latents_gt = latents_gt * vae.config.scaling_factor # Convert images to latent space
  
                  encoder_hidden_states = text_encoder(batch["input_caption"].to(accelerator.device))[0]
  
                  controlnet_image = batch["conditioning_pixel_values"].to(accelerator.device)
                  controlnet_image_encode = 2*controlnet_image-1
                  if args.is_module:
                      vae_encode_condition_hidden_states = vae.module.encode(controlnet_image_encode).latent_dist.sample()
                      vae_encode_condition_hidden_states = vae_encode_condition_hidden_states * vae.module.config.scaling_factor
                  else:
                      vae_encode_condition_hidden_states = vae.encode(controlnet_image_encode).latent_dist.sample()
                      vae_encode_condition_hidden_states = vae_encode_condition_hidden_states * vae.config.scaling_factor # Convert images to latent space
                                  
                  if global_step > args.begin_disc:
                      lambda_l2 = args.lambda_l2
                      lambda_lpips = args.lambda_lpips
                      lambda_disc = args.lambda_disc
                      lambda_disc_train = args.lambda_disc_train
                  else:
                      lambda_l2 = args.lambda_l2
                      lambda_lpips = 0
                      lambda_disc = 0
                      lambda_disc_train = args.lambda_disc_train
  
                  noise = torch.randn_like(latents_gt)
                  bsz = latents_gt.shape[0]
                  
                  timesteps = args.start_timesteps * torch.ones(latents_gt.shape[0]).to(accelerator.device)
                  timesteps = timesteps.long()
  
                  # Add noise to the latents according to the noise magnitude at each timestep
                  # (this is the forward diffusion process)
                  if args.start_timesteps==1:
                      noisy_latents = noise_scheduler.add_noise(vae_encode_condition_hidden_states, noise, timesteps)
                      noisy_latents = noisy_latents.to(dtype=weight_dtype)
                      encoder_hidden_states = encoder_hidden_states.to(dtype=weight_dtype)
                      controlnet_image = controlnet_image.to(dtype=weight_dtype)
                      vae_encode_condition_hidden_states = vae_encode_condition_hidden_states.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,
                          vae_encode_condition_hidden_states=vae_encode_condition_hidden_states,
                      )
  
                      # 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
  
                      # Predict x0 for T
                      x0_T = noisy_latents - model_pred
                  else:
                      # Sample noise based on LR (controlnet_image) or a Random Noise?
                      if args.is_start_lr:
                          noisy_latents = noise_scheduler.add_noise(vae_encode_condition_hidden_states, noise, timesteps)
                          noisy_latents = noisy_latents.to(dtype=weight_dtype)
                      else:
                          noisy_latents = noise_scheduler.add_noise(latents_gt, noise, timesteps)
                          noisy_latents = noisy_latents.to(dtype=weight_dtype)
  
                      encoder_hidden_states = encoder_hidden_states.to(dtype=weight_dtype)
                      controlnet_image = controlnet_image.to(dtype=weight_dtype)
                      vae_encode_condition_hidden_states = vae_encode_condition_hidden_states.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,
                              vae_encode_condition_hidden_states=vae_encode_condition_hidden_states,
                      )
  
                      # Predict the noise residual
                      model_pred = unet(
                          noisy_latents,
                          timesteps,
                          encoder_hidden_states=encoder_hidden_states,
                          down_block_additional_residuals=down_block_res_samples,
                          mid_block_additional_residual=mid_block_res_sample,
                          return_dict=False,
                      )[0]
  
                      # Predict x0 for T
                      x0_T = predict_start_from_noise(noisy_latents, timesteps[0], model_pred)
  
                  if num_time_steps!=1:
                      # Re-add noise on x0_tmax
                      noise2 = torch.randn_like(latents_gt)
                      timesteps = t_max * torch.ones(model_pred.shape[0]).to(accelerator.device)
                      timesteps = timesteps.long()
                      latents = noise_scheduler.add_noise(x0_T, noise2, timesteps[0])
  
  
                      # Denoising loop
                      for i, t in enumerate(timesteps_loop):
                          
                          # controlnet_latent_model_input = noise_scheduler.scale_model_input(latents, t)
                          latents = latents.to(dtype=weight_dtype)
                          down_block_res_samples, mid_block_res_sample = controlnet(
                              latents,
                              t,
                              encoder_hidden_states=encoder_hidden_states,
                              controlnet_cond=controlnet_image,
                              return_dict=False,
                              vae_encode_condition_hidden_states=vae_encode_condition_hidden_states,
                          )
  
                          # predict the noise residual
                          noise_pred = unet(
                              latents,
                              t,
                              encoder_hidden_states=encoder_hidden_states,
                              down_block_additional_residuals=down_block_res_samples,
                              mid_block_additional_residual=mid_block_res_sample,
                              return_dict=False,
                          )[0]
  
                          # compute the previous noisy sample x_t -> x_t-1
                          latents_old = latents
                          latents = noise_scheduler.step(noise_pred, t, latents, return_dict=False)[0]
  
                      x0_tmin = predict_start_from_noise(latents_old, t, noise_pred)
                      latents = x0_tmin
                      latents = latents.to(dtype=torch.float32)
                  else:
                      latents = x0_T.to(dtype=torch.float32)
  
          
              # optimize the generator: vae decoder
              discriminatornet.requires_grad_(False)
              if args.is_module:
                  image = vae.module.decode(latents / vae.module.config.scaling_factor, return_dict=False)[0].clamp(-1, 1)
              else:
                  image = vae.decode(latents / vae.config.scaling_factor, return_dict=False)[0].clamp(-1, 1)
              # compute the discriminator loss & update parameters
              _, cls_lr = model_fea(F.interpolate(controlnet_image, size=518, mode='bilinear'))
  
              # compute the generator loss
              pred_fake, _ = discriminatornet(image, cls_lr.detach())
              pred_fake = torch.cat(pred_fake, dim=1)
              gan_loss = -torch.mean(pred_fake)
  
              loss_x0 = F.mse_loss(image.float(), pixel_values.float(), reduction="mean") * lambda_l2
              if lambda_lpips != 0:
                  loss_lpips = net_lpips(image.float(), pixel_values.float()).mean() * lambda_lpips
                  loss_x0 = loss_lpips + loss_x0
  
              loss = loss_x0 + lambda_disc * gan_loss
  
              accelerator.backward(loss)
  
              if accelerator.sync_gradients:
                  params_to_clip = vae.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)
  
              # update discriminator
              discriminatornet.requires_grad_(True)
              if args.is_module:
                  discriminatornet.module.dino.requires_grad_(False)
              else:
                  discriminatornet.dino.requires_grad_(False)
              pred_real, features = discriminatornet(pixel_values, cls_lr.detach())
              pred_fake, _ = discriminatornet(image.detach(), cls_lr.detach())
              pred_fake = torch.cat(pred_fake, dim=1)
  
              pred_real = torch.cat(pred_real, dim=1)
              loss_real = torch.mean(torch.relu(1.0 - pred_real)) * lambda_disc_train
  
              loss_fake = torch.mean(torch.relu(1.0 + pred_fake)) * lambda_disc_train
              loss_disc = loss_real + loss_fake
  
              accelerator.backward(loss_disc)
              if accelerator.sync_gradients:
                  params_to_clip = discriminatornet.parameters()
                  accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
              optimizer_disc.step()
              lr_scheduler_disc.step()
              optimizer_disc.zero_grad(set_to_none=args.set_grads_to_none)
              model_fea.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:
  
                      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:
                  if False:
                      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:
      vae = accelerator.unwrap_model(vae)
      vae.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()