git init

video_super_resolution/color_fix.py

+'''
+# --------------------------------------------------------------------------------
+#   Color fixed script from Li Yi (https://github.com/pkuliyi2015/sd-webui-stablesr/blob/master/srmodule/colorfix.py)
+# --------------------------------------------------------------------------------
+'''
+
+import torch
+from PIL import Image
+from torch import Tensor
+from torch.nn import functional as F
+
+from torchvision.transforms import ToTensor, ToPILImage
+from einops import rearrange
+
+def adain_color_fix(target: Image, source: Image):
+    # Convert images to tensors
+    target = rearrange(target, 'T H W C -> T C H W') / 255
+    source = (source + 1) / 2
+
+    # Apply adaptive instance normalization
+    result_tensor_list = []
+    for i in range(0, target.shape[0]):
+        result_tensor_list.append(adaptive_instance_normalization(target[i].unsqueeze(0), source[i].unsqueeze(0)))
+
+    # Convert tensor back to image
+    result_tensor = torch.cat(result_tensor_list, dim=0).clamp_(0.0, 1.0)
+    result_video = rearrange(result_tensor, "T C H W -> T H W C") * 255
+
+    return result_video
+
+def wavelet_color_fix(target, source):
+    # Convert images to tensors
+    target = rearrange(target, 'T H W C -> T C H W') / 255
+    source = (source + 1) / 2
+
+    # Apply wavelet reconstruction
+    result_tensor_list = []
+    for i in range(0, target.shape[0]):
+        result_tensor_list.append(wavelet_reconstruction(target[i].unsqueeze(0), source[i].unsqueeze(0)))
+
+    # Convert tensor back to image
+    result_tensor = torch.cat(result_tensor_list, dim=0).clamp_(0.0, 1.0)
+    result_video = rearrange(result_tensor, "T C H W -> T H W C") * 255
+
+    return result_video
+
+def calc_mean_std(feat: Tensor, eps=1e-5):
+    """Calculate mean and std for adaptive_instance_normalization.
+    Args:
+        feat (Tensor): 4D tensor.
+        eps (float): A small value added to the variance to avoid
+            divide-by-zero. Default: 1e-5.
+    """
+    size = feat.size()
+    assert len(size) == 4, 'The input feature should be 4D tensor.'
+    b, c = size[:2]
+    feat_var = feat.reshape(b, c, -1).var(dim=2) + eps
+    feat_std = feat_var.sqrt().reshape(b, c, 1, 1)
+    feat_mean = feat.reshape(b, c, -1).mean(dim=2).reshape(b, c, 1, 1)
+    return feat_mean, feat_std
+
+def adaptive_instance_normalization(content_feat:Tensor, style_feat:Tensor):
+    """Adaptive instance normalization.
+    Adjust the reference features to have the similar color and illuminations
+    as those in the degradate features.
+    Args:
+        content_feat (Tensor): The reference feature.
+        style_feat (Tensor): The degradate features.
+    """
+    size = content_feat.size()
+    style_mean, style_std = calc_mean_std(style_feat)
+    content_mean, content_std = calc_mean_std(content_feat)
+    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
+    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
+
+def wavelet_blur(image: Tensor, radius: int):
+    """
+    Apply wavelet blur to the input tensor.
+    """
+    # input shape: (1, 3, H, W)
+    # convolution kernel
+    kernel_vals = [
+        [0.0625, 0.125, 0.0625],
+        [0.125, 0.25, 0.125],
+        [0.0625, 0.125, 0.0625],
+    ]
+    kernel = torch.tensor(kernel_vals, dtype=image.dtype, device=image.device)
+    # add channel dimensions to the kernel to make it a 4D tensor
+    kernel = kernel[None, None]
+    # repeat the kernel across all input channels
+    kernel = kernel.repeat(3, 1, 1, 1)
+    image = F.pad(image, (radius, radius, radius, radius), mode='replicate')
+    # apply convolution
+    output = F.conv2d(image, kernel, groups=3, dilation=radius)
+    return output
+
+def wavelet_decomposition(image: Tensor, levels=5):
+    """
+    Apply wavelet decomposition to the input tensor.
+    This function only returns the low frequency & the high frequency.
+    """
+    high_freq = torch.zeros_like(image)
+    for i in range(levels):
+        radius = 2 ** i
+        low_freq = wavelet_blur(image, radius)
+        high_freq += (image - low_freq)
+        image = low_freq
+
+    return high_freq, low_freq
+
+def wavelet_reconstruction(content_feat:Tensor, style_feat:Tensor):
+    """
+    Apply wavelet decomposition, so that the content will have the same color as the style.
+    """
+    # calculate the wavelet decomposition of the content feature
+    content_high_freq, content_low_freq = wavelet_decomposition(content_feat)
+    del content_low_freq
+    # calculate the wavelet decomposition of the style feature
+    style_high_freq, style_low_freq = wavelet_decomposition(style_feat)
+    del style_high_freq
+    # reconstruct the content feature with the style's high frequency
+    return content_high_freq + style_low_freq
\ No newline at end of file

video_super_resolution/dataset.py

+import os
+import random
+import glob
+import torchvision
+from einops import rearrange
+from torch.utils import data as data
+import torch.nn.functional as F
+from torchvision import transforms
+from PIL import Image
+
+class PairedCaptionVideoDataset(data.Dataset):
+    def __init__(
+            self,
+            root_folders=None,
+            null_text_ratio=0.5,
+            num_frames=16
+    ):
+        super(PairedCaptionVideoDataset, self).__init__()
+
+        self.null_text_ratio = null_text_ratio
+        self.lr_list = []
+        self.gt_list = []
+        self.tag_path_list = []
+        self.num_frames = num_frames
+
+        # root_folders = root_folders.split(',')
+        for root_folder in root_folders:
+            lr_path = root_folder +'/lq'
+            tag_path = root_folder +'/text'
+            gt_path = root_folder +'/gt'
+
+            self.lr_list += glob.glob(os.path.join(lr_path, '*.mp4'))
+            self.gt_list += glob.glob(os.path.join(gt_path, '*.mp4'))
+            self.tag_path_list += glob.glob(os.path.join(tag_path, '*.txt'))
+
+        assert len(self.lr_list) == len(self.gt_list)
+        assert len(self.lr_list) == len(self.tag_path_list)
+
+    def __getitem__(self, index):
+
+        gt_path = self.gt_list[index]
+        vframes_gt, _, info = torchvision.io.read_video(filename=gt_path, pts_unit="sec", output_format="TCHW")
+        fps = info['video_fps']
+        vframes_gt = (rearrange(vframes_gt, "T C H W -> C T H W") / 255) * 2 - 1
+
+        lq_path = self.lr_list[index]
+        vframes_lq, _, _ = torchvision.io.read_video(filename=lq_path, pts_unit="sec", output_format="TCHW")
+        vframes_lq = (rearrange(vframes_lq, "T C H W -> C T H W") / 255) * 2 - 1
+
+        if random.random() < self.null_text_ratio:
+            tag = ''
+        else:
+            tag_path = self.tag_path_list[index]
+            with open(tag_path, 'r', encoding='utf-8') as file:
+                tag = file.read()
+
+        return {"gt": vframes_gt[:, :self.num_frames, :, :], "lq": vframes_lq[:, :self.num_frames, :, :], "text": tag, 'fps': fps}
+
+    def __len__(self):
+        return len(self.gt_list)
+
+
+class PairedCaptionImageDataset(data.Dataset):
+    def __init__(
+            self,
+            root_folder=None,
+    ):
+        super(PairedCaptionImageDataset, self).__init__()
+
+        self.lr_list = []
+        self.gt_list = []
+        self.tag_path_list = []
+
+        lr_path = root_folder +'/sr_bicubic'
+        gt_path = root_folder +'/gt'
+
+        self.lr_list += glob.glob(os.path.join(lr_path, '*.png'))
+        self.gt_list += glob.glob(os.path.join(gt_path, '*.png'))
+
+        assert len(self.lr_list) == len(self.gt_list)
+
+        self.img_preproc = transforms.Compose([       
+            transforms.ToTensor(),
+        ])
+
+        # Define the crop size (e.g., 256x256)
+        crop_size = (720, 1280)
+
+        # CenterCrop transform
+        self.center_crop = transforms.CenterCrop(crop_size)
+
+    def __getitem__(self, index):
+
+        gt_path = self.gt_list[index]
+        gt_img = Image.open(gt_path).convert('RGB')
+        gt_img = self.center_crop(self.img_preproc(gt_img))
+        
+        lq_path = self.lr_list[index]
+        lq_img = Image.open(lq_path).convert('RGB')
+        lq_img = self.center_crop(self.img_preproc(lq_img))
+
+        example = dict()
+        
+        example["lq"] = (lq_img.squeeze(0) * 2.0 - 1.0).unsqueeze(1)
+        example["gt"] = (gt_img.squeeze(0) * 2.0 - 1.0).unsqueeze(1)
+        example["text"] = ""
+
+        return example
+
+    def __len__(self):
+        return len(self.gt_list)
\ No newline at end of file

video_super_resolution/scripts/inference_sr.py

+import os
+import torch
+from argparse import ArgumentParser, Namespace
+import json
+from typing import Any, Dict, List, Mapping, Tuple
+from easydict import EasyDict
+import time
+
+import sys
+base_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../'))
+sys.path.append(base_path)
+from video_to_video.video_to_video_model import VideoToVideo_sr
+from video_to_video.utils.seed import setup_seed
+from video_to_video.utils.logger import get_logger
+from video_super_resolution.color_fix import adain_color_fix
+
+from inference_utils import *
+
+logger = get_logger()
+
+
+class STAR():
+    def __init__(self, 
+                 result_dir='./results/',
+                 file_name='000_video.mp4',
+                 model_path='',
+                 solver_mode='fast',
+                 steps=15,
+                 guide_scale=7.5,
+                 upscale=4,
+                 max_chunk_len=32,
+                 ):
+        self.model_path=model_path
+        logger.info('checkpoint_path: {}'.format(self.model_path))
+
+        self.result_dir = result_dir
+        self.file_name = file_name
+        os.makedirs(self.result_dir, exist_ok=True)
+
+        model_cfg = EasyDict(__name__='model_cfg')
+        model_cfg.model_path = self.model_path
+        self.model = VideoToVideo_sr(model_cfg)
+
+        steps = 15 if solver_mode == 'fast' else steps
+        self.solver_mode=solver_mode
+        self.steps=steps
+        self.guide_scale=guide_scale
+        self.upscale = upscale
+        self.max_chunk_len=max_chunk_len
+
+    def enhance_a_video(self, video_path, prompt):
+        logger.info('input video path: {}'.format(video_path))
+        text = prompt
+        logger.info('text: {}'.format(text))
+        caption = text + self.model.positive_prompt
+
+        input_frames, input_fps = load_video(video_path)
+        logger.info('input fps: {}'.format(input_fps))
+
+        video_data = preprocess(input_frames[:32])     ###
+        _, _, h, w = video_data.shape
+        logger.info('input resolution: {}'.format((h, w)))
+        target_h, target_w = h * self.upscale, w * self.upscale   # adjust_resolution(h, w, up_scale=4)
+        logger.info('target resolution: {}'.format((target_h, target_w)))
+
+        pre_data = {'video_data': video_data, 'y': caption}
+        pre_data['target_res'] = (target_h, target_w)
+
+        total_noise_levels = 900
+        setup_seed(666)
+        start_time = time.time()    ###
+
+        with torch.no_grad():
+            data_tensor = collate_fn(pre_data, 'cuda:0')
+            output = self.model.test(data_tensor, total_noise_levels, steps=self.steps, \
+                                solver_mode=self.solver_mode, guide_scale=self.guide_scale, \
+                                max_chunk_len=self.max_chunk_len
+                                )
+        end_time = time.time()    ###
+        print("Infer time:", end_time-start_time, "s", flush=True)   ###
+        print("output.shape:", output.shape, flush=True)    ###
+
+        output = tensor2vid(output)
+
+        # Using color fix
+        output = adain_color_fix(output, video_data)
+
+        save_video(output, self.result_dir, self.file_name, fps=input_fps)
+        return os.path.join(self.result_dir, self.file_name)
+    
+
+def parse_args():
+    parser = ArgumentParser()
+    
+    parser.add_argument("--input_path", required=True, type=str, help="input video path")
+    parser.add_argument("--save_dir", type=str, default='results', help="save directory")
+    parser.add_argument("--file_name", type=str, help="file name")
+    parser.add_argument("--model_path", type=str, default='./pretrained_weight/model.pt', help="model path")
+    parser.add_argument("--prompt", type=str, default='a good video', help="prompt")
+    parser.add_argument("--upscale", type=int, default=1, help='up-scale')
+    parser.add_argument("--max_chunk_len", type=int, default=32, help='max_chunk_len')
+
+    parser.add_argument("--cfg", type=float, default=7.5)
+    parser.add_argument("--solver_mode", type=str, default='fast', help='fast | normal')
+    parser.add_argument("--steps", type=int, default=5)
+
+    return parser.parse_args()
+
+def main():
+    
+    args = parse_args()
+
+    input_path = args.input_path
+    prompt = args.prompt
+    model_path = args.model_path
+    save_dir = args.save_dir
+    file_name = args.file_name
+    upscale = args.upscale
+    max_chunk_len = args.max_chunk_len
+
+    steps = args.steps
+    solver_mode = args.solver_mode
+    guide_scale = args.cfg
+
+    assert solver_mode in ('fast', 'normal')
+
+    star = STAR(
+                result_dir=save_dir,
+                file_name=file_name,
+                model_path=model_path,
+                solver_mode=solver_mode,
+                steps=steps,
+                guide_scale=guide_scale,
+                upscale=upscale,
+                max_chunk_len=max_chunk_len,
+                )
+
+    star.enhance_a_video(input_path, prompt)
+
+
+if __name__ == '__main__':
+    main()

video_super_resolution/scripts/inference_sr.sh

+#!/bin/bash
+
+# Folder paths
+video_folder_path='./input/video'
+txt_file_path='./input/text/prompt.txt'
+
+# Get all .mp4 files in the folder using find to handle special characters
+mapfile -t mp4_files < <(find "$video_folder_path" -type f -name "*.mp4")
+
+# Print the list of MP4 files
+echo "MP4 files to be processed:"
+for mp4_file in "${mp4_files[@]}"; do
+    echo "$mp4_file"
+done
+
+# Read lines from the text file, skipping empty lines
+mapfile -t lines < <(grep -v '^\s*$' "$txt_file_path")
+
+# The number of video frames processed simultaneously during each denoising process.
+frame_length=32
+
+# Debugging output
+echo "Number of MP4 files: ${#mp4_files[@]}"
+echo "Number of lines in the text file: ${#lines[@]}"
+
+# Ensure the number of video files matches the number of lines
+if [ ${#mp4_files[@]} -ne ${#lines[@]} ]; then
+    echo "Number of MP4 files and lines in the text file do not match."
+    exit 1
+fi
+
+# Loop through video files and corresponding lines
+for i in "${!mp4_files[@]}"; do
+    mp4_file="${mp4_files[$i]}"
+    line="${lines[$i]}"
+    
+    # Extract the filename without the extension
+    file_name=$(basename "$mp4_file" .mp4)
+    
+    echo "Processing video file: $mp4_file with prompt: $line"
+        
+    # Run Python script with parameters
+    python \
+        ./video_super_resolution/scripts/inference_sr.py \
+        --solver_mode 'normal' \
+        --steps 5 \
+        --input_path "${mp4_file}" \
+        --model_path ./pretrained_weight/I2VGen-XL-based/light_deg.pt \
+        --prompt "${line}" \
+        --upscale 1 \
+        --max_chunk_len ${frame_length} \
+        --file_name "${file_name}.mp4" \
+        --save_dir ./results
+done
+
+echo "All videos processed successfully."

video_super_resolution/scripts/train_sr.py

+#!/usr/bin/env python
+# coding=utf-8
+
+import argparse
+import os
+from pathlib import Path
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import ProjectConfiguration
+from tqdm.auto import tqdm
+from diffusers.optimization import get_scheduler
+from diffusers.utils import check_min_version
+
+import torch
+import torch.nn.functional as F
+import torch.fft
+from typing import Tuple
+
+import sys
+base_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../'))
+sys.path.append(base_path)
+
+from video_to_video.modules import *
+from video_to_video.diffusion.diffusion_sdedit import GaussianDiffusion
+from video_to_video.diffusion.schedules_sdedit import noise_schedule
+from video_to_video.utils.logger import get_logger
+from video_super_resolution.dataset import PairedCaptionVideoDataset
+
+from diffusers import AutoencoderKLTemporalDecoder
+
+# 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 get_model_numel(model: torch.nn.Module) -> Tuple[int, int]:
+    num_params = 0
+    num_params_trainable = 0
+    for p in model.parameters():
+        num_params += p.numel()
+        if p.requires_grad:
+            num_params_trainable += p.numel()
+    return num_params, num_params_trainable
+
+def format_numel_str(numel: int) -> str:
+    B = 1024**3
+    M = 1024**2
+    K = 1024
+    if numel >= B:
+        return f"{numel / B:.2f} B"
+    elif numel >= M:
+        return f"{numel / M:.2f} M"
+    elif numel >= K:
+        return f"{numel / K:.2f} K"
+    else:
+        return f"{numel}"
+
+def parse_args(input_args=None):
+    parser = argparse.ArgumentParser(description="Simple example of a ControlNet training script.")
+    parser.add_argument(
+        "--pretrained_model_path",
+        type=str,
+        default="",
+        required=True,
+        help="Path to pretrained model or model identifier from huggingface.co/models.",
+    )
+    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(
+        "--output_dir",
+        type=str,
+        default="",
+        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=1 * 8, 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=50000,
+        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
+    )
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=500,
+        help=(
+            "Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
+            "In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
+            "Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
+            "See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
+            "instructions."
+        ),
+    )
+    parser.add_argument(
+        "--checkpoints_total_limit",
+        type=int,
+        default=None,
+        help=("Max number of checkpoints to store."),
+    )
+    parser.add_argument(
+        "--resume_from_checkpoint",
+        type=str,
+        default=None,
+        help=(
+            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
+            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
+        ),
+    )
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+    parser.add_argument(
+        "--gradient_checkpointing",
+        action="store_true",
+        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
+    )
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=5e-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(
+        "--train_data_dir",
+        type=str,
+        default='NOTHING',
+        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(
+        "--num_frames",
+        type=int,
+        default=32,
+        help=(
+            "Length of each training video"
+        ),
+    )
+    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(
+        "--tracker_project_name",
+        type=str,
+        default="SeeSR",
+        help=(
+            "The `project_name` argument passed to Accelerator.init_trackers for"
+            " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
+        ),
+    )
+    
+    if input_args is not None:
+        args = parser.parse_args(input_args)
+    else:
+        args = parser.parse_args()
+
+    if args.dataset_name is None and args.train_data_dir is None:
+        raise ValueError("Specify either `--dataset_name` or `--train_data_dir`")
+
+    if args.dataset_name is not None and args.train_data_dir is not None:
+        raise ValueError("Specify only one of `--dataset_name` or `--train_data_dir`")
+
+    if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
+        raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
+
+    if args.validation_prompt is not None and args.validation_image is None:
+        raise ValueError("`--validation_image` must be set if `--validation_prompt` is set")
+
+    if args.validation_prompt is None and args.validation_image is not None:
+        raise ValueError("`--validation_prompt` must be set if `--validation_image` is set")
+
+    if (
+        args.validation_image is not None
+        and args.validation_prompt is not None
+        and len(args.validation_image) != 1
+        and len(args.validation_prompt) != 1
+        and len(args.validation_image) != len(args.validation_prompt)
+    ):
+        raise ValueError(
+            "Must provide either 1 `--validation_image`, 1 `--validation_prompt`,"
+            " or the same number of `--validation_prompt`s and `--validation_image`s"
+        )
+
+    return args
+
+
+args = parse_args()
+logging_dir = Path(args.output_dir, args.logging_dir)
+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,
+    split_batches=True,  # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be divided by the number of processes assuming batches are multiplied by the number of processes
+)
+
+
+###-------------------------------------
+# Bulid Model
+###------------------------------------
+
+# text_encoder
+text_encoder = FrozenOpenCLIPEmbedder(pretrained="laion2b_s32b_b79k")
+text_encoder.requires_grad_(False)
+logger.info(f'Build text encoder with CLIP')
+
+# U-Net with ControlNet
+model = ControlledV2VUNet()
+load_dict = torch.load(args.pretrained_model_path, map_location='cpu')
+if 'state_dict' in load_dict:
+    load_dict = load_dict['state_dict']
+
+incompatible_keys = model.load_state_dict(load_dict, strict=False)
+logger.info('Load model path {}, with local status {}'.format(args.pretrained_model_path, incompatible_keys))
+model_numel, model_numel_trainable = get_model_numel(model)
+logger.info(
+    f"Total model params: {format_numel_str(model_numel)}"
+)
+
+# Noise scheduler
+sigmas = noise_schedule(
+    schedule='logsnr_cosine_interp',
+    n=1000,
+    zero_terminal_snr=True,
+    scale_min=2.0,
+    scale_max=4.0)
+noise_scheduler = GaussianDiffusion(sigmas=sigmas)
+logger.info('Build noise_scheduler with GaussianDiffusion')
+
+# Temporal VAE
+vae = AutoencoderKLTemporalDecoder.from_pretrained(
+            "stabilityai/stable-video-diffusion-img2vid", subfolder="vae", variant="fp16"
+        )
+vae.eval()
+vae.requires_grad_(False)
+logger.info('Build Temporal VAE')
+
+###-------------------------------------
+# Bulid dataset & dataloader
+###-------------------------------------
+train_dataset = PairedCaptionVideoDataset(
+    root_folders=[
+        args.train_data_dir
+    ], 
+    num_frames=args.num_frames,
+)
+
+train_dataloader = torch.utils.data.DataLoader(
+train_dataset,
+num_workers=args.dataloader_num_workers,
+batch_size=args.train_batch_size,
+shuffle=False
+)
+
+# 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
+    )
+
+###-------------------------------------
+# Optimizer creation & lr_scheduler
+###-------------------------------------
+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
+
+print(f'=================Optimize ControlNet ======================')
+
+# For training VideoControlNet
+
+params_to_optimize = set()
+
+params_to_optimize.update(model.VideoControlNet.parameters())
+
+for name, param in model.named_parameters():
+    if 'local' in name:
+        print(f'{name} will be optimized')
+        params_to_optimize.add(param)
+
+params_to_optimize = list(params_to_optimize)
+
+# Calculate the total number of parameters
+total_params = sum(param.numel() for param in params_to_optimize)
+total_params_million = total_params / 1_000_000
+print(f"Total number of trainable parameters to optimize: {total_params_million:.2f} million")
+
+
+print(f'start to load optimizer...')
+
+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,
+)
+
+# Scheduler and math around the number of training steps.
+overrode_max_train_steps = False
+num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+if args.max_train_steps is None:
+    args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+    overrode_max_train_steps = True
+
+lr_scheduler = get_scheduler(
+    args.lr_scheduler,
+    optimizer=optimizer,
+    num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
+    num_training_steps=args.max_train_steps * accelerator.num_processes,
+    num_cycles=args.lr_num_cycles,
+    power=args.lr_power,
+)
+
+# Prepare everything with `accelerator`.
+model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+    model, optimizer, train_dataloader, lr_scheduler
+)
+
+# For mixed precision training we cast the text_encoder and vae weights to half-precision
+# as these models are only used for inference, keeping weights in full precision is not required.
+weight_dtype = torch.float32
+if accelerator.mixed_precision == "fp16":
+    weight_dtype = torch.float16
+elif accelerator.mixed_precision == "bf16":
+    weight_dtype = torch.bfloat16
+
+# Move vae and text_encoder to device and cast to weight_dtype
+vae.to(accelerator.device, dtype=weight_dtype)
+text_encoder.to(accelerator.device, dtype=weight_dtype)
+
+def tensor2latent(t, vae):
+    video_length = t.shape[2]
+    t = rearrange(t, "b c f h w -> (b f) c h w")
+    chunk_size = 1
+    latents_list = []
+    for ind in range(0,t.shape[0],chunk_size):
+        latents_list.append(vae.encode(t[ind:ind+chunk_size]).latent_dist.sample())
+    latents = torch.cat(latents_list, dim=0)
+    latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length)
+    latents = latents * vae.config.scaling_factor
+    return latents
+
+def temporal_vae_decode(z, num_f):
+    return vae.decode(z/vae.config.scaling_factor, num_frames=num_f).sample
+
+def vae_decode_chunk(z, chunk_size=3):
+    z = rearrange(z, "b c f h w -> (b f) c h w")
+    video = []
+    for ind in range(0, z.shape[0], chunk_size):
+        num_f = z[ind:ind+chunk_size].shape[0]
+        video.append(temporal_vae_decode(z[ind:ind+chunk_size],num_f))
+    video = torch.cat(video)
+    return video
+
+
+def fourier_transform(x, balance=None):
+    """
+    Apply Fourier transform to the input tensor and separate it into low-frequency and high-frequency components.
+
+    Args:
+    x (torch.Tensor): Input tensor of shape [batch_size, channels, height, width]
+    balance (torch.Tensor or float, optional): Learnable balance parameter for adjusting the cutoff frequency.
+
+    Returns:
+    low_freq (torch.Tensor): Low-frequency components (with real and imaginary parts)
+    high_freq (torch.Tensor): High-frequency components (with real and imaginary parts)
+    """
+    # Perform 2D Real Fourier transform (rfft2 only computes positive frequencies)
+    x = x.to(torch.float32)
+    fft_x = torch.fft.rfft2(x, dim=(-2, -1))
+    
+    # Calculate magnitude of frequency components
+    magnitude = torch.abs(fft_x)
+
+    # Set cutoff based on balance or default to the 80th percentile of the magnitude for low frequency
+    if balance is None:
+        # Downsample the magnitude to reduce computation for large tensors
+        subsample_size = 10000  # Adjust based on available memory and tensor size
+        if magnitude.numel() > subsample_size:
+            # Randomly select a subset of values to approximate the quantile
+            magnitude_sample = magnitude.flatten()[torch.randint(0, magnitude.numel(), (subsample_size,))]
+            cutoff = torch.quantile(magnitude_sample, 0.8)  # 80th percentile for low frequency
+        else:
+            cutoff = torch.quantile(magnitude, 0.8)  # 80th percentile for low frequency
+    else:
+        # balance is clamped for safety and used to scale the mean-based cutoff
+        cutoff = magnitude.mean() * (1 + 10 * balance)
+
+    # Smooth mask using sigmoid to ensure gradients can pass through
+    sharpness = 10  # A parameter to control the sharpness of the transition
+    low_freq_mask = torch.sigmoid(sharpness * (cutoff - magnitude))
+    
+    # High-frequency mask can be derived from low-frequency mask (1 - low_freq_mask)
+    high_freq_mask = 1 - low_freq_mask
+    
+    # Separate low and high frequencies using smooth masks
+    low_freq = fft_x * low_freq_mask
+    high_freq = fft_x * high_freq_mask
+
+    # Return real and imaginary parts separately
+    low_freq = torch.stack([low_freq.real, low_freq.imag], dim=-1)
+    high_freq = torch.stack([high_freq.real, high_freq.imag], dim=-1)
+    
+    return low_freq, high_freq
+
+
+def extract_frequencies(video: torch.Tensor, balance=None):
+    """
+    Extract high-frequency and low-frequency components of a video using Fourier transform.
+
+    Args:
+    video (torch.Tensor): Input video tensor of shape [batch_size, channels, frames, height, width]
+
+    Returns:
+    low_freq (torch.Tensor): Low-frequency components of the video
+    high_freq (torch.Tensor): High-frequency components of the video
+    """
+    # batch_size, channels, frames, _, _ = video.shape
+    video = rearrange(video, 'b c t h w -> (b t) c h w')  # Reshape for Fourier transform
+
+    # Apply Fourier transform to each frame
+    low_freq, high_freq = fourier_transform(video, balance=balance)
+
+    return low_freq, high_freq
+
+###-------------------------------------
+# Train
+###-------------------------------------
+progress_bar = tqdm(
+    range(0, args.max_train_steps),
+    initial=0,
+    desc="Steps",
+    # Only show the progress bar once on each machine.
+    disable=not accelerator.is_local_main_process,
+)
+global_step = 0
+
+for epoch in range(0, args.num_train_epochs):
+    for step, batch in enumerate(train_dataloader):
+        torch.cuda.empty_cache()
+        with accelerator.accumulate(model):
+            video_data = batch.pop('gt').to(accelerator.device, dtype=weight_dtype) # [b, c, t, h, w]
+            lq = batch.pop('lq').to(accelerator.device, dtype=weight_dtype)
+            text = batch.pop('text')
+
+            with torch.no_grad():
+                # Process hq & lq video
+                video_data_feature = tensor2latent(video_data, vae)
+                lq_feature = tensor2latent(lq, vae)
+
+                # Process text
+                model_kwargs = {}
+                model_kwargs['y'] = text_encoder(text).detach()
+
+            # Diffusion process
+            bsz = video_data_feature.shape[0]
+            timesteps = torch.randint(0, 1000, (bsz,), device=video_data_feature.device)
+            timesteps = timesteps.long()
+            noise = torch.randn_like(video_data_feature)
+            noised_video = noise_scheduler.diffuse(video_data_feature, timesteps, noise=noise)
+            
+            # == video meta info ==
+            for k, v in batch.items():
+                if isinstance(v, torch.Tensor):
+                    model_kwargs[k] = v.to(accelerator.device, weight_dtype)
+            
+            model_kwargs['hint'] = lq_feature
+
+            # Predict the velocity
+            out = model(noised_video, timesteps, **model_kwargs)
+            target = noise_scheduler.get_velocity(x0=video_data_feature, xt=noised_video, t=timesteps)
+            
+            # get the low-freq & high-freq from x0
+            pred_x0 = noise_scheduler.get_x0(v=out, xt=noised_video, t=timesteps).to(accelerator.device, weight_dtype)
+            # Learning the cutoff frequency
+            with torch.no_grad():
+                pred_x0 = vae_decode_chunk(pred_x0, chunk_size=3).permute(1, 0, 2, 3).unsqueeze(0)
+            low_freq_pred_x0, high_freq_pred_x0 = extract_frequencies(pred_x0)
+            low_freq_x0, high_freq_x0 = extract_frequencies(video_data)
+
+            # v-prediction loss
+            loss_v = F.mse_loss(out.float(), target.float(), reduction="mean")
+
+            # timestep-aware loss
+            alpha = 2
+            ct = (timesteps/999) ** alpha
+            loss_low = F.l1_loss(low_freq_pred_x0.float(), low_freq_x0.float(), reduction="mean")
+            loss_high = F.l1_loss(high_freq_pred_x0.float(), high_freq_x0.float(), reduction="mean")
+            loss_t = 0.01*(ct * loss_low + (1 - ct) * loss_high)
+
+            # Calculate the loss & Backword
+            beta = 1
+            weight_t = 1 - timesteps/999
+            loss = loss_v + beta * weight_t * loss_t
+            accelerator.backward(loss)
+            if accelerator.sync_gradients:
+                params_to_clip = list(model.module.VideoControlNet.parameters())
+                accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
+            optimizer.step()
+            lr_scheduler.step()
+            model.zero_grad()
+
+            # Save weights
+            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}")
+            
+
+            logs = {"loss_high": loss_high.detach().item(), "loss_low": loss_low.detach().item(), "loss_v": loss_v.detach().item(), "total_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
+
+accelerator.end_training()
\ No newline at end of file

video_super_resolution/scripts/train_sr.sh

+#!/bin/bash
+
+torchrun \
+  --nnodes=1 \
+  --nproc_per_node=8 \
+  ./video_super_resolution/scripts/train_sr.py \
+  --pretrained_model_path '' \
+  --train_batch_size 1 \
+  --max_train_steps 15000 \
+  --checkpointing_steps 500 \
+  --learning_rate 5e-5 \
+  --train_data_dir '' \
+  --num_frames 32 \
+  --output_dir ''