Add files using upload-large-folder tool

Helios-main/eval/6_get_drifting_motion_smoothness.py

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+import argparse
+import glob
+import json
+import os
+import re
+
+import cv2
+import numpy as np
+import pandas as pd
+import torch
+from omegaconf import OmegaConf
+from tqdm import tqdm
+
+from utils.third_party.amt.utils.build_utils import build_from_cfg
+from utils.third_party.amt.utils.utils import InputPadder, check_dim_and_resize, img2tensor, tensor2img
+from utils.utils import align_dimension
+
+
+# Percentage of frames to use for start/end portions
+DRIFT_RATIO = 0.15
+
+
+class FrameProcess:
+    def __init__(self, height=384, width=640):
+        self.height = height
+        self.width = width
+
+    def get_frames(self, video_path):
+        """Extract frames from MP4 video"""
+        frame_list = []
+        video = cv2.VideoCapture(video_path)
+
+        original_width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+        original_height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        original_aspect_ratio = original_width / original_height
+
+        if self.width > self.height:
+            target_width = self.width
+            target_height = int(self.width / original_aspect_ratio)
+        else:
+            target_height = self.height
+            target_width = int(self.height * original_aspect_ratio)
+
+        target_height = align_dimension(target_height, 2)
+        target_width = align_dimension(target_width, 2)
+
+        while video.isOpened():
+            success, frame = video.read()
+            if success:
+                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                frame = cv2.resize(frame, (target_width, target_height))
+                frame_list.append(frame)
+            else:
+                break
+        video.release()
+        assert frame_list != [], "No frames extracted from video"
+        return frame_list
+
+    def extract_frame(self, frame_list, start_from=0):
+        extract = []
+        for i in range(start_from, len(frame_list), 2):
+            extract.append(frame_list[i])
+        return extract
+
+
+class MotionSmoothnessEvaluator:
+    def __init__(self, config, ckpt, height=384, width=640, device="cuda"):
+        self.device = device
+        self.config = config
+        self.ckpt = ckpt
+        self.niters = 1
+        self.height = height
+        self.width = width
+        self.initialization()
+        self.load_model()
+
+    def load_model(self):
+        """Load AMT model"""
+        cfg_path = self.config
+        ckpt_path = self.ckpt
+        network_cfg = OmegaConf.load(cfg_path).network
+        network_name = network_cfg.name
+        print(f"Loading [{network_name}] from [{ckpt_path}]...")
+
+        self.model = build_from_cfg(network_cfg)
+        ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False)
+        self.model.load_state_dict(ckpt["state_dict"])
+        self.model = self.model.to(self.device)
+        self.model.eval()
+
+    def initialization(self):
+        """Initialize parameters based on device"""
+        if self.device.type == "cuda":
+            self.anchor_resolution = 1024 * 512
+            self.anchor_memory = 1500 * 1024**2
+            self.anchor_memory_bias = 2500 * 1024**2
+            self.vram_avail = torch.cuda.get_device_properties(self.device).total_memory
+        else:
+            self.anchor_resolution = 8192 * 8192
+            self.anchor_memory = 1
+            self.anchor_memory_bias = 0
+            self.vram_avail = 1
+
+        self.embt = torch.tensor(1 / 2).float().view(1, 1, 1, 1).to(self.device)
+        self.fp = FrameProcess(height=self.height, width=self.width)
+
+    def compute_motion_score_on_frames(self, frames):
+        """Calculate motion smoothness score for a list of frames"""
+        if len(frames) < 4:
+            return 0.0
+
+        frame_list = self.fp.extract_frame(frames, start_from=0)
+        if len(frame_list) < 2:
+            return 0.0
+
+        # Convert to tensors
+        inputs = [img2tensor(frame).to(self.device) for frame in frame_list]
+
+        inputs = check_dim_and_resize(inputs)
+        h, w = inputs[0].shape[-2:]
+        scale = (
+            self.anchor_resolution
+            / (h * w)
+            * np.sqrt((self.vram_avail - self.anchor_memory_bias) / self.anchor_memory)
+        )
+        scale = 1 if scale > 1 else scale
+        scale = 1 / np.floor(1 / np.sqrt(scale) * 16) * 16
+
+        padding = int(16 / scale)
+        padder = InputPadder(inputs[0].shape, padding)
+        inputs = padder.pad(*inputs)
+
+        # Frame interpolation
+        iters = int(self.niters)
+        for i in range(iters):
+            outputs = [inputs[0]]
+            for in_0, in_1 in zip(inputs[:-1], inputs[1:]):
+                in_0 = in_0.to(self.device)
+                in_1 = in_1.to(self.device)
+                with torch.no_grad():
+                    imgt_pred = self.model(in_0, in_1, self.embt, scale_factor=scale, eval=True)["imgt_pred"]
+                outputs += [imgt_pred.cpu(), in_1.cpu()]
+            inputs = outputs
+
+        # Calculate VFI score
+        outputs = padder.unpad(*outputs)
+        outputs = [tensor2img(out) for out in outputs]
+        vfi_score = self.vfi_score(frames, outputs)
+        norm = (255.0 - vfi_score) / 255.0
+
+        return norm
+
+    def vfi_score(self, ori_frames, interpolate_frames):
+        """Calculate video frame interpolation quality score"""
+        ori = self.fp.extract_frame(ori_frames, start_from=1)
+        interpolate = self.fp.extract_frame(interpolate_frames, start_from=1)
+
+        scores = []
+        min_len = min(len(ori), len(interpolate))
+        for i in range(min_len):
+            scores.append(self.get_diff(ori[i], interpolate[i]))
+
+        if len(scores) == 0:
+            return 0.0
+        return np.mean(np.array(scores))
+
+    def get_diff(self, img1, img2):
+        """Calculate absolute difference between two images"""
+        img = cv2.absdiff(img1, img2)
+        return np.mean(img)
+
+    def evaluate_drifting(self, video_path):
+        """
+        Evaluate drifting motion smoothness for a single video.
+        Returns: (drift_score, start_score, end_score)
+        """
+        frames = self.fp.get_frames(video_path)
+        total_frames = len(frames)
+        num_drift_frames = max(4, int(total_frames * DRIFT_RATIO))  # Need at least 4 frames for AMT
+
+        # Extract start and end portions
+        start_frames = frames[:num_drift_frames]
+        end_frames = frames[-num_drift_frames:]
+
+        # Calculate scores for each portion
+        start_score = self.compute_motion_score_on_frames(start_frames)
+        end_score = self.compute_motion_score_on_frames(end_frames)
+
+        # Calculate drift as absolute difference
+        drift_score = abs(start_score - end_score)
+
+        return drift_score, start_score, end_score
+
+
+def main(args):
+    baseline_name = os.path.basename(args.video_dir)
+    output_path = os.path.join(args.output_path, baseline_name)
+    output_json_path = os.path.join(output_path, "drifting_motion_smoothness_results.json")
+
+    # Set device
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+
+    # Load CSV file
+    if not os.path.exists(args.input_csv):
+        raise FileNotFoundError(f"CSV file not found: {args.input_csv}")
+
+    df = pd.read_csv(args.input_csv)
+    df_dict = df.set_index("id").to_dict("index")
+
+    # Validate CSV columns
+    required_columns = ["id", "duration"]
+    for col in required_columns:
+        if col not in df.columns:
+            raise ValueError(f"CSV must contain '{col}' column. Found columns: {df.columns.tolist()}")
+
+    # Load existing results if available
+    existing_results = {}
+    if os.path.exists(output_json_path):
+        print(f"Found existing results at {output_json_path}, loading...")
+        with open(output_json_path, "r") as f:
+            existing_data = json.load(f)
+            for item in existing_data.get("per_video_results", []):
+                existing_results[item["id"]] = item
+        print(f"Loaded {len(existing_results)} existing results")
+
+    # Get video files
+    video_files = glob.glob(os.path.join(args.video_dir, "*_*_ori*.mp4"))
+    video_files.sort(key=lambda x: int(re.search(r"(\d+)_", os.path.basename(x)).group(1)))
+    print(f"\nFound {len(video_files)} videos in directory")
+
+    # Check which videos need processing
+    results = []
+    drift_scores = []
+    videos_to_process = []
+
+    for video_path in video_files:
+        video_name = os.path.basename(video_path)
+        parts = video_name.replace(".mp4", "").split("_")
+        video_id = int(parts[0])
+
+        if video_id not in df_dict:
+            print(f"Warning: Video {video_name} (id={video_id}) not found in CSV, skipping")
+            continue
+
+        # Check if already processed
+        if video_id in existing_results:
+            # Use existing result
+            results.append(existing_results[video_id])
+            drift_scores.append(existing_results[video_id]["drift_motion_smoothness_score"])
+        else:
+            # Need to process
+            videos_to_process.append((video_path, video_id, video_name))
+
+    print(f"Already processed: {len(existing_results)} videos")
+    print(f"Need to process: {len(videos_to_process)} videos")
+
+    # Process remaining videos
+    if videos_to_process:
+        # Load model
+        print("Loading AMT model...")
+        evaluator = MotionSmoothnessEvaluator(
+            args.config, args.smoothness_model_path, height=args.height, width=args.width, device=device
+        )
+
+        print("\nEvaluating remaining videos...")
+        for video_path, video_id, video_name in tqdm(videos_to_process):
+            try:
+                drift_score, start_score, end_score = evaluator.evaluate_drifting(video_path)
+
+                result_item = {
+                    "id": video_id,
+                    "video_name": video_name,
+                    "drift_motion_smoothness_score": float(drift_score),
+                    "start_motion_smoothness_score": float(start_score),
+                    "end_motion_smoothness_score": float(end_score),
+                }
+                results.append(result_item)
+                drift_scores.append(drift_score)
+
+            except Exception as e:
+                print(f"Error processing {video_name}: {str(e)}")
+                continue
+    else:
+        print("No videos to process. Skipping evaluation.")
+        return
+
+    # Sort all results by video_id
+    results_sorted = sorted(results, key=lambda x: x["id"])
+
+    # Calculate overall metrics
+    if drift_scores:
+        avg_drift = sum(drift_scores) / len(drift_scores)
+
+        output = {
+            "metric": "drifting_motion_smoothness",
+            "description": f"Start-end contrast of motion smoothness (first/last {DRIFT_RATIO * 100:.0f}% frames)",
+            "average_drift_score": avg_drift,
+            "num_videos": len(drift_scores),
+            "per_video_results": results_sorted,
+        }
+
+        # Save results
+        os.makedirs(output_path, exist_ok=True)
+        with open(output_json_path, "w") as f:
+            json.dump(output, f, indent=2)
+
+        print(f"\n{'=' * 60}")
+        print("Results Summary:")
+        print(f"{'=' * 60}")
+        print(f"Average Drifting Motion Smoothness Score: {avg_drift:.4f}")
+        print("(Lower is better - indicates less motion quality drift)")
+        print(f"Number of videos evaluated: {len(drift_scores)}")
+        print(f"Results saved to: {output_json_path}")
+        print(f"{'=' * 60}\n")
+    else:
+        print("No videos were successfully evaluated!")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Evaluate drifting motion smoothness using AMT model")
+
+    # Input/Output arguments
+    parser.add_argument("--height", type=str, default=384)
+    parser.add_argument("--width", type=str, default=640)
+    parser.add_argument("--input_csv", type=str, default="playground/helios_t2v_prompts.csv")
+    parser.add_argument("--video_dir", type=str, default="playground/toy-video")
+    parser.add_argument("--output_path", type=str, default="playground/results")
+
+    # Model arguments
+    parser.add_argument("--config", type=str, default="checkpoints/AMT-S.yaml")
+    parser.add_argument("--smoothness_model_path", type=str, default="checkpoints/amt_model/amt-s.pth")
+
+    args = parser.parse_args()
+
+    main(args)

Helios-main/eval/checkpoints/get_checkpoints.sh

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+hf download BestWishYsh/HeliosBench-Weights --local-dir ./

Helios-main/eval/utils/convert_json_to_excel.py

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+import argparse
+import json
+
+import pandas as pd
+
+
+CUSTOM_ORDER = [
+    "total_weighted_rating",
+    "aesthetic",
+    "motion_amplitude",
+    "motion_smoothness",
+    "semantic",
+    "naturalness",
+    "drifting_aesthetic",
+    "drifting_motion_smoothness",
+    "drifting_semantic",
+    "drifting_naturalness",
+]
+
+SELECTED_METRICS = [
+    "total_weighted_rating",
+    "aesthetic",
+    "motion_amplitude",
+    "motion_smoothness",
+    "semantic",
+    "naturalness",
+]
+
+
+def json_to_excel(json_path, excel_path=None, use_selected_metrics=False, show_raw_values=False, score_type=""):
+    with open(json_path, "r") as f:
+        data = json.load(f)
+
+    models_data = data["models"]
+    df = pd.DataFrame.from_dict(models_data, orient="index")
+
+    df.reset_index(inplace=True)
+    df.rename(columns={"index": "model_name"}, inplace=True)
+
+    if use_selected_metrics:
+        available_cols = ["model_name"] + [col for col in SELECTED_METRICS if col in df.columns]
+        df = df[available_cols]
+        print(f"Selected {len(available_cols) - 1} metrics from available metrics")
+
+    valid_order = ["model_name"] + [col for col in CUSTOM_ORDER if col in df.columns]
+    df = df[valid_order]
+    print(f"Kept {len(valid_order) - 1} metrics as specified in CUSTOM_ORDER")
+
+    if excel_path is None:
+        excel_path = json_path.rsplit(".", 1)[0] + f"_{score_type}" + ".xlsx"
+
+    with pd.ExcelWriter(excel_path, engine="openpyxl") as writer:
+        df.to_excel(writer, sheet_name="Models", index=False)
+
+        metadata = pd.DataFrame(
+            {
+                "Property": ["timestamp", "num_models", "num_metrics", "filtered", "format"],
+                "Value": [
+                    data.get("timestamp", "N/A"),
+                    data.get("num_models", len(models_data)),
+                    len(df.columns) - 1,
+                    "Yes" if use_selected_metrics else "No",
+                    "Raw Values" if show_raw_values else "Percentage",
+                ],
+            }
+        )
+        metadata.to_excel(writer, sheet_name="Metadata", index=False)
+
+        worksheet = writer.sheets["Models"]
+        for idx, col in enumerate(df.columns):
+            max_length = max(df[col].astype(str).apply(len).max(), len(col))
+            if idx < 26:
+                col_letter = chr(65 + idx)
+            else:
+                col_letter = chr(65 + idx // 26 - 1) + chr(65 + idx % 26)
+            worksheet.column_dimensions[col_letter].width = min(max_length + 2, 50)
+
+            if col != "model_name" and pd.api.types.is_numeric_dtype(df[col]):
+                for row in range(2, len(df) + 2):  # Start from row 2 (after header)
+                    cell = worksheet[f"{col_letter}{row}"]
+                    if cell.value is not None:
+                        if col == "total_weighted_rating":
+                            cell.number_format = "0.00"
+                        elif show_raw_values:
+                            cell.number_format = "0"
+                        else:
+                            cell.value = cell.value * 100
+                            cell.number_format = '0.00"%"'
+
+    print(f"Conversion successful! Output file: {excel_path}")
+    print(f"Processed {len(df)} models with {len(df.columns) - 1} metrics")
+    print(f"Format: {'Raw values' if show_raw_values else 'Percentage'}")
+
+    return excel_path
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--json_file", type=str, required=True, help="Input JSON file path")
+    parser.add_argument(
+        "--excel_file",
+        type=str,
+        required=True,
+        help="Output Excel file path (optional, defaults to input filename.xlsx)",
+    )
+    parser.add_argument("--filter", action="store_true", help="Use only metrics defined in SELECTED_METRICS list")
+    parser.add_argument(
+        "--score_type",
+        type=str,
+        choices=["raw", "normalized", "rating"],
+        default="rating",
+        help="Type of scores to use: 'raw', 'normalized', or 'rating'",
+    )
+
+    args = parser.parse_args()
+
+    if args.score_type == "rating":
+        raw_value = True
+    else:
+        raw_value = False
+
+    try:
+        json_to_excel(
+            args.json_file,
+            args.excel_file,
+            use_selected_metrics=args.filter,
+            show_raw_values=raw_value,
+            score_type=args.score_type,
+        )
+    except FileNotFoundError:
+        print(f"Error: File not found {args.json_file}")
+    except json.JSONDecodeError:
+        print(f"Error: {args.json_file} is not a valid JSON file")
+    except Exception as e:
+        print(f"Error: {e}")

Helios-main/eval/utils/extract_short_from_long.py

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+import argparse
+import os
+import subprocess
+from pathlib import Path
+
+
+def extract_first_n_frames(input_root, output_root, num_frames=81):
+    input_path = Path(input_root)
+    output_path = Path(output_root)
+
+    for subfolder in input_path.iterdir():
+        if not subfolder.is_dir():
+            continue
+
+        print(f"Processing folder: {subfolder.name}")
+
+        output_subfolder = output_path / subfolder.name
+        output_subfolder.mkdir(parents=True, exist_ok=True)
+
+        video_files = list(subfolder.glob("*.mp4"))
+
+        for i, video_file in enumerate(video_files, 1):
+            original_name = video_file.stem
+            if "_ori" in original_name:
+                new_name = original_name.rsplit("_ori", 1)[0] + f"_ori{num_frames}.mp4"
+            else:
+                new_name = video_file.name
+
+            output_file = output_subfolder / new_name
+            if os.path.exists(output_file):
+                print(f"Skipping existing file: {output_file}")
+                continue
+
+            cmd = [
+                "ffmpeg",
+                "-i",
+                str(video_file),
+                "-vframes",
+                str(num_frames),  # Extract only first N frames
+                "-map",
+                "0",  # Copy all streams (video + audio)
+                "-c",
+                "copy",  # Try direct copy (fastest, preserves all parameters)
+                "-y",
+                str(output_file),
+            ]
+
+            try:
+                result = subprocess.run(cmd, capture_output=True)
+
+                if result.returncode != 0:
+                    print(f"    Direct copy failed for {video_file.name}, re-encoding...")
+                    cmd = [
+                        "ffmpeg",
+                        "-i",
+                        str(video_file),
+                        "-vframes",
+                        str(num_frames),
+                        "-c:v",
+                        "libx264",  # Re-encode video
+                        "-qp",
+                        "0",  # Lossless quality
+                        "-c:a",
+                        "copy",  # Copy audio directly
+                        "-map",
+                        "0",  # Copy all streams
+                        "-y",
+                        str(output_file),
+                    ]
+                    subprocess.run(cmd, check=True, capture_output=True)
+
+                print(f"  [{i}/{len(video_files)}] {video_file.name} -> {new_name}")
+            except subprocess.CalledProcessError as e:
+                print(f"  Error processing {video_file.name}: {e}")
+                continue
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Extract first N frames from videos")
+    parser.add_argument(
+        "--input",
+        type=str,
+        default="long",
+        help="Input root directory (default: current directory)",
+    )
+    parser.add_argument(
+        "--output",
+        type=str,
+        default="short/0_from_long",
+        help="Output root directory (default: ./output_frames)",
+    )
+    parser.add_argument("--frames", type=int, default=81, help="Number of frames to extract (default: 81)")
+
+    args = parser.parse_args()
+
+    extract_first_n_frames(args.input, args.output, args.frames)
+    print("\nDone!")

Helios-main/eval/utils/third_party/ViCLIP/simple_tokenizer.py

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+import gzip
+import html
+import os
+import subprocess
+from functools import lru_cache
+
+import ftfy
+import regex as re
+
+
+def default_bpe():
+    tokenizer_file = os.path.join("checkpoints", "ViCLIP/bpe_simple_vocab_16e6.txt.gz")
+    if not os.path.exists(tokenizer_file):
+        print(f"Downloading ViCLIP tokenizer to {tokenizer_file}")
+        wget_command = [
+            "wget",
+            "https://raw.githubusercontent.com/openai/CLIP/main/clip/bpe_simple_vocab_16e6.txt.gz",
+            "-P",
+            os.path.dirname(tokenizer_file),
+        ]
+        subprocess.run(wget_command)
+    return tokenizer_file
+
+
+@lru_cache()
+def bytes_to_unicode():
+    """
+    Returns list of utf-8 byte and a corresponding list of unicode strings.
+    The reversible bpe codes work on unicode strings.
+    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
+    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
+    This is a signficant percentage of your normal, say, 32K bpe vocab.
+    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+    And avoids mapping to whitespace/control characters the bpe code barfs on.
+    """
+    bs = (
+        list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
+    )
+    cs = bs[:]
+    n = 0
+    for b in range(2**8):
+        if b not in bs:
+            bs.append(b)
+            cs.append(2**8 + n)
+            n += 1
+    cs = [chr(n) for n in cs]
+    return dict(zip(bs, cs))
+
+
+def get_pairs(word):
+    """Return set of symbol pairs in a word.
+    Word is represented as tuple of symbols (symbols being variable-length strings).
+    """
+    pairs = set()
+    prev_char = word[0]
+    for char in word[1:]:
+        pairs.add((prev_char, char))
+        prev_char = char
+    return pairs
+
+
+def basic_clean(text):
+    text = ftfy.fix_text(text)
+    text = html.unescape(html.unescape(text))
+    return text.strip()
+
+
+def whitespace_clean(text):
+    text = re.sub(r"\s+", " ", text)
+    text = text.strip()
+    return text
+
+
+class SimpleTokenizer(object):
+    def __init__(self, bpe_path: str = default_bpe()):
+        self.byte_encoder = bytes_to_unicode()
+        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+        merges = gzip.open(bpe_path).read().decode("utf-8").split("\n")
+        merges = merges[1 : 49152 - 256 - 2 + 1]
+        merges = [tuple(merge.split()) for merge in merges]
+        vocab = list(bytes_to_unicode().values())
+        vocab = vocab + [v + "</w>" for v in vocab]
+        for merge in merges:
+            vocab.append("".join(merge))
+        vocab.extend(["<|startoftext|>", "<|endoftext|>"])
+        self.encoder = dict(zip(vocab, range(len(vocab))))
+        self.decoder = {v: k for k, v in self.encoder.items()}
+        self.bpe_ranks = dict(zip(merges, range(len(merges))))
+        self.cache = {"<|startoftext|>": "<|startoftext|>", "<|endoftext|>": "<|endoftext|>"}
+        self.pat = re.compile(
+            r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
+            re.IGNORECASE,
+        )
+
+    def bpe(self, token):
+        if token in self.cache:
+            return self.cache[token]
+        word = tuple(token[:-1]) + (token[-1] + "</w>",)
+        pairs = get_pairs(word)
+
+        if not pairs:
+            return token + "</w>"
+
+        while True:
+            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+            if bigram not in self.bpe_ranks:
+                break
+            first, second = bigram
+            new_word = []
+            i = 0
+            while i < len(word):
+                try:
+                    j = word.index(first, i)
+                    new_word.extend(word[i:j])
+                    i = j
+                except Exception:
+                    new_word.extend(word[i:])
+                    break
+
+                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+                    new_word.append(first + second)
+                    i += 2
+                else:
+                    new_word.append(word[i])
+                    i += 1
+            new_word = tuple(new_word)
+            word = new_word
+            if len(word) == 1:
+                break
+            else:
+                pairs = get_pairs(word)
+        word = " ".join(word)
+        self.cache[token] = word
+        return word
+
+    def encode(self, text):
+        bpe_tokens = []
+        text = whitespace_clean(basic_clean(text)).lower()
+        for token in re.findall(self.pat, text):
+            token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
+            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" "))
+        return bpe_tokens
+
+    def decode(self, tokens):
+        text = "".join([self.decoder[token] for token in tokens])
+        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors="replace").replace("</w>", " ")
+        return text

Helios-main/eval/utils/third_party/ViCLIP/viclip.py

@@ -0,0 +1,216 @@
+import logging
+import os
+
+import torch
+from torch import nn
+
+from .simple_tokenizer import SimpleTokenizer as _Tokenizer
+from .viclip_text import clip_text_l14
+from .viclip_vision import clip_joint_l14
+
+
+logger = logging.getLogger(__name__)
+
+
+class ViCLIP(nn.Module):
+    """docstring for ViCLIP"""
+
+    def __init__(
+        self,
+        tokenizer=None,
+        pretrain=os.path.join(os.path.dirname(os.path.abspath(__file__)), "ViClip-InternVid-10M-FLT.pth"),
+        freeze_text=True,
+    ):
+        super(ViCLIP, self).__init__()
+        if tokenizer:
+            self.tokenizer = tokenizer
+        else:
+            self.tokenizer = _Tokenizer()
+        self.max_txt_l = 32
+
+        self.vision_encoder_name = "vit_l14"
+
+        self.vision_encoder_pretrained = False
+        self.inputs_image_res = 224
+        self.vision_encoder_kernel_size = 1
+        self.vision_encoder_center = True
+        self.video_input_num_frames = 8
+        self.vision_encoder_drop_path_rate = 0.1
+        self.vision_encoder_checkpoint_num = 24
+        self.is_pretrain = pretrain
+        self.vision_width = 1024
+        self.text_width = 768
+        self.embed_dim = 768
+        self.masking_prob = 0.9
+
+        self.text_encoder_name = "vit_l14"
+        self.text_encoder_pretrained = False  #'bert-base-uncased'
+        self.text_encoder_d_model = 768
+
+        self.text_encoder_vocab_size = 49408
+
+        # create modules.
+        self.vision_encoder = self.build_vision_encoder()
+        self.text_encoder = self.build_text_encoder()
+
+        self.temp = nn.parameter.Parameter(torch.ones([]) * 1 / 100.0)
+        self.temp_min = 1 / 100.0
+
+        if pretrain:
+            logger.info(f"Load pretrained weights from {pretrain}")
+            state_dict = torch.load(pretrain, map_location="cpu", weights_only=False)["model"]
+            self.load_state_dict(state_dict)
+
+        # Freeze weights
+        if freeze_text:
+            self.freeze_text()
+
+    def freeze_text(self):
+        """freeze text encoder"""
+        for p in self.text_encoder.parameters():
+            p.requires_grad = False
+
+    def no_weight_decay(self):
+        ret = {"temp"}
+        ret.update({"vision_encoder." + k for k in self.vision_encoder.no_weight_decay()})
+        ret.update({"text_encoder." + k for k in self.text_encoder.no_weight_decay()})
+
+        return ret
+
+    def forward(self, image, text, raw_text, idx, log_generation=None, return_sims=False):
+        """forward and calculate loss.
+
+        Args:
+            image (torch.Tensor): The input images. Shape: [B,T,C,H,W].
+            text (dict): TODO
+            idx (torch.Tensor): TODO
+
+        Returns: TODO
+
+        """
+        self.clip_contrastive_temperature()
+
+        vision_embeds = self.encode_vision(image)
+        text_embeds = self.encode_text(raw_text)
+        if return_sims:
+            sims = torch.nn.functional.normalize(vision_embeds, dim=-1) @ torch.nn.functional.normalize(
+                text_embeds, dim=-1
+            ).transpose(0, 1)
+            return sims
+
+        # calculate loss
+
+        ## VTC loss
+        loss_vtc = self.clip_loss.vtc_loss(vision_embeds, text_embeds, idx, self.temp, all_gather=True)
+
+        return {
+            "loss_vtc": loss_vtc,
+        }
+
+    def encode_vision(self, image, test=False):
+        """encode image / videos as features.
+
+        Args:
+            image (torch.Tensor): The input images.
+            test (bool): Whether testing.
+
+        Returns: tuple.
+            - vision_embeds (torch.Tensor): The features of all patches. Shape: [B,T,L,C].
+            - pooled_vision_embeds (torch.Tensor): The pooled features. Shape: [B,T,C].
+
+        """
+        if image.ndim == 5:
+            image = image.permute(0, 2, 1, 3, 4).contiguous()
+        else:
+            image = image.unsqueeze(2)
+
+        if not test and self.masking_prob > 0.0:
+            return self.vision_encoder(image, masking_prob=self.masking_prob)
+
+        return self.vision_encoder(image)
+
+    def encode_text(self, text):
+        """encode text.
+        Args:
+            text (dict): The output of huggingface's `PreTrainedTokenizer`. contains keys:
+                - input_ids (torch.Tensor): Token ids to be fed to a model. Shape: [B,L].
+                - attention_mask (torch.Tensor): The mask indicate padded tokens. Shape: [B,L]. 0 is padded token.
+                - other keys refer to "https://huggingface.co/docs/transformers/v4.21.2/en/main_classes/tokenizer#transformers.PreTrainedTokenizer.__call__".
+        Returns: tuple.
+            - text_embeds (torch.Tensor): The features of all tokens. Shape: [B,L,C].
+            - pooled_text_embeds (torch.Tensor): The pooled features. Shape: [B,C].
+
+        """
+        device = next(self.text_encoder.parameters()).device
+        text = self.text_encoder.tokenize(text, context_length=self.max_txt_l).to(device)
+        text_embeds = self.text_encoder(text)
+        return text_embeds
+
+    @torch.no_grad()
+    def clip_contrastive_temperature(self, min_val=0.001, max_val=0.5):
+        """Seems only used during pre-training"""
+        self.temp.clamp_(min=self.temp_min)
+
+    def build_vision_encoder(self):
+        """build vision encoder
+        Returns: (vision_encoder, vision_layernorm). Each is a `nn.Module`.
+
+        """
+        encoder_name = self.vision_encoder_name
+        if encoder_name != "vit_l14":
+            raise ValueError(f"Not implemented: {encoder_name}")
+        vision_encoder = clip_joint_l14(
+            pretrained=self.vision_encoder_pretrained,
+            input_resolution=self.inputs_image_res,
+            kernel_size=self.vision_encoder_kernel_size,
+            center=self.vision_encoder_center,
+            num_frames=self.video_input_num_frames,
+            drop_path=self.vision_encoder_drop_path_rate,
+            checkpoint_num=self.vision_encoder_checkpoint_num,
+        )
+        return vision_encoder
+
+    def build_text_encoder(self):
+        """build text_encoder and possiblly video-to-text multimodal fusion encoder.
+        Returns: nn.Module. The text encoder
+
+        """
+        encoder_name = self.text_encoder_name
+        if encoder_name != "vit_l14":
+            raise ValueError(f"Not implemented: {encoder_name}")
+        text_encoder = clip_text_l14(
+            pretrained=self.text_encoder_pretrained,
+            embed_dim=self.text_encoder_d_model,
+            context_length=self.max_txt_l,
+            vocab_size=self.text_encoder_vocab_size,
+            checkpoint_num=0,
+        )
+
+        return text_encoder
+
+    def get_text_encoder(self):
+        """get text encoder, used for text and cross-modal encoding"""
+        encoder = self.text_encoder
+        return encoder.bert if hasattr(encoder, "bert") else encoder
+
+    def get_text_features(self, input_text, tokenizer, text_feature_dict={}):
+        if input_text in text_feature_dict:
+            return text_feature_dict[input_text]
+        text_template = f"{input_text}"
+        with torch.no_grad():
+            # text_token = tokenizer.encode(text_template).cuda()
+            text_features = self.encode_text(text_template).float()
+            text_features /= text_features.norm(dim=-1, keepdim=True)
+            text_feature_dict[input_text] = text_features
+        return text_features
+
+    def get_vid_features(self, input_frames):
+        with torch.no_grad():
+            clip_feat = self.encode_vision(input_frames, test=True).float()
+            clip_feat /= clip_feat.norm(dim=-1, keepdim=True)
+        return clip_feat
+
+    def get_predict_label(self, clip_feature, text_feats_tensor, top=5):
+        label_probs = (100.0 * clip_feature @ text_feats_tensor.T).softmax(dim=-1)
+        top_probs, top_labels = label_probs.cpu().topk(top, dim=-1)
+        return top_probs, top_labels

Helios-main/eval/utils/third_party/ViCLIP/viclip_text.py

@@ -0,0 +1,260 @@
+import functools
+import logging
+import os
+from collections import OrderedDict
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from pkg_resources import packaging
+from torch import nn
+
+from .simple_tokenizer import SimpleTokenizer as _Tokenizer
+
+
+logger = logging.getLogger(__name__)
+
+
+MODEL_PATH = "https://huggingface.co/laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K"
+_MODELS = {
+    "ViT-L/14": os.path.join(MODEL_PATH, "vit_l14_text.pth"),
+}
+
+
+class LayerNorm(nn.LayerNorm):
+    """Subclass torch's LayerNorm to handle fp16."""
+
+    def forward(self, x: torch.Tensor):
+        orig_type = x.dtype
+        ret = super().forward(x.type(torch.float32))
+        return ret.type(orig_type)
+
+
+class QuickGELU(nn.Module):
+    def forward(self, x: torch.Tensor):
+        return x * torch.sigmoid(1.702 * x)
+
+
+class ResidualAttentionBlock(nn.Module):
+    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
+        super().__init__()
+
+        self.attn = nn.MultiheadAttention(d_model, n_head)
+        self.ln_1 = LayerNorm(d_model)
+        self.mlp = nn.Sequential(
+            OrderedDict(
+                [
+                    ("c_fc", nn.Linear(d_model, d_model * 4)),
+                    ("gelu", QuickGELU()),
+                    ("c_proj", nn.Linear(d_model * 4, d_model)),
+                ]
+            )
+        )
+        self.ln_2 = LayerNorm(d_model)
+        self.attn_mask = attn_mask
+
+    def attention(self, x: torch.Tensor):
+        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
+        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
+
+    def forward(self, x: torch.Tensor):
+        x = x + self.attention(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class Transformer(nn.Module):
+    def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None, checkpoint_num: int = 0):
+        super().__init__()
+        self.width = width
+        self.layers = layers
+        self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
+
+        self.checkpoint_num = checkpoint_num
+
+    def forward(self, x: torch.Tensor):
+        if self.checkpoint_num > 0:
+            segments = min(self.checkpoint_num, len(self.resblocks))
+            return checkpoint.checkpoint_sequential(self.resblocks, segments, x)
+        else:
+            return self.resblocks(x)
+
+
+class CLIP_TEXT(nn.Module):
+    def __init__(
+        self,
+        embed_dim: int,
+        context_length: int,
+        vocab_size: int,
+        transformer_width: int,
+        transformer_heads: int,
+        transformer_layers: int,
+        checkpoint_num: int,
+    ):
+        super().__init__()
+
+        self.context_length = context_length
+        self._tokenizer = _Tokenizer()
+
+        self.transformer = Transformer(
+            width=transformer_width,
+            layers=transformer_layers,
+            heads=transformer_heads,
+            attn_mask=self.build_attention_mask(),
+            checkpoint_num=checkpoint_num,
+        )
+
+        self.vocab_size = vocab_size
+        self.token_embedding = nn.Embedding(vocab_size, transformer_width)
+        self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
+        self.ln_final = LayerNorm(transformer_width)
+
+        self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
+
+    def no_weight_decay(self):
+        return {"token_embedding", "positional_embedding"}
+
+    @functools.lru_cache(maxsize=None)
+    def build_attention_mask(self):
+        # lazily create causal attention mask, with full attention between the vision tokens
+        # pytorch uses additive attention mask; fill with -inf
+        mask = torch.empty(self.context_length, self.context_length)
+        mask.fill_(float("-inf"))
+        mask.triu_(1)  # zero out the lower diagonal
+        return mask
+
+    def tokenize(self, texts, context_length=77, truncate=True):
+        """
+        Returns the tokenized representation of given input string(s)
+        Parameters
+        ----------
+        texts : Union[str, List[str]]
+            An input string or a list of input strings to tokenize
+        context_length : int
+            The context length to use; all CLIP models use 77 as the context length
+        truncate: bool
+            Whether to truncate the text in case its encoding is longer than the context length
+        Returns
+        -------
+        A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length].
+        We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long.
+        """
+        if isinstance(texts, str):
+            texts = [texts]
+
+        sot_token = self._tokenizer.encoder["<|startoftext|>"]
+        eot_token = self._tokenizer.encoder["<|endoftext|>"]
+        all_tokens = [[sot_token] + self._tokenizer.encode(text) + [eot_token] for text in texts]
+        if packaging.version.parse(torch.__version__) < packaging.version.parse("1.8.0"):
+            result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
+        else:
+            result = torch.zeros(len(all_tokens), context_length, dtype=torch.int)
+
+        for i, tokens in enumerate(all_tokens):
+            if len(tokens) > context_length:
+                if truncate:
+                    tokens = tokens[:context_length]
+                    tokens[-1] = eot_token
+                else:
+                    raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
+            result[i, : len(tokens)] = torch.tensor(tokens)
+
+        return result
+
+    def forward(self, text):
+        x = self.token_embedding(text)  # [batch_size, n_ctx, d_model]
+
+        x = x + self.positional_embedding
+        x = x.permute(1, 0, 2)  # NLD -> LND
+        x = self.transformer(x)
+        x = x.permute(1, 0, 2)  # LND -> NLD
+        x = self.ln_final(x)
+
+        # x.shape = [batch_size, n_ctx, transformer.width]
+        # take features from the eot embedding (eot_token is the highest number in each sequence)
+        x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
+
+        return x
+
+
+def clip_text_b16(
+    embed_dim=512,
+    context_length=77,
+    vocab_size=49408,
+    transformer_width=512,
+    transformer_heads=8,
+    transformer_layers=12,
+):
+    raise NotImplementedError
+    model = CLIP_TEXT(embed_dim, context_length, vocab_size, transformer_width, transformer_heads, transformer_layers)
+    pretrained = _MODELS["ViT-B/16"]
+    logger.info(f"Load pretrained weights from {pretrained}")
+    state_dict = torch.load(pretrained, map_location="cpu")
+    model.load_state_dict(state_dict, strict=False)
+    return model.eval()
+
+
+def clip_text_l14(
+    embed_dim=768,
+    context_length=77,
+    vocab_size=49408,
+    transformer_width=768,
+    transformer_heads=12,
+    transformer_layers=12,
+    checkpoint_num=0,
+    pretrained=True,
+):
+    model = CLIP_TEXT(
+        embed_dim,
+        context_length,
+        vocab_size,
+        transformer_width,
+        transformer_heads,
+        transformer_layers,
+        checkpoint_num,
+    )
+    if pretrained:
+        if isinstance(pretrained, str) and pretrained != "bert-base-uncased":
+            pretrained = _MODELS[pretrained]
+        else:
+            pretrained = _MODELS["ViT-L/14"]
+        logger.info(f"Load pretrained weights from {pretrained}")
+        state_dict = torch.load(pretrained, map_location="cpu")
+        if context_length != state_dict["positional_embedding"].size(0):
+            # assert context_length < state_dict["positional_embedding"].size(0), "Cannot increase context length."
+            print(f"Resize positional embedding from {state_dict['positional_embedding'].size(0)} to {context_length}")
+            if context_length < state_dict["positional_embedding"].size(0):
+                state_dict["positional_embedding"] = state_dict["positional_embedding"][:context_length]
+            else:
+                state_dict["positional_embedding"] = F.pad(
+                    state_dict["positional_embedding"],
+                    (0, 0, 0, context_length - state_dict["positional_embedding"].size(0)),
+                    value=0,
+                )
+
+        message = model.load_state_dict(state_dict, strict=False)
+        print(f"Load pretrained weights from {pretrained}: {message}")
+    return model.eval()
+
+
+def clip_text_l14_336(
+    embed_dim=768,
+    context_length=77,
+    vocab_size=49408,
+    transformer_width=768,
+    transformer_heads=12,
+    transformer_layers=12,
+):
+    raise NotImplementedError
+    model = CLIP_TEXT(embed_dim, context_length, vocab_size, transformer_width, transformer_heads, transformer_layers)
+    pretrained = _MODELS["ViT-L/14_336"]
+    logger.info(f"Load pretrained weights from {pretrained}")
+    state_dict = torch.load(pretrained, map_location="cpu")
+    model.load_state_dict(state_dict, strict=False)
+    return model.eval()
+
+
+def build_clip(config):
+    model_cls = config.text_encoder.clip_teacher
+    model = eval(model_cls)()
+    return model

Helios-main/eval/utils/third_party/ViCLIP/viclip_vision.py

@@ -0,0 +1,364 @@
+#!/usr/bin/env python
+import logging
+import os
+from collections import OrderedDict
+
+import torch
+import torch.utils.checkpoint as checkpoint
+from einops import rearrange
+from timm.layers import DropPath
+from timm.models import register_model
+from torch import nn
+
+
+logger = logging.getLogger(__name__)
+
+
+def load_temp_embed_with_mismatch(temp_embed_old, temp_embed_new, add_zero=True):
+    """
+    Add/Remove extra temporal_embeddings as needed.
+    https://arxiv.org/abs/2104.00650 shows adding zero paddings works.
+
+    temp_embed_old: (1, num_frames_old, 1, d)
+    temp_embed_new: (1, num_frames_new, 1, d)
+    add_zero: bool, if True, add zero, else, interpolate trained embeddings.
+    """
+    # TODO zero pad
+    num_frms_new = temp_embed_new.shape[1]
+    num_frms_old = temp_embed_old.shape[1]
+    logger.info(f"Load temporal_embeddings, lengths: {num_frms_old}-->{num_frms_new}")
+    if num_frms_new > num_frms_old:
+        if add_zero:
+            temp_embed_new[:, :num_frms_old] = temp_embed_old  # untrained embeddings are zeros.
+        else:
+            pass
+            # temp_embed_new = interpolate_temporal_pos_embed(temp_embed_old, num_frms_new)
+    elif num_frms_new < num_frms_old:
+        temp_embed_new = temp_embed_old[:, :num_frms_new]
+    else:  # =
+        temp_embed_new = temp_embed_old
+    return temp_embed_new
+
+
+MODEL_PATH = "https://pjlab-gvm-data.oss-cn-shanghai.aliyuncs.com/internvideo/viclip/"
+_MODELS = {
+    "ViT-L/14": os.path.join(MODEL_PATH, "ViClip-InternVid-10M-FLT.pth"),
+}
+
+
+class QuickGELU(nn.Module):
+    def forward(self, x):
+        return x * torch.sigmoid(1.702 * x)
+
+
+class ResidualAttentionBlock(nn.Module):
+    def __init__(self, d_model, n_head, drop_path=0.0, attn_mask=None, dropout=0.0):
+        super().__init__()
+
+        self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.attn = nn.MultiheadAttention(d_model, n_head, dropout=dropout)
+        self.ln_1 = nn.LayerNorm(d_model)
+        self.mlp = nn.Sequential(
+            OrderedDict(
+                [
+                    ("c_fc", nn.Linear(d_model, d_model * 4)),
+                    ("gelu", QuickGELU()),
+                    ("drop1", nn.Dropout(dropout)),
+                    ("c_proj", nn.Linear(d_model * 4, d_model)),
+                    ("drop2", nn.Dropout(dropout)),
+                ]
+            )
+        )
+        self.ln_2 = nn.LayerNorm(d_model)
+        self.attn_mask = attn_mask
+
+    def attention(self, x):
+        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
+        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
+
+    def forward(self, x):
+        x = x + self.drop_path1(self.attention(self.ln_1(x)))
+        x = x + self.drop_path2(self.mlp(self.ln_2(x)))
+        return x
+
+
+class Transformer(nn.Module):
+    def __init__(self, width, layers, heads, drop_path=0.0, checkpoint_num=0, dropout=0.0):
+        super().__init__()
+        dpr = [x.item() for x in torch.linspace(0, drop_path, layers)]
+        self.resblocks = nn.ModuleList()
+        for idx in range(layers):
+            self.resblocks.append(ResidualAttentionBlock(width, heads, drop_path=dpr[idx], dropout=dropout))
+        self.checkpoint_num = checkpoint_num
+
+    def forward(self, x):
+        for idx, blk in enumerate(self.resblocks):
+            if idx < self.checkpoint_num:
+                x = checkpoint.checkpoint(blk, x, use_reentrant=False)
+            else:
+                x = blk(x)
+        return x
+
+
+class VisionTransformer(nn.Module):
+    def __init__(
+        self,
+        input_resolution,
+        patch_size,
+        width,
+        layers,
+        heads,
+        output_dim=None,
+        kernel_size=1,
+        num_frames=8,
+        drop_path=0,
+        checkpoint_num=0,
+        dropout=0.0,
+        temp_embed=True,
+    ):
+        super().__init__()
+        self.output_dim = output_dim
+        self.conv1 = nn.Conv3d(
+            3,
+            width,
+            (kernel_size, patch_size, patch_size),
+            (kernel_size, patch_size, patch_size),
+            (0, 0, 0),
+            bias=False,
+        )
+
+        scale = width**-0.5
+        self.class_embedding = nn.Parameter(scale * torch.randn(width))
+        self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
+        self.ln_pre = nn.LayerNorm(width)
+        if temp_embed:
+            self.temporal_positional_embedding = nn.Parameter(torch.zeros(1, num_frames, width))
+
+        self.transformer = Transformer(
+            width, layers, heads, drop_path=drop_path, checkpoint_num=checkpoint_num, dropout=dropout
+        )
+
+        self.ln_post = nn.LayerNorm(width)
+        if output_dim is not None:
+            self.proj = nn.Parameter(torch.empty(width, output_dim))
+        else:
+            self.proj = None
+
+        self.dropout = nn.Dropout(dropout)
+
+    def get_num_layers(self):
+        return len(self.transformer.resblocks)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {"positional_embedding", "class_embedding", "temporal_positional_embedding"}
+
+    def mask_tokens(self, inputs, masking_prob=0.0):
+        B, L, _ = inputs.shape
+
+        # This is different from text as we are masking a fix number of tokens
+        Lm = int(masking_prob * L)
+        masked_indices = torch.zeros(B, L)
+        indices = torch.argsort(torch.rand_like(masked_indices), dim=-1)[:, :Lm]
+        batch_indices = torch.arange(masked_indices.shape[0]).unsqueeze(-1).expand_as(indices)
+        masked_indices[batch_indices, indices] = 1
+
+        masked_indices = masked_indices.bool()
+
+        return inputs[~masked_indices].reshape(B, -1, inputs.shape[-1])
+
+    def forward(self, x, masking_prob=0.0):
+        x = self.conv1(x)  # shape = [*, width, grid, grid]
+        B, C, T, H, W = x.shape
+        x = x.permute(0, 2, 3, 4, 1).reshape(B * T, H * W, C)
+
+        x = torch.cat(
+            [
+                self.class_embedding.to(x.dtype)
+                + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),
+                x,
+            ],
+            dim=1,
+        )  # shape = [*, grid ** 2 + 1, width]
+        x = x + self.positional_embedding.to(x.dtype)
+
+        # temporal pos
+        cls_tokens = x[:B, :1, :]
+        x = x[:, 1:]
+        x = rearrange(x, "(b t) n m -> (b n) t m", b=B, t=T)
+        if hasattr(self, "temporal_positional_embedding"):
+            if x.size(1) == 1:
+                # This is a workaround for unused parameter issue
+                x = x + self.temporal_positional_embedding.mean(1)
+            else:
+                x = x + self.temporal_positional_embedding
+        x = rearrange(x, "(b n) t m -> b (n t) m", b=B, t=T)
+
+        if masking_prob > 0.0:
+            x = self.mask_tokens(x, masking_prob)
+
+        x = torch.cat((cls_tokens, x), dim=1)
+
+        x = self.ln_pre(x)
+
+        x = x.permute(1, 0, 2)  # BND -> NBD
+        x = self.transformer(x)
+
+        x = self.ln_post(x)
+
+        if self.proj is not None:
+            x = self.dropout(x[0]) @ self.proj
+        else:
+            x = x.permute(1, 0, 2)  # NBD -> BND
+
+        return x
+
+
+def inflate_weight(weight_2d, time_dim, center=True):
+    logger.info(f"Init center: {center}")
+    if center:
+        weight_3d = torch.zeros(*weight_2d.shape)
+        weight_3d = weight_3d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
+        middle_idx = time_dim // 2
+        weight_3d[:, :, middle_idx, :, :] = weight_2d
+    else:
+        weight_3d = weight_2d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
+        weight_3d = weight_3d / time_dim
+    return weight_3d
+
+
+def load_state_dict(model, state_dict, input_resolution=224, patch_size=16, center=True):
+    state_dict_3d = model.state_dict()
+    for k in state_dict.keys():
+        if k in state_dict_3d.keys() and state_dict[k].shape != state_dict_3d[k].shape:
+            if len(state_dict_3d[k].shape) <= 2:
+                logger.info(f"Ignore: {k}")
+                continue
+            logger.info(f"Inflate: {k}, {state_dict[k].shape} => {state_dict_3d[k].shape}")
+            time_dim = state_dict_3d[k].shape[2]
+            state_dict[k] = inflate_weight(state_dict[k], time_dim, center=center)
+
+    pos_embed_checkpoint = state_dict["positional_embedding"]
+    embedding_size = pos_embed_checkpoint.shape[-1]
+    num_patches = (input_resolution // patch_size) ** 2
+    orig_size = int((pos_embed_checkpoint.shape[-2] - 1) ** 0.5)
+    new_size = int(num_patches**0.5)
+    if orig_size != new_size:
+        logger.info(f"Pos_emb from {orig_size} to {new_size}")
+        extra_tokens = pos_embed_checkpoint[:1]
+        pos_tokens = pos_embed_checkpoint[1:]
+        pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
+        pos_tokens = torch.nn.functional.interpolate(
+            pos_tokens, size=(new_size, new_size), mode="bicubic", align_corners=False
+        )
+        pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(0, 2)
+        new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=0)
+        state_dict["positional_embedding"] = new_pos_embed
+
+    message = model.load_state_dict(state_dict, strict=False)
+    logger.info(f"Load pretrained weights: {message}")
+
+
+@register_model
+def clip_joint_b16(pretrained=True, input_resolution=224, kernel_size=1, center=True, num_frames=8, drop_path=0.0):
+    model = VisionTransformer(
+        input_resolution=input_resolution,
+        patch_size=16,
+        width=768,
+        layers=12,
+        heads=12,
+        output_dim=512,
+        kernel_size=kernel_size,
+        num_frames=num_frames,
+        drop_path=drop_path,
+    )
+    raise NotImplementedError
+    if pretrained:
+        logger.info("load pretrained weights")
+        state_dict = torch.load(_MODELS["ViT-B/16"], map_location="cpu")
+        load_state_dict(model, state_dict, input_resolution=input_resolution, patch_size=16, center=center)
+    return model.eval()
+
+
+@register_model
+def clip_joint_l14(
+    pretrained=False,
+    input_resolution=224,
+    kernel_size=1,
+    center=True,
+    num_frames=8,
+    drop_path=0.0,
+    checkpoint_num=0,
+    dropout=0.0,
+):
+    model = VisionTransformer(
+        input_resolution=input_resolution,
+        patch_size=14,
+        width=1024,
+        layers=24,
+        heads=16,
+        output_dim=768,
+        kernel_size=kernel_size,
+        num_frames=num_frames,
+        drop_path=drop_path,
+        checkpoint_num=checkpoint_num,
+        dropout=dropout,
+    )
+    if pretrained:
+        if isinstance(pretrained, str):
+            model_name = pretrained
+        else:
+            model_name = "ViT-L/14"
+        logger.info("load pretrained weights")
+        state_dict = torch.load(_MODELS[model_name], map_location="cpu")
+        load_state_dict(model, state_dict, input_resolution=input_resolution, patch_size=14, center=center)
+    return model.eval()
+
+
+@register_model
+def clip_joint_l14_336(pretrained=True, input_resolution=336, kernel_size=1, center=True, num_frames=8, drop_path=0.0):
+    raise NotImplementedError
+    model = VisionTransformer(
+        input_resolution=input_resolution,
+        patch_size=14,
+        width=1024,
+        layers=24,
+        heads=16,
+        output_dim=768,
+        kernel_size=kernel_size,
+        num_frames=num_frames,
+        drop_path=drop_path,
+    )
+    if pretrained:
+        logger.info("load pretrained weights")
+        state_dict = torch.load(_MODELS["ViT-L/14_336"], map_location="cpu")
+        load_state_dict(model, state_dict, input_resolution=input_resolution, patch_size=14, center=center)
+    return model.eval()
+
+
+def interpolate_pos_embed_vit(state_dict, new_model):
+    key = "vision_encoder.temporal_positional_embedding"
+    if key in state_dict:
+        vision_temp_embed_new = new_model.state_dict()[key]
+        vision_temp_embed_new = vision_temp_embed_new.unsqueeze(2)  # [1, n, d] -> [1, n, 1, d]
+        vision_temp_embed_old = state_dict[key]
+        vision_temp_embed_old = vision_temp_embed_old.unsqueeze(2)
+
+        state_dict[key] = load_temp_embed_with_mismatch(
+            vision_temp_embed_old, vision_temp_embed_new, add_zero=False
+        ).squeeze(2)
+
+    key = "text_encoder.positional_embedding"
+    if key in state_dict:
+        text_temp_embed_new = new_model.state_dict()[key]
+        text_temp_embed_new = text_temp_embed_new.unsqueeze(0).unsqueeze(2)  # [n, d] -> [1, n, 1, d]
+        text_temp_embed_old = state_dict[key]
+        text_temp_embed_old = text_temp_embed_old.unsqueeze(0).unsqueeze(2)
+
+        state_dict[key] = (
+            load_temp_embed_with_mismatch(text_temp_embed_old, text_temp_embed_new, add_zero=False)
+            .squeeze(2)
+            .squeeze(0)
+        )
+    return state_dict

Helios-main/eval/utils/third_party/amt/LICENSE

@@ -0,0 +1,176 @@
+## creative commons
+
+# Attribution-NonCommercial 4.0 International
+
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+
+### Using Creative Commons Public Licenses
+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+b. To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions.
+
+c. No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor.
+
+d. Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority.
+
+> Creative Commons is not a party to its public licenses. Notwithstanding, Creative Commons may elect to apply one of its public licenses to material it publishes and in those instances will be considered the “Licensor.” Except for the limited purpose of indicating that material is shared under a Creative Commons public license or as otherwise permitted by the Creative Commons policies published at [creativecommons.org/policies](http://creativecommons.org/policies), Creative Commons does not authorize the use of the trademark “Creative Commons” or any other trademark or logo of Creative Commons without its prior written consent including, without limitation, in connection with any unauthorized modifications to any of its public licenses or any other arrangements, understandings, or agreements concerning use of licensed material. For the avoidance of doubt, this paragraph does not form part of the public licenses.
+>
+> Creative Commons may be contacted at creativecommons.org
+
+
+### Commercial licensing opportunities
+For commercial uses of the Model & Software, please send email to cmm[AT]nankai.edu.cn
+
+Citation:
+
+@inproceedings{licvpr23amt,
+    title     = {AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation},
+    author    = {Li, Zhen and Zhu, Zuo-Liang and Han, Ling-Hao and Hou, Qibin and Guo, Chun-Le and Cheng, Ming-Ming},
+    booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+    year      = {2023}
+}
+
+Copyright (c) 2023 MCG-NKU

Helios-main/eval/utils/third_party/amt/README.md

@@ -0,0 +1,167 @@
+# AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation
+
+
+This repository contains the official implementation of the following paper:
+> **AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation**<br>
+> [Zhen Li](https://paper99.github.io/)<sup>\*</sup>, [Zuo-Liang Zhu](https://nk-cs-zzl.github.io/)<sup>\*</sup>, [Ling-Hao Han](https://scholar.google.com/citations?user=0ooNdgUAAAAJ&hl=en), [Qibin Hou](https://scholar.google.com/citations?hl=en&user=fF8OFV8AAAAJ&view_op=list_works), [Chun-Le Guo](https://scholar.google.com/citations?hl=en&user=RZLYwR0AAAAJ),  [Ming-Ming Cheng](https://mmcheng.net/cmm)<br>
+> (\* denotes equal contribution) <br>
+> Nankai University <br>
+> In CVPR 2023<br>
+
+[[Paper](https://arxiv.org/abs/2304.09790)]
+[[Project Page](https://nk-cs-zzl.github.io/projects/amt/index.html)]
+[[Web demos](#web-demos)]
+[Video]
+
+AMT is a **lightweight, fast, and accurate** algorithm for Frame Interpolation. 
+It aims to provide practical solutions for **video generation** from **a few given frames (at least two frames)**.
+
+![Demo gif](assets/amt_demo.gif)
+* More examples can be found in our [project page](https://nk-cs-zzl.github.io/projects/amt/index.html).
+
+## Web demos
+Integrated into [Hugging Face Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/NKU-AMT/AMT)
+
+Try AMT to interpolate between two or more images at [![PyTTI-Tools:FILM](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1IeVO5BmLouhRh6fL2z_y18kgubotoaBq?usp=sharing)
+
+
+## Change Log
+- **Apr 20, 2023**: Our code is publicly available.
+
+
+## Method Overview
+![pipeline](https://user-images.githubusercontent.com/21050959/229420451-65951bd0-732c-4f09-9121-f291a3862d6e.png)
+
+For technical details, please refer to the [method.md](docs/method.md) file, or read the full report on [arXiv](https://arxiv.org/abs/2304.09790).
+
+## Dependencies and Installation
+1. Clone Repo
+
+   ```bash
+   git clone https://github.com/MCG-NKU/AMT.git
+   ```
+
+2. Create Conda Environment and Install Dependencies
+
+   ```bash
+   conda env create -f environment.yaml
+   conda activate amt
+   ```
+3. Download pretrained models for demos from [Pretrained Models](#pretrained-models) and place them to the `pretrained` folder
+
+## Quick Demo
+
+**Note that the selected pretrained model (`[CKPT_PATH]`) needs to match the config file (`[CFG]`).**
+
+ > Creating a video demo, increasing $n$ will slow down the motion in the video. (With $m$ input frames, `[N_ITER]` $=n$ corresponds to $2^n\times (m-1)+1$ output frames.)
+
+
+ ```bash
+ python demos/demo_2x.py -c [CFG] -p [CKPT] -n [N_ITER] -i [INPUT] -o [OUT_PATH] -r [FRAME_RATE]
+ # e.g. [INPUT]
+ # -i could be a video / a regular expression / a folder contains multiple images
+ # -i demo.mp4 (video)/img_*.png (regular expression)/img0.png img1.png (images)/demo_input (folder)
+
+ # e.g. a simple usage
+ python demos/demo_2x.py -c cfgs/AMT-S.yaml -p pretrained/amt-s.pth -n 6 -i assets/quick_demo/img0.png assets/quick_demo/img1.png
+
+ ```
+
+ + Note: Please enable `--save_images` for saving the output images (Save speed will be slowed down if there are too many output images)
+ + Input type supported: `a video` / `a regular expression` / `multiple images` / `a folder containing input frames`.
+ + Results are in the `[OUT_PATH]` (default is `results/2x`) folder.
+
+## Pretrained Models
+
+<p id="Pretrained"></p>
+
+<table>
+<thead>
+  <tr>
+    <th> Dataset </th>
+    <th> :link: Download Links </th>
+    <th> Config file </th>
+    <th> Trained on </th>
+    <th> Arbitrary/Fixed </th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td>AMT-S</td>
+    <th> [<a href="https://drive.google.com/file/d/1WmOKmQmd6pnLpID8EpUe-TddFpJuavrL/view?usp=share_link">Google Driver</a>][<a href="https://pan.baidu.com/s/1yGaNLeb9TG5-81t0skrOUA?pwd=f66n">Baidu Cloud</a>][<a href="https://huggingface.co/lalala125/AMT/resolve/main/amt-s.pth">Hugging Face</a>] </th>
+    <th> [<a href="cfgs/AMT-S.yaml">cfgs/AMT-S</a>] </th>
+    <th>Vimeo90k</th>
+    <th>Fixed</th>
+  </tr>
+  <tr>
+    <td>AMT-L</td>
+    <th>[<a href="https://drive.google.com/file/d/1UyhYpAQLXMjFA55rlFZ0kdiSVTL7oU-z/view?usp=share_link">Google Driver</a>][<a href="https://pan.baidu.com/s/1qI4fBgS405Bd4Wn1R3Gbeg?pwd=nbne">Baidu Cloud</a>][<a href="https://huggingface.co/lalala125/AMT/resolve/main/amt-l.pth">Hugging Face</a>]</th>
+    <th> [<a href="cfgs/AMT-L.yaml">cfgs/AMT-L</a>] </th>
+    <th>Vimeo90k</th>
+    <th>Fixed</th>
+  </tr>
+  <tr>
+    <td>AMT-G</td>
+    <th>[<a href="https://drive.google.com/file/d/1yieLtKh4ei3gOrLN1LhKSP_9157Q-mtP/view?usp=share_link">Google Driver</a>][<a href="https://pan.baidu.com/s/1AjmQVziQut1bXgQnDcDKvA?pwd=caf6">Baidu Cloud</a>][<a href="https://huggingface.co/lalala125/AMT/resolve/main/amt-g.pth">Hugging Face</a>] </th>
+    <th> [<a href="cfgs/AMT-G.yaml">cfgs/AMT-G</a>] </th>
+    <th>Vimeo90k</th>
+    <th>Fixed</th>
+  </tr>
+  <tr>
+    <td>AMT-S</td>
+    <th>[<a href="https://drive.google.com/file/d/1f1xAF0EDm-rjDdny8_aLyeedfM0QL4-C/view?usp=share_link">Google Driver</a>][<a href="https://pan.baidu.com/s/1eZtoULyduQM8AkXeYEBOEw?pwd=8hy3">Baidu Cloud</a>][<a href="https://huggingface.co/lalala125/AMT/resolve/main/gopro_amt-s.pth">Hugging Face</a>] </th>
+    <th> [<a href="cfgs/AMT-S_gopro.yaml">cfgs/AMT-S_gopro</a>] </th>
+    <th>GoPro</th>
+    <th>Arbitrary</th>
+  </tr>
+</tbody>
+</table>
+
+## Training and Evaluation
+
+Please refer to [develop.md](docs/develop.md) to learn how to benchmark the AMT and how to train a new AMT model from scratch.
+
+
+## Citation
+   If you find our repo useful for your research, please consider citing our paper:
+
+   ```bibtex
+   @inproceedings{licvpr23amt,
+      title={AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation},
+      author={Li, Zhen and Zhu, Zuo-Liang and Han, Ling-Hao and Hou, Qibin and Guo, Chun-Le and Cheng, Ming-Ming},
+      booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+      year={2023}
+   }
+   ```
+
+
+## License
+This code is licensed under the [Creative Commons Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/) for non-commercial use only.
+Please note that any commercial use of this code requires formal permission prior to use.
+
+## Contact
+
+For technical questions, please contact `zhenli1031[AT]gmail.com` and `nkuzhuzl[AT]gmail.com`.
+
+For commercial licensing, please contact `cmm[AT]nankai.edu.cn`
+
+## Acknowledgement
+
+We thank Jia-Wen Xiao, Zheng-Peng Duan, Rui-Qi Wu, and Xin Jin for proof reading.
+We thank [Zhewei Huang](https://github.com/hzwer) for his suggestions.
+
+Here are some great resources we benefit from:
+
+- [IFRNet](https://github.com/ltkong218/IFRNet) and [RIFE](https://github.com/megvii-research/ECCV2022-RIFE) for data processing, benchmarking, and loss designs.
+- [RAFT](https://github.com/princeton-vl/RAFT), [M2M-VFI](https://github.com/feinanshan/M2M_VFI), and [GMFlow](https://github.com/haofeixu/gmflow) for inspirations.
+- [FILM](https://github.com/google-research/frame-interpolation) for Web demo reference.
+
+
+**If you develop/use AMT in your projects, welcome to let us know. We will list your projects in this repository.**
+
+We also thank all of our contributors.
+
+<a href="https://github.com/MCG-NKU/AMT/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=MCG-NKU/AMT" />
+</a>
+

Helios-main/eval/utils/third_party/amt/benchmarks/adobe240.py

@@ -0,0 +1,65 @@
+import argparse
+import sys
+
+import numpy as np
+import torch
+import tqdm
+from omegaconf import OmegaConf
+
+
+sys.path.append(".")
+from datasets.adobe_datasets import Adobe240_Dataset
+from metrics.psnr_ssim import calculate_psnr, calculate_ssim
+
+from utils.build_utils import build_from_cfg
+
+
+parser = argparse.ArgumentParser(
+    prog="AMT",
+    description="Adobe240 evaluation",
+)
+parser.add_argument("-c", "--config", default="cfgs/AMT-S_gopro.yaml")
+parser.add_argument(
+    "-p",
+    "--ckpt",
+    default="pretrained/gopro_amt-s.pth",
+)
+parser.add_argument(
+    "-r",
+    "--root",
+    default="data/Adobe240/test_frames",
+)
+args = parser.parse_args()
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+cfg_path = args.config
+ckpt_path = args.ckpt
+root = args.root
+
+network_cfg = OmegaConf.load(cfg_path).network
+network_name = network_cfg.name
+model = build_from_cfg(network_cfg)
+ckpt = torch.load(ckpt_path)
+model.load_state_dict(ckpt["state_dict"])
+model = model.to(device)
+model.eval()
+
+dataset = Adobe240_Dataset(dataset_dir=root, augment=False)
+
+psnr_list = []
+ssim_list = []
+pbar = tqdm.tqdm(dataset, total=len(dataset))
+for data in pbar:
+    input_dict = {}
+    for k, v in data.items():
+        input_dict[k] = v.to(device).unsqueeze(0)
+    with torch.no_grad():
+        imgt_pred = model(**input_dict)["imgt_pred"]
+        psnr = calculate_psnr(imgt_pred, input_dict["imgt"])
+        ssim = calculate_ssim(imgt_pred, input_dict["imgt"])
+    psnr_list.append(psnr)
+    ssim_list.append(ssim)
+    avg_psnr = np.mean(psnr_list)
+    avg_ssim = np.mean(ssim_list)
+    desc_str = f"[{network_name}/Adobe240] psnr: {avg_psnr:.02f}, ssim: {avg_ssim:.04f}"
+    pbar.set_description_str(desc_str)

Helios-main/eval/utils/third_party/amt/benchmarks/gopro.py

@@ -0,0 +1,65 @@
+import argparse
+import sys
+
+import numpy as np
+import torch
+import tqdm
+from omegaconf import OmegaConf
+
+
+sys.path.append(".")
+from datasets.gopro_datasets import GoPro_Test_Dataset
+from metrics.psnr_ssim import calculate_psnr, calculate_ssim
+
+from utils.build_utils import build_from_cfg
+
+
+parser = argparse.ArgumentParser(
+    prog="AMT",
+    description="GOPRO evaluation",
+)
+parser.add_argument("-c", "--config", default="cfgs/AMT-S_gopro.yaml")
+parser.add_argument(
+    "-p",
+    "--ckpt",
+    default="pretrained/gopro_amt-s.pth",
+)
+parser.add_argument(
+    "-r",
+    "--root",
+    default="data/GOPRO",
+)
+args = parser.parse_args()
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+cfg_path = args.config
+ckpt_path = args.ckpt
+root = args.root
+
+network_cfg = OmegaConf.load(cfg_path).network
+network_name = network_cfg.name
+model = build_from_cfg(network_cfg)
+ckpt = torch.load(ckpt_path)
+model.load_state_dict(ckpt["state_dict"])
+model = model.to(device)
+model.eval()
+
+dataset = GoPro_Test_Dataset(dataset_dir=root)
+
+psnr_list = []
+ssim_list = []
+pbar = tqdm.tqdm(dataset, total=len(dataset))
+for data in pbar:
+    input_dict = {}
+    for k, v in data.items():
+        input_dict[k] = v.to(device).unsqueeze(0)
+    with torch.no_grad():
+        imgt_pred = model(**input_dict)["imgt_pred"]
+        psnr = calculate_psnr(imgt_pred, input_dict["imgt"])
+        ssim = calculate_ssim(imgt_pred, input_dict["imgt"])
+    psnr_list.append(psnr)
+    ssim_list.append(ssim)
+    avg_psnr = np.mean(psnr_list)
+    avg_ssim = np.mean(ssim_list)
+    desc_str = f"[{network_name}/GOPRO] psnr: {avg_psnr:.02f}, ssim: {avg_ssim:.04f}"
+    pbar.set_description_str(desc_str)

Helios-main/eval/utils/third_party/amt/benchmarks/snu_film.py

@@ -0,0 +1,76 @@
+import argparse
+import os
+import os.path as osp
+import sys
+
+import numpy as np
+import torch
+import tqdm
+from omegaconf import OmegaConf
+
+
+sys.path.append(".")
+from metrics.psnr_ssim import calculate_psnr, calculate_ssim
+
+from utils.build_utils import build_from_cfg
+from utils.utils import InputPadder, img2tensor, read
+
+
+def parse_path(path):
+    path_list = path.split("/")
+    new_path = osp.join(*path_list[-3:])
+    return new_path
+
+
+parser = argparse.ArgumentParser(
+    prog="AMT",
+    description="SNU-FILM evaluation",
+)
+parser.add_argument("-c", "--config", default="cfgs/AMT-S.yaml")
+parser.add_argument("-p", "--ckpt", default="pretrained/amt-s.pth")
+parser.add_argument("-r", "--root", default="data/SNU_FILM")
+args = parser.parse_args()
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+cfg_path = args.config
+ckpt_path = args.ckpt
+root = args.root
+
+network_cfg = OmegaConf.load(cfg_path).network
+network_name = network_cfg.name
+model = build_from_cfg(network_cfg)
+ckpt = torch.load(ckpt_path)
+model.load_state_dict(ckpt["state_dict"])
+model = model.to(device)
+model.eval()
+
+divisor = 20
+scale_factor = 0.8
+splits = ["easy", "medium", "hard", "extreme"]
+for split in splits:
+    with open(os.path.join(root, f"test-{split}.txt"), "r") as fr:
+        file_list = [l.strip().split(" ") for l in fr.readlines()]
+    pbar = tqdm.tqdm(file_list, total=len(file_list))
+
+    psnr_list = []
+    ssim_list = []
+    for name in pbar:
+        img0 = img2tensor(read(osp.join(root, parse_path(name[0])))).to(device)
+        imgt = img2tensor(read(osp.join(root, parse_path(name[1])))).to(device)
+        img1 = img2tensor(read(osp.join(root, parse_path(name[2])))).to(device)
+        padder = InputPadder(img0.shape, divisor)
+        img0, img1 = padder.pad(img0, img1)
+
+        embt = torch.tensor(1 / 2).float().view(1, 1, 1, 1).to(device)
+        imgt_pred = model(img0, img1, embt, scale_factor=scale_factor, eval=True)["imgt_pred"]
+        imgt_pred = padder.unpad(imgt_pred)
+
+        psnr = calculate_psnr(imgt_pred, imgt).detach().cpu().numpy()
+        ssim = calculate_ssim(imgt_pred, imgt).detach().cpu().numpy()
+
+        psnr_list.append(psnr)
+        ssim_list.append(ssim)
+        avg_psnr = np.mean(psnr_list)
+        avg_ssim = np.mean(ssim_list)
+        desc_str = f"[{network_name}/SNU-FILM] [{split}] psnr: {avg_psnr:.02f}, ssim: {avg_ssim:.04f}"
+        pbar.set_description_str(desc_str)

Helios-main/eval/utils/third_party/amt/benchmarks/speed_parameters.py

@@ -0,0 +1,41 @@
+import argparse
+import sys
+import time
+
+import torch
+from omegaconf import OmegaConf
+
+
+sys.path.append(".")
+from utils.build_utils import build_from_cfg
+
+
+parser = argparse.ArgumentParser(
+    prog="AMT",
+    description="Speed&parameter benchmark",
+)
+parser.add_argument("-c", "--config", default="cfgs/AMT-S.yaml")
+args = parser.parse_args()
+
+cfg_path = args.config
+network_cfg = OmegaConf.load(cfg_path).network
+model = build_from_cfg(network_cfg)
+model = model.cuda()
+model.eval()
+
+img0 = torch.randn(1, 3, 256, 448).cuda()
+img1 = torch.randn(1, 3, 256, 448).cuda()
+embt = torch.tensor(1 / 2).float().view(1, 1, 1, 1).cuda()
+
+with torch.no_grad():
+    for i in range(100):
+        out = model(img0, img1, embt, eval=True)
+    torch.cuda.synchronize()
+    time_stamp = time.time()
+    for i in range(1000):
+        out = model(img0, img1, embt, eval=True)
+    torch.cuda.synchronize()
+    print("Time: {:.5f}s".format((time.time() - time_stamp) / 1))
+
+total = sum([param.nelement() for param in model.parameters()])
+print("Parameters: {:.2f}M".format(total / 1e6))

Helios-main/eval/utils/third_party/amt/benchmarks/ucf101.py

@@ -0,0 +1,63 @@
+import argparse
+import os
+import os.path as osp
+import sys
+
+import numpy as np
+import torch
+import tqdm
+from omegaconf import OmegaConf
+
+
+sys.path.append(".")
+from metrics.psnr_ssim import calculate_psnr, calculate_ssim
+
+from utils.build_utils import build_from_cfg
+from utils.utils import img2tensor, read
+
+
+parser = argparse.ArgumentParser(
+    prog="AMT",
+    description="UCF101 evaluation",
+)
+parser.add_argument("-c", "--config", default="cfgs/AMT-S.yaml")
+parser.add_argument("-p", "--ckpt", default="pretrained/amt-s.pth")
+parser.add_argument("-r", "--root", default="data/ucf101_interp_ours")
+args = parser.parse_args()
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+cfg_path = args.config
+ckpt_path = args.ckpt
+root = args.root
+
+network_cfg = OmegaConf.load(cfg_path).network
+network_name = network_cfg.name
+model = build_from_cfg(network_cfg)
+ckpt = torch.load(ckpt_path)
+model.load_state_dict(ckpt["state_dict"])
+model = model.to(device)
+model.eval()
+
+dirs = sorted(os.listdir(root))
+psnr_list = []
+ssim_list = []
+pbar = tqdm.tqdm(dirs, total=len(dirs))
+for d in pbar:
+    dir_path = osp.join(root, d)
+    I0 = img2tensor(read(osp.join(dir_path, "frame_00.png"))).to(device)
+    I1 = img2tensor(read(osp.join(dir_path, "frame_01_gt.png"))).to(device)
+    I2 = img2tensor(read(osp.join(dir_path, "frame_02.png"))).to(device)
+    embt = torch.tensor(1 / 2).float().view(1, 1, 1, 1).to(device)
+
+    I1_pred = model(I0, I2, embt, eval=True)["imgt_pred"]
+
+    psnr = calculate_psnr(I1_pred, I1).detach().cpu().numpy()
+    ssim = calculate_ssim(I1_pred, I1).detach().cpu().numpy()
+
+    psnr_list.append(psnr)
+    ssim_list.append(ssim)
+
+    avg_psnr = np.mean(psnr_list)
+    avg_ssim = np.mean(ssim_list)
+    desc_str = f"[{network_name}/UCF101] psnr: {avg_psnr:.02f}, ssim: {avg_ssim:.04f}"
+    pbar.set_description_str(desc_str)

Helios-main/eval/utils/third_party/amt/benchmarks/vimeo90k.py

@@ -0,0 +1,75 @@
+import argparse
+import os.path as osp
+import sys
+
+import numpy as np
+import torch
+import tqdm
+from omegaconf import OmegaConf
+
+
+sys.path.append(".")
+from metrics.psnr_ssim import calculate_psnr, calculate_ssim
+
+from utils.build_utils import build_from_cfg
+from utils.utils import img2tensor, read
+
+
+parser = argparse.ArgumentParser(
+    prog="AMT",
+    description="Vimeo90K evaluation",
+)
+parser.add_argument("-c", "--config", default="cfgs/AMT-S.yaml")
+parser.add_argument(
+    "-p",
+    "--ckpt",
+    default="pretrained/amt-s.pth",
+)
+parser.add_argument(
+    "-r",
+    "--root",
+    default="data/vimeo_triplet",
+)
+args = parser.parse_args()
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+cfg_path = args.config
+ckpt_path = args.ckpt
+root = args.root
+
+network_cfg = OmegaConf.load(cfg_path).network
+network_name = network_cfg.name
+model = build_from_cfg(network_cfg)
+ckpt = torch.load(ckpt_path)
+model.load_state_dict(ckpt["state_dict"])
+model = model.to(device)
+model.eval()
+
+with open(osp.join(root, "tri_testlist.txt"), "r") as fr:
+    file_list = fr.readlines()
+
+psnr_list = []
+ssim_list = []
+
+pbar = tqdm.tqdm(file_list, total=len(file_list))
+for name in pbar:
+    name = str(name).strip()
+    if len(name) <= 1:
+        continue
+    dir_path = osp.join(root, "sequences", name)
+    I0 = img2tensor(read(osp.join(dir_path, "im1.png"))).to(device)
+    I1 = img2tensor(read(osp.join(dir_path, "im2.png"))).to(device)
+    I2 = img2tensor(read(osp.join(dir_path, "im3.png"))).to(device)
+    embt = torch.tensor(1 / 2).float().view(1, 1, 1, 1).to(device)
+
+    I1_pred = model(I0, I2, embt, scale_factor=1.0, eval=True)["imgt_pred"]
+
+    psnr = calculate_psnr(I1_pred, I1).detach().cpu().numpy()
+    ssim = calculate_ssim(I1_pred, I1).detach().cpu().numpy()
+
+    psnr_list.append(psnr)
+    ssim_list.append(ssim)
+    avg_psnr = np.mean(psnr_list)
+    avg_ssim = np.mean(ssim_list)
+    desc_str = f"[{network_name}/Vimeo90K] psnr: {avg_psnr:.02f}, ssim: {avg_ssim:.04f}"
+    pbar.set_description_str(desc_str)

Helios-main/eval/utils/third_party/amt/benchmarks/vimeo90k_tta.py

@@ -0,0 +1,76 @@
+import argparse
+import os.path as osp
+import sys
+
+import numpy as np
+import torch
+import tqdm
+from omegaconf import OmegaConf
+
+
+sys.path.append(".")
+from metrics.psnr_ssim import calculate_psnr, calculate_ssim
+
+from utils.build_utils import build_from_cfg
+from utils.utils import img2tensor, read
+
+
+parser = argparse.ArgumentParser(
+    prog="AMT",
+    description="Vimeo90K evaluation (with Test-Time Augmentation)",
+)
+parser.add_argument("-c", "--config", default="cfgs/AMT-S.yaml")
+parser.add_argument(
+    "p",
+    "--ckpt",
+    default="pretrained/amt-s.pth",
+)
+parser.add_argument(
+    "-r",
+    "--root",
+    default="data/vimeo_triplet",
+)
+args = parser.parse_args()
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+cfg_path = args.config
+ckpt_path = args.ckpt
+root = args.root
+
+network_cfg = OmegaConf.load(cfg_path).network
+network_name = network_cfg.name
+model = build_from_cfg(network_cfg)
+ckpt = torch.load(ckpt_path)
+model.load_state_dict(ckpt["state_dict"])
+model = model.to(device)
+model.eval()
+
+with open(osp.join(root, "tri_testlist.txt"), "r") as fr:
+    file_list = fr.readlines()
+
+psnr_list = []
+ssim_list = []
+
+pbar = tqdm.tqdm(file_list, total=len(file_list))
+for name in pbar:
+    name = str(name).strip()
+    if len(name) <= 1:
+        continue
+    dir_path = osp.join(root, "sequences", name)
+    I0 = img2tensor(read(osp.join(dir_path, "im1.png"))).to(device)
+    I1 = img2tensor(read(osp.join(dir_path, "im2.png"))).to(device)
+    I2 = img2tensor(read(osp.join(dir_path, "im3.png"))).to(device)
+    embt = torch.tensor(1 / 2).float().view(1, 1, 1, 1).to(device)
+
+    I1_pred1 = model(I0, I2, embt, scale_factor=1.0, eval=True)["imgt_pred"]
+    I1_pred2 = model(torch.flip(I0, [2]), torch.flip(I2, [2]), embt, scale_factor=1.0, eval=True)["imgt_pred"]
+    I1_pred = I1_pred1 / 2 + torch.flip(I1_pred2, [2]) / 2
+    psnr = calculate_psnr(I1_pred, I1).detach().cpu().numpy()
+    ssim = calculate_ssim(I1_pred, I1).detach().cpu().numpy()
+
+    psnr_list.append(psnr)
+    ssim_list.append(ssim)
+    avg_psnr = np.mean(psnr_list)
+    avg_ssim = np.mean(ssim_list)
+    desc_str = f"[{network_name}/Vimeo90K] psnr: {avg_psnr:.02f}, ssim: {avg_ssim:.04f}"
+    pbar.set_description_str(desc_str)

Helios-main/eval/utils/third_party/amt/datasets/adobe_datasets.py

@@ -0,0 +1,77 @@
+import os
+import sys
+
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+
+sys.path.append(".")
+from datasets.gopro_datasets import (
+    center_crop_woflow,
+    random_crop_woflow,
+    random_horizontal_flip_woflow,
+    random_resize_woflow,
+    random_reverse_channel_woflow,
+    random_reverse_time_woflow,
+    random_rotate_woflow,
+    random_vertical_flip_woflow,
+)
+
+from utils.utils import img2tensor, read
+
+
+class Adobe240_Dataset(Dataset):
+    def __init__(self, dataset_dir="data/adobe240/test_frames", interFrames=7, augment=True):
+        super().__init__()
+        self.augment = augment
+        self.interFrames = interFrames
+        self.setLength = interFrames + 2
+        self.dataset_dir = os.path.join(dataset_dir)
+        video_list = os.listdir(self.dataset_dir)[9::10]
+        self.frames_list = []
+        self.file_list = []
+        for video in video_list:
+            frames = sorted(os.listdir(os.path.join(self.dataset_dir, video)))
+            n_sets = (len(frames) - self.setLength) // (interFrames + 1) + 1
+            videoInputs = [
+                frames[(interFrames + 1) * i : (interFrames + 1) * i + self.setLength] for i in range(n_sets)
+            ]
+            videoInputs = [[os.path.join(video, f) for f in group] for group in videoInputs]
+            self.file_list.extend(videoInputs)
+
+    def __getitem__(self, idx):
+        clip_idx = idx // self.interFrames
+        embt_idx = idx % self.interFrames
+        imgpaths = [os.path.join(self.dataset_dir, fp) for fp in self.file_list[clip_idx]]
+        pick_idxs = list(range(0, self.setLength, self.interFrames + 1))
+        imgt_beg = self.setLength // 2 - self.interFrames // 2
+        imgt_end = self.setLength // 2 + self.interFrames // 2 + self.interFrames % 2
+        imgt_idx = list(range(imgt_beg, imgt_end))
+        input_paths = [imgpaths[idx] for idx in pick_idxs]
+        imgt_paths = [imgpaths[idx] for idx in imgt_idx]
+
+        img0 = np.array(read(input_paths[0]))
+        imgt = np.array(read(imgt_paths[embt_idx]))
+        img1 = np.array(read(input_paths[1]))
+        embt = torch.from_numpy(np.array((embt_idx + 1) / (self.interFrames + 1)).reshape(1, 1, 1).astype(np.float32))
+
+        if self.augment:
+            img0, imgt, img1 = random_resize_woflow(img0, imgt, img1, p=0.1)
+            img0, imgt, img1 = random_crop_woflow(img0, imgt, img1, crop_size=(224, 224))
+            img0, imgt, img1 = random_reverse_channel_woflow(img0, imgt, img1, p=0.5)
+            img0, imgt, img1 = random_vertical_flip_woflow(img0, imgt, img1, p=0.3)
+            img0, imgt, img1 = random_horizontal_flip_woflow(img0, imgt, img1, p=0.5)
+            img0, imgt, img1 = random_rotate_woflow(img0, imgt, img1, p=0.05)
+            img0, imgt, img1, embt = random_reverse_time_woflow(img0, imgt, img1, embt=embt, p=0.5)
+        else:
+            img0, imgt, img1 = center_crop_woflow(img0, imgt, img1, crop_size=(512, 512))
+
+        img0 = img2tensor(img0).squeeze(0)
+        imgt = img2tensor(imgt).squeeze(0)
+        img1 = img2tensor(img1).squeeze(0)
+
+        return {"img0": img0.float(), "imgt": imgt.float(), "img1": img1.float(), "embt": embt}
+
+    def __len__(self):
+        return len(self.file_list) * self.interFrames

Helios-main/eval/utils/third_party/amt/datasets/gopro_datasets.py

@@ -0,0 +1,213 @@
+import os
+import random
+
+import cv2
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+from utils.utils import img2tensor, read
+
+
+def random_resize_woflow(img0, imgt, img1, p=0.1):
+    if random.uniform(0, 1) < p:
+        img0 = cv2.resize(img0, dsize=None, fx=2.0, fy=2.0, interpolation=cv2.INTER_LINEAR)
+        imgt = cv2.resize(imgt, dsize=None, fx=2.0, fy=2.0, interpolation=cv2.INTER_LINEAR)
+        img1 = cv2.resize(img1, dsize=None, fx=2.0, fy=2.0, interpolation=cv2.INTER_LINEAR)
+    return img0, imgt, img1
+
+
+def random_crop_woflow(img0, imgt, img1, crop_size=(224, 224)):
+    h, w = crop_size[0], crop_size[1]
+    ih, iw, _ = img0.shape
+    x = np.random.randint(0, ih - h + 1)
+    y = np.random.randint(0, iw - w + 1)
+    img0 = img0[x : x + h, y : y + w, :]
+    imgt = imgt[x : x + h, y : y + w, :]
+    img1 = img1[x : x + h, y : y + w, :]
+    return img0, imgt, img1
+
+
+def center_crop_woflow(img0, imgt, img1, crop_size=(512, 512)):
+    h, w = crop_size[0], crop_size[1]
+    ih, iw, _ = img0.shape
+    img0 = img0[ih // 2 - h // 2 : ih // 2 + h // 2, iw // 2 - w // 2 : iw // 2 + w // 2, :]
+    imgt = imgt[ih // 2 - h // 2 : ih // 2 + h // 2, iw // 2 - w // 2 : iw // 2 + w // 2, :]
+    img1 = img1[ih // 2 - h // 2 : ih // 2 + h // 2, iw // 2 - w // 2 : iw // 2 + w // 2, :]
+    return img0, imgt, img1
+
+
+def random_reverse_channel_woflow(img0, imgt, img1, p=0.5):
+    if random.uniform(0, 1) < p:
+        img0 = img0[:, :, ::-1]
+        imgt = imgt[:, :, ::-1]
+        img1 = img1[:, :, ::-1]
+    return img0, imgt, img1
+
+
+def random_vertical_flip_woflow(img0, imgt, img1, p=0.3):
+    if random.uniform(0, 1) < p:
+        img0 = img0[::-1]
+        imgt = imgt[::-1]
+        img1 = img1[::-1]
+    return img0, imgt, img1
+
+
+def random_horizontal_flip_woflow(img0, imgt, img1, p=0.5):
+    if random.uniform(0, 1) < p:
+        img0 = img0[:, ::-1]
+        imgt = imgt[:, ::-1]
+        img1 = img1[:, ::-1]
+    return img0, imgt, img1
+
+
+def random_rotate_woflow(img0, imgt, img1, p=0.05):
+    if random.uniform(0, 1) < p:
+        img0 = img0.transpose((1, 0, 2))
+        imgt = imgt.transpose((1, 0, 2))
+        img1 = img1.transpose((1, 0, 2))
+    return img0, imgt, img1
+
+
+def random_reverse_time_woflow(img0, imgt, img1, embt, p=0.5):
+    if random.uniform(0, 1) < p:
+        tmp = img1
+        img1 = img0
+        img0 = tmp
+    embt = 1 - embt
+    return img0, imgt, img1, embt
+
+
+class GoPro_Train_Dataset(Dataset):
+    def __init__(self, dataset_dir="data/GOPRO", interFrames=7, augment=True):
+        self.dataset_dir = dataset_dir + "/train"
+        self.interFrames = interFrames
+        self.augment = augment
+        self.setLength = interFrames + 2
+        video_list = [
+            "GOPR0372_07_00",
+            "GOPR0374_11_01",
+            "GOPR0378_13_00",
+            "GOPR0384_11_01",
+            "GOPR0384_11_04",
+            "GOPR0477_11_00",
+            "GOPR0868_11_02",
+            "GOPR0884_11_00",
+            "GOPR0372_07_01",
+            "GOPR0374_11_02",
+            "GOPR0379_11_00",
+            "GOPR0384_11_02",
+            "GOPR0385_11_00",
+            "GOPR0857_11_00",
+            "GOPR0871_11_01",
+            "GOPR0374_11_00",
+            "GOPR0374_11_03",
+            "GOPR0380_11_00",
+            "GOPR0384_11_03",
+            "GOPR0386_11_00",
+            "GOPR0868_11_01",
+            "GOPR0881_11_00",
+        ]
+        self.frames_list = []
+        self.file_list = []
+        for video in video_list:
+            frames = sorted(os.listdir(os.path.join(self.dataset_dir, video)))
+            n_sets = (len(frames) - self.setLength) // (interFrames + 1) + 1
+            videoInputs = [
+                frames[(interFrames + 1) * i : (interFrames + 1) * i + self.setLength] for i in range(n_sets)
+            ]
+            videoInputs = [[os.path.join(video, f) for f in group] for group in videoInputs]
+            self.file_list.extend(videoInputs)
+
+    def __len__(self):
+        return len(self.file_list) * self.interFrames
+
+    def __getitem__(self, idx):
+        clip_idx = idx // self.interFrames
+        embt_idx = idx % self.interFrames
+        imgpaths = [os.path.join(self.dataset_dir, fp) for fp in self.file_list[clip_idx]]
+        pick_idxs = list(range(0, self.setLength, self.interFrames + 1))
+        imgt_beg = self.setLength // 2 - self.interFrames // 2
+        imgt_end = self.setLength // 2 + self.interFrames // 2 + self.interFrames % 2
+        imgt_idx = list(range(imgt_beg, imgt_end))
+        input_paths = [imgpaths[idx] for idx in pick_idxs]
+        imgt_paths = [imgpaths[idx] for idx in imgt_idx]
+
+        embt = torch.from_numpy(np.array((embt_idx + 1) / (self.interFrames + 1)).reshape(1, 1, 1).astype(np.float32))
+        img0 = np.array(read(input_paths[0]))
+        imgt = np.array(read(imgt_paths[embt_idx]))
+        img1 = np.array(read(input_paths[1]))
+
+        if self.augment:
+            img0, imgt, img1 = random_resize_woflow(img0, imgt, img1, p=0.1)
+            img0, imgt, img1 = random_crop_woflow(img0, imgt, img1, crop_size=(224, 224))
+            img0, imgt, img1 = random_reverse_channel_woflow(img0, imgt, img1, p=0.5)
+            img0, imgt, img1 = random_vertical_flip_woflow(img0, imgt, img1, p=0.3)
+            img0, imgt, img1 = random_horizontal_flip_woflow(img0, imgt, img1, p=0.5)
+            img0, imgt, img1 = random_rotate_woflow(img0, imgt, img1, p=0.05)
+            img0, imgt, img1, embt = random_reverse_time_woflow(img0, imgt, img1, embt=embt, p=0.5)
+        else:
+            img0, imgt, img1 = center_crop_woflow(img0, imgt, img1, crop_size=(512, 512))
+
+        img0 = img2tensor(img0.copy()).squeeze(0)
+        imgt = img2tensor(imgt.copy()).squeeze(0)
+        img1 = img2tensor(img1.copy()).squeeze(0)
+
+        return {"img0": img0.float(), "imgt": imgt.float(), "img1": img1.float(), "embt": embt}
+
+
+class GoPro_Test_Dataset(Dataset):
+    def __init__(self, dataset_dir="data/GOPRO", interFrames=7):
+        self.dataset_dir = dataset_dir + "/test"
+        self.interFrames = interFrames
+        self.setLength = interFrames + 2
+        video_list = [
+            "GOPR0384_11_00",
+            "GOPR0385_11_01",
+            "GOPR0410_11_00",
+            "GOPR0862_11_00",
+            "GOPR0869_11_00",
+            "GOPR0881_11_01",
+            "GOPR0384_11_05",
+            "GOPR0396_11_00",
+            "GOPR0854_11_00",
+            "GOPR0868_11_00",
+            "GOPR0871_11_00",
+        ]
+        self.frames_list = []
+        self.file_list = []
+        for video in video_list:
+            frames = sorted(os.listdir(os.path.join(self.dataset_dir, video)))
+            n_sets = (len(frames) - self.setLength) // (interFrames + 1) + 1
+            videoInputs = [
+                frames[(interFrames + 1) * i : (interFrames + 1) * i + self.setLength] for i in range(n_sets)
+            ]
+            videoInputs = [[os.path.join(video, f) for f in group] for group in videoInputs]
+            self.file_list.extend(videoInputs)
+
+    def __len__(self):
+        return len(self.file_list) * self.interFrames
+
+    def __getitem__(self, idx):
+        clip_idx = idx // self.interFrames
+        embt_idx = idx % self.interFrames
+        imgpaths = [os.path.join(self.dataset_dir, fp) for fp in self.file_list[clip_idx]]
+        pick_idxs = list(range(0, self.setLength, self.interFrames + 1))
+        imgt_beg = self.setLength // 2 - self.interFrames // 2
+        imgt_end = self.setLength // 2 + self.interFrames // 2 + self.interFrames % 2
+        imgt_idx = list(range(imgt_beg, imgt_end))
+        input_paths = [imgpaths[idx] for idx in pick_idxs]
+        imgt_paths = [imgpaths[idx] for idx in imgt_idx]
+
+        img0 = np.array(read(input_paths[0]))
+        imgt = np.array(read(imgt_paths[embt_idx]))
+        img1 = np.array(read(input_paths[1]))
+
+        img0, imgt, img1 = center_crop_woflow(img0, imgt, img1, crop_size=(512, 512))
+
+        img0 = img2tensor(img0).squeeze(0)
+        imgt = img2tensor(imgt).squeeze(0)
+        img1 = img2tensor(img1).squeeze(0)
+
+        embt = torch.from_numpy(np.array((embt_idx + 1) / (self.interFrames + 1)).reshape(1, 1, 1).astype(np.float32))
+        return {"img0": img0.float(), "imgt": imgt.float(), "img1": img1.float(), "embt": embt}

Helios-main/eval/utils/third_party/amt/datasets/vimeo_datasets.py

@@ -0,0 +1,168 @@
+import os
+import random
+
+import cv2
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+from utils.utils import read
+
+
+def random_resize(img0, imgt, img1, flow, p=0.1):
+    if random.uniform(0, 1) < p:
+        img0 = cv2.resize(img0, dsize=None, fx=2.0, fy=2.0, interpolation=cv2.INTER_LINEAR)
+        imgt = cv2.resize(imgt, dsize=None, fx=2.0, fy=2.0, interpolation=cv2.INTER_LINEAR)
+        img1 = cv2.resize(img1, dsize=None, fx=2.0, fy=2.0, interpolation=cv2.INTER_LINEAR)
+        flow = cv2.resize(flow, dsize=None, fx=2.0, fy=2.0, interpolation=cv2.INTER_LINEAR) * 2.0
+    return img0, imgt, img1, flow
+
+
+def random_crop(img0, imgt, img1, flow, crop_size=(224, 224)):
+    h, w = crop_size[0], crop_size[1]
+    ih, iw, _ = img0.shape
+    x = np.random.randint(0, ih - h + 1)
+    y = np.random.randint(0, iw - w + 1)
+    img0 = img0[x : x + h, y : y + w, :]
+    imgt = imgt[x : x + h, y : y + w, :]
+    img1 = img1[x : x + h, y : y + w, :]
+    flow = flow[x : x + h, y : y + w, :]
+    return img0, imgt, img1, flow
+
+
+def random_reverse_channel(img0, imgt, img1, flow, p=0.5):
+    if random.uniform(0, 1) < p:
+        img0 = img0[:, :, ::-1]
+        imgt = imgt[:, :, ::-1]
+        img1 = img1[:, :, ::-1]
+    return img0, imgt, img1, flow
+
+
+def random_vertical_flip(img0, imgt, img1, flow, p=0.3):
+    if random.uniform(0, 1) < p:
+        img0 = img0[::-1]
+        imgt = imgt[::-1]
+        img1 = img1[::-1]
+        flow = flow[::-1]
+        flow = np.concatenate((flow[:, :, 0:1], -flow[:, :, 1:2], flow[:, :, 2:3], -flow[:, :, 3:4]), 2)
+    return img0, imgt, img1, flow
+
+
+def random_horizontal_flip(img0, imgt, img1, flow, p=0.5):
+    if random.uniform(0, 1) < p:
+        img0 = img0[:, ::-1]
+        imgt = imgt[:, ::-1]
+        img1 = img1[:, ::-1]
+        flow = flow[:, ::-1]
+        flow = np.concatenate((-flow[:, :, 0:1], flow[:, :, 1:2], -flow[:, :, 2:3], flow[:, :, 3:4]), 2)
+    return img0, imgt, img1, flow
+
+
+def random_rotate(img0, imgt, img1, flow, p=0.05):
+    if random.uniform(0, 1) < p:
+        img0 = img0.transpose((1, 0, 2))
+        imgt = imgt.transpose((1, 0, 2))
+        img1 = img1.transpose((1, 0, 2))
+        flow = flow.transpose((1, 0, 2))
+        flow = np.concatenate((flow[:, :, 1:2], flow[:, :, 0:1], flow[:, :, 3:4], flow[:, :, 2:3]), 2)
+    return img0, imgt, img1, flow
+
+
+def random_reverse_time(img0, imgt, img1, flow, p=0.5):
+    if random.uniform(0, 1) < p:
+        tmp = img1
+        img1 = img0
+        img0 = tmp
+        flow = np.concatenate((flow[:, :, 2:4], flow[:, :, 0:2]), 2)
+    return img0, imgt, img1, flow
+
+
+class Vimeo90K_Train_Dataset(Dataset):
+    def __init__(self, dataset_dir="data/vimeo_triplet", flow_dir=None, augment=True, crop_size=(224, 224)):
+        self.dataset_dir = dataset_dir
+        self.augment = augment
+        self.crop_size = crop_size
+        self.img0_list = []
+        self.imgt_list = []
+        self.img1_list = []
+        self.flow_t0_list = []
+        self.flow_t1_list = []
+        if flow_dir is None:
+            flow_dir = "flow"
+        with open(os.path.join(dataset_dir, "tri_trainlist.txt"), "r") as f:
+            for i in f:
+                name = str(i).strip()
+                if len(name) <= 1:
+                    continue
+                self.img0_list.append(os.path.join(dataset_dir, "sequences", name, "im1.png"))
+                self.imgt_list.append(os.path.join(dataset_dir, "sequences", name, "im2.png"))
+                self.img1_list.append(os.path.join(dataset_dir, "sequences", name, "im3.png"))
+                self.flow_t0_list.append(os.path.join(dataset_dir, flow_dir, name, "flow_t0.flo"))
+                self.flow_t1_list.append(os.path.join(dataset_dir, flow_dir, name, "flow_t1.flo"))
+
+    def __len__(self):
+        return len(self.imgt_list)
+
+    def __getitem__(self, idx):
+        img0 = read(self.img0_list[idx])
+        imgt = read(self.imgt_list[idx])
+        img1 = read(self.img1_list[idx])
+        flow_t0 = read(self.flow_t0_list[idx])
+        flow_t1 = read(self.flow_t1_list[idx])
+        flow = np.concatenate((flow_t0, flow_t1), 2).astype(np.float64)
+
+        if self.augment:
+            img0, imgt, img1, flow = random_resize(img0, imgt, img1, flow, p=0.1)
+            img0, imgt, img1, flow = random_crop(img0, imgt, img1, flow, crop_size=self.crop_size)
+            img0, imgt, img1, flow = random_reverse_channel(img0, imgt, img1, flow, p=0.5)
+            img0, imgt, img1, flow = random_vertical_flip(img0, imgt, img1, flow, p=0.3)
+            img0, imgt, img1, flow = random_horizontal_flip(img0, imgt, img1, flow, p=0.5)
+            img0, imgt, img1, flow = random_rotate(img0, imgt, img1, flow, p=0.05)
+            img0, imgt, img1, flow = random_reverse_time(img0, imgt, img1, flow, p=0.5)
+
+        img0 = torch.from_numpy(img0.transpose((2, 0, 1)).astype(np.float32) / 255.0)
+        imgt = torch.from_numpy(imgt.transpose((2, 0, 1)).astype(np.float32) / 255.0)
+        img1 = torch.from_numpy(img1.transpose((2, 0, 1)).astype(np.float32) / 255.0)
+        flow = torch.from_numpy(flow.transpose((2, 0, 1)).astype(np.float32))
+        embt = torch.from_numpy(np.array(1 / 2).reshape(1, 1, 1).astype(np.float32))
+
+        return {"img0": img0.float(), "imgt": imgt.float(), "img1": img1.float(), "flow": flow.float(), "embt": embt}
+
+
+class Vimeo90K_Test_Dataset(Dataset):
+    def __init__(self, dataset_dir="data/vimeo_triplet"):
+        self.dataset_dir = dataset_dir
+        self.img0_list = []
+        self.imgt_list = []
+        self.img1_list = []
+        self.flow_t0_list = []
+        self.flow_t1_list = []
+        with open(os.path.join(dataset_dir, "tri_testlist.txt"), "r") as f:
+            for i in f:
+                name = str(i).strip()
+                if len(name) <= 1:
+                    continue
+                self.img0_list.append(os.path.join(dataset_dir, "sequences", name, "im1.png"))
+                self.imgt_list.append(os.path.join(dataset_dir, "sequences", name, "im2.png"))
+                self.img1_list.append(os.path.join(dataset_dir, "sequences", name, "im3.png"))
+                self.flow_t0_list.append(os.path.join(dataset_dir, "flow", name, "flow_t0.flo"))
+                self.flow_t1_list.append(os.path.join(dataset_dir, "flow", name, "flow_t1.flo"))
+
+    def __len__(self):
+        return len(self.imgt_list)
+
+    def __getitem__(self, idx):
+        img0 = read(self.img0_list[idx])
+        imgt = read(self.imgt_list[idx])
+        img1 = read(self.img1_list[idx])
+        flow_t0 = read(self.flow_t0_list[idx])
+        flow_t1 = read(self.flow_t1_list[idx])
+        flow = np.concatenate((flow_t0, flow_t1), 2)
+
+        img0 = torch.from_numpy(img0.transpose((2, 0, 1)).astype(np.float32) / 255.0)
+        imgt = torch.from_numpy(imgt.transpose((2, 0, 1)).astype(np.float32) / 255.0)
+        img1 = torch.from_numpy(img1.transpose((2, 0, 1)).astype(np.float32) / 255.0)
+        flow = torch.from_numpy(flow.transpose((2, 0, 1)).astype(np.float32))
+        embt = torch.from_numpy(np.array(1 / 2).reshape(1, 1, 1).astype(np.float32))
+
+        return {"img0": img0.float(), "imgt": imgt.float(), "img1": img1.float(), "flow": flow.float(), "embt": embt}

Helios-main/eval/utils/third_party/amt/docs/develop.md

@@ -0,0 +1,239 @@
+# Development for evaluation and training
+
+- [Datasets](#Datasets)
+- [Pretrained Models](#pretrained-models)
+- [Evaluation](#evaluation)
+- [Training](#training)
+
+## Datasets<p id="Datasets"></p>
+First, please prepare standard datasets for evaluation and training.
+
+We present most of prevailing datasets in video frame interpolation, though some are not used in our project. Hope this collection could help your research. 
+
+<table>
+<thead>
+  <tr>
+    <th> Dataset </th>
+    <th> :link: Source </th>
+    <th> Train/Eval </th>
+    <th> Arbitrary/Fixed </th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td>Vimeo90k</td>
+    <th><a href="http://toflow.csail.mit.edu/">ToFlow (IJCV 2019)</a></th>
+    <th>Both</th>
+    <th>Fixed</th>
+  </tr>
+  <tr>
+    <td>ATD-12K</td>
+    <th><a href="https://github.com/lisiyao21/AnimeInterp">AnimeInterp (CVPR 2021)</a></th>
+    <th>Both</th>
+    <th>Fixed</th>
+  </tr>
+  <tr>
+    <td>SNU-FILM</td>
+    <th><a href="https://myungsub.github.io/CAIN/">CAIN (AAAI 2021)</a></th>
+    <th>Eval</th>
+    <th>Fixed</th>
+  </tr>
+  <tr>
+    <td>UCF101</td>
+    <th><a href="https://drive.google.com/file/d/0B7EVK8r0v71pdHBNdXB6TE1wSTQ/view?resourcekey=0-r6ihCy20h3kbgZ3ZdimPiA">Google Driver</a></th>
+    <th>Eval</th>
+    <th>Fixed</th>
+  </tr>
+  <tr>
+    <td>HD</td>
+    <th><a href="https://github.com/baowenbo/MEMC-Net">MEMC-Net (TPAMI 2018)</a>/<a href="https://github.com/baowenbo/MEMC-Net">Google Driver</a></th>
+    <th>Eval</th>
+    <th>Fixed</th>
+  </tr>
+  <tr>
+    <td>Xiph-2k/-4k</td>
+    <th><a href="https://github.com/sniklaus/softmax-splatting/blob/master/benchmark_xiph.py">SoftSplat (CVPR 2020)</a></th>
+    <th>Eval</th>
+    <th>Fixed</th>
+  </tr>
+  <tr>
+    <td>MiddleBury</td>
+    <th><a href="https://vision.middlebury.edu/flow/data/">MiddleBury</a></th>
+    <th>Eval</th>
+    <th>Fixed</th>
+  </tr>
+  <tr>
+    <td>GoPro</td>
+    <th><a href="https://seungjunnah.github.io/Datasets/gopro">GoPro</a></th>
+    <th>Both</th>
+    <th>Arbitrary</th>
+  </tr>
+  <tr>
+    <td>Adobe240fps</td>
+    <th><a href="http://www.cs.ubc.ca/labs/imager/tr/2017/DeepVideoDeblurring">DBN (CVPR 2017)</a></th>
+    <th>Both</th>
+    <th>Arbitrary</th>
+  </tr>
+   <tr>
+    <td>X4K1000FPS</td>
+    <th><a href="https://github.com/JihyongOh/XVFI">XVFI (ICCV 2021)</a></th>
+    <th>Both</th>
+    <th>Arbitrary</th>
+  </tr>
+</tbody>
+</table>
+
+
+## Pretrained Models
+
+<p id="Pretrained"></p>
+
+<table>
+<thead>
+  <tr>
+    <th> Dataset </th>
+    <th> :link: Download Links </th>
+    <th> Config file </th>
+    <th> Trained on </th>
+    <th> Arbitrary/Fixed </th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td>AMT-S</td>
+    <th> [<a href="https://drive.google.com/file/d/1WmOKmQmd6pnLpID8EpUe-TddFpJuavrL/view?usp=share_link">Google Driver</a>][<a href="https://pan.baidu.com/s/1yGaNLeb9TG5-81t0skrOUA?pwd=f66n">Baidu Cloud</a>]</th>
+    <th> [<a href="../cfgs/AMT-S.yaml">cfgs/AMT-S</a>] </th>
+    <th>Vimeo90k</th>
+    <th>Fixed</th>
+  </tr>
+  <tr>
+    <td>AMT-L</td>
+    <th>[<a href="https://drive.google.com/file/d/1UyhYpAQLXMjFA55rlFZ0kdiSVTL7oU-z/view?usp=share_link">Google Driver</a>][<a href="https://pan.baidu.com/s/1qI4fBgS405Bd4Wn1R3Gbeg?pwd=nbne">Baidu Cloud</a>]</th>
+    <th> [<a href="../cfgs/AMT-L.yaml">cfgs/AMT-L</a>] </th>
+    <th>Vimeo90k</th>
+    <th>Fixed</th>
+  </tr>
+  <tr>
+    <td>AMT-G</td>
+    <th>[<a href="https://drive.google.com/file/d/1yieLtKh4ei3gOrLN1LhKSP_9157Q-mtP/view?usp=share_link">Google Driver</a>][<a href="https://pan.baidu.com/s/1AjmQVziQut1bXgQnDcDKvA?pwd=caf6">Baidu Cloud</a>]</th>
+    <th> [<a href="../cfgs/AMT-G.yaml">cfgs/AMT-G</a>] </th>
+    <th>Vimeo90k</th>
+    <th>Fixed</th>
+  </tr>
+  <tr>
+    <td>AMT-S</td>
+    <th>[<a href="https://drive.google.com/file/d/1f1xAF0EDm-rjDdny8_aLyeedfM0QL4-C/view?usp=share_link">Google Driver</a>][<a href="https://pan.baidu.com/s/1eZtoULyduQM8AkXeYEBOEw?pwd=8hy3">Baidu Cloud</a>]</th>
+    <th> [<a href="../cfgs/AMT-S_gopro.yaml">cfgs/AMT-S_gopro</a>] </th>
+    <th>GoPro</th>
+    <th>Arbitrary</th>
+  </tr>
+</tbody>
+</table>
+
+## Evaluation
+Before evaluation, you should:
+
+1. Check the dataroot is organized as follows:
+
+```shell
+./data
+├── Adobe240
+│   ├── original_high_fps_videos
+│   └── test_frames # using ffmpeg to extract 240 fps frames from `original_high_fps_videos`
+├── GOPRO
+│   ├── test
+│   └── train
+├── SNU_FILM
+│   ├── GOPRO_test
+│   ├── test-easy.txt
+│   ├── test-extreme.txt
+│   ├── test-hard.txt
+│   ├── test-medium.txt
+│   └── YouTube_test
+├── ucf101_interp_ours
+│   ├── 1
+│   ├── 1001
+│   └── ...
+└── vimeo_triplet
+    ├── readme.txt
+    ├── sequences
+    ├── tri_testlist.txt
+    └── tri_trainlist.txt
+```
+
+2. Download the provided [pretrained models](#pretrained-models).
+
+Then, you can perform evaluation as follows:
+
++ Run all benchmarks for fixed-time models.
+
+    ```shell
+    sh ./scripts/benchmark_fixed.sh [CFG] [CKPT_PATH]
+    ## e.g.
+    sh ./scripts/benchmark_fixed.sh cfgs/AMT-S.yaml pretrained/amt-s.pth
+    ```
+
++ Run all benchmarks for arbitrary-time models.
+
+    ```shell
+    sh ./scripts/benchmark_arbitrary.sh [CFG] [CKPT_PATH]
+    ## e.g.
+    sh ./scripts/benchmark_arbitrary.sh cfgs/AMT-S.yaml pretrained/gopro_amt-s.pth
+    ```
+
++ Run a single benchmark for fixed-time models. *You can custom data paths in this case*.
+
+    ```shell
+    python [BENCHMARK] -c [CFG] -p [CKPT_PATH] -r [DATAROOT]
+    ## e.g.
+    python benchmarks/vimeo90k.py -c cfgs/AMT-S.yaml -p pretrained/amt-s.pth -r data/vimeo_triplet
+    ```
+
++ Run the inference speed & model size comparisons using:
+
+    ```shell
+    python speed_parameters.py -c [CFG]
+    ## e.g.
+    python speed_parameters.py -c cfgs/AMT-S.yaml
+    ```
+
+
+## Training
+
+Before training, please first prepare the optical flows (which are used for supervision).
+
+We need to install `cupy` first before flow generation:
+
+```shell
+conda activate amt # satisfying `requirement.txt`
+conda install -c conda-forge cupy
+```
+
+
+After installing `cupy`, we can generate optical flows by the following command:
+
+```shell
+python flow_generation/gen_flow.py -r [DATA_ROOT]
+## e.g.
+python flow_generation/gen_flow.py -r data/vimeo_triplet
+```
+
+After obtaining the optical flow of the training data,
+run the following commands for training (DDP mode):
+
+```shell
+ sh ./scripts/train.sh [NUM_GPU] [CFG] [MASTER_PORT]
+ ## e.g.
+ sh ./scripts/train.sh 2 cfgs/AMT-S.yaml 14514
+```
+
+Our training configuration files are provided in [`cfgs`](../cfgs). Please carefully check the `dataset_dir` is suitable for you.
+
+
+Note:
+
+- If you intend to turn off DDP training, you can switch the key `distributed` from `true` 
+to `false` in the config file.
+
+- If you do not use wandb, you can switch the key `logger.use_wandb` from `true` 
+to `false` in the config file.

Helios-main/eval/utils/third_party/amt/docs/method.md

@@ -0,0 +1,126 @@
+# Illustration of AMT
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/21050959/229420451-65951bd0-732c-4f09-9121-f291a3862d6e.png" width="1200">
+</p>
+
+### :rocket: Highlights:
+
++ [**Good tradeoff**](#good-tradeoff) between performance and efficiency.
+
++ [**All-pairs correlation**](#all-pairs-correlation) for modeling large motions during interpolation.
+
++ A [**plug-and-play operator**](#multi-field-refinement) to improve the diversity of predicted task-oriented flows, further **boosting the interpolation performance**.
+
+
+## Good Tradeoff
+
+<p align="left">
+<img src="https://user-images.githubusercontent.com/21050959/229470703-2f386d62-d26c-46a3-af97-ddfc4270678a.png" width="500">
+</p>
+
+We examine the proposed AMT on several public benchmarks with different model scales, showing strong performance and high efficiency in contrast to the SOTA methods (see Figure). Our small model outperforms [IFRNet-B](https://arxiv.org/abs/2205.14620), a SOTA lightweight model, by **\+0.17dB PSNR** on Vimeo90K with **only 60% of its FLOPs and parameters**. For large-scale setting, our AMT exceeds the previous SOTA (i.e., [IFRNet-L](https://arxiv.org/abs/2205.14620)) by **+0.15 dB PSNR** on Vimeo90K with **75% of its FLOPs and 65% of its parameters**. Besides, we provide a huge model for comparison
+with the SOTA transformer-based method [VFIFormer](https://arxiv.org/abs/2205.07230). Our convolution-based AMT shows a **comparable performance** but only needs **nearly 23× less computational cost** compared to VFIFormer. 
+
+Considering its effectiveness, we hope our AMT could bring a new perspective for the architecture design in efficient frame interpolation.
+
+## All-pairs correlation
+
+We build all-pairs correlation to effectively model large motions during interpolation.
+
+Here is an example about the update operation at a single scale in AMT:
+
+```python
+  # Construct bidirectional correlation volumes
+  fmap0, fmap1 = self.feat_encoder([img0_, img1_]) # [B, C, H//8, W//8]
+  corr_fn = BidirCorrBlock(fmap0, fmap1, radius=self.radius, num_levels=self.corr_levels)
+  
+  # Correlation scaled lookup (bilateral -> bidirectional)
+  t1_scale = 1. / embt
+  t0_scale = 1. / (1. - embt)
+  coord = coords_grid(b, h // 8, w // 8, img0.device)
+  corr0, corr1 = corr_fn(coord + flow1 * t1_scale, coord + flow0 * t0_scale) 
+  corr = torch.cat([corr0, corr1], dim=1)
+  flow = torch.cat([flow0, flow1], dim=1)
+  
+  # Update both intermediate feature and bilateral flows
+  delta_feat, delta_flow = self.update(feat, flow, corr)
+  delta_flow0, delta_flow1 = torch.chunk(delta_flow, 2, 1)
+  flow0 = flow0 + delta_flow0
+  flow1= flow1 + delta_flow1
+  feat = feat + delta_feat
+
+```
+
+Note: we extend above operations to each pyramid scale (except for the last one), which guarantees the consistency of flows on the coarse scale.
+
+### ⏫ performance gain
+|                         | Vimeo 90k | Hard  | Extreme |
+|-------------------------|-----------|-------|---------|
+| Baseline                | 35.60     | 30.39 | 25.06   |
+| + All-pairs correlation | 35.97 (**+0.37**)  | 30.60 (**+0.21**) | 25.30 (**+0.24**)  |
+
+More ablations can be found in the [paper](https://arxiv.org/abs/2304.09790).
+
+## Multi-field Refinement
+
+For most frame interpolation methods which are based on backward warping, the common formulation for
+interpolating the final intermediate frame $I_{t}$ is:
+
+$I_{t} = M \odot \mathcal{W}(I_{0}, F_{t\rightarrow 0}) + (1 - M) \odot \mathcal{W}(I_{1}, F_{t\rightarrow 1}) + R$
+
+Above formualtion only utilizes **one set of** bilateral optical flows $F_{t\rightarrow 0}$ and $F_{t\rightarrow 1}$, occulusion masks $M$, and residuals $R$.
+
+Multi-field refinement aims to improve the common formulation of backward warping.
+Specifically, we first predict **multiple** bilateral optical flows (accompanied by the corresponding masks and residuals) through simply enlarging the output channels of the last decoder. 
+Then, we use aforementioned equation to genearate each interpolated candidate frame. Finally, we obtain the final interpolated frame through combining candidate frames using stacked convolutional layers.
+
+Please refer to [this code snippet](../networks/blocks/multi_flow.py#L46) for the details of the first step.
+Please refer to [this code snippet](../networks/blocks/multi_flow.py#L10) for the details of the last two steps.
+
+### 🌟 easy to use
+The proposed multi-field refinement can be **easily migrated to any frame interpolation model** to improve the performance.
+
+Code examples are shown below:
+
+```python
+
+# (At the __init__ stage) Initialize a decoder that predicts multiple flow fields (accompanied by the corresponding masks and residuals) 
+self.decoder1 = MultiFlowDecoder(channels[0], skip_channels, num_flows)
+...
+
+# (At the forward stage) Predict multiple flow fields (accompanied by the corresponding masks and residuals) 
+up_flow0_1, up_flow1_1, mask, img_res = self.decoder1(ft_1_, f0_1, f1_1, up_flow0_2, up_flow1_2)
+# Merge multiple predictions 
+imgt_pred = multi_flow_combine(self.comb_block, img0, img1, up_flow0_1, up_flow1_1,  # self.comb_block stacks two convolutional layers
+                                                            mask, img_res, mean_)
+
+```
+
+### ⏫ performance gain
+
+| # Number of flow pairs | Vimeo 90k     | Hard          | Extreme       |
+|------------------------|---------------|---------------|---------------|
+| Baseline (1 pair)      | 35.84         | 30.52         | 25.25         |
+| 3 pairs                | 35.97 (**+0.13**) | 30.60 (**+0.08**) | 25.30 (**+0.05**) |
+| 5 pairs                | 36.00 (**+0.16**) | 30.63 (**+0.11**) | 25.33 (**+0.08**) |
+
+## Comparison with SOTA methods
+<p align="left">
+<img src="https://user-images.githubusercontent.com/21050959/230716340-dea52895-1713-4857-97e5-48cdff9c478f.png" width="1200">
+</p>
+
+
+## Discussions 
+
+We encountered the challenges about the novelty issue during the rebuttal process.
+
+We are ready to clarify again here:
+
+1. We consider the estimation of task-oriented flows from **the perspective of architecture formulation rather than loss function designs** in previous works. The detailed analysis can be found in Sec. 1 of the main paper. We introduce all-pairs correlation to strengthen the ability
+in motion modeling, which guarantees **the consistency of flows on the coarse scale**. We employ multi-field refinement to **ensure diversity for the flow regions that need to be task-specific at the finest scale**. The two designs also enable our AMT to capture large motions and successfully handle occlusion regions with high efficiency. As a consequence, they both bring noticeable performance improvements, as shown in the ablations. 
+2. The frame interpolation task is closely related to the **motion modeling**. We strongly believe that a [RAFT-style](https://arxiv.org/abs/2003.12039) approach to motion modeling would be beneficial for the frame interpolation task. However, such style **has not been well studied** in the recent frame interpolation literature. Experimental results show that **all-pairs correlation is very important for the performance gain**. We also involve many novel and task-specific designs
+beyond the original RAFT. For other task-related design choices, our volume design, scaled lookup strategy, content update, and cross-scale update way have good performance gains on challenging cases (i.e., Hard and Extreme). Besides, if we discard all design choices (but remaining multi-field refinement) and follow the original RAFT to retrain a new model, **the PSNR values will dramatically decrease** (-0.20dB on Vimeo, -0.33dB on Hard, and -0.39dB on Extreme). 
+3.  [M2M-VFI](https://arxiv.org/abs/2204.03513) is the most relevant to our multi-field refinement. It also generates multiple flows through the decoder and prepares warped candidates in the image domain. However, there are **five key differences** between our multi-field refinement and M2M-VFI. **First**, our method generates the candidate frames by backward warping rather than forward warping in M2M-VFI. The proposed multi-field refinement aims to improve the common formulation of backward warping (see Eqn.~(4) in the main paper). **Second**, while M2M-VFI predicts multiple flows to overcome the hole issue and artifacts in overlapped regions caused by forward warping, we aim to alleviate the ambiguity issue in the occluded areas and motion boundaries by enhancing the diversity of flows. **Third**, M2M-VFI needs to estimate bidirectional flows first through an off-the-shelf optical flow estimator and then predict multiple bilateral flows through a motion refinement network. On the contrary, we directly estimate multiple bilateral flows in a one-stage network. In this network, we first estimate one pair of bilateral flows at the coarse scale and then derive multiple groups of fine-grained bilateral flows from the coarse flow pairs. **Fourth**, M2M-VFI jointly estimates two reliability maps together with all pairs of bilateral flows, which can be further used to fuse the overlapping pixels caused by forward warping. As shown in Eqn. (5) of the main paper, we estimate not only an occlusion mask but a residual content for cooperating with each pair of bilateral flows. The residual content is used to compensate for the unreliable details after warping. This design has been investigated in Tab. 2e of the main paper. **Fifth**, we stack two convolutional layers to adaptively merge candidate frames, while M2M-VFI normalizes the sum of all candidate frames through a pre-computed weighting map 
+
+More discussions and details can be found in the [appendix](https://arxiv.org/abs/2304.09790) of our paper.

Helios-main/eval/utils/third_party/amt/environment.yaml

@@ -0,0 +1,19 @@
+name: amt
+channels:
+  - pytorch
+  - conda-forge
+  - defaults
+dependencies:
+  - python=3.8.5
+  - pip=20.3
+  - cudatoolkit=11.3
+  - pytorch=1.11.0
+  - torchvision=0.12.0
+  - numpy=1.21.5
+  - pip:
+    - opencv-python==4.1.2.30
+    - imageio==2.19.3
+    - omegaconf==2.3.0
+    - Pillow==9.4.0
+    - tqdm==4.64.1
+    - wandb==0.12.21

Helios-main/eval/utils/third_party/amt/losses/loss.py

@@ -0,0 +1,201 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Loss(nn.Module):
+    def __init__(self, loss_weight, keys, mapping=None) -> None:
+        """
+        mapping: map the kwargs keys into desired ones.
+        """
+        super().__init__()
+        self.loss_weight = loss_weight
+        self.keys = keys
+        self.mapping = mapping
+        if isinstance(mapping, dict):
+            self.mapping = {k: v for k, v in mapping if v in keys}
+
+    def forward(self, **kwargs):
+        params = {k: v for k, v in kwargs.items() if k in self.keys}
+        if self.mapping is not None:
+            for k, v in kwargs.items():
+                if self.mapping.get(k) is not None:
+                    params[self.mapping[k]] = v
+
+        return self._forward(**params) * self.loss_weight
+
+    def _forward(self, **kwargs):
+        pass
+
+
+class CharbonnierLoss(Loss):
+    def __init__(self, loss_weight, keys) -> None:
+        super().__init__(loss_weight, keys)
+
+    def _forward(self, imgt_pred, imgt):
+        diff = imgt_pred - imgt
+        loss = ((diff**2 + 1e-6) ** 0.5).mean()
+        return loss
+
+
+class AdaCharbonnierLoss(Loss):
+    def __init__(self, loss_weight, keys) -> None:
+        super().__init__(loss_weight, keys)
+
+    def _forward(self, imgt_pred, imgt, weight):
+        alpha = weight / 2
+        epsilon = 10 ** (-(10 * weight - 1) / 3)
+
+        diff = imgt_pred - imgt
+        loss = ((diff**2 + epsilon**2) ** alpha).mean()
+        return loss
+
+
+class TernaryLoss(Loss):
+    def __init__(self, loss_weight, keys, patch_size=7):
+        super().__init__(loss_weight, keys)
+        self.patch_size = patch_size
+        out_channels = patch_size * patch_size
+        self.w = np.eye(out_channels).reshape((patch_size, patch_size, 1, out_channels))
+        self.w = np.transpose(self.w, (3, 2, 0, 1))
+        self.w = torch.tensor(self.w, dtype=torch.float32)
+
+    def transform(self, tensor):
+        self.w = self.w.to(tensor.device)
+        tensor_ = tensor.mean(dim=1, keepdim=True)
+        patches = F.conv2d(tensor_, self.w, padding=self.patch_size // 2, bias=None)
+        loc_diff = patches - tensor_
+        loc_diff_norm = loc_diff / torch.sqrt(0.81 + loc_diff**2)
+        return loc_diff_norm
+
+    def valid_mask(self, tensor):
+        padding = self.patch_size // 2
+        b, c, h, w = tensor.size()
+        inner = torch.ones(b, 1, h - 2 * padding, w - 2 * padding).type_as(tensor)
+        mask = F.pad(inner, [padding] * 4)
+        return mask
+
+    def _forward(self, imgt_pred, imgt):
+        loc_diff_x = self.transform(imgt_pred)
+        loc_diff_y = self.transform(imgt)
+        diff = loc_diff_x - loc_diff_y.detach()
+        dist = (diff**2 / (0.1 + diff**2)).mean(dim=1, keepdim=True)
+        mask = self.valid_mask(imgt_pred)
+        loss = (dist * mask).mean()
+        return loss
+
+
+class GeometryLoss(Loss):
+    def __init__(self, loss_weight, keys, patch_size=3):
+        super().__init__(loss_weight, keys)
+        self.patch_size = patch_size
+        out_channels = patch_size * patch_size
+        self.w = np.eye(out_channels).reshape((patch_size, patch_size, 1, out_channels))
+        self.w = np.transpose(self.w, (3, 2, 0, 1))
+        self.w = torch.tensor(self.w).float()
+
+    def transform(self, tensor):
+        b, c, h, w = tensor.size()
+        self.w = self.w.to(tensor.device)
+        tensor_ = tensor.reshape(b * c, 1, h, w)
+        patches = F.conv2d(tensor_, self.w, padding=self.patch_size // 2, bias=None)
+        loc_diff = patches - tensor_
+        loc_diff_ = loc_diff.reshape(b, c * (self.patch_size**2), h, w)
+        loc_diff_norm = loc_diff_ / torch.sqrt(0.81 + loc_diff_**2)
+        return loc_diff_norm
+
+    def valid_mask(self, tensor):
+        padding = self.patch_size // 2
+        b, c, h, w = tensor.size()
+        inner = torch.ones(b, 1, h - 2 * padding, w - 2 * padding).type_as(tensor)
+        mask = F.pad(inner, [padding] * 4)
+        return mask
+
+    def _forward(self, ft_pred, ft_gt):
+        loss = 0.0
+        for pred, gt in zip(ft_pred, ft_gt):
+            loc_diff_x = self.transform(pred)
+            loc_diff_y = self.transform(gt)
+            diff = loc_diff_x - loc_diff_y
+            dist = (diff**2 / (0.1 + diff**2)).mean(dim=1, keepdim=True)
+            mask = self.valid_mask(pred)
+            loss = loss + (dist * mask).mean()
+        return loss
+
+
+class IFRFlowLoss(Loss):
+    def __init__(self, loss_weight, keys, beta=0.3) -> None:
+        super().__init__(loss_weight, keys)
+        self.beta = beta
+        self.ada_cb_loss = AdaCharbonnierLoss(1.0, ["imgt_pred", "imgt", "weight"])
+
+    def _forward(self, flow0_pred, flow1_pred, flow):
+        robust_weight0 = self.get_robust_weight(flow0_pred[0], flow[:, 0:2])
+        robust_weight1 = self.get_robust_weight(flow1_pred[0], flow[:, 2:4])
+        loss = 0
+        for lvl in range(1, len(flow0_pred)):
+            scale_factor = 2**lvl
+            loss = loss + self.ada_cb_loss(
+                **{
+                    "imgt_pred": self.resize(flow0_pred[lvl], scale_factor),
+                    "imgt": flow[:, 0:2],
+                    "weight": robust_weight0,
+                }
+            )
+            loss = loss + self.ada_cb_loss(
+                **{
+                    "imgt_pred": self.resize(flow1_pred[lvl], scale_factor),
+                    "imgt": flow[:, 2:4],
+                    "weight": robust_weight1,
+                }
+            )
+        return loss
+
+    def resize(self, x, scale_factor):
+        return scale_factor * F.interpolate(x, scale_factor=scale_factor, mode="bilinear", align_corners=False)
+
+    def get_robust_weight(self, flow_pred, flow_gt):
+        epe = ((flow_pred.detach() - flow_gt) ** 2).sum(dim=1, keepdim=True) ** 0.5
+        robust_weight = torch.exp(-self.beta * epe)
+        return robust_weight
+
+
+class MultipleFlowLoss(Loss):
+    def __init__(self, loss_weight, keys, beta=0.3) -> None:
+        super().__init__(loss_weight, keys)
+        self.beta = beta
+        self.ada_cb_loss = AdaCharbonnierLoss(1.0, ["imgt_pred", "imgt", "weight"])
+
+    def _forward(self, flow0_pred, flow1_pred, flow):
+        robust_weight0 = self.get_mutli_flow_robust_weight(flow0_pred[0], flow[:, 0:2])
+        robust_weight1 = self.get_mutli_flow_robust_weight(flow1_pred[0], flow[:, 2:4])
+        loss = 0
+        for lvl in range(1, len(flow0_pred)):
+            scale_factor = 2**lvl
+            loss = loss + self.ada_cb_loss(
+                **{
+                    "imgt_pred": self.resize(flow0_pred[lvl], scale_factor),
+                    "imgt": flow[:, 0:2],
+                    "weight": robust_weight0,
+                }
+            )
+            loss = loss + self.ada_cb_loss(
+                **{
+                    "imgt_pred": self.resize(flow1_pred[lvl], scale_factor),
+                    "imgt": flow[:, 2:4],
+                    "weight": robust_weight1,
+                }
+            )
+        return loss
+
+    def resize(self, x, scale_factor):
+        return scale_factor * F.interpolate(x, scale_factor=scale_factor, mode="bilinear", align_corners=False)
+
+    def get_mutli_flow_robust_weight(self, flow_pred, flow_gt):
+        b, num_flows, c, h, w = flow_pred.shape
+        flow_pred = flow_pred.view(b, num_flows, c, h, w)
+        flow_gt = flow_gt.repeat(1, num_flows, 1, 1).view(b, num_flows, c, h, w)
+        epe = ((flow_pred.detach() - flow_gt) ** 2).sum(dim=2, keepdim=True).max(1)[0] ** 0.5
+        robust_weight = torch.exp(-self.beta * epe)
+        return robust_weight

Helios-main/eval/utils/third_party/amt/metrics/psnr_ssim.py

@@ -0,0 +1,142 @@
+from math import exp
+
+import torch
+import torch.nn.functional as F
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def gaussian(window_size, sigma):
+    gauss = torch.Tensor([exp(-((x - window_size // 2) ** 2) / float(2 * sigma**2)) for x in range(window_size)])
+    return gauss / gauss.sum()
+
+
+def create_window(window_size, channel=1):
+    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
+    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0).to(device)
+    window = _2D_window.expand(channel, 1, window_size, window_size).contiguous()
+    return window
+
+
+def create_window_3d(window_size, channel=1):
+    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
+    _2D_window = _1D_window.mm(_1D_window.t())
+    _3D_window = _2D_window.unsqueeze(2) @ (_1D_window.t())
+    window = _3D_window.expand(1, channel, window_size, window_size, window_size).contiguous().to(device)
+    return window
+
+
+def ssim(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None):
+    if val_range is None:
+        if torch.max(img1) > 128:
+            max_val = 255
+        else:
+            max_val = 1
+
+        if torch.min(img1) < -0.5:
+            min_val = -1
+        else:
+            min_val = 0
+        L = max_val - min_val
+    else:
+        L = val_range
+
+    padd = 0
+    (_, channel, height, width) = img1.size()
+    if window is None:
+        real_size = min(window_size, height, width)
+        window = create_window(real_size, channel=channel).to(img1.device)
+
+    mu1 = F.conv2d(F.pad(img1, (5, 5, 5, 5), mode="replicate"), window, padding=padd, groups=channel)
+    mu2 = F.conv2d(F.pad(img2, (5, 5, 5, 5), mode="replicate"), window, padding=padd, groups=channel)
+
+    mu1_sq = mu1.pow(2)
+    mu2_sq = mu2.pow(2)
+    mu1_mu2 = mu1 * mu2
+
+    sigma1_sq = F.conv2d(F.pad(img1 * img1, (5, 5, 5, 5), "replicate"), window, padding=padd, groups=channel) - mu1_sq
+    sigma2_sq = F.conv2d(F.pad(img2 * img2, (5, 5, 5, 5), "replicate"), window, padding=padd, groups=channel) - mu2_sq
+    sigma12 = F.conv2d(F.pad(img1 * img2, (5, 5, 5, 5), "replicate"), window, padding=padd, groups=channel) - mu1_mu2
+
+    C1 = (0.01 * L) ** 2
+    C2 = (0.03 * L) ** 2
+
+    v1 = 2.0 * sigma12 + C2
+    v2 = sigma1_sq + sigma2_sq + C2
+    cs = torch.mean(v1 / v2)
+
+    ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2)
+
+    if size_average:
+        ret = ssim_map.mean()
+    else:
+        ret = ssim_map.mean(1).mean(1).mean(1)
+
+    if full:
+        return ret, cs
+    return ret
+
+
+def calculate_ssim(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None):
+    if val_range is None:
+        if torch.max(img1) > 128:
+            max_val = 255
+        else:
+            max_val = 1
+
+        if torch.min(img1) < -0.5:
+            min_val = -1
+        else:
+            min_val = 0
+        L = max_val - min_val
+    else:
+        L = val_range
+
+    padd = 0
+    (_, _, height, width) = img1.size()
+    if window is None:
+        real_size = min(window_size, height, width)
+        window = create_window_3d(real_size, channel=1).to(img1.device)
+
+    img1 = img1.unsqueeze(1)
+    img2 = img2.unsqueeze(1)
+
+    mu1 = F.conv3d(F.pad(img1, (5, 5, 5, 5, 5, 5), mode="replicate"), window, padding=padd, groups=1)
+    mu2 = F.conv3d(F.pad(img2, (5, 5, 5, 5, 5, 5), mode="replicate"), window, padding=padd, groups=1)
+
+    mu1_sq = mu1.pow(2)
+    mu2_sq = mu2.pow(2)
+    mu1_mu2 = mu1 * mu2
+
+    sigma1_sq = F.conv3d(F.pad(img1 * img1, (5, 5, 5, 5, 5, 5), "replicate"), window, padding=padd, groups=1) - mu1_sq
+    sigma2_sq = F.conv3d(F.pad(img2 * img2, (5, 5, 5, 5, 5, 5), "replicate"), window, padding=padd, groups=1) - mu2_sq
+    sigma12 = F.conv3d(F.pad(img1 * img2, (5, 5, 5, 5, 5, 5), "replicate"), window, padding=padd, groups=1) - mu1_mu2
+
+    C1 = (0.01 * L) ** 2
+    C2 = (0.03 * L) ** 2
+
+    v1 = 2.0 * sigma12 + C2
+    v2 = sigma1_sq + sigma2_sq + C2
+    cs = torch.mean(v1 / v2)
+
+    ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2)
+
+    if size_average:
+        ret = ssim_map.mean()
+    else:
+        ret = ssim_map.mean(1).mean(1).mean(1)
+
+    if full:
+        return ret, cs
+    return ret.detach().cpu().numpy()
+
+
+def calculate_psnr(img1, img2):
+    psnr = -10 * torch.log10(((img1 - img2) * (img1 - img2)).mean())
+    return psnr.detach().cpu().numpy()
+
+
+def calculate_ie(img1, img2):
+    ie = torch.abs(torch.round(img1 * 255.0) - torch.round(img2 * 255.0)).mean()
+    return ie.detach().cpu().numpy()

Helios-main/eval/utils/third_party/amt/networks/AMT-G.py

@@ -0,0 +1,156 @@
+import torch
+import torch.nn as nn
+
+from utils.third_party.amt.networks.blocks.feat_enc import LargeEncoder
+from utils.third_party.amt.networks.blocks.ifrnet import Encoder, InitDecoder, IntermediateDecoder, resize
+from utils.third_party.amt.networks.blocks.multi_flow import MultiFlowDecoder, multi_flow_combine
+from utils.third_party.amt.networks.blocks.raft import BasicUpdateBlock, BidirCorrBlock, coords_grid
+
+
+class Model(nn.Module):
+    def __init__(self, corr_radius=3, corr_lvls=4, num_flows=5, channels=[84, 96, 112, 128], skip_channels=84):
+        super(Model, self).__init__()
+        self.radius = corr_radius
+        self.corr_levels = corr_lvls
+        self.num_flows = num_flows
+
+        self.feat_encoder = LargeEncoder(output_dim=128, norm_fn="instance", dropout=0.0)
+        self.encoder = Encoder(channels, large=True)
+        self.decoder4 = InitDecoder(channels[3], channels[2], skip_channels)
+        self.decoder3 = IntermediateDecoder(channels[2], channels[1], skip_channels)
+        self.decoder2 = IntermediateDecoder(channels[1], channels[0], skip_channels)
+        self.decoder1 = MultiFlowDecoder(channels[0], skip_channels, num_flows)
+
+        self.update4 = self._get_updateblock(112, None)
+        self.update3_low = self._get_updateblock(96, 2.0)
+        self.update2_low = self._get_updateblock(84, 4.0)
+
+        self.update3_high = self._get_updateblock(96, None)
+        self.update2_high = self._get_updateblock(84, None)
+
+        self.comb_block = nn.Sequential(
+            nn.Conv2d(3 * self.num_flows, 6 * self.num_flows, 7, 1, 3),
+            nn.PReLU(6 * self.num_flows),
+            nn.Conv2d(6 * self.num_flows, 3, 7, 1, 3),
+        )
+
+    def _get_updateblock(self, cdim, scale_factor=None):
+        return BasicUpdateBlock(
+            cdim=cdim,
+            hidden_dim=192,
+            flow_dim=64,
+            corr_dim=256,
+            corr_dim2=192,
+            fc_dim=188,
+            scale_factor=scale_factor,
+            corr_levels=self.corr_levels,
+            radius=self.radius,
+        )
+
+    def _corr_scale_lookup(self, corr_fn, coord, flow0, flow1, embt, downsample=1):
+        # convert t -> 0 to 0 -> 1 | convert t -> 1 to 1 -> 0
+        # based on linear assumption
+        t1_scale = 1.0 / embt
+        t0_scale = 1.0 / (1.0 - embt)
+        if downsample != 1:
+            inv = 1 / downsample
+            flow0 = inv * resize(flow0, scale_factor=inv)
+            flow1 = inv * resize(flow1, scale_factor=inv)
+
+        corr0, corr1 = corr_fn(coord + flow1 * t1_scale, coord + flow0 * t0_scale)
+        corr = torch.cat([corr0, corr1], dim=1)
+        flow = torch.cat([flow0, flow1], dim=1)
+        return corr, flow
+
+    def forward(self, img0, img1, embt, scale_factor=1.0, eval=False, **kwargs):
+        mean_ = torch.cat([img0, img1], 2).mean(1, keepdim=True).mean(2, keepdim=True).mean(3, keepdim=True)
+        img0 = img0 - mean_
+        img1 = img1 - mean_
+        img0_ = resize(img0, scale_factor) if scale_factor != 1.0 else img0
+        img1_ = resize(img1, scale_factor) if scale_factor != 1.0 else img1
+        b, _, h, w = img0_.shape
+        coord = coords_grid(b, h // 8, w // 8, img0.device)
+
+        fmap0, fmap1 = self.feat_encoder([img0_, img1_])  # [1, 128, H//8, W//8]
+        corr_fn = BidirCorrBlock(fmap0, fmap1, radius=self.radius, num_levels=self.corr_levels)
+
+        # f0_1: [1, c0, H//2, W//2] | f0_2: [1, c1, H//4, W//4]
+        # f0_3: [1, c2, H//8, W//8] | f0_4: [1, c3, H//16, W//16]
+        f0_1, f0_2, f0_3, f0_4 = self.encoder(img0_)
+        f1_1, f1_2, f1_3, f1_4 = self.encoder(img1_)
+
+        ######################################### the 4th decoder #########################################
+        up_flow0_4, up_flow1_4, ft_3_ = self.decoder4(f0_4, f1_4, embt)
+        corr_4, flow_4 = self._corr_scale_lookup(corr_fn, coord, up_flow0_4, up_flow1_4, embt, downsample=1)
+
+        # residue update with lookup corr
+        delta_ft_3_, delta_flow_4 = self.update4(ft_3_, flow_4, corr_4)
+        delta_flow0_4, delta_flow1_4 = torch.chunk(delta_flow_4, 2, 1)
+        up_flow0_4 = up_flow0_4 + delta_flow0_4
+        up_flow1_4 = up_flow1_4 + delta_flow1_4
+        ft_3_ = ft_3_ + delta_ft_3_
+
+        ######################################### the 3rd decoder #########################################
+        up_flow0_3, up_flow1_3, ft_2_ = self.decoder3(ft_3_, f0_3, f1_3, up_flow0_4, up_flow1_4)
+        corr_3, flow_3 = self._corr_scale_lookup(corr_fn, coord, up_flow0_3, up_flow1_3, embt, downsample=2)
+
+        # residue update with lookup corr
+        delta_ft_2_, delta_flow_3 = self.update3_low(ft_2_, flow_3, corr_3)
+        delta_flow0_3, delta_flow1_3 = torch.chunk(delta_flow_3, 2, 1)
+        up_flow0_3 = up_flow0_3 + delta_flow0_3
+        up_flow1_3 = up_flow1_3 + delta_flow1_3
+        ft_2_ = ft_2_ + delta_ft_2_
+
+        # residue update with lookup corr (hr)
+        corr_3 = resize(corr_3, scale_factor=2.0)
+        up_flow_3 = torch.cat([up_flow0_3, up_flow1_3], dim=1)
+        delta_ft_2_, delta_up_flow_3 = self.update3_high(ft_2_, up_flow_3, corr_3)
+        ft_2_ += delta_ft_2_
+        up_flow0_3 += delta_up_flow_3[:, 0:2]
+        up_flow1_3 += delta_up_flow_3[:, 2:4]
+
+        ######################################### the 2nd decoder #########################################
+        up_flow0_2, up_flow1_2, ft_1_ = self.decoder2(ft_2_, f0_2, f1_2, up_flow0_3, up_flow1_3)
+        corr_2, flow_2 = self._corr_scale_lookup(corr_fn, coord, up_flow0_2, up_flow1_2, embt, downsample=4)
+
+        # residue update with lookup corr
+        delta_ft_1_, delta_flow_2 = self.update2_low(ft_1_, flow_2, corr_2)
+        delta_flow0_2, delta_flow1_2 = torch.chunk(delta_flow_2, 2, 1)
+        up_flow0_2 = up_flow0_2 + delta_flow0_2
+        up_flow1_2 = up_flow1_2 + delta_flow1_2
+        ft_1_ = ft_1_ + delta_ft_1_
+
+        # residue update with lookup corr (hr)
+        corr_2 = resize(corr_2, scale_factor=4.0)
+        up_flow_2 = torch.cat([up_flow0_2, up_flow1_2], dim=1)
+        delta_ft_1_, delta_up_flow_2 = self.update2_high(ft_1_, up_flow_2, corr_2)
+        ft_1_ += delta_ft_1_
+        up_flow0_2 += delta_up_flow_2[:, 0:2]
+        up_flow1_2 += delta_up_flow_2[:, 2:4]
+
+        ######################################### the 1st decoder #########################################
+        up_flow0_1, up_flow1_1, mask, img_res = self.decoder1(ft_1_, f0_1, f1_1, up_flow0_2, up_flow1_2)
+
+        if scale_factor != 1.0:
+            up_flow0_1 = resize(up_flow0_1, scale_factor=(1.0 / scale_factor)) * (1.0 / scale_factor)
+            up_flow1_1 = resize(up_flow1_1, scale_factor=(1.0 / scale_factor)) * (1.0 / scale_factor)
+            mask = resize(mask, scale_factor=(1.0 / scale_factor))
+            img_res = resize(img_res, scale_factor=(1.0 / scale_factor))
+
+        # Merge multiple predictions
+        imgt_pred = multi_flow_combine(self.comb_block, img0, img1, up_flow0_1, up_flow1_1, mask, img_res, mean_)
+        imgt_pred = torch.clamp(imgt_pred, 0, 1)
+
+        if eval:
+            return {
+                "imgt_pred": imgt_pred,
+            }
+        else:
+            up_flow0_1 = up_flow0_1.reshape(b, self.num_flows, 2, h, w)
+            up_flow1_1 = up_flow1_1.reshape(b, self.num_flows, 2, h, w)
+            return {
+                "imgt_pred": imgt_pred,
+                "flow0_pred": [up_flow0_1, up_flow0_2, up_flow0_3, up_flow0_4],
+                "flow1_pred": [up_flow1_1, up_flow1_2, up_flow1_3, up_flow1_4],
+                "ft_pred": [ft_1_, ft_2_, ft_3_],
+            }

Helios-main/eval/utils/third_party/amt/networks/AMT-L.py

@@ -0,0 +1,140 @@
+import torch
+import torch.nn as nn
+
+from utils.third_party.amt.networks.blocks.feat_enc import (
+    BasicEncoder,
+)
+from utils.third_party.amt.networks.blocks.ifrnet import Encoder, InitDecoder, IntermediateDecoder, resize
+from utils.third_party.amt.networks.blocks.multi_flow import MultiFlowDecoder, multi_flow_combine
+from utils.third_party.amt.networks.blocks.raft import BasicUpdateBlock, BidirCorrBlock, coords_grid
+
+
+class Model(nn.Module):
+    def __init__(self, corr_radius=3, corr_lvls=4, num_flows=5, channels=[48, 64, 72, 128], skip_channels=48):
+        super(Model, self).__init__()
+        self.radius = corr_radius
+        self.corr_levels = corr_lvls
+        self.num_flows = num_flows
+
+        self.feat_encoder = BasicEncoder(output_dim=128, norm_fn="instance", dropout=0.0)
+        self.encoder = Encoder([48, 64, 72, 128], large=True)
+
+        self.decoder4 = InitDecoder(channels[3], channels[2], skip_channels)
+        self.decoder3 = IntermediateDecoder(channels[2], channels[1], skip_channels)
+        self.decoder2 = IntermediateDecoder(channels[1], channels[0], skip_channels)
+        self.decoder1 = MultiFlowDecoder(channels[0], skip_channels, num_flows)
+
+        self.update4 = self._get_updateblock(72, None)
+        self.update3 = self._get_updateblock(64, 2.0)
+        self.update2 = self._get_updateblock(48, 4.0)
+
+        self.comb_block = nn.Sequential(
+            nn.Conv2d(3 * self.num_flows, 6 * self.num_flows, 7, 1, 3),
+            nn.PReLU(6 * self.num_flows),
+            nn.Conv2d(6 * self.num_flows, 3, 7, 1, 3),
+        )
+
+    def _get_updateblock(self, cdim, scale_factor=None):
+        return BasicUpdateBlock(
+            cdim=cdim,
+            hidden_dim=128,
+            flow_dim=48,
+            corr_dim=256,
+            corr_dim2=160,
+            fc_dim=124,
+            scale_factor=scale_factor,
+            corr_levels=self.corr_levels,
+            radius=self.radius,
+        )
+
+    def _corr_scale_lookup(self, corr_fn, coord, flow0, flow1, embt, downsample=1):
+        # convert t -> 0 to 0 -> 1 | convert t -> 1 to 1 -> 0
+        # based on linear assumption
+        t1_scale = 1.0 / embt
+        t0_scale = 1.0 / (1.0 - embt)
+        if downsample != 1:
+            inv = 1 / downsample
+            flow0 = inv * resize(flow0, scale_factor=inv)
+            flow1 = inv * resize(flow1, scale_factor=inv)
+
+        corr0, corr1 = corr_fn(coord + flow1 * t1_scale, coord + flow0 * t0_scale)
+        corr = torch.cat([corr0, corr1], dim=1)
+        flow = torch.cat([flow0, flow1], dim=1)
+        return corr, flow
+
+    def forward(self, img0, img1, embt, scale_factor=1.0, eval=False, **kwargs):
+        mean_ = torch.cat([img0, img1], 2).mean(1, keepdim=True).mean(2, keepdim=True).mean(3, keepdim=True)
+        img0 = img0 - mean_
+        img1 = img1 - mean_
+        img0_ = resize(img0, scale_factor) if scale_factor != 1.0 else img0
+        img1_ = resize(img1, scale_factor) if scale_factor != 1.0 else img1
+        b, _, h, w = img0_.shape
+        coord = coords_grid(b, h // 8, w // 8, img0.device)
+
+        fmap0, fmap1 = self.feat_encoder([img0_, img1_])  # [1, 128, H//8, W//8]
+        corr_fn = BidirCorrBlock(fmap0, fmap1, radius=self.radius, num_levels=self.corr_levels)
+
+        # f0_1: [1, c0, H//2, W//2] | f0_2: [1, c1, H//4, W//4]
+        # f0_3: [1, c2, H//8, W//8] | f0_4: [1, c3, H//16, W//16]
+        f0_1, f0_2, f0_3, f0_4 = self.encoder(img0_)
+        f1_1, f1_2, f1_3, f1_4 = self.encoder(img1_)
+
+        ######################################### the 4th decoder #########################################
+        up_flow0_4, up_flow1_4, ft_3_ = self.decoder4(f0_4, f1_4, embt)
+        corr_4, flow_4 = self._corr_scale_lookup(corr_fn, coord, up_flow0_4, up_flow1_4, embt, downsample=1)
+
+        # residue update with lookup corr
+        delta_ft_3_, delta_flow_4 = self.update4(ft_3_, flow_4, corr_4)
+        delta_flow0_4, delta_flow1_4 = torch.chunk(delta_flow_4, 2, 1)
+        up_flow0_4 = up_flow0_4 + delta_flow0_4
+        up_flow1_4 = up_flow1_4 + delta_flow1_4
+        ft_3_ = ft_3_ + delta_ft_3_
+
+        ######################################### the 3rd decoder #########################################
+        up_flow0_3, up_flow1_3, ft_2_ = self.decoder3(ft_3_, f0_3, f1_3, up_flow0_4, up_flow1_4)
+        corr_3, flow_3 = self._corr_scale_lookup(corr_fn, coord, up_flow0_3, up_flow1_3, embt, downsample=2)
+
+        # residue update with lookup corr
+        delta_ft_2_, delta_flow_3 = self.update3(ft_2_, flow_3, corr_3)
+        delta_flow0_3, delta_flow1_3 = torch.chunk(delta_flow_3, 2, 1)
+        up_flow0_3 = up_flow0_3 + delta_flow0_3
+        up_flow1_3 = up_flow1_3 + delta_flow1_3
+        ft_2_ = ft_2_ + delta_ft_2_
+
+        ######################################### the 2nd decoder #########################################
+        up_flow0_2, up_flow1_2, ft_1_ = self.decoder2(ft_2_, f0_2, f1_2, up_flow0_3, up_flow1_3)
+        corr_2, flow_2 = self._corr_scale_lookup(corr_fn, coord, up_flow0_2, up_flow1_2, embt, downsample=4)
+
+        # residue update with lookup corr
+        delta_ft_1_, delta_flow_2 = self.update2(ft_1_, flow_2, corr_2)
+        delta_flow0_2, delta_flow1_2 = torch.chunk(delta_flow_2, 2, 1)
+        up_flow0_2 = up_flow0_2 + delta_flow0_2
+        up_flow1_2 = up_flow1_2 + delta_flow1_2
+        ft_1_ = ft_1_ + delta_ft_1_
+
+        ######################################### the 1st decoder #########################################
+        up_flow0_1, up_flow1_1, mask, img_res = self.decoder1(ft_1_, f0_1, f1_1, up_flow0_2, up_flow1_2)
+
+        if scale_factor != 1.0:
+            up_flow0_1 = resize(up_flow0_1, scale_factor=(1.0 / scale_factor)) * (1.0 / scale_factor)
+            up_flow1_1 = resize(up_flow1_1, scale_factor=(1.0 / scale_factor)) * (1.0 / scale_factor)
+            mask = resize(mask, scale_factor=(1.0 / scale_factor))
+            img_res = resize(img_res, scale_factor=(1.0 / scale_factor))
+
+        # Merge multiple predictions
+        imgt_pred = multi_flow_combine(self.comb_block, img0, img1, up_flow0_1, up_flow1_1, mask, img_res, mean_)
+        imgt_pred = torch.clamp(imgt_pred, 0, 1)
+
+        if eval:
+            return {
+                "imgt_pred": imgt_pred,
+            }
+        else:
+            up_flow0_1 = up_flow0_1.reshape(b, self.num_flows, 2, h, w)
+            up_flow1_1 = up_flow1_1.reshape(b, self.num_flows, 2, h, w)
+            return {
+                "imgt_pred": imgt_pred,
+                "flow0_pred": [up_flow0_1, up_flow0_2, up_flow0_3, up_flow0_4],
+                "flow1_pred": [up_flow1_1, up_flow1_2, up_flow1_3, up_flow1_4],
+                "ft_pred": [ft_1_, ft_2_, ft_3_],
+            }

Helios-main/eval/utils/third_party/amt/networks/AMT-S.py

@@ -0,0 +1,139 @@
+import torch
+import torch.nn as nn
+
+from utils.third_party.amt.networks.blocks.feat_enc import SmallEncoder
+from utils.third_party.amt.networks.blocks.ifrnet import Encoder, InitDecoder, IntermediateDecoder, resize
+from utils.third_party.amt.networks.blocks.multi_flow import MultiFlowDecoder, multi_flow_combine
+from utils.third_party.amt.networks.blocks.raft import BidirCorrBlock, SmallUpdateBlock, coords_grid
+
+
+class Model(nn.Module):
+    def __init__(self, corr_radius=3, corr_lvls=4, num_flows=3, channels=[20, 32, 44, 56], skip_channels=20):
+        super(Model, self).__init__()
+        self.radius = corr_radius
+        self.corr_levels = corr_lvls
+        self.num_flows = num_flows
+        self.channels = channels
+        self.skip_channels = skip_channels
+
+        self.feat_encoder = SmallEncoder(output_dim=84, norm_fn="instance", dropout=0.0)
+        self.encoder = Encoder(channels)
+
+        self.decoder4 = InitDecoder(channels[3], channels[2], skip_channels)
+        self.decoder3 = IntermediateDecoder(channels[2], channels[1], skip_channels)
+        self.decoder2 = IntermediateDecoder(channels[1], channels[0], skip_channels)
+        self.decoder1 = MultiFlowDecoder(channels[0], skip_channels, num_flows)
+
+        self.update4 = self._get_updateblock(44)
+        self.update3 = self._get_updateblock(32, 2)
+        self.update2 = self._get_updateblock(20, 4)
+
+        self.comb_block = nn.Sequential(
+            nn.Conv2d(3 * num_flows, 6 * num_flows, 3, 1, 1),
+            nn.PReLU(6 * num_flows),
+            nn.Conv2d(6 * num_flows, 3, 3, 1, 1),
+        )
+
+    def _get_updateblock(self, cdim, scale_factor=None):
+        return SmallUpdateBlock(
+            cdim=cdim,
+            hidden_dim=76,
+            flow_dim=20,
+            corr_dim=64,
+            fc_dim=68,
+            scale_factor=scale_factor,
+            corr_levels=self.corr_levels,
+            radius=self.radius,
+        )
+
+    def _corr_scale_lookup(self, corr_fn, coord, flow0, flow1, embt, downsample=1):
+        # convert t -> 0 to 0 -> 1 | convert t -> 1 to 1 -> 0
+        # based on linear assumption
+        t1_scale = 1.0 / embt
+        t0_scale = 1.0 / (1.0 - embt)
+        if downsample != 1:
+            inv = 1 / downsample
+            flow0 = inv * resize(flow0, scale_factor=inv)
+            flow1 = inv * resize(flow1, scale_factor=inv)
+
+        corr0, corr1 = corr_fn(coord + flow1 * t1_scale, coord + flow0 * t0_scale)
+        corr = torch.cat([corr0, corr1], dim=1)
+        flow = torch.cat([flow0, flow1], dim=1)
+        return corr, flow
+
+    def forward(self, img0, img1, embt, scale_factor=1.0, eval=False, **kwargs):
+        mean_ = torch.cat([img0, img1], 2).mean(1, keepdim=True).mean(2, keepdim=True).mean(3, keepdim=True)
+        img0 = img0 - mean_
+        img1 = img1 - mean_
+        img0_ = resize(img0, scale_factor) if scale_factor != 1.0 else img0
+        img1_ = resize(img1, scale_factor) if scale_factor != 1.0 else img1
+        b, _, h, w = img0_.shape
+        coord = coords_grid(b, h // 8, w // 8, img0.device)
+
+        fmap0, fmap1 = self.feat_encoder([img0_, img1_])  # [1, 128, H//8, W//8]
+        corr_fn = BidirCorrBlock(fmap0, fmap1, radius=self.radius, num_levels=self.corr_levels)
+
+        # f0_1: [1, c0, H//2, W//2] | f0_2: [1, c1, H//4, W//4]
+        # f0_3: [1, c2, H//8, W//8] | f0_4: [1, c3, H//16, W//16]
+        f0_1, f0_2, f0_3, f0_4 = self.encoder(img0_)
+        f1_1, f1_2, f1_3, f1_4 = self.encoder(img1_)
+
+        ######################################### the 4th decoder #########################################
+        up_flow0_4, up_flow1_4, ft_3_ = self.decoder4(f0_4, f1_4, embt)
+        corr_4, flow_4 = self._corr_scale_lookup(corr_fn, coord, up_flow0_4, up_flow1_4, embt, downsample=1)
+
+        # residue update with lookup corr
+        delta_ft_3_, delta_flow_4 = self.update4(ft_3_, flow_4, corr_4)
+        delta_flow0_4, delta_flow1_4 = torch.chunk(delta_flow_4, 2, 1)
+        up_flow0_4 = up_flow0_4 + delta_flow0_4
+        up_flow1_4 = up_flow1_4 + delta_flow1_4
+        ft_3_ = ft_3_ + delta_ft_3_
+
+        ######################################### the 3rd decoder #########################################
+        up_flow0_3, up_flow1_3, ft_2_ = self.decoder3(ft_3_, f0_3, f1_3, up_flow0_4, up_flow1_4)
+        corr_3, flow_3 = self._corr_scale_lookup(corr_fn, coord, up_flow0_3, up_flow1_3, embt, downsample=2)
+
+        # residue update with lookup corr
+        delta_ft_2_, delta_flow_3 = self.update3(ft_2_, flow_3, corr_3)
+        delta_flow0_3, delta_flow1_3 = torch.chunk(delta_flow_3, 2, 1)
+        up_flow0_3 = up_flow0_3 + delta_flow0_3
+        up_flow1_3 = up_flow1_3 + delta_flow1_3
+        ft_2_ = ft_2_ + delta_ft_2_
+
+        ######################################### the 2nd decoder #########################################
+        up_flow0_2, up_flow1_2, ft_1_ = self.decoder2(ft_2_, f0_2, f1_2, up_flow0_3, up_flow1_3)
+        corr_2, flow_2 = self._corr_scale_lookup(corr_fn, coord, up_flow0_2, up_flow1_2, embt, downsample=4)
+
+        # residue update with lookup corr
+        delta_ft_1_, delta_flow_2 = self.update2(ft_1_, flow_2, corr_2)
+        delta_flow0_2, delta_flow1_2 = torch.chunk(delta_flow_2, 2, 1)
+        up_flow0_2 = up_flow0_2 + delta_flow0_2
+        up_flow1_2 = up_flow1_2 + delta_flow1_2
+        ft_1_ = ft_1_ + delta_ft_1_
+
+        ######################################### the 1st decoder #########################################
+        up_flow0_1, up_flow1_1, mask, img_res = self.decoder1(ft_1_, f0_1, f1_1, up_flow0_2, up_flow1_2)
+
+        if scale_factor != 1.0:
+            up_flow0_1 = resize(up_flow0_1, scale_factor=(1.0 / scale_factor)) * (1.0 / scale_factor)
+            up_flow1_1 = resize(up_flow1_1, scale_factor=(1.0 / scale_factor)) * (1.0 / scale_factor)
+            mask = resize(mask, scale_factor=(1.0 / scale_factor))
+            img_res = resize(img_res, scale_factor=(1.0 / scale_factor))
+
+        # Merge multiple predictions
+        imgt_pred = multi_flow_combine(self.comb_block, img0, img1, up_flow0_1, up_flow1_1, mask, img_res, mean_)
+        imgt_pred = torch.clamp(imgt_pred, 0, 1)
+
+        if eval:
+            return {
+                "imgt_pred": imgt_pred,
+            }
+        else:
+            up_flow0_1 = up_flow0_1.reshape(b, self.num_flows, 2, h, w)
+            up_flow1_1 = up_flow1_1.reshape(b, self.num_flows, 2, h, w)
+            return {
+                "imgt_pred": imgt_pred,
+                "flow0_pred": [up_flow0_1, up_flow0_2, up_flow0_3, up_flow0_4],
+                "flow1_pred": [up_flow1_1, up_flow1_2, up_flow1_3, up_flow1_4],
+                "ft_pred": [ft_1_, ft_2_, ft_3_],
+            }

Helios-main/eval/utils/third_party/amt/networks/IFRNet.py

@@ -0,0 +1,153 @@
+import torch
+import torch.nn as nn
+
+from utils.third_party.amt.networks.blocks.ifrnet import (
+    ResBlock,
+    convrelu,
+    resize,
+)
+from utils.third_party.amt.utils.flow_utils import warp
+
+
+class Encoder(nn.Module):
+    def __init__(self):
+        super(Encoder, self).__init__()
+        self.pyramid1 = nn.Sequential(convrelu(3, 32, 3, 2, 1), convrelu(32, 32, 3, 1, 1))
+        self.pyramid2 = nn.Sequential(convrelu(32, 48, 3, 2, 1), convrelu(48, 48, 3, 1, 1))
+        self.pyramid3 = nn.Sequential(convrelu(48, 72, 3, 2, 1), convrelu(72, 72, 3, 1, 1))
+        self.pyramid4 = nn.Sequential(convrelu(72, 96, 3, 2, 1), convrelu(96, 96, 3, 1, 1))
+
+    def forward(self, img):
+        f1 = self.pyramid1(img)
+        f2 = self.pyramid2(f1)
+        f3 = self.pyramid3(f2)
+        f4 = self.pyramid4(f3)
+        return f1, f2, f3, f4
+
+
+class Decoder4(nn.Module):
+    def __init__(self):
+        super(Decoder4, self).__init__()
+        self.convblock = nn.Sequential(
+            convrelu(192 + 1, 192), ResBlock(192, 32), nn.ConvTranspose2d(192, 76, 4, 2, 1, bias=True)
+        )
+
+    def forward(self, f0, f1, embt):
+        b, c, h, w = f0.shape
+        embt = embt.repeat(1, 1, h, w)
+        f_in = torch.cat([f0, f1, embt], 1)
+        f_out = self.convblock(f_in)
+        return f_out
+
+
+class Decoder3(nn.Module):
+    def __init__(self):
+        super(Decoder3, self).__init__()
+        self.convblock = nn.Sequential(
+            convrelu(220, 216), ResBlock(216, 32), nn.ConvTranspose2d(216, 52, 4, 2, 1, bias=True)
+        )
+
+    def forward(self, ft_, f0, f1, up_flow0, up_flow1):
+        f0_warp = warp(f0, up_flow0)
+        f1_warp = warp(f1, up_flow1)
+        f_in = torch.cat([ft_, f0_warp, f1_warp, up_flow0, up_flow1], 1)
+        f_out = self.convblock(f_in)
+        return f_out
+
+
+class Decoder2(nn.Module):
+    def __init__(self):
+        super(Decoder2, self).__init__()
+        self.convblock = nn.Sequential(
+            convrelu(148, 144), ResBlock(144, 32), nn.ConvTranspose2d(144, 36, 4, 2, 1, bias=True)
+        )
+
+    def forward(self, ft_, f0, f1, up_flow0, up_flow1):
+        f0_warp = warp(f0, up_flow0)
+        f1_warp = warp(f1, up_flow1)
+        f_in = torch.cat([ft_, f0_warp, f1_warp, up_flow0, up_flow1], 1)
+        f_out = self.convblock(f_in)
+        return f_out
+
+
+class Decoder1(nn.Module):
+    def __init__(self):
+        super(Decoder1, self).__init__()
+        self.convblock = nn.Sequential(
+            convrelu(100, 96), ResBlock(96, 32), nn.ConvTranspose2d(96, 8, 4, 2, 1, bias=True)
+        )
+
+    def forward(self, ft_, f0, f1, up_flow0, up_flow1):
+        f0_warp = warp(f0, up_flow0)
+        f1_warp = warp(f1, up_flow1)
+        f_in = torch.cat([ft_, f0_warp, f1_warp, up_flow0, up_flow1], 1)
+        f_out = self.convblock(f_in)
+        return f_out
+
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.encoder = Encoder()
+        self.decoder4 = Decoder4()
+        self.decoder3 = Decoder3()
+        self.decoder2 = Decoder2()
+        self.decoder1 = Decoder1()
+
+    def forward(self, img0, img1, embt, scale_factor=1.0, eval=False, **kwargs):
+        mean_ = torch.cat([img0, img1], 2).mean(1, keepdim=True).mean(2, keepdim=True).mean(3, keepdim=True)
+        img0 = img0 - mean_
+        img1 = img1 - mean_
+
+        img0_ = resize(img0, scale_factor) if scale_factor != 1.0 else img0
+        img1_ = resize(img1, scale_factor) if scale_factor != 1.0 else img1
+
+        f0_1, f0_2, f0_3, f0_4 = self.encoder(img0_)
+        f1_1, f1_2, f1_3, f1_4 = self.encoder(img1_)
+
+        out4 = self.decoder4(f0_4, f1_4, embt)
+        up_flow0_4 = out4[:, 0:2]
+        up_flow1_4 = out4[:, 2:4]
+        ft_3_ = out4[:, 4:]
+
+        out3 = self.decoder3(ft_3_, f0_3, f1_3, up_flow0_4, up_flow1_4)
+        up_flow0_3 = out3[:, 0:2] + 2.0 * resize(up_flow0_4, scale_factor=2.0)
+        up_flow1_3 = out3[:, 2:4] + 2.0 * resize(up_flow1_4, scale_factor=2.0)
+        ft_2_ = out3[:, 4:]
+
+        out2 = self.decoder2(ft_2_, f0_2, f1_2, up_flow0_3, up_flow1_3)
+        up_flow0_2 = out2[:, 0:2] + 2.0 * resize(up_flow0_3, scale_factor=2.0)
+        up_flow1_2 = out2[:, 2:4] + 2.0 * resize(up_flow1_3, scale_factor=2.0)
+        ft_1_ = out2[:, 4:]
+
+        out1 = self.decoder1(ft_1_, f0_1, f1_1, up_flow0_2, up_flow1_2)
+        up_flow0_1 = out1[:, 0:2] + 2.0 * resize(up_flow0_2, scale_factor=2.0)
+        up_flow1_1 = out1[:, 2:4] + 2.0 * resize(up_flow1_2, scale_factor=2.0)
+        up_mask_1 = torch.sigmoid(out1[:, 4:5])
+        up_res_1 = out1[:, 5:]
+
+        if scale_factor != 1.0:
+            up_flow0_1 = resize(up_flow0_1, scale_factor=(1.0 / scale_factor)) * (1.0 / scale_factor)
+            up_flow1_1 = resize(up_flow1_1, scale_factor=(1.0 / scale_factor)) * (1.0 / scale_factor)
+            up_mask_1 = resize(up_mask_1, scale_factor=(1.0 / scale_factor))
+            up_res_1 = resize(up_res_1, scale_factor=(1.0 / scale_factor))
+
+        img0_warp = warp(img0, up_flow0_1)
+        img1_warp = warp(img1, up_flow1_1)
+        imgt_merge = up_mask_1 * img0_warp + (1 - up_mask_1) * img1_warp + mean_
+        imgt_pred = imgt_merge + up_res_1
+        imgt_pred = torch.clamp(imgt_pred, 0, 1)
+
+        if eval:
+            return {
+                "imgt_pred": imgt_pred,
+            }
+        else:
+            return {
+                "imgt_pred": imgt_pred,
+                "flow0_pred": [up_flow0_1, up_flow0_2, up_flow0_3, up_flow0_4],
+                "flow1_pred": [up_flow1_1, up_flow1_2, up_flow1_3, up_flow1_4],
+                "ft_pred": [ft_1_, ft_2_, ft_3_],
+                "img0_warp": img0_warp,
+                "img1_warp": img1_warp,
+            }

Helios-main/eval/utils/third_party/amt/scripts/train.sh

@@ -0,0 +1,6 @@
+NUM_GPU=$1
+CFG=$2
+PORT=$3
+python -m torch.distributed.launch \
+--nproc_per_node $NUM_GPU \
+--master_port $PORT train.py -c $CFG

Helios-main/eval/utils/third_party/amt/train.py

@@ -0,0 +1,69 @@
+import argparse
+import datetime
+import importlib
+import os
+from shutil import copyfile
+
+import torch
+import torch.distributed as dist
+from omegaconf import OmegaConf
+
+from utils.dist_utils import (
+    get_world_size,
+)
+from utils.utils import seed_all
+
+
+parser = argparse.ArgumentParser(description="VFI")
+parser.add_argument("-c", "--config", type=str)
+parser.add_argument("-p", "--port", default="23455", type=str)
+parser.add_argument("--local_rank", default="0")
+
+args = parser.parse_args()
+
+
+def main_worker(rank, config):
+    if "local_rank" not in config:
+        config["local_rank"] = config["global_rank"] = rank
+    if torch.cuda.is_available():
+        print(f"Rank {rank} is available")
+        config["device"] = f"cuda:{rank}"
+        if config["distributed"]:
+            dist.init_process_group(backend="nccl", timeout=datetime.timedelta(seconds=5400))
+    else:
+        config["device"] = "cpu"
+
+    cfg_name = os.path.basename(args.config).split(".")[0]
+    config["exp_name"] = cfg_name + "_" + config["exp_name"]
+    config["save_dir"] = os.path.join(config["save_dir"], config["exp_name"])
+
+    if (not config["distributed"]) or rank == 0:
+        os.makedirs(config["save_dir"], exist_ok=True)
+        os.makedirs(f"{config['save_dir']}/ckpts", exist_ok=True)
+        config_path = os.path.join(config["save_dir"], args.config.split("/")[-1])
+        if not os.path.isfile(config_path):
+            copyfile(args.config, config_path)
+        print("[**] create folder {}".format(config["save_dir"]))
+
+    trainer_name = config.get("trainer_type", "base_trainer")
+    print(f"using GPU {rank} for training")
+    if rank == 0:
+        print(trainer_name)
+    trainer_pack = importlib.import_module("trainers." + trainer_name)
+    trainer = trainer_pack.Trainer(config)
+
+    trainer.train()
+
+
+if __name__ == "__main__":
+    torch.backends.cudnn.benchmark = True
+    cfg = OmegaConf.load(args.config)
+    seed_all(cfg.seed)
+    rank = int(args.local_rank)
+    torch.cuda.set_device(torch.device(f"cuda:{rank}"))
+    # setting distributed cfgurations
+    cfg["world_size"] = get_world_size()
+    cfg["local_rank"] = rank
+    if rank == 0:
+        print("world_size: ", cfg["world_size"])
+    main_worker(rank, cfg)

Helios-main/eval/utils/third_party/amt/trainers/base_trainer.py

@@ -0,0 +1,248 @@
+import logging
+import os.path as osp
+import time
+from collections import OrderedDict
+
+import numpy as np
+import torch
+import wandb
+from metrics.psnr_ssim import calculate_psnr
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.optim import AdamW
+from torch.utils.data import DataLoader
+from torch.utils.data.distributed import DistributedSampler
+
+from utils.build_utils import build_from_cfg
+from utils.utils import AverageMeterGroups
+
+from .logger import CustomLogger
+
+
+class Trainer:
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.rank = self.config["local_rank"]
+        init_log = self._init_logger()
+        self._init_dataset()
+        self._init_loss()
+        self.model_name = config["exp_name"]
+        self.model = build_from_cfg(config.network).to(self.config.device)
+
+        if config["distributed"]:
+            self.model = DDP(
+                self.model,
+                device_ids=[self.rank],
+                output_device=self.rank,
+                broadcast_buffers=True,
+                find_unused_parameters=False,
+            )
+
+        init_log += str(self.model)
+        self.optimizer = AdamW(self.model.parameters(), lr=config.lr, weight_decay=config.weight_decay)
+        if self.rank == 0:
+            print(init_log)
+        self.logger(init_log)
+        self.resume_training()
+
+    def resume_training(self):
+        ckpt_path = self.config.get("resume_state")
+        if ckpt_path is not None:
+            ckpt = torch.load(self.config["resume_state"])
+            if self.config["distributed"]:
+                self.model.module.load_state_dict(ckpt["state_dict"])
+            else:
+                self.model.load_state_dict(ckpt["state_dict"])
+            self.optimizer.load_state_dict(ckpt["optim"])
+            self.resume_epoch = ckpt.get("epoch")
+            self.logger(f"load model from {ckpt_path} and training resumes from epoch {self.resume_epoch}")
+        else:
+            self.resume_epoch = 0
+
+    def _init_logger(self):
+        init_log = ""
+        console_cfg = {
+            "level": logging.INFO,
+            "format": "%(asctime)s %(filename)s[line:%(lineno)d]%(levelname)s %(message)s",
+            "datefmt": "%a, %d %b %Y %H:%M:%S",
+            "filename": f"{self.config['save_dir']}/log",
+            "filemode": "w",
+        }
+        tb_cfg = {"log_dir": osp.join(self.config["save_dir"], "tb_logger")}
+        wandb_cfg = None
+        use_wandb = self.config["logger"].get("use_wandb", False)
+        if use_wandb:
+            resume_id = self.config["logger"].get("resume_id", None)
+            if resume_id:
+                wandb_id = resume_id
+                resume = "allow"
+                init_log += f"Resume wandb logger with id={wandb_id}."
+            else:
+                wandb_id = wandb.util.generate_id()
+                resume = "never"
+
+            wandb_cfg = {
+                "id": wandb_id,
+                "resume": resume,
+                "name": osp.basename(self.config["save_dir"]),
+                "config": self.config,
+                "project": "YOUR PROJECT",
+                "entity": "YOUR ENTITY",
+                "sync_tensorboard": True,
+            }
+            init_log += f"Use wandb logger with id={wandb_id}; project=[YOUR PROJECT]."
+        self.logger = CustomLogger(console_cfg, tb_cfg, wandb_cfg, self.rank)
+        return init_log
+
+    def _init_dataset(self):
+        dataset_train = build_from_cfg(self.config.data.train)
+        dataset_val = build_from_cfg(self.config.data.val)
+
+        self.sampler = DistributedSampler(
+            dataset_train, num_replicas=self.config["world_size"], rank=self.config["local_rank"]
+        )
+        self.config.data.train_loader.batch_size //= self.config["world_size"]
+        self.loader_train = DataLoader(
+            dataset_train, **self.config.data.train_loader, pin_memory=True, drop_last=True, sampler=self.sampler
+        )
+
+        self.loader_val = DataLoader(
+            dataset_val, **self.config.data.val_loader, pin_memory=True, shuffle=False, drop_last=False
+        )
+
+    def _init_loss(self):
+        self.loss_dict = {}
+        for loss_cfg in self.config.losses:
+            loss = build_from_cfg(loss_cfg)
+            self.loss_dict[loss_cfg["nickname"]] = loss
+
+    def set_lr(self, optimizer, lr):
+        for param_group in optimizer.param_groups:
+            param_group["lr"] = lr
+
+    def get_lr(self, iters):
+        ratio = 0.5 * (1.0 + np.cos(iters / (self.config["epochs"] * self.loader_train.__len__()) * np.pi))
+        lr = (self.config["lr"] - self.config["lr_min"]) * ratio + self.config["lr_min"]
+        return lr
+
+    def train(self):
+        local_rank = self.config["local_rank"]
+        best_psnr = 0.0
+        loss_group = AverageMeterGroups()
+        time_group = AverageMeterGroups()
+        iters_per_epoch = self.loader_train.__len__()
+        iters = self.resume_epoch * iters_per_epoch
+        total_iters = self.config["epochs"] * iters_per_epoch
+
+        start_t = time.time()
+        total_t = 0
+        for epoch in range(self.resume_epoch, self.config["epochs"]):
+            self.sampler.set_epoch(epoch)
+            for data in self.loader_train:
+                for k, v in data.items():
+                    data[k] = v.to(self.config["device"])
+                data_t = time.time() - start_t
+
+                lr = self.get_lr(iters)
+                self.set_lr(self.optimizer, lr)
+
+                self.optimizer.zero_grad()
+                results = self.model(**data)
+                total_loss = torch.tensor(0.0, device=self.config["device"])
+                for name, loss in self.loss_dict.items():
+                    l = loss(**results, **data)
+                    loss_group.update({name: l.cpu().data})
+                    total_loss += l
+                total_loss.backward()
+                self.optimizer.step()
+
+                iters += 1
+
+                iter_t = time.time() - start_t
+                total_t += iter_t
+                time_group.update({"data_t": data_t, "iter_t": iter_t})
+
+                if (iters + 1) % 100 == 0 and local_rank == 0:
+                    tpi = total_t / (iters - self.resume_epoch * iters_per_epoch)
+                    eta = total_iters * tpi
+                    remainder = (total_iters - iters) * tpi
+                    eta = self.eta_format(eta)
+
+                    remainder = self.eta_format(remainder)
+                    log_str = f"[{self.model_name}]epoch:{epoch + 1}/{self.config['epochs']} "
+                    log_str += f"iter:{iters + 1}/{self.config['epochs'] * iters_per_epoch} "
+                    log_str += f"time:{time_group.avg('iter_t'):.3f}({time_group.avg('data_t'):.3f}) "
+                    log_str += f"lr:{lr:.3e} eta:{remainder}({eta})\n"
+                    for name in self.loss_dict.keys():
+                        avg_l = loss_group.avg(name)
+                        log_str += f"{name}:{avg_l:.3e} "
+                        self.logger(tb_msg=[f"loss/{name}", avg_l, iters])
+                    log_str += f"best:{best_psnr:.2f}dB\n\n"
+                    self.logger(log_str)
+                    loss_group.reset()
+                    time_group.reset()
+                start_t = time.time()
+
+            if (epoch + 1) % self.config["eval_interval"] == 0 and local_rank == 0:
+                psnr, eval_t = self.evaluate(epoch)
+                total_t += eval_t
+                self.logger(tb_msg=["eval/psnr", psnr, epoch])
+                if psnr > best_psnr:
+                    best_psnr = psnr
+                    self.save("psnr_best.pth", epoch)
+                    if self.logger.enable_wandb:
+                        wandb.run.summary["best_psnr"] = best_psnr
+                if (epoch + 1) % 50 == 0:
+                    self.save(f"epoch_{epoch + 1}.pth", epoch)
+                self.save("latest.pth", epoch)
+
+        self.logger.close()
+
+    def evaluate(self, epoch):
+        psnr_list = []
+        time_stamp = time.time()
+        for i, data in enumerate(self.loader_val):
+            for k, v in data.items():
+                data[k] = v.to(self.config["device"])
+
+            with torch.no_grad():
+                results = self.model(**data, eval=True)
+                imgt_pred = results["imgt_pred"]
+                for j in range(data["img0"].shape[0]):
+                    psnr = calculate_psnr(imgt_pred[j].detach().unsqueeze(0), data["imgt"][j].unsqueeze(0)).cpu().data
+                    psnr_list.append(psnr)
+
+        eval_time = time.time() - time_stamp
+
+        self.logger(
+            "eval epoch:{}/{} time:{:.2f} psnr:{:.3f}".format(
+                epoch + 1, self.config["epochs"], eval_time, np.array(psnr_list).mean()
+            )
+        )
+        return np.array(psnr_list).mean(), eval_time
+
+    def save(self, name, epoch):
+        save_path = "{}/{}/{}".format(self.config["save_dir"], "ckpts", name)
+        ckpt = OrderedDict(epoch=epoch)
+        if self.config["distributed"]:
+            ckpt["state_dict"] = self.model.module.state_dict()
+        else:
+            ckpt["state_dict"] = self.model.state_dict()
+        ckpt["optim"] = self.optimizer.state_dict()
+        torch.save(ckpt, save_path)
+
+    def eta_format(self, eta):
+        time_str = ""
+        if eta >= 3600:
+            hours = int(eta // 3600)
+            eta -= hours * 3600
+            time_str = f"{hours}"
+
+        if eta >= 60:
+            mins = int(eta // 60)
+            eta -= mins * 60
+            time_str = f"{time_str}:{mins:02}"
+
+        eta = int(eta)
+        time_str = f"{time_str}:{eta:02}"
+        return time_str

Helios-main/eval/utils/third_party/amt/trainers/logger.py

@@ -0,0 +1,62 @@
+import logging
+import os.path as osp
+import shutil
+import time
+
+import wandb
+from torch.utils.tensorboard import SummaryWriter
+
+
+def mv_archived_logger(name):
+    timestamp = time.strftime("%Y-%m-%d_%H:%M:%S_", time.localtime())
+    basename = "archived_" + timestamp + osp.basename(name)
+    archived_name = osp.join(osp.dirname(name), basename)
+    shutil.move(name, archived_name)
+
+
+class CustomLogger:
+    def __init__(self, common_cfg, tb_cfg=None, wandb_cfg=None, rank=0):
+        global global_logger
+        self.rank = rank
+
+        if self.rank == 0:
+            self.logger = logging.getLogger("VFI")
+            self.logger.setLevel(logging.INFO)
+            format_str = logging.Formatter(common_cfg["format"])
+
+            console_handler = logging.StreamHandler()
+            console_handler.setFormatter(format_str)
+
+            if osp.exists(common_cfg["filename"]):
+                mv_archived_logger(common_cfg["filename"])
+
+            file_handler = logging.FileHandler(common_cfg["filename"], common_cfg["filemode"])
+            file_handler.setFormatter(format_str)
+
+            self.logger.addHandler(console_handler)
+            self.logger.addHandler(file_handler)
+            self.tb_logger = None
+
+            self.enable_wandb = False
+
+            if wandb_cfg is not None:
+                self.enable_wandb = True
+                wandb.init(**wandb_cfg)
+
+            if tb_cfg is not None:
+                self.tb_logger = SummaryWriter(**tb_cfg)
+
+        global_logger = self
+
+    def __call__(self, msg=None, level=logging.INFO, tb_msg=None):
+        if self.rank != 0:
+            return
+        if msg is not None:
+            self.logger.log(level, msg)
+
+        if self.tb_logger is not None and tb_msg is not None:
+            self.tb_logger.add_scalar(*tb_msg)
+
+    def close(self):
+        if self.rank == 0 and self.enable_wandb:
+            wandb.finish()

Helios-main/eval/utils/third_party/amt/utils/build_utils.py

@@ -0,0 +1,17 @@
+import importlib
+import os
+import sys
+
+
+CUR_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(os.path.join(CUR_DIR, "../"))
+
+
+def base_build_fn(module, cls, params):
+    return getattr(importlib.import_module(module, package=None), cls)(**params)
+
+
+def build_from_cfg(config):
+    module, cls = config["name"].rsplit(".", 1)
+    params = config.get("params", {})
+    return base_build_fn(module, cls, params)

Helios-main/eval/utils/third_party/amt/utils/dist_utils.py

@@ -0,0 +1,48 @@
+import os
+
+import torch
+
+
+def get_world_size():
+    """Find OMPI world size without calling mpi functions
+    :rtype: int
+    """
+    if os.environ.get("PMI_SIZE") is not None:
+        return int(os.environ.get("PMI_SIZE") or 1)
+    elif os.environ.get("OMPI_COMM_WORLD_SIZE") is not None:
+        return int(os.environ.get("OMPI_COMM_WORLD_SIZE") or 1)
+    else:
+        return torch.cuda.device_count()
+
+
+def get_global_rank():
+    """Find OMPI world rank without calling mpi functions
+    :rtype: int
+    """
+    if os.environ.get("PMI_RANK") is not None:
+        return int(os.environ.get("PMI_RANK") or 0)
+    elif os.environ.get("OMPI_COMM_WORLD_RANK") is not None:
+        return int(os.environ.get("OMPI_COMM_WORLD_RANK") or 0)
+    else:
+        return 0
+
+
+def get_local_rank():
+    """Find OMPI local rank without calling mpi functions
+    :rtype: int
+    """
+    if os.environ.get("MPI_LOCALRANKID") is not None:
+        return int(os.environ.get("MPI_LOCALRANKID") or 0)
+    elif os.environ.get("OMPI_COMM_WORLD_LOCAL_RANK") is not None:
+        return int(os.environ.get("OMPI_COMM_WORLD_LOCAL_RANK") or 0)
+    else:
+        return 0
+
+
+def get_master_ip():
+    if os.environ.get("AZ_BATCH_MASTER_NODE") is not None:
+        return os.environ.get("AZ_BATCH_MASTER_NODE").split(":")[0]
+    elif os.environ.get("AZ_BATCHAI_MPI_MASTER_NODE") is not None:
+        return os.environ.get("AZ_BATCHAI_MPI_MASTER_NODE")
+    else:
+        return "127.0.0.1"

Helios-main/eval/utils/third_party/amt/utils/flow_utils.py

@@ -0,0 +1,126 @@
+import numpy as np
+import torch
+import torch.nn.functional as F
+from PIL import ImageFile
+
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+
+def warp(img, flow):
+    B, _, H, W = flow.shape
+    xx = torch.linspace(-1.0, 1.0, W).view(1, 1, 1, W).expand(B, -1, H, -1)
+    yy = torch.linspace(-1.0, 1.0, H).view(1, 1, H, 1).expand(B, -1, -1, W)
+    grid = torch.cat([xx, yy], 1).to(img)
+    flow_ = torch.cat([flow[:, 0:1, :, :] / ((W - 1.0) / 2.0), flow[:, 1:2, :, :] / ((H - 1.0) / 2.0)], 1)
+    grid_ = (grid + flow_).permute(0, 2, 3, 1)
+    output = F.grid_sample(input=img, grid=grid_, mode="bilinear", padding_mode="border", align_corners=True)
+    return output
+
+
+def make_colorwheel():
+    """
+    Generates a color wheel for optical flow visualization as presented in:
+        Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007)
+        URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf
+    Code follows the original C++ source code of Daniel Scharstein.
+    Code follows the the Matlab source code of Deqing Sun.
+    Returns:
+        np.ndarray: Color wheel
+    """
+
+    RY = 15
+    YG = 6
+    GC = 4
+    CB = 11
+    BM = 13
+    MR = 6
+
+    ncols = RY + YG + GC + CB + BM + MR
+    colorwheel = np.zeros((ncols, 3))
+    col = 0
+
+    # RY
+    colorwheel[0:RY, 0] = 255
+    colorwheel[0:RY, 1] = np.floor(255 * np.arange(0, RY) / RY)
+    col = col + RY
+    # YG
+    colorwheel[col : col + YG, 0] = 255 - np.floor(255 * np.arange(0, YG) / YG)
+    colorwheel[col : col + YG, 1] = 255
+    col = col + YG
+    # GC
+    colorwheel[col : col + GC, 1] = 255
+    colorwheel[col : col + GC, 2] = np.floor(255 * np.arange(0, GC) / GC)
+    col = col + GC
+    # CB
+    colorwheel[col : col + CB, 1] = 255 - np.floor(255 * np.arange(CB) / CB)
+    colorwheel[col : col + CB, 2] = 255
+    col = col + CB
+    # BM
+    colorwheel[col : col + BM, 2] = 255
+    colorwheel[col : col + BM, 0] = np.floor(255 * np.arange(0, BM) / BM)
+    col = col + BM
+    # MR
+    colorwheel[col : col + MR, 2] = 255 - np.floor(255 * np.arange(MR) / MR)
+    colorwheel[col : col + MR, 0] = 255
+    return colorwheel
+
+
+def flow_uv_to_colors(u, v, convert_to_bgr=False):
+    """
+    Applies the flow color wheel to (possibly clipped) flow components u and v.
+    According to the C++ source code of Daniel Scharstein
+    According to the Matlab source code of Deqing Sun
+    Args:
+        u (np.ndarray): Input horizontal flow of shape [H,W]
+        v (np.ndarray): Input vertical flow of shape [H,W]
+        convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
+    Returns:
+        np.ndarray: Flow visualization image of shape [H,W,3]
+    """
+    flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)
+    colorwheel = make_colorwheel()  # shape [55x3]
+    ncols = colorwheel.shape[0]
+    rad = np.sqrt(np.square(u) + np.square(v))
+    a = np.arctan2(-v, -u) / np.pi
+    fk = (a + 1) / 2 * (ncols - 1)
+    k0 = np.floor(fk).astype(np.int32)
+    k1 = k0 + 1
+    k1[k1 == ncols] = 0
+    f = fk - k0
+    for i in range(colorwheel.shape[1]):
+        tmp = colorwheel[:, i]
+        col0 = tmp[k0] / 255.0
+        col1 = tmp[k1] / 255.0
+        col = (1 - f) * col0 + f * col1
+        idx = rad <= 1
+        col[idx] = 1 - rad[idx] * (1 - col[idx])
+        col[~idx] = col[~idx] * 0.75  # out of range
+        # Note the 2-i => BGR instead of RGB
+        ch_idx = 2 - i if convert_to_bgr else i
+        flow_image[:, :, ch_idx] = np.floor(255 * col)
+    return flow_image
+
+
+def flow_to_image(flow_uv, clip_flow=None, convert_to_bgr=False):
+    """
+    Expects a two dimensional flow image of shape.
+    Args:
+        flow_uv (np.ndarray): Flow UV image of shape [H,W,2]
+        clip_flow (float, optional): Clip maximum of flow values. Defaults to None.
+        convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
+    Returns:
+        np.ndarray: Flow visualization image of shape [H,W,3]
+    """
+    assert flow_uv.ndim == 3, "input flow must have three dimensions"
+    assert flow_uv.shape[2] == 2, "input flow must have shape [H,W,2]"
+    if clip_flow is not None:
+        flow_uv = np.clip(flow_uv, 0, clip_flow)
+    u = flow_uv[:, :, 0]
+    v = flow_uv[:, :, 1]
+    rad = np.sqrt(np.square(u) + np.square(v))
+    rad_max = np.max(rad)
+    epsilon = 1e-5
+    u = u / (rad_max + epsilon)
+    v = v / (rad_max + epsilon)
+    return flow_uv_to_colors(u, v, convert_to_bgr)

Helios-main/eval/utils/third_party/amt/utils/utils.py

@@ -0,0 +1,315 @@
+import random
+import re
+import sys
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from imageio import imread, imwrite
+from PIL import ImageFile
+
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+
+class AverageMeter:
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0.0
+        self.avg = 0.0
+        self.sum = 0.0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+
+class AverageMeterGroups:
+    def __init__(self) -> None:
+        self.meter_dict = {}
+
+    def update(self, dict, n=1):
+        for name, val in dict.items():
+            if self.meter_dict.get(name) is None:
+                self.meter_dict[name] = AverageMeter()
+            self.meter_dict[name].update(val, n)
+
+    def reset(self, name=None):
+        if name is None:
+            for v in self.meter_dict.values():
+                v.reset()
+        else:
+            meter = self.meter_dict.get(name)
+            if meter is not None:
+                meter.reset()
+
+    def avg(self, name):
+        meter = self.meter_dict.get(name)
+        if meter is not None:
+            return meter.avg
+
+
+class InputPadder:
+    """Pads images such that dimensions are divisible by divisor"""
+
+    def __init__(self, dims, divisor=16):
+        self.ht, self.wd = dims[-2:]
+        pad_ht = (((self.ht // divisor) + 1) * divisor - self.ht) % divisor
+        pad_wd = (((self.wd // divisor) + 1) * divisor - self.wd) % divisor
+        self._pad = [pad_wd // 2, pad_wd - pad_wd // 2, pad_ht // 2, pad_ht - pad_ht // 2]
+
+    def pad(self, *inputs):
+        if len(inputs) == 1:
+            return F.pad(inputs[0], self._pad, mode="replicate")
+        else:
+            return [F.pad(x, self._pad, mode="replicate") for x in inputs]
+
+    def unpad(self, *inputs):
+        if len(inputs) == 1:
+            return self._unpad(inputs[0])
+        else:
+            return [self._unpad(x) for x in inputs]
+
+    def _unpad(self, x):
+        ht, wd = x.shape[-2:]
+        c = [self._pad[2], ht - self._pad[3], self._pad[0], wd - self._pad[1]]
+        return x[..., c[0] : c[1], c[2] : c[3]]
+
+
+def img2tensor(img):
+    if img.shape[-1] > 3:
+        img = img[:, :, :3]
+    return torch.tensor(img).permute(2, 0, 1).unsqueeze(0) / 255.0
+
+
+def tensor2img(img_t):
+    return (img_t * 255.0).detach().squeeze(0).permute(1, 2, 0).cpu().numpy().clip(0, 255).astype(np.uint8)
+
+
+def seed_all(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def read(file):
+    if file.endswith(".float3"):
+        return readFloat(file)
+    elif file.endswith(".flo"):
+        return readFlow(file)
+    elif file.endswith(".ppm"):
+        return readImage(file)
+    elif file.endswith(".pgm"):
+        return readImage(file)
+    elif file.endswith(".png"):
+        return readImage(file)
+    elif file.endswith(".jpg"):
+        return readImage(file)
+    elif file.endswith(".pfm"):
+        return readPFM(file)[0]
+    else:
+        raise Exception("don't know how to read %s" % file)
+
+
+def write(file, data):
+    if file.endswith(".float3"):
+        return writeFloat(file, data)
+    elif file.endswith(".flo"):
+        return writeFlow(file, data)
+    elif file.endswith(".ppm"):
+        return writeImage(file, data)
+    elif file.endswith(".pgm"):
+        return writeImage(file, data)
+    elif file.endswith(".png"):
+        return writeImage(file, data)
+    elif file.endswith(".jpg"):
+        return writeImage(file, data)
+    elif file.endswith(".pfm"):
+        return writePFM(file, data)
+    else:
+        raise Exception("don't know how to write %s" % file)
+
+
+def readPFM(file):
+    file = open(file, "rb")
+
+    color = None
+    width = None
+    height = None
+    scale = None
+    endian = None
+
+    header = file.readline().rstrip()
+    if header.decode("ascii") == "PF":
+        color = True
+    elif header.decode("ascii") == "Pf":
+        color = False
+    else:
+        raise Exception("Not a PFM file.")
+
+    dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii"))
+    if dim_match:
+        width, height = list(map(int, dim_match.groups()))
+    else:
+        raise Exception("Malformed PFM header.")
+
+    scale = float(file.readline().decode("ascii").rstrip())
+    if scale < 0:
+        endian = "<"
+        scale = -scale
+    else:
+        endian = ">"
+
+    data = np.fromfile(file, endian + "f")
+    shape = (height, width, 3) if color else (height, width)
+
+    data = np.reshape(data, shape)
+    data = np.flipud(data)
+    return data, scale
+
+
+def writePFM(file, image, scale=1):
+    file = open(file, "wb")
+
+    color = None
+
+    if image.dtype.name != "float32":
+        raise Exception("Image dtype must be float32.")
+
+    image = np.flipud(image)
+
+    if len(image.shape) == 3 and image.shape[2] == 3:
+        color = True
+    elif len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1:
+        color = False
+    else:
+        raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")
+
+    file.write("PF\n" if color else "Pf\n".encode())
+    file.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))
+
+    endian = image.dtype.byteorder
+
+    if endian == "<" or endian == "=" and sys.byteorder == "little":
+        scale = -scale
+
+    file.write("%f\n".encode() % scale)
+
+    image.tofile(file)
+
+
+def readFlow(name):
+    if name.endswith(".pfm") or name.endswith(".PFM"):
+        return readPFM(name)[0][:, :, 0:2]
+
+    f = open(name, "rb")
+
+    header = f.read(4)
+    if header.decode("utf-8") != "PIEH":
+        raise Exception("Flow file header does not contain PIEH")
+
+    width = np.fromfile(f, np.int32, 1).squeeze()
+    height = np.fromfile(f, np.int32, 1).squeeze()
+
+    flow = np.fromfile(f, np.float32, width * height * 2).reshape((height, width, 2))
+
+    return flow.astype(np.float32)
+
+
+def readImage(name):
+    if name.endswith(".pfm") or name.endswith(".PFM"):
+        data = readPFM(name)[0]
+        if len(data.shape) == 3:
+            return data[:, :, 0:3]
+        else:
+            return data
+    return imread(name)
+
+
+def writeImage(name, data):
+    if name.endswith(".pfm") or name.endswith(".PFM"):
+        return writePFM(name, data, 1)
+    return imwrite(name, data)
+
+
+def writeFlow(name, flow):
+    f = open(name, "wb")
+    f.write("PIEH".encode("utf-8"))
+    np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f)
+    flow = flow.astype(np.float32)
+    flow.tofile(f)
+
+
+def readFloat(name):
+    f = open(name, "rb")
+
+    if (f.readline().decode("utf-8")) != "float\n":
+        raise Exception("float file %s did not contain <float> keyword" % name)
+
+    dim = int(f.readline())
+
+    dims = []
+    count = 1
+    for i in range(0, dim):
+        d = int(f.readline())
+        dims.append(d)
+        count *= d
+
+    dims = list(reversed(dims))
+
+    data = np.fromfile(f, np.float32, count).reshape(dims)
+    if dim > 2:
+        data = np.transpose(data, (2, 1, 0))
+        data = np.transpose(data, (1, 0, 2))
+
+    return data
+
+
+def writeFloat(name, data):
+    f = open(name, "wb")
+
+    dim = len(data.shape)
+    if dim > 3:
+        raise Exception("bad float file dimension: %d" % dim)
+
+    f.write(("float\n").encode("ascii"))
+    f.write(("%d\n" % dim).encode("ascii"))
+
+    if dim == 1:
+        f.write(("%d\n" % data.shape[0]).encode("ascii"))
+    else:
+        f.write(("%d\n" % data.shape[1]).encode("ascii"))
+        f.write(("%d\n" % data.shape[0]).encode("ascii"))
+        for i in range(2, dim):
+            f.write(("%d\n" % data.shape[i]).encode("ascii"))
+
+    data = data.astype(np.float32)
+    if dim == 2:
+        data.tofile(f)
+
+    else:
+        np.transpose(data, (2, 0, 1)).tofile(f)
+
+
+def check_dim_and_resize(tensor_list):
+    shape_list = []
+    for t in tensor_list:
+        shape_list.append(t.shape[2:])
+
+    if len(set(shape_list)) > 1:
+        desired_shape = shape_list[0]
+        print(f"Inconsistent size of input video frames. All frames will be resized to {desired_shape}")
+
+        resize_tensor_list = []
+        for t in tensor_list:
+            resize_tensor_list.append(torch.nn.functional.interpolate(t, size=tuple(desired_shape), mode="bilinear"))
+
+        tensor_list = resize_tensor_list
+
+    return tensor_list

Helios-main/eval/utils/utils.py

@@ -0,0 +1,297 @@
+import random
+
+import numpy as np
+import torch
+from PIL import Image, ImageSequence
+from torchvision import transforms
+from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
+from video_reader import PyVideoReader
+
+
+try:
+    from torchvision.transforms import InterpolationMode
+
+    BICUBIC = InterpolationMode.BICUBIC
+    BILINEAR = InterpolationMode.BILINEAR
+except ImportError:
+    BICUBIC = Image.BICUBIC
+    BILINEAR = Image.BILINEAR
+
+
+def clip_transform(n_px):
+    return Compose(
+        [
+            Resize(n_px, interpolation=BICUBIC, antialias=False),
+            CenterCrop(n_px),
+            transforms.Lambda(lambda x: x.float().div(255.0)),
+            Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
+        ]
+    )
+
+
+def clip_transform_Image(n_px):
+    return Compose(
+        [
+            Resize(n_px, interpolation=BICUBIC, antialias=False),
+            CenterCrop(n_px),
+            ToTensor(),
+            Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
+        ]
+    )
+
+
+def get_frame_indices(num_frames, vlen, sample="rand", fix_start=None, input_fps=1, max_num_frames=-1):
+    if sample in ["rand", "middle"]:  # uniform sampling
+        acc_samples = min(num_frames, vlen)
+        # split the video into `acc_samples` intervals, and sample from each interval.
+        intervals = np.linspace(start=0, stop=vlen, num=acc_samples + 1).astype(int)
+        ranges = []
+        for idx, interv in enumerate(intervals[:-1]):
+            ranges.append((interv, intervals[idx + 1] - 1))
+        if sample == "rand":
+            try:
+                frame_indices = [random.choice(range(x[0], x[1])) for x in ranges]
+            except Exception:
+                frame_indices = np.random.permutation(vlen)[:acc_samples]
+                frame_indices.sort()
+                frame_indices = list(frame_indices)
+        elif fix_start is not None:
+            frame_indices = [x[0] + fix_start for x in ranges]
+        elif sample == "middle":
+            frame_indices = [(x[0] + x[1]) // 2 for x in ranges]
+        else:
+            raise NotImplementedError
+
+        if len(frame_indices) < num_frames:  # padded with last frame
+            padded_frame_indices = [frame_indices[-1]] * num_frames
+            padded_frame_indices[: len(frame_indices)] = frame_indices
+            frame_indices = padded_frame_indices
+    elif "fps" in sample:  # fps0.5, sequentially sample frames at 0.5 fps
+        output_fps = float(sample[3:])
+        duration = float(vlen) / input_fps
+        delta = 1 / output_fps  # gap between frames, this is also the clip length each frame represents
+        frame_seconds = np.arange(0 + delta / 2, duration + delta / 2, delta)
+        frame_indices = np.around(frame_seconds * input_fps).astype(int)
+        frame_indices = [e for e in frame_indices if e < vlen]
+        if max_num_frames > 0 and len(frame_indices) > max_num_frames:
+            frame_indices = frame_indices[:max_num_frames]
+            # frame_indices = np.linspace(0 + delta / 2, duration + delta / 2, endpoint=False, num=max_num_frames)
+    else:
+        raise ValueError
+    return frame_indices
+
+
+def align_dimension(value, alignment=2):
+    return int(round(value / alignment) * alignment)
+
+
+def load_video(video_path, data_transform=None, num_frames=None, return_tensor=True, width=None, height=None):
+    if video_path.endswith(".gif"):
+        frame_ls = []
+        img = Image.open(video_path)
+        for frame in ImageSequence.Iterator(img):
+            frame = frame.convert("RGB")
+            frame = np.array(frame).astype(np.uint8)
+            frame_ls.append(frame)
+        buffer = np.array(frame_ls).astype(np.uint8)
+    elif video_path.endswith(".png"):
+        frame = Image.open(video_path)
+        frame = frame.convert("RGB")
+        frame = np.array(frame).astype(np.uint8)
+        frame_ls = [frame]
+        buffer = np.array(frame_ls)
+    elif video_path.endswith(".mp4"):
+        vr = PyVideoReader(video_path, threads=0)
+        if width is not None and height is not None:
+            (_, original_height, original_width) = vr.get_shape()
+            original_aspect_ratio = original_width / original_height
+            if width > height:
+                target_width = width
+                target_height = int(width / original_aspect_ratio)
+            else:
+                target_height = height
+                target_width = int(height * original_aspect_ratio)
+            target_height = align_dimension(target_height, 2)
+            target_width = align_dimension(target_width, 2)
+        vr = PyVideoReader(video_path, target_height=target_height, target_width=target_width, threads=0)
+        buffer = vr.decode()
+        vr = None
+        del vr
+    else:
+        raise NotImplementedError
+
+    frames = buffer
+    if num_frames and not video_path.endswith(".mp4"):
+        frame_indices = get_frame_indices(num_frames, len(frames), sample="middle")
+        frames = frames[frame_indices]
+
+    if data_transform:
+        frames = data_transform(frames)
+    elif return_tensor:
+        frames = torch.Tensor(frames)
+        frames = frames.permute(0, 3, 1, 2)  # (T, C, H, W), torch.uint8
+
+    return frames
+
+
+def read_frames_decord_by_fps(
+    video_path,
+    sample_fps=2,
+    sample="rand",
+    fix_start=None,
+    max_num_frames=-1,
+    trimmed30=False,
+    num_frames=8,
+    width=None,
+    height=None,
+):
+    vr_info = PyVideoReader(video_path, threads=0)
+    (vlen, original_height, original_width) = vr_info.get_shape()
+    fps = vr_info.get_fps()
+    duration = vlen / float(fps)
+    vr_info = None
+    del vr_info
+
+    if trimmed30 and duration > 30:
+        duration = 30
+        vlen = int(30 * float(fps))
+
+    target_width = None
+    target_height = None
+    if width is not None and height is not None:
+        original_aspect_ratio = original_width / original_height
+        if width > height:
+            target_width = width
+            target_height = int(width / original_aspect_ratio)
+        else:
+            target_height = height
+            target_width = int(height * original_aspect_ratio)
+        target_height = align_dimension(target_height, 2)
+        target_width = align_dimension(target_width, 2)
+
+    frame_indices = get_frame_indices(
+        num_frames, vlen, sample=sample, fix_start=fix_start, input_fps=fps, max_num_frames=max_num_frames
+    )
+
+    vr = PyVideoReader(video_path, target_height=target_height, target_width=target_width, threads=0)
+    buffer = vr.decode()
+    vr = None
+    del vr
+
+    frames = buffer[frame_indices]
+    if not isinstance(frames, torch.Tensor):
+        frames = torch.from_numpy(frames)
+
+    frames = frames.permute(0, 3, 1, 2)  # (T, H, W, C) -> (T, C, H, W)
+
+    return frames
+
+
+def load_video_frames(video_path, start_ratio=0.0, end_ratio=1.0, num_frames=8, height=384, width=640):
+    # First pass: get video shape
+    vr = PyVideoReader(video_path, threads=0)
+    (total_frames, original_height, original_width) = vr.get_shape()
+
+    # Calculate target dimensions maintaining aspect ratio
+    original_aspect_ratio = original_width / original_height
+    if width > height:
+        target_width = width
+        target_height = int(width / original_aspect_ratio)
+    else:
+        target_height = height
+        target_width = int(height * original_aspect_ratio)
+
+    target_height = align_dimension(target_height, 2)
+    target_width = align_dimension(target_width, 2)
+
+    # Calculate frame range
+    start_frame = int(total_frames * start_ratio)
+    end_frame = int(total_frames * end_ratio)
+    portion_length = end_frame - start_frame
+
+    if portion_length < num_frames:
+        # Expand the range to accommodate num_frames
+        needed_frames = num_frames - portion_length
+        expansion = needed_frames / 2
+
+        # Try to expand symmetrically
+        new_start = max(0, start_frame - int(np.ceil(expansion)))
+        new_end = min(total_frames, end_frame + int(np.floor(expansion)))
+
+        # If still not enough, expand further in available direction
+        if new_end - new_start < num_frames:
+            if new_start == 0:
+                new_end = min(total_frames, new_start + num_frames)
+            elif new_end == total_frames:
+                new_start = max(0, new_end - num_frames)
+
+        start_frame = new_start
+        end_frame = new_end
+        portion_length = end_frame - start_frame
+
+        # Now sample frames
+        frame_indices = np.linspace(start_frame, end_frame - 1, num_frames, dtype=int)
+    else:
+        # Sample uniformly from the portion
+        step = portion_length / num_frames
+        frame_indices = [int(start_frame + i * step) for i in range(num_frames)]
+
+    # Ensure indices are within bounds
+    frame_indices = [min(idx, total_frames - 1) for idx in frame_indices]
+
+    # Second pass: decode only needed frames with target dimensions
+    vr = PyVideoReader(video_path, target_height=target_height, target_width=target_width, threads=0)
+    frames = vr.get_batch(frame_indices)  # Only decode needed frames (num_frames, H, W, C)
+
+    # Convert to tensor if needed and permute to (T, C, H, W)
+    if not isinstance(frames, torch.Tensor):
+        frames = torch.from_numpy(frames)
+    frames = frames.permute(0, 3, 1, 2)  # (T, C, H, W)
+
+    # Clean up
+    vr = None
+    del vr
+
+    return frames
+
+
+def extract_video_segment(input_path, output_path, start_ratio, end_ratio):
+    """
+    尽可能保持原视频编码参数
+    """
+    import ffmpeg
+
+    # 获取原视频信息
+    probe = ffmpeg.probe(input_path)
+    video_stream = next(s for s in probe["streams"] if s["codec_type"] == "video")
+
+    duration = float(probe["format"]["duration"])
+    start_time = duration * start_ratio
+    segment_duration = duration * (end_ratio - start_ratio)
+
+    # 检测原视频编码参数
+    orig_codec = video_stream.get("codec_name", "h264")
+    orig_pix_fmt = video_stream.get("pix_fmt", "yuv420p")
+
+    # 如果原视频是 h264/h265,使用相同编码器
+    if orig_codec in ["h264", "hevc"]:
+        codec_name = "libx264" if orig_codec == "h264" else "libx265"
+    else:
+        codec_name = "libx264"  # fallback
+
+    (
+        ffmpeg.input(input_path, ss=start_time)
+        .output(
+            output_path,
+            t=segment_duration,
+            vcodec=codec_name,
+            crf=0,
+            preset="medium",
+            pix_fmt=orig_pix_fmt,
+            acodec="copy",
+            vsync="cfr",
+            map_metadata=0,
+        )
+        .overwrite_output()
+        .run(quiet=True)
+    )

Helios-main/helios/diffusers_version/pipeline_helios_diffusers.py

@@ -0,0 +1,1359 @@
+# Copyright 2025 The Helios Team and The HuggingFace 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
+# limitations under the License.
+
+import html
+import math
+from itertools import accumulate
+from typing import Any, Callable
+
+import numpy as np
+import regex as re
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, UMT5EncoderModel
+
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.image_processor import PipelineImageInput
+from diffusers.loaders import HeliosLoraLoaderMixin
+from diffusers.models import AutoencoderKLWan, HeliosTransformer3DModel
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers import HeliosScheduler
+from diffusers.utils import is_ftfy_available, is_torch_xla_available, logging, replace_example_docstring
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.video_processor import VideoProcessor
+
+from ..pipelines.pipeline_output import HeliosPipelineOutput
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+if is_ftfy_available():
+    import ftfy
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```python
+        >>> import torch
+        >>> from diffusers.utils import export_to_video
+        >>> from diffusers import AutoencoderKLWan, HeliosPipeline
+
+        >>> # Available models: BestWishYsh/Helios-Base, BestWishYsh/Helios-Mid, BestWishYsh/Helios-Distilled
+        >>> model_id = "BestWishYsh/Helios-Base"
+        >>> vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+        >>> pipe = HeliosPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
+        >>> pipe.to("cuda")
+
+        >>> prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window."
+        >>> negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
+
+        >>> output = pipe(
+        ...     prompt=prompt,
+        ...     negative_prompt=negative_prompt,
+        ...     height=384,
+        ...     width=640,
+        ...     num_frames=132,
+        ...     guidance_scale=5.0,
+        ... ).frames[0]
+        >>> export_to_video(output, "output.mp4", fps=24)
+        ```
+"""
+
+
+def optimized_scale(positive_flat, negative_flat):
+    positive_flat = positive_flat.float()
+    negative_flat = negative_flat.float()
+    # Calculate dot production
+    dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
+    # Squared norm of uncondition
+    squared_norm = torch.sum(negative_flat**2, dim=1, keepdim=True) + 1e-8
+    # st_star = v_cond^T * v_uncond / ||v_uncond||^2
+    st_star = dot_product / squared_norm
+    return st_star
+
+
+def basic_clean(text):
+    text = ftfy.fix_text(text)
+    text = html.unescape(html.unescape(text))
+    return text.strip()
+
+
+def whitespace_clean(text):
+    text = re.sub(r"\s+", " ", text)
+    text = text.strip()
+    return text
+
+
+def prompt_clean(text):
+    text = whitespace_clean(basic_clean(text))
+    return text
+
+
+# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.15,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+
+class HeliosPipeline(DiffusionPipeline, HeliosLoraLoaderMixin):
+    r"""
+    Pipeline for text-to-video / image-to-video / video-to-video generation using Helios.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
+
+    Args:
+        tokenizer ([`T5Tokenizer`]):
+            Tokenizer from [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5Tokenizer),
+            specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.
+        text_encoder ([`T5EncoderModel`]):
+            [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
+            the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.
+        transformer ([`HeliosTransformer3DModel`]):
+            Conditional Transformer to denoise the input latents.
+        scheduler ([`HeliosScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKLWan`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
+    """
+
+    model_cpu_offload_seq = "text_encoder->transformer->vae"
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+    _optional_components = ["transformer"]
+
+    def __init__(
+        self,
+        tokenizer: AutoTokenizer,
+        text_encoder: UMT5EncoderModel,
+        vae: AutoencoderKLWan,
+        scheduler: HeliosScheduler,
+        transformer: HeliosTransformer3DModel,
+        is_cfg_zero_star: bool = False,
+        is_distilled: bool = False,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            transformer=transformer,
+            scheduler=scheduler,
+        )
+        self.register_to_config(is_cfg_zero_star=is_cfg_zero_star)
+        self.register_to_config(is_distilled=is_distilled)
+        self.vae_scale_factor_temporal = self.vae.config.scale_factor_temporal if getattr(self, "vae", None) else 4
+        self.vae_scale_factor_spatial = self.vae.config.scale_factor_spatial if getattr(self, "vae", None) else 8
+        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
+
+    def _get_t5_prompt_embeds(
+        self,
+        prompt: str | list[str] = None,
+        num_videos_per_prompt: int = 1,
+        max_sequence_length: int = 226,
+        device: torch.device | None = None,
+        dtype: torch.dtype | None = None,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        prompt = [prompt_clean(u) for u in prompt]
+        batch_size = len(prompt)
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            add_special_tokens=True,
+            return_attention_mask=True,
+            return_tensors="pt",
+        )
+        text_input_ids, mask = text_inputs.input_ids, text_inputs.attention_mask
+        seq_lens = mask.gt(0).sum(dim=1).long()
+
+        prompt_embeds = self.text_encoder(text_input_ids.to(device), mask.to(device)).last_hidden_state
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+        prompt_embeds = [u[:v] for u, v in zip(prompt_embeds, seq_lens)]
+        prompt_embeds = torch.stack(
+            [torch.cat([u, u.new_zeros(max_sequence_length - u.size(0), u.size(1))]) for u in prompt_embeds], dim=0
+        )
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
+
+        return prompt_embeds, text_inputs.attention_mask.bool()
+
+    def encode_prompt(
+        self,
+        prompt: str | list[str],
+        negative_prompt: str | list[str] | None = None,
+        do_classifier_free_guidance: bool = True,
+        num_videos_per_prompt: int = 1,
+        prompt_embeds: torch.Tensor | None = None,
+        negative_prompt_embeds: torch.Tensor | None = None,
+        max_sequence_length: int = 226,
+        device: torch.device | None = None,
+        dtype: torch.dtype | None = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `list[str]`, *optional*):
+                prompt to be encoded
+            negative_prompt (`str` or `list[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
+                Whether to use classifier free guidance or not.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            device: (`torch.device`, *optional*):
+                torch device
+            dtype: (`torch.dtype`, *optional*):
+                torch dtype
+        """
+        device = device or self._execution_device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_embeds, _ = self._get_t5_prompt_embeds(
+                prompt=prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+
+            negative_prompt_embeds, _ = self._get_t5_prompt_embeds(
+                prompt=negative_prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        return prompt_embeds, negative_prompt_embeds
+
+    def check_inputs(
+        self,
+        prompt,
+        negative_prompt,
+        height,
+        width,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
+        image=None,
+        video=None,
+        use_interpolate_prompt=False,
+        num_videos_per_prompt=None,
+        interpolate_time_list=None,
+        interpolation_steps=None,
+        guidance_scale=None,
+    ):
+        if height % 16 != 0 or width % 16 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`: {negative_prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        elif negative_prompt is not None and (
+            not isinstance(negative_prompt, str) and not isinstance(negative_prompt, list)
+        ):
+            raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")
+
+        if image is not None and video is not None:
+            raise ValueError("image and video cannot be provided simultaneously")
+
+        if use_interpolate_prompt:
+            assert num_videos_per_prompt == 1, f"num_videos_per_prompt must be 1, got {num_videos_per_prompt}"
+            assert isinstance(prompt, list), "prompt must be a list"
+            assert len(prompt) == len(interpolate_time_list), (
+                f"Length mismatch: {len(prompt)} vs {len(interpolate_time_list)}"
+            )
+            assert min(interpolate_time_list) > interpolation_steps, (
+                f"Minimum value {min(interpolate_time_list)} must be greater than {interpolation_steps}"
+            )
+
+        if guidance_scale > 1.0 and self.config.is_distilled:
+            logger.warning(f"Guidance scale {guidance_scale} is ignored for step-wise distilled models.")
+
+    def prepare_latents(
+        self,
+        batch_size: int,
+        num_channels_latents: int = 16,
+        height: int = 384,
+        width: int = 640,
+        num_frames: int = 33,
+        dtype: torch.dtype | None = None,
+        device: torch.device | None = None,
+        generator: torch.Generator | list[torch.Generator] | None = None,
+        latents: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        if latents is not None:
+            return latents.to(device=device, dtype=dtype)
+
+        num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
+        shape = (
+            batch_size,
+            num_channels_latents,
+            num_latent_frames,
+            int(height) // self.vae_scale_factor_spatial,
+            int(width) // self.vae_scale_factor_spatial,
+        )
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        return latents
+
+    def prepare_image_latents(
+        self,
+        image: torch.Tensor,
+        latents_mean: torch.Tensor,
+        latents_std: torch.Tensor,
+        num_latent_frames_per_chunk: int,
+        dtype: torch.dtype | None = None,
+        device: torch.device | None = None,
+        generator: torch.Generator | list[torch.Generator] | None = None,
+        latents: torch.Tensor | None = None,
+        fake_latents: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        device = device or self._execution_device
+        if latents is None:
+            image = image.unsqueeze(2).to(device=device, dtype=self.vae.dtype)
+            latents = self.vae.encode(image).latent_dist.sample(generator=generator)
+            latents = (latents - latents_mean) * latents_std
+        if fake_latents is None:
+            min_frames = (num_latent_frames_per_chunk - 1) * self.vae_scale_factor_temporal + 1
+            fake_video = image.repeat(1, 1, min_frames, 1, 1).to(device=device, dtype=self.vae.dtype)
+            fake_latents_full = self.vae.encode(fake_video).latent_dist.sample(generator=generator)
+            fake_latents_full = (fake_latents_full - latents_mean) * latents_std
+            fake_latents = fake_latents_full[:, :, -1:, :, :]
+        return latents.to(device=device, dtype=dtype), fake_latents.to(device=device, dtype=dtype)
+
+    def prepare_video_latents(
+        self,
+        video: torch.Tensor,
+        latents_mean: torch.Tensor,
+        latents_std: torch.Tensor,
+        num_latent_frames_per_chunk: int,
+        dtype: torch.dtype | None = None,
+        device: torch.device | None = None,
+        generator: torch.Generator | list[torch.Generator] | None = None,
+        latents: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        device = device or self._execution_device
+        video = video.to(device=device, dtype=self.vae.dtype)
+        if latents is None:
+            num_frames = video.shape[2]
+            min_frames = (num_latent_frames_per_chunk - 1) * self.vae_scale_factor_temporal + 1
+            num_chunks = num_frames // min_frames
+            if num_chunks == 0:
+                raise ValueError(
+                    f"Video must have at least {min_frames} frames "
+                    f"(got {num_frames} frames). "
+                    f"Required: (num_latent_frames_per_chunk - 1) * {self.vae_scale_factor_temporal} + 1 = ({num_latent_frames_per_chunk} - 1) * {self.vae_scale_factor_temporal} + 1 = {min_frames}"
+                )
+            total_valid_frames = num_chunks * min_frames
+            start_frame = num_frames - total_valid_frames
+
+            first_frame = video[:, :, 0:1, :, :]
+            first_frame_latent = self.vae.encode(first_frame).latent_dist.sample(generator=generator)
+            first_frame_latent = (first_frame_latent - latents_mean) * latents_std
+
+            latents_chunks = []
+            for i in range(num_chunks):
+                chunk_start = start_frame + i * min_frames
+                chunk_end = chunk_start + min_frames
+                video_chunk = video[:, :, chunk_start:chunk_end, :, :]
+                chunk_latents = self.vae.encode(video_chunk).latent_dist.sample(generator=generator)
+                chunk_latents = (chunk_latents - latents_mean) * latents_std
+                latents_chunks.append(chunk_latents)
+            latents = torch.cat(latents_chunks, dim=2)
+        return first_frame_latent.to(device=device, dtype=dtype), latents.to(device=device, dtype=dtype)
+
+    def interpolate_prompt_embeds(
+        self,
+        prompt_embeds_1: torch.Tensor,
+        prompt_embeds_2: torch.Tensor,
+        interpolation_steps: int = 3,
+    ):
+        x = torch.lerp(
+            prompt_embeds_1,
+            prompt_embeds_2,
+            torch.linspace(0, 1, steps=interpolation_steps).unsqueeze(1).unsqueeze(2).to(prompt_embeds_1),
+        )
+        interpolated_prompt_embeds = list(x.chunk(interpolation_steps, dim=0))
+        return interpolated_prompt_embeds
+
+    def sample_block_noise(
+        self,
+        batch_size,
+        channel,
+        num_frames,
+        height,
+        width,
+        patch_size: tuple[int, ...] = (1, 2, 2),
+        device: torch.device | None = None,
+        generator: torch.Generator | None = None,
+    ):
+        # NOTE: A generator must be provided to ensure correct and reproducible results.
+        # Creating a default generator here is a fallback only — without a fixed seed,
+        # the output will be non-deterministic and may produce incorrect results in CP context.
+        if generator is None:
+            generator = torch.Generator(device=device)
+        elif isinstance(generator, list):
+            generator = generator[0]
+
+        gamma = self.scheduler.config.gamma
+        _, ph, pw = patch_size
+        block_size = ph * pw
+
+        cov = (
+            torch.eye(block_size, device=device) * (1 + gamma)
+            - torch.ones(block_size, block_size, device=device) * gamma
+        )
+        cov += torch.eye(block_size, device=device) * 1e-8
+        cov = cov.float()  # Upcast to fp32 for numerical stability — cholesky is unreliable in fp16/bf16.
+
+        L = torch.linalg.cholesky(cov)
+        block_number = batch_size * channel * num_frames * (height // ph) * (width // pw)
+        z = torch.randn(block_number, block_size, generator=generator, device=generator.device).to(device=device)
+        noise = z @ L.T
+
+        noise = noise.view(batch_size, channel, num_frames, height // ph, width // pw, ph, pw)
+        noise = noise.permute(0, 1, 2, 3, 5, 4, 6).reshape(batch_size, channel, num_frames, height, width)
+
+        return noise
+
+    def stage1_sample(
+        self,
+        latents: torch.Tensor = None,
+        prompt_embeds: torch.Tensor = None,
+        negative_prompt_embeds: torch.Tensor = None,
+        timesteps: torch.Tensor = None,
+        guidance_scale: float | None = 5.0,
+        indices_hidden_states: torch.Tensor = None,
+        indices_latents_history_short: torch.Tensor = None,
+        indices_latents_history_mid: torch.Tensor = None,
+        indices_latents_history_long: torch.Tensor = None,
+        latents_history_short: torch.Tensor = None,
+        latents_history_mid: torch.Tensor = None,
+        latents_history_long: torch.Tensor = None,
+        attention_kwargs: dict | None = None,
+        device: torch.device | None = None,
+        transformer_dtype: torch.dtype = None,
+        generator: torch.Generator | None = None,
+        num_warmup_steps: int | None = None,
+        # ------------ CFG Zero ------------
+        use_zero_init: bool | None = True,
+        zero_steps: int | None = 1,
+        # ------------ Callback ------------
+        callback_on_step_end: Callable[[int, int], None] | PipelineCallback | MultiPipelineCallbacks | None = None,
+        callback_on_step_end_tensor_inputs: list[str] = ["latents"],
+        progress_bar=None,
+    ):
+        batch_size = latents.shape[0]
+
+        for i, t in enumerate(timesteps):
+            if self.interrupt:
+                continue
+
+            self._current_timestep = t
+            timestep = t.expand(latents.shape[0])
+
+            latent_model_input = latents.to(transformer_dtype)
+            with self.transformer.cache_context("cond"):
+                noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    timestep=timestep,
+                    encoder_hidden_states=prompt_embeds,
+                    indices_hidden_states=indices_hidden_states,
+                    indices_latents_history_short=indices_latents_history_short,
+                    indices_latents_history_mid=indices_latents_history_mid,
+                    indices_latents_history_long=indices_latents_history_long,
+                    latents_history_short=latents_history_short.to(transformer_dtype),
+                    latents_history_mid=latents_history_mid.to(transformer_dtype),
+                    latents_history_long=latents_history_long.to(transformer_dtype),
+                    attention_kwargs=attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+            if self.do_classifier_free_guidance:
+                with self.transformer.cache_context("uncond"):
+                    noise_uncond = self.transformer(
+                        hidden_states=latent_model_input,
+                        timestep=timestep,
+                        encoder_hidden_states=negative_prompt_embeds,
+                        indices_hidden_states=indices_hidden_states,
+                        indices_latents_history_short=indices_latents_history_short,
+                        indices_latents_history_mid=indices_latents_history_mid,
+                        indices_latents_history_long=indices_latents_history_long,
+                        latents_history_short=latents_history_short.to(transformer_dtype),
+                        latents_history_mid=latents_history_mid.to(transformer_dtype),
+                        latents_history_long=latents_history_long.to(transformer_dtype),
+                        attention_kwargs=attention_kwargs,
+                        return_dict=False,
+                    )[0]
+
+                if self.config.is_cfg_zero_star:
+                    noise_pred_text = noise_pred
+                    positive_flat = noise_pred_text.view(batch_size, -1)
+                    negative_flat = noise_uncond.view(batch_size, -1)
+
+                    alpha = optimized_scale(positive_flat, negative_flat)
+                    alpha = alpha.view(batch_size, *([1] * (len(noise_pred_text.shape) - 1)))
+                    alpha = alpha.to(noise_pred_text.dtype)
+
+                    if (i <= zero_steps) and use_zero_init:
+                        noise_pred = noise_pred_text * 0.0
+                    else:
+                        noise_pred = noise_uncond * alpha + guidance_scale * (noise_pred_text - noise_uncond * alpha)
+                else:
+                    noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)
+
+            latents = self.scheduler.step(
+                noise_pred,
+                t,
+                latents,
+                return_dict=False,
+            )[0]
+
+            if callback_on_step_end is not None:
+                callback_kwargs = {}
+                for k in callback_on_step_end_tensor_inputs:
+                    callback_kwargs[k] = locals()[k]
+                callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                latents = callback_outputs.pop("latents", latents)
+                prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+            if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                progress_bar.update()
+
+            if XLA_AVAILABLE:
+                xm.mark_step()
+
+        return latents
+
+    def stage2_sample(
+        self,
+        latents: torch.Tensor = None,
+        pyramid_num_stages: int = None,
+        pyramid_num_inference_steps_list: list[int] = None,
+        prompt_embeds: torch.Tensor = None,
+        negative_prompt_embeds: torch.Tensor = None,
+        guidance_scale: float | None = 5.0,
+        indices_hidden_states: torch.Tensor = None,
+        indices_latents_history_short: torch.Tensor = None,
+        indices_latents_history_mid: torch.Tensor = None,
+        indices_latents_history_long: torch.Tensor = None,
+        latents_history_short: torch.Tensor = None,
+        latents_history_mid: torch.Tensor = None,
+        latents_history_long: torch.Tensor = None,
+        attention_kwargs: dict | None = None,
+        device: torch.device | None = None,
+        transformer_dtype: torch.dtype = None,
+        generator: torch.Generator | None = None,
+        # ------------ CFG Zero ------------
+        use_zero_init: bool | None = True,
+        zero_steps: int | None = 1,
+        # -------------- DMD --------------
+        is_amplify_first_chunk: bool = False,
+        # ------------ Callback ------------
+        callback_on_step_end: Callable[[int, int], None] | PipelineCallback | MultiPipelineCallbacks | None = None,
+        callback_on_step_end_tensor_inputs: list[str] = ["latents"],
+        progress_bar=None,
+    ):
+        batch_size, num_channel, num_frames, height, width = latents.shape
+        latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, num_channel, height, width)
+        for _ in range(pyramid_num_stages - 1):
+            height //= 2
+            width //= 2
+            latents = (
+                F.interpolate(
+                    latents,
+                    size=(height, width),
+                    mode="bilinear",
+                )
+                * 2
+            )
+        latents = latents.reshape(batch_size, num_frames, num_channel, height, width).permute(0, 2, 1, 3, 4)
+
+        batch_size = latents.shape[0]
+        start_point_list = None
+        if self.config.is_distilled:
+            start_point_list = [latents]
+
+        i = 0
+        for i_s in range(pyramid_num_stages):
+            patch_size = self.transformer.config.patch_size
+            image_seq_len = (latents.shape[-1] * latents.shape[-2] * latents.shape[-3]) // (
+                patch_size[0] * patch_size[1] * patch_size[2]
+            )
+            mu = calculate_shift(
+                image_seq_len,
+                self.scheduler.config.get("base_image_seq_len", 256),
+                self.scheduler.config.get("max_image_seq_len", 4096),
+                self.scheduler.config.get("base_shift", 0.5),
+                self.scheduler.config.get("max_shift", 1.15),
+            )
+            self.scheduler.set_timesteps(
+                pyramid_num_inference_steps_list[i_s],
+                i_s,
+                device=device,
+                mu=mu,
+                is_amplify_first_chunk=is_amplify_first_chunk,
+            )
+            timesteps = self.scheduler.timesteps
+
+            if i_s > 0:
+                height *= 2
+                width *= 2
+                num_frames = latents.shape[2]
+                latents = latents.permute(0, 2, 1, 3, 4).reshape(
+                    batch_size * num_frames, num_channel, height // 2, width // 2
+                )
+                latents = F.interpolate(latents, size=(height, width), mode="nearest")
+                latents = latents.reshape(batch_size, num_frames, num_channel, height, width).permute(0, 2, 1, 3, 4)
+                # Fix the stage
+                ori_sigma = 1 - self.scheduler.ori_start_sigmas[i_s]  # the original coeff of signal
+                gamma = self.scheduler.config.gamma
+                alpha = 1 / (math.sqrt(1 + (1 / gamma)) * (1 - ori_sigma) + ori_sigma)
+                beta = alpha * (1 - ori_sigma) / math.sqrt(gamma)
+
+                batch_size, channel, num_frames, height, width = latents.shape
+                noise = self.sample_block_noise(
+                    batch_size, channel, num_frames, height, width, patch_size, device, generator
+                )
+                noise = noise.to(device=device, dtype=transformer_dtype)
+                latents = alpha * latents + beta * noise  # To fix the block artifact
+
+                if self.config.is_distilled:
+                    start_point_list.append(latents)
+
+            for idx, t in enumerate(timesteps):
+                timestep = t.expand(latents.shape[0]).to(torch.int64)
+
+                with self.transformer.cache_context("cond"):
+                    noise_pred = self.transformer(
+                        hidden_states=latents.to(transformer_dtype),
+                        timestep=timestep,
+                        encoder_hidden_states=prompt_embeds,
+                        attention_kwargs=attention_kwargs,
+                        return_dict=False,
+                        indices_hidden_states=indices_hidden_states,
+                        indices_latents_history_short=indices_latents_history_short,
+                        indices_latents_history_mid=indices_latents_history_mid,
+                        indices_latents_history_long=indices_latents_history_long,
+                        latents_history_short=latents_history_short.to(transformer_dtype),
+                        latents_history_mid=latents_history_mid.to(transformer_dtype),
+                        latents_history_long=latents_history_long.to(transformer_dtype),
+                    )[0]
+
+                if self.do_classifier_free_guidance:
+                    with self.transformer.cache_context("uncond"):
+                        noise_uncond = self.transformer(
+                            hidden_states=latents.to(transformer_dtype),
+                            timestep=timestep,
+                            encoder_hidden_states=negative_prompt_embeds,
+                            attention_kwargs=attention_kwargs,
+                            return_dict=False,
+                            indices_hidden_states=indices_hidden_states,
+                            indices_latents_history_short=indices_latents_history_short,
+                            indices_latents_history_mid=indices_latents_history_mid,
+                            indices_latents_history_long=indices_latents_history_long,
+                            latents_history_short=latents_history_short.to(transformer_dtype),
+                            latents_history_mid=latents_history_mid.to(transformer_dtype),
+                            latents_history_long=latents_history_long.to(transformer_dtype),
+                        )[0]
+
+                    if self.config.is_cfg_zero_star:
+                        noise_pred_text = noise_pred
+                        positive_flat = noise_pred_text.view(batch_size, -1)
+                        negative_flat = noise_uncond.view(batch_size, -1)
+
+                        alpha = optimized_scale(positive_flat, negative_flat)
+                        alpha = alpha.view(batch_size, *([1] * (len(noise_pred_text.shape) - 1)))
+                        alpha = alpha.to(noise_pred_text.dtype)
+
+                        if (i_s == 0 and idx <= zero_steps) and use_zero_init:
+                            noise_pred = noise_pred_text * 0.0
+                        else:
+                            noise_pred = noise_uncond * alpha + guidance_scale * (
+                                noise_pred_text - noise_uncond * alpha
+                            )
+                    else:
+                        noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)
+
+                latents = self.scheduler.step(
+                    noise_pred,
+                    t,
+                    latents,
+                    generator=generator,
+                    return_dict=False,
+                    cur_sampling_step=idx,
+                    dmd_noisy_tensor=start_point_list[i_s] if start_point_list is not None else None,
+                    dmd_sigmas=self.scheduler.sigmas,
+                    dmd_timesteps=self.scheduler.timesteps,
+                    all_timesteps=timesteps,
+                )[0]
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+                i += 1
+
+        return latents
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1.0
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def current_timestep(self):
+        return self._current_timestep
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: str | list[str] = None,
+        negative_prompt: str | list[str] = None,
+        height: int = 384,
+        width: int = 640,
+        num_frames: int = 132,
+        num_inference_steps: int = 50,
+        sigmas: list[float] = None,
+        guidance_scale: float = 5.0,
+        num_videos_per_prompt: int | None = 1,
+        generator: torch.Generator | list[torch.Generator] | None = None,
+        latents: torch.Tensor | None = None,
+        prompt_embeds: torch.Tensor | None = None,
+        negative_prompt_embeds: torch.Tensor | None = None,
+        output_type: str | None = "np",
+        return_dict: bool = True,
+        attention_kwargs: dict[str, Any] | None = None,
+        callback_on_step_end: Callable[[int, int], None] | PipelineCallback | MultiPipelineCallbacks | None = None,
+        callback_on_step_end_tensor_inputs: list[str] = ["latents"],
+        max_sequence_length: int = 512,
+        # ------------ I2V ------------
+        image: PipelineImageInput | None = None,
+        image_latents: torch.Tensor | None = None,
+        fake_image_latents: torch.Tensor | None = None,
+        add_noise_to_image_latents: bool = True,
+        image_noise_sigma_min: float = 0.111,
+        image_noise_sigma_max: float = 0.135,
+        # ------------ V2V ------------
+        video: PipelineImageInput | None = None,
+        video_latents: torch.Tensor | None = None,
+        add_noise_to_video_latents: bool = True,
+        video_noise_sigma_min: float = 0.111,
+        video_noise_sigma_max: float = 0.135,
+        # ------------ Interactive ------------
+        use_interpolate_prompt: bool = False,
+        interpolate_time_list: list = [7, 7, 7],
+        interpolation_steps: int = 3,
+        # ------------ Stage 1 ------------
+        history_sizes: list = [16, 2, 1],
+        num_latent_frames_per_chunk: int = 9,
+        keep_first_frame: bool = True,
+        is_skip_first_chunk: bool = False,
+        # ------------ Stage 2 ------------
+        is_enable_stage2: bool = False,
+        pyramid_num_stages: int = 3,
+        pyramid_num_inference_steps_list: list = [10, 10, 10],
+        # ------------ CFG Zero ------------
+        use_zero_init: bool | None = True,
+        zero_steps: int | None = 1,
+        # ------------ DMD ------------
+        is_amplify_first_chunk: bool = False,
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            prompt (`str` or `list[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, pass `prompt_embeds` instead.
+            negative_prompt (`str` or `list[str]`, *optional*):
+                The prompt or prompts to avoid during image generation. If not defined, pass `negative_prompt_embeds`
+                instead. Ignored when not using guidance (`guidance_scale` < `1`).
+            height (`int`, defaults to `384`):
+                The height in pixels of the generated image.
+            width (`int`, defaults to `640`):
+                The width in pixels of the generated image.
+            num_frames (`int`, defaults to `132`):
+                The number of frames in the generated video.
+            num_inference_steps (`int`, defaults to `50`):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, defaults to `5.0`):
+                Guidance scale as defined in [Classifier-Free Diffusion
+                Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
+                of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
+                `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
+                the text `prompt`, usually at the expense of lower image quality.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `list[torch.Generator]`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            latents (`torch.Tensor`, *optional*):
+                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor is generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            output_type (`str`, *optional*, defaults to `"np"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`HeliosPipelineOutput`] instead of a plain tuple.
+            attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
+                A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
+                each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
+                DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
+                list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`list`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int`, defaults to `512`):
+                The maximum sequence length of the text encoder. If the prompt is longer than this, it will be
+                truncated. If the prompt is shorter, it will be padded to this length.
+
+        Examples:
+
+        Returns:
+            [`~HeliosPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`HeliosPipelineOutput`] is returned, otherwise a `tuple` is returned where
+                the first element is a list with the generated images and the second element is a list of `bool`s
+                indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.
+        """
+
+        if image is not None and video is not None:
+            raise ValueError("image and video cannot be provided simultaneously")
+
+        history_sizes = sorted(history_sizes, reverse=True)  # From big to small
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            negative_prompt,
+            height,
+            width,
+            prompt_embeds,
+            negative_prompt_embeds,
+            callback_on_step_end_tensor_inputs,
+            image,
+            video,
+            use_interpolate_prompt,
+            num_videos_per_prompt,
+            interpolate_time_list,
+            interpolation_steps,
+            guidance_scale,
+        )
+
+        num_frames = max(num_frames, 1)
+
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+        self._current_timestep = None
+        self._interrupt = False
+
+        device = self._execution_device
+        vae_dtype = self.vae.dtype
+
+        latents_mean = (
+            torch.tensor(self.vae.config.latents_mean)
+            .view(1, self.vae.config.z_dim, 1, 1, 1)
+            .to(device, self.vae.dtype)
+        )
+        latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
+            device, self.vae.dtype
+        )
+
+        # 2. Define call parameters
+        if use_interpolate_prompt or (prompt is not None and isinstance(prompt, str)):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        # 3. Encode input prompt
+        if use_interpolate_prompt:
+            interpolate_interval_idx = None
+            interpolate_embeds = None
+            interpolate_cumulative_list = list(accumulate(interpolate_time_list))
+
+        all_prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            num_videos_per_prompt=num_videos_per_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            max_sequence_length=max_sequence_length,
+            device=device,
+        )
+
+        transformer_dtype = self.transformer.dtype
+        all_prompt_embeds = all_prompt_embeds.to(transformer_dtype)
+        if negative_prompt_embeds is not None:
+            if use_interpolate_prompt:
+                negative_prompt_embeds = negative_prompt_embeds[0].unsqueeze(0)
+            negative_prompt_embeds = negative_prompt_embeds.to(transformer_dtype)
+
+        # 4. Prepare image or video
+        if image is not None:
+            image = self.video_processor.preprocess(image, height=height, width=width)
+            image_latents, fake_image_latents = self.prepare_image_latents(
+                image,
+                latents_mean=latents_mean,
+                latents_std=latents_std,
+                num_latent_frames_per_chunk=num_latent_frames_per_chunk,
+                dtype=torch.float32,
+                device=device,
+                generator=generator,
+                latents=image_latents,
+                fake_latents=fake_image_latents,
+            )
+
+        if image_latents is not None and add_noise_to_image_latents:
+            image_noise_sigma = (
+                torch.rand(1, device=device, generator=generator) * (image_noise_sigma_max - image_noise_sigma_min)
+                + image_noise_sigma_min
+            )
+            image_latents = (
+                image_noise_sigma * randn_tensor(image_latents.shape, generator=generator, device=device)
+                + (1 - image_noise_sigma) * image_latents
+            )
+            fake_image_noise_sigma = (
+                torch.rand(1, device=device, generator=generator) * (video_noise_sigma_max - video_noise_sigma_min)
+                + video_noise_sigma_min
+            )
+            fake_image_latents = (
+                fake_image_noise_sigma * randn_tensor(fake_image_latents.shape, generator=generator, device=device)
+                + (1 - fake_image_noise_sigma) * fake_image_latents
+            )
+
+        if video is not None:
+            video = self.video_processor.preprocess_video(video, height=height, width=width)
+            image_latents, video_latents = self.prepare_video_latents(
+                video,
+                latents_mean=latents_mean,
+                latents_std=latents_std,
+                num_latent_frames_per_chunk=num_latent_frames_per_chunk,
+                dtype=torch.float32,
+                device=device,
+                generator=generator,
+                latents=video_latents,
+            )
+
+        if video_latents is not None and add_noise_to_video_latents:
+            image_noise_sigma = (
+                torch.rand(1, device=device, generator=generator) * (image_noise_sigma_max - image_noise_sigma_min)
+                + image_noise_sigma_min
+            )
+            image_latents = (
+                image_noise_sigma * randn_tensor(image_latents.shape, generator=generator, device=device)
+                + (1 - image_noise_sigma) * image_latents
+            )
+
+            noisy_latents_chunks = []
+            num_latent_chunks = video_latents.shape[2] // num_latent_frames_per_chunk
+            for i in range(num_latent_chunks):
+                chunk_start = i * num_latent_frames_per_chunk
+                chunk_end = chunk_start + num_latent_frames_per_chunk
+                latent_chunk = video_latents[:, :, chunk_start:chunk_end, :, :]
+
+                chunk_frames = latent_chunk.shape[2]
+                frame_sigmas = (
+                    torch.rand(chunk_frames, device=device, generator=generator)
+                    * (video_noise_sigma_max - video_noise_sigma_min)
+                    + video_noise_sigma_min
+                )
+                frame_sigmas = frame_sigmas.view(1, 1, chunk_frames, 1, 1)
+
+                noisy_chunk = (
+                    frame_sigmas * randn_tensor(latent_chunk.shape, generator=generator, device=device)
+                    + (1 - frame_sigmas) * latent_chunk
+                )
+                noisy_latents_chunks.append(noisy_chunk)
+            video_latents = torch.cat(noisy_latents_chunks, dim=2)
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels
+        window_num_frames = (num_latent_frames_per_chunk - 1) * self.vae_scale_factor_temporal + 1
+        num_latent_chunk = max(1, (num_frames + window_num_frames - 1) // window_num_frames)
+        num_history_latent_frames = sum(history_sizes)
+        history_video = None
+        total_generated_latent_frames = 0
+
+        if not keep_first_frame:
+            history_sizes[-1] = history_sizes[-1] + 1
+        history_latents = torch.zeros(
+            batch_size,
+            num_channels_latents,
+            num_history_latent_frames,
+            height // self.vae_scale_factor_spatial,
+            width // self.vae_scale_factor_spatial,
+            device=device,
+            dtype=torch.float32,
+        )
+        if fake_image_latents is not None:
+            history_latents = torch.cat([history_latents[:, :, :-1, :, :], fake_image_latents], dim=2)
+            total_generated_latent_frames += 1
+        if video_latents is not None:
+            history_frames = history_latents.shape[2]
+            video_frames = video_latents.shape[2]
+            if video_frames < history_frames:
+                keep_frames = history_frames - video_frames
+                history_latents = torch.cat([history_latents[:, :, :keep_frames, :, :], video_latents], dim=2)
+            else:
+                history_latents = video_latents
+            total_generated_latent_frames += video_latents.shape[2]
+
+        if keep_first_frame:
+            indices = torch.arange(0, sum([1, *history_sizes, num_latent_frames_per_chunk]))
+            (
+                indices_prefix,
+                indices_latents_history_long,
+                indices_latents_history_mid,
+                indices_latents_history_1x,
+                indices_hidden_states,
+            ) = indices.split([1, *history_sizes, num_latent_frames_per_chunk], dim=0)
+            indices_latents_history_short = torch.cat([indices_prefix, indices_latents_history_1x], dim=0)
+        else:
+            indices = torch.arange(0, sum([*history_sizes, num_latent_frames_per_chunk]))
+            (
+                indices_latents_history_long,
+                indices_latents_history_mid,
+                indices_latents_history_short,
+                indices_hidden_states,
+            ) = indices.split([*history_sizes, num_latent_frames_per_chunk], dim=0)
+        indices_hidden_states = indices_hidden_states.unsqueeze(0)
+        indices_latents_history_short = indices_latents_history_short.unsqueeze(0)
+        indices_latents_history_mid = indices_latents_history_mid.unsqueeze(0)
+        indices_latents_history_long = indices_latents_history_long.unsqueeze(0)
+
+        # 6. Denoising loop
+        if use_interpolate_prompt:
+            if num_latent_chunk < max(interpolate_cumulative_list):
+                num_latent_chunk = sum(interpolate_cumulative_list)
+                print(f"Update num_latent_chunk to: {num_latent_chunk}")
+
+        if not is_enable_stage2:
+            patch_size = self.transformer.config.patch_size
+            image_seq_len = (
+                num_latent_frames_per_chunk
+                * (height // self.vae_scale_factor_spatial)
+                * (width // self.vae_scale_factor_spatial)
+                // (patch_size[0] * patch_size[1] * patch_size[2])
+            )
+            sigmas = np.linspace(0.999, 0.0, num_inference_steps + 1)[:-1] if sigmas is None else sigmas
+            mu = calculate_shift(
+                image_seq_len,
+                self.scheduler.config.get("base_image_seq_len", 256),
+                self.scheduler.config.get("max_image_seq_len", 4096),
+                self.scheduler.config.get("base_shift", 0.5),
+                self.scheduler.config.get("max_shift", 1.15),
+            )
+
+        for k in range(num_latent_chunk):
+            if use_interpolate_prompt:
+                assert num_latent_chunk >= max(interpolate_cumulative_list)
+
+                current_interval_idx = 0
+                for idx, cumulative_val in enumerate(interpolate_cumulative_list):
+                    if k < cumulative_val:
+                        current_interval_idx = idx
+                        break
+
+                if current_interval_idx == 0:
+                    prompt_embeds = all_prompt_embeds[0].unsqueeze(0)
+                else:
+                    interval_start = interpolate_cumulative_list[current_interval_idx - 1]
+                    position_in_interval = k - interval_start
+
+                    if position_in_interval < interpolation_steps:
+                        if interpolate_embeds is None or interpolate_interval_idx != current_interval_idx:
+                            interpolate_embeds = self.interpolate_prompt_embeds(
+                                prompt_embeds_1=all_prompt_embeds[current_interval_idx - 1].unsqueeze(0),
+                                prompt_embeds_2=all_prompt_embeds[current_interval_idx].unsqueeze(0),
+                                interpolation_steps=interpolation_steps,
+                            )
+                            interpolate_interval_idx = current_interval_idx
+
+                        prompt_embeds = interpolate_embeds[position_in_interval]
+                    else:
+                        prompt_embeds = all_prompt_embeds[current_interval_idx].unsqueeze(0)
+            else:
+                prompt_embeds = all_prompt_embeds
+
+            is_first_chunk = k == 0
+            is_second_chunk = k == 1
+            if keep_first_frame:
+                latents_history_long, latents_history_mid, latents_history_1x = history_latents[
+                    :, :, -num_history_latent_frames:
+                ].split(history_sizes, dim=2)
+                if image_latents is None and is_first_chunk:
+                    latents_prefix = torch.zeros(
+                        (
+                            batch_size,
+                            num_channels_latents,
+                            1,
+                            latents_history_1x.shape[-2],
+                            latents_history_1x.shape[-1],
+                        ),
+                        device=device,
+                        dtype=latents_history_1x.dtype,
+                    )
+                else:
+                    latents_prefix = image_latents
+                latents_history_short = torch.cat([latents_prefix, latents_history_1x], dim=2)
+            else:
+                latents_history_long, latents_history_mid, latents_history_short = history_latents[
+                    :, :, -num_history_latent_frames:
+                ].split(history_sizes, dim=2)
+
+            latents = self.prepare_latents(
+                batch_size,
+                num_channels_latents,
+                height,
+                width,
+                window_num_frames,
+                dtype=torch.float32,
+                device=device,
+                generator=generator,
+                latents=None,
+            )
+
+            if not is_enable_stage2:
+                self.scheduler.set_timesteps(num_inference_steps, device=device, sigmas=sigmas, mu=mu)
+                timesteps = self.scheduler.timesteps
+                num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+                self._num_timesteps = len(timesteps)
+            else:
+                num_inference_steps = (
+                    sum(pyramid_num_inference_steps_list) * 2
+                    if is_amplify_first_chunk and self.config.is_distilled and is_first_chunk
+                    else sum(pyramid_num_inference_steps_list)
+                )
+
+            with self.progress_bar(total=num_inference_steps) as progress_bar:
+                if is_enable_stage2:
+                    latents = self.stage2_sample(
+                        latents=latents,
+                        pyramid_num_stages=pyramid_num_stages,
+                        pyramid_num_inference_steps_list=pyramid_num_inference_steps_list,
+                        prompt_embeds=prompt_embeds,
+                        negative_prompt_embeds=negative_prompt_embeds,
+                        guidance_scale=guidance_scale,
+                        indices_hidden_states=indices_hidden_states,
+                        indices_latents_history_short=indices_latents_history_short,
+                        indices_latents_history_mid=indices_latents_history_mid,
+                        indices_latents_history_long=indices_latents_history_long,
+                        latents_history_short=latents_history_short,
+                        latents_history_mid=latents_history_mid,
+                        latents_history_long=latents_history_long,
+                        attention_kwargs=attention_kwargs,
+                        device=device,
+                        transformer_dtype=transformer_dtype,
+                        # ------------ CFG Zero ------------
+                        use_zero_init=use_zero_init,
+                        zero_steps=zero_steps,
+                        # -------------- DMD --------------
+                        is_amplify_first_chunk=is_amplify_first_chunk and is_first_chunk,
+                        # ------------ Callback ------------
+                        callback_on_step_end=callback_on_step_end,
+                        callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+                        progress_bar=progress_bar,
+                    )
+                else:
+                    latents = self.stage1_sample(
+                        latents=latents,
+                        prompt_embeds=prompt_embeds,
+                        negative_prompt_embeds=negative_prompt_embeds,
+                        timesteps=timesteps,
+                        guidance_scale=guidance_scale,
+                        indices_hidden_states=indices_hidden_states,
+                        indices_latents_history_short=indices_latents_history_short,
+                        indices_latents_history_mid=indices_latents_history_mid,
+                        indices_latents_history_long=indices_latents_history_long,
+                        latents_history_short=latents_history_short,
+                        latents_history_mid=latents_history_mid,
+                        latents_history_long=latents_history_long,
+                        attention_kwargs=attention_kwargs,
+                        device=device,
+                        transformer_dtype=transformer_dtype,
+                        generator=generator,
+                        num_warmup_steps=num_warmup_steps,
+                        # ------------ CFG Zero ------------
+                        use_zero_init=use_zero_init,
+                        zero_steps=zero_steps,
+                        # ------------ Callback ------------
+                        callback_on_step_end=callback_on_step_end,
+                        callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+                        progress_bar=progress_bar,
+                    )
+
+                if keep_first_frame and (
+                    (is_first_chunk and image_latents is None) or (is_skip_first_chunk and is_second_chunk)
+                ):
+                    image_latents = latents[:, :, 0:1, :, :]
+
+                total_generated_latent_frames += latents.shape[2]
+                history_latents = torch.cat([history_latents, latents], dim=2)
+                real_history_latents = history_latents[:, :, -total_generated_latent_frames:]
+                current_latents = (
+                    real_history_latents[:, :, -num_latent_frames_per_chunk:].to(vae_dtype) / latents_std
+                    + latents_mean
+                )
+                current_video = self.vae.decode(current_latents, return_dict=False)[0]
+
+                if history_video is None:
+                    history_video = current_video
+                else:
+                    history_video = torch.cat([history_video, current_video], dim=2)
+
+        self._current_timestep = None
+
+        if output_type != "latent":
+            generated_frames = history_video.size(2)
+            generated_frames = (
+                generated_frames - 1
+            ) // self.vae_scale_factor_temporal * self.vae_scale_factor_temporal + 1
+            history_video = history_video[:, :, :generated_frames]
+            video = self.video_processor.postprocess_video(history_video, output_type=output_type)
+        else:
+            video = real_history_latents
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (video,)
+
+        return HeliosPipelineOutput(frames=video)