ComfyUI-Tween/nodes.py

import os
import glob
import logging
import shutil
import subprocess
import tempfile
import torch
import folder_paths
from comfy.utils import ProgressBar

from .inference import BiMVFIModel, EMAVFIModel, SGMVFIModel
from .bim_vfi_arch import clear_backwarp_cache
from .ema_vfi_arch import clear_warp_cache as clear_ema_warp_cache
from .sgm_vfi_arch import clear_warp_cache as clear_sgm_warp_cache

logger = logging.getLogger("Tween")

# Google Drive file ID for the pretrained BIM-VFI model
GDRIVE_FILE_ID = "18Wre7XyRtu_wtFRzcsit6oNfHiFRt9vC"
MODEL_FILENAME = "bim_vfi.pth"

# Google Drive folder ID for EMA-VFI pretrained models
EMA_GDRIVE_FOLDER_ID = "16jUa3HkQ85Z5lb5gce1yoaWkP-rdCd0o"
EMA_DEFAULT_MODEL = "ours_t.pkl"

# Register model folders with ComfyUI
MODEL_DIR = os.path.join(folder_paths.models_dir, "bim-vfi")
if not os.path.exists(MODEL_DIR):
    os.makedirs(MODEL_DIR, exist_ok=True)

EMA_MODEL_DIR = os.path.join(folder_paths.models_dir, "ema-vfi")
if not os.path.exists(EMA_MODEL_DIR):
    os.makedirs(EMA_MODEL_DIR, exist_ok=True)

# Google Drive folder ID for SGM-VFI pretrained models
SGM_GDRIVE_FOLDER_ID = "1S5O6W0a7XQDHgBtP9HnmoxYEzWBIzSJq"
SGM_DEFAULT_MODEL = "ours-1-2-points.pth"

SGM_MODEL_DIR = os.path.join(folder_paths.models_dir, "sgm-vfi")
if not os.path.exists(SGM_MODEL_DIR):
    os.makedirs(SGM_MODEL_DIR, exist_ok=True)


def get_available_models():
    """List available checkpoint files in the bim-vfi model directory."""
    models = []
    if os.path.isdir(MODEL_DIR):
        for f in os.listdir(MODEL_DIR):
            if f.endswith((".pth", ".pt", ".ckpt", ".safetensors")):
                models.append(f)
    if not models:
        models.append(MODEL_FILENAME)  # Will trigger auto-download
    return sorted(models)


def download_model_from_gdrive(file_id, dest_path):
    """Download a file from Google Drive using gdown."""
    try:
        import gdown
    except ImportError:
        raise RuntimeError(
            "gdown is required to auto-download the BIM-VFI model. "
            "Install it with: pip install gdown"
        )
    url = f"https://drive.google.com/uc?id={file_id}"
    logger.info(f"Downloading BIM-VFI model to {dest_path}...")
    gdown.download(url, dest_path, quiet=False)
    if not os.path.exists(dest_path):
        raise RuntimeError(f"Failed to download model to {dest_path}")
    logger.info("Download complete.")


class LoadBIMVFIModel:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "model_path": (get_available_models(), {
                    "default": MODEL_FILENAME,
                    "tooltip": "Checkpoint file from models/bim-vfi/. Auto-downloads on first use if missing.",
                }),
                "auto_pyr_level": ("BOOLEAN", {
                    "default": True,
                    "tooltip": "Automatically select pyramid level based on input resolution: <540p=3, 540p=5, 1080p=6, 4K=7. Disable to use manual pyr_level.",
                }),
                "pyr_level": ("INT", {
                    "default": 3, "min": 3, "max": 7, "step": 1,
                    "tooltip": "Manual pyramid levels for coarse-to-fine processing. Only used when auto_pyr_level is disabled. More levels = captures larger motion but slower.",
                }),
            }
        }

    RETURN_TYPES = ("BIM_VFI_MODEL",)
    RETURN_NAMES = ("model",)
    FUNCTION = "load_model"
    CATEGORY = "video/BIM-VFI"

    def load_model(self, model_path, auto_pyr_level, pyr_level):
        full_path = os.path.join(MODEL_DIR, model_path)

        if not os.path.exists(full_path):
            logger.info(f"Model not found at {full_path}, attempting download...")
            download_model_from_gdrive(GDRIVE_FILE_ID, full_path)

        wrapper = BiMVFIModel(
            checkpoint_path=full_path,
            pyr_level=pyr_level,
            auto_pyr_level=auto_pyr_level,
            device="cpu",
        )

        mode = "auto" if auto_pyr_level else f"manual ({pyr_level})"
        logger.info(f"BIM-VFI model loaded (pyr_level={mode})")
        return (wrapper,)


class BIMVFIInterpolate:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE", {
                    "tooltip": "Input image batch. Output frame count: 2x=(2N-1), 4x=(4N-3), 8x=(8N-7).",
                }),
                "model": ("BIM_VFI_MODEL", {
                    "tooltip": "BIM-VFI model from the Load BIM-VFI Model node.",
                }),
                "multiplier": ([2, 4, 8], {
                    "default": 2,
                    "tooltip": "Frame rate multiplier. 2x=one interpolation pass, 4x=two recursive passes, 8x=three. Higher = more frames but longer processing.",
                }),
                "clear_cache_after_n_frames": ("INT", {
                    "default": 10, "min": 1, "max": 100, "step": 1,
                    "tooltip": "Clear CUDA cache every N frame pairs to prevent VRAM buildup. Lower = less VRAM but slower. Ignored when all_on_gpu is enabled.",
                }),
                "keep_device": ("BOOLEAN", {
                    "default": True,
                    "tooltip": "Keep model on GPU between frame pairs. Faster but uses ~200MB VRAM constantly. Disable to free VRAM between pairs (slower due to CPU-GPU transfers).",
                }),
                "all_on_gpu": ("BOOLEAN", {
                    "default": False,
                    "tooltip": "Store all intermediate frames on GPU instead of CPU. Much faster (no transfers) but requires enough VRAM for all frames. Recommended for 48GB+ cards.",
                }),
                "batch_size": ("INT", {
                    "default": 1, "min": 1, "max": 64, "step": 1,
                    "tooltip": "Number of frame pairs to process simultaneously. Higher = faster but uses more VRAM. Start with 1, increase until VRAM is full. Recommended: 1 for 8GB, 2-4 for 24GB, 4-16 for 48GB+.",
                }),
                "chunk_size": ("INT", {
                    "default": 0, "min": 0, "max": 10000, "step": 1,
                    "tooltip": "Process input frames in chunks of this size (0=disabled). Bounds VRAM usage during processing but the full output is still assembled in RAM. To bound RAM, use the Segment Interpolate node instead. Result is identical to processing all at once.",
                }),
            }
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("images",)
    FUNCTION = "interpolate"
    CATEGORY = "video/BIM-VFI"

    def _interpolate_frames(self, frames, model, num_passes, batch_size,
                            device, storage_device, keep_device, all_on_gpu,
                            clear_cache_after_n_frames, pbar, step_ref):
        """Run all interpolation passes on a chunk of frames.

        Args:
            frames: [N, C, H, W] tensor on storage_device
            step_ref: list with single int, mutable counter for progress bar
        Returns:
            Interpolated frames as [M, C, H, W] tensor on storage_device
        """
        for pass_idx in range(num_passes):
            new_frames = []
            num_pairs = frames.shape[0] - 1
            pairs_since_clear = 0

            for i in range(0, num_pairs, batch_size):
                batch_end = min(i + batch_size, num_pairs)
                actual_batch = batch_end - i

                frames0 = frames[i:batch_end]
                frames1 = frames[i + 1:batch_end + 1]

                if not keep_device:
                    model.to(device)

                mids = model.interpolate_batch(frames0, frames1, time_step=0.5)
                mids = mids.to(storage_device)

                if not keep_device:
                    model.to("cpu")

                for j in range(actual_batch):
                    new_frames.append(frames[i + j:i + j + 1])
                    new_frames.append(mids[j:j+1])

                step_ref[0] += actual_batch
                pbar.update_absolute(step_ref[0])

                pairs_since_clear += actual_batch
                if not all_on_gpu and pairs_since_clear >= clear_cache_after_n_frames and torch.cuda.is_available():
                    clear_backwarp_cache()
                    torch.cuda.empty_cache()
                    pairs_since_clear = 0

            new_frames.append(frames[-1:])
            frames = torch.cat(new_frames, dim=0)

            if not all_on_gpu and torch.cuda.is_available():
                clear_backwarp_cache()
                torch.cuda.empty_cache()

        return frames

    @staticmethod
    def _count_steps(num_frames, num_passes):
        """Count total interpolation steps for a given input frame count."""
        n = num_frames
        total = 0
        for _ in range(num_passes):
            total += n - 1
            n = 2 * n - 1
        return total

    def interpolate(self, images, model, multiplier, clear_cache_after_n_frames,
                    keep_device, all_on_gpu, batch_size, chunk_size):
        if images.shape[0] < 2:
            return (images,)

        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        num_passes = {2: 1, 4: 2, 8: 3}[multiplier]

        if all_on_gpu:
            keep_device = True

        storage_device = device if all_on_gpu else torch.device("cpu")

        # Convert from ComfyUI [B, H, W, C] to model [B, C, H, W]
        all_frames = images.permute(0, 3, 1, 2).to(storage_device)
        total_input = all_frames.shape[0]

        # Build chunk boundaries (1-frame overlap between consecutive chunks)
        if chunk_size < 2 or chunk_size >= total_input:
            chunks = [(0, total_input)]
        else:
            chunks = []
            start = 0
            while start < total_input - 1:
                end = min(start + chunk_size, total_input)
                chunks.append((start, end))
                start = end - 1  # overlap by 1 frame
                if end == total_input:
                    break

        # Calculate total progress steps across all chunks
        total_steps = sum(self._count_steps(ce - cs, num_passes) for cs, ce in chunks)
        pbar = ProgressBar(total_steps)
        step_ref = [0]

        if keep_device:
            model.to(device)

        result_chunks = []
        for chunk_idx, (chunk_start, chunk_end) in enumerate(chunks):
            chunk_frames = all_frames[chunk_start:chunk_end].clone()

            chunk_result = self._interpolate_frames(
                chunk_frames, model, num_passes, batch_size,
                device, storage_device, keep_device, all_on_gpu,
                clear_cache_after_n_frames, pbar, step_ref,
            )

            # Skip first frame of subsequent chunks (duplicate of previous chunk's last frame)
            if chunk_idx > 0:
                chunk_result = chunk_result[1:]

            # Move completed chunk to CPU to bound memory when chunking
            if len(chunks) > 1:
                chunk_result = chunk_result.cpu()

            result_chunks.append(chunk_result)

        result = torch.cat(result_chunks, dim=0)
        # Convert back to ComfyUI [B, H, W, C], on CPU
        result = result.cpu().permute(0, 2, 3, 1)
        return (result,)


class BIMVFISegmentInterpolate(BIMVFIInterpolate):
    """Process a numbered segment of the input batch.

    Chain multiple instances with Save nodes between them to bound peak RAM.
    The model pass-through output forces sequential execution so each segment
    saves and frees from RAM before the next starts.
    """

    @classmethod
    def INPUT_TYPES(cls):
        base = BIMVFIInterpolate.INPUT_TYPES()
        base["required"]["segment_index"] = ("INT", {
            "default": 0, "min": 0, "max": 10000, "step": 1,
            "tooltip": "Which segment to process (0-based). Bounds RAM by only producing this segment's output frames, "
                       "unlike chunk_size which bounds VRAM but still assembles the full output in RAM. "
                       "Chain the model output to the next Segment Interpolate to force sequential execution.",
        })
        base["required"]["segment_size"] = ("INT", {
            "default": 500, "min": 2, "max": 10000, "step": 1,
            "tooltip": "Number of input frames per segment. Adjacent segments overlap by 1 frame for seamless stitching. "
                       "Smaller = less peak RAM per segment. Save each segment's output to disk before the next runs.",
        })
        return base

    RETURN_TYPES = ("IMAGE", "BIM_VFI_MODEL")
    RETURN_NAMES = ("images", "model")
    FUNCTION = "interpolate"
    CATEGORY = "video/BIM-VFI"

    def interpolate(self, images, model, multiplier, clear_cache_after_n_frames,
                    keep_device, all_on_gpu, batch_size, chunk_size,
                    segment_index, segment_size):
        total_input = images.shape[0]

        # Compute segment boundaries (1-frame overlap)
        start = segment_index * (segment_size - 1)
        end = min(start + segment_size, total_input)

        if start >= total_input - 1:
            # Past the end — return empty single frame + model
            return (images[:1], model)

        segment_images = images[start:end]
        is_continuation = segment_index > 0

        # Delegate to the parent interpolation logic
        (result,) = super().interpolate(
            segment_images, model, multiplier, clear_cache_after_n_frames,
            keep_device, all_on_gpu, batch_size, chunk_size,
        )

        if is_continuation:
            result = result[1:]  # skip duplicate boundary frame

        return (result, model)


class TweenConcatVideos:
    """Concatenate segment video files into a single video using ffmpeg.

    Connect the model output from the last Segment Interpolate node to ensure
    this runs only after all segments have been saved to disk.
    """

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "model": ("*", {
                    "tooltip": "Connect from the last Segment Interpolate's model output (any model type). "
                               "This ensures concatenation runs only after all segments are saved.",
                }),
                "output_directory": ("STRING", {
                    "default": "",
                    "tooltip": "Directory containing the segment video files. "
                               "Leave empty to use ComfyUI's default output directory.",
                }),
                "filename_prefix": ("STRING", {
                    "default": "segment",
                    "tooltip": "Filename prefix used when saving segments with VHS Video Combine. "
                               "Matches files like segment_00001.mp4, segment_00002.mp4, etc.",
                }),
                "output_filename": ("STRING", {
                    "default": "final_video.mp4",
                    "tooltip": "Name of the concatenated output file. Saved in the same directory.",
                }),
                "delete_segments": ("BOOLEAN", {
                    "default": False,
                    "tooltip": "Delete the individual segment files after successful concatenation. "
                               "Useful to avoid leftover files that would pollute the next run.",
                }),
            }
        }

    RETURN_TYPES = ("STRING",)
    RETURN_NAMES = ("video_path",)
    OUTPUT_NODE = True
    FUNCTION = "concat"
    CATEGORY = "video/Tween"

    @staticmethod
    def _find_ffmpeg():
        ffmpeg_path = shutil.which("ffmpeg")
        if ffmpeg_path is None:
            try:
                from imageio_ffmpeg import get_ffmpeg_exe
                ffmpeg_path = get_ffmpeg_exe()
            except ImportError:
                pass
        if ffmpeg_path is None:
            raise RuntimeError(
                "ffmpeg not found. Install ffmpeg or pip install imageio-ffmpeg."
            )
        return ffmpeg_path

    def concat(self, model, output_directory, filename_prefix, output_filename, delete_segments):
        # Resolve output directory
        out_dir = output_directory.strip()
        if not out_dir:
            out_dir = folder_paths.get_output_directory()

        if not os.path.isdir(out_dir):
            raise ValueError(f"Output directory does not exist: {out_dir}")

        # Find segment files matching the prefix
        safe_prefix = glob.escape(filename_prefix)
        segments = []
        for ext in ("mp4", "webm", "mkv"):
            segments.extend(
                glob.glob(os.path.join(out_dir, f"{safe_prefix}_*.{ext}"))
            )
        segments.sort()

        if not segments:
            raise FileNotFoundError(
                f"No segment files found matching '{filename_prefix}_*' "
                f"in {out_dir}"
            )

        logger.info(f"Found {len(segments)} segment(s) to concatenate")

        # Write ffmpeg concat list to a temp file
        fd, concat_list_path = tempfile.mkstemp(suffix=".txt", prefix="bimvfi_concat_")
        try:
            with os.fdopen(fd, "w") as f:
                f.write("ffconcat version 1.0\n")
                for seg in segments:
                    # ffconcat escaping: \ -> \\, ' -> \'
                    escaped = os.path.abspath(seg).replace("\\", "\\\\").replace("'", "\\'")
                    f.write(f"file '{escaped}'\n")

            output_path = os.path.join(out_dir, output_filename)
            ffmpeg = self._find_ffmpeg()

            cmd = [
                ffmpeg,
                "-y",
                "-f", "concat",
                "-safe", "0",
                "-i", concat_list_path,
                "-c", "copy",
                output_path,
            ]

            logger.info(f"Running: {' '.join(cmd)}")
            result = subprocess.run(
                cmd, capture_output=True, text=True, check=False
            )

            if result.returncode != 0:
                raise RuntimeError(
                    f"ffmpeg concat failed (exit {result.returncode}):\n"
                    f"{result.stderr}"
                )

            logger.info(f"Concatenated video saved to {output_path}")

            if delete_segments:
                for seg in segments:
                    try:
                        os.remove(seg)
                    except OSError as e:
                        logger.warning(f"Failed to delete segment {seg}: {e}")
                logger.info(f"Deleted {len(segments)} segment file(s)")
        finally:
            if os.path.exists(concat_list_path):
                os.remove(concat_list_path)

        return (output_path,)


# ---------------------------------------------------------------------------
# EMA-VFI nodes
# ---------------------------------------------------------------------------

def get_available_ema_models():
    """List available checkpoint files in the ema-vfi model directory."""
    models = []
    if os.path.isdir(EMA_MODEL_DIR):
        for f in os.listdir(EMA_MODEL_DIR):
            if f.endswith((".pkl", ".pth", ".pt", ".ckpt", ".safetensors")):
                models.append(f)
    if not models:
        models.append(EMA_DEFAULT_MODEL)  # Will trigger auto-download
    return sorted(models)


def download_ema_model_from_gdrive(folder_id, dest_path):
    """Download EMA-VFI model from Google Drive folder using gdown."""
    try:
        import gdown
    except ImportError:
        raise RuntimeError(
            "gdown is required to auto-download the EMA-VFI model. "
            "Install it with: pip install gdown"
        )
    filename = os.path.basename(dest_path)
    url = f"https://drive.google.com/drive/folders/{folder_id}"
    logger.info(f"Downloading {filename} from Google Drive folder to {dest_path}...")
    os.makedirs(os.path.dirname(dest_path), exist_ok=True)
    gdown.download_folder(url, output=os.path.dirname(dest_path), quiet=False, remaining_ok=True)
    if not os.path.exists(dest_path):
        raise RuntimeError(
            f"Failed to download {filename}. Please download manually from "
            f"https://drive.google.com/drive/folders/{folder_id} "
            f"and place it in {os.path.dirname(dest_path)}"
        )
    logger.info("Download complete.")


class LoadEMAVFIModel:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "model_path": (get_available_ema_models(), {
                    "default": EMA_DEFAULT_MODEL,
                    "tooltip": "Checkpoint file from models/ema-vfi/. Auto-downloads on first use if missing. "
                               "Variant (large/small) and timestep support are auto-detected from filename.",
                }),
                "tta": ("BOOLEAN", {
                    "default": False,
                    "tooltip": "Test-time augmentation: flip input and average with unflipped result. "
                               "~2x slower but slightly better quality. Recommended for large model only.",
                }),
            }
        }

    RETURN_TYPES = ("EMA_VFI_MODEL",)
    RETURN_NAMES = ("model",)
    FUNCTION = "load_model"
    CATEGORY = "video/EMA-VFI"

    def load_model(self, model_path, tta):
        full_path = os.path.join(EMA_MODEL_DIR, model_path)

        if not os.path.exists(full_path):
            logger.info(f"Model not found at {full_path}, attempting download...")
            download_ema_model_from_gdrive(EMA_GDRIVE_FOLDER_ID, full_path)

        wrapper = EMAVFIModel(
            checkpoint_path=full_path,
            variant="auto",
            tta=tta,
            device="cpu",
        )

        t_mode = "arbitrary" if wrapper.supports_arbitrary_t else "fixed (0.5)"
        logger.info(f"EMA-VFI model loaded (variant={wrapper.variant_name}, timestep={t_mode}, tta={tta})")
        return (wrapper,)


class EMAVFIInterpolate:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE", {
                    "tooltip": "Input image batch. Output frame count: 2x=(2N-1), 4x=(4N-3), 8x=(8N-7).",
                }),
                "model": ("EMA_VFI_MODEL", {
                    "tooltip": "EMA-VFI model from the Load EMA-VFI Model node.",
                }),
                "multiplier": ([2, 4, 8], {
                    "default": 2,
                    "tooltip": "Frame rate multiplier. 2x=one interpolation pass, 4x=two recursive passes, 8x=three. Higher = more frames but longer processing.",
                }),
                "clear_cache_after_n_frames": ("INT", {
                    "default": 10, "min": 1, "max": 100, "step": 1,
                    "tooltip": "Clear CUDA cache every N frame pairs to prevent VRAM buildup. Lower = less VRAM but slower. Ignored when all_on_gpu is enabled.",
                }),
                "keep_device": ("BOOLEAN", {
                    "default": True,
                    "tooltip": "Keep model on GPU between frame pairs. Faster but uses more VRAM constantly. Disable to free VRAM between pairs (slower due to CPU-GPU transfers).",
                }),
                "all_on_gpu": ("BOOLEAN", {
                    "default": False,
                    "tooltip": "Store all intermediate frames on GPU instead of CPU. Much faster (no transfers) but requires enough VRAM for all frames. Recommended for 48GB+ cards.",
                }),
                "batch_size": ("INT", {
                    "default": 1, "min": 1, "max": 64, "step": 1,
                    "tooltip": "Number of frame pairs to process simultaneously. Higher = faster but uses more VRAM. Start with 1, increase until VRAM is full.",
                }),
                "chunk_size": ("INT", {
                    "default": 0, "min": 0, "max": 10000, "step": 1,
                    "tooltip": "Process input frames in chunks of this size (0=disabled). Bounds VRAM usage during processing but the full output is still assembled in RAM. To bound RAM, use the Segment Interpolate node instead.",
                }),
            }
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("images",)
    FUNCTION = "interpolate"
    CATEGORY = "video/EMA-VFI"

    def _interpolate_frames(self, frames, model, num_passes, batch_size,
                            device, storage_device, keep_device, all_on_gpu,
                            clear_cache_after_n_frames, pbar, step_ref):
        """Run all interpolation passes on a chunk of frames."""
        for pass_idx in range(num_passes):
            new_frames = []
            num_pairs = frames.shape[0] - 1
            pairs_since_clear = 0

            for i in range(0, num_pairs, batch_size):
                batch_end = min(i + batch_size, num_pairs)
                actual_batch = batch_end - i

                frames0 = frames[i:batch_end]
                frames1 = frames[i + 1:batch_end + 1]

                if not keep_device:
                    model.to(device)

                mids = model.interpolate_batch(frames0, frames1, time_step=0.5)
                mids = mids.to(storage_device)

                if not keep_device:
                    model.to("cpu")

                for j in range(actual_batch):
                    new_frames.append(frames[i + j:i + j + 1])
                    new_frames.append(mids[j:j+1])

                step_ref[0] += actual_batch
                pbar.update_absolute(step_ref[0])

                pairs_since_clear += actual_batch
                if not all_on_gpu and pairs_since_clear >= clear_cache_after_n_frames and torch.cuda.is_available():
                    clear_ema_warp_cache()
                    torch.cuda.empty_cache()
                    pairs_since_clear = 0

            new_frames.append(frames[-1:])
            frames = torch.cat(new_frames, dim=0)

            if not all_on_gpu and torch.cuda.is_available():
                clear_ema_warp_cache()
                torch.cuda.empty_cache()

        return frames

    @staticmethod
    def _count_steps(num_frames, num_passes):
        """Count total interpolation steps for a given input frame count."""
        n = num_frames
        total = 0
        for _ in range(num_passes):
            total += n - 1
            n = 2 * n - 1
        return total

    def interpolate(self, images, model, multiplier, clear_cache_after_n_frames,
                    keep_device, all_on_gpu, batch_size, chunk_size):
        if images.shape[0] < 2:
            return (images,)

        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        num_passes = {2: 1, 4: 2, 8: 3}[multiplier]

        if all_on_gpu:
            keep_device = True

        storage_device = device if all_on_gpu else torch.device("cpu")

        # Convert from ComfyUI [B, H, W, C] to model [B, C, H, W]
        all_frames = images.permute(0, 3, 1, 2).to(storage_device)
        total_input = all_frames.shape[0]

        # Build chunk boundaries (1-frame overlap between consecutive chunks)
        if chunk_size < 2 or chunk_size >= total_input:
            chunks = [(0, total_input)]
        else:
            chunks = []
            start = 0
            while start < total_input - 1:
                end = min(start + chunk_size, total_input)
                chunks.append((start, end))
                start = end - 1  # overlap by 1 frame
                if end == total_input:
                    break

        # Calculate total progress steps across all chunks
        total_steps = sum(self._count_steps(ce - cs, num_passes) for cs, ce in chunks)
        pbar = ProgressBar(total_steps)
        step_ref = [0]

        if keep_device:
            model.to(device)

        result_chunks = []
        for chunk_idx, (chunk_start, chunk_end) in enumerate(chunks):
            chunk_frames = all_frames[chunk_start:chunk_end].clone()

            chunk_result = self._interpolate_frames(
                chunk_frames, model, num_passes, batch_size,
                device, storage_device, keep_device, all_on_gpu,
                clear_cache_after_n_frames, pbar, step_ref,
            )

            # Skip first frame of subsequent chunks (duplicate of previous chunk's last frame)
            if chunk_idx > 0:
                chunk_result = chunk_result[1:]

            # Move completed chunk to CPU to bound memory when chunking
            if len(chunks) > 1:
                chunk_result = chunk_result.cpu()

            result_chunks.append(chunk_result)

        result = torch.cat(result_chunks, dim=0)
        # Convert back to ComfyUI [B, H, W, C], on CPU
        result = result.cpu().permute(0, 2, 3, 1)
        return (result,)


class EMAVFISegmentInterpolate(EMAVFIInterpolate):
    """Process a numbered segment of the input batch for EMA-VFI.

    Chain multiple instances with Save nodes between them to bound peak RAM.
    The model pass-through output forces sequential execution so each segment
    saves and frees from RAM before the next starts.
    """

    @classmethod
    def INPUT_TYPES(cls):
        base = EMAVFIInterpolate.INPUT_TYPES()
        base["required"]["segment_index"] = ("INT", {
            "default": 0, "min": 0, "max": 10000, "step": 1,
            "tooltip": "Which segment to process (0-based). Bounds RAM by only producing this segment's output frames, "
                       "unlike chunk_size which bounds VRAM but still assembles the full output in RAM. "
                       "Chain the model output to the next Segment Interpolate to force sequential execution.",
        })
        base["required"]["segment_size"] = ("INT", {
            "default": 500, "min": 2, "max": 10000, "step": 1,
            "tooltip": "Number of input frames per segment. Adjacent segments overlap by 1 frame for seamless stitching. "
                       "Smaller = less peak RAM per segment. Save each segment's output to disk before the next runs.",
        })
        return base

    RETURN_TYPES = ("IMAGE", "EMA_VFI_MODEL")
    RETURN_NAMES = ("images", "model")
    FUNCTION = "interpolate"
    CATEGORY = "video/EMA-VFI"

    def interpolate(self, images, model, multiplier, clear_cache_after_n_frames,
                    keep_device, all_on_gpu, batch_size, chunk_size,
                    segment_index, segment_size):
        total_input = images.shape[0]

        # Compute segment boundaries (1-frame overlap)
        start = segment_index * (segment_size - 1)
        end = min(start + segment_size, total_input)

        if start >= total_input - 1:
            # Past the end — return empty single frame + model
            return (images[:1], model)

        segment_images = images[start:end]
        is_continuation = segment_index > 0

        # Delegate to the parent interpolation logic
        (result,) = super().interpolate(
            segment_images, model, multiplier, clear_cache_after_n_frames,
            keep_device, all_on_gpu, batch_size, chunk_size,
        )

        if is_continuation:
            result = result[1:]  # skip duplicate boundary frame

        return (result, model)


# ---------------------------------------------------------------------------
# SGM-VFI nodes
# ---------------------------------------------------------------------------

def get_available_sgm_models():
    """List available checkpoint files in the sgm-vfi model directory."""
    models = []
    if os.path.isdir(SGM_MODEL_DIR):
        for f in os.listdir(SGM_MODEL_DIR):
            if f.endswith((".pkl", ".pth", ".pt", ".ckpt", ".safetensors")):
                models.append(f)
    if not models:
        models.append(SGM_DEFAULT_MODEL)  # Will trigger auto-download
    return sorted(models)


def download_sgm_model_from_gdrive(folder_id, dest_path):
    """Download SGM-VFI model from Google Drive folder using gdown."""
    try:
        import gdown
    except ImportError:
        raise RuntimeError(
            "gdown is required to auto-download the SGM-VFI model. "
            "Install it with: pip install gdown"
        )
    filename = os.path.basename(dest_path)
    url = f"https://drive.google.com/drive/folders/{folder_id}"
    logger.info(f"Downloading {filename} from Google Drive folder to {dest_path}...")
    os.makedirs(os.path.dirname(dest_path), exist_ok=True)
    gdown.download_folder(url, output=os.path.dirname(dest_path), quiet=False, remaining_ok=True)
    if not os.path.exists(dest_path):
        raise RuntimeError(
            f"Failed to download {filename}. Please download manually from "
            f"https://drive.google.com/drive/folders/{folder_id} "
            f"and place it in {os.path.dirname(dest_path)}"
        )
    logger.info("Download complete.")


class LoadSGMVFIModel:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "model_path": (get_available_sgm_models(), {
                    "default": SGM_DEFAULT_MODEL,
                    "tooltip": "Checkpoint file from models/sgm-vfi/. Auto-downloads on first use if missing. "
                               "Variant (base/small) is auto-detected from filename.",
                }),
                "tta": ("BOOLEAN", {
                    "default": False,
                    "tooltip": "Test-time augmentation: flip input and average with unflipped result. "
                               "~2x slower but slightly better quality.",
                }),
                "num_key_points": ("FLOAT", {
                    "default": 0.5, "min": 0.0, "max": 1.0, "step": 0.05,
                    "tooltip": "Sparsity of global matching. 0.0 = global matching everywhere (slower, better for large motion). "
                               "Higher = sparser keypoints (faster). Default 0.5 is a good balance.",
                }),
            }
        }

    RETURN_TYPES = ("SGM_VFI_MODEL",)
    RETURN_NAMES = ("model",)
    FUNCTION = "load_model"
    CATEGORY = "video/SGM-VFI"

    def load_model(self, model_path, tta, num_key_points):
        full_path = os.path.join(SGM_MODEL_DIR, model_path)

        if not os.path.exists(full_path):
            logger.info(f"Model not found at {full_path}, attempting download...")
            download_sgm_model_from_gdrive(SGM_GDRIVE_FOLDER_ID, full_path)

        wrapper = SGMVFIModel(
            checkpoint_path=full_path,
            variant="auto",
            num_key_points=num_key_points,
            tta=tta,
            device="cpu",
        )

        logger.info(f"SGM-VFI model loaded (variant={wrapper.variant_name}, num_key_points={num_key_points}, tta={tta})")
        return (wrapper,)


class SGMVFIInterpolate:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE", {
                    "tooltip": "Input image batch. Output frame count: 2x=(2N-1), 4x=(4N-3), 8x=(8N-7).",
                }),
                "model": ("SGM_VFI_MODEL", {
                    "tooltip": "SGM-VFI model from the Load SGM-VFI Model node.",
                }),
                "multiplier": ([2, 4, 8], {
                    "default": 2,
                    "tooltip": "Frame rate multiplier. 2x=one interpolation pass, 4x=two recursive passes, 8x=three. Higher = more frames but longer processing.",
                }),
                "clear_cache_after_n_frames": ("INT", {
                    "default": 10, "min": 1, "max": 100, "step": 1,
                    "tooltip": "Clear CUDA cache every N frame pairs to prevent VRAM buildup. Lower = less VRAM but slower. Ignored when all_on_gpu is enabled.",
                }),
                "keep_device": ("BOOLEAN", {
                    "default": True,
                    "tooltip": "Keep model on GPU between frame pairs. Faster but uses more VRAM constantly. Disable to free VRAM between pairs (slower due to CPU-GPU transfers).",
                }),
                "all_on_gpu": ("BOOLEAN", {
                    "default": False,
                    "tooltip": "Store all intermediate frames on GPU instead of CPU. Much faster (no transfers) but requires enough VRAM for all frames. Recommended for 48GB+ cards.",
                }),
                "batch_size": ("INT", {
                    "default": 1, "min": 1, "max": 64, "step": 1,
                    "tooltip": "Number of frame pairs to process simultaneously. Higher = faster but uses more VRAM. Start with 1, increase until VRAM is full.",
                }),
                "chunk_size": ("INT", {
                    "default": 0, "min": 0, "max": 10000, "step": 1,
                    "tooltip": "Process input frames in chunks of this size (0=disabled). Bounds VRAM usage during processing but the full output is still assembled in RAM. To bound RAM, use the Segment Interpolate node instead.",
                }),
            }
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("images",)
    FUNCTION = "interpolate"
    CATEGORY = "video/SGM-VFI"

    def _interpolate_frames(self, frames, model, num_passes, batch_size,
                            device, storage_device, keep_device, all_on_gpu,
                            clear_cache_after_n_frames, pbar, step_ref):
        """Run all interpolation passes on a chunk of frames."""
        for pass_idx in range(num_passes):
            new_frames = []
            num_pairs = frames.shape[0] - 1
            pairs_since_clear = 0

            for i in range(0, num_pairs, batch_size):
                batch_end = min(i + batch_size, num_pairs)
                actual_batch = batch_end - i

                frames0 = frames[i:batch_end]
                frames1 = frames[i + 1:batch_end + 1]

                if not keep_device:
                    model.to(device)

                mids = model.interpolate_batch(frames0, frames1, time_step=0.5)
                mids = mids.to(storage_device)

                if not keep_device:
                    model.to("cpu")

                for j in range(actual_batch):
                    new_frames.append(frames[i + j:i + j + 1])
                    new_frames.append(mids[j:j+1])

                step_ref[0] += actual_batch
                pbar.update_absolute(step_ref[0])

                pairs_since_clear += actual_batch
                if not all_on_gpu and pairs_since_clear >= clear_cache_after_n_frames and torch.cuda.is_available():
                    clear_sgm_warp_cache()
                    torch.cuda.empty_cache()
                    pairs_since_clear = 0

            new_frames.append(frames[-1:])
            frames = torch.cat(new_frames, dim=0)

            if not all_on_gpu and torch.cuda.is_available():
                clear_sgm_warp_cache()
                torch.cuda.empty_cache()

        return frames

    @staticmethod
    def _count_steps(num_frames, num_passes):
        """Count total interpolation steps for a given input frame count."""
        n = num_frames
        total = 0
        for _ in range(num_passes):
            total += n - 1
            n = 2 * n - 1
        return total

    def interpolate(self, images, model, multiplier, clear_cache_after_n_frames,
                    keep_device, all_on_gpu, batch_size, chunk_size):
        if images.shape[0] < 2:
            return (images,)

        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        num_passes = {2: 1, 4: 2, 8: 3}[multiplier]

        if all_on_gpu:
            keep_device = True

        storage_device = device if all_on_gpu else torch.device("cpu")

        # Convert from ComfyUI [B, H, W, C] to model [B, C, H, W]
        all_frames = images.permute(0, 3, 1, 2).to(storage_device)
        total_input = all_frames.shape[0]

        # Build chunk boundaries (1-frame overlap between consecutive chunks)
        if chunk_size < 2 or chunk_size >= total_input:
            chunks = [(0, total_input)]
        else:
            chunks = []
            start = 0
            while start < total_input - 1:
                end = min(start + chunk_size, total_input)
                chunks.append((start, end))
                start = end - 1  # overlap by 1 frame
                if end == total_input:
                    break

        # Calculate total progress steps across all chunks
        total_steps = sum(self._count_steps(ce - cs, num_passes) for cs, ce in chunks)
        pbar = ProgressBar(total_steps)
        step_ref = [0]

        if keep_device:
            model.to(device)

        result_chunks = []
        for chunk_idx, (chunk_start, chunk_end) in enumerate(chunks):
            chunk_frames = all_frames[chunk_start:chunk_end].clone()

            chunk_result = self._interpolate_frames(
                chunk_frames, model, num_passes, batch_size,
                device, storage_device, keep_device, all_on_gpu,
                clear_cache_after_n_frames, pbar, step_ref,
            )

            # Skip first frame of subsequent chunks (duplicate of previous chunk's last frame)
            if chunk_idx > 0:
                chunk_result = chunk_result[1:]

            # Move completed chunk to CPU to bound memory when chunking
            if len(chunks) > 1:
                chunk_result = chunk_result.cpu()

            result_chunks.append(chunk_result)

        result = torch.cat(result_chunks, dim=0)
        # Convert back to ComfyUI [B, H, W, C], on CPU
        result = result.cpu().permute(0, 2, 3, 1)
        return (result,)


class SGMVFISegmentInterpolate(SGMVFIInterpolate):
    """Process a numbered segment of the input batch for SGM-VFI.

    Chain multiple instances with Save nodes between them to bound peak RAM.
    The model pass-through output forces sequential execution so each segment
    saves and frees from RAM before the next starts.
    """

    @classmethod
    def INPUT_TYPES(cls):
        base = SGMVFIInterpolate.INPUT_TYPES()
        base["required"]["segment_index"] = ("INT", {
            "default": 0, "min": 0, "max": 10000, "step": 1,
            "tooltip": "Which segment to process (0-based). Bounds RAM by only producing this segment's output frames, "
                       "unlike chunk_size which bounds VRAM but still assembles the full output in RAM. "
                       "Chain the model output to the next Segment Interpolate to force sequential execution.",
        })
        base["required"]["segment_size"] = ("INT", {
            "default": 500, "min": 2, "max": 10000, "step": 1,
            "tooltip": "Number of input frames per segment. Adjacent segments overlap by 1 frame for seamless stitching. "
                       "Smaller = less peak RAM per segment. Save each segment's output to disk before the next runs.",
        })
        return base

    RETURN_TYPES = ("IMAGE", "SGM_VFI_MODEL")
    RETURN_NAMES = ("images", "model")
    FUNCTION = "interpolate"
    CATEGORY = "video/SGM-VFI"

    def interpolate(self, images, model, multiplier, clear_cache_after_n_frames,
                    keep_device, all_on_gpu, batch_size, chunk_size,
                    segment_index, segment_size):
        total_input = images.shape[0]

        # Compute segment boundaries (1-frame overlap)
        start = segment_index * (segment_size - 1)
        end = min(start + segment_size, total_input)

        if start >= total_input - 1:
            # Past the end — return empty single frame + model
            return (images[:1], model)

        segment_images = images[start:end]
        is_continuation = segment_index > 0

        # Delegate to the parent interpolation logic
        (result,) = super().interpolate(
            segment_images, model, multiplier, clear_cache_after_n_frames,
            keep_device, all_on_gpu, batch_size, chunk_size,
        )

        if is_continuation:
            result = result[1:]  # skip duplicate boundary frame

        return (result, model)