m1_4kandClaude Sonnet 4.6
f0d2ae802a
- Input/output are now standard ComfyUI IMAGE tensors [B, H, W, C] float32 in [0,1], compatible with any Load/Save Video node - Remove file-path and folder_paths dependencies from the node - Add interpolate_batch_2x() to phase_interpolation.py for frame-list based processing - Output frame count: B*2-1 (original frames interleaved with synthesized midpoints) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
98 lines
3.2 KiB
Python
98 lines
3.2 KiB
Python
"""
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ComfyUI custom node: Phase-Based Frame Interpolation (2x)
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Input : IMAGE tensor [B, H, W, C] float32 [0, 1] — batch of video frames
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Output : IMAGE tensor [B*2-1, H, W, C] float32 [0, 1] — 2x interpolated frames
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Connects directly to any Load Video / Save Video node that uses the
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standard ComfyUI IMAGE batch format.
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"""
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import numpy as np
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import torch
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from .phase_interpolation import interpolate_batch_2x
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class PhaseBasedFrameInterpolation:
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"""
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2x Frame Interpolation via Phase-Based Fourier Method.
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Takes a batch of video frames as a standard ComfyUI IMAGE tensor,
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inserts one phase-interpolated frame between every consecutive pair,
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and returns the doubled frame batch as an IMAGE tensor.
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Connect: Load Video → [this node] → Save Video / Preview
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": (
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"IMAGE",
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{
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"tooltip": "Batch of video frames [B, H, W, C] from a Load Video node.",
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},
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),
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"pyramid_levels": (
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"INT",
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{
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"default": 6,
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"min": 3,
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"max": 10,
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"step": 1,
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"tooltip": (
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"Number of complex pyramid levels. "
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"More levels = finer phase unwrapping but slower."
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),
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},
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),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
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FUNCTION = "interpolate"
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CATEGORY = "video/interpolation"
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DESCRIPTION = (
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"2x frame interpolation using Fourier-transform phase differences. "
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"Accepts an IMAGE batch from a Load Video node, inserts one synthesized "
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"frame between every consecutive pair, and returns the 2x frame batch. "
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"Connect the output to a Save Video or Preview node."
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)
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def interpolate(self, images: torch.Tensor, pyramid_levels: int):
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# images: [B, H, W, C] float32 in [0, 1]
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B, H, W, C = images.shape
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if B < 2:
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raise ValueError("Need at least 2 frames for interpolation.")
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# Convert tensor to list of uint8 RGB numpy arrays
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np_frames = (images.cpu().numpy() * 255.0).clip(0, 255).astype(np.uint8)
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frame_list = [np_frames[i] for i in range(B)]
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print(f"[PhaseInterp] Input: {B} frames Output: {B * 2 - 1} frames")
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# Run phase-based 2x interpolation
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out_list = interpolate_batch_2x(frame_list, num_levels=pyramid_levels)
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# Convert back to [B_out, H, W, C] float32 tensor in [0, 1]
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out_np = np.stack(out_list, axis=0).astype(np.float32) / 255.0
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out_tensor = torch.from_numpy(out_np)
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return (out_tensor,)
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# ---------------------------------------------------------------------------
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# Node registration
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# ---------------------------------------------------------------------------
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NODE_CLASS_MAPPINGS = {
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"PhaseBasedFrameInterpolation": PhaseBasedFrameInterpolation,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"PhaseBasedFrameInterpolation": "Phase-Based Frame Interpolation (2x)",
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}
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