nodes/stuttlepress-ComfyUI-Wan-VACE-Prep
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VFS: add Wan-friendly resolution toggle

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stuttlepress
2026-04-05 13:06:40 -05:00
parent 2b67355a63
commit 2cd4f5536c
1 changed files with 19 additions and 1 deletions
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frame_selector.py
+19 -1
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@@ -1,3 +1,4 @@
import math
import os
import torch
import numpy as np
@@ -22,6 +23,7 @@ class VisualFrameSelector:
"end_frame": ("INT", {"default": 0, "min": 0, "max": 1000000, "step": 1}),
"current_frame": ("INT", {"default": 0, "min": 0, "max": 1000000, "step": 1}),
"output_all_frames": ("BOOLEAN", {"default": False}),
"divisible_by_16": ("BOOLEAN", {"default": False, "tooltip": "Snap output frame dimensions up to the nearest multiple of 16 (required by many video AI models)."}),
},
}
@@ -128,7 +130,7 @@ class VisualFrameSelector:
# Main
# ------------------------------------------------------------------
def load_frames(self, video, current_frame=0, start_frame=0, end_frame=0, output_all_frames=False):
def load_frames(self, video, current_frame=0, start_frame=0, end_frame=0, output_all_frames=False, divisible_by_16=False):
video_path = folder_paths.get_annotated_filepath(video)
if not os.path.exists(video_path):
@@ -201,6 +203,22 @@ class VisualFrameSelector:
else:
all_tensor = torch.zeros((1, 1, 1, 3), dtype=torch.float32)
# --- Snap resolution to nearest multiple of 16 (ceiling) ----------
if divisible_by_16:
import torch.nn.functional as F
src_h, src_w = selected_tensor.shape[1], selected_tensor.shape[2]
snap_w = math.ceil(src_w / 16) * 16
snap_h = math.ceil(src_h / 16) * 16
if snap_w != src_w or snap_h != src_h:
t = selected_tensor.permute(0, 3, 1, 2)
t = F.interpolate(t, size=(snap_h, snap_w), mode="bilinear", align_corners=False)
selected_tensor = t.permute(0, 2, 3, 1)
if output_all_frames and all_tensor.shape[1] > 1:
t = all_tensor.permute(0, 3, 1, 2)
t = F.interpolate(t, size=(snap_h, snap_w), mode="bilinear", align_corners=False)
all_tensor = t.permute(0, 2, 3, 1)
print(f"[VisualFrameSelector] divisible_by_16: {src_w}×{src_h} → {snap_w}×{snap_h}")
container.close()
container = None