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Add oversampled image output to all VFI Interpolate nodes

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Second IMAGE output exposes the full power-of-2 oversampled frames
before target FPS selection. Identical to the first output when
target_fps=0. Document the new output in README.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Ethanfel
2026-02-14 17:39:58 +01:00
parent b2d7d3b634
commit 2f1cc17f5c
2 changed files with 41 additions and 24 deletions
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60
nodes.py
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@@ -202,8 +202,8 @@ class BIMVFIInterpolate:
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("images",)
RETURN_TYPES = ("IMAGE", "IMAGE")
RETURN_NAMES = ("images", "oversampled")
FUNCTION = "interpolate"
CATEGORY = "video/BIM-VFI"
@@ -275,7 +275,7 @@ class BIMVFIInterpolate:
keep_device, all_on_gpu, batch_size, chunk_size,
source_fps=0.0, target_fps=0.0):
if images.shape[0] < 2:
return (images,)
return (images, images)
# Target FPS mode: auto-compute multiplier from fps ratio
use_target_fps = target_fps > 0 and source_fps > 0
@@ -285,7 +285,7 @@ class BIMVFIInterpolate:
# Downsampling or same fps — select from input directly
all_frames = images.permute(0, 3, 1, 2)
result = _select_target_fps_frames(all_frames, source_fps, target_fps, mult, all_frames.shape[0])
return (result.cpu().permute(0, 2, 3, 1),)
return (result.cpu().permute(0, 2, 3, 1), images)
else:
num_passes = {2: 1, 4: 2, 8: 3}[multiplier]
mult = multiplier
@@ -344,13 +344,16 @@ class BIMVFIInterpolate:
result = torch.cat(result_chunks, dim=0)
# Convert oversampled to ComfyUI format for second output
oversampled = result.cpu().permute(0, 2, 3, 1)
# Target FPS: select frames from oversampled result
if use_target_fps:
result = _select_target_fps_frames(result, source_fps, target_fps, mult, total_input)
# Convert back to ComfyUI [B, H, W, C], on CPU
result = result.cpu().permute(0, 2, 3, 1)
return (result,)
return (result, oversampled)
class BIMVFISegmentInterpolate(BIMVFIInterpolate):
@@ -461,7 +464,7 @@ class BIMVFISegmentInterpolate(BIMVFIInterpolate):
# Standard multiplier mode
is_continuation = segment_index > 0
(result,) = super().interpolate(
(result, _) = super().interpolate(
segment_images, model, multiplier, clear_cache_after_n_frames,
keep_device, all_on_gpu, batch_size, chunk_size,
)
@@ -737,8 +740,8 @@ class EMAVFIInterpolate:
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("images",)
RETURN_TYPES = ("IMAGE", "IMAGE")
RETURN_NAMES = ("images", "oversampled")
FUNCTION = "interpolate"
CATEGORY = "video/EMA-VFI"
@@ -803,7 +806,7 @@ class EMAVFIInterpolate:
keep_device, all_on_gpu, batch_size, chunk_size,
source_fps=0.0, target_fps=0.0):
if images.shape[0] < 2:
return (images,)
return (images, images)
# Target FPS mode: auto-compute multiplier from fps ratio
use_target_fps = target_fps > 0 and source_fps > 0
@@ -812,7 +815,7 @@ class EMAVFIInterpolate:
if num_passes == 0:
all_frames = images.permute(0, 3, 1, 2)
result = _select_target_fps_frames(all_frames, source_fps, target_fps, mult, all_frames.shape[0])
return (result.cpu().permute(0, 2, 3, 1),)
return (result.cpu().permute(0, 2, 3, 1), images)
else:
num_passes = {2: 1, 4: 2, 8: 3}[multiplier]
mult = multiplier
@@ -871,13 +874,16 @@ class EMAVFIInterpolate:
result = torch.cat(result_chunks, dim=0)
# Convert oversampled to ComfyUI format for second output
oversampled = result.cpu().permute(0, 2, 3, 1)
# Target FPS: select frames from oversampled result
if use_target_fps:
result = _select_target_fps_frames(result, source_fps, target_fps, mult, total_input)
# Convert back to ComfyUI [B, H, W, C], on CPU
result = result.cpu().permute(0, 2, 3, 1)
return (result,)
return (result, oversampled)
class EMAVFISegmentInterpolate(EMAVFIInterpolate):
@@ -985,7 +991,7 @@ class EMAVFISegmentInterpolate(EMAVFIInterpolate):
# Standard multiplier mode
is_continuation = segment_index > 0
(result,) = super().interpolate(
(result, _) = super().interpolate(
segment_images, model, multiplier, clear_cache_after_n_frames,
keep_device, all_on_gpu, batch_size, chunk_size,
)
@@ -1128,8 +1134,8 @@ class SGMVFIInterpolate:
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("images",)
RETURN_TYPES = ("IMAGE", "IMAGE")
RETURN_NAMES = ("images", "oversampled")
FUNCTION = "interpolate"
CATEGORY = "video/SGM-VFI"
@@ -1194,7 +1200,7 @@ class SGMVFIInterpolate:
keep_device, all_on_gpu, batch_size, chunk_size,
source_fps=0.0, target_fps=0.0):
if images.shape[0] < 2:
return (images,)
return (images, images)
# Target FPS mode: auto-compute multiplier from fps ratio
use_target_fps = target_fps > 0 and source_fps > 0
@@ -1203,7 +1209,7 @@ class SGMVFIInterpolate:
if num_passes == 0:
all_frames = images.permute(0, 3, 1, 2)
result = _select_target_fps_frames(all_frames, source_fps, target_fps, mult, all_frames.shape[0])
return (result.cpu().permute(0, 2, 3, 1),)
return (result.cpu().permute(0, 2, 3, 1), images)
else:
num_passes = {2: 1, 4: 2, 8: 3}[multiplier]
mult = multiplier
@@ -1262,13 +1268,16 @@ class SGMVFIInterpolate:
result = torch.cat(result_chunks, dim=0)
# Convert oversampled to ComfyUI format for second output
oversampled = result.cpu().permute(0, 2, 3, 1)
# Target FPS: select frames from oversampled result
if use_target_fps:
result = _select_target_fps_frames(result, source_fps, target_fps, mult, total_input)
# Convert back to ComfyUI [B, H, W, C], on CPU
result = result.cpu().permute(0, 2, 3, 1)
return (result,)
return (result, oversampled)
class SGMVFISegmentInterpolate(SGMVFIInterpolate):
@@ -1376,7 +1385,7 @@ class SGMVFISegmentInterpolate(SGMVFIInterpolate):
# Standard multiplier mode
is_continuation = segment_index > 0
(result,) = super().interpolate(
(result, _) = super().interpolate(
segment_images, model, multiplier, clear_cache_after_n_frames,
keep_device, all_on_gpu, batch_size, chunk_size,
)
@@ -1536,8 +1545,8 @@ class GIMMVFIInterpolate:
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("images",)
RETURN_TYPES = ("IMAGE", "IMAGE")
RETURN_NAMES = ("images", "oversampled")
FUNCTION = "interpolate"
CATEGORY = "video/GIMM-VFI"
@@ -1647,7 +1656,7 @@ class GIMMVFIInterpolate:
batch_size, chunk_size,
source_fps=0.0, target_fps=0.0):
if images.shape[0] < 2:
return (images,)
return (images, images)
# Target FPS mode: auto-compute multiplier from fps ratio
use_target_fps = target_fps > 0 and source_fps > 0
@@ -1656,7 +1665,7 @@ class GIMMVFIInterpolate:
if num_passes == 0:
all_frames = images.permute(0, 3, 1, 2)
result = _select_target_fps_frames(all_frames, source_fps, target_fps, mult, all_frames.shape[0])
return (result.cpu().permute(0, 2, 3, 1),)
return (result.cpu().permute(0, 2, 3, 1), images)
# Override multiplier for single_pass mode
multiplier = mult
else:
@@ -1732,13 +1741,16 @@ class GIMMVFIInterpolate:
result = torch.cat(result_chunks, dim=0)
# Convert oversampled to ComfyUI format for second output
oversampled = result.cpu().permute(0, 2, 3, 1)
# Target FPS: select frames from oversampled result
if use_target_fps:
result = _select_target_fps_frames(result, source_fps, target_fps, mult, total_input)
# Convert back to ComfyUI [B, H, W, C], on CPU
result = result.cpu().permute(0, 2, 3, 1)
return (result,)
return (result, oversampled)
class GIMMVFISegmentInterpolate(GIMMVFIInterpolate):
@@ -1856,7 +1868,7 @@ class GIMMVFISegmentInterpolate(GIMMVFIInterpolate):
# Standard multiplier mode
is_continuation = segment_index > 0
(result,) = super().interpolate(
(result, _) = super().interpolate(
segment_images, model, multiplier, single_pass,
clear_cache_after_n_frames, keep_device, all_on_gpu,
batch_size, chunk_size,