nodes/kijai-ComfyUI-NativeLooping_testing
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Fix total_frames mode

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kijai
2026-06-16 01:45:37 +03:00
parent fceb066b77
commit 82fff6130e
1 changed files with 18 additions and 31 deletions
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nodes.py
+18 -31
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@@ -107,9 +107,7 @@ class TensorLoopOpen(io.ComfyNode):
comfy.utils.ProgressBar(total_frames_val, node_id=open_node_id).update_absolute(accumulated_count)
elif count > 0:
comfy.utils.ProgressBar(count, node_id=open_node_id).update_absolute(count - remaining)
# The initial_value link survives loop-body cloning, so this is valid on every iteration
has_initial = initial_value is not None
loop_state = {"remaining": remaining, "accum": accum, "previous_value": previous_value, "count": count, "open_node_id": open_node_id, "total_frames": total_frames_val, "blend_overlap": blend_overlap, "has_initial": has_initial}
loop_state = {"remaining": remaining, "accum": accum, "previous_value": previous_value, "count": count, "open_node_id": open_node_id, "total_frames": total_frames_val, "blend_overlap": blend_overlap}
return io.NodeOutput(loop_state, previous_value, accumulated_count, current_iteration)
@@ -146,10 +144,10 @@ class TensorLoopClose(io.ComfyNode):
"- start/end: cut the duplicates from the start or end of each iteration's output\n"
"- fade_linear/fade_smooth: crossfade the end of each iteration into the start of the next", options=[
io.DynamicCombo.Option("disabled", []),
_overlap_option("start", "Number of frames to cut from the START of each iteration's output. Use when the model re-generates its context frames at the start of its output (most common for video continuation). The first iteration is trimmed only when initial_value is connected."),
_overlap_option("end", "Number of frames to cut from the END of each iteration's output. Use when the model generates look-ahead frames at the end of its output that the next iteration re-generates. When initial_value is connected, the first iteration's start is also trimmed."),
_overlap_option("fade_linear", "Number of overlapping frames to crossfade: the end of each iteration is blended into the start of the next with a linear ramp. When initial_value is connected, the first iteration's start is trimmed instead of blended."),
_overlap_option("fade_smooth", "Number of overlapping frames to crossfade: the end of each iteration is blended into the start of the next with a smoothstep (ease in/out) ramp. When initial_value is connected, the first iteration's start is trimmed instead of blended."),
_overlap_option("start", "Number of frames to cut from the START of each iteration's output. Use when the model re-generates its context frames at the start of its output (most common for video continuation). The first generation is always kept whole; only iterations 2+ are trimmed."),
_overlap_option("end", "Number of frames to cut from the END of each iteration's output. Use when the model generates look-ahead frames at the end of its output that the next iteration re-generates. The trimmed tail of the final iteration is re-appended."),
_overlap_option("fade_linear", "Number of overlapping frames to crossfade: the end of each iteration is blended into the start of the next with a linear ramp. The first generation is kept whole."),
_overlap_option("fade_smooth", "Number of overlapping frames to crossfade: the end of each iteration is blended into the start of the next with a smoothstep (ease in/out) ramp. The first generation is kept whole."),
]),
io.Boolean.Input("stop", optional=True, default=False, raw_link=True, force_input=True,
tooltip="Optional early stop signal from inside the loop body. When True, the loop stops after the current iteration regardless of remaining iterations or total_frames target."),
@@ -166,7 +164,7 @@ class TensorLoopClose(io.ComfyNode):
graph = GraphBuilder()
open_id = flow_control[0]
unpack = graph.node("_ImageAccumStateUnpack", loop_state=[open_id, 0])
# unpack: 0=remaining, 1=accum, 2=previous_value, 3=accumulated_count, 4=count, 5=open_node_id, 6=total_frames, 7=has_initial
# unpack: 0=remaining, 1=accum, 2=previous_value, 3=accumulated_count, 4=count, 5=open_node_id, 6=total_frames
sub = graph.node("_IntOperations", operation="subtract", a=unpack.out(0), b=1)
overlap_mode = "disabled"
@@ -179,25 +177,16 @@ class TensorLoopClose(io.ComfyNode):
if accumulate:
to_accum = processed
if overlap_frames > 0 and overlap_mode != "disabled":
# Iteration 1's start overlaps with initial_value (external context the model
# re-generates), so it is only trimmed when initial_value was actually connected.
# _BatchOps is a no-op at amount=0, so the trim amounts double as the conditionals.
is_first = graph.node("_IntOperations", a=unpack.out(3), b=0, operation="==")
first_with_init = graph.node("_IntOperations", a=is_first.out(0), b=unpack.out(7), operation="multiply")
# The first generation has no preceding chunk, so it is kept whole; only the seams
# of iterations 2+ are trimmed. start_trim is 0 on iteration 1 (a _BatchOps no-op).
if overlap_mode == "start":
# Iterations 2+ always overlap the previous iteration's tail
not_first = graph.node("_IntOperations", a=unpack.out(3), b=0, operation=">")
do_trim = graph.node("_IntOperations", a=not_first.out(0), b=first_with_init.out(0), operation="add")
else:
# end/fade keep iteration starts (end trims tails, fade blends post-loop)
do_trim = first_with_init
start_trim = graph.node("_IntOperations", a=do_trim.out(0), b=overlap_frames, operation="multiply")
to_trim = processed
if overlap_mode == "end":
to_trim = graph.node("_BatchOps", batch=processed, operation="trim_end", amount=overlap_frames).out(0)
to_accum = graph.node("_BatchOps", batch=to_trim, operation="trim_start", amount=start_trim.out(0)).out(0)
iter_index = graph.node("_IntOperations", a=unpack.out(4), b=unpack.out(0), operation="subtract")
not_first = graph.node("_IntOperations", a=iter_index.out(0), b=0, operation=">")
start_trim = graph.node("_IntOperations", a=not_first.out(0), b=overlap_frames, operation="multiply")
to_accum = graph.node("_BatchOps", batch=processed, operation="trim_start", amount=start_trim.out(0)).out(0)
elif overlap_mode == "end":
to_accum = graph.node("_BatchOps", batch=processed, operation="trim_end", amount=overlap_frames).out(0)
# fade modes blend the seams post-loop
accum_out = graph.node("_AccumulateNode", to_add=to_accum, accumulation=accum_out).out(0)
@@ -681,8 +670,8 @@ class _ImageAccumStatePack(io.ComfyNode):
if total_frames > 0:
should_continue = accumulated_count < total_frames
# Bail if the last iteration added nothing — the loop would never reach the target
if accumulated_count == prev_accumulated_count:
# Bail if the last iteration made no progress — the target is unreachable
if accumulated_count <= prev_accumulated_count:
should_continue = False
comfy.utils.ProgressBar(total_frames, node_id=open_node_id).update_absolute(accumulated_count)
else:
@@ -714,7 +703,6 @@ class _ImageAccumStateUnpack(io.ComfyNode):
io.Int.Output("count"),
io.AnyType.Output("open_node_id"),
io.Int.Output("total_frames"),
io.Int.Output("has_initial"),
],
)
@@ -727,8 +715,7 @@ class _ImageAccumStateUnpack(io.ComfyNode):
open_node_id = loop_state.get("open_node_id")
total_frames = loop_state.get("total_frames", 0)
accumulated_count = _accum_count(accum, loop_state.get("blend_overlap", 0))
has_initial = int(bool(loop_state.get("has_initial", False)))
return io.NodeOutput(remaining, accum, previous_value, accumulated_count, count, open_node_id, total_frames, has_initial)
return io.NodeOutput(remaining, accum, previous_value, accumulated_count, count, open_node_id, total_frames)