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This commit is contained in:
CGlide
2026-05-22 16:29:52 +02:00
committed by GitHub
parent d791b85549
commit 9f2807826e
14 changed files with 310 additions and 105 deletions
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63
ltx_sequencer.py
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@@ -1,3 +1,4 @@
import json
from comfy_extras.nodes_lt import get_noise_mask, LTXVAddGuide
import torch
import comfy.utils
@@ -55,12 +56,14 @@ class LTXSequencer(LTXVAddGuide):
node_id="LTXSequencer",
display_name="LTX Sequencer",
category="WhatDreamsCost",
description="Add multiple guide images at specified frame indices or seconds with strengths. Number of widgets is dynamically configured.",
description="Add multiple guide images at specified frame indices or seconds. Auto-syncs to Prompt Relay invisibly via the positive wire.",
inputs=inputs,
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent", tooltip="Video latent with added guides"),
# ---> ADDED SYNC LOG OUTPUT <---
io.String.Output(display_name="sync_log", tooltip="Outputs a readable text block showing the actual times used for each image"),
],
)
@@ -89,29 +92,63 @@ class LTXSequencer(LTXVAddGuide):
insert_mode = kwargs.get("insert_mode", "frames")
frame_rate = kwargs.get("frame_rate", 24)
# ---> EXTRACT GHOST SYNC DATA <---
timeline_data = None
if positive is not None and len(positive) > 0:
if "prompt_relay_timeline" in positive[0][1]:
try:
timeline_data = json.loads(positive[0][1]["prompt_relay_timeline"])
except json.JSONDecodeError:
pass
# Prepare the log text
sync_log_lines = []
if timeline_data:
sync_log_lines.append("=== TIMELINE SYNC ENABLED ===")
sync_log_lines.append(f"Invisibly Synced via Positive Wire! Using {insert_mode.upper()}:")
else:
sync_log_lines.append("=== MANUAL MODE ===")
sync_log_lines.append(f"No timeline detected, or Sync turned OFF. Using manual UI inputs ({insert_mode}):")
# Process inputs up to num_images, extracting dynamic frame/strength values from kwargs
for i in range(1, num_images + 1):
# Skip if this image index exceeds the batch
if i > batch_size:
sync_log_lines.append(f"Image #{i}: Skipped (No image loaded in batch)")
continue
img = multi_input[i-1:i] # Extract the single image frame from the batch
if img is None:
continue
# Calculate the final frame index based on the chosen mode
f_idx = None
if insert_mode == "frames":
f_idx = kwargs.get(f"insert_frame_{i}")
elif insert_mode == "seconds":
sec = kwargs.get(f"insert_second_{i}")
if sec is not None:
f_idx = int(sec * frame_rate)
strength = kwargs.get(f"strength_{i}", 1.0)
# 1. AUTO-SYNC
if timeline_data and "starts_frames" in timeline_data and (i - 1) < len(timeline_data["starts_frames"]):
display_frame = timeline_data["starts_frames"][i - 1]
display_sec = timeline_data["starts_seconds"][i - 1]
if insert_mode == "frames":
f_idx = display_frame
sync_log_lines.append(f"-> Image #{i} (Strength: {strength}): Synced to Segment #{i} start @ {display_frame} frames")
elif insert_mode == "seconds":
f_idx = int(display_sec * frame_rate)
sync_log_lines.append(f"-> Image #{i} (Strength: {strength}): Synced to Segment #{i} start @ {display_sec:.2f} seconds (Frame {f_idx})")
# 2. MANUAL FALLBACK
else:
if insert_mode == "frames":
f_idx = kwargs.get(f"insert_frame_{i}")
sync_log_lines.append(f"-> Image #{i} (Strength: {strength}): Manual input @ {f_idx} frames")
elif insert_mode == "seconds":
sec = kwargs.get(f"insert_second_{i}")
if sec is not None:
f_idx = int(sec * frame_rate)
sync_log_lines.append(f"-> Image #{i} (Strength: {strength}): Manual input @ {sec:.2f} seconds (Frame {f_idx})")
if f_idx is None:
continue
strength = kwargs.get(f"strength_{i}", 1.0)
# Execution logic mirrored from LTXVAddGuideMulti
image_1, t = cls.encode(vae, latent_width, latent_height, img, scale_factors)
@@ -130,4 +167,8 @@ class LTXSequencer(LTXVAddGuide):
scale_factors,
)
return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask})
sync_log_str = "\n".join(sync_log_lines)
print(f"\n[LTX Sequencer]\n{sync_log_str}\n")
# Returned the newly formatted sync log string at the end of NodeOutput
return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask}, sync_log_str)