1122 lines
53 KiB
Python
1122 lines
53 KiB
Python
# --- START OF FILE ltx_director.py ---
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import logging
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import json
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import base64
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import io as _io
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import math
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import time
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import numpy as np
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import torch
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import av
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from PIL import Image
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import os
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import folder_paths
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import comfy.model_management
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from comfy_api.latest import io
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from .prompt_relay import (
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get_raw_tokenizer,
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map_token_indices,
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build_segments,
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create_mask_fn,
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distribute_segment_lengths,
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)
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from .patches import detect_model_type, apply_patches
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log = logging.getLogger(__name__)
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# Custom socket type shared with LTXSequencer
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GuideData = io.Custom("GUIDE_DATA")
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# --- Licon MSR engine fixed parameters (were node widgets; hardcoded for a clean UI) ---
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# Slideshow length. 41 is the Licon training default (valid: 17 / 25 / 33 / 41).
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MSR_PREFIX_FRAMES = 41
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# latent_downscale_factor for the IC-LoRA reference frames. Tied to how the MSR LoRA was
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# trained; Licon MSR V1 uses full-resolution references (1.0). This is INDEPENDENT of any
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# multi-stage scale_by / x2 upscaling, which LTXDirectorGuide handles downstream.
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MSR_LATENT_DOWNSCALE = 1.0
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def _preprocess_prompts_with_characters(global_prompt, local_prompts, char1="", char2="", char3=""):
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"""Invisibly swaps out @character1/@char1 tags with their high-fidelity VLM descriptions."""
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gp = global_prompt
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char1 = char1 if char1 else ""
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char2 = char2 if char2 else ""
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char3 = char3 if char3 else ""
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# Process Global Prompt
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for tag in ["@character1", "@char1"]:
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if tag in gp:
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gp = gp.replace(tag, char1)
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for tag in ["@character2", "@char2"]:
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if tag in gp:
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gp = gp.replace(tag, char2)
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for tag in ["@character3", "@char3"]:
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if tag in gp:
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gp = gp.replace(tag, char3)
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# Process Local Timeline Prompts
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locals_list = [p.strip() for p in local_prompts.split("|")] if local_prompts else []
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processed_locals = []
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for lp in locals_list:
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for tag in ["@character1", "@char1"]:
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if tag in lp:
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lp = lp.replace(tag, char1)
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for tag in ["@character2", "@char2"]:
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if tag in lp:
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lp = lp.replace(tag, char2)
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for tag in ["@character3", "@char3"]:
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if tag in lp:
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lp = lp.replace(tag, char3)
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processed_locals.append(lp)
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return gp, " | ".join(processed_locals)
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def _format_timeline_to_text(global_prompt, duration_frames, frame_rate, epsilon,
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custom_width, custom_height, resize_method,
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timeline_data, local_prompts, segment_lengths, guide_strength):
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lines = []
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lines.append("LTX Director Timeline Export")
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lines.append("============================")
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lines.append("")
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lines.append("=== Global Parameters ===")
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lines.append(f"Global Prompt:\n{global_prompt}\n")
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fr = float(frame_rate) if frame_rate else 24.0
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if fr <= 0: fr = 24.0
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lines.append(f"Duration: {duration_frames} frames ({(duration_frames / fr):.2f}s @ {fr} FPS)")
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lines.append(f"Epsilon (Penalty Decay): {epsilon}")
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if custom_width > 0 or custom_height > 0:
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lines.append(f"Target Dimensions: {custom_width}x{custom_height} (Resize Method: {resize_method})")
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else:
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lines.append("Target Dimensions: Auto (Based on first image)")
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lines.append("")
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lines.append("=== Timeline Segments ===")
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# --- 1. Text Prompts (Extracted from direct inputs) ---
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locals_list = [p.strip() for p in local_prompts.split("|")] if local_prompts else []
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lengths_list = [l.strip() for l in segment_lengths.split(",")] if segment_lengths else []
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if locals_list and any(locals_list):
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lines.append("\n--- Text Prompts ---")
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current_frame = 0.0
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for i, prompt in enumerate(locals_list):
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try:
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len_f = float(lengths_list[i]) if i < len(lengths_list) and lengths_list[i] else 0.0
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except ValueError:
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len_f = 0.0
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start_f = current_frame
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end_f = start_f + len_f
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start_s = start_f / fr
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end_s = end_f / fr
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len_s = len_f / fr
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lines.append(f"\n[Prompt {i+1}]")
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lines.append(f"Time: {start_s:.2f}s - {end_s:.2f}s (Duration: {len_s:.2f}s)")
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lines.append(f"Frames: {start_f:.1f} - {end_f:.1f} (Length: {len_f:.1f})")
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lines.append(f"Prompt:\n{prompt}")
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lines.append("-" * 40)
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current_frame += len_f
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# --- 2. Images & Audio (Extracted from JSON) ---
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try:
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tdata = json.loads(timeline_data) if timeline_data else {}
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segs = tdata.get("segments", [])
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img_segs = [s for s in segs if s.get("type", "image") == "image"]
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img_segs.sort(key=lambda s: float(s.get("start", 0)))
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if img_segs:
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lines.append("\n--- Image Guides ---")
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strengths = [float(x.strip()) for x in guide_strength.split(",")] if guide_strength and guide_strength.strip() else []
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for i, seg in enumerate(img_segs):
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start_f = float(seg.get("start", 0))
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start_s = start_f / fr
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strength = strengths[i] if i < len(strengths) else 1.0
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lines.append(f"\n[Image {i+1}]")
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lines.append(f"Time Inserted: {start_s:.2f}s (Frame {start_f:.1f})")
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lines.append(f"Guide Strength: {strength}")
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lines.append("-" * 40)
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audio_segs = [s for s in segs if s.get("type", "audio") == "audio"]
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audio_segs.sort(key=lambda s: float(s.get("start", 0)))
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if audio_segs:
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lines.append("\n--- Audio Segments ---")
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for i, seg in enumerate(audio_segs):
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start_f = float(seg.get("start", 0))
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len_f = float(seg.get("length", 0))
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start_s = start_f / fr
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len_s = len_f / fr
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file_name = seg.get("fileName", "Unknown")
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lines.append(f"\n[Audio {i+1}] {file_name}")
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lines.append(f"Time: {start_s:.2f}s (Frame {start_f:.1f}) | Duration: {len_s:.2f}s")
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lines.append("-" * 40)
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except Exception as e:
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lines.append(f"\n[Note: Could not parse detailed timeline JSON for images/audio. Error: {e}]")
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return "\n".join(lines)
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def _convert_to_latent_lengths(pixel_lengths, temporal_stride, latent_frames):
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if not pixel_lengths:
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return []
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total_pixel = sum(pixel_lengths)
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if total_pixel <= 0:
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return [1] * len(pixel_lengths)
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naive_total = max(1, round(total_pixel / temporal_stride))
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target_total = min(latent_frames, naive_total)
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if target_total >= latent_frames - 1:
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target_total = latent_frames
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exact = [p * target_total / total_pixel for p in pixel_lengths]
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result = [int(e) for e in exact]
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diff = target_total - sum(result)
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if diff > 0:
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order = sorted(range(len(exact)), key=lambda i: -(exact[i] - int(exact[i])))
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for k in range(diff):
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result[order[k % len(order)]] += 1
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for i in range(len(result)):
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if result[i] < 1:
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max_idx = max(range(len(result)), key=lambda j: result[j])
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if result[max_idx] > 1:
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result[max_idx] -= 1
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result[i] = 1
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return result
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def _encode_relay(model, clip, latent, global_prompt, local_prompts, segment_lengths, epsilon):
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for name, val in (("global_prompt", global_prompt),
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("local_prompts", local_prompts),
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("segment_lengths", segment_lengths)):
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if val is None:
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raise ValueError(
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f"PromptRelay: '{name}' arrived as None. "
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"Likely causes: a stale workflow JSON saved with null, the timeline "
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"editor's web extension failing to load, or an upstream node returning None. "
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"Set the field to an empty string or fix the upstream connection."
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)
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locals_list = [p.strip() for p in local_prompts.split("|")]
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for p in locals_list:
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if not p:
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raise ValueError("There is a segment on the timeline missing a prompt!")
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if not locals_list or (len(locals_list) == 1 and not locals_list[0]):
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raise ValueError("At least one local prompt is required.")
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arch, patch_size, temporal_stride = detect_model_type(model)
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samples = latent["samples"]
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latent_frames = samples.shape[2]
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tokens_per_frame = (samples.shape[3] // patch_size[1]) * (samples.shape[4] // patch_size[2])
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parsed_lengths = None
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if segment_lengths.strip():
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pixel_lengths = [int(float(x.strip())) for x in segment_lengths.split(",") if x.strip()]
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parsed_lengths = _convert_to_latent_lengths(pixel_lengths, temporal_stride, latent_frames)
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raw_tokenizer = get_raw_tokenizer(clip)
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full_prompt, token_ranges = map_token_indices(raw_tokenizer, global_prompt, locals_list)
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log.info("[PromptRelay] Global: tokens [0:%d] (%d tokens)", token_ranges[0][0], token_ranges[0][0])
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for i, (s, e) in enumerate(token_ranges):
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log.info("[PromptRelay] Segment %d: tokens [%d:%d] (%d tokens)", i, s, e, e - s)
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conditioning = clip.encode_from_tokens_scheduled(clip.tokenize(full_prompt))
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effective_lengths = distribute_segment_lengths(len(locals_list), latent_frames, parsed_lengths)
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log.info(
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"[PromptRelay] Latent: %d frames, %d tokens/frame, segments: %s",
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latent_frames, tokens_per_frame, effective_lengths,
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)
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q_token_idx = build_segments(token_ranges, effective_lengths, epsilon, None)
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mask_fn = create_mask_fn(q_token_idx, tokens_per_frame, latent_frames)
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patched = model.clone()
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apply_patches(patched, arch, mask_fn)
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return patched, conditioning
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def _execute_comfy_node(node_class, **kwargs):
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"""Executes any ComfyUI node class programmatically by resolving its entrypoint dynamically."""
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if hasattr(node_class, "execute"):
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return node_class.execute(**kwargs)
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node_instance = node_class()
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func_name = getattr(node_class, "FUNCTION", None)
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if func_name is None:
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for candidate in ("generate", "encode", "process", "execute"):
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if hasattr(node_instance, candidate):
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func_name = candidate
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break
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if func_name is not None:
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func = getattr(node_instance, func_name)
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import inspect
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sig = inspect.signature(func)
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filtered_kwargs = {}
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for param in sig.parameters.values():
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if param.name in kwargs:
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filtered_kwargs[param.name] = kwargs[param.name]
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elif param.default is not inspect.Parameter.empty:
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pass
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elif param.kind == inspect.Parameter.VAR_KEYWORD:
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filtered_kwargs.update(kwargs)
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break
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return func(**filtered_kwargs)
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else:
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raise AttributeError(f"Could not resolve entrypoint for node class {node_class.__name__}")
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def _unpack(out):
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"""Safely extracts positional returns from ComfyUI API outputs."""
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try:
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return out[0], out[1], out[2]
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except Exception:
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t = tuple(out)
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return t[0], t[1], t[2]
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def _load_image_source(b64_or_url: str, filename: str = None) -> torch.Tensor:
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if not b64_or_url and not filename:
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return torch.zeros((1, 512, 512, 3), dtype=torch.float32)
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if b64_or_url and "view?" in b64_or_url:
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try:
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from urllib.parse import urlparse, parse_qs
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parsed = urlparse(b64_or_url)
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q = parse_qs(parsed.query)
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fname = q.get("filename", [None])[0]
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subfolder = q.get("subfolder", [""])[0]
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if fname:
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file_path = os.path.join(folder_paths.get_input_directory(), subfolder, fname)
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if os.path.exists(file_path):
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img = Image.open(file_path).convert("RGB")
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arr = np.array(img, dtype=np.float32) / 255.0
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return torch.from_numpy(arr).unsqueeze(0)
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except Exception as e:
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log.debug(f"[PromptRelay] URL parsing failed for {b64_or_url}: {e}")
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if filename:
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file_path = os.path.join(folder_paths.get_input_directory(), filename)
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if os.path.exists(file_path):
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img = Image.open(file_path).convert("RGB")
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arr = np.array(img, dtype=np.float32) / 255.0
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return torch.from_numpy(arr).unsqueeze(0)
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if b64_or_url:
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try:
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b64_str = b64_or_url
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if "," in b64_str:
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b64_str = b64_str.split(",", 1)[1]
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img_bytes = base64.b64decode(b64_str)
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img = Image.open(_io.BytesIO(img_bytes)).convert("RGB")
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arr = np.array(img, dtype=np.float32) / 255.0
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return torch.from_numpy(arr).unsqueeze(0)
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except Exception as e:
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log.debug(f"[PromptRelay] Base64 decoding failed: {e}")
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return torch.zeros((1, 512, 512, 3), dtype=torch.float32)
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def _load_image_tensor(seg: dict) -> torch.Tensor:
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if seg.get("imageFile"):
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file_path = os.path.join(folder_paths.get_input_directory(), seg["imageFile"])
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if os.path.exists(file_path):
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img = Image.open(file_path).convert("RGB")
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arr = np.array(img, dtype=np.float32) / 255.0
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return torch.from_numpy(arr).unsqueeze(0)
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b64_str = seg.get("imageB64", "")
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if b64_str and "view?" in b64_str:
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try:
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from urllib.parse import urlparse, parse_qs
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parsed = urlparse(b64_str)
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q = parse_qs(parsed.query)
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fname = q.get("filename", [None])[0]
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subfolder = q.get("subfolder", [""])[0]
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if fname:
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file_path = os.path.join(folder_paths.get_input_directory(), subfolder, fname)
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if os.path.exists(file_path):
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img = Image.open(file_path).convert("RGB")
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arr = np.array(img, dtype=np.float32) / 255.0
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return torch.from_numpy(arr).unsqueeze(0)
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except Exception as e:
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log.debug(f"[PromptRelay] URL parsing failed for {b64_str}: {e}")
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if not b64_str or b64_str.startswith("/view?"):
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return torch.zeros((1, 512, 512, 3), dtype=torch.float32)
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if "," in b64_str:
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b64_str = b64_str.split(",", 1)[1]
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try:
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img_bytes = base64.b64decode(b64_str)
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img = Image.open(_io.BytesIO(img_bytes)).convert("RGB")
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arr = np.array(img, dtype=np.float32) / 255.0
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return torch.from_numpy(arr).unsqueeze(0)
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except:
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return torch.zeros((1, 512, 512, 3), dtype=torch.float32)
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def _resize_image(tensor: torch.Tensor, target_w: int, target_h: int, method: str, divisible_by: int) -> torch.Tensor:
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from PIL import Image as _PilImage
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def snap(val, div):
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return max(div, (val // div) * div)
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tw = snap(target_w, divisible_by)
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th = snap(target_h, divisible_by)
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img_np = (tensor[0].cpu().numpy() * 255.0).clip(0, 255).astype(np.uint8)
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pil = _PilImage.fromarray(img_np)
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src_w, src_h = pil.size
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if method == "stretch to fit":
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resized = pil.resize((tw, th), _PilImage.LANCZOS)
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elif method == "maintain aspect ratio":
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ratio = min(tw / src_w, th / src_h)
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new_w = int(src_w * ratio)
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new_h = int(src_h * ratio)
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new_w = snap(new_w, divisible_by)
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new_h = snap(new_h, divisible_by)
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resized = pil.resize((new_w, new_h), _PilImage.LANCZOS)
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elif method == "pad":
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ratio = min(tw / src_w, th / src_h)
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new_w = snap(int(src_w * ratio), divisible_by)
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new_h = snap(int(src_h * ratio), divisible_by)
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inner = pil.resize((new_w, new_h), _PilImage.LANCZOS)
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resized = _PilImage.new("RGB", (tw, th), (0, 0, 0))
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resized.paste(inner, ((tw - new_w) // 2, (th - new_h) // 2))
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elif method == "crop":
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ratio = max(tw / src_w, th / src_h)
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new_w = int(src_w * ratio)
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new_h = int(src_h * ratio)
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inner = pil.resize((new_w, new_h), _PilImage.LANCZOS)
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left = (new_w - tw) // 2
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top = (new_h - th) // 2
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resized = inner.crop((left, top, left + tw, top + th))
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else:
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resized = pil.resize((tw, th), _PilImage.LANCZOS)
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arr = np.array(resized, dtype=np.float32) / 255.0
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return torch.from_numpy(arr).unsqueeze(0)
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def _compress_image(tensor: torch.Tensor, crf: int) -> torch.Tensor:
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if crf == 0:
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return tensor
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img = tensor[0]
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h = (img.shape[0] // 2) * 2
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w = (img.shape[1] // 2) * 2
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img_np = (img[:h, :w] * 255.0).byte().cpu().numpy()
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try:
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buf = _io.BytesIO()
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container = av.open(buf, mode="w", format="mp4")
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stream = container.add_stream("libx264", rate=1)
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stream.width = w
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stream.height = h
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stream.pix_fmt = "yuv420p"
|
|
stream.options = {"crf": str(crf), "preset": "ultrafast"}
|
|
frame = av.VideoFrame.from_ndarray(img_np, format="rgb24")
|
|
for pkt in stream.encode(frame):
|
|
container.mux(pkt)
|
|
for pkt in stream.encode(None):
|
|
container.mux(pkt)
|
|
container.close()
|
|
|
|
buf.seek(0)
|
|
container_r = av.open(buf, mode="r")
|
|
decoded = None
|
|
for frame_r in container_r.decode(video=0):
|
|
decoded = frame_r.to_ndarray(format="rgb24")
|
|
break
|
|
container_r.close()
|
|
|
|
if decoded is None:
|
|
return tensor
|
|
arr = torch.from_numpy(decoded.astype(np.float32) / 255.0).to(tensor.device, tensor.dtype)
|
|
out = tensor.clone()
|
|
out[0, :h, :w] = arr
|
|
return out
|
|
except Exception as e:
|
|
log.warning("[PromptRelay] img_compression encode/decode failed: %s", e)
|
|
return tensor
|
|
|
|
|
|
def _build_combined_audio(timeline_data_str: str, duration_frames: int, frame_rate: float) -> dict:
|
|
target_sr = 44100
|
|
total_samples = max(1, int(math.ceil(duration_frames / frame_rate * target_sr)))
|
|
empty_audio = {"waveform": torch.zeros((1, 2, total_samples), dtype=torch.float32), "sample_rate": target_sr}
|
|
|
|
if not timeline_data_str:
|
|
return empty_audio
|
|
|
|
try:
|
|
data = json.loads(timeline_data_str)
|
|
audio_segs = data.get("audioSegments", [])
|
|
except Exception:
|
|
return empty_audio
|
|
|
|
if not audio_segs:
|
|
return empty_audio
|
|
|
|
out_waveform = torch.zeros((2, total_samples), dtype=torch.float32)
|
|
|
|
for seg in audio_segs:
|
|
buffer = None
|
|
if seg.get("audioFile"):
|
|
file_path = os.path.join(folder_paths.get_input_directory(), seg["audioFile"])
|
|
if os.path.exists(file_path):
|
|
with open(file_path, "rb") as f:
|
|
buffer = _io.BytesIO(f.read())
|
|
|
|
if not buffer and seg.get("audioB64"):
|
|
b64 = seg.get("audioB64")
|
|
if "," in b64:
|
|
b64 = b64.split(",", 1)[1]
|
|
try:
|
|
audio_bytes = base64.b64decode(b64)
|
|
buffer = _io.BytesIO(audio_bytes)
|
|
except:
|
|
pass
|
|
|
|
if not buffer:
|
|
continue
|
|
|
|
try:
|
|
clip_frames = []
|
|
|
|
with av.open(buffer) as container:
|
|
stream = container.streams.audio[0]
|
|
resampler = av.AudioResampler(format='fltp', layout='stereo', rate=target_sr)
|
|
|
|
for frame in container.decode(stream):
|
|
for resampled_frame in resampler.resample(frame):
|
|
clip_frames.append(torch.from_numpy(resampled_frame.to_ndarray()))
|
|
|
|
for resampled_frame in resampler.resample(None):
|
|
clip_frames.append(torch.from_numpy(resampled_frame.to_ndarray()))
|
|
|
|
if not clip_frames:
|
|
continue
|
|
|
|
waveform = torch.cat(clip_frames, dim=1)
|
|
|
|
trim_start_frames = float(seg.get("trimStart", 0))
|
|
length_frames = float(seg.get("length", 1))
|
|
start_frames = float(seg.get("start", 0))
|
|
|
|
start_sample_src = int(trim_start_frames / frame_rate * target_sr)
|
|
length_samples = int(length_frames / frame_rate * target_sr)
|
|
end_sample_src = start_sample_src + length_samples
|
|
|
|
if start_sample_src < 0: start_sample_src = 0
|
|
if end_sample_src > waveform.shape[1]:
|
|
end_sample_src = waveform.shape[1]
|
|
|
|
actual_length = end_sample_src - start_sample_src
|
|
if actual_length <= 0: continue
|
|
|
|
clip_waveform = waveform[:, start_sample_src:end_sample_src]
|
|
|
|
start_sample_dst = int(start_frames / frame_rate * target_sr)
|
|
|
|
if start_sample_dst >= out_waveform.shape[1]:
|
|
continue
|
|
|
|
end_sample_dst = start_sample_dst + actual_length
|
|
|
|
if end_sample_dst > out_waveform.shape[1]:
|
|
actual_length = out_waveform.shape[1] - start_sample_dst
|
|
clip_waveform = clip_waveform[:, :actual_length]
|
|
end_sample_dst = start_sample_dst + actual_length
|
|
|
|
if actual_length <= 0:
|
|
continue
|
|
|
|
out_waveform[:, start_sample_dst:end_sample_dst] += clip_waveform
|
|
|
|
except Exception as e:
|
|
log.warning("[PromptRelay] Audio process error for segment %s: %s", seg.get("fileName"), e)
|
|
continue
|
|
|
|
return {"waveform": out_waveform.unsqueeze(0), "sample_rate": target_sr}
|
|
|
|
|
|
# Register API endpoint for instant export
|
|
try:
|
|
import server
|
|
from aiohttp import web
|
|
|
|
@server.PromptServer.instance.routes.post("/ltx_director/export_timeline")
|
|
async def export_timeline_endpoint(request):
|
|
try:
|
|
data = await request.json()
|
|
global_prompt = data.get("global_prompt", "")
|
|
timeline_data = data.get("timeline_data", "{}")
|
|
duration_frames = int(data.get("duration_frames", 0))
|
|
frame_rate = float(data.get("frame_rate", 24))
|
|
epsilon = float(data.get("epsilon", 0.001))
|
|
custom_width = int(data.get("custom_width", 0))
|
|
custom_height = int(data.get("custom_height", 0))
|
|
resize_method = data.get("resize_method", "maintain aspect ratio")
|
|
local_prompts = data.get("local_prompts", "")
|
|
segment_lengths = data.get("segment_lengths", "")
|
|
guide_strength = data.get("guide_strength", "")
|
|
|
|
formatted_text = _format_timeline_to_text(
|
|
global_prompt, duration_frames, frame_rate, epsilon,
|
|
custom_width, custom_height, resize_method,
|
|
timeline_data, local_prompts, segment_lengths, guide_strength
|
|
)
|
|
|
|
out_dir = folder_paths.get_output_directory()
|
|
filename = f"ltx_director_prompts_{int(time.time())}.txt"
|
|
filepath = os.path.join(out_dir, filename)
|
|
|
|
with open(filepath, "w", encoding="utf-8") as f:
|
|
f.write(formatted_text)
|
|
|
|
return web.json_response({
|
|
"status": "success",
|
|
"filepath": filepath,
|
|
"filename": filename,
|
|
"content": formatted_text
|
|
})
|
|
except Exception as e:
|
|
return web.json_response({"status": "error", "message": str(e)}, status=500)
|
|
except Exception as e:
|
|
pass
|
|
|
|
# Register API endpoint for instant character analysis via local Ollama
|
|
try:
|
|
import server
|
|
from aiohttp import web
|
|
import aiohttp
|
|
|
|
@server.PromptServer.instance.routes.post("/ltx_director/analyze_character")
|
|
async def analyze_character_endpoint(request):
|
|
try:
|
|
data = await request.json()
|
|
image_b64 = data.get("image_b64", "")
|
|
char_index = int(data.get("char_index", 0))
|
|
|
|
if not image_b64:
|
|
return web.json_response({"status": "error", "message": "No image provided for analysis."})
|
|
|
|
b64_list = image_b64 if isinstance(image_b64, list) else [image_b64]
|
|
cleaned_b64_list = []
|
|
for b64 in b64_list:
|
|
if "," in b64:
|
|
b64 = b64.split(",", 1)[1]
|
|
cleaned_b64_list.append(b64)
|
|
|
|
if not cleaned_b64_list:
|
|
return web.json_response({"status": "error", "message": "No valid base64 images decoded."})
|
|
|
|
# Restored your preferred model configuration
|
|
model_name = "huihui_ai/qwen3.5-abliterated:2b"
|
|
prompt = (
|
|
"Describe the character's physical appearance in two concise sentences. "
|
|
"Specify their hair color/style, face details, and their clothing type/color. "
|
|
"Keep the entire response very brief."
|
|
)
|
|
|
|
log.info(f"[PromptRelay] Analyzing Character {char_index+1} with local Ollama model '{model_name}'...")
|
|
|
|
payload = {
|
|
"model": model_name,
|
|
"prompt": prompt,
|
|
"images": cleaned_b64_list,
|
|
"stream": False,
|
|
"keep_alive": 0
|
|
}
|
|
|
|
ollama_url = "http://127.0.0.1:11434/api/generate"
|
|
|
|
async with aiohttp.ClientSession() as session:
|
|
try:
|
|
async with session.post(ollama_url, json=payload, timeout=60) as response:
|
|
if response.status != 200:
|
|
err_txt = await response.text()
|
|
return web.json_response({
|
|
"status": "error",
|
|
"message": f"Ollama returned HTTP {response.status}: {err_txt}"
|
|
})
|
|
|
|
resp_json = await response.json()
|
|
generated_text = resp_json.get("response", "").strip()
|
|
except aiohttp.ClientConnectorError:
|
|
return web.json_response({
|
|
"status": "error",
|
|
"message": (
|
|
f"Could not connect to Ollama. Please ensure Ollama is installed and "
|
|
f"running, and that you have pulled the model via 'ollama run {model_name}'."
|
|
)
|
|
})
|
|
|
|
if "<think>" in generated_text:
|
|
generated_text = generated_text.split("</think>")[-1].strip()
|
|
|
|
log.info(f"[PromptRelay] Analysis complete: {generated_text}")
|
|
return web.json_response({"status": "success", "description": generated_text})
|
|
|
|
except Exception as e:
|
|
log.error(f"[PromptRelay] Failed to analyze character: {e}")
|
|
return web.json_response({"status": "error", "message": str(e)}, status=500)
|
|
except Exception as e:
|
|
pass
|
|
|
|
|
|
class LTXDirector(io.ComfyNode):
|
|
"""WYSIWYG timeline variant — segments and lengths come from a visual editor in the node UI."""
|
|
|
|
@classmethod
|
|
def define_schema(cls):
|
|
return io.Schema(
|
|
node_id="LTXDirectorCS",
|
|
display_name="LTX Director CS",
|
|
category="WhatDreamsCost CS",
|
|
description=(
|
|
"Same as Prompt Relay Encode, but local prompts and segment lengths are edited "
|
|
"visually as draggable blocks on a timeline. The duration_frames input only sets the "
|
|
"timeline scale (pixel space) — actual frame count is still read from the latent."
|
|
),
|
|
inputs=[
|
|
io.Model.Input("model"),
|
|
io.Clip.Input("clip"),
|
|
io.Conditioning.Input("negative", optional=True, tooltip="Optional. Connect your negative prompt conditioning here. Highly recommended for Licon MSR to prevent noise artifacts."),
|
|
io.Vae.Input("vae", optional=True, tooltip="Optional. Connect the LTX Autoencoder/VAE here to natively encode the MSR visual reference slideshow into the latent prefix."),
|
|
io.Vae.Input("audio_vae", optional=True, tooltip="Optional. Connect an Audio VAE to generate audio latents."),
|
|
io.Latent.Input("optional_latent", optional=True, tooltip="Optional. Connect a latent to override the auto-generated one."),
|
|
io.String.Input("global_prompt", multiline=True, default="", tooltip="Conditions the entire video. Anchors persistent characters, objects, and scene context."),
|
|
io.Int.Input("duration_frames", default=120, min=1, max=10000, step=1, tooltip="Total timeline length in pixel-space frames. Used by the editor for visual scale only."),
|
|
io.Float.Input("duration_seconds", default=5.0, min=0.1, max=1000.0, step=0.01, tooltip="Total timeline duration in seconds (computed/synced from frames)."),
|
|
io.String.Input("timeline_data", default="", tooltip="JSON state of the timeline editor (auto-managed; do not edit by hand)."),
|
|
io.Boolean.Input("use_custom_audio", default=False, tooltip="Toggle between using timeline audio (ON) and generating audio from scratch (OFF)."),
|
|
io.String.Input("local_prompts", multiline=True, default="", tooltip="Auto-populated from the timeline editor."),
|
|
io.String.Input("segment_lengths", default="", tooltip="Auto-populated from the timeline editor (pixel-space frame counts)."),
|
|
io.Float.Input("epsilon", default=0.001, min=0.0001, max=0.99, step=0.0001, tooltip="Penalty decay parameter. Values below ~0.1 all produce sharp boundaries (paper default 0.001). For softer transitions, try 0.5 or higher."),
|
|
io.Float.Input("frame_rate", default=24.0, min=1.0, max=240.0, step=1.0, tooltip="Frames per second — only affects how time is displayed in the timeline editor when time_units is set to 'seconds'."),
|
|
io.Combo.Input("display_mode", options=["frames", "seconds"], default="seconds", tooltip="Display the ruler, segment ranges, length input, and total in frames or seconds. Internal storage is always pixel-space frames."),
|
|
io.String.Input("guide_strength", default="", tooltip="Auto-populated from the timeline editor (comma-separated guide strengths for image segments)."),
|
|
io.Int.Input("custom_width", default=0, min=0, max=8192, step=1, tooltip="Target output width for all image segments. Set to 0 to use the original image width."),
|
|
io.Int.Input("custom_height", default=0, min=0, max=8192, step=1, tooltip="Target output height for all image segments. Set to 0 to use the original image height."),
|
|
io.Combo.Input("resize_method", options=["maintain aspect ratio", "stretch to fit", "pad", "crop"], default="maintain aspect ratio", tooltip="How to resize image segments to fit the target dimensions."),
|
|
io.Int.Input("divisible_by", default=32, min=1, max=256, step=1, tooltip="Snap the final output image dimensions to be divisible by this number (e.g. 32 for LTX)."),
|
|
io.Int.Input("img_compression", default=18, min=0, max=100, step=1, tooltip="H.264 CRF compression to apply to each guide image. 0 = no compression, higher = more artefacts."),
|
|
io.Combo.Input("reference_mode", options=["Ghost Mask (End)", "Licon MSR (Prefix)"], default="Ghost Mask (End)", tooltip="Choose whether to hide the references at the end with attention masks (Ghost Mask) or stack them sequentially at the beginning (Licon MSR Prefix) for the MSR LoRA."),
|
|
io.Boolean.Input("save_prompts_to_file", default=False, optional=True, tooltip="Save the timeline prompts and parameters to a text file in your ComfyUI output directory during execution."),
|
|
io.Float.Input("reference_strength", default=1.0, min=0.0, max=5.0, step=0.05, optional=True, tooltip="Guide strength for the reference images."),
|
|
],
|
|
outputs=[
|
|
io.Model.Output(display_name="model"),
|
|
io.Conditioning.Output(display_name="positive"),
|
|
io.Conditioning.Output(display_name="negative"),
|
|
io.Latent.Output(display_name="video_latent"),
|
|
io.Latent.Output(display_name="audio_latent"),
|
|
GuideData.Output(display_name="guide_data"),
|
|
io.Float.Output(display_name="frame_rate"),
|
|
io.Audio.Output(display_name="combined_audio"),
|
|
io.Int.Output(display_name="clean_latent_frames"),
|
|
io.Int.Output(display_name="clean_pixel_frames"),
|
|
],
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, model, clip, negative=None, vae=None, audio_vae=None, optional_latent=None,
|
|
global_prompt="", duration_frames=120, duration_seconds=5.0,
|
|
timeline_data="", use_custom_audio=False, local_prompts="", segment_lengths="",
|
|
epsilon=1e-3, frame_rate=24.0, display_mode="seconds", guide_strength="",
|
|
custom_width=0, custom_height=0, resize_method="maintain aspect ratio",
|
|
divisible_by=32, img_compression=18,
|
|
reference_mode="Ghost Mask (End)",
|
|
save_prompts_to_file=False, reference_strength=1.0) -> io.NodeOutput:
|
|
|
|
if isinstance(optional_latent, list):
|
|
if len(optional_latent) > 0:
|
|
optional_latent = optional_latent[0]
|
|
else:
|
|
optional_latent = None
|
|
|
|
clean_pixel_frames = duration_frames + 1
|
|
clean_latent_frames = ((clean_pixel_frames - 1) // 8) + 1
|
|
|
|
guide_data = {"images": [], "insert_frames": [], "strengths": [], "frame_rate": float(frame_rate)}
|
|
derived_w, derived_h = custom_width, custom_height
|
|
char_images = []
|
|
char_slot_images = [] # per-slot tensors, for MSR @char tag filtering
|
|
|
|
char1_val, char2_val, char3_val = "", "", ""
|
|
try:
|
|
tdata = json.loads(timeline_data) if timeline_data else {}
|
|
characters = tdata.get("characters", [])
|
|
if len(characters) > 0: char1_val = characters[0].get("description", "")
|
|
if len(characters) > 1: char2_val = characters[1].get("description", "")
|
|
if len(characters) > 2: char3_val = characters[2].get("description", "")
|
|
|
|
for idx, char_info in enumerate(characters):
|
|
images_list = char_info.get("images", [])
|
|
legacy_b64 = char_info.get("imageB64", "")
|
|
if legacy_b64 and not images_list:
|
|
images_list = [{"b64": legacy_b64, "name": char_info.get("fileName", "")}]
|
|
|
|
slot_tensors = []
|
|
for img_info in images_list:
|
|
image_b64 = img_info.get("b64", "")
|
|
file_name = img_info.get("name", "")
|
|
tensor = _load_image_source(image_b64, file_name)
|
|
char_images.append(tensor)
|
|
slot_tensors.append(tensor)
|
|
char_slot_images.append(slot_tensors) # keep slot boundaries for @char filtering
|
|
except Exception as e:
|
|
log.warning("[PromptRelay] Could not process character slot inputs: %s", e)
|
|
|
|
try:
|
|
tdata = json.loads(timeline_data) if timeline_data else {}
|
|
img_segs = [
|
|
s for s in tdata.get("segments", [])
|
|
if s.get("type", "image") == "image"
|
|
and (s.get("imageFile") or s.get("imageB64"))
|
|
and int(s.get("start", 0)) < duration_frames
|
|
]
|
|
img_segs.sort(key=lambda s: s["start"])
|
|
strengths = [float(x.strip()) for x in guide_strength.split(",") if x.strip()] if guide_strength.strip() else []
|
|
|
|
for idx, seg in enumerate(img_segs):
|
|
tensor = _load_image_tensor(seg)
|
|
src_h, src_w = tensor.shape[1], tensor.shape[2]
|
|
|
|
def snap(val, div):
|
|
return max(div, (val // div) * div)
|
|
|
|
if custom_width > 0 and custom_height > 0:
|
|
tensor = _resize_image(tensor, custom_width, custom_height, resize_method, divisible_by)
|
|
elif custom_width > 0:
|
|
tgt_w = snap(custom_width, divisible_by)
|
|
tgt_h = snap(int(src_h * tgt_w / src_w), divisible_by)
|
|
tensor = _resize_image(tensor, tgt_w, tgt_h, "stretch to fit", divisible_by)
|
|
elif custom_height > 0:
|
|
tgt_h = snap(custom_height, divisible_by)
|
|
tgt_w = snap(int(src_w * tgt_h / src_h), divisible_by)
|
|
tensor = _resize_image(tensor, tgt_w, tgt_h, "stretch to fit", divisible_by)
|
|
else:
|
|
tensor = _resize_image(tensor, src_w, src_h, "maintain aspect ratio", divisible_by)
|
|
|
|
if img_compression > 0:
|
|
tensor = _compress_image(tensor, img_compression)
|
|
|
|
if idx == 0:
|
|
derived_h = tensor.shape[1]
|
|
derived_w = tensor.shape[2]
|
|
|
|
strength = strengths[idx] if idx < len(strengths) else 1.0
|
|
guide_data["images"].append(tensor)
|
|
guide_data["insert_frames"].append(int(seg["start"]))
|
|
guide_data["strengths"].append(float(strength))
|
|
|
|
# Prevent crashes if completely empty
|
|
if not guide_data["images"] and not char_images:
|
|
w = derived_w if derived_w > 0 else 768
|
|
h = derived_h if derived_h > 0 else 512
|
|
w = (w // 32) * 32
|
|
h = (h // 32) * 32
|
|
|
|
dummy_image = torch.zeros((1, h, w, 3), dtype=torch.float32)
|
|
guide_data["images"].append(dummy_image)
|
|
guide_data["insert_frames"].append(0)
|
|
guide_data["strengths"].append(0.0)
|
|
|
|
derived_w = w
|
|
derived_h = h
|
|
except Exception as e:
|
|
log.warning("[PromptRelay] Could not build guide_data: %s", e)
|
|
|
|
derived_w = max(32, (derived_w // 32) * 32) if derived_w > 0 else 768
|
|
derived_h = max(32, (derived_h // 32) * 32) if derived_h > 0 else 512
|
|
|
|
if reference_mode == "Licon MSR (Prefix)":
|
|
processed_global, processed_local = global_prompt, local_prompts
|
|
else:
|
|
processed_global, processed_local = _preprocess_prompts_with_characters(
|
|
global_prompt, local_prompts, char1_val, char2_val, char3_val
|
|
)
|
|
|
|
# If no global prompt was entered, fall back to the first timeline/local prompt as
|
|
# the global anchor (restores prior behaviour; avoids an empty tail segment in the
|
|
# relay, which would otherwise trip the 'segment missing a prompt' guard).
|
|
if not (processed_global or "").strip() and processed_local:
|
|
processed_global = processed_local.split("|")[0].strip()
|
|
|
|
# ====== LICON MSR DIRECTOR ENGINE (relay-aware) ======
|
|
# Axis separation, so the relay and IC-LoRA never fight over the same bookkeeping:
|
|
# - Character slots -> IDENTITY. Built into the IC-LoRA slideshow AND pinned as
|
|
# one identity keyframe each (the MSR LoRA's job).
|
|
# - Timeline images -> SCENE/background. The first seeds the slideshow's background
|
|
# slot; every timeline image is also pinned at its own
|
|
# frame_idx as a positional scene reference.
|
|
# - Timeline text -> the relay's per-segment attention mask over the clean region.
|
|
# The relay conditioning is encoded FIRST, then threaded THROUGH the IC-LoRA guide
|
|
# calls, so keyframe metadata + guide_attention_entries land on the real conditioning
|
|
# natively. The Director bakes everything it conditions on, so the relay mask length
|
|
# stays self-consistent with the latent and nothing downstream needs to add frames.
|
|
if reference_mode == "Licon MSR (Prefix)":
|
|
if vae is None:
|
|
raise ValueError("Licon MSR (Prefix) mode requires connecting the VAE to LTX Director!")
|
|
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
IC_Lora_Guide_Class = NODE_CLASS_MAPPINGS.get("LTXAddVideoICLoRAGuide")
|
|
if IC_Lora_Guide_Class is None:
|
|
raise ValueError("LTXAddVideoICLoRAGuide node not found! Please install the ComfyUI-LTXVideo repository.")
|
|
|
|
# 1. Identity references. In MSR the reference IMAGE drives identity, so we
|
|
# honour the @charN tags by selecting ONLY the slots the prompt references.
|
|
# @char1/@character1 -> slot 0, @char2 -> slot 1, @char3 -> slot 2.
|
|
# No tag referenced (or referenced slots empty) -> use every filled slot.
|
|
_prompt_text = (global_prompt or "") + " " + (local_prompts or "")
|
|
_tag_pairs = [("@character1", "@char1"), ("@character2", "@char2"), ("@character3", "@char3")]
|
|
_referenced_slots = [i for i, tags in enumerate(_tag_pairs) if any(t in _prompt_text for t in tags)]
|
|
_selected = []
|
|
for _slot in _referenced_slots:
|
|
if _slot < len(char_slot_images):
|
|
_selected.extend(char_slot_images[_slot])
|
|
if not _selected:
|
|
_selected = char_images # fallback: no tags, or tagged slots had no image
|
|
log.info("[PromptRelay] MSR slideshow subjects: referenced slots=%s, images=%d", _referenced_slots, len(_selected))
|
|
identity_images = [
|
|
_resize_image(c, derived_w, derived_h, resize_method, divisible_by)
|
|
for c in _selected
|
|
]
|
|
|
|
# 2. Scene references: every timeline image (already loaded/resized into guide_data
|
|
# upstream). Snapshot them, then clear guide_data so the downstream LTXDirectorGuide
|
|
# is a no-op -- the Director bakes them itself for a self-consistent latent length.
|
|
scene_images = list(guide_data["images"])
|
|
scene_frames = list(guide_data["insert_frames"])
|
|
scene_strengths = list(guide_data["strengths"])
|
|
guide_data = {"images": [], "insert_frames": [], "strengths": [], "frame_rate": float(frame_rate)}
|
|
|
|
# 3. Slideshow background slide = first timeline image, else black.
|
|
if scene_images:
|
|
bg_slide = scene_images[0]
|
|
if bg_slide.shape[1] != derived_h or bg_slide.shape[2] != derived_w:
|
|
bg_slide = _resize_image(bg_slide, derived_w, derived_h, resize_method, divisible_by)
|
|
else:
|
|
bg_slide = torch.zeros((1, derived_h, derived_w, 3), dtype=torch.float32)
|
|
|
|
# 4. Build the slideshow exactly like Licon MSR: characters then background,
|
|
# distributed across MSR_PREFIX_FRAMES.
|
|
slideshow_sources = identity_images + [bg_slide]
|
|
base_count = MSR_PREFIX_FRAMES // len(slideshow_sources)
|
|
remainder = MSR_PREFIX_FRAMES % len(slideshow_sources)
|
|
slideshow_tensors = []
|
|
for index, src_img in enumerate(slideshow_sources):
|
|
repeats = base_count + (1 if index < remainder else 0)
|
|
slideshow_tensors.extend([src_img[0]] * repeats)
|
|
slideshow_video = torch.stack(slideshow_tensors)
|
|
|
|
# 5. PER-STAGE reference handoff. In two-stage pipelines the references must be
|
|
# (re)encoded at EACH stage's resolution; a footprint baked here at full res breaks
|
|
# when scale_by shrinks the latent downstream. So the Director no longer injects --
|
|
# it hands the raw full-res slideshow + keyframe images to LTXDirectorGuide, which
|
|
# encodes and injects them at its own resolution. The length budget is purely
|
|
# TEMPORAL (scale-invariant): LTXVCropGuides always trims the slideshow's temporal
|
|
# footprint (prefix_latents), so the guide node pads the generated region so that
|
|
# (pad + keyframes) == prefix_latents at whatever resolution it runs.
|
|
keyframe_images = slideshow_sources # chars + bg (same set as the slideshow)
|
|
num_keyframes = len(keyframe_images)
|
|
prefix_latents = ((MSR_PREFIX_FRAMES - 1) // 8) + 1
|
|
tail_latents = prefix_latents # pad + keyframes, filled in per stage
|
|
total_runtime_latents = clean_latent_frames + tail_latents
|
|
tail_pixels = tail_latents * 8
|
|
|
|
# 6. Relay conditioning over the runtime length. This is temporal, so the mask is
|
|
# valid at every stage regardless of scale_by: clean -> locals, tail -> global.
|
|
local_part = processed_local.strip() if processed_local.strip() else processed_global
|
|
clean_lengths = segment_lengths.strip() if segment_lengths.strip() else str(duration_frames)
|
|
injected_local = f"{local_part} | {processed_global}"
|
|
injected_lengths = f"{clean_lengths},{tail_pixels}"
|
|
|
|
dummy_full = {"samples": torch.zeros(
|
|
[1, 128, total_runtime_latents, derived_h // 32, derived_w // 32],
|
|
device=comfy.model_management.intermediate_device(),
|
|
)}
|
|
|
|
# FALLBACK (relay vs LoRA attention conflict): replace the next call with
|
|
# patched = model.clone()
|
|
# conditioning = clip.encode_from_tokens_scheduled(clip.tokenize(processed_global))
|
|
patched, conditioning = _encode_relay(
|
|
model, clip, dummy_full, processed_global, injected_local, injected_lengths, epsilon
|
|
)
|
|
|
|
if negative is None:
|
|
neg_tokens = clip.tokenize("worst quality, blurry, low resolution, jittery, low geometry, bad details")
|
|
conditioning_neg = clip.encode_from_tokens_scheduled(neg_tokens)
|
|
else:
|
|
conditioning_neg = negative
|
|
|
|
# 7. Clean base latent: the TRUE clean region only (C frames), at full target res.
|
|
# LTXDirectorGuide scales it per stage, pads it, and appends the keyframes.
|
|
latent = {
|
|
"samples": torch.zeros([1, 128, clean_latent_frames, derived_h // 32, derived_w // 32], device=comfy.model_management.intermediate_device()),
|
|
"noise_mask": torch.ones((1, 1, clean_latent_frames, derived_h // 32, derived_w // 32), dtype=torch.float32, device=comfy.model_management.intermediate_device()),
|
|
}
|
|
|
|
# 8. Hand the RAW full-res references to the guide node via guide_data (no new socket).
|
|
# Each stage VAE-encodes them at its own scale, so Stage 2 sees full-res detail.
|
|
guide_data = {
|
|
"images": [], "insert_frames": [], "strengths": [], "frame_rate": float(frame_rate),
|
|
"msr": {
|
|
"slideshow": slideshow_video, # [F, H, W, 3] full-res montage
|
|
"keyframes": keyframe_images, # list of [1, H, W, 3] full-res refs
|
|
"prefix_latents": int(prefix_latents),
|
|
"strength": float(reference_strength),
|
|
"downscale": float(MSR_LATENT_DOWNSCALE),
|
|
"clean_latent_frames": int(clean_latent_frames),
|
|
},
|
|
}
|
|
|
|
total_latents = total_runtime_latents # clean + prefix (what the sampler sees)
|
|
|
|
log.info(
|
|
"[PromptRelay] Licon MSR Engine (per-stage): clean=%d, tail=%d (prefix), "
|
|
"%d keyframes handed to LTXDirectorGuide for per-resolution injection.",
|
|
clean_latent_frames, tail_latents, num_keyframes,
|
|
)
|
|
else: # Standard "Ghost Mask (End)"
|
|
total_latents = clean_latent_frames + len(char_images)
|
|
|
|
if char_images:
|
|
for i, single_ref in enumerate(char_images):
|
|
ref_tensor = _resize_image(single_ref, derived_w, derived_h, resize_method, divisible_by)
|
|
if img_compression > 0:
|
|
ref_tensor = _compress_image(ref_tensor, img_compression)
|
|
guide_data["images"].append(ref_tensor)
|
|
insert_point = (clean_latent_frames + i) * 8
|
|
guide_data["insert_frames"].append(insert_point)
|
|
guide_data["strengths"].append(float(reference_strength))
|
|
|
|
dummy_latent = {"samples": torch.zeros([1, 128, total_latents, derived_h // 32, derived_w // 32], device=comfy.model_management.intermediate_device())}
|
|
|
|
patched, conditioning = _encode_relay(
|
|
model, clip, dummy_latent, processed_global, processed_local, segment_lengths, epsilon,
|
|
)
|
|
|
|
if optional_latent is None:
|
|
latent = {
|
|
"samples": torch.zeros([1, 128, total_latents, derived_h // 32, derived_w // 32], device=comfy.model_management.intermediate_device()),
|
|
"noise_mask": torch.ones((1, 1, total_latents, derived_h // 32, derived_w // 32), dtype=torch.float32, device=comfy.model_management.intermediate_device())
|
|
}
|
|
else:
|
|
latent = optional_latent
|
|
|
|
if negative is None:
|
|
neg_tokens = clip.tokenize("worst quality, blurry, low resolution, jittery, low geometry, bad details")
|
|
conditioning_neg = clip.encode_from_tokens_scheduled(neg_tokens)
|
|
else:
|
|
conditioning_neg = negative
|
|
|
|
ltxv_length = ((total_latents - 1) * 8) + 1
|
|
audio_out = _build_combined_audio(timeline_data, ltxv_length, float(frame_rate))
|
|
audio_latent = {}
|
|
|
|
if audio_vae is not None:
|
|
def get_empty_latent():
|
|
inner = getattr(audio_vae, "first_stage_model", audio_vae)
|
|
z_channels = audio_vae.latent_channels
|
|
audio_freq = inner.latent_frequency_bins
|
|
num_audio_latents = inner.num_of_latents_from_frames(ltxv_length, float(frame_rate))
|
|
audio_latents = torch.zeros(
|
|
(1, z_channels, num_audio_latents, audio_freq),
|
|
device=comfy.model_management.intermediate_device(),
|
|
)
|
|
return {"samples": audio_latents, "type": "audio"}
|
|
|
|
if use_custom_audio:
|
|
try:
|
|
if audio_out is not None:
|
|
waveform = audio_out["waveform"]
|
|
if waveform.ndim == 2:
|
|
waveform = waveform.unsqueeze(0)
|
|
if waveform.ndim != 3:
|
|
raise ValueError(f"Expected custom audio waveform with 2 or 3 dims, got shape {tuple(waveform.shape)}")
|
|
|
|
if hasattr(audio_vae, "first_stage_model"):
|
|
latent_samples = audio_vae.encode(waveform.movedim(1, -1))
|
|
else:
|
|
latent_samples = audio_vae.encode({
|
|
"waveform": waveform,
|
|
"sample_rate": audio_out["sample_rate"],
|
|
})
|
|
|
|
if latent_samples.numel() == 0:
|
|
raise ValueError("Encoded audio latent is empty (0 elements).")
|
|
|
|
audio_mask_tensor = torch.full(
|
|
(1, latent_samples.shape[-2], latent_samples.shape[-1]),
|
|
0.0,
|
|
dtype=torch.float32,
|
|
device=comfy.model_management.intermediate_device()
|
|
)
|
|
|
|
audio_latent = {
|
|
"samples": latent_samples,
|
|
"type": "audio",
|
|
"noise_mask": audio_mask_tensor.reshape((-1, 1, audio_mask_tensor.shape[-2], audio_mask_tensor.shape[-1]))
|
|
}
|
|
else:
|
|
raise ValueError("No audio waveform to encode.")
|
|
except Exception as e:
|
|
log.warning("[PromptRelay] Failed to generate custom audio latent: %s", e)
|
|
raise e
|
|
else:
|
|
try:
|
|
audio_latent = get_empty_latent()
|
|
except Exception as e:
|
|
log.warning("[PromptRelay] Could not generate empty audio latent: %s", e)
|
|
raise e
|
|
|
|
return io.NodeOutput(
|
|
patched,
|
|
conditioning,
|
|
conditioning_neg,
|
|
latent,
|
|
audio_latent,
|
|
guide_data,
|
|
float(frame_rate),
|
|
audio_out,
|
|
clean_latent_frames,
|
|
clean_pixel_frames
|
|
)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"LTXDirector": LTXDirector,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"PromptRelayEncodeTimeline": "Prompt Relay Encode (Timeline)",
|
|
} |