1393 lines
57 KiB
Python
1393 lines
57 KiB
Python
from __future__ import annotations
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import copy
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import glob
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import json
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import logging
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import os
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import shutil
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import subprocess
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import tempfile
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import uuid
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import wave
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from pathlib import Path
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import numpy as np
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import server
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import torch
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from comfy.cli_args import args
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from comfy_api.latest import Types
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try:
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from safetensors.torch import load_file as _safetensors_load_file # type: ignore[import]
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from safetensors.torch import save_file as _safetensors_save_file # type: ignore[import]
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except Exception:
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_safetensors_load_file = None
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_safetensors_save_file = None
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log = logging.getLogger("IAMCCS.LTX2.SegmentQueue")
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# Carmine Cristallo Scalzi AI reasearch (IAMCCS) - patreon.com/IAMCCS
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def _bridge_dir() -> Path:
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try:
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import folder_paths # type: ignore[import]
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base = Path(folder_paths.get_output_directory())
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except Exception:
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base = Path("output")
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path = base / "iamccs_ltx2_bridges"
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path.mkdir(parents=True, exist_ok=True)
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return path
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def _bridge_path(bridge_name: str, render_id: str) -> Path:
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safe_bridge = str(bridge_name or "ltx2_bridge").strip() or "ltx2_bridge"
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safe_render = str(render_id or "").strip() or "default"
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return _bridge_dir() / f"{safe_bridge}_{safe_render}.png"
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def _latent_bridge_path(render_id: str) -> Path:
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safe_render = str(render_id or "").strip() or "default"
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return _bridge_dir() / f"ltx2_latent_bridge_{safe_render}.safetensors"
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def _latent_bridge_manifest_path(render_id: str) -> Path:
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safe_render = str(render_id or "").strip() or "default"
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return _bridge_dir() / f"ltx2_latent_bridge_{safe_render}.json"
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def _resolve_latent_bridge_payload_path(render_id: str) -> Path:
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manifest_path = _latent_bridge_manifest_path(render_id)
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legacy_path = _latent_bridge_path(render_id)
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if manifest_path.exists():
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try:
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data = json.loads(manifest_path.read_text(encoding="utf-8"))
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payload_name = data.get("current_file")
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if payload_name:
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payload_path = _bridge_dir() / str(payload_name)
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if payload_path.exists():
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return payload_path
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except Exception:
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pass
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return legacy_path
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def _write_latent_bridge_manifest(render_id: str, payload_path: Path) -> None:
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manifest_path = _latent_bridge_manifest_path(render_id)
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manifest_path.write_text(
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json.dumps({"current_file": payload_path.name}, ensure_ascii=True, indent=2),
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encoding="utf-8",
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)
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def _new_latent_bridge_payload_path(render_id: str) -> Path:
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safe_render = str(render_id or "").strip() or "default"
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return _bridge_dir() / f"ltx2_latent_bridge_{safe_render}_{uuid.uuid4().hex[:10]}.safetensors"
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def _save_latent_bridge(
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render_id: str,
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latent_tail: torch.Tensor,
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latent_full: torch.Tensor | None = None,
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latent_reference: torch.Tensor | None = None,
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seed_offset_latent_frames: int | None = None,
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overlap_latent_frames: int | None = None,
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) -> None:
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path = _new_latent_bridge_payload_path(render_id)
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path.parent.mkdir(parents=True, exist_ok=True)
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tail_cpu = latent_tail.detach().cpu().contiguous()
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full_cpu = (
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latent_full.detach().cpu().contiguous()
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if torch.is_tensor(latent_full)
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else None
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)
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reference_cpu = (
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latent_reference.detach().cpu().contiguous()
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if torch.is_tensor(latent_reference)
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else None
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)
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seed_offset_cpu = torch.tensor([int(seed_offset_latent_frames or 0)], dtype=torch.int64)
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overlap_latent_cpu = torch.tensor([int(overlap_latent_frames or 0)], dtype=torch.int64)
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if _safetensors_save_file is not None:
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tensors = {"latent_tail": tail_cpu}
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metadata = {"frames": str(int(tail_cpu.shape[2]))}
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if full_cpu is not None:
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tensors["latent_full"] = full_cpu
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metadata["full_frames"] = str(int(full_cpu.shape[2]))
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if reference_cpu is not None:
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tensors["latent_reference"] = reference_cpu
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metadata["reference_frames"] = str(int(reference_cpu.shape[2]))
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tensors["latent_seed_offset"] = seed_offset_cpu
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tensors["latent_overlap_frames"] = overlap_latent_cpu
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metadata["seed_offset_latent_frames"] = str(int(seed_offset_cpu.item()))
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metadata["overlap_latent_frames"] = str(int(overlap_latent_cpu.item()))
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_safetensors_save_file(tensors, str(path), metadata=metadata)
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_write_latent_bridge_manifest(render_id, path)
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return
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payload = {"latent_tail": tail_cpu}
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if full_cpu is not None:
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payload["latent_full"] = full_cpu
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if reference_cpu is not None:
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payload["latent_reference"] = reference_cpu
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payload["latent_seed_offset"] = seed_offset_cpu
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payload["latent_overlap_frames"] = overlap_latent_cpu
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torch.save(payload, str(path))
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_write_latent_bridge_manifest(render_id, path)
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def _load_latent_bridge(path: Path) -> dict[str, torch.Tensor | None]:
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if _safetensors_load_file is not None:
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tensors = _safetensors_load_file(str(path))
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return {
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"latent_tail": tensors["latent_tail"],
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"latent_full": tensors.get("latent_full"),
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"latent_reference": tensors.get("latent_reference"),
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"latent_seed_offset": tensors.get("latent_seed_offset"),
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"latent_overlap_frames": tensors.get("latent_overlap_frames"),
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}
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data = torch.load(str(path), map_location="cpu", weights_only=False)
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if torch.is_tensor(data):
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return {"latent_tail": data, "latent_full": None, "latent_reference": None, "latent_seed_offset": None, "latent_overlap_frames": None}
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if isinstance(data, dict):
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if "latent_tail" in data:
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return {
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"latent_tail": data["latent_tail"],
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"latent_full": data.get("latent_full"),
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"latent_reference": data.get("latent_reference"),
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"latent_seed_offset": data.get("latent_seed_offset"),
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"latent_overlap_frames": data.get("latent_overlap_frames"),
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}
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if "samples" in data and torch.is_tensor(data["samples"]):
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return {"latent_tail": data["samples"], "latent_full": data["samples"], "latent_reference": None, "latent_seed_offset": None, "latent_overlap_frames": None}
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raise ValueError(f"Unsupported latent bridge payload: {path}")
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def _pixel_frames_to_latent_frames(pixel_frames: int, time_scale_factor: int) -> int:
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pixel_frames = int(pixel_frames)
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if pixel_frames <= 0:
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return 0
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time_scale_factor = max(int(time_scale_factor), 1)
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return 1 + max(0, (pixel_frames - 1) // time_scale_factor)
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def _get_time_scale_factor_from_vae(vae) -> int:
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ts = getattr(vae, "downscale_index_formula", None)
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if ts and isinstance(ts, (tuple, list)) and len(ts) >= 1:
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try:
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return int(ts[0])
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except Exception:
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pass
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return 8
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def _save_last_frame_png(path: Path, image_tensor: torch.Tensor) -> None:
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from PIL import Image # type: ignore[import]
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path.parent.mkdir(parents=True, exist_ok=True)
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image = image_tensor.detach().cpu().float().clamp(0, 1)
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arr = (image.numpy() * 255.0).round().astype(np.uint8)
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Image.fromarray(arr).save(str(path))
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def _load_png(path: Path) -> torch.Tensor:
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from PIL import Image # type: ignore[import]
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arr = np.asarray(Image.open(str(path)).convert("RGB"), dtype=np.float32) / 255.0
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return torch.from_numpy(arr).unsqueeze(0)
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def _find_ffmpeg() -> str | None:
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forced = os.environ.get("VHS_FORCE_FFMPEG_PATH")
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if forced and os.path.isfile(forced):
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return forced
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try:
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from imageio_ffmpeg import get_ffmpeg_exe
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ffmpeg_path = get_ffmpeg_exe()
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if ffmpeg_path and os.path.isfile(ffmpeg_path):
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return ffmpeg_path
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except Exception:
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pass
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return shutil.which("ffmpeg")
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def _normalize_prompt_keys(prompt):
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if prompt is None:
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return None
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return {str(key): value for key, value in prompt.items()}
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def _normalize_outputs_to_execute(outputs_to_execute):
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if outputs_to_execute is None:
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return None
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return [str(item) for item in outputs_to_execute]
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def _infer_output_nodes(prompt):
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import nodes # type: ignore[import]
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normalized_prompt = _normalize_prompt_keys(prompt) or {}
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outputs = []
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for node_id, node in normalized_prompt.items():
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class_type = node.get("class_type")
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class_def = nodes.NODE_CLASS_MAPPINGS.get(class_type)
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if class_def is not None and getattr(class_def, "OUTPUT_NODE", False):
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outputs.append(str(node_id))
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return outputs
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def _get_current_queue_item():
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prompt_server = getattr(server.PromptServer, "instance", None)
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if prompt_server is None or getattr(prompt_server, "prompt_queue", None) is None:
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raise RuntimeError("PromptServer prompt queue is unavailable")
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currently_running = getattr(prompt_server.prompt_queue, "currently_running", {})
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if not currently_running:
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raise RuntimeError("No currently running prompt was found")
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current = next(iter(currently_running.values()))
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if len(current) == 6:
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(_, _, prompt, extra_data, outputs_to_execute, sensitive) = current
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else:
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(_, _, prompt, extra_data, outputs_to_execute) = current
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sensitive = {}
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return prompt, extra_data, outputs_to_execute, sensitive
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def _enqueue_prompt(prompt, extra_data=None, outputs_to_execute=None, sensitive=None):
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prompt_server = getattr(server.PromptServer, "instance", None)
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if prompt_server is None or getattr(prompt_server, "prompt_queue", None) is None:
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raise RuntimeError("PromptServer prompt queue is unavailable")
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prompt_queue = prompt_server.prompt_queue
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prompt = _normalize_prompt_keys(prompt)
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try:
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_, current_extra_data, current_outputs_to_execute, current_sensitive = _get_current_queue_item()
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except Exception:
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current_extra_data = None
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current_outputs_to_execute = None
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current_sensitive = None
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if extra_data is None:
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extra_data = current_extra_data if current_extra_data is not None else {}
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if sensitive is None:
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sensitive = current_sensitive if current_sensitive is not None else {}
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if outputs_to_execute is None:
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outputs_to_execute = current_outputs_to_execute
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outputs_to_execute = _normalize_outputs_to_execute(outputs_to_execute)
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if outputs_to_execute is None:
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outputs_to_execute = _infer_output_nodes(prompt)
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if not outputs_to_execute:
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raise RuntimeError("No output nodes were found for the requeued prompt")
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number = -prompt_server.number
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prompt_server.number += 1
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prompt_id = str(server.uuid.uuid4())
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log.info("[IAMCCS LTX2] Queueing next segment prompt %s with outputs %s", prompt_id, outputs_to_execute)
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prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute, sensitive))
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def _build_metadata(prompt, extra_pnginfo):
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if args.disable_metadata:
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return None
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metadata = {}
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if extra_pnginfo is not None:
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metadata.update(extra_pnginfo)
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if prompt is not None:
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metadata["prompt"] = prompt
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return metadata or None
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def _resolve_output_location(filename_prefix, video):
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import folder_paths # type: ignore[import]
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width, height = video.get_dimensions()
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full_output_folder, filename, counter, subfolder, _resolved_prefix = folder_paths.get_save_image_path(
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filename_prefix,
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folder_paths.get_output_directory(),
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width,
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height,
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)
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os.makedirs(full_output_folder, exist_ok=True)
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next_counter = max(int(counter or 1), 1)
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while True:
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unique_base_name = f"{filename}_{next_counter:05d}"
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existing_matches = glob.glob(os.path.join(full_output_folder, f"{unique_base_name}*"))
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if not existing_matches:
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break
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next_counter += 1
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return full_output_folder, unique_base_name, subfolder
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def _video_extension():
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return Types.VideoContainer.get_extension("auto")
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def _segment_filename(base_name, render_id, segment_index):
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return f"{base_name}_{render_id}_seg_{segment_index + 1:04d}.{_video_extension()}"
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def _final_filename(base_name, render_id):
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return f"{base_name}_{render_id}_full.{_video_extension()}"
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def _concat_segments(segment_paths, output_path):
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ffmpeg_path = _find_ffmpeg()
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if ffmpeg_path is None:
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raise RuntimeError("ffmpeg was not found, so segment videos could not be concatenated.")
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missing_paths = [segment_path for segment_path in segment_paths if not os.path.exists(segment_path)]
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if missing_paths:
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missing_lines = "\n".join(missing_paths)
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raise RuntimeError(f"Segment merge aborted because these files are missing:\n{missing_lines}")
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with tempfile.NamedTemporaryFile("w", suffix=".txt", delete=False, encoding="utf-8") as handle:
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list_path = handle.name
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for segment_path in segment_paths:
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escaped = segment_path.replace("'", "'\\''")
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handle.write(f"file '{escaped}'\n")
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cmd = [
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ffmpeg_path,
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"-y",
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"-f",
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"concat",
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"-safe",
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"0",
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"-i",
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list_path,
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"-fflags",
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"+genpts",
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"-avoid_negative_ts",
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"make_zero",
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"-c:v",
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"copy",
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"-c:a",
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"aac",
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"-b:a",
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"192k",
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"-ar",
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"48000",
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"-movflags",
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"+faststart",
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output_path,
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]
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result = subprocess.run(cmd, capture_output=True, text=True)
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try:
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os.remove(list_path)
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except Exception:
|
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pass
|
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if result.returncode != 0:
|
|
stderr = result.stderr.strip() or result.stdout.strip()
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raise RuntimeError(f"ffmpeg concat failed: {stderr}")
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|
|
|
|
|
def _normalize_audio(audio):
|
|
waveform = None
|
|
sample_rate = None
|
|
if isinstance(audio, dict):
|
|
waveform = audio.get("waveform")
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sample_rate = audio.get("sample_rate")
|
|
elif hasattr(audio, "get"):
|
|
try:
|
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waveform = audio.get("waveform")
|
|
sample_rate = audio.get("sample_rate")
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except Exception:
|
|
waveform = None
|
|
sample_rate = None
|
|
|
|
if waveform is None and hasattr(audio, "waveform"):
|
|
waveform = getattr(audio, "waveform")
|
|
sample_rate = getattr(audio, "sample_rate", sample_rate)
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|
|
|
if waveform is None and isinstance(audio, (tuple, list)) and audio:
|
|
waveform = audio[0]
|
|
if len(audio) > 1:
|
|
sample_rate = audio[1]
|
|
|
|
if waveform is None:
|
|
return None, None
|
|
|
|
if not torch.is_tensor(waveform):
|
|
waveform = torch.as_tensor(waveform)
|
|
|
|
if waveform.ndim == 1:
|
|
waveform = waveform.unsqueeze(0).unsqueeze(0)
|
|
elif waveform.ndim == 2:
|
|
waveform = waveform.unsqueeze(0)
|
|
|
|
return waveform.detach().cpu().float(), int(sample_rate or 48000)
|
|
|
|
|
|
def _write_audio_wav(audio, wav_path: str):
|
|
waveform, sample_rate = _normalize_audio(audio)
|
|
if waveform is None:
|
|
return False
|
|
|
|
audio_np = waveform.squeeze(0).numpy()
|
|
if audio_np.ndim == 1:
|
|
audio_np = audio_np[np.newaxis, :]
|
|
audio_np = np.nan_to_num(audio_np, nan=0.0, posinf=0.0, neginf=0.0)
|
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audio_np = np.clip(audio_np, -1.0, 1.0)
|
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pcm = (audio_np.T * 32767.0).round().astype(np.int16)
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|
|
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with wave.open(wav_path, "wb") as wav_file:
|
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wav_file.setnchannels(int(pcm.shape[1]))
|
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wav_file.setsampwidth(2)
|
|
wav_file.setframerate(int(sample_rate))
|
|
wav_file.writeframes(pcm.tobytes())
|
|
return True
|
|
|
|
|
|
def _trim_audio_leading(audio, trim_frames: int, frame_rate: float):
|
|
waveform, sample_rate = _normalize_audio(audio)
|
|
if waveform is None:
|
|
return audio
|
|
|
|
trim_frames = max(0, int(trim_frames))
|
|
frame_rate = float(max(0.001, frame_rate))
|
|
trim_samples = int(round((float(trim_frames) / frame_rate) * float(sample_rate)))
|
|
if trim_samples <= 0:
|
|
return {"waveform": waveform, "sample_rate": sample_rate}
|
|
|
|
total_samples = int(waveform.shape[-1])
|
|
trim_samples = min(trim_samples, max(0, total_samples - 1))
|
|
return {
|
|
"waveform": waveform[:, :, trim_samples:],
|
|
"sample_rate": sample_rate,
|
|
}
|
|
|
|
|
|
def _save_segment_from_images(images, audio, frame_rate, output_path):
|
|
ffmpeg_path = _find_ffmpeg()
|
|
if ffmpeg_path is None:
|
|
raise RuntimeError("ffmpeg was not found, so the segment video could not be written.")
|
|
|
|
if images is None or int(images.shape[0]) <= 0:
|
|
raise ValueError("images input is required when no VIDEO object is provided.")
|
|
|
|
frame_rate = float(max(0.001, frame_rate))
|
|
with tempfile.TemporaryDirectory(prefix="iamccs_ltx2_seg_") as temp_dir:
|
|
from PIL import Image # type: ignore[import]
|
|
|
|
for index in range(int(images.shape[0])):
|
|
frame = images[index].detach().cpu().float().clamp(0, 1)
|
|
arr = (frame.numpy() * 255.0).round().astype(np.uint8)
|
|
Image.fromarray(arr).save(os.path.join(temp_dir, f"frame_{index:05d}.png"))
|
|
|
|
wav_path = os.path.join(temp_dir, "audio.wav")
|
|
has_audio = audio is not None and _write_audio_wav(audio, wav_path)
|
|
|
|
cmd = [
|
|
ffmpeg_path,
|
|
"-y",
|
|
"-framerate",
|
|
f"{frame_rate:.6f}",
|
|
"-i",
|
|
os.path.join(temp_dir, "frame_%05d.png"),
|
|
]
|
|
if has_audio:
|
|
cmd += ["-i", wav_path]
|
|
cmd += [
|
|
"-c:v",
|
|
"libx264",
|
|
"-preset",
|
|
"medium",
|
|
"-crf",
|
|
"18",
|
|
"-pix_fmt",
|
|
"yuv420p",
|
|
]
|
|
if has_audio:
|
|
cmd += ["-c:a", "aac", "-b:a", "192k", "-ar", "48000", "-shortest"]
|
|
cmd += ["-movflags", "+faststart", output_path]
|
|
|
|
result = subprocess.run(cmd, capture_output=True, text=True)
|
|
if result.returncode != 0:
|
|
stderr = result.stderr.strip() or result.stdout.strip()
|
|
raise RuntimeError(f"ffmpeg segment encode failed: {stderr}")
|
|
|
|
|
|
class IAMCCS_LTX2_LastFrameBridgeLoad:
|
|
# Carmine Cristallo Scalzi AI reasearch (IAMCCS) - patreon.com/IAMCCS
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"bridge_name": ("STRING", {"default": "ltx2_detailer_bridge"}),
|
|
"segment_index": ("INT", {"default": 0, "min": 0, "max": 1000000, "step": 1}),
|
|
},
|
|
"optional": {
|
|
"render_id": ("STRING", {"default": ""}),
|
|
"fallback_image": ("IMAGE",),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", "INT", "STRING")
|
|
RETURN_NAMES = ("image", "exists", "report")
|
|
FUNCTION = "load"
|
|
CATEGORY = "IAMCCS/LTX-2"
|
|
|
|
def load(self, bridge_name, segment_index, render_id="", fallback_image=None):
|
|
segment_index = int(segment_index)
|
|
active_render_id = str(render_id or "").strip()
|
|
if segment_index <= 0 or not active_render_id:
|
|
if fallback_image is None:
|
|
raise ValueError("No bridge available for segment 0. Connect fallback_image for the first pass.")
|
|
return (fallback_image, 0, "LTX2 last-frame bridge: using fallback_image for initial segment")
|
|
|
|
bridge_path = _bridge_path(bridge_name, active_render_id)
|
|
if bridge_path.exists():
|
|
image = _load_png(bridge_path)
|
|
return (image, 1, f"LTX2 last-frame bridge: loaded {bridge_path.name}")
|
|
|
|
if fallback_image is not None:
|
|
return (fallback_image, 0, f"LTX2 last-frame bridge missing ({bridge_path.name}), using fallback_image")
|
|
|
|
raise FileNotFoundError(f"Last-frame bridge not found: {bridge_path}")
|
|
|
|
|
|
class IAMCCS_LTX2_LastFrameBridgeSave:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"images": ("IMAGE",),
|
|
"bridge_name": ("STRING", {"default": "ltx2_detailer_bridge"}),
|
|
"render_id": ("STRING", {"default": ""}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", "STRING")
|
|
RETURN_NAMES = ("images", "report")
|
|
FUNCTION = "save"
|
|
CATEGORY = "IAMCCS/LTX-2"
|
|
|
|
def save(self, images, bridge_name, render_id):
|
|
active_render_id = str(render_id or "").strip()
|
|
if not active_render_id:
|
|
return (images, "LTX2 last-frame bridge save disabled: render_id empty")
|
|
|
|
if images is None or not torch.is_tensor(images) or images.ndim != 4 or images.shape[0] <= 0:
|
|
raise ValueError("images must be an IMAGE batch [N,H,W,C] with at least one frame")
|
|
|
|
last_frame = images[-1]
|
|
bridge_path = _bridge_path(bridge_name, active_render_id)
|
|
_save_last_frame_png(bridge_path, last_frame)
|
|
report = f"LTX2 last-frame bridge: saved {bridge_path.name}"
|
|
log.info("[IAMCCS LTX2] %s", report)
|
|
return (images, report)
|
|
|
|
|
|
class IAMCCS_LTX2_LongVideoWrapperPrep:
|
|
# Carmine Cristallo Scalzi AI reasearch (IAMCCS) - patreon.com/IAMCCS
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"source_images": ("IMAGE",),
|
|
"song_duration_s": ("FLOAT", {"default": 180.0, "min": 0.01, "max": 36000.0, "step": 0.01}),
|
|
"fps": ("FLOAT", {"default": 24.0, "min": 0.001, "max": 240.0, "step": 0.01}),
|
|
"segment_duration_s": ("FLOAT", {"default": 10.0, "min": 0.01, "max": 3600.0, "step": 0.01}),
|
|
"segment_index": ("INT", {"default": 0, "min": 0, "max": 1000000, "step": 1}),
|
|
},
|
|
"optional": {
|
|
"render_id": ("STRING", {"default": ""}),
|
|
"bridge_name": ("STRING", {"default": "ltx2_detailer_bridge"}),
|
|
"use_bridge_anchor": ("BOOLEAN", {"default": False}),
|
|
"planning_mode": (["manual_segment_seconds", "auto_profile"], {"default": "manual_segment_seconds"}),
|
|
"content_profile": (["videoclip", "monologue"], {"default": "videoclip"}),
|
|
"overlap_frames": ("INT", {"default": 9, "min": 0, "max": 4096, "step": 1}),
|
|
"ltx_round_mode": (["up", "nearest", "down"], {"default": "up"}),
|
|
"head_k_frames": ("INT", {"default": 1, "min": 1, "max": 64, "step": 1}),
|
|
"head_mode": (["hard_lock", "linear_blend", "ramp"], {"default": "hard_lock"}),
|
|
"head_blend_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05}),
|
|
"min_frames": ("INT", {"default": 25, "min": 1, "max": 1000000, "step": 1}),
|
|
"min_frames_mode": (["repeat_last", "error"], {"default": "repeat_last"}),
|
|
"min_frames_ltx_fix": (["none", "up", "down", "nearest"], {"default": "up"}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", "INT", "INT", "STRING", "STRING", "INT", "STRING", "INT")
|
|
RETURN_NAMES = ("images", "current_segment", "total_segments", "segment_report", "plan_report", "trim_head_frames", "render_id", "continuation_trim_head_frames")
|
|
FUNCTION = "prepare"
|
|
CATEGORY = "IAMCCS/LTX-2"
|
|
|
|
@staticmethod
|
|
def _ensure_image_batch(images: torch.Tensor) -> torch.Tensor:
|
|
if images is None or not torch.is_tensor(images) or images.ndim != 4:
|
|
raise ValueError("source_images must be an IMAGE tensor batch with shape [N,H,W,C]")
|
|
return images
|
|
|
|
@staticmethod
|
|
def _extract_range(images: torch.Tensor, start_index: int, end_index: int) -> torch.Tensor:
|
|
total = int(images.shape[0])
|
|
start_index = max(0, min(int(start_index), total - 1))
|
|
end_index = max(start_index + 1, min(int(end_index), total))
|
|
return images[start_index:end_index].clone()
|
|
|
|
@staticmethod
|
|
def _first_frame(images: torch.Tensor) -> torch.Tensor:
|
|
return images[:1].clone()
|
|
|
|
@staticmethod
|
|
def _resize_to(image: torch.Tensor, target_h: int, target_w: int) -> torch.Tensor:
|
|
if int(image.shape[1]) == target_h and int(image.shape[2]) == target_w:
|
|
return image
|
|
import torch.nn.functional as F
|
|
|
|
x = image.permute(0, 3, 1, 2)
|
|
x = F.interpolate(x.float(), size=(target_h, target_w), mode="bilinear", align_corners=False)
|
|
return x.permute(0, 2, 3, 1).clamp(0.0, 1.0).to(image.dtype)
|
|
|
|
@classmethod
|
|
def _broadcast_ref(cls, ref: torch.Tensor, count: int, target_h: int, target_w: int) -> torch.Tensor:
|
|
ref = cls._resize_to(ref, target_h, target_w)
|
|
if int(ref.shape[0]) == count:
|
|
return ref
|
|
if int(ref.shape[0]) == 1:
|
|
return ref.repeat((count, 1, 1, 1))
|
|
return ref[:count]
|
|
|
|
@staticmethod
|
|
def _blend_weights(count: int, mode: str, max_strength: float) -> list[float]:
|
|
if mode == "hard_lock":
|
|
return [1.0] * count
|
|
if mode == "linear_blend":
|
|
return [max_strength] * count
|
|
if count == 1:
|
|
return [max_strength]
|
|
return [max_strength * float(index + 1) / float(count) for index in range(count)]
|
|
|
|
@classmethod
|
|
def _apply_head_anchor(cls, images: torch.Tensor, first_frame: torch.Tensor, k_frames: int, mode: str, blend_strength: float) -> torch.Tensor:
|
|
if first_frame is None:
|
|
return images
|
|
total = int(images.shape[0])
|
|
if total <= 0:
|
|
return images
|
|
k = max(1, min(int(k_frames), total))
|
|
weights = cls._blend_weights(k, str(mode or "hard_lock"), float(max(0.0, min(1.0, blend_strength))))
|
|
out = images.clone()
|
|
ref = cls._broadcast_ref(first_frame, k, int(out.shape[1]), int(out.shape[2]))
|
|
for idx in range(k):
|
|
strength = weights[idx]
|
|
out[idx] = ((1.0 - strength) * out[idx].float() + strength * ref[idx].float()).clamp(0.0, 1.0).to(out.dtype)
|
|
return out
|
|
|
|
@staticmethod
|
|
def _fix_ltx_frames(frames: int, mode: str) -> int:
|
|
frames = max(1, int(frames))
|
|
rem = (frames - 1) % 8
|
|
if rem == 0:
|
|
return frames
|
|
down = max(1, frames - rem)
|
|
up = frames + (8 - rem)
|
|
if mode == "down":
|
|
return down
|
|
if mode == "nearest":
|
|
return up if (up - frames) <= (frames - down) else down
|
|
return up
|
|
|
|
@classmethod
|
|
def _ensure_min_frames(cls, images: torch.Tensor, min_frames: int, mode: str, ltx_fix: str) -> tuple[torch.Tensor, int, str]:
|
|
frames_in = int(images.shape[0])
|
|
target_frames = max(frames_in, max(1, int(min_frames)))
|
|
if str(ltx_fix or "none") != "none":
|
|
target_frames = cls._fix_ltx_frames(target_frames, str(ltx_fix))
|
|
if frames_in >= target_frames:
|
|
return images, frames_in, f"EnsureMinFrames: ok ({frames_in}) | min={min_frames} | ltx_fix={ltx_fix}"
|
|
if str(mode or "repeat_last") == "error":
|
|
raise ValueError(f"EnsureMinFrames: got {frames_in} frames, required at least {target_frames}")
|
|
pad = target_frames - frames_in
|
|
out = torch.cat([images, images[-1:, ...].repeat((pad, 1, 1, 1))], dim=0)
|
|
return out, target_frames, f"EnsureMinFrames: repeat_last {frames_in} -> {target_frames} | min={min_frames} | ltx_fix={ltx_fix}"
|
|
|
|
def prepare(
|
|
self,
|
|
source_images,
|
|
song_duration_s,
|
|
fps,
|
|
segment_duration_s,
|
|
segment_index,
|
|
render_id="",
|
|
bridge_name="ltx2_detailer_bridge",
|
|
use_bridge_anchor=False,
|
|
planning_mode="manual_segment_seconds",
|
|
content_profile="videoclip",
|
|
overlap_frames=0,
|
|
ltx_round_mode="up",
|
|
head_k_frames=1,
|
|
head_mode="hard_lock",
|
|
head_blend_strength=1.0,
|
|
min_frames=25,
|
|
min_frames_mode="repeat_last",
|
|
min_frames_ltx_fix="up",
|
|
):
|
|
source_images = self._ensure_image_batch(source_images)
|
|
|
|
from .iamccs_ltx2_tools import IAMCCS_SegmentPlanner, IAMCCS_SourceRangeFromSegmentPlan
|
|
|
|
plan = IAMCCS_SegmentPlanner().plan(
|
|
song_duration_s=float(song_duration_s),
|
|
fps=float(fps),
|
|
segment_duration_s=float(segment_duration_s),
|
|
planning_mode=str(planning_mode),
|
|
content_profile=str(content_profile),
|
|
overlap_frames=int(overlap_frames),
|
|
ltx_round_mode=str(ltx_round_mode),
|
|
segment_index=int(segment_index),
|
|
)
|
|
|
|
total_segments = int(plan[4])
|
|
continuation_raw_frames = int(plan[3])
|
|
current_segment = int(plan[8])
|
|
current_segment_raw_frames = int(plan[9])
|
|
current_segment_unique_frames = int(plan[10])
|
|
current_segment_start_frames = int(plan[11])
|
|
trim_head_frames = max(0, current_segment_raw_frames - current_segment_unique_frames)
|
|
continuation_trim_head_frames = max(0, continuation_raw_frames - current_segment_unique_frames)
|
|
plan_report = str(plan[7])
|
|
plan_segment_report = str(plan[16])
|
|
|
|
range_info = IAMCCS_SourceRangeFromSegmentPlan().derive(
|
|
segment_index=current_segment,
|
|
current_segment_raw_frames=current_segment_raw_frames,
|
|
current_segment_unique_frames=current_segment_unique_frames,
|
|
current_segment_start_frames=current_segment_start_frames,
|
|
)
|
|
range_start_index = int(range_info[0])
|
|
range_end_index = int(range_info[1])
|
|
|
|
segment_images = self._extract_range(source_images, range_start_index, range_end_index)
|
|
fallback_image = self._first_frame(segment_images)
|
|
bridge_report = "bridge=disabled"
|
|
active_render_id = str(render_id or "").strip() or uuid.uuid4().hex[:10]
|
|
use_bridge_anchor = bool(use_bridge_anchor)
|
|
if use_bridge_anchor and current_segment > 0 and active_render_id:
|
|
bridge_path = _bridge_path(str(bridge_name), active_render_id)
|
|
if bridge_path.exists():
|
|
fallback_image = _load_png(bridge_path)
|
|
bridge_report = f"bridge=loaded:{bridge_path.name}"
|
|
else:
|
|
bridge_report = f"bridge=missing:{bridge_path.name},fallback"
|
|
elif current_segment <= 0:
|
|
bridge_report = "bridge=initial_segment"
|
|
|
|
anchored_images = segment_images
|
|
if use_bridge_anchor:
|
|
anchored_images = self._apply_head_anchor(
|
|
segment_images,
|
|
fallback_image,
|
|
k_frames=int(head_k_frames),
|
|
mode=str(head_mode),
|
|
blend_strength=float(head_blend_strength),
|
|
)
|
|
final_images, final_frames, min_report = self._ensure_min_frames(
|
|
anchored_images,
|
|
min_frames=int(min_frames),
|
|
mode=str(min_frames_mode),
|
|
ltx_fix=str(min_frames_ltx_fix),
|
|
)
|
|
|
|
segment_report = (
|
|
f"{plan_segment_report} | range=[{range_start_index}..{range_end_index}) | "
|
|
f"bridge={bridge_report} | trim_head={trim_head_frames}f | prepared_frames={final_frames} | {min_report}"
|
|
)
|
|
return (
|
|
final_images,
|
|
current_segment,
|
|
total_segments,
|
|
segment_report,
|
|
plan_report,
|
|
int(trim_head_frames),
|
|
active_render_id,
|
|
int(continuation_trim_head_frames),
|
|
)
|
|
|
|
|
|
class IAMCCS_LTX2_LongVideoWrapperPrepDisk(IAMCCS_LTX2_LongVideoWrapperPrep):
|
|
# Carmine Cristallo Scalzi AI reasearch (IAMCCS) - patreon.com/IAMCCS
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"frames_dir": ("STRING", {"default": "iamccs_source_frames/source_video"}),
|
|
"song_duration_s": ("FLOAT", {"default": 180.0, "min": 0.01, "max": 36000.0, "step": 0.01}),
|
|
"fps": ("FLOAT", {"default": 24.0, "min": 0.001, "max": 240.0, "step": 0.01}),
|
|
"segment_duration_s": ("FLOAT", {"default": 10.0, "min": 0.01, "max": 3600.0, "step": 0.01}),
|
|
"segment_index": ("INT", {"default": 0, "min": 0, "max": 1000000, "step": 1}),
|
|
},
|
|
"optional": {
|
|
"render_id": ("STRING", {"default": ""}),
|
|
"bridge_name": ("STRING", {"default": "ltx2_detailer_bridge"}),
|
|
"use_bridge_anchor": ("BOOLEAN", {"default": False}),
|
|
"planning_mode": (["manual_segment_seconds", "auto_profile"], {"default": "manual_segment_seconds"}),
|
|
"content_profile": (["videoclip", "monologue"], {"default": "videoclip"}),
|
|
"overlap_frames": ("INT", {"default": 9, "min": 0, "max": 4096, "step": 1}),
|
|
"ltx_round_mode": (["up", "nearest", "down"], {"default": "up"}),
|
|
"head_k_frames": ("INT", {"default": 1, "min": 1, "max": 64, "step": 1}),
|
|
"head_mode": (["hard_lock", "linear_blend", "ramp"], {"default": "hard_lock"}),
|
|
"head_blend_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05}),
|
|
"min_frames": ("INT", {"default": 25, "min": 1, "max": 1000000, "step": 1}),
|
|
"min_frames_mode": (["repeat_last", "error"], {"default": "repeat_last"}),
|
|
"min_frames_ltx_fix": (["none", "up", "down", "nearest"], {"default": "up"}),
|
|
},
|
|
}
|
|
|
|
FUNCTION = "prepare_disk"
|
|
|
|
def prepare_disk(
|
|
self,
|
|
frames_dir,
|
|
song_duration_s,
|
|
fps,
|
|
segment_duration_s,
|
|
segment_index,
|
|
render_id="",
|
|
bridge_name="ltx2_detailer_bridge",
|
|
use_bridge_anchor=False,
|
|
planning_mode="manual_segment_seconds",
|
|
content_profile="videoclip",
|
|
overlap_frames=0,
|
|
ltx_round_mode="up",
|
|
head_k_frames=1,
|
|
head_mode="hard_lock",
|
|
head_blend_strength=1.0,
|
|
min_frames=25,
|
|
min_frames_mode="repeat_last",
|
|
min_frames_ltx_fix="up",
|
|
):
|
|
from .iamccs_ltx2_extension_module import IAMCCS_LoadImagesFromDirLite
|
|
from .iamccs_ltx2_tools import IAMCCS_SegmentPlanner, IAMCCS_SourceRangeFromSegmentPlan
|
|
|
|
plan = IAMCCS_SegmentPlanner().plan(
|
|
song_duration_s=float(song_duration_s),
|
|
fps=float(fps),
|
|
segment_duration_s=float(segment_duration_s),
|
|
planning_mode=str(planning_mode),
|
|
content_profile=str(content_profile),
|
|
overlap_frames=int(overlap_frames),
|
|
ltx_round_mode=str(ltx_round_mode),
|
|
segment_index=int(segment_index),
|
|
)
|
|
|
|
total_segments = int(plan[4])
|
|
continuation_raw_frames = int(plan[3])
|
|
current_segment = int(plan[8])
|
|
current_segment_raw_frames = int(plan[9])
|
|
current_segment_unique_frames = int(plan[10])
|
|
current_segment_start_frames = int(plan[11])
|
|
trim_head_frames = max(0, current_segment_raw_frames - current_segment_unique_frames)
|
|
continuation_trim_head_frames = max(0, continuation_raw_frames - current_segment_unique_frames)
|
|
plan_report = str(plan[7])
|
|
plan_segment_report = str(plan[16])
|
|
|
|
range_info = IAMCCS_SourceRangeFromSegmentPlan().derive(
|
|
segment_index=current_segment,
|
|
current_segment_raw_frames=current_segment_raw_frames,
|
|
current_segment_unique_frames=current_segment_unique_frames,
|
|
current_segment_start_frames=current_segment_start_frames,
|
|
)
|
|
range_start_index = int(range_info[0])
|
|
range_end_index = int(range_info[1])
|
|
|
|
load_result = IAMCCS_LoadImagesFromDirLite().load(
|
|
directory=str(frames_dir),
|
|
mode="range",
|
|
count=int(current_segment_raw_frames),
|
|
start_index=int(range_start_index),
|
|
end_index=int(range_end_index),
|
|
)
|
|
segment_images = self._ensure_image_batch(load_result[0])
|
|
load_report = str(load_result[2])
|
|
fallback_image = self._first_frame(segment_images)
|
|
bridge_report = "bridge=disabled"
|
|
active_render_id = str(render_id or "").strip() or uuid.uuid4().hex[:10]
|
|
use_bridge_anchor = bool(use_bridge_anchor)
|
|
if use_bridge_anchor and current_segment > 0 and active_render_id:
|
|
bridge_path = _bridge_path(str(bridge_name), active_render_id)
|
|
if bridge_path.exists():
|
|
fallback_image = _load_png(bridge_path)
|
|
bridge_report = f"bridge=loaded:{bridge_path.name}"
|
|
else:
|
|
bridge_report = f"bridge=missing:{bridge_path.name},fallback"
|
|
elif current_segment <= 0:
|
|
bridge_report = "bridge=initial_segment"
|
|
|
|
anchored_images = segment_images
|
|
if use_bridge_anchor:
|
|
anchored_images = self._apply_head_anchor(
|
|
segment_images,
|
|
fallback_image,
|
|
k_frames=int(head_k_frames),
|
|
mode=str(head_mode),
|
|
blend_strength=float(head_blend_strength),
|
|
)
|
|
final_images, final_frames, min_report = self._ensure_min_frames(
|
|
anchored_images,
|
|
min_frames=int(min_frames),
|
|
mode=str(min_frames_mode),
|
|
ltx_fix=str(min_frames_ltx_fix),
|
|
)
|
|
|
|
segment_report = (
|
|
f"{plan_segment_report} | range=[{range_start_index}..{range_end_index}) | "
|
|
f"load={load_report} | bridge={bridge_report} | trim_head={trim_head_frames}f | "
|
|
f"prepared_frames={final_frames} | {min_report}"
|
|
)
|
|
return (
|
|
final_images,
|
|
current_segment,
|
|
total_segments,
|
|
segment_report,
|
|
plan_report,
|
|
int(trim_head_frames),
|
|
active_render_id,
|
|
int(continuation_trim_head_frames),
|
|
)
|
|
|
|
|
|
class IAMCCS_LTX2_SegmentQueueLoop:
|
|
# Carmine Cristallo Scalzi AI reasearch (IAMCCS) - patreon.com/IAMCCS
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"current_segment": ("INT", {"forceInput": True}),
|
|
"total_segments": ("INT", {"forceInput": True}),
|
|
},
|
|
"optional": {
|
|
"video": ("VIDEO",),
|
|
"images": ("IMAGE",),
|
|
"audio": ("AUDIO",),
|
|
"frame_rate": ("FLOAT", {"default": 24.0, "min": 0.001, "max": 240.0, "step": 0.01}),
|
|
"bridge_images": ("IMAGE",),
|
|
"enabled": ("BOOLEAN", {"default": True}),
|
|
"filename_prefix": ("STRING", {"default": "IAMCCS/LTX2_segment"}),
|
|
"merge_segments": ("BOOLEAN", {"default": True}),
|
|
"keep_segments": ("BOOLEAN", {"default": True}),
|
|
"render_id": ("STRING", {"default": ""}),
|
|
"segment_base_name": ("STRING", {"default": ""}),
|
|
"save_last_frame_bridge": ("BOOLEAN", {"default": True}),
|
|
"bridge_name": ("STRING", {"default": "ltx2_detailer_bridge"}),
|
|
"trim_head_frames": ("INT", {"default": 0, "min": 0, "max": 256, "step": 1}),
|
|
"trim_head_frames_on_continuation": ("INT", {"default": 1, "min": 0, "max": 64, "step": 1}),
|
|
"source_frame_rate": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 240.0, "step": 0.01}),
|
|
},
|
|
"hidden": {
|
|
"prompt": "PROMPT",
|
|
"unique_id": "UNIQUE_ID",
|
|
"extra_pnginfo": "EXTRA_PNGINFO",
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ()
|
|
FUNCTION = "queue_next"
|
|
OUTPUT_NODE = True
|
|
CATEGORY = "IAMCCS/LTX-2"
|
|
|
|
def queue_next(
|
|
self,
|
|
current_segment,
|
|
total_segments,
|
|
video=None,
|
|
images=None,
|
|
audio=None,
|
|
frame_rate=24.0,
|
|
bridge_images=None,
|
|
enabled=True,
|
|
filename_prefix="IAMCCS/LTX2_segment",
|
|
merge_segments=True,
|
|
keep_segments=True,
|
|
render_id="",
|
|
segment_base_name="",
|
|
save_last_frame_bridge=True,
|
|
bridge_name="ltx2_detailer_bridge",
|
|
trim_head_frames=0,
|
|
trim_head_frames_on_continuation=1,
|
|
source_frame_rate=0.0,
|
|
prompt=None,
|
|
unique_id=None,
|
|
extra_pnginfo=None,
|
|
):
|
|
current_segment = int(current_segment)
|
|
total_segments = int(total_segments)
|
|
enabled = bool(enabled)
|
|
merge_segments = bool(merge_segments)
|
|
keep_segments = bool(keep_segments)
|
|
save_last_frame_bridge = bool(save_last_frame_bridge)
|
|
trim_head_frames = max(0, int(trim_head_frames))
|
|
trim_head_frames_on_continuation = max(0, int(trim_head_frames_on_continuation))
|
|
source_frame_rate = max(0.0, float(source_frame_rate))
|
|
|
|
if not render_id and not segment_base_name and current_segment > 0:
|
|
raise RuntimeError(
|
|
f"Fresh run is starting from segment {current_segment + 1} instead of segment 1. "
|
|
"Reset segment_index to 0 or reload the workflow before running again."
|
|
)
|
|
|
|
active_render_id = str(render_id or "").strip() or uuid.uuid4().hex[:10]
|
|
if video is not None:
|
|
output_folder, resolved_base_name, _subfolder = _resolve_output_location(filename_prefix, video)
|
|
else:
|
|
try:
|
|
import folder_paths # type: ignore[import]
|
|
|
|
output_folder = folder_paths.get_output_directory()
|
|
except Exception:
|
|
output_folder = os.getcwd()
|
|
prefix_parts = str(filename_prefix or "IAMCCS/LTX2_segment").replace("\\", "/").split("/")
|
|
subfolder_parts = prefix_parts[:-1]
|
|
resolved_base_name = prefix_parts[-1] if prefix_parts[-1] else "LTX2_segment"
|
|
if subfolder_parts:
|
|
output_folder = os.path.join(output_folder, *subfolder_parts)
|
|
os.makedirs(output_folder, exist_ok=True)
|
|
active_base_name = str(segment_base_name or "").strip() or resolved_base_name
|
|
segment_name = _segment_filename(active_base_name, active_render_id, current_segment)
|
|
segment_path = os.path.join(output_folder, segment_name)
|
|
|
|
effective_prompt = prompt
|
|
effective_extra_pnginfo = extra_pnginfo
|
|
try:
|
|
live_prompt, live_extra_data, _, _ = _get_current_queue_item()
|
|
if live_prompt is not None:
|
|
effective_prompt = live_prompt
|
|
if effective_extra_pnginfo is None:
|
|
effective_extra_pnginfo = live_extra_data.get("extra_pnginfo", None)
|
|
except Exception:
|
|
pass
|
|
|
|
images_to_save = images
|
|
audio_to_save = audio
|
|
effective_trim_head_frames = trim_head_frames if trim_head_frames > 0 else trim_head_frames_on_continuation
|
|
if current_segment > 0 and effective_trim_head_frames > 0 and images is not None:
|
|
scaled_trim_head_frames = effective_trim_head_frames
|
|
if source_frame_rate > 0.0 and frame_rate > 0.0:
|
|
scaled_trim_head_frames = max(
|
|
0,
|
|
int(round(float(effective_trim_head_frames) * float(frame_rate) / float(source_frame_rate))),
|
|
)
|
|
available_frames = int(images.shape[0])
|
|
trim_frames = min(scaled_trim_head_frames, max(0, available_frames - 1))
|
|
if trim_frames > 0:
|
|
images_to_save = images[trim_frames:, ...]
|
|
audio_to_save = _trim_audio_leading(audio, trim_frames, frame_rate)
|
|
log.info(
|
|
"[IAMCCS LTX2] Trimmed %s leading frame(s) from continuation segment %s/%s before save (base=%s, output_fps=%.3f, source_fps=%.3f)",
|
|
trim_frames,
|
|
current_segment + 1,
|
|
total_segments,
|
|
effective_trim_head_frames,
|
|
float(frame_rate),
|
|
float(source_frame_rate),
|
|
)
|
|
|
|
metadata = _build_metadata(effective_prompt, effective_extra_pnginfo)
|
|
if video is not None:
|
|
video.save_to(segment_path, format=Types.VideoContainer("auto"), codec="auto", metadata=metadata)
|
|
else:
|
|
_save_segment_from_images(images=images_to_save, audio=audio_to_save, frame_rate=frame_rate, output_path=segment_path)
|
|
log.info("[IAMCCS LTX2] Saved segment %s/%s to %s", current_segment + 1, total_segments, segment_path)
|
|
|
|
bridge_images = bridge_images if bridge_images is not None else images
|
|
if save_last_frame_bridge and bridge_images is not None and int(bridge_images.shape[0]) > 0:
|
|
bridge_path = _bridge_path(bridge_name, active_render_id)
|
|
_save_last_frame_png(bridge_path, bridge_images[-1])
|
|
log.info("[IAMCCS LTX2] Saved last-frame bridge %s", bridge_path)
|
|
|
|
if not enabled:
|
|
return {"ui": {"text": [f"Saved segment {current_segment + 1}/{total_segments}: {segment_name}"]}}
|
|
|
|
next_segment = current_segment + 1
|
|
if next_segment >= total_segments:
|
|
ui_text = [f"Saved segment {current_segment + 1}/{total_segments}: {segment_name}"]
|
|
if merge_segments:
|
|
segment_paths = [
|
|
os.path.join(output_folder, _segment_filename(active_base_name, active_render_id, index))
|
|
for index in range(total_segments)
|
|
]
|
|
final_name = _final_filename(active_base_name, active_render_id)
|
|
final_path = os.path.join(output_folder, final_name)
|
|
_concat_segments(segment_paths, final_path)
|
|
ui_text.append(f"Merged final video: {final_name}")
|
|
if not keep_segments:
|
|
for path in segment_paths:
|
|
if os.path.exists(path):
|
|
os.remove(path)
|
|
return {"ui": {"text": ui_text}}
|
|
|
|
base_prompt = effective_prompt
|
|
if base_prompt is None:
|
|
live_prompt, _, _, _ = _get_current_queue_item()
|
|
base_prompt = live_prompt
|
|
prompt_copy = copy.deepcopy(_normalize_prompt_keys(base_prompt))
|
|
|
|
loop_updated = False
|
|
updated_segment_nodes = 0
|
|
for node_id, node in prompt_copy.items():
|
|
inputs = node.setdefault("inputs", {})
|
|
if "segment_index" in inputs:
|
|
inputs["segment_index"] = next_segment
|
|
updated_segment_nodes += 1
|
|
if "render_id" in inputs:
|
|
inputs["render_id"] = active_render_id
|
|
if node.get("class_type") == "IAMCCS_LTX2_LongVideoWrapperPrep":
|
|
inputs["render_id"] = active_render_id
|
|
is_current_loop = unique_id is not None and node_id == str(unique_id)
|
|
is_loop_fallback = unique_id is None and node.get("class_type") == "IAMCCS_LTX2_SegmentQueueLoop"
|
|
if is_current_loop or is_loop_fallback:
|
|
inputs["render_id"] = active_render_id
|
|
inputs["segment_base_name"] = active_base_name
|
|
loop_updated = True
|
|
|
|
if updated_segment_nodes == 0:
|
|
raise ValueError("IAMCCS_LTX2_SegmentQueueLoop could not find any node with a segment_index input.")
|
|
if not loop_updated:
|
|
raise ValueError("IAMCCS_LTX2_SegmentQueueLoop could not update its own render_id in the prompt.")
|
|
|
|
_enqueue_prompt(prompt_copy)
|
|
return {
|
|
"ui": {
|
|
"text": [
|
|
f"Saved segment {current_segment + 1}/{total_segments}: {segment_name}",
|
|
f"Queued segment {next_segment + 1}/{total_segments}",
|
|
]
|
|
}
|
|
}
|
|
|
|
|
|
class IAMCCS_LTX2_LoadLatentBridge:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"latents": ("LATENT",),
|
|
"segment_index": ("INT", {"default": 0, "min": 0, "max": 1000000, "step": 1}),
|
|
"render_id": ("STRING", {"default": ""}),
|
|
"temporal_overlap": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1}),
|
|
"temporal_overlap_cond_strength": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
},
|
|
"optional": {
|
|
"vae": ("VAE",),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("LATENT", "STRING")
|
|
RETURN_NAMES = ("conditioned_latents", "report")
|
|
FUNCTION = "condition"
|
|
CATEGORY = "IAMCCS/LTX-2"
|
|
|
|
def condition(self, latents, segment_index, render_id, temporal_overlap, temporal_overlap_cond_strength, vae=None):
|
|
segment_index = int(segment_index)
|
|
overlap_px = max(0, int(temporal_overlap))
|
|
active_render_id = str(render_id or "").strip()
|
|
if segment_index <= 0 or not active_render_id or overlap_px <= 0:
|
|
return (latents, "latent_bridge=initial_or_disabled")
|
|
|
|
bridge_path = _resolve_latent_bridge_payload_path(active_render_id)
|
|
if not bridge_path.exists():
|
|
return (latents, f"latent_bridge=missing:{bridge_path.name}")
|
|
|
|
time_scale = _get_time_scale_factor_from_vae(vae) if vae is not None else 8
|
|
overlap_f = _pixel_frames_to_latent_frames(overlap_px, time_scale)
|
|
if overlap_f <= 0:
|
|
return (latents, "latent_bridge=overlap_zero")
|
|
|
|
out = {k: v for k, v in latents.items()}
|
|
samples = out["samples"].clone()
|
|
noise_mask = out.get("noise_mask")
|
|
if noise_mask is None or not torch.is_tensor(noise_mask):
|
|
noise_mask = torch.ones(
|
|
(samples.shape[0], 1, samples.shape[2], 1, 1),
|
|
device=samples.device,
|
|
dtype=torch.float32,
|
|
)
|
|
else:
|
|
noise_mask = noise_mask.clone()
|
|
|
|
bridge_payload = _load_latent_bridge(bridge_path)
|
|
prev_tail = bridge_payload["latent_tail"]
|
|
if prev_tail is None:
|
|
return (latents, "latent_bridge=empty")
|
|
prev_tail = prev_tail.to(device=samples.device, dtype=samples.dtype)
|
|
saved_overlap = bridge_payload.get("latent_overlap_frames")
|
|
if torch.is_tensor(saved_overlap) and int(saved_overlap.numel()) > 0:
|
|
overlap_f = int(saved_overlap.flatten()[0].item())
|
|
overlap_f = min(int(overlap_f), int(prev_tail.shape[2]), int(samples.shape[2]))
|
|
if overlap_f <= 0:
|
|
return (latents, "latent_bridge=empty")
|
|
|
|
samples[:, :, :overlap_f, :, :] = prev_tail[:, :, -overlap_f:, :, :]
|
|
noise_mask[:, :, :overlap_f, :, :] = 1.0 - float(temporal_overlap_cond_strength)
|
|
out["samples"] = samples
|
|
out["noise_mask"] = noise_mask
|
|
prev_full = bridge_payload.get("latent_full")
|
|
if torch.is_tensor(prev_full) and prev_full.ndim == 5:
|
|
out["iamccs_prev_latents"] = {"samples": prev_full.to(device=samples.device, dtype=samples.dtype)}
|
|
reference_latent = bridge_payload.get("latent_reference")
|
|
if torch.is_tensor(reference_latent) and reference_latent.ndim == 5:
|
|
out["iamccs_reference_latents"] = {"samples": reference_latent.to(device=samples.device, dtype=samples.dtype)}
|
|
seed_offset = bridge_payload.get("latent_seed_offset")
|
|
if torch.is_tensor(seed_offset) and int(seed_offset.numel()) > 0:
|
|
out["iamccs_seed_offset"] = int(seed_offset.flatten()[0].item())
|
|
report = f"latent_bridge=loaded:{bridge_path.name} overlap_px={overlap_px} overlap_lat={overlap_f}"
|
|
log.info("[IAMCCS LTX2] %s", report)
|
|
return (out, report)
|
|
|
|
|
|
class IAMCCS_LTX2_SaveLatentBridge:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"latents": ("LATENT",),
|
|
"render_id": ("STRING", {"default": ""}),
|
|
"temporal_overlap": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1}),
|
|
},
|
|
"optional": {
|
|
"vae": ("VAE",),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("LATENT", "STRING")
|
|
RETURN_NAMES = ("latents", "report")
|
|
FUNCTION = "save"
|
|
CATEGORY = "IAMCCS/LTX-2"
|
|
|
|
def save(self, latents, render_id, temporal_overlap, vae=None):
|
|
active_render_id = str(render_id or "").strip()
|
|
overlap_px = max(0, int(temporal_overlap))
|
|
if not active_render_id or overlap_px <= 0:
|
|
return (latents, "latent_bridge=save_disabled")
|
|
|
|
samples = latents.get("samples")
|
|
if not torch.is_tensor(samples) or samples.ndim != 5:
|
|
return (latents, "latent_bridge=invalid_latents")
|
|
|
|
time_scale = _get_time_scale_factor_from_vae(vae) if vae is not None else 8
|
|
overlap_f = _pixel_frames_to_latent_frames(overlap_px, time_scale)
|
|
overlap_f = min(max(0, int(overlap_f)), int(samples.shape[2]))
|
|
if overlap_f <= 0:
|
|
return (latents, "latent_bridge=save_overlap_zero")
|
|
|
|
tail = samples[:, :, -overlap_f:, :, :]
|
|
bridge_path = _resolve_latent_bridge_payload_path(active_render_id)
|
|
previous_seed_offset = 0
|
|
reference_latent = samples
|
|
if bridge_path.exists():
|
|
try:
|
|
existing_payload = _load_latent_bridge(bridge_path)
|
|
existing_seed_offset = existing_payload.get("latent_seed_offset")
|
|
if torch.is_tensor(existing_seed_offset) and int(existing_seed_offset.numel()) > 0:
|
|
previous_seed_offset = int(existing_seed_offset.flatten()[0].item())
|
|
existing_reference = existing_payload.get("latent_reference")
|
|
if torch.is_tensor(existing_reference) and existing_reference.ndim == 5:
|
|
reference_latent = existing_reference.to(dtype=samples.dtype)
|
|
except Exception:
|
|
previous_seed_offset = 0
|
|
reference_latent = samples
|
|
next_seed_offset = previous_seed_offset + max(1, int(samples.shape[2]) - overlap_f)
|
|
_save_latent_bridge(
|
|
active_render_id,
|
|
tail,
|
|
samples,
|
|
reference_latent,
|
|
next_seed_offset,
|
|
overlap_f,
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|
)
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report = f"latent_bridge=saved:{bridge_path.name} overlap_px={overlap_px} overlap_lat={overlap_f}"
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|
log.info("[IAMCCS LTX2] %s", report)
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|
return (latents, report)
|
|
|
|
|
|
class IAMCCS_LTX2_BlendLatentBridge:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"latents": ("LATENT",),
|
|
"segment_index": ("INT", {"default": 0, "min": 0, "max": 1000000, "step": 1}),
|
|
"render_id": ("STRING", {"default": ""}),
|
|
"temporal_overlap": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1}),
|
|
},
|
|
"optional": {
|
|
"vae": ("VAE",),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("LATENT", "STRING")
|
|
RETURN_NAMES = ("latents", "report")
|
|
FUNCTION = "blend"
|
|
CATEGORY = "IAMCCS/LTX-2"
|
|
|
|
def blend(self, latents, segment_index, render_id, temporal_overlap, vae=None):
|
|
if latents.get("iamccs_bridge_transition_applied"):
|
|
return (latents, "latent_bridge_blend=handled_by_extend_sampler")
|
|
|
|
segment_index = int(segment_index)
|
|
overlap_px = max(0, int(temporal_overlap))
|
|
active_render_id = str(render_id or "").strip()
|
|
if segment_index <= 0 or not active_render_id or overlap_px <= 0:
|
|
return (latents, "latent_bridge_blend=initial_or_disabled")
|
|
|
|
bridge_path = _resolve_latent_bridge_payload_path(active_render_id)
|
|
if not bridge_path.exists():
|
|
return (latents, f"latent_bridge_blend=missing:{bridge_path.name}")
|
|
|
|
samples = latents.get("samples")
|
|
if not torch.is_tensor(samples) or samples.ndim != 5:
|
|
return (latents, "latent_bridge_blend=invalid_latents")
|
|
|
|
time_scale = _get_time_scale_factor_from_vae(vae) if vae is not None else 8
|
|
overlap_f = _pixel_frames_to_latent_frames(overlap_px, time_scale)
|
|
bridge_payload = _load_latent_bridge(bridge_path)
|
|
prev_tail = bridge_payload["latent_tail"]
|
|
if prev_tail is None:
|
|
return (latents, "latent_bridge_blend=empty")
|
|
prev_tail = prev_tail.to(device=samples.device, dtype=samples.dtype)
|
|
saved_overlap = bridge_payload.get("latent_overlap_frames")
|
|
if torch.is_tensor(saved_overlap) and int(saved_overlap.numel()) > 0:
|
|
overlap_f = int(saved_overlap.flatten()[0].item())
|
|
overlap_f = min(int(overlap_f), int(prev_tail.shape[2]), int(samples.shape[2]))
|
|
if overlap_f <= 0:
|
|
return (latents, "latent_bridge_blend=empty")
|
|
|
|
out = {k: v for k, v in latents.items()}
|
|
blended_samples = samples.clone()
|
|
alpha = torch.linspace(0.0, 1.0, steps=overlap_f, device=samples.device, dtype=samples.dtype).view(1, 1, overlap_f, 1, 1)
|
|
prev = prev_tail[:, :, -overlap_f:, :, :]
|
|
cur = blended_samples[:, :, :overlap_f, :, :]
|
|
blended_samples[:, :, :overlap_f, :, :] = prev * (1.0 - alpha) + cur * alpha
|
|
out["samples"] = blended_samples
|
|
report = f"latent_bridge_blend=applied:{bridge_path.name} overlap_px={overlap_px} overlap_lat={overlap_f}"
|
|
log.info("[IAMCCS LTX2] %s", report)
|
|
return (out, report)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"IAMCCS_LTX2_LastFrameBridgeSave": IAMCCS_LTX2_LastFrameBridgeSave,
|
|
"IAMCCS_LTX2_LastFrameBridgeLoad": IAMCCS_LTX2_LastFrameBridgeLoad,
|
|
"IAMCCS_LTX2_BlendLatentBridge": IAMCCS_LTX2_BlendLatentBridge,
|
|
"IAMCCS_LTX2_LongVideoWrapperPrep": IAMCCS_LTX2_LongVideoWrapperPrep,
|
|
"IAMCCS_LTX2_SegmentQueueLoop": IAMCCS_LTX2_SegmentQueueLoop,
|
|
"IAMCCS_LTX2_LoadLatentBridge": IAMCCS_LTX2_LoadLatentBridge,
|
|
"IAMCCS_LTX2_SaveLatentBridge": IAMCCS_LTX2_SaveLatentBridge,
|
|
}
|
|
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"IAMCCS_LTX2_LastFrameBridgeSave": "LTX-2 Last Frame Bridge Save 🖼️💾",
|
|
"IAMCCS_LTX2_LastFrameBridgeLoad": "LTX-2 Last Frame Bridge Load 🖼️",
|
|
"IAMCCS_LTX2_BlendLatentBridge": "LTX-2 Blend Latent Bridge 🎚️",
|
|
"IAMCCS_LTX2_LongVideoWrapperPrep": "LTX-2 Long Video Wrapper Prep 🧰",
|
|
"IAMCCS_LTX2_SegmentQueueLoop": "LTX-2 Segment Queue Loop 🔁",
|
|
"IAMCCS_LTX2_LoadLatentBridge": "LTX-2 Load Latent Bridge 🧬",
|
|
"IAMCCS_LTX2_SaveLatentBridge": "LTX-2 Save Latent Bridge 💾",
|
|
} |