Maxed-Out-99andClaude Fable 5
9cd58baf89
Snapshot of ~1 year of accumulated work prior to restructuring: smart loaders merge, checkpoint loader/saver, live preview, model storage auto-registration, LTX2 power lora loader, and misc web UI. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
3094 lines
118 KiB
Python
3094 lines
118 KiB
Python
from __future__ import annotations
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import os, re, glob, json, hashlib, copy
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from collections import deque
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from typing import Any, Dict, Tuple, Optional, List, Union
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import torch
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from safetensors import safe_open
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import folder_paths
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import comfy.utils
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import comfy.model_management
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from comfy.cli_args import args
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from nodes import KSamplerAdvanced
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import node_helpers, nodes
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# Comfy API
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try:
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from comfy_api.latest import io, ui
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from comfy_api.input import VideoInput
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from comfy_api.input_impl import VideoFromFile, VideoFromComponents
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from comfy_api.util import VideoComponents, VideoContainer, VideoCodec
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HAVE_COMFY_API = True
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except Exception as _e:
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io = None
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ui = None
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VideoInput = None
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VideoFromFile = None
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VideoFromComponents = None
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VideoComponents = None
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VideoContainer = None
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VideoCodec = None
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HAVE_COMFY_API = False
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print(f"[ComfyUI-MaxedOut] comfy_api not available in wan22nodes: {_e}")
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from server import PromptServer
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from aiohttp import web
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VIDEO_EXTS = {".mp4", ".mov", ".mkv", ".webm", ".avi"}
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routes = PromptServer.instance.routes
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def _sort_paths_newest_first(paths: List[str]) -> List[str]:
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"""Sort file paths by mtime desc (newest first), stable by normalized path."""
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def _mtime(path: str) -> float:
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try:
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return os.path.getmtime(path)
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except OSError:
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return 0.0
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return sorted(
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paths,
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key=lambda p: (-_mtime(p), p.replace("\\", "/").lower()),
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)
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def _sort_latent_options_by_folder(options: List[str], root: str = "") -> List[str]:
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"""
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Order relative '.latent' option paths so that the combo's prev/next arrows
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stay confined to one folder before moving on, newest first:
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folderA/file1, folderA/file2, ..., folderB/file1, ...
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Folders are ordered by the mtime of their most recently modified file (so
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a folder that just received a new file jumps back to the top), and files
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within each folder are newest first.
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"""
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def _mtime(rel: str) -> float:
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if not root:
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return 0.0
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try:
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return os.path.getmtime(os.path.join(root, rel))
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except OSError:
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return 0.0
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folder_of = lambda rel: rel.replace("\\", "/").rsplit("/", 1)[0] if "/" in rel.replace("\\", "/") else ""
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folder_latest: Dict[str, float] = {}
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for rel in options:
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folder = folder_of(rel)
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m = _mtime(rel)
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if m > folder_latest.get(folder, -1.0):
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folder_latest[folder] = m
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def key(rel: str):
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folder = folder_of(rel)
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return (-folder_latest.get(folder, 0.0), -_mtime(rel))
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return sorted(options, key=key)
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def _list_latent_subfolders(latents_root: str) -> List[str]:
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"""
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List latent subfolders recursively (e.g. "a", "a/b"), newest first by
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latest latent mtime in each branch.
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"""
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files = glob.glob(os.path.join(latents_root, "**", "*.latent"), recursive=True)
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if not files:
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return []
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folder_latest_mtime: Dict[str, float] = {}
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for file_path in files:
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rel_dir = os.path.relpath(os.path.dirname(file_path), latents_root).replace(os.sep, "/").strip("/")
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if not rel_dir or rel_dir == ".":
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continue
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try:
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mtime = os.path.getmtime(file_path)
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except OSError:
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mtime = 0.0
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# Include each ancestor so both "a" and "a/b" appear as options.
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parts = [p for p in rel_dir.split("/") if p]
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for i in range(1, len(parts) + 1):
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branch = "/".join(parts[:i])
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prev = folder_latest_mtime.get(branch, -1.0)
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if mtime > prev:
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folder_latest_mtime[branch] = mtime
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return [
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folder
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for folder, _ in sorted(
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folder_latest_mtime.items(),
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key=lambda kv: (-kv[1], kv[0].lower()),
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)
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]
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@routes.get("/mxd/videos/input")
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async def mxd_list_input_videos(request):
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"""
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Return a JSON list of *video* files under the input folder (relative paths),
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sorted by last modified time (newest first) so the combo's 'first' entry
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is always the latest render.
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"""
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input_dir = folder_paths.get_input_directory()
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entries = []
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for root, _, filenames in os.walk(input_dir):
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for name in filenames:
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ext = os.path.splitext(name)[1].lower()
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if ext in VIDEO_EXTS:
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full = os.path.join(root, name)
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rel = os.path.relpath(full, input_dir).replace("\\", "/")
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try:
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mtime = os.path.getmtime(full)
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except OSError:
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mtime = 0
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entries.append((mtime, rel))
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# 🔁 Sort newest → oldest, to match Comfy's internal behavior
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entries.sort(key=lambda x: x[0], reverse=True)
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files = [rel for _, rel in entries]
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return web.json_response(files)
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@routes.get("/mxd/latents/files")
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async def mxd_list_latent_files(request):
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"""
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Fresh re-scan of input/latents for .latent files. Used by the run_folder
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queuing loop (refresh_before_run) to pick up files a still-running
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workflow is writing concurrently, instead of relying on the dropdown
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list captured whenever the node's combo was last populated.
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"""
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latents_root = os.path.join(folder_paths.get_input_directory(), "latents")
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os.makedirs(latents_root, exist_ok=True)
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files = glob.glob(os.path.join(latents_root, "**", "*.latent"), recursive=True)
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options = [os.path.relpath(f, latents_root).replace(os.sep, "/") for f in files]
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options = _sort_latent_options_by_folder(options, latents_root)
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return web.json_response(options)
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# ---------- SaveLatent (saves latent + conditioning + optional trim_latent) ----------
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class SaveLatent_I2V_MXD:
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"""
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Default latent saver, works for t2v, i2v, and VACE 2.2. Persists:
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• latent tensor -> .latent
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• pos/neg CONDITIONING -> .cond.pt
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• optional trim_latent value (VACE 2.2) -> .cond.pt
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"""
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TITLE = "Save Latent MXD"
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CATEGORY = "MXD/Latents"
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OUTPUT_NODE = True
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RETURN_TYPES = ()
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FUNCTION = "save_only"
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"samples": ("LATENT", {"tooltip": "Latent to save."}),
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"positive": ("CONDITIONING", {"tooltip": "Positive CONDITIONING to save alongside the latent."}),
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"negative": ("CONDITIONING", {"tooltip": "Negative CONDITIONING to save alongside the latent."}),
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"filename_prefix": ("STRING", {"default": "ComfyUI", "tooltip": "Prefix for saved files"}),
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},
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"optional": {
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"trim_latent": ("INT", {
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"default": 0,
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"min": 0,
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"max": 10000,
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"step": 1,
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"tooltip": "VACE 2.2 trim_latent value to preserve with this latent. Usually 0 or 1. Ignored for t2v/i2v."
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}),
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},
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"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"},
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}
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def save_only(self, samples, positive, negative, filename_prefix="ComfyUI",
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trim_latent=0, prompt=None, extra_pnginfo=None, unique_id=None):
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_save_i2v_latent_bundle(
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samples=samples,
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positive=positive,
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negative=negative,
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filename_prefix=filename_prefix,
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prompt=prompt,
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extra_pnginfo=extra_pnginfo,
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unique_id=unique_id,
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sidecar_extra={"trim_latent": _coerce_trim_latent(trim_latent)},
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)
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return {}
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# ---------- Helpers ----------
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def _load_latent_file(latent_path: str) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any], List[str]]:
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"""
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Load safetensors latent with Comfy metadata.
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Returns (samples_dict, metadata_dict, keys_list)
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"""
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with safe_open(latent_path, framework="pt", device="cpu") as f:
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keys = list(f.keys())
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# prefer explicit key we write
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if "latent_tensor" in keys:
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t = f.get_tensor("latent_tensor").float().contiguous()
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else:
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# fall back (some variants might save using a different name)
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first = keys[0]
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t = f.get_tensor(first).float().contiguous()
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meta = f.metadata() or {}
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# if ancient format, rescale (match Comfy behavior)
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if "latent_format_version_0" not in keys:
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t = t * (1.0 / 0.18215)
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return {"samples": t}, meta, keys
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def _safe_json_loads(s: Union[str, bytes, None]) -> Optional[Dict[str, Any]]:
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if s is None:
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return None
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if isinstance(s, bytes):
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try:
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s = s.decode("utf-8", "ignore")
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except Exception:
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return None
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if not isinstance(s, str):
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return None
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try:
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return json.loads(s)
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except Exception:
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# sometimes double-encoded in metadata
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try:
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return json.loads(json.loads(s))
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except Exception:
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return None
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def _workflow_node_bbox(nodes: List[Dict[str, Any]]) -> Optional[Tuple[float, float, float, float]]:
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"""(min_x, min_y, max_x, max_y) over a litegraph 'nodes' list. None if no positions found."""
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xs0, ys0, xs1, ys1 = [], [], [], []
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for n in nodes:
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if not isinstance(n, dict):
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continue
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pos = n.get("pos")
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if isinstance(pos, list) and len(pos) >= 2:
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x, y = pos[0], pos[1]
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elif isinstance(pos, dict):
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x, y = pos.get("0", 0), pos.get("1", 0)
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else:
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continue
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size = n.get("size")
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w = size[0] if isinstance(size, (list, tuple)) and len(size) >= 1 else 200
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h = size[1] if isinstance(size, (list, tuple)) and len(size) >= 2 else 100
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xs0.append(x); ys0.append(y); xs1.append(x + w); ys1.append(y + h)
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if not xs0:
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return None
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return (min(xs0), min(ys0), max(xs1), max(ys1))
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def _offset_workflow_nodes(nodes: List[Dict[str, Any]], dx: float, dy: float) -> None:
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for n in nodes:
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if not isinstance(n, dict):
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continue
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pos = n.get("pos")
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if isinstance(pos, list) and len(pos) >= 2:
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pos[0] = pos[0] + dx
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pos[1] = pos[1] + dy
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elif isinstance(pos, dict):
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if "0" in pos: pos["0"] = pos["0"] + dx
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if "1" in pos: pos["1"] = pos["1"] + dy
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def _merge_prior_workflow_into_current(prior_workflow_json: Optional[str], current_workflow: Any) -> Any:
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"""
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Merge a previously-saved workflow graph (embedded in a loaded .latent file) into the
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workflow graph of the run that's currently saving. The prior graph's nodes/links/groups
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are copied in with fresh ids and shifted to sit to the left of the current graph, wrapped
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in a labelled group - so dragging the final video into ComfyUI shows both stages at once,
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the same as if you'd copy/pasted the first workflow onto the second one's canvas.
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Best-effort: on any parse/shape problem, returns current_workflow untouched.
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"""
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if not prior_workflow_json or not isinstance(current_workflow, dict):
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return current_workflow
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try:
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prior = json.loads(prior_workflow_json) if isinstance(prior_workflow_json, str) else prior_workflow_json
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if not isinstance(prior, dict):
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return current_workflow
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prior_nodes = prior.get("nodes")
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if not isinstance(prior_nodes, list) or not prior_nodes:
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return current_workflow
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merged = copy.deepcopy(current_workflow)
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current_nodes = merged.get("nodes")
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if not isinstance(current_nodes, list):
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current_nodes = []
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merged["nodes"] = current_nodes
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prior_nodes = copy.deepcopy(prior_nodes)
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prior_links = copy.deepcopy(prior.get("links")) if isinstance(prior.get("links"), list) else []
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prior_groups = copy.deepcopy(prior.get("groups")) if isinstance(prior.get("groups"), list) else []
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# ---- remap node ids so they can't collide with the current graph ----
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current_last_node_id = merged.get("last_node_id")
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if not isinstance(current_last_node_id, int):
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current_last_node_id = max((n.get("id", 0) for n in current_nodes if isinstance(n, dict)), default=0)
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next_node_id = current_last_node_id + 1
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node_id_map: Dict[Any, int] = {}
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for n in prior_nodes:
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if not isinstance(n, dict) or "id" not in n:
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continue
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node_id_map[n["id"]] = next_node_id
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n["id"] = next_node_id
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next_node_id += 1
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# ---- remap link ids the same way ----
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current_last_link_id = merged.get("last_link_id")
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if not isinstance(current_last_link_id, int):
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current_last_link_id = max(
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(l[0] for l in (merged.get("links") or []) if isinstance(l, list) and l), default=0
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)
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next_link_id = current_last_link_id + 1
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link_id_map: Dict[Any, int] = {}
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for l in prior_links:
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if isinstance(l, list) and l:
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link_id_map[l[0]] = next_link_id
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next_link_id += 1
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for n in prior_nodes:
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if not isinstance(n, dict):
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continue
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for inp in (n.get("inputs") or []):
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if isinstance(inp, dict) and inp.get("link") is not None:
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inp["link"] = link_id_map.get(inp["link"], inp["link"])
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for out in (n.get("outputs") or []):
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if isinstance(out, dict) and isinstance(out.get("links"), list):
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out["links"] = [link_id_map.get(x, x) for x in out["links"]]
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remapped_links = []
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for l in prior_links:
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if not isinstance(l, list) or len(l) < 5:
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continue
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new_l = list(l)
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new_l[0] = link_id_map.get(l[0], l[0])
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new_l[1] = node_id_map.get(l[1], l[1])
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new_l[3] = node_id_map.get(l[3], l[3])
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remapped_links.append(new_l)
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# ---- shift the prior graph so it sits to the left of the current one ----
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current_bbox = _workflow_node_bbox(current_nodes)
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prior_bbox = _workflow_node_bbox(prior_nodes)
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margin = 400
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if current_bbox and prior_bbox:
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dx = (current_bbox[0] - margin) - prior_bbox[2]
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dy = current_bbox[1] - prior_bbox[1]
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||
else:
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dx, dy = 0, 0
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_offset_workflow_nodes(prior_nodes, dx, dy)
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for g in prior_groups:
|
||
if not isinstance(g, dict):
|
||
continue
|
||
b = g.get("bounding")
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||
if isinstance(b, list) and len(b) >= 2:
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b[0] = b[0] + dx
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b[1] = b[1] + dy
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# wrap the prior graph in a labelled group so it's obvious what it is
|
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wrapper_group = None
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||
prior_bbox_shifted = _workflow_node_bbox(prior_nodes)
|
||
if prior_bbox_shifted:
|
||
pad = 60
|
||
wrapper_group = {
|
||
"title": "Prior stage (loaded latent's source workflow)",
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||
"bounding": [
|
||
prior_bbox_shifted[0] - pad,
|
||
prior_bbox_shifted[1] - pad - 40,
|
||
(prior_bbox_shifted[2] - prior_bbox_shifted[0]) + pad * 2,
|
||
(prior_bbox_shifted[3] - prior_bbox_shifted[1]) + pad * 2 + 40,
|
||
],
|
||
"color": "#3f789e",
|
||
"font_size": 24,
|
||
}
|
||
|
||
merged["nodes"] = current_nodes + prior_nodes
|
||
merged["links"] = (merged.get("links") or []) + remapped_links
|
||
groups = list(merged.get("groups") or []) + prior_groups
|
||
if wrapper_group:
|
||
groups.append(wrapper_group)
|
||
merged["groups"] = groups
|
||
merged["last_node_id"] = next_node_id - 1
|
||
merged["last_link_id"] = next_link_id - 1
|
||
return merged
|
||
except Exception as e:
|
||
print(f"[SaveVideoMXD] Could not merge prior stage workflow into embedded metadata: {e}")
|
||
return current_workflow
|
||
|
||
|
||
def _node_sort_key(node_id: str) -> Tuple[int, Union[int, str]]:
|
||
s = str(node_id)
|
||
try:
|
||
return (0, int(s))
|
||
except Exception:
|
||
return (1, s)
|
||
|
||
|
||
def _normalize_prompt_graph(prompt_json: Any) -> Dict[str, Any]:
|
||
if not isinstance(prompt_json, dict):
|
||
return {}
|
||
graph = prompt_json.get("prompt", prompt_json)
|
||
return graph if isinstance(graph, dict) else {}
|
||
|
||
|
||
def _get_graph_node(graph: Dict[str, Any], node_id: Any) -> Optional[Dict[str, Any]]:
|
||
if node_id is None or not isinstance(graph, dict):
|
||
return None
|
||
node = graph.get(str(node_id))
|
||
return node if isinstance(node, dict) else None
|
||
|
||
|
||
def _iter_graph_nodes_sorted(graph: Dict[str, Any]) -> List[Tuple[str, Dict[str, Any]]]:
|
||
nodes: List[Tuple[str, Dict[str, Any]]] = []
|
||
for node_id, node in graph.items():
|
||
if isinstance(node, dict):
|
||
nodes.append((str(node_id), node))
|
||
nodes.sort(key=lambda pair: _node_sort_key(pair[0]))
|
||
return nodes
|
||
|
||
|
||
def _linked_node_id(value: Any) -> Optional[str]:
|
||
if isinstance(value, (list, tuple)) and len(value) >= 1:
|
||
return str(value[0])
|
||
return None
|
||
|
||
|
||
def _is_ksampler_node(node: Any) -> bool:
|
||
if not isinstance(node, dict):
|
||
return False
|
||
return "KSampler" in str(node.get("class_type", ""))
|
||
|
||
|
||
def _collect_upstream_linked_node_ids(node: Dict[str, Any]) -> List[str]:
|
||
inputs = node.get("inputs", {})
|
||
if not isinstance(inputs, dict):
|
||
return []
|
||
|
||
seen = set()
|
||
ordered = []
|
||
|
||
# Prefer latent-carrying links first.
|
||
for key in ("samples", "latent", "latent_image"):
|
||
linked = _linked_node_id(inputs.get(key))
|
||
if linked is not None and linked not in seen:
|
||
seen.add(linked)
|
||
ordered.append(linked)
|
||
|
||
# Then search all other connected inputs in stable order.
|
||
for key, value in inputs.items():
|
||
if key in ("samples", "latent", "latent_image"):
|
||
continue
|
||
linked = _linked_node_id(value)
|
||
if linked is not None and linked not in seen:
|
||
seen.add(linked)
|
||
ordered.append(linked)
|
||
|
||
return ordered
|
||
|
||
|
||
def _find_upstream_ksampler_node_id(graph: Dict[str, Any], start_node_id: Any) -> Optional[str]:
|
||
if not isinstance(graph, dict) or start_node_id is None:
|
||
return None
|
||
|
||
queue: deque[str] = deque([str(start_node_id)])
|
||
visited = set()
|
||
|
||
while queue:
|
||
node_id = queue.popleft()
|
||
if node_id in visited:
|
||
continue
|
||
visited.add(node_id)
|
||
|
||
node = _get_graph_node(graph, node_id)
|
||
if not node:
|
||
continue
|
||
if _is_ksampler_node(node):
|
||
return node_id
|
||
|
||
for upstream_id in _collect_upstream_linked_node_ids(node):
|
||
if upstream_id not in visited:
|
||
queue.append(upstream_id)
|
||
|
||
return None
|
||
|
||
|
||
def _extract_ksampler_params(node: Dict[str, Any]) -> Dict[str, Any]:
|
||
inputs = node.get("inputs", {}) if isinstance(node, dict) else {}
|
||
if not isinstance(inputs, dict):
|
||
inputs = {}
|
||
|
||
out: Dict[str, Any] = {}
|
||
|
||
def set_int(key: str):
|
||
if key in inputs:
|
||
try:
|
||
out[key] = int(inputs[key])
|
||
except Exception:
|
||
pass
|
||
|
||
def set_float(key: str):
|
||
if key in inputs:
|
||
try:
|
||
out[key] = float(inputs[key])
|
||
except Exception:
|
||
pass
|
||
|
||
def set_str(key: str):
|
||
if key in inputs and not isinstance(inputs[key], (list, tuple, dict)):
|
||
try:
|
||
out[key] = str(inputs[key]).strip()
|
||
except Exception:
|
||
pass
|
||
|
||
set_int("steps")
|
||
set_float("cfg")
|
||
set_str("sampler_name")
|
||
set_str("scheduler")
|
||
set_int("start_at_step")
|
||
set_int("end_at_step")
|
||
|
||
return out
|
||
|
||
|
||
def _attach_source_ksampler_metadata(meta: Dict[str, Any], prompt: Any, unique_id: Any) -> None:
|
||
if not isinstance(meta, dict):
|
||
return
|
||
|
||
graph = _normalize_prompt_graph(prompt)
|
||
if not graph:
|
||
return
|
||
|
||
save_node_id = str(unique_id) if unique_id is not None else ""
|
||
if not save_node_id:
|
||
return
|
||
|
||
save_node = _get_graph_node(graph, save_node_id)
|
||
if not save_node:
|
||
return
|
||
|
||
source_candidates = _collect_upstream_linked_node_ids(save_node)
|
||
if not source_candidates:
|
||
return
|
||
|
||
source_ksampler_id = None
|
||
for start_id in source_candidates:
|
||
source_ksampler_id = _find_upstream_ksampler_node_id(graph, start_id)
|
||
if source_ksampler_id:
|
||
break
|
||
|
||
if not source_ksampler_id:
|
||
return
|
||
|
||
source_node = _get_graph_node(graph, source_ksampler_id)
|
||
if not source_node:
|
||
return
|
||
|
||
meta["mxd_source_save_node_id"] = save_node_id
|
||
meta["mxd_source_ksampler_node_id"] = source_ksampler_id
|
||
try:
|
||
meta["mxd_source_ksampler_params"] = json.dumps(_extract_ksampler_params(source_node))
|
||
except Exception:
|
||
pass
|
||
|
||
|
||
def _build_latent_metadata(prompt=None, extra_pnginfo=None, unique_id=None, extra_meta=None):
|
||
if args.disable_metadata:
|
||
return None
|
||
|
||
meta = {}
|
||
if prompt is not None:
|
||
try:
|
||
meta["prompt"] = json.dumps(prompt)
|
||
except Exception:
|
||
pass
|
||
if extra_pnginfo is not None:
|
||
for k, v in extra_pnginfo.items():
|
||
try:
|
||
meta[k] = json.dumps(v)
|
||
except Exception:
|
||
pass
|
||
if isinstance(extra_meta, dict):
|
||
for k, v in extra_meta.items():
|
||
try:
|
||
meta[str(k)] = json.dumps(v)
|
||
except Exception:
|
||
pass
|
||
_attach_source_ksampler_metadata(meta, prompt, unique_id)
|
||
return meta
|
||
|
||
|
||
def _save_i2v_latent_bundle(
|
||
samples,
|
||
positive,
|
||
negative,
|
||
filename_prefix="I2V",
|
||
prompt=None,
|
||
extra_pnginfo=None,
|
||
unique_id=None,
|
||
sidecar_extra=None,
|
||
):
|
||
latents_dir = os.path.join(folder_paths.get_input_directory(), "latents")
|
||
os.makedirs(latents_dir, exist_ok=True)
|
||
|
||
full_output_folder, filename, counter, _subfolder, _filename_prefix = folder_paths.get_save_image_path(
|
||
filename_prefix, latents_dir
|
||
)
|
||
|
||
extra_meta = sidecar_extra if isinstance(sidecar_extra, dict) else None
|
||
meta = _build_latent_metadata(
|
||
prompt=prompt,
|
||
extra_pnginfo=extra_pnginfo,
|
||
unique_id=unique_id,
|
||
extra_meta=extra_meta,
|
||
)
|
||
|
||
latent_path = os.path.join(full_output_folder, f"{filename}_{counter:05}_.latent")
|
||
payload = {
|
||
"latent_tensor": samples["samples"].contiguous(),
|
||
"latent_format_version_0": torch.tensor([]),
|
||
}
|
||
comfy.utils.save_torch_file(payload, latent_path, metadata=meta)
|
||
|
||
sidecar = {"positive": positive, "negative": negative}
|
||
if isinstance(sidecar_extra, dict):
|
||
sidecar.update(sidecar_extra)
|
||
torch.save(sidecar, latent_path.replace(".latent", ".cond.pt"))
|
||
return latent_path
|
||
|
||
|
||
def _load_i2v_conditioning_sidecar(latent_path):
|
||
cond_path = latent_path.replace(".latent", ".cond.pt")
|
||
if not os.path.exists(cond_path):
|
||
return [], [], {}
|
||
|
||
try:
|
||
data = torch.load(cond_path, map_location="cpu")
|
||
except Exception:
|
||
return [], [], {}
|
||
|
||
if not isinstance(data, dict):
|
||
return [], [], {}
|
||
|
||
return data.get("positive", []), data.get("negative", []), data
|
||
|
||
|
||
def _coerce_trim_latent(value, default=0):
|
||
try:
|
||
if isinstance(value, str):
|
||
parsed = _safe_json_loads(value)
|
||
value = parsed if parsed is not None else value
|
||
return int(value)
|
||
except Exception:
|
||
return int(default)
|
||
|
||
|
||
def _extract_prompt_text_from_ksampler(graph: Dict[str, Any], ks_node: Dict[str, Any]) -> Tuple[str, str]:
|
||
pos = ""
|
||
neg = ""
|
||
|
||
inputs = ks_node.get("inputs", {}) if isinstance(ks_node, dict) else {}
|
||
if not isinstance(inputs, dict):
|
||
return pos, neg
|
||
|
||
def _text_from_clip(link_value: Any) -> str:
|
||
node_id = _linked_node_id(link_value)
|
||
if node_id is None:
|
||
return ""
|
||
node = _get_graph_node(graph, node_id) or {}
|
||
if node.get("class_type") == "CLIPTextEncode":
|
||
return str(node.get("inputs", {}).get("text", "")).strip()
|
||
return ""
|
||
|
||
pos = _text_from_clip(inputs.get("positive"))
|
||
neg = _text_from_clip(inputs.get("negative"))
|
||
return pos, neg
|
||
|
||
|
||
def _extract_params_from_prompt_json(
|
||
prompt_json: Dict[str, Any],
|
||
meta: Optional[Dict[str, Any]] = None,
|
||
) -> Tuple[str, str, int, float, str, str, int]:
|
||
"""
|
||
Returns: (positive, negative, steps, cfg, sampler_name, scheduler, end_at_step)
|
||
parsed from the saved Comfy prompt graph with deterministic KSampler selection.
|
||
"""
|
||
pos = ""
|
||
neg = ""
|
||
steps = 20
|
||
cfg = 8.0
|
||
sampler_name = ""
|
||
scheduler = ""
|
||
end_at_step = 0
|
||
|
||
graph = _normalize_prompt_graph(prompt_json)
|
||
if not isinstance(graph, dict):
|
||
return pos, neg, steps, cfg, sampler_name, scheduler, end_at_step
|
||
|
||
ks_node = None
|
||
extracted_params: Dict[str, Any] = {}
|
||
|
||
# 1) Source KSampler id saved directly in latent metadata.
|
||
if isinstance(meta, dict):
|
||
raw_ks = meta.get("mxd_source_ksampler_node_id")
|
||
if raw_ks is not None:
|
||
candidate = _get_graph_node(graph, str(raw_ks))
|
||
if candidate and _is_ksampler_node(candidate):
|
||
ks_node = candidate
|
||
|
||
# 2) Source save node id -> trace upstream to nearest KSampler.
|
||
if ks_node is None and isinstance(meta, dict):
|
||
raw_save = meta.get("mxd_source_save_node_id")
|
||
if raw_save is not None:
|
||
save_node = _get_graph_node(graph, str(raw_save))
|
||
if save_node:
|
||
for start_id in _collect_upstream_linked_node_ids(save_node):
|
||
trace_id = _find_upstream_ksampler_node_id(graph, start_id)
|
||
if trace_id:
|
||
candidate = _get_graph_node(graph, trace_id)
|
||
if candidate and _is_ksampler_node(candidate):
|
||
ks_node = candidate
|
||
break
|
||
|
||
# 3) Legacy fallback: last KSampler node in graph.
|
||
if ks_node is None:
|
||
for _, node in _iter_graph_nodes_sorted(graph):
|
||
if _is_ksampler_node(node):
|
||
ks_node = node
|
||
|
||
if not ks_node:
|
||
return pos, neg, steps, cfg, sampler_name, scheduler, end_at_step
|
||
|
||
pos, neg = _extract_prompt_text_from_ksampler(graph, ks_node)
|
||
extracted_params = _extract_ksampler_params(ks_node)
|
||
|
||
if "steps" in extracted_params:
|
||
steps = int(extracted_params["steps"])
|
||
if "cfg" in extracted_params:
|
||
cfg = float(extracted_params["cfg"])
|
||
if "end_at_step" in extracted_params:
|
||
end_at_step = int(extracted_params["end_at_step"])
|
||
if "sampler_name" in extracted_params:
|
||
sampler_name = str(extracted_params["sampler_name"]).strip()
|
||
if "scheduler" in extracted_params:
|
||
scheduler = str(extracted_params["scheduler"]).strip()
|
||
|
||
# Fallback to saved parameter snapshot if graph parse is incomplete.
|
||
if isinstance(meta, dict):
|
||
saved_params = _safe_json_loads(meta.get("mxd_source_ksampler_params"))
|
||
if isinstance(saved_params, dict):
|
||
if "steps" in saved_params and "steps" not in extracted_params:
|
||
try:
|
||
steps = int(saved_params["steps"])
|
||
except Exception:
|
||
pass
|
||
if "cfg" in saved_params and "cfg" not in extracted_params:
|
||
try:
|
||
cfg = float(saved_params["cfg"])
|
||
except Exception:
|
||
pass
|
||
if "end_at_step" in saved_params and "end_at_step" not in extracted_params:
|
||
try:
|
||
end_at_step = int(saved_params["end_at_step"])
|
||
except Exception:
|
||
pass
|
||
if "sampler_name" in saved_params and "sampler_name" not in extracted_params:
|
||
try:
|
||
sampler_name = str(saved_params["sampler_name"]).strip()
|
||
except Exception:
|
||
pass
|
||
if "scheduler" in saved_params and "scheduler" not in extracted_params:
|
||
try:
|
||
scheduler = str(saved_params["scheduler"]).strip()
|
||
except Exception:
|
||
pass
|
||
|
||
return pos, neg, steps, cfg, sampler_name, scheduler, end_at_step
|
||
|
||
# ---------- Load a single latent (WITH Comfy params, consistent with folder version) ----------
|
||
# ---------- Load one latent (conditioning + sampler params + optional trim_latent) ----------
|
||
class LoadLatent_I2V_MXD:
|
||
"""
|
||
Default single-latent loader: sampler settings, CONDITIONING (positive/negative), and
|
||
an optional trim_latent value, all read from the .latent file and its .cond.pt sidecar.
|
||
"""
|
||
DESCRIPTION = """Load one latent and return conditioning and sampler settings."""
|
||
TITLE = "Load Latent MXD"
|
||
CATEGORY = "MXD/Latents"
|
||
FUNCTION = "load"
|
||
|
||
RETURN_TYPES = (
|
||
"FLOAT", # shift
|
||
"CONDITIONING", # positive conditioning
|
||
"CONDITIONING", # negative conditioning
|
||
"LATENT",
|
||
"INT",
|
||
"FLOAT",
|
||
"STRING",
|
||
"STRING",
|
||
"INT",
|
||
"STRING",
|
||
"INT", # trim_latent
|
||
"STRING", # high_workflow
|
||
)
|
||
RETURN_NAMES = (
|
||
"shift",
|
||
"positive",
|
||
"negative",
|
||
"samples",
|
||
"steps",
|
||
"cfg",
|
||
"sampler_name",
|
||
"scheduler",
|
||
"end_at_step",
|
||
"filename_prefix",
|
||
"trim_latent",
|
||
"high_workflow",
|
||
)
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(s):
|
||
latents_root = os.path.join(folder_paths.get_input_directory(), "latents")
|
||
os.makedirs(latents_root, exist_ok=True)
|
||
files = glob.glob(os.path.join(latents_root, "**", "*.latent"), recursive=True)
|
||
# Clean dropdown display (no "latents/" prefix)
|
||
options = [os.path.relpath(f, latents_root).replace(os.sep, "/") for f in files]
|
||
# Group by folder so the combo's prev/next arrows walk one folder at a time.
|
||
options = _sort_latent_options_by_folder(options, latents_root)
|
||
|
||
ks_inputs = KSamplerAdvanced.INPUT_TYPES().get("required", {})
|
||
samplers_enum = ks_inputs.get("sampler_name", ("STRING",))[0]
|
||
schedulers_enum = ks_inputs.get("scheduler", ("STRING",))[0]
|
||
|
||
s.RETURN_TYPES = (
|
||
"FLOAT", "CONDITIONING", "CONDITIONING", "LATENT",
|
||
"INT", "FLOAT", samplers_enum, schedulers_enum,
|
||
"INT", "STRING", "INT", "STRING",
|
||
)
|
||
s._SAMPLERS_ENUM = samplers_enum
|
||
s._SCHEDULERS_ENUM = schedulers_enum
|
||
|
||
return {
|
||
"required": {
|
||
"latent": (options, ),
|
||
"run_folder": ("BOOLEAN", {
|
||
"default": False,
|
||
"tooltip": "When enabled, hitting Queue Prompt auto-queues every latent in this file's folder, one after another, instead of just the selected file.",
|
||
}),
|
||
"refresh_before_run": ("BOOLEAN", {
|
||
"default": False,
|
||
"tooltip": "When run_folder is on, re-scan the latents folder for new files right before the queuing loop starts, instead of using the dropdown list as of whenever it was last populated. Use this when another workflow is still writing latents into this folder as you queue this one.",
|
||
}),
|
||
}
|
||
}
|
||
|
||
def _coerce_enum(self, value, enum_values):
|
||
try:
|
||
return value if (enum_values and value in enum_values) else (enum_values[0] if enum_values else value)
|
||
except Exception:
|
||
return value
|
||
|
||
def _strip_counter(self, name: str) -> str:
|
||
# Only strip the trailing pattern we generate when saving: "_<5digits>_"
|
||
# Preserve numeric-only base names like "96".
|
||
stem, _ = os.path.splitext(name)
|
||
m = re.match(r"^(.*?)(?:_\d{5}_)$", stem)
|
||
return m.group(1) if m else stem
|
||
|
||
def _extract_sd3_shift(self, meta: dict, prompt_json: dict | None) -> float:
|
||
"""
|
||
Find SD3 'shift' in several places:
|
||
1) flat meta["shift"]
|
||
2) nested in prompt/workflow JSON:
|
||
- nodes[].{type|class_type} == "ModelSamplingSD3" -> inputs.shift or widgets_values[0]
|
||
- runtime-style prompt dict mapping IDs -> {..., class_type: "ModelSamplingSD3"}
|
||
Falls back to 5.0 if not found.
|
||
"""
|
||
def try_float(x):
|
||
try:
|
||
return float(x)
|
||
except Exception:
|
||
return None
|
||
|
||
# 1) flat meta
|
||
if isinstance(meta, dict):
|
||
v = try_float(meta.get("shift"))
|
||
if v is not None:
|
||
return v
|
||
|
||
# parse any JSON-like strings present in meta
|
||
def safe_load(x):
|
||
try:
|
||
return _safe_json_loads(x) if isinstance(x, str) else x
|
||
except Exception:
|
||
return None
|
||
|
||
# Search helper over various JSON shapes
|
||
def search_container(obj):
|
||
# Direct dict containing shift
|
||
if isinstance(obj, dict):
|
||
if "shift" in obj:
|
||
v = try_float(obj.get("shift"))
|
||
if v is not None:
|
||
return v
|
||
|
||
# Comfy "nodes": [ {...}, ... ]
|
||
nodes = obj.get("nodes")
|
||
if isinstance(nodes, list):
|
||
# take the last SD3 node (most recent in graph)
|
||
ms_nodes = [n for n in nodes if isinstance(n, dict) and (
|
||
n.get("type") == "ModelSamplingSD3" or
|
||
n.get("class_type") == "ModelSamplingSD3" or
|
||
(isinstance(n.get("properties"), dict) and n["properties"].get("Node name for S&R") == "ModelSamplingSD3")
|
||
)]
|
||
if ms_nodes:
|
||
nd = ms_nodes[-1]
|
||
# Prefer explicit inputs.shift if present and literal
|
||
inp = nd.get("inputs")
|
||
if isinstance(inp, dict) and "shift" in inp:
|
||
vv = inp["shift"]
|
||
# ignore connection like [node_id, idx]
|
||
if not isinstance(vv, (list, tuple)):
|
||
v2 = try_float(vv)
|
||
if v2 is not None:
|
||
return v2
|
||
# Fallback: first widget is shift for SD3 (as seen in your JSON)
|
||
w = nd.get("widgets_values")
|
||
if isinstance(w, list) and len(w) >= 1:
|
||
v2 = try_float(w[0])
|
||
if v2 is not None:
|
||
return v2
|
||
|
||
# Runtime prompt map: {"42": {"class_type":"ModelSamplingSD3", "inputs":{...}, "widgets_values":[...]}, ...}
|
||
# Heuristic: values that are dicts with class_type keys
|
||
has_ct = [v for v in obj.values() if isinstance(v, dict) and "class_type" in v]
|
||
if has_ct:
|
||
for nd in has_ct:
|
||
if nd.get("class_type") == "ModelSamplingSD3":
|
||
inp = nd.get("inputs", {})
|
||
if isinstance(inp, dict) and "shift" in inp:
|
||
vv = inp["shift"]
|
||
if not isinstance(vv, (list, tuple)):
|
||
v2 = try_float(vv)
|
||
if v2 is not None:
|
||
return v2
|
||
w = nd.get("widgets_values")
|
||
if isinstance(w, list) and len(w) >= 1:
|
||
v2 = try_float(w[0])
|
||
if v2 is not None:
|
||
return v2
|
||
|
||
# Lists / nested
|
||
if isinstance(obj, list):
|
||
for it in obj:
|
||
v = search_container(it)
|
||
if v is not None:
|
||
return v
|
||
return None
|
||
|
||
# 2) Look in provided prompt_json
|
||
v = search_container(prompt_json)
|
||
if v is not None:
|
||
return v
|
||
|
||
# Also look in common meta fields that can hold the full workflow/prompt
|
||
for key in ("workflow", "prompt", "extra_pnginfo"):
|
||
candidate = meta.get(key)
|
||
cand_obj = safe_load(candidate)
|
||
if isinstance(cand_obj, dict) or isinstance(cand_obj, list):
|
||
v = search_container(cand_obj)
|
||
if v is not None:
|
||
return v
|
||
# extra_pnginfo can nest "workflow"/"prompt" again
|
||
if isinstance(cand_obj, dict):
|
||
for subkey in ("workflow", "prompt"):
|
||
sub = safe_load(cand_obj.get(subkey))
|
||
if isinstance(sub, dict) or isinstance(sub, list):
|
||
v = search_container(sub)
|
||
if v is not None:
|
||
return v
|
||
|
||
# default
|
||
return 5.0
|
||
|
||
@classmethod
|
||
def IS_CHANGED(s, latent):
|
||
# Fix path lookup (add "latents/" prefix back)
|
||
p = folder_paths.get_annotated_filepath(f"latents/{latent}")
|
||
m = hashlib.sha256()
|
||
with open(p, "rb") as f:
|
||
m.update(f.read())
|
||
side = p.replace(".latent", ".cond.pt")
|
||
if os.path.exists(side):
|
||
with open(side, "rb") as f:
|
||
m.update(f.read())
|
||
return m.digest().hex()
|
||
|
||
@classmethod
|
||
def VALIDATE_INPUTS(s, latent):
|
||
check_path = latent if latent.startswith("latents/") else f"latents/{latent}"
|
||
try:
|
||
folder_paths.get_annotated_filepath(check_path)
|
||
except Exception:
|
||
return f"Invalid latent file: {latent}"
|
||
return True
|
||
|
||
def load(self, latent, run_folder=False, refresh_before_run=False):
|
||
# Ensure we prepend "latents/" if missing, but don't duplicate it
|
||
if not latent.startswith("latents/"):
|
||
latent_path = folder_paths.get_annotated_filepath(f"latents/{latent}")
|
||
else:
|
||
latent_path = folder_paths.get_annotated_filepath(latent)
|
||
|
||
sample_dict, meta, _ = _load_latent_file(latent_path)
|
||
t = sample_dict["samples"]
|
||
|
||
if isinstance(t, torch.Tensor) and t.dim() >= 4 and t.size(0) > 1:
|
||
samples = {"samples": t[0:1].contiguous()}
|
||
elif isinstance(t, torch.Tensor) and t.dim() >= 4 and t.size(0) == 1:
|
||
samples = {"samples": t}
|
||
else:
|
||
samples = {"samples": t.unsqueeze(0)}
|
||
|
||
prompt_json = _safe_json_loads(meta.get("prompt"))
|
||
_pos_text, _neg_text, steps, cfg, sampler_name, scheduler, end_at_step = \
|
||
_extract_params_from_prompt_json(prompt_json or {}, meta)
|
||
|
||
# SD3 shift (not in KSamplerAdvanced, but we want it)
|
||
shift = self._extract_sd3_shift(meta, prompt_json)
|
||
|
||
sampler_name = self._coerce_enum(sampler_name, getattr(self.__class__, "_SAMPLERS_ENUM", ()))
|
||
scheduler = self._coerce_enum(scheduler, getattr(self.__class__, "_SCHEDULERS_ENUM", ()))
|
||
|
||
def normalize_folder(part: str) -> str:
|
||
part = part.replace("\\", "/").strip("/")
|
||
if not part:
|
||
return ""
|
||
segments = [seg for seg in part.split("/") if seg]
|
||
if segments and segments[0].lower() == "latents":
|
||
segments = segments[1:]
|
||
return "/".join(segments)
|
||
|
||
folder_part = normalize_folder(os.path.dirname(latent))
|
||
base_name = os.path.basename(latent_path)
|
||
clean_stem = self._strip_counter(base_name)
|
||
prefix = f"{folder_part}/{clean_stem}" if folder_part else clean_stem
|
||
|
||
# Raw workflow JSON embedded when this latent was saved (empty string if none).
|
||
source_workflow = meta.get("workflow") or ""
|
||
|
||
positive_conditioning, negative_conditioning, sidecar = _load_i2v_conditioning_sidecar(latent_path)
|
||
trim_latent = _coerce_trim_latent(sidecar.get("trim_latent", meta.get("trim_latent", 0)))
|
||
|
||
return (
|
||
float(shift),
|
||
positive_conditioning,
|
||
negative_conditioning,
|
||
samples,
|
||
int(steps),
|
||
float(cfg),
|
||
sampler_name,
|
||
scheduler,
|
||
int(end_at_step),
|
||
prefix,
|
||
trim_latent,
|
||
source_workflow,
|
||
)
|
||
|
||
# ---------- Load multiple latents from a folder (conditioning + sampler params + optional trim_latent) ----------
|
||
class LoadLatents_FromFolder_I2V_MXD:
|
||
"""
|
||
Default folder/batch loader: same outputs as LoadLatent_I2V_MXD, one set per latent
|
||
found in the folder.
|
||
"""
|
||
DESCRIPTION = """Load all latents in a folder with conditioning and sampler settings."""
|
||
TITLE = "Load Latent Batch MXD"
|
||
CATEGORY = "MXD/Latents"
|
||
FUNCTION = "load_batch_i2v"
|
||
|
||
RETURN_TYPES = (
|
||
"FLOAT", # shift
|
||
"CONDITIONING", # positive conditioning
|
||
"CONDITIONING", # negative conditioning
|
||
"LATENT",
|
||
"INT",
|
||
"FLOAT",
|
||
"STRING", # will be replaced with sampler enum in INPUT_TYPES
|
||
"STRING", # will be replaced with scheduler enum in INPUT_TYPES
|
||
"INT",
|
||
"STRING",
|
||
"INT", # trim_latent
|
||
)
|
||
RETURN_NAMES = (
|
||
"shift",
|
||
"positive",
|
||
"negative",
|
||
"samples",
|
||
"steps",
|
||
"cfg",
|
||
"sampler_name",
|
||
"scheduler",
|
||
"end_at_step",
|
||
"filename_prefix",
|
||
"trim_latent",
|
||
)
|
||
|
||
OUTPUT_IS_LIST = (True,) * 11
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(s):
|
||
# Same folder logic as the single loader
|
||
latents_root = os.path.join(folder_paths.get_input_directory(), "latents")
|
||
os.makedirs(latents_root, exist_ok=True)
|
||
subs = [""] + _list_latent_subfolders(latents_root)
|
||
|
||
# Pull live enums from KSamplerAdvanced so sampler/scheduler wire cleanly
|
||
from nodes import KSamplerAdvanced
|
||
ks_inputs = KSamplerAdvanced.INPUT_TYPES().get("required", {})
|
||
samplers_enum = ks_inputs.get("sampler_name", ("STRING",))[0]
|
||
schedulers_enum = ks_inputs.get("scheduler", ("STRING",))[0]
|
||
|
||
s.RETURN_TYPES = (
|
||
"FLOAT", # shift
|
||
"CONDITIONING", # positive conditioning
|
||
"CONDITIONING", # negative conditioning
|
||
"LATENT",
|
||
"INT",
|
||
"FLOAT",
|
||
samplers_enum, # enum type for sampler_name
|
||
schedulers_enum, # enum type for scheduler
|
||
"INT",
|
||
"STRING",
|
||
"INT",
|
||
)
|
||
s._SAMPLERS_ENUM = samplers_enum
|
||
s._SCHEDULERS_ENUM = schedulers_enum
|
||
|
||
return {"required": {"subfolder": (subs, )}}
|
||
|
||
def _coerce_enum(self, value, enum_values):
|
||
try:
|
||
return value if (enum_values and value in enum_values) else (enum_values[0] if enum_values else value)
|
||
except Exception:
|
||
return value
|
||
|
||
def _strip_counter(self, name: str) -> str:
|
||
stem, _ = os.path.splitext(name)
|
||
m = re.match(r"^(.*?)(?:_\d{5}_)$", stem)
|
||
return m.group(1) if m else stem
|
||
|
||
def _extract_sd3_shift(self, meta: dict, prompt_json: dict | None) -> float:
|
||
def try_float(x):
|
||
try: return float(x)
|
||
except Exception: return None
|
||
|
||
if isinstance(meta, dict):
|
||
v = try_float(meta.get("shift"))
|
||
if v is not None: return v
|
||
|
||
def safe_load(x):
|
||
try: return _safe_json_loads(x) if isinstance(x, str) else x
|
||
except Exception: return None
|
||
|
||
def search_container(obj):
|
||
if isinstance(obj, dict):
|
||
if "shift" in obj:
|
||
v = try_float(obj.get("shift"))
|
||
if v is not None: return v
|
||
nodes = obj.get("nodes")
|
||
if isinstance(nodes, list):
|
||
ms_nodes = [n for n in nodes if isinstance(n, dict) and (
|
||
n.get("type") == "ModelSamplingSD3" or
|
||
n.get("class_type") == "ModelSamplingSD3" or
|
||
(isinstance(n.get("properties"), dict) and n["properties"].get("Node name for S&R") == "ModelSamplingSD3")
|
||
)]
|
||
if ms_nodes:
|
||
nd = ms_nodes[-1]
|
||
inp = nd.get("inputs")
|
||
if isinstance(inp, dict) and "shift" in inp:
|
||
vv = inp["shift"]
|
||
if not isinstance(vv, (list, tuple)):
|
||
v2 = try_float(vv)
|
||
if v2 is not None: return v2
|
||
w = nd.get("widgets_values")
|
||
if isinstance(w, list) and len(w) >= 1:
|
||
v2 = try_float(w[0])
|
||
if v2 is not None: return v2
|
||
has_ct = [v for v in obj.values() if isinstance(v, dict) and "class_type" in v]
|
||
for nd in has_ct:
|
||
if nd.get("class_type") == "ModelSamplingSD3":
|
||
inp = nd.get("inputs", {})
|
||
if isinstance(inp, dict) and "shift" in inp:
|
||
vv = inp["shift"]
|
||
if not isinstance(vv, (list, tuple)):
|
||
v2 = try_float(vv)
|
||
if v2 is not None: return v2
|
||
w = nd.get("widgets_values")
|
||
if isinstance(w, list) and len(w) >= 1:
|
||
v2 = try_float(w[0])
|
||
if v2 is not None: return v2
|
||
if isinstance(obj, list):
|
||
for it in obj:
|
||
v = search_container(it)
|
||
if v is not None: return v
|
||
return None
|
||
|
||
v = search_container(prompt_json)
|
||
if v is not None: return v
|
||
|
||
for key in ("workflow", "prompt", "extra_pnginfo"):
|
||
candidate = meta.get(key)
|
||
cand_obj = safe_load(candidate)
|
||
if isinstance(cand_obj, (dict, list)):
|
||
v = search_container(cand_obj)
|
||
if v is not None: return v
|
||
if isinstance(cand_obj, dict):
|
||
for subkey in ("workflow", "prompt"):
|
||
sub = safe_load(cand_obj.get(subkey))
|
||
if isinstance(sub, (dict, list)):
|
||
v = search_container(sub)
|
||
if v is not None: return v
|
||
|
||
return 5.0
|
||
|
||
def load_batch_i2v(self, subfolder):
|
||
latents_root = os.path.join(folder_paths.get_input_directory(), "latents")
|
||
base = os.path.join(latents_root, subfolder) if subfolder else latents_root
|
||
files = glob.glob(os.path.join(base, "**", "*.latent"), recursive=True)
|
||
files = _sort_paths_newest_first(files)
|
||
if not files:
|
||
raise RuntimeError(f"[LoadLatents_FromFolder_I2V_MXD] No .latent files found in '{base}'.")
|
||
|
||
shifts, samples_list = [], []
|
||
positives, negatives = [], []
|
||
steps_list, cfgs, samplers, schedulers, end_steps = [], [], [], [], []
|
||
filename_prefixes, trims = [], []
|
||
|
||
for path in files:
|
||
sample_dict, meta, _ = _load_latent_file(path)
|
||
t = sample_dict["samples"]
|
||
|
||
if isinstance(t, torch.Tensor) and t.dim() >= 4 and t.size(0) > 1:
|
||
slices = [t[i:i+1].contiguous() for i in range(t.size(0))]
|
||
else:
|
||
slices = [t if (isinstance(t, torch.Tensor) and t.dim() >= 4 and t.size(0) == 1)
|
||
else t.unsqueeze(0)]
|
||
|
||
prompt_json = _safe_json_loads(meta.get("prompt"))
|
||
_pos_text, _neg_text, n_steps, cfg, sampler_name, scheduler, end_at_step = \
|
||
_extract_params_from_prompt_json(prompt_json or {}, meta)
|
||
|
||
sampler_name = self._coerce_enum(sampler_name, getattr(self.__class__, "_SAMPLERS_ENUM", ()))
|
||
scheduler = self._coerce_enum(scheduler, getattr(self.__class__, "_SCHEDULERS_ENUM", ()))
|
||
shift_val = self._extract_sd3_shift(meta, prompt_json)
|
||
|
||
positive_conditioning, negative_conditioning, sidecar = _load_i2v_conditioning_sidecar(path)
|
||
trim_latent = _coerce_trim_latent(sidecar.get("trim_latent", meta.get("trim_latent", 0)))
|
||
|
||
folder_part = subfolder if subfolder else ""
|
||
clean_stem = self._strip_counter(os.path.basename(path))
|
||
prefix = os.path.join(folder_part, clean_stem) if folder_part else clean_stem
|
||
|
||
for sl in slices:
|
||
shifts.append(float(shift_val))
|
||
positives.append(positive_conditioning)
|
||
negatives.append(negative_conditioning)
|
||
samples_list.append({"samples": sl})
|
||
steps_list.append(int(n_steps))
|
||
cfgs.append(float(cfg))
|
||
samplers.append(sampler_name)
|
||
schedulers.append(scheduler)
|
||
end_steps.append(int(end_at_step))
|
||
filename_prefixes.append(prefix)
|
||
trims.append(trim_latent)
|
||
|
||
return (
|
||
shifts,
|
||
positives,
|
||
negatives,
|
||
samples_list,
|
||
steps_list,
|
||
cfgs,
|
||
samplers,
|
||
schedulers,
|
||
end_steps,
|
||
filename_prefixes,
|
||
trims,
|
||
)
|
||
|
||
# ---------- Pipe variants: bundle all the loader outputs into one wire ----------
|
||
class LoadLatent_I2V_Pipe_MXD(LoadLatent_I2V_MXD):
|
||
"""
|
||
Same loading logic as LoadLatent_I2V_MXD, but bundles every value into a single
|
||
MXD_LATENT_PIPE output so switching between the single/batch loaders is a one-wire swap.
|
||
Unpack with LatentPipeUnpack_MXD.
|
||
"""
|
||
TITLE = "Load Latent Pipe MXD"
|
||
CATEGORY = "MXD/Latents"
|
||
FUNCTION = "load_pipe"
|
||
|
||
RETURN_TYPES = ("MXD_LATENT_PIPE",)
|
||
RETURN_NAMES = ("latent_pipe",)
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(s):
|
||
inputs = LoadLatent_I2V_MXD.INPUT_TYPES.__func__(s)
|
||
s.RETURN_TYPES = ("MXD_LATENT_PIPE",)
|
||
return inputs
|
||
|
||
def load_pipe(self, latent, run_folder=False, refresh_before_run=False):
|
||
(
|
||
shift, positive, negative, samples,
|
||
steps, cfg, sampler_name, scheduler,
|
||
end_at_step, prefix, trim_latent, source_workflow,
|
||
) = self.load(latent, run_folder, refresh_before_run)
|
||
|
||
pipe = {
|
||
"shift": shift,
|
||
"positive": positive,
|
||
"negative": negative,
|
||
"samples": samples,
|
||
"steps": steps,
|
||
"cfg": cfg,
|
||
"sampler_name": sampler_name,
|
||
"scheduler": scheduler,
|
||
"end_at_step": end_at_step,
|
||
"filename_prefix": prefix,
|
||
"trim_latent": trim_latent,
|
||
"high_workflow": source_workflow,
|
||
}
|
||
return (pipe,)
|
||
|
||
|
||
class LoadLatents_FromFolder_I2V_Pipe_MXD(LoadLatents_FromFolder_I2V_MXD):
|
||
"""
|
||
Same loading logic as LoadLatents_FromFolder_I2V_MXD, but bundles every value into a
|
||
single MXD_LATENT_PIPE output per item. Unpack with LatentPipeUnpack_MXD.
|
||
"""
|
||
TITLE = "Load Latent Batch Pipe MXD"
|
||
CATEGORY = "MXD/Latents"
|
||
FUNCTION = "load_batch_pipe"
|
||
|
||
RETURN_TYPES = ("MXD_LATENT_PIPE",)
|
||
RETURN_NAMES = ("latent_pipe",)
|
||
OUTPUT_IS_LIST = (True,)
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(s):
|
||
inputs = LoadLatents_FromFolder_I2V_MXD.INPUT_TYPES.__func__(s)
|
||
s.RETURN_TYPES = ("MXD_LATENT_PIPE",)
|
||
return inputs
|
||
|
||
def load_batch_pipe(self, subfolder):
|
||
(
|
||
shifts, positives, negatives, samples_list,
|
||
steps_list, cfgs, samplers, schedulers,
|
||
end_steps, filename_prefixes, trims,
|
||
) = self.load_batch_i2v(subfolder)
|
||
|
||
pipes = []
|
||
for i in range(len(samples_list)):
|
||
pipes.append({
|
||
"shift": shifts[i],
|
||
"positive": positives[i],
|
||
"negative": negatives[i],
|
||
"samples": samples_list[i],
|
||
"steps": steps_list[i],
|
||
"cfg": cfgs[i],
|
||
"sampler_name": samplers[i],
|
||
"scheduler": schedulers[i],
|
||
"end_at_step": end_steps[i],
|
||
"filename_prefix": filename_prefixes[i],
|
||
"trim_latent": trims[i],
|
||
})
|
||
return (pipes,)
|
||
|
||
|
||
class LatentPipeUnpack_MXD:
|
||
"""
|
||
Splits an MXD_LATENT_PIPE back into shift, conditioning, samples, and sampler settings.
|
||
Works with any MXD latent pipe loader (single or batch, I2V or VACE 2.2) - missing
|
||
fields like trim_latent just fall back to a safe default.
|
||
"""
|
||
DESCRIPTION = """Split a latent pipe back into shift, positive, negative, samples, and sampler settings."""
|
||
TITLE = "Unpack Latent Pipe MXD"
|
||
CATEGORY = "MXD/Latents"
|
||
FUNCTION = "unpack"
|
||
|
||
RETURN_TYPES = (
|
||
"FLOAT", "CONDITIONING", "CONDITIONING", "LATENT",
|
||
"INT", "FLOAT", "STRING", "STRING", "INT", "STRING", "INT", "STRING",
|
||
)
|
||
RETURN_NAMES = (
|
||
"shift", "positive", "negative", "samples",
|
||
"steps", "cfg", "sampler_name", "scheduler",
|
||
"end_at_step", "filename_prefix", "trim_latent", "high_workflow",
|
||
)
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(s):
|
||
from nodes import KSamplerAdvanced
|
||
ks_inputs = KSamplerAdvanced.INPUT_TYPES().get("required", {})
|
||
samplers_enum = ks_inputs.get("sampler_name", ("STRING",))[0]
|
||
schedulers_enum = ks_inputs.get("scheduler", ("STRING",))[0]
|
||
|
||
s.RETURN_TYPES = (
|
||
"FLOAT", "CONDITIONING", "CONDITIONING", "LATENT",
|
||
"INT", "FLOAT", samplers_enum, schedulers_enum,
|
||
"INT", "STRING", "INT", "STRING",
|
||
)
|
||
s._SAMPLERS_ENUM = samplers_enum
|
||
s._SCHEDULERS_ENUM = schedulers_enum
|
||
|
||
return {"required": {"latent_pipe": ("MXD_LATENT_PIPE",)}}
|
||
|
||
def _coerce_enum(self, value, enum_values):
|
||
try:
|
||
return value if (enum_values and value in enum_values) else (enum_values[0] if enum_values else value)
|
||
except Exception:
|
||
return value
|
||
|
||
def unpack(self, latent_pipe):
|
||
sampler_name = self._coerce_enum(latent_pipe.get("sampler_name"), getattr(self.__class__, "_SAMPLERS_ENUM", ()))
|
||
scheduler = self._coerce_enum(latent_pipe.get("scheduler"), getattr(self.__class__, "_SCHEDULERS_ENUM", ()))
|
||
|
||
return (
|
||
latent_pipe.get("shift", 0.0),
|
||
latent_pipe.get("positive", []),
|
||
latent_pipe.get("negative", []),
|
||
latent_pipe.get("samples"),
|
||
latent_pipe.get("steps", 0),
|
||
latent_pipe.get("cfg", 0.0),
|
||
sampler_name,
|
||
scheduler,
|
||
latent_pipe.get("end_at_step", 0),
|
||
latent_pipe.get("filename_prefix", ""),
|
||
latent_pipe.get("trim_latent", 0),
|
||
latent_pipe.get("high_workflow", ""),
|
||
)
|
||
|
||
# ---------- Empty latent image generator (for video nodes) ----------
|
||
class Wan2_2EmptyLatentImageMXD:
|
||
"""
|
||
Utility node for WAN 2.2 workflows.
|
||
Generates an empty latent tensor at common video-friendly resolutions.
|
||
"""
|
||
|
||
DESCRIPTION = """Create an empty WAN 2.2 latent at a preset resolution."""
|
||
TITLE = "WAN2.2 Empty Latent Image"
|
||
CATEGORY = "WAN2.2/Latent"
|
||
|
||
RESOLUTIONS = {
|
||
"— 720p —": None,
|
||
"Widescreen (16:9) 1280×720": (1280, 720),
|
||
|
||
"— 480p —": None,
|
||
"Widescreen (16:9) 832×480": (832, 480),
|
||
"Square (1:1) 624×624": (624, 624),
|
||
}
|
||
|
||
RETURN_TYPES = ("LATENT",)
|
||
FUNCTION = "generate"
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
options = list(cls.RESOLUTIONS.keys())
|
||
return {
|
||
"required": {
|
||
"resolution": (
|
||
options,
|
||
{"default": "Square (1:1) 960×960", "tooltip": "Select target resolution preset."}
|
||
),
|
||
"vertical": (
|
||
"BOOLEAN",
|
||
{"default": False, "label_on": "Vertical", "label_off": "Landscape",
|
||
"tooltip": "Swap width/height for vertical orientation."}
|
||
),
|
||
"batch_size": (
|
||
"INT",
|
||
{"default": 1, "min": 1, "max": 4096, "tooltip": "Number of latents to generate."}
|
||
),
|
||
}
|
||
}
|
||
|
||
def generate(self, resolution, vertical, batch_size):
|
||
size = self.RESOLUTIONS.get(resolution)
|
||
if size is None:
|
||
raise ValueError(f"'{resolution}' is a header or invalid option.")
|
||
|
||
w, h = size
|
||
if vertical:
|
||
w, h = h, w
|
||
|
||
# Safety: ensure divisible by 8
|
||
if (w % 8) or (h % 8):
|
||
raise ValueError(f"Resolution must be divisible by 8. Got {w}x{h}.")
|
||
|
||
# WAN video length always t=1
|
||
t = 1
|
||
|
||
latent = torch.zeros(
|
||
[batch_size, 16, t, h // 8, w // 8],
|
||
device=comfy.model_management.intermediate_device()
|
||
)
|
||
return ({"samples": latent},)
|
||
|
||
# ---------- Empty latent video generator with presets (for video nodes) ----------
|
||
class wan22EmptyHunyuanLatentVideoMXD:
|
||
"""
|
||
Exactly like core EmptyHunyuanLatentVideo, but width/height are replaced
|
||
with valid WAN 2.2 resolution presets and a vertical toggle.
|
||
"""
|
||
|
||
RETURN_TYPES = ("LATENT",)
|
||
FUNCTION = "generate"
|
||
CATEGORY = "latent/video"
|
||
|
||
# ✅ Cleaned, WAN 2.2–accurate presets
|
||
RESOLUTIONS = {
|
||
"— 720p —": None,
|
||
"Widescreen (16:9) 1280×720": (1280, 720),
|
||
"Square (1:1) 1024×1024": (1024, 1024),
|
||
|
||
"— 480p —": None,
|
||
"Widescreen (16:9) 832×480": (832, 480),
|
||
"Square (1:1) 624×624": (624, 624),
|
||
}
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
options = list(cls.RESOLUTIONS.keys())
|
||
return {
|
||
"required": {
|
||
"resolution": (
|
||
options,
|
||
{"default": "Widescreen (16:9) 832×480"}
|
||
),
|
||
"vertical": (
|
||
"BOOLEAN",
|
||
{"default": False, "label_on": "Vertical", "label_off": "Landscape"}
|
||
),
|
||
"length": (
|
||
"INT",
|
||
{"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}
|
||
),
|
||
"batch_size": (
|
||
"INT",
|
||
{"default": 1, "min": 1, "max": 4096}
|
||
),
|
||
}
|
||
}
|
||
|
||
def generate(self, resolution, vertical, length, batch_size):
|
||
size = self.RESOLUTIONS.get(resolution)
|
||
if size is None:
|
||
raise ValueError(f"'{resolution}' is not a selectable resolution.")
|
||
w, h = size
|
||
if vertical:
|
||
w, h = h, w
|
||
|
||
# identical to core behavior:
|
||
t = ((length - 1) // 4) + 1
|
||
latent = torch.zeros(
|
||
[batch_size, 16, t, h // 8, w // 8],
|
||
device=comfy.model_management.intermediate_device()
|
||
)
|
||
return ({"samples": latent},)
|
||
# ---------- WAN 2.2 Image to Video (no scaling; expects pre-sized input) ----------
|
||
if HAVE_COMFY_API:
|
||
class Wan22ImageToVideoMXD(io.ComfyNode):
|
||
@classmethod
|
||
def define_schema(cls):
|
||
return io.Schema(
|
||
node_id="Wan22ImageToVideoMXD",
|
||
display_name="WAN 2.2 Image to Video MXD",
|
||
category="conditioning/video_models",
|
||
description="WAN 2.2 image to video without scaling or CLIP vision.",
|
||
inputs=[
|
||
io.Conditioning.Input("positive"),
|
||
io.Conditioning.Input("negative"),
|
||
io.Vae.Input("vae"),
|
||
io.Int.Input("length", default=81, min=1, max=16384, step=4),
|
||
io.Int.Input("batch_size", default=1, min=1, max=4096),
|
||
io.Image.Input("start_image", optional=False),
|
||
],
|
||
outputs=[
|
||
io.Conditioning.Output(display_name="positive"),
|
||
io.Conditioning.Output(display_name="negative"),
|
||
io.Latent.Output(display_name="latent"),
|
||
],
|
||
)
|
||
|
||
@classmethod
|
||
def execute(cls, positive, negative, vae, length, batch_size, start_image) -> io.NodeOutput:
|
||
if start_image is None:
|
||
raise ValueError("start_image must be provided (already pre-sized).")
|
||
|
||
frames_in, ih, iw, ch = start_image.shape
|
||
frames_used = min(frames_in, length)
|
||
t = ((length - 1) // 4) + 1
|
||
|
||
latent = torch.zeros(
|
||
[batch_size, 16, t, ih // 8, iw // 8],
|
||
device=comfy.model_management.intermediate_device()
|
||
)
|
||
|
||
# create placeholder image tensor
|
||
image = torch.ones(
|
||
(length, ih, iw, ch),
|
||
device=start_image.device,
|
||
dtype=start_image.dtype
|
||
) * 0.5
|
||
image[:frames_used] = start_image[:frames_used]
|
||
|
||
# encode using VAE
|
||
concat_latent_image = vae.encode(image[:, :, :, :3])
|
||
|
||
# mask zeros out the frames used
|
||
mask = torch.ones(
|
||
(1, 1, t, concat_latent_image.shape[-2], concat_latent_image.shape[-1]),
|
||
device=image.device,
|
||
dtype=image.dtype
|
||
)
|
||
mask[:, :, :((frames_used - 1) // 4) + 1] = 0.0
|
||
|
||
positive = node_helpers.conditioning_set_values(
|
||
positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask}
|
||
)
|
||
negative = node_helpers.conditioning_set_values(
|
||
negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask}
|
||
)
|
||
|
||
out_latent = {"samples": latent}
|
||
return io.NodeOutput(positive, negative, out_latent)
|
||
|
||
# ---- Canonical WAN 2.2 buckets ----
|
||
BUCKETS_480 = [(832,480), (480,832), (624,624)] # 16:9, 9:16, 1:1
|
||
BUCKETS_720 = [(1280,720), (720,1280), (1024,1024)] # 16:9, 9:16, 1:1
|
||
SQUARE_TOL = 0.03 # exact-ish square passthrough tolerance
|
||
AUTO_SQUARE_MAX_AR = 1.25 # Auto may crop to square when the source is within 25% of 1:1.
|
||
|
||
def _ar(w, h):
|
||
return w / max(1, h)
|
||
|
||
def _safe_hw(w, h):
|
||
w = max(16, min(w, nodes.MAX_RESOLUTION))
|
||
h = max(16, min(h, nodes.MAX_RESOLUTION))
|
||
return w, h
|
||
|
||
def _floor16(x):
|
||
x = int(x) // 16 * 16
|
||
return max(16, x)
|
||
|
||
def _ceil16(x):
|
||
x = (int(x) + 15) // 16 * 16
|
||
return max(16, x)
|
||
|
||
def _is_squareish(w, h, tol=SQUARE_TOL):
|
||
r = _ar(w, h)
|
||
return abs(r - 1.0) <= tol
|
||
|
||
def _is_auto_square_candidate(w, h):
|
||
r = _ar(w, h)
|
||
return max(r, 1.0 / max(r, 1e-9)) <= AUTO_SQUARE_MAX_AR
|
||
|
||
def _wan22_tier_from_area(iw, ih):
|
||
area = iw * ih
|
||
area_480 = 832 * 480
|
||
area_720 = 1280 * 720
|
||
return "480p" if abs(area - area_480) / area_480 <= abs(area - area_720) / area_720 else "720p"
|
||
|
||
def _wan22_square_bucket(tier, iw=None, ih=None):
|
||
if tier == "720p":
|
||
return (1024, 1024)
|
||
if tier == "480p":
|
||
return (624, 624)
|
||
return (1024, 1024) if _wan22_tier_from_area(iw, ih) == "720p" else (624, 624)
|
||
|
||
def _wan22_oriented_bucket(tier, orientation, iw=None, ih=None):
|
||
if tier == "Auto":
|
||
tier = _wan22_tier_from_area(iw, ih)
|
||
if orientation == "Tall":
|
||
return (480, 832) if tier == "480p" else (720, 1280)
|
||
if orientation == "Wide":
|
||
return (832, 480) if tier == "480p" else (1280, 720)
|
||
return _wan22_square_bucket(tier, iw, ih)
|
||
|
||
def _closest_bucket(img_w, img_h, bucket_list, cover=False):
|
||
"""
|
||
Pick the best (bw,bh) from bucket_list for this image.
|
||
Uses scale closeness + AR diff to rank.
|
||
"""
|
||
in_ar = _ar(img_w, img_h)
|
||
best, best_key = None, (float("inf"), 0.0)
|
||
for bw, bh in bucket_list:
|
||
s = max(bw/img_w, bh/img_h) if cover else min(bw/img_w, bh/img_h)
|
||
ar_diff = abs(_ar(bw, bh) - in_ar)
|
||
key = (abs(1.0 - s), ar_diff)
|
||
if key < best_key:
|
||
best_key, best = key, (bw, bh)
|
||
return best
|
||
|
||
def _resize_then_center_crop(img, out_w, out_h):
|
||
"""
|
||
Resize to cover target (ensures >= target on both sides after ceil16),
|
||
then center-crop. No padding.
|
||
"""
|
||
t, ih, iw, c = img.shape
|
||
s = max(out_w / iw, out_h / ih)
|
||
tw = _ceil16(iw * s)
|
||
th = _ceil16(ih * s)
|
||
tmp = comfy.utils.common_upscale(img.movedim(-1, 1), tw, th, "bilinear", "center").movedim(1, -1)
|
||
y0 = max(0, (th - out_h) // 2)
|
||
x0 = max(0, (tw - out_w) // 2)
|
||
return tmp[:, y0:y0+out_h, x0:x0+out_w, :]
|
||
|
||
def _resize_fit_inside(img, out_w, out_h):
|
||
"""
|
||
Resize to fit inside target (ensures <= target on both sides via floor16),
|
||
and return the resized tensor only. No padding.
|
||
"""
|
||
t, ih, iw, c = img.shape
|
||
s = min(out_w / iw, out_h / ih)
|
||
tw = _floor16(iw * s)
|
||
th = _floor16(ih * s)
|
||
tw, th = _safe_hw(tw, th)
|
||
resized = comfy.utils.common_upscale(img.movedim(-1, 1), tw, th, "bilinear", "center").movedim(1, -1)
|
||
return resized, tw, th
|
||
|
||
def _validate_image_batch_4d(image, node_name, input_name):
|
||
if image is None:
|
||
raise ValueError(f"[{node_name}] '{input_name}' is required.")
|
||
if not torch.is_tensor(image):
|
||
raise TypeError(f"[{node_name}] '{input_name}' must be an IMAGE torch tensor, got {type(image).__name__}.")
|
||
if image.ndim != 4:
|
||
raise ValueError(f"[{node_name}] '{input_name}' must have shape [T,H,W,C], got {tuple(image.shape)}.")
|
||
if image.shape[0] <= 0:
|
||
raise ValueError(f"[{node_name}] '{input_name}' contains zero images/frames.")
|
||
if image.shape[1] <= 0 or image.shape[2] <= 0 or image.shape[3] <= 0:
|
||
raise ValueError(f"[{node_name}] '{input_name}' has invalid dimensions {tuple(image.shape)}.")
|
||
return image
|
||
|
||
def _resize_to_explicit_resolution(img, out_w, out_h, match_mode="crop_to_match"):
|
||
"""
|
||
Resize IMAGE batch to an explicit resolution.
|
||
- crop_to_match: cover + center crop (exact output)
|
||
- fit_inside_only: preserve AR, no crop (may be smaller)
|
||
- stretch_exact: force exact output (distorts AR)
|
||
"""
|
||
out_w = int(out_w)
|
||
out_h = int(out_h)
|
||
if out_w <= 0 or out_h <= 0:
|
||
raise ValueError(f"Invalid target resolution {out_w}x{out_h}.")
|
||
|
||
if match_mode == "crop_to_match":
|
||
return _resize_then_center_crop(img, out_w, out_h)
|
||
|
||
if match_mode == "fit_inside_only":
|
||
_, ih, iw, _ = img.shape
|
||
s = min(out_w / max(1, iw), out_h / max(1, ih))
|
||
tw = max(1, min(out_w, int(iw * s)))
|
||
th = max(1, min(out_h, int(ih * s)))
|
||
return comfy.utils.common_upscale(img.movedim(-1, 1), tw, th, "bilinear", "center").movedim(1, -1)
|
||
|
||
if match_mode == "stretch_exact":
|
||
return comfy.utils.common_upscale(img.movedim(-1, 1), out_w, out_h, "bilinear", "center").movedim(1, -1)
|
||
|
||
raise ValueError(
|
||
f"Invalid match_mode '{match_mode}'. Expected one of: crop_to_match, fit_inside_only, stretch_exact."
|
||
)
|
||
|
||
# ---------- WAN22_I2V_Image_Scaler_MXD ----------
|
||
# Adds a new “Safe Auto” mode for video extend workflows.
|
||
# Normal modes (Auto / 480p / 720p) behave exactly as before.
|
||
# “Safe Auto” adds passthrough + strict checks to prevent failures on WAN 2.2 extend.
|
||
|
||
_WAN22_VALID_RES = {
|
||
(832, 480), (480, 832),
|
||
(1280, 720), (720, 1280),
|
||
(624, 624), (1024, 1024),
|
||
}
|
||
|
||
def _wan22_is_valid_dim(w, h):
|
||
return (w, h) in _WAN22_VALID_RES
|
||
|
||
|
||
def _wan22_pick_bucket(iw, ih, tier, crop_to_fit, aspect_mode="Auto"):
|
||
if tier == "Safe Auto":
|
||
tier = "Auto"
|
||
|
||
if aspect_mode in ("Tall", "Wide", "Square"):
|
||
return _wan22_oriented_bucket(tier, aspect_mode, iw, ih)
|
||
|
||
is_squareish = _is_squareish(iw, ih)
|
||
is_landscape = iw >= ih
|
||
|
||
# --- Square handling ---
|
||
if is_squareish or (crop_to_fit and _is_auto_square_candidate(iw, ih)):
|
||
return _wan22_square_bucket(tier, iw, ih)
|
||
|
||
# --- Explicit tiers ---
|
||
if tier == "480p":
|
||
return _closest_bucket(iw, ih, [(832, 480)] if is_landscape else [(480, 832)], cover=crop_to_fit)
|
||
if tier == "720p":
|
||
return _closest_bucket(iw, ih, [(1280, 720)] if is_landscape else [(720, 1280)], cover=crop_to_fit)
|
||
|
||
# --- Auto tier logic ---
|
||
buckets_480 = [(832, 480)] if is_landscape else [(480, 832)]
|
||
buckets_720 = [(1280, 720)] if is_landscape else [(720, 1280)]
|
||
iw_ih = iw * ih
|
||
area_480, area_720 = 832 * 480, 1280 * 720
|
||
scale_to_480 = abs(iw_ih - area_480) / area_480
|
||
scale_to_720 = abs(iw_ih - area_720) / area_720
|
||
|
||
# prefer minimal scaling
|
||
if iw <= 832 and ih <= 480:
|
||
return _closest_bucket(iw, ih, buckets_480, cover=crop_to_fit)
|
||
return _closest_bucket(iw, ih, buckets_480 if scale_to_480 <= scale_to_720 else buckets_720, cover=crop_to_fit)
|
||
|
||
|
||
def _wan22_scale_image_core(image, tier="Auto", crop_to_fit=False, aspect_mode="Auto"):
|
||
"""
|
||
Shared WAN 2.2 scaler core.
|
||
Returns (scaled_image, out_w, out_h, did_passthrough).
|
||
"""
|
||
_, ih, iw, _ = image.shape
|
||
|
||
# --- Safe Auto logic ---
|
||
if tier == "Safe Auto":
|
||
# passthrough if already WAN-safe
|
||
if _wan22_is_valid_dim(iw, ih):
|
||
return image, iw, ih, True
|
||
|
||
area = iw * ih
|
||
area_480, area_720 = 832 * 480, 1280 * 720
|
||
min_area, max_area = int(area_480 * 0.5), int(area_720 * 1.8)
|
||
|
||
if area < min_area or area > max_area:
|
||
size_label = "small" if area < min_area else "large"
|
||
raise ValueError(
|
||
f"[WAN22_I2V_Image_Scaler_MXD] Input resolution {iw}x{ih} is too {size_label} for WAN 2.2 video buckets.\n"
|
||
"WAN 2.2 works best around:\n"
|
||
" - 480p tier ~= 832x480 (or 480x832)\n"
|
||
" - 720p tier ~= 1280x720 (or 720x1280)\n"
|
||
" - Squares: 624x624 or 1024x1024\n\n"
|
||
"Please use a source closer to 480p/720p, or first process it "
|
||
"through your WAN 2.2 workflow. This ensures extend runs without mismatch."
|
||
)
|
||
# fallback to Auto scaling
|
||
tier = "Auto"
|
||
|
||
# --- Normal path (Auto / 480p / 720p) ---
|
||
bw, bh = _wan22_pick_bucket(iw, ih, tier, crop_to_fit, aspect_mode=aspect_mode)
|
||
if crop_to_fit:
|
||
bw, bh = _safe_hw(_ceil16(bw), _ceil16(bh))
|
||
out = _resize_then_center_crop(image, bw, bh)
|
||
else:
|
||
bw, bh = _safe_hw(_floor16(bw), _floor16(bh))
|
||
out, _, _ = _resize_fit_inside(image, bw, bh)
|
||
|
||
return out, int(out.shape[2]), int(out.shape[1]), False
|
||
|
||
|
||
def _resample_video_frames_to_fps(frames, in_fps, out_fps):
|
||
"""
|
||
Resample a frame sequence to a target FPS using nearest-frame selection.
|
||
Preserves clip duration approximately by dropping/duplicating frames,
|
||
instead of only changing FPS metadata (which changes playback speed).
|
||
Returns (frames_out, fps_out, changed).
|
||
"""
|
||
if frames is None or frames.ndim != 4:
|
||
raise ValueError("Expected frame tensor with shape [T,H,W,C].")
|
||
|
||
if in_fps is None:
|
||
raise ValueError("Input video FPS is missing; cannot force FPS safely.")
|
||
|
||
in_fps = float(in_fps)
|
||
out_fps = float(out_fps)
|
||
if in_fps <= 0:
|
||
raise ValueError(f"Invalid input FPS: {in_fps}")
|
||
if out_fps <= 0:
|
||
raise ValueError(f"Invalid target FPS: {out_fps}")
|
||
|
||
if frames.shape[0] <= 1:
|
||
return frames, float(out_fps), False
|
||
|
||
if abs(in_fps - out_fps) < 1e-6:
|
||
return frames, float(out_fps), False
|
||
|
||
n_in = int(frames.shape[0])
|
||
# Match the first/last frame span, then pick nearest frames on that timeline.
|
||
n_out = max(1, int(round(((n_in - 1) * out_fps) / in_fps)) + 1)
|
||
if n_out == n_in:
|
||
# Frame count may stay the same for near-equal FPS; metadata still becomes exact.
|
||
return frames, float(out_fps), False
|
||
|
||
idx = torch.linspace(0, n_in - 1, steps=n_out, device=frames.device)
|
||
idx = idx.round().to(dtype=torch.long)
|
||
out = frames.index_select(0, idx)
|
||
return out, float(out_fps), True
|
||
|
||
|
||
def _select_frames_start_end(frames, count=1, offset=1, mode="end"):
|
||
total = int(frames.shape[0])
|
||
if total <= 0:
|
||
raise ValueError("No frames available for selection.")
|
||
|
||
# Clamp offset and count
|
||
offset = max(1, min(offset, total))
|
||
count = max(1, min(count, total - offset + 1))
|
||
|
||
if mode == "start":
|
||
start_idx = offset - 1
|
||
end_idx = start_idx + count
|
||
selected = frames[start_idx:end_idx].clone()
|
||
elif mode == "end":
|
||
start_idx = max(0, total - offset - count + 1)
|
||
end_idx = start_idx + count
|
||
selected = frames[start_idx:end_idx].clone()
|
||
else:
|
||
raise ValueError(f"Invalid mode '{mode}'. Expected 'start' or 'end'.")
|
||
|
||
return selected
|
||
|
||
|
||
class WAN22_I2V_Image_Scaler_MXD:
|
||
"""
|
||
MXD Image Scaler for WAN 2.2 (NO PADDING)
|
||
- Modes: Auto / 480p / 720p (legacy "Safe Auto" still accepted)
|
||
- Fit (no pad): proportional resize ≤ target; returns resized dims.
|
||
- Crop (no pad): resize-to-cover then center-crop to exact target.
|
||
- Square handling:
|
||
* Auto & 480p: ~square → 624×624
|
||
* 720p: ~square -> 1024x1024
|
||
- “Safe Auto”:
|
||
* If input is already a valid WAN 2.2 bucket, passthrough.
|
||
* If input is far outside 480p–720p range, error early.
|
||
* Otherwise, same logic as Auto.
|
||
* Perfect for video-extend workflows.
|
||
"""
|
||
|
||
TITLE = "Image Bucket Scaler MXD (No Pad)"
|
||
CATEGORY = "image/processing"
|
||
RETURN_TYPES = ("IMAGE",)
|
||
FUNCTION = "scale"
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"image": ("IMAGE",),
|
||
"tier": (["Auto", "480p", "720p"], {"default": "Auto"}),
|
||
"crop_to_fit": ("BOOLEAN", {
|
||
"default": True,
|
||
"label_on": "Perfect Fit (Crops Edges)",
|
||
"label_off": "Closest Fit (No Crop)"
|
||
}),
|
||
"aspect_mode": (["Auto", "Tall", "Wide", "Square"], {
|
||
"default": "Auto",
|
||
"tooltip": "Auto picks wide/tall/square from the source. Use Square/Tall/Wide to force the target bucket shape."
|
||
}),
|
||
}
|
||
}
|
||
|
||
# -----------------------------
|
||
# Internal helpers
|
||
# -----------------------------
|
||
def _pick_bucket(self, iw, ih, tier, crop_to_fit):
|
||
is_squareish = _is_squareish(iw, ih)
|
||
is_landscape = iw >= ih
|
||
|
||
# --- Square handling ---
|
||
if is_squareish:
|
||
if tier == "720p":
|
||
return (1024, 1024)
|
||
else:
|
||
return (624, 624)
|
||
|
||
# --- Explicit tiers ---
|
||
if tier == "480p":
|
||
return _closest_bucket(iw, ih, [(832, 480)] if is_landscape else [(480, 832)], cover=crop_to_fit)
|
||
if tier == "720p":
|
||
return _closest_bucket(iw, ih, [(1280, 720)] if is_landscape else [(720, 1280)], cover=crop_to_fit)
|
||
|
||
# --- Auto tier logic ---
|
||
buckets_480 = [(832, 480)] if is_landscape else [(480, 832)]
|
||
buckets_720 = [(1280, 720)] if is_landscape else [(720, 1280)]
|
||
iw_ih = iw * ih
|
||
area_480, area_720 = 832 * 480, 1280 * 720
|
||
scale_to_480 = abs(iw_ih - area_480) / area_480
|
||
scale_to_720 = abs(iw_ih - area_720) / area_720
|
||
|
||
# prefer minimal scaling
|
||
if iw <= 832 and ih <= 480:
|
||
return _closest_bucket(iw, ih, buckets_480, cover=crop_to_fit)
|
||
return _closest_bucket(iw, ih, buckets_480 if scale_to_480 <= scale_to_720 else buckets_720, cover=crop_to_fit)
|
||
|
||
# -----------------------------
|
||
# Main function
|
||
# -----------------------------
|
||
def scale(self, image, tier="Auto", crop_to_fit=False, aspect_mode="Auto"):
|
||
# Keep legacy "Safe Auto" values from old workflows working, but expose only one Auto in UI.
|
||
internal_tier = "Safe Auto" if tier == "Auto" else tier
|
||
out, _, _, _ = _wan22_scale_image_core(
|
||
image,
|
||
tier=internal_tier,
|
||
crop_to_fit=crop_to_fit,
|
||
aspect_mode=aspect_mode,
|
||
)
|
||
return (out,)
|
||
|
||
_, ih, iw, _ = image.shape
|
||
|
||
# --- Safe Auto logic ---
|
||
if tier == "Safe Auto":
|
||
# passthrough if already WAN-safe
|
||
if _wan22_is_valid_dim(iw, ih):
|
||
return (image,)
|
||
|
||
area = iw * ih
|
||
area_480, area_720 = 832 * 480, 1280 * 720
|
||
min_area, max_area = int(area_480 * 0.5), int(area_720 * 1.8)
|
||
|
||
if area < min_area or area > max_area:
|
||
size_label = "small" if area < min_area else "large"
|
||
raise ValueError(
|
||
f"[WAN22_I2V_Image_Scaler_MXD] Input resolution {iw}x{ih} is too {size_label} for WAN 2.2 video buckets.\n"
|
||
"WAN 2.2 works best around:\n"
|
||
" • 480p tier ≈ 832×480 (or 480×832)\n"
|
||
" • 720p tier ≈ 1280×720 (or 720×1280)\n"
|
||
" • Squares: 624×624 or 1024×1024\n\n"
|
||
"Please use a source closer to 480p/720p, or first process it "
|
||
"through your WAN 2.2 workflow. This ensures extend runs without mismatch."
|
||
)
|
||
# fallback to Auto scaling
|
||
tier = "Auto"
|
||
|
||
# --- Normal path (Auto / 480p / 720p) ---
|
||
bw, bh = self._pick_bucket(iw, ih, tier, crop_to_fit)
|
||
is_squareish = _is_squareish(iw, ih)
|
||
|
||
if is_squareish:
|
||
crop_to_fit = False
|
||
|
||
if crop_to_fit:
|
||
bw, bh = _safe_hw(_ceil16(bw), _ceil16(bh))
|
||
out = _resize_then_center_crop(image, bw, bh)
|
||
else:
|
||
bw, bh = _safe_hw(_floor16(bw), _floor16(bh))
|
||
out, _, _ = _resize_fit_inside(image, bw, bh)
|
||
|
||
return (out,)
|
||
|
||
class WAN22_I2V_Match_Resolution_MXD:
|
||
"""
|
||
Match a second image (or image batch) to a reference image resolution for WAN 2.2
|
||
first/last-frame workflows.
|
||
"""
|
||
TITLE = "WAN 2.2 I2V Match Resolution"
|
||
CATEGORY = "image/processing"
|
||
RETURN_TYPES = ("IMAGE",)
|
||
RETURN_NAMES = ("matched_image",)
|
||
FUNCTION = "match_resolution"
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"reference_image": ("IMAGE", {
|
||
"tooltip": "Reference size source (usually the first image after WAN bucket scaling)."
|
||
}),
|
||
"image_to_match": ("IMAGE", {
|
||
"tooltip": "Image or batch to resize using the reference image resolution."
|
||
}),
|
||
"match_mode": (["crop_to_match", "fit_inside_only", "stretch_exact"], {
|
||
"default": "crop_to_match",
|
||
"tooltip": "crop_to_match = exact size via cover+center crop; fit_inside_only = no crop, may be smaller; stretch_exact = exact size with distortion."
|
||
}),
|
||
"enforce_wan_bucket": ("BOOLEAN", {
|
||
"default": False,
|
||
"label_on": "Validate WAN Bucket",
|
||
"label_off": "No WAN Validation",
|
||
"tooltip": "If enabled, reference_image must already be a WAN 2.2 bucket size."
|
||
}),
|
||
}
|
||
}
|
||
|
||
def match_resolution(self, reference_image, image_to_match, match_mode="crop_to_match", enforce_wan_bucket=False):
|
||
node_name = "WAN22_I2V_Match_Resolution_MXD"
|
||
reference_image = _validate_image_batch_4d(reference_image, node_name, "reference_image")
|
||
image_to_match = _validate_image_batch_4d(image_to_match, node_name, "image_to_match")
|
||
|
||
_, ref_h, ref_w, _ = reference_image.shape
|
||
|
||
if enforce_wan_bucket and not _wan22_is_valid_dim(ref_w, ref_h):
|
||
raise ValueError(
|
||
f"[{node_name}] Reference image resolution {ref_w}x{ref_h} is not a valid WAN 2.2 bucket.\n"
|
||
"Valid WAN 2.2 buckets are:\n"
|
||
" - 832x480 / 480x832\n"
|
||
" - 1280x720 / 720x1280\n"
|
||
" - 624x624 / 1024x1024\n\n"
|
||
"Recommended workflow:\n"
|
||
" 1. Scale the first image with 'Image Scaler Wan 2.2 I2V MXD'\n"
|
||
" 2. Use this node to match the second image to the scaled first image"
|
||
)
|
||
|
||
matched = _resize_to_explicit_resolution(
|
||
image_to_match,
|
||
out_w=ref_w,
|
||
out_h=ref_h,
|
||
match_mode=match_mode,
|
||
)
|
||
return (matched,)
|
||
|
||
# ---------- MXD Frames Select Start/End (from start or end of sequence) ----------
|
||
class Frames_Select_StartEnd_MXD:
|
||
def __init__(self):
|
||
pass
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"frames": ("IMAGE",),
|
||
"count": ("INT", {
|
||
"default": 1,
|
||
"min": 1,
|
||
"max": 10000,
|
||
"tooltip": "Number of frames to select"
|
||
}),
|
||
"offset": ("INT", {
|
||
"default": 1,
|
||
"min": 1,
|
||
"max": 10000,
|
||
"tooltip": "How far into the video to start selection (from start or end)"
|
||
}),
|
||
"mode": (["start", "end"], {
|
||
"default": "end",
|
||
"tooltip": "Select frames from the start or end of the sequence"
|
||
}),
|
||
},
|
||
}
|
||
|
||
RETURN_TYPES = ("IMAGE",)
|
||
RETURN_NAMES = ("image",)
|
||
FUNCTION = "main"
|
||
CATEGORY = "MXD/images"
|
||
|
||
def main(self, frames=None, count=1, offset=1, mode="end"):
|
||
selected = _select_frames_start_end(frames, count=count, offset=offset, mode=mode)
|
||
return (selected,)
|
||
|
||
total = frames.shape[0]
|
||
|
||
# Clamp offset and count
|
||
offset = max(1, min(offset, total))
|
||
count = max(1, min(count, total - offset + 1))
|
||
|
||
if mode == "start":
|
||
start_idx = offset - 1
|
||
end_idx = start_idx + count
|
||
selected = frames[start_idx:end_idx].clone()
|
||
else: # mode == "end"
|
||
start_idx = max(0, total - offset - count + 1)
|
||
end_idx = start_idx + count
|
||
selected = frames[start_idx:end_idx].clone()
|
||
|
||
return (selected,)
|
||
|
||
# ---------- MXD Frames Select Start/End (from start or end of sequence) ----------
|
||
class Frames_Remove_From_Start_MXD:
|
||
def __init__(self):
|
||
pass
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"frames": ("IMAGE",),
|
||
"count": ("INT", {
|
||
"default": 10,
|
||
"min": 1,
|
||
"max": 10000,
|
||
"tooltip": "Number of frames to remove from the start"
|
||
}),
|
||
},
|
||
}
|
||
|
||
RETURN_TYPES = ("IMAGE",)
|
||
RETURN_NAMES = ("image",)
|
||
FUNCTION = "main"
|
||
CATEGORY = "MXD/images"
|
||
|
||
def main(self, frames=None, count=10):
|
||
# ✅ Skip the first `count` frames instead of keeping them
|
||
frames_after = frames[count:].clone()
|
||
return (frames_after,)
|
||
|
||
|
||
if HAVE_COMFY_API:
|
||
class CombineVideos_MXD:
|
||
"""
|
||
Combine two VIDEO inputs end-to-end (sequentially).
|
||
"""
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"front_video": ("VIDEO", {"tooltip": "The first video (plays first)"}),
|
||
"back_video": ("VIDEO", {"tooltip": "The second video (plays after the first)"}),
|
||
},
|
||
}
|
||
|
||
RETURN_TYPES = ("VIDEO",)
|
||
RETURN_NAMES = ("video",)
|
||
FUNCTION = "combine"
|
||
CATEGORY = "MXD/video"
|
||
|
||
def combine(self, front_video, back_video):
|
||
comp_a = front_video.get_components()
|
||
comp_b = back_video.get_components()
|
||
|
||
# Check frame rate consistency
|
||
if comp_a.frame_rate != comp_b.frame_rate:
|
||
raise ValueError(f"FPS mismatch: {comp_a.frame_rate} vs {comp_b.frame_rate}")
|
||
|
||
# ✅ Correct way: concatenate frame tensors along batch/time dimension (dim=0)
|
||
frames_a = torch.stack(comp_a.images) if isinstance(comp_a.images, list) else comp_a.images
|
||
frames_b = torch.stack(comp_b.images) if isinstance(comp_b.images, list) else comp_b.images
|
||
if frames_a.shape[1] != frames_b.shape[1] or frames_a.shape[2] != frames_b.shape[2]:
|
||
raise ValueError(
|
||
"Resolution mismatch in CombineVideos_MXD: "
|
||
f"front_video={frames_a.shape[2]}x{frames_a.shape[1]}, "
|
||
f"back_video={frames_b.shape[2]}x{frames_b.shape[1]}. "
|
||
"Use 'WAN 2.2 Video Prep I2V MXD' before WAN generation so scaled base video and generated clip match."
|
||
)
|
||
combined_images = torch.cat([frames_a, frames_b], dim=0)
|
||
|
||
# ✅ Combine audio sequentially
|
||
combined_audio = None
|
||
if comp_a.audio is not None or comp_b.audio is not None:
|
||
def _extract_audio(audio_obj):
|
||
if audio_obj is None:
|
||
return None, None, None, None
|
||
if torch.is_tensor(audio_obj):
|
||
return audio_obj, None, "tensor", None
|
||
if isinstance(audio_obj, dict):
|
||
wave_key = "waveform" if "waveform" in audio_obj else ("samples" if "samples" in audio_obj else None)
|
||
if wave_key is None or not torch.is_tensor(audio_obj.get(wave_key)):
|
||
raise TypeError(f"Unsupported audio dict format. Keys: {list(audio_obj.keys())}")
|
||
return audio_obj[wave_key], audio_obj.get("sample_rate"), "dict", wave_key
|
||
waveform = getattr(audio_obj, "waveform", None)
|
||
sample_rate = getattr(audio_obj, "sample_rate", None)
|
||
if torch.is_tensor(waveform):
|
||
return waveform, sample_rate, "object", None
|
||
raise TypeError(f"Unsupported audio payload type: {type(audio_obj).__name__}")
|
||
|
||
wave_a, sr_a, kind_a, wave_key_a = _extract_audio(comp_a.audio)
|
||
wave_b, sr_b, kind_b, wave_key_b = _extract_audio(comp_b.audio)
|
||
rank_a = wave_a.ndim if wave_a is not None else None
|
||
rank_b = wave_b.ndim if wave_b is not None else None
|
||
|
||
def _to_bct(w):
|
||
if w is None:
|
||
return None
|
||
if w.ndim == 1:
|
||
return w.unsqueeze(0).unsqueeze(0) # [1,1,T]
|
||
if w.ndim == 2:
|
||
return w.unsqueeze(0) # [1,C,T]
|
||
if w.ndim == 3:
|
||
return w # [B,C,T]
|
||
raise ValueError(f"Unsupported audio tensor rank: {w.ndim}")
|
||
|
||
wave_a = _to_bct(wave_a)
|
||
wave_b = _to_bct(wave_b)
|
||
|
||
if wave_a is None and wave_b is not None:
|
||
wave_a = torch.zeros((wave_b.shape[0], wave_b.shape[1], 0), dtype=wave_b.dtype, device=wave_b.device)
|
||
if wave_b is None and wave_a is not None:
|
||
wave_b = torch.zeros((wave_a.shape[0], wave_a.shape[1], 0), dtype=wave_a.dtype, device=wave_a.device)
|
||
|
||
if wave_a is not None and wave_b is not None:
|
||
if wave_a.shape[0] != wave_b.shape[0]:
|
||
if wave_a.shape[0] == 1:
|
||
wave_a = wave_a.expand(wave_b.shape[0], -1, -1)
|
||
elif wave_b.shape[0] == 1:
|
||
wave_b = wave_b.expand(wave_a.shape[0], -1, -1)
|
||
else:
|
||
raise ValueError(f"Audio batch mismatch: {wave_a.shape[0]} vs {wave_b.shape[0]}")
|
||
|
||
if wave_a.shape[1] != wave_b.shape[1]:
|
||
if wave_a.shape[1] == 1:
|
||
wave_a = wave_a.expand(-1, wave_b.shape[1], -1)
|
||
elif wave_b.shape[1] == 1:
|
||
wave_b = wave_b.expand(-1, wave_a.shape[1], -1)
|
||
else:
|
||
raise ValueError(f"Audio channel mismatch: {wave_a.shape[1]} vs {wave_b.shape[1]}")
|
||
|
||
if sr_a is not None and sr_b is not None and sr_a != sr_b:
|
||
raise ValueError(f"Audio sample-rate mismatch: {sr_a} vs {sr_b}")
|
||
|
||
combined_wave = torch.cat([wave_a, wave_b], dim=2)
|
||
out_sr = sr_a if sr_a is not None else sr_b
|
||
|
||
target_rank = rank_a if rank_a is not None else rank_b
|
||
if target_rank == 1 and combined_wave.shape[0] == 1 and combined_wave.shape[1] == 1:
|
||
combined_wave = combined_wave.squeeze(0).squeeze(0)
|
||
elif target_rank == 2 and combined_wave.shape[0] == 1:
|
||
combined_wave = combined_wave.squeeze(0)
|
||
|
||
out_kind = kind_a if kind_a is not None else kind_b
|
||
if out_kind == "dict":
|
||
out_key = wave_key_a if kind_a == "dict" else wave_key_b
|
||
combined_audio = {out_key or "waveform": combined_wave}
|
||
if out_sr is not None:
|
||
combined_audio["sample_rate"] = out_sr
|
||
else:
|
||
combined_audio = combined_wave
|
||
|
||
|
||
|
||
combined_video = VideoFromComponents(
|
||
VideoComponents(
|
||
images=combined_images,
|
||
audio=combined_audio,
|
||
frame_rate=comp_a.frame_rate,
|
||
)
|
||
)
|
||
|
||
return (combined_video,)
|
||
|
||
class WAN22_I2V_Video_Prep_MXD:
|
||
"""
|
||
Prepare a source video for iterative WAN 2.2 extension:
|
||
- scale entire video using WAN bucket logic
|
||
- output the scaled frame batch directly
|
||
- keep default workflow simple for common use
|
||
"""
|
||
CATEGORY = "MXD/video"
|
||
FUNCTION = "prepare"
|
||
RETURN_TYPES = ("VIDEO", "IMAGE", "FLOAT")
|
||
RETURN_NAMES = ("scaled_video", "images", "fps")
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"video": ("VIDEO",),
|
||
"tier": (["Auto", "480p", "720p"], {"default": "Auto"}),
|
||
"crop_to_fit": ("BOOLEAN", {
|
||
"default": True,
|
||
"label_on": "Perfect Fit (Crops Edges)",
|
||
"label_off": "Closest Fit (No Crop)"
|
||
}),
|
||
"force_fps": ("BOOLEAN", {
|
||
"default": False,
|
||
"label_on": "Force FPS",
|
||
"label_off": "Keep Source FPS",
|
||
"tooltip": "When enabled, resample frames (drop/duplicate) and set exact target fps."
|
||
}),
|
||
"target_fps": ("INT", {
|
||
"default": 16,
|
||
"min": 1,
|
||
"max": 1000,
|
||
"step": 1,
|
||
"tooltip": "Used when Force FPS is enabled. Output video fps will be set exactly to this value."
|
||
}),
|
||
"aspect_mode": (["Auto", "Tall", "Wide", "Square"], {
|
||
"default": "Auto",
|
||
"tooltip": "Auto picks wide/tall/square from the source. Use Square/Tall/Wide to force the target bucket shape."
|
||
}),
|
||
},
|
||
}
|
||
|
||
def prepare(self, video, tier="Auto", crop_to_fit=True, force_fps=False, target_fps=16, aspect_mode="Auto"):
|
||
comp = video.get_components()
|
||
if isinstance(comp.images, list):
|
||
if len(comp.images) == 0:
|
||
raise ValueError("[WAN22_I2V_Video_Prep_MXD] Input video has zero frames.")
|
||
frames = torch.stack(comp.images)
|
||
else:
|
||
frames = comp.images
|
||
|
||
if frames is None:
|
||
raise ValueError("[WAN22_I2V_Video_Prep_MXD] Input video has no frames.")
|
||
if frames.ndim == 3:
|
||
frames = frames.unsqueeze(0)
|
||
if frames.ndim != 4:
|
||
raise ValueError(f"[WAN22_I2V_Video_Prep_MXD] Unexpected frame tensor shape: {tuple(frames.shape)}")
|
||
if frames.shape[0] <= 0:
|
||
raise ValueError("[WAN22_I2V_Video_Prep_MXD] Input video has zero frames.")
|
||
|
||
out_frame_rate = float(comp.frame_rate) if comp.frame_rate is not None else None
|
||
if force_fps:
|
||
frames, out_frame_rate, _ = _resample_video_frames_to_fps(
|
||
frames, comp.frame_rate, target_fps
|
||
)
|
||
|
||
# "Auto" in video prep uses the safer extend-friendly behavior.
|
||
# Keep accepting legacy "Safe Auto" values from older saved workflows.
|
||
internal_tier = "Safe Auto" if tier == "Auto" else tier
|
||
scaled_frames, _, _, _ = _wan22_scale_image_core(
|
||
frames,
|
||
tier=internal_tier,
|
||
crop_to_fit=crop_to_fit,
|
||
aspect_mode=aspect_mode,
|
||
)
|
||
|
||
scaled_video = VideoFromComponents(
|
||
VideoComponents(
|
||
images=scaled_frames,
|
||
audio=comp.audio,
|
||
frame_rate=out_frame_rate,
|
||
)
|
||
)
|
||
|
||
fps = float(out_frame_rate) if out_frame_rate is not None else 0.0
|
||
return (scaled_video, scaled_frames, fps)
|
||
|
||
# ---------- Load Video MXD (video-only picker with refresh) ----------
|
||
class LoadVideoMXD:
|
||
"""Load a video from /input with a refresh button (videos only)."""
|
||
|
||
CATEGORY = "image/video"
|
||
FUNCTION = "load"
|
||
RETURN_TYPES = ("VIDEO", "STRING")
|
||
RETURN_NAMES = ("video", "video_path")
|
||
TITLE = "Load Video MXD"
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"file": ("COMBO", {
|
||
# Only allow video uploads in the picker
|
||
"video_upload": True,
|
||
# Custom route that returns ONLY videos in /input
|
||
"remote": {
|
||
"route": "/mxd/videos/input",
|
||
"refresh_button": True,
|
||
"control_after_refresh": "first",
|
||
},
|
||
}),
|
||
}
|
||
}
|
||
|
||
# --- helpers --------------------------------------------------------------
|
||
|
||
@staticmethod
|
||
def _resolve_video_path(file: str) -> str:
|
||
"""
|
||
Try to resolve `file` in a backwards-compatible way:
|
||
1. If it's an annotated path, let folder_paths handle it.
|
||
2. Otherwise treat it as relative to the input directory.
|
||
"""
|
||
# 1) Try annotated style (old workflows / uploads)
|
||
try:
|
||
return folder_paths.get_annotated_filepath(file)
|
||
except Exception:
|
||
pass
|
||
|
||
# 2) Fall back to /input relative
|
||
base = folder_paths.get_input_directory()
|
||
candidate = os.path.join(base, file)
|
||
if os.path.isfile(candidate):
|
||
return candidate
|
||
|
||
# If all else fails, just return what we got (will error later)
|
||
return candidate
|
||
|
||
@staticmethod
|
||
def _is_video_file(path: str) -> bool:
|
||
_, ext = os.path.splitext(path)
|
||
return ext.lower() in VIDEO_EXTS
|
||
|
||
# --- main function --------------------------------------------------------
|
||
|
||
def load(self, file: str):
|
||
video_path = self._resolve_video_path(file)
|
||
|
||
if not os.path.isfile(video_path):
|
||
raise FileNotFoundError(f"[LoadVideoMXD] File not found: {video_path}")
|
||
|
||
if not self._is_video_file(video_path):
|
||
raise ValueError(f"[LoadVideoMXD] Not a video file: {video_path}")
|
||
|
||
print(f"[LoadVideoMXD] Loaded exactly: {video_path}")
|
||
return (VideoFromFile(video_path), video_path)
|
||
|
||
# --- nice-to-haves --------------------------------------------------------
|
||
|
||
@classmethod
|
||
def IS_CHANGED(cls, file: str):
|
||
try:
|
||
p = cls._resolve_video_path(file)
|
||
return os.path.getmtime(p)
|
||
except Exception:
|
||
return 0
|
||
|
||
@classmethod
|
||
def VALIDATE_INPUTS(cls, file: str):
|
||
# First, try the annotated path (for backwards compat)
|
||
if folder_paths.exists_annotated_filepath(file):
|
||
resolved = folder_paths.get_annotated_filepath(file)
|
||
if not cls._is_video_file(resolved):
|
||
return f"This node only accepts video files ({', '.join(sorted(VIDEO_EXTS))})."
|
||
return True
|
||
|
||
# Then, try treating it as /input-relative
|
||
base = folder_paths.get_input_directory()
|
||
candidate = os.path.join(base, file)
|
||
if os.path.isfile(candidate):
|
||
if not cls._is_video_file(candidate):
|
||
return f"This node only accepts video files ({', '.join(sorted(VIDEO_EXTS))})."
|
||
return True
|
||
|
||
return f"Invalid video file: {file}"
|
||
|
||
# ---------- Save Video MXD ----------
|
||
class SaveVideoMXD(io.ComfyNode):
|
||
@classmethod
|
||
def define_schema(cls):
|
||
return io.Schema(
|
||
node_id="SaveVideoMXD",
|
||
display_name="Save Video MXD",
|
||
category="image/video",
|
||
description="Saves the input video to your ComfyUI output directory.",
|
||
inputs=[
|
||
io.Video.Input("video", tooltip="The video to save."),
|
||
io.String.Input("filename_prefix", default="video/ComfyUI", tooltip="The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."),
|
||
io.Combo.Input("format", options=VideoContainer.as_input(), default="auto", tooltip="The format to save the video as."),
|
||
io.Combo.Input("codec", options=VideoCodec.as_input(), default="auto", tooltip="The codec to use for the video."),
|
||
io.Boolean.Input(
|
||
"embed_workflow",
|
||
default=True,
|
||
label_on="embed",
|
||
label_off="skip",
|
||
tooltip="When high_workflow is connected, merge it into this video's embedded workflow "
|
||
"so dragging the final video into ComfyUI shows both the high-noise stage and "
|
||
"this stage together.",
|
||
),
|
||
io.String.Input(
|
||
"high_workflow",
|
||
optional=True,
|
||
force_input=True,
|
||
tooltip="Connect a Load Latent node's 'high_workflow' output here to carry the "
|
||
"high-noise stage's workflow into this video's metadata.",
|
||
),
|
||
],
|
||
hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
|
||
is_output_node=True,
|
||
)
|
||
|
||
@classmethod
|
||
def execute(cls, video: VideoInput, filename_prefix: str, format: str, codec: str,
|
||
embed_workflow: bool = True, high_workflow: str = "") -> io.NodeOutput:
|
||
width, height = video.get_dimensions()
|
||
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
|
||
filename_prefix,
|
||
folder_paths.get_output_directory(),
|
||
width,
|
||
height
|
||
)
|
||
|
||
saved_metadata = None
|
||
if not args.disable_metadata:
|
||
metadata = {}
|
||
if cls.hidden.extra_pnginfo is not None:
|
||
metadata.update(cls.hidden.extra_pnginfo)
|
||
if cls.hidden.prompt is not None:
|
||
metadata["prompt"] = cls.hidden.prompt
|
||
if embed_workflow and high_workflow:
|
||
current_workflow = metadata.get("workflow")
|
||
merged_workflow = _merge_prior_workflow_into_current(high_workflow, current_workflow)
|
||
if merged_workflow is not current_workflow:
|
||
metadata["workflow"] = merged_workflow
|
||
if len(metadata) > 0:
|
||
saved_metadata = metadata
|
||
|
||
file = f"{filename}_{counter:05}_.{VideoContainer.get_extension(format)}"
|
||
video.save_to(
|
||
os.path.join(full_output_folder, file),
|
||
format=VideoContainer(format),
|
||
codec=codec,
|
||
metadata=saved_metadata
|
||
)
|
||
|
||
return io.NodeOutput(ui=ui.PreviewVideo([ui.SavedResult(file, subfolder, io.FolderType.output)]))
|
||
|
||
class PreviewVideoMXD(io.ComfyNode):
|
||
@classmethod
|
||
def define_schema(cls):
|
||
return io.Schema(
|
||
node_id="PreviewVideoMXD",
|
||
display_name="Preview Video MXD",
|
||
category="image/video",
|
||
description="Preview a video without saving output (optional pass-through).",
|
||
inputs=[
|
||
io.Video.Input("input_video", tooltip="Video to preview."),
|
||
],
|
||
outputs=[
|
||
io.Video.Output("output_video", tooltip="Passes the same video forward."),
|
||
],
|
||
# Allow this node to run even when output_video is not connected.
|
||
is_output_node=True,
|
||
)
|
||
|
||
@classmethod
|
||
def execute(cls, input_video: VideoInput):
|
||
# Save a temporary H264 file so ComfyUI has something to preview
|
||
out_dir = os.path.join(folder_paths.get_output_directory(), "previews")
|
||
os.makedirs(out_dir, exist_ok=True)
|
||
|
||
preview_path = os.path.join(out_dir, "preview_temp.mp4")
|
||
input_video.save_to(preview_path, format="mp4", codec="h264")
|
||
|
||
# ✅ Return the raw video object (not a tuple)
|
||
return io.NodeOutput(
|
||
input_video,
|
||
ui=ui.PreviewVideo([
|
||
ui.SavedResult("preview_temp.mp4", "previews", io.FolderType.output)
|
||
])
|
||
)
|
||
|
||
|
||
class GroupVideoFramesMXD:
|
||
CATEGORY = "MXD/Video"
|
||
TITLE = "Group Video Frames (MXD)"
|
||
RETURN_TYPES = ("IMAGE",)
|
||
RETURN_NAMES = ("IMAGE_GROUPS",)
|
||
OUTPUT_IS_LIST = (True,)
|
||
FUNCTION = "group_frames"
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"frames": ("IMAGE",),
|
||
"group_size": ("INT", {"default": 81, "min": 1, "max": 5000, "step": 1}),
|
||
}
|
||
}
|
||
|
||
def group_frames(self, frames, group_size):
|
||
import math, torch
|
||
|
||
all_frames = list(frames)
|
||
total = len(all_frames)
|
||
num_groups = math.ceil(total / group_size)
|
||
grouped_tensors = []
|
||
|
||
for i in range(num_groups):
|
||
start = i * group_size
|
||
end = min(start + group_size, total)
|
||
group = all_frames[start:end]
|
||
|
||
clean = []
|
||
for f in group:
|
||
# ✅ drop redundant singleton batch dim if present
|
||
if f.ndim == 4 and f.shape[0] == 1:
|
||
f = f.squeeze(0) # (H,W,C)
|
||
# ✅ ensure shape (H,W,C)
|
||
if f.ndim != 3:
|
||
print(f"[GroupVideoFramesMXD] weird frame shape {f.shape}")
|
||
continue
|
||
clean.append(f)
|
||
|
||
# ✅ stack back to (N,H,W,C)
|
||
if len(clean) == 0:
|
||
continue
|
||
stacked = torch.stack(clean, dim=0)
|
||
grouped_tensors.append(stacked)
|
||
|
||
print(f"[GroupVideoFramesMXD] Split {total} frames into {len(grouped_tensors)} groups of up to {group_size}.")
|
||
return (grouped_tensors,)
|
||
|
||
if HAVE_COMFY_API:
|
||
class Wan22FirstLastImageToVideoMXD(io.ComfyNode):
|
||
@classmethod
|
||
def define_schema(cls):
|
||
return io.Schema(
|
||
node_id="Wan22FirstLastImageToVideoMXD",
|
||
display_name="WAN 2.2 First & Last I2V MXD",
|
||
category="conditioning/video_models",
|
||
inputs=[
|
||
io.Conditioning.Input("positive"),
|
||
io.Conditioning.Input("negative"),
|
||
io.Vae.Input("vae"),
|
||
io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
|
||
io.Int.Input("batch_size", default=1, min=1, max=4096),
|
||
io.Image.Input("start_image", optional=True),
|
||
io.Image.Input("end_image", optional=True),
|
||
],
|
||
outputs=[
|
||
io.Conditioning.Output(display_name="positive"),
|
||
io.Conditioning.Output(display_name="negative"),
|
||
io.Latent.Output(display_name="latent"),
|
||
],
|
||
)
|
||
|
||
@classmethod
|
||
def execute(cls, positive, negative, vae, length, batch_size, start_image=None, end_image=None) -> io.NodeOutput:
|
||
spacial_scale = vae.spacial_compression_encode()
|
||
|
||
# Assume incoming images are already pre-sized by upstream nodes.
|
||
height, width = start_image.shape[1], start_image.shape[2] if start_image is not None else (vae.latent_channels * spacial_scale, vae.latent_channels * spacial_scale)
|
||
|
||
latent = torch.zeros(
|
||
[batch_size, vae.latent_channels, ((length - 1) // 4) + 1, height // spacial_scale, width // spacial_scale],
|
||
device=comfy.model_management.intermediate_device()
|
||
)
|
||
|
||
image = torch.ones((length, height, width, 3)) * 0.5
|
||
mask = torch.ones((1, 1, latent.shape[2] * 4, latent.shape[-2], latent.shape[-1]))
|
||
|
||
if start_image is not None:
|
||
image[:start_image.shape[0]] = start_image
|
||
mask[:, :, :start_image.shape[0] + 3] = 0.0
|
||
|
||
if end_image is not None:
|
||
image[-end_image.shape[0]:] = end_image
|
||
mask[:, :, -end_image.shape[0]:] = 0.0
|
||
|
||
concat_latent_image = vae.encode(image[:, :, :, :3])
|
||
mask = mask.view(1, mask.shape[2] // 4, 4, mask.shape[3], mask.shape[4]).transpose(1, 2)
|
||
|
||
positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
|
||
negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
|
||
|
||
out_latent = {"samples": latent}
|
||
return io.NodeOutput(positive, negative, out_latent)
|
||
|
||
|
||
# ============================================================
|
||
# LTX Video Image Scaler MXD
|
||
# ============================================================
|
||
# Official LTX-2.3 rules (Lightricks model card + example workflows):
|
||
# - Width & height must be divisible by 32; frame count must be 8n+1.
|
||
# - The distilled two-stage workflow generates Stage 1 low-res, then the
|
||
# ltx-2.3-spatial-upscaler-x2 doubles it (exactly 2x) for Stage 2.
|
||
# - The one published two-stage resolution is Stage 1 960x544 -> 1920x1088.
|
||
#
|
||
# Tiers below are FINAL (Stage 2) sizes; Stage 1 is exactly half. Finals are
|
||
# kept /64 so Stage 1 stays /32 (the latent constraint). Only the 1080p 16:9
|
||
# row is officially published by Lightricks; the portrait/square rows and the
|
||
# 720p/576p tiers are /32-aligned siblings at the same pixel budget.
|
||
#
|
||
# Buckets (FINAL size, all /64) -> Stage 1 (half, all /32):
|
||
# 1080p: 1920x1088 / 1088x1920 / 1408x1408 (Stage 1: 960x544 / 544x960 / 704x704)
|
||
# 720p: 1280x704 / 704x1280 / 960x960 (Stage 1: 640x352 / 352x640 / 480x480)
|
||
# 576p: 1024x576 / 576x1024 / 768x768 (Stage 1: 512x288 / 288x512 / 384x384)
|
||
#
|
||
# Fit (no pad): proportional resize <= target, /64 aligned.
|
||
# Crop (no pad): resize-to-cover then center-crop to exact bucket.
|
||
# Square images map to each tier's square bucket.
|
||
# ============================================================
|
||
|
||
_LTX_BUCKETS = {
|
||
"1080p": {"landscape": (1920, 1088), "portrait": (1088, 1920), "square": (1408, 1408)},
|
||
"720p": {"landscape": (1280, 704), "portrait": (704, 1280), "square": (960, 960)},
|
||
"576p": {"landscape": (1024, 576), "portrait": (576, 1024), "square": (768, 768)},
|
||
}
|
||
|
||
|
||
def _ceil32(x):
|
||
x = (int(x) + 31) // 32 * 32
|
||
return max(32, x)
|
||
|
||
|
||
def _floor32(x):
|
||
x = int(x) // 32 * 32
|
||
return max(32, x)
|
||
|
||
|
||
def _floor64(x):
|
||
x = int(x) // 64 * 64
|
||
return max(64, x)
|
||
|
||
|
||
def _ltx_stage1_dims(final_w, final_h):
|
||
"""Return Stage 1 dimensions that upscale exactly to the final size."""
|
||
return max(32, int(final_w) // 2), max(32, int(final_h) // 2)
|
||
|
||
|
||
def _ltx_resize_fit_inside(img, out_w, out_h):
|
||
"""Resize to fit inside (out_w, out_h), output /64 aligned on both sides."""
|
||
_, ih, iw, _ = img.shape
|
||
s = min(out_w / iw, out_h / ih)
|
||
tw = _floor64(iw * s)
|
||
th = _floor64(ih * s)
|
||
tw = max(32, min(tw, nodes.MAX_RESOLUTION))
|
||
th = max(32, min(th, nodes.MAX_RESOLUTION))
|
||
resized = comfy.utils.common_upscale(img.movedim(-1, 1), tw, th, "bilinear", "center").movedim(1, -1)
|
||
return resized, tw, th
|
||
|
||
|
||
def _ltx_resize_then_center_crop(img, out_w, out_h):
|
||
"""Resize to cover (out_w, out_h) then center-crop to exact /32 target."""
|
||
_, ih, iw, _ = img.shape
|
||
s = max(out_w / iw, out_h / ih)
|
||
tw = _ceil32(iw * s)
|
||
th = _ceil32(ih * s)
|
||
tmp = comfy.utils.common_upscale(img.movedim(-1, 1), tw, th, "bilinear", "center").movedim(1, -1)
|
||
y0 = max(0, (th - out_h) // 2)
|
||
x0 = max(0, (tw - out_w) // 2)
|
||
return tmp[:, y0:y0+out_h, x0:x0+out_w, :]
|
||
|
||
|
||
def _ltx_pick_bucket(iw, ih, tier):
|
||
"""Pick the landscape / portrait / square bucket for the given tier."""
|
||
tier_map = _LTX_BUCKETS[tier]
|
||
if _is_squareish(iw, ih):
|
||
return tier_map["square"]
|
||
return tier_map["landscape"] if iw >= ih else tier_map["portrait"]
|
||
|
||
|
||
def _ltx_scale_image_core(image, tier="1080p", crop_to_fit=True):
|
||
"""
|
||
Core LTX scaler. Returns (scaled_image, final_w, final_h, stage1_w, stage1_h).
|
||
'tier' is the FINAL (Stage 2) size budget; Stage 1 is exactly half.
|
||
"""
|
||
_, ih, iw, _ = image.shape
|
||
|
||
bw, bh = _ltx_pick_bucket(iw, ih, tier)
|
||
|
||
if _is_squareish(iw, ih):
|
||
crop_to_fit = False
|
||
|
||
if crop_to_fit:
|
||
out = _ltx_resize_then_center_crop(image, bw, bh)
|
||
else:
|
||
out, bw, bh = _ltx_resize_fit_inside(image, bw, bh)
|
||
|
||
final_w = int(out.shape[2])
|
||
final_h = int(out.shape[1])
|
||
stage1_w, stage1_h = _ltx_stage1_dims(final_w, final_h)
|
||
return out, final_w, final_h, stage1_w, stage1_h
|
||
|
||
|
||
class LTX_Image_Scaler_MXD:
|
||
"""
|
||
MXD Image Scaler for LTX Video (distilled two-stage workflow).
|
||
|
||
'tier' is the FINAL (Stage 2) size; Stage 1 is exactly half. Finals are /64
|
||
so Stage 1 stays /32 (the LTX latent constraint). Wire stage1_width /
|
||
stage1_height into the empty latent for the low-res pass; the spatial
|
||
upscaler-x2 then doubles it back to the final size.
|
||
|
||
Tiers (final / Stage 1):
|
||
1080p 1920x1088 (official 16:9) / 1088x1920 / 1408x1408 -> half
|
||
720p 1280x704 / 704x1280 / 960x960 -> half
|
||
576p 1024x576 / 576x1024 / 768x768 -> half
|
||
|
||
Modes:
|
||
Perfect Fit (Crops Edges) resize-to-cover + center-crop to exact bucket.
|
||
Closest Fit (No Crop) proportional resize, /64-aligned; may be smaller.
|
||
|
||
Square images (within +-3% of 1:1) map to the tier's square bucket.
|
||
Outputs the scaled image at final size plus the Stage 1 dimensions.
|
||
"""
|
||
|
||
TITLE = "LTX Video Image Scaler MXD"
|
||
CATEGORY = "image/processing"
|
||
RETURN_TYPES = ("IMAGE", "INT", "INT")
|
||
RETURN_NAMES = ("image", "width", "height")
|
||
FUNCTION = "scale"
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"image": ("IMAGE",),
|
||
"tier": (["1080p", "720p", "576p"], {"default": "1080p"}),
|
||
"crop_to_fit": ("BOOLEAN", {
|
||
"default": True,
|
||
"label_on": "Crop Edges",
|
||
"label_off": "Closest Fit (No Crop)",
|
||
}),
|
||
}
|
||
}
|
||
|
||
def scale(self, image, tier="1080p", crop_to_fit=True):
|
||
image = _validate_image_batch_4d(image, "LTX_Image_Scaler_MXD", "image")
|
||
out, _final_w, _final_h, stage1_w, stage1_h = _ltx_scale_image_core(
|
||
image, tier=tier, crop_to_fit=crop_to_fit
|
||
)
|
||
return (out, stage1_w, stage1_h)
|
||
|
||
|
||
class PadImageForOutpaintingMXD:
|
||
SEARCH_ALIASES = ["extend canvas", "expand image", "outpaint pad"]
|
||
|
||
RETURN_TYPES = ("IMAGE", "MASK")
|
||
FUNCTION = "expand_image"
|
||
CATEGORY = "image/transform"
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"image": ("IMAGE",),
|
||
"left": ("INT", {"default": 0, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 2}),
|
||
"top": ("INT", {"default": 0, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 2}),
|
||
"right": ("INT", {"default": 0, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 2}),
|
||
"bottom": ("INT", {"default": 0, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 2}),
|
||
"round_to": (["None", "2", "8", "16", "32", "64"], {"default": "16"}),
|
||
}
|
||
}
|
||
|
||
@staticmethod
|
||
def _nearest_multiple(value: int, multiple: int, padded: bool) -> int:
|
||
if multiple <= 1 or value % multiple == 0:
|
||
return value
|
||
lower = (value // multiple) * multiple
|
||
upper = lower + multiple
|
||
if lower <= 0:
|
||
return upper
|
||
if not padded:
|
||
return lower
|
||
return lower if value - lower <= upper - value else upper
|
||
|
||
@staticmethod
|
||
def _axis_plan(size: int, before: int, after: int, multiple: int) -> Tuple[int, int, int, int, int]:
|
||
target = size + before + after
|
||
if multiple > 1:
|
||
target = PadImageForOutpaintingMXD._nearest_multiple(target, multiple, before + after > 0)
|
||
|
||
delta = target - (size + before + after)
|
||
if delta < 0:
|
||
remove = -delta
|
||
from_after = min(after, remove)
|
||
after -= from_after
|
||
remove -= from_after
|
||
from_before = min(before, remove)
|
||
before -= from_before
|
||
remove -= from_before
|
||
crop_before = remove // 2
|
||
crop_after = remove - crop_before
|
||
else:
|
||
crop_before = 0
|
||
crop_after = 0
|
||
if before > 0 and after > 0:
|
||
add_before = delta // 2
|
||
before += add_before
|
||
after += delta - add_before
|
||
elif before > 0:
|
||
before += delta
|
||
else:
|
||
after += delta
|
||
|
||
final_size = size - crop_before - crop_after + before + after
|
||
if final_size <= 0:
|
||
raise ValueError("[PadImageForOutpaintingMXD] Rounding removed the full image on one axis.")
|
||
return before, after, crop_before, crop_after, final_size
|
||
|
||
def expand_image(self, image, left, top, right, bottom, round_to="16"):
|
||
image = _validate_image_batch_4d(image, "PadImageForOutpaintingMXD", "image")
|
||
batch, height, width, channels = image.size()
|
||
multiple = 1 if round_to == "None" else int(round_to)
|
||
|
||
left, right, crop_left, crop_right, final_width = self._axis_plan(width, left, right, multiple)
|
||
top, bottom, crop_top, crop_bottom, final_height = self._axis_plan(height, top, bottom, multiple)
|
||
|
||
cropped = image[:, crop_top:height - crop_bottom, crop_left:width - crop_right, :]
|
||
crop_height = cropped.shape[1]
|
||
crop_width = cropped.shape[2]
|
||
|
||
new_image = torch.full(
|
||
(batch, final_height, final_width, channels),
|
||
0.5,
|
||
dtype=image.dtype,
|
||
device=image.device,
|
||
)
|
||
new_image[:, top:top + crop_height, left:left + crop_width, :] = cropped
|
||
|
||
mask = torch.ones(
|
||
(final_height, final_width),
|
||
dtype=torch.float32,
|
||
device=image.device,
|
||
)
|
||
mask[top:top + crop_height, left:left + crop_width] = 0.0
|
||
|
||
return (new_image, mask.unsqueeze(0))
|
||
|
||
|
||
# ---------- Node registration ----------
|
||
NODE_CLASS_MAPPINGS = {
|
||
"Wan2_2EmptyLatentImageMXD": Wan2_2EmptyLatentImageMXD,
|
||
"wan22EmptyHunyuanLatentVideoMXD": wan22EmptyHunyuanLatentVideoMXD,
|
||
"SaveLatent_I2V_MXD": SaveLatent_I2V_MXD,
|
||
"LoadLatent_I2V_MXD": LoadLatent_I2V_MXD,
|
||
"LoadLatents_FromFolder_I2V_MXD": LoadLatents_FromFolder_I2V_MXD,
|
||
"LoadLatent_I2V_Pipe_MXD": LoadLatent_I2V_Pipe_MXD,
|
||
"LoadLatents_FromFolder_I2V_Pipe_MXD": LoadLatents_FromFolder_I2V_Pipe_MXD,
|
||
"LatentPipeUnpack_MXD": LatentPipeUnpack_MXD,
|
||
"WAN22_I2V_Image_Scaler_MXD": WAN22_I2V_Image_Scaler_MXD,
|
||
"LTX_Image_Scaler_MXD": LTX_Image_Scaler_MXD,
|
||
"WAN22_I2V_Match_Resolution_MXD": WAN22_I2V_Match_Resolution_MXD,
|
||
"Frames_Remove_From_Start_MXD": Frames_Remove_From_Start_MXD,
|
||
"GroupVideoFramesMXD": GroupVideoFramesMXD,
|
||
"Frames_Select_StartEnd_MXD": Frames_Select_StartEnd_MXD,
|
||
"PadImageForOutpaintingMXD": PadImageForOutpaintingMXD,
|
||
}
|
||
|
||
if HAVE_COMFY_API:
|
||
NODE_CLASS_MAPPINGS.update({
|
||
"Wan22ImageToVideoMXD": Wan22ImageToVideoMXD,
|
||
"WAN22_I2V_Video_Prep_MXD": WAN22_I2V_Video_Prep_MXD,
|
||
"CombineVideos_MXD": CombineVideos_MXD,
|
||
"LoadVideoMXD": LoadVideoMXD,
|
||
"SaveVideoMXD": SaveVideoMXD,
|
||
"PreviewVideoMXD": PreviewVideoMXD,
|
||
"Wan22FirstLastImageToVideoMXD": Wan22FirstLastImageToVideoMXD,
|
||
})
|
||
|
||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||
"Wan2_2EmptyLatentImageMXD": "Wan 2.2 Empty Latent Image MXD",
|
||
"wan22EmptyHunyuanLatentVideoMXD": "WAN2.2 Empty Latent Video MXD",
|
||
"SaveLatent_I2V_MXD": "Save Latent MXD",
|
||
"LoadLatent_I2V_MXD": "Load Latent MXD",
|
||
"LoadLatents_FromFolder_I2V_MXD": "Load Latent Batch MXD",
|
||
"LoadLatent_I2V_Pipe_MXD": "Load Latent Pipe MXD",
|
||
"LoadLatents_FromFolder_I2V_Pipe_MXD": "Load Latent Batch Pipe MXD",
|
||
"LatentPipeUnpack_MXD": "Unpack Latent Pipe MXD",
|
||
"WAN22_I2V_Image_Scaler_MXD": "Image Scaler Wan 2.2 I2V MXD",
|
||
"LTX_Image_Scaler_MXD": "LTX Video Image Scaler MXD",
|
||
"WAN22_I2V_Match_Resolution_MXD": "Match Resolution Wan 2.2 I2V MXD",
|
||
"Frames_Remove_From_Start_MXD": "Remove Frames From Start MXD",
|
||
"GroupVideoFramesMXD": "Group Video Frames MXD",
|
||
"Frames_Select_StartEnd_MXD": "Select Frames MXD",
|
||
"PadImageForOutpaintingMXD": "Pad Image for Outpainting MXD",
|
||
}
|
||
|
||
if HAVE_COMFY_API:
|
||
NODE_DISPLAY_NAME_MAPPINGS.update({
|
||
"Wan22ImageToVideoMXD": "Wan 2.2 Image to Video MXD",
|
||
"WAN22_I2V_Video_Prep_MXD": "WAN 2.2 Video Prep I2V MXD",
|
||
"CombineVideos_MXD": "Combine Videos MXD",
|
||
"LoadVideoMXD": "Load Video MXD",
|
||
"SaveVideoMXD": "Save Video MXD",
|
||
"PreviewVideoMXD": "Preview Video MXD",
|
||
"Wan22FirstLastImageToVideoMXD": "Wan 2.2 I2V First & Last Frame MXD",
|
||
})
|
||
|
||
def _add_mxd_aliases(class_map, display_map):
|
||
alias_sources = {}
|
||
for key in list(class_map.keys()):
|
||
if "MXD" in key.upper():
|
||
continue
|
||
alias = f"{key} MXD"
|
||
if alias in class_map:
|
||
continue
|
||
class_map[alias] = class_map[key]
|
||
alias_sources[alias] = key
|
||
for alias, source in alias_sources.items():
|
||
if alias not in display_map:
|
||
display_map[alias] = display_map.get(source, alias)
|
||
return alias_sources
|
||
|
||
_add_mxd_aliases(NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS)
|