Ethanfel
6c7f618bb0
Return None for scores_list when scores_info input is not connected, and skip writing score metadata in that case for both images and videos. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
431 lines
18 KiB
Python
431 lines
18 KiB
Python
import os
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import sys
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import platform
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import torch
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import numpy as np
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from PIL import Image, ExifTags
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from PIL.PngImagePlugin import PngInfo
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import concurrent.futures
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import re
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import time
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import glob
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import json
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import subprocess
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import shutil
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import stat
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import tempfile
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import urllib.request
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import zipfile
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import tarfile
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_NODE_DIR = os.path.dirname(os.path.abspath(__file__))
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_FFMPEG_DIR = os.path.join(_NODE_DIR, "ffmpeg_bin")
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def _get_ffmpeg():
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"""Find or download a ffmpeg binary. Search order:
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1. Bundled binary in this node's ffmpeg_bin/ folder
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2. imageio_ffmpeg (shipped by VideoHelperSuite)
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3. System PATH
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4. Auto-download a static build into ffmpeg_bin/
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"""
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system = platform.system()
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exe_name = "ffmpeg.exe" if system == "Windows" else "ffmpeg"
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local_bin = os.path.join(_FFMPEG_DIR, exe_name)
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# 1. Already downloaded
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if os.path.isfile(local_bin):
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return local_bin
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# 2. imageio_ffmpeg
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try:
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import imageio_ffmpeg
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path = imageio_ffmpeg.get_ffmpeg_exe()
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if path and os.path.isfile(path):
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return path
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except Exception:
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pass
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# 3. System PATH
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system_bin = shutil.which("ffmpeg")
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if system_bin:
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return system_bin
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# 4. Auto-download static build
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print("xx- FastSaver: ffmpeg not found. Downloading static build...")
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os.makedirs(_FFMPEG_DIR, exist_ok=True)
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urls = {
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("Linux", "x86_64"): "https://johnvansickle.com/ffmpeg/releases/ffmpeg-release-amd64-static.tar.xz",
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("Linux", "aarch64"): "https://johnvansickle.com/ffmpeg/releases/ffmpeg-release-arm64-static.tar.xz",
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("Windows", "AMD64"): "https://www.gyan.dev/ffmpeg/builds/ffmpeg-release-essentials.zip",
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}
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machine = platform.machine()
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key = (system, machine)
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url = urls.get(key)
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if not url:
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raise RuntimeError(
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f"No automatic ffmpeg download available for {system}/{machine}. "
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f"Please install ffmpeg manually or place a binary in {_FFMPEG_DIR}"
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)
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archive_path = os.path.join(_FFMPEG_DIR, "ffmpeg_archive")
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try:
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urllib.request.urlretrieve(url, archive_path)
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if url.endswith(".tar.xz"):
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with tarfile.open(archive_path, "r:xz") as tar:
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for member in tar.getmembers():
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if member.name.endswith("/ffmpeg") and member.isfile():
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member.name = exe_name
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tar.extract(member, _FFMPEG_DIR)
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break
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elif url.endswith(".zip"):
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with zipfile.ZipFile(archive_path, "r") as zf:
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for name in zf.namelist():
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if name.endswith("bin/ffmpeg.exe"):
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data = zf.read(name)
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with open(local_bin, "wb") as f:
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f.write(data)
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break
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finally:
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if os.path.exists(archive_path):
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os.remove(archive_path)
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if not os.path.isfile(local_bin):
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raise RuntimeError(f"Failed to extract ffmpeg to {local_bin}")
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# Make executable on Linux/Mac
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if system != "Windows":
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os.chmod(local_bin, os.stat(local_bin).st_mode | stat.S_IEXEC)
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print(f"xx- FastSaver: ffmpeg downloaded to {local_bin}")
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return local_bin
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class FastAbsoluteSaver:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"images": ("IMAGE", ),
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"output_path": ("STRING", {"default": "D:\\Datasets\\Sharp_Output"}),
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"filename_prefix": ("STRING", {"default": "frame"}),
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# --- FORMAT SWITCH ---
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"save_format": (["png", "webp", "mp4", "webm"], ),
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# --- NAMING CONTROL ---
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"use_timestamp": ("BOOLEAN", {"default": False, "label": "Add Timestamp (Unique)"}),
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"auto_increment": ("BOOLEAN", {"default": True, "label": "Auto-Increment Counter (Scan Folder)"}),
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"counter_digits": ("INT", {"default": 4, "min": 1, "max": 12, "step": 1, "label": "Number Padding (000X)"}),
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"filename_with_score": ("BOOLEAN", {"default": False, "label": "Append Score to Filename"}),
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# --- METADATA & WORKFLOW ---
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"metadata_key": ("STRING", {"default": "sharpness_score"}),
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"save_workflow_metadata": ("BOOLEAN", {"default": False, "label": "Save ComfyUI Workflow (Graph)"}),
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# --- PERFORMANCE ---
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"max_threads": ("INT", {"default": 0, "min": 0, "max": 128, "step": 1, "label": "Max Threads (0=Auto)"}),
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# --- WEBP SPECIFIC ---
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"webp_lossless": ("BOOLEAN", {"default": True, "label": "WebP Lossless"}),
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"webp_quality": ("INT", {"default": 100, "min": 0, "max": 100, "step": 1}),
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"webp_method": ("INT", {"default": 4, "min": 0, "max": 6, "step": 1}),
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# --- VIDEO SPECIFIC ---
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"video_fps": ("INT", {"default": 24, "min": 1, "max": 120, "step": 1, "label": "Video FPS"}),
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"video_crf": ("INT", {"default": 18, "min": 0, "max": 51, "step": 1, "label": "Video CRF (0=Lossless, 51=Worst)"}),
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"video_pixel_format": (["yuv420p", "yuv444p"], {"label": "Pixel Format"}),
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},
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"optional": {
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"scores_info": ("STRING", {"forceInput": True}),
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},
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# Hidden inputs used to capture the workflow graph
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"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
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}
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RETURN_TYPES = ()
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FUNCTION = "save_images_fast"
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OUTPUT_NODE = True
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CATEGORY = "BetaHelper/IO"
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def parse_info(self, info_str, batch_size):
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if not info_str:
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# No scores connected - return None for scores to indicate "not provided"
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return ([0]*batch_size, None)
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matches = re.findall(r"F:(\d+).*?Score:\s*(\d+(\.\d+)?)", info_str)
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frames = []
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scores = []
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for m in matches:
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try:
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frames.append(int(m[0]))
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scores.append(float(m[1]))
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except ValueError:
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pass
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if len(frames) < batch_size:
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missing = batch_size - len(frames)
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frames.extend([0] * missing)
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scores.extend([0.0] * missing)
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return frames[:batch_size], scores[:batch_size]
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def get_start_index(self, output_path, prefix):
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# Scans the directory ONCE to find the highest existing number.
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print(f"xx- FastSaver: Scanning folder for existing '{prefix}' files...")
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files = glob.glob(os.path.join(output_path, f"{prefix}*.*"))
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max_idx = 0
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# Check specifically for prefix_NUMBER pattern to avoid confusing timestamps
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pattern = re.compile(rf"{re.escape(prefix)}_(\d+)")
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for f in files:
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fname = os.path.basename(f)
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match = pattern.match(fname)
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if match:
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try:
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val = int(match.group(1))
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if val > max_idx:
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max_idx = val
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except ValueError:
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continue
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print(f"xx- FastSaver: Found highest index {max_idx}. Starting at {max_idx + 1}")
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return max_idx + 1
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def save_single_image(self, tensor_img, full_path, score, key_name, fmt, lossless, quality, method,
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save_workflow, prompt_data, extra_data):
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try:
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array = 255. * tensor_img.cpu().numpy()
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img = Image.fromarray(np.clip(array, 0, 255).astype(np.uint8))
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# --- METADATA PREPARATION ---
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meta_png = PngInfo()
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exif_bytes = None
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# 1. Custom Score Metadata (only if score was actually provided)
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if fmt == "png":
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if score is not None:
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meta_png.add_text(key_name, str(score))
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meta_png.add_text("software", "ComfyUI_Parallel_Node")
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# 2. ComfyUI Workflow Metadata (If requested)
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if save_workflow:
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# Prepare JSON payloads
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workflow_json = json.dumps(extra_data.get("workflow", {})) if extra_data else "{}"
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prompt_json = json.dumps(prompt_data) if prompt_data else "{}"
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if fmt == "png":
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# Standard PNG text chunks
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meta_png.add_text("prompt", prompt_json)
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meta_png.add_text("workflow", workflow_json)
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elif fmt == "webp":
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# WebP: Embed in Exif UserComment (Standard ComfyUI method)
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# We construct a JSON dict containing the workflow
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exif_payload = {
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"prompt": prompt_data,
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"workflow": extra_data.get("workflow", {}) if extra_data else {}
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}
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# We also add the custom score here for WebP readers that check Exif
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if score is not None:
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exif_payload[key_name] = score
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user_comment = json.dumps(exif_payload)
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# Create Exif data with tag 0x9286 (UserComment)
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exif_dict = {
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ExifTags.IFD.Exif: {
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0x9286: user_comment.encode('utf-8')
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}
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}
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# Pillow requires raw bytes for 'exif='
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# Since we want to avoid 'piexif' dependency, we do a lightweight workaround:
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# We just save the image. Pillow WebP writer doesn't support easy Exif writing
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# without external libs or pre-existing exif.
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#
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# FALLBACK: If we can't write complex Exif easily without piexif,
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# we will skip WebP workflow embedding to keep this node dependency-free.
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#
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# BUT, ComfyUI users expect it.
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# Strategy: If format is WebP and workflow is ON, we assume
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# the user is okay with a slightly slower save or we skip it if dependencies missing.
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# For this "Fast" node, we will skip the complex Exif write to prevent errors/bloat.
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pass
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# --- SAVING ---
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if fmt == "png":
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img.save(full_path, format="PNG", pnginfo=meta_png, compress_level=1)
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elif fmt == "webp":
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img.save(full_path, format="WEBP", lossless=lossless, quality=quality, method=method)
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return True
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except Exception as e:
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print(f"xx- Error saving {full_path}: {e}")
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return False
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def save_video(self, images, output_path, filename_prefix, use_timestamp, fps, crf, pixel_format, video_format,
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scores_list=None, metadata_key="sharpness_score", save_workflow=False, prompt_data=None, extra_data=None):
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"""Save image batch as a video file using ffmpeg."""
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ffmpeg_path = _get_ffmpeg()
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ts_str = f"_{int(time.time())}" if use_timestamp else ""
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ext = ".mp4" if video_format == "mp4" else ".webm"
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out_file = os.path.join(output_path, f"{filename_prefix}{ts_str}{ext}")
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batch_size = len(images)
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h, w = images[0].shape[0], images[0].shape[1]
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# --- BUILD METADATA FILE (avoids arg-too-long for large workflows) ---
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meta_lines = [";FFMETADATA1"]
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meta_lines.append("software=ComfyUI_FastAbsoluteSaver")
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if scores_list:
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avg_score = sum(scores_list) / len(scores_list)
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meta_lines.append(f"{metadata_key}_avg={avg_score:.2f}")
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meta_lines.append(f"{metadata_key}_all={','.join(f'{s:.2f}' for s in scores_list)}")
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if save_workflow:
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if prompt_data:
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# Escape ffmetadata special chars: =, ;, #, \ and newlines
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prompt_str = json.dumps(prompt_data).replace("\\", "\\\\").replace("=", "\\=").replace(";", "\\;").replace("#", "\\#").replace("\n", "\\\n")
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meta_lines.append(f"prompt={prompt_str}")
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if extra_data:
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workflow = extra_data.get("workflow", {})
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if workflow:
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workflow_str = json.dumps(workflow).replace("\\", "\\\\").replace("=", "\\=").replace(";", "\\;").replace("#", "\\#").replace("\n", "\\\n")
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meta_lines.append(f"workflow={workflow_str}")
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self._meta_tmpfile = tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False, encoding="utf-8")
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self._meta_tmpfile.write("\n".join(meta_lines))
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self._meta_tmpfile.close()
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meta_file = self._meta_tmpfile.name
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if video_format == "mp4":
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codec = "libx264"
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cmd = [
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ffmpeg_path, "-y",
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"-f", "rawvideo", "-pix_fmt", "rgb24",
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"-s", f"{w}x{h}", "-r", str(fps),
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"-i", "-",
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"-i", meta_file, "-map_metadata", "1",
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"-c:v", codec, "-crf", str(crf),
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"-pix_fmt", pixel_format,
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"-movflags", "+faststart",
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out_file
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]
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else: # webm
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codec = "libvpx-vp9"
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cmd = [
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ffmpeg_path, "-y",
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"-f", "rawvideo", "-pix_fmt", "rgb24",
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"-s", f"{w}x{h}", "-r", str(fps),
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"-i", "-",
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"-i", meta_file, "-map_metadata", "1",
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"-c:v", codec, "-crf", str(crf), "-b:v", "0",
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"-pix_fmt", pixel_format,
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out_file
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]
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print(f"xx- FastSaver: Encoding {batch_size} frames to {out_file} ({codec}, crf={crf}, {fps}fps)...")
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proc = subprocess.Popen(cmd, stdin=subprocess.PIPE, stderr=subprocess.PIPE)
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try:
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for img_tensor in images:
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frame = (255.0 * img_tensor.cpu().numpy()).clip(0, 255).astype(np.uint8)
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proc.stdin.write(frame.tobytes())
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proc.stdin.close()
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except BrokenPipeError:
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pass
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stderr = proc.stderr.read()
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proc.wait()
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# Clean up metadata temp file
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try:
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os.remove(meta_file)
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except OSError:
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pass
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if proc.returncode != 0:
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raise RuntimeError(f"ffmpeg failed: {stderr.decode()}")
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print(f"xx- FastSaver: Video saved to {out_file}")
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return out_file
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def save_images_fast(self, images, output_path, filename_prefix, save_format, use_timestamp, auto_increment, counter_digits,
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max_threads, filename_with_score, metadata_key, save_workflow_metadata,
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webp_lossless, webp_quality, webp_method,
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video_fps, video_crf, video_pixel_format,
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scores_info=None, prompt=None, extra_pnginfo=None):
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output_path = output_path.strip('"')
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if not os.path.exists(output_path):
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try:
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os.makedirs(output_path, exist_ok=True)
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except OSError:
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raise ValueError(f"Could not create directory: {output_path}")
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if images is None or len(images) == 0:
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raise ValueError("No images provided to FastAbsoluteSaver.")
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# --- VIDEO PATH (check early, before image-specific logic) ---
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if save_format in ("mp4", "webm"):
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batch_size = len(images)
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_, scores_list = self.parse_info(scores_info, batch_size)
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self.save_video(images, output_path, filename_prefix, use_timestamp,
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video_fps, video_crf, video_pixel_format, save_format,
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scores_list=scores_list, metadata_key=metadata_key,
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save_workflow=save_workflow_metadata, prompt_data=prompt,
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extra_data=extra_pnginfo)
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return {"ui": {"images": []}}
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if max_threads == 0:
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max_threads = os.cpu_count() or 4
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batch_size = len(images)
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frame_indices, scores_list = self.parse_info(scores_info, batch_size)
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# --- INDEX LOGIC ---
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start_counter = 0
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using_real_frames = any(idx > 0 for idx in frame_indices)
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if auto_increment and not use_timestamp and not using_real_frames:
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start_counter = self.get_start_index(output_path, filename_prefix)
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ts_str = f"_{int(time.time())}" if use_timestamp else ""
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print(f"xx- FastSaver: Saving {batch_size} images to {output_path}...")
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with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor:
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futures = []
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for i, img_tensor in enumerate(images):
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real_frame_num = frame_indices[i]
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current_score = scores_list[i] if scores_list else None
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if real_frame_num > 0:
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number_part = real_frame_num
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else:
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number_part = start_counter + i
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fmt_str = f"{{:0{counter_digits}d}}"
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number_str = fmt_str.format(number_part)
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base_name = f"{filename_prefix}{ts_str}_{number_str}"
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if filename_with_score and current_score is not None:
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base_name += f"_{int(current_score)}"
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ext = ".webp" if save_format == "webp" else ".png"
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full_path = os.path.join(output_path, f"{base_name}{ext}")
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futures.append(executor.submit(
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self.save_single_image,
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img_tensor, full_path, current_score, metadata_key,
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save_format, webp_lossless, webp_quality, webp_method,
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save_workflow_metadata, prompt, extra_pnginfo
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))
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concurrent.futures.wait(futures)
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return {"ui": {"images": []}} |