149 lines
5.8 KiB
Python
149 lines
5.8 KiB
Python
import os
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import torch
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import numpy as np
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from PIL import Image
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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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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"], ),
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# --- NAMING CONTROL ---
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"use_timestamp": ("BOOLEAN", {"default": True, "label": "Add Timestamp (Unique)"}),
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"counter_digits": ("INT", {"default": 3, "min": 1, "max": 12, "step": 1, "label": "Number Padding (00X)"}),
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"filename_with_score": ("BOOLEAN", {"default": False, "label": "Append Score to Filename"}),
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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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# --- METADATA ---
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"metadata_key": ("STRING", {"default": "sharpness_score"}),
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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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},
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"optional": {
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"scores_info": ("STRING", {"forceInput": True}),
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}
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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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return ([0]*batch_size, [0.0]*batch_size)
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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 save_single_image(self, tensor_img, full_path, score, key_name, fmt, lossless, quality, method):
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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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if fmt == "png":
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metadata = PngInfo()
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metadata.add_text(key_name, str(score))
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metadata.add_text("software", "ComfyUI_Parallel_Node")
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img.save(full_path, format="PNG", pnginfo=metadata, compress_level=1)
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elif fmt == "webp":
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img.save(full_path, format="WEBP",
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lossless=lossless,
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quality=quality,
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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_images_fast(self, images, output_path, filename_prefix, save_format, use_timestamp, counter_digits,
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max_threads, filename_with_score, metadata_key, webp_lossless, webp_quality, webp_method, scores_info=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 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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# Pre-calculate timestamp once for the whole batch if needed
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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]
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# Logic: If we have real frame numbers (from Loader), use them.
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# If NOT (or if frame is 0), use the loop index 'i' (0, 1, 2...)
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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 = i
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# Format string using dynamic padding size (e.g. :05d)
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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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# Construct Name: prefix + timestamp + number
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# Case 1: frame_173000_001.png (Timestamp ON)
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# Case 2: frame_001.png (Timestamp OFF)
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base_name = f"{filename_prefix}{ts_str}_{number_str}"
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if filename_with_score:
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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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))
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concurrent.futures.wait(futures)
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return {"ui": {"images": []}} |