| | import json |
| | import os |
| | import re |
| |
|
| | from datetime import datetime |
| |
|
| | from PIL import Image |
| | from PIL.PngImagePlugin import PngInfo |
| | import numpy as np |
| |
|
| | import piexif |
| | import piexif.helper |
| |
|
| | import folder_paths |
| | from comfy.cli_args import args |
| |
|
| | from .base import BaseNode |
| |
|
| | from ..capture import Capture |
| | from .. import hook |
| | from ..trace import Trace |
| |
|
| | from ..defs.combo import SAMPLER_SELECTION_METHOD |
| |
|
| |
|
| | |
| | class SaveImageWithMetaData(BaseNode): |
| | SAVE_FILE_FORMATS = ["png", "jpeg", "webp"] |
| |
|
| | def __init__(self): |
| | self.output_dir = folder_paths.get_output_directory() |
| | self.type = "output" |
| | self.prefix_append = "" |
| | self.compress_level = 4 |
| |
|
| | @classmethod |
| | def INPUT_TYPES(s): |
| | return { |
| | "required": { |
| | "images": ("IMAGE",), |
| | "filename_prefix": ("STRING", {"default": "ComfyUI"}), |
| | "sampler_selection_method": (SAMPLER_SELECTION_METHOD,), |
| | "sampler_selection_node_id": ( |
| | "INT", |
| | {"default": 0, "min": 0, "max": 999999999, "step": 1}, |
| | ), |
| | "file_format": (s.SAVE_FILE_FORMATS,), |
| | }, |
| | "optional": { |
| | "lossless_webp": ("BOOLEAN", {"default": True}), |
| | "quality": ("INT", {"default": 100, "min": 1, "max": 100}), |
| | "save_workflow_json": ("BOOLEAN", {"default": False}), |
| | "add_counter_to_filename": ("BOOLEAN", {"default": True}), |
| | }, |
| | "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, |
| | } |
| |
|
| | RETURN_TYPES = () |
| | FUNCTION = "save_images" |
| |
|
| | OUTPUT_NODE = True |
| |
|
| | pattern_format = re.compile(r"(%[^%]+%)") |
| |
|
| | def save_images( |
| | self, |
| | images, |
| | filename_prefix="ComfyUI", |
| | sampler_selection_method=SAMPLER_SELECTION_METHOD[0], |
| | sampler_selection_node_id=0, |
| | file_format="png", |
| | lossless_webp=True, |
| | quality=100, |
| | save_workflow_json=False, |
| | add_counter_to_filename=True, |
| | prompt=None, |
| | extra_pnginfo=None, |
| | ): |
| | pnginfo_dict_src = self.gen_pnginfo( |
| | sampler_selection_method, sampler_selection_node_id |
| | ) |
| | results = list() |
| | for index, image in enumerate(images): |
| | i = 255.0 * image.cpu().numpy() |
| | img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) |
| |
|
| | pnginfo_dict = pnginfo_dict_src.copy() |
| | if len(images) >= 2: |
| | pnginfo_dict["Batch index"] = index |
| | pnginfo_dict["Batch size"] = len(images) |
| |
|
| | metadata = None |
| | parameters = "" |
| | if not args.disable_metadata: |
| | metadata = PngInfo() |
| | parameters = Capture.gen_parameters_str(pnginfo_dict) |
| | if pnginfo_dict: |
| | metadata.add_text("parameters", parameters) |
| | if prompt is not None: |
| | metadata.add_text("prompt", json.dumps(prompt)) |
| | if extra_pnginfo is not None: |
| | for x in extra_pnginfo: |
| | metadata.add_text(x, json.dumps(extra_pnginfo[x])) |
| |
|
| | filename_prefix = self.format_filename(filename_prefix, pnginfo_dict) |
| | output_path = os.path.join(self.output_dir, filename_prefix) |
| | if not os.path.exists(os.path.dirname(output_path)): |
| | os.makedirs(os.path.dirname(output_path), exist_ok=True) |
| | ( |
| | full_output_folder, |
| | filename, |
| | counter, |
| | subfolder, |
| | filename_prefix, |
| | ) = folder_paths.get_save_image_path( |
| | filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0] |
| | ) |
| | base_filename = filename |
| | if add_counter_to_filename: |
| | base_filename += f"_{counter:05}_" |
| | file = base_filename + "." + file_format |
| | file_path = os.path.join(full_output_folder, file) |
| |
|
| | if file_format == "png": |
| | img.save( |
| | file_path, |
| | pnginfo=metadata, |
| | compress_level=self.compress_level, |
| | ) |
| | else: |
| | img.save( |
| | file_path, |
| | optimize=True, |
| | quality=quality, |
| | lossless=lossless_webp, |
| | ) |
| | exif_bytes = piexif.dump( |
| | { |
| | "Exif": { |
| | piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump( |
| | parameters, encoding="unicode" |
| | ), |
| | }, |
| | } |
| | ) |
| | piexif.insert(exif_bytes, file_path) |
| |
|
| | if save_workflow_json: |
| | file_path_workflow = os.path.join( |
| | full_output_folder, f"{base_filename}.json" |
| | ) |
| | with open(file_path_workflow, "w", encoding="utf-8") as f: |
| | json.dump(extra_pnginfo["workflow"], f) |
| |
|
| | results.append( |
| | {"filename": file, "subfolder": subfolder, "type": self.type} |
| | ) |
| | counter += 1 |
| |
|
| | return {"ui": {"images": results}} |
| |
|
| | @classmethod |
| | def gen_pnginfo(cls, sampler_selection_method, sampler_selection_node_id): |
| | |
| | inputs = Capture.get_inputs() |
| |
|
| | |
| | trace_tree_from_this_node = Trace.trace( |
| | hook.current_save_image_node_id, hook.current_prompt |
| | ) |
| | inputs_before_this_node = Trace.filter_inputs_by_trace_tree( |
| | inputs, trace_tree_from_this_node |
| | ) |
| | sampler_node_id = Trace.find_sampler_node_id( |
| | trace_tree_from_this_node, |
| | sampler_selection_method, |
| | sampler_selection_node_id, |
| | ) |
| |
|
| | |
| | trace_tree_from_sampler_node = Trace.trace(sampler_node_id, hook.current_prompt) |
| | inputs_before_sampler_node = Trace.filter_inputs_by_trace_tree( |
| | inputs, trace_tree_from_sampler_node |
| | ) |
| |
|
| | |
| | pnginfo_dict = Capture.gen_pnginfo_dict( |
| | inputs_before_sampler_node, inputs_before_this_node |
| | ) |
| | return pnginfo_dict |
| |
|
| | @classmethod |
| | def format_filename(cls, filename, pnginfo_dict): |
| | result = re.findall(cls.pattern_format, filename) |
| | for segment in result: |
| | parts = segment.replace("%", "").split(":") |
| | key = parts[0] |
| | if key == "seed": |
| | filename = filename.replace(segment, str(pnginfo_dict.get("Seed", ""))) |
| | elif key == "width": |
| | w = pnginfo_dict.get("Size", "x").split("x")[0] |
| | filename = filename.replace(segment, str(w)) |
| | elif key == "height": |
| | w = pnginfo_dict.get("Size", "x").split("x")[1] |
| | filename = filename.replace(segment, str(w)) |
| | elif key == "pprompt": |
| | prompt = pnginfo_dict.get("Positive prompt", "").replace("\n", " ") |
| | if len(parts) >= 2: |
| | length = int(parts[1]) |
| | prompt = prompt[:length] |
| | filename = filename.replace(segment, prompt.strip()) |
| | elif key == "nprompt": |
| | prompt = pnginfo_dict.get("Negative prompt", "").replace("\n", " ") |
| | if len(parts) >= 2: |
| | length = int(parts[1]) |
| | prompt = prompt[:length] |
| | filename = filename.replace(segment, prompt.strip()) |
| | elif key == "model": |
| | model = pnginfo_dict.get("Model", "") |
| | model = os.path.splitext(os.path.basename(model))[0] |
| | if len(parts) >= 2: |
| | length = int(parts[1]) |
| | model = model[:length] |
| | filename = filename.replace(segment, model) |
| | elif key == "date": |
| | now = datetime.now() |
| | date_table = { |
| | "yyyy": now.year, |
| | "MM": now.month, |
| | "dd": now.day, |
| | "hh": now.hour, |
| | "mm": now.minute, |
| | "ss": now.second, |
| | } |
| | if len(parts) >= 2: |
| | date_format = parts[1] |
| | for k, v in date_table.items(): |
| | date_format = date_format.replace(k, str(v).zfill(len(k))) |
| | filename = filename.replace(segment, date_format) |
| | else: |
| | date_format = "yyyyMMddhhmmss" |
| | for k, v in date_table.items(): |
| | date_format = date_format.replace(k, str(v).zfill(len(k))) |
| | filename = filename.replace(segment, date_format) |
| |
|
| | return filename |
| |
|
