Update image and video workflow nodes
This commit is contained in:
o1key
+478
-1
@@ -3,10 +3,23 @@ o1key GPT Image 节点
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支持 gpt-image-1 / gpt-image-1.5 模型的文生图、图生图、图像编辑（带蒙版）
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"""
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import os
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import time
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from typing import List, Optional, Tuple
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from PIL import Image
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from ..clients.gpt_image_client import GptImageClient
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from ..utils.image_utils import parse_batch_prompts
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from ..utils.image_utils import parse_batch_prompts, pil_to_tensor, tensor_to_pil
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from ..utils.config import NETWORK_ROUTE_OPTIONS, get_base_url_by_route
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from ..utils.file_utils import (
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ImageInfo,
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generate_timestamp_filename,
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load_images_from_folder,
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pair_images_by_name,
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pair_images_cartesian,
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save_image,
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)
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try:
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from comfy.model_management import processing_interrupted, InterruptProcessingException
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@@ -16,6 +29,18 @@ except ImportError:
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processing_interrupted = lambda: False
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InterruptProcessingException = RuntimeError
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try:
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from comfy.utils import ProgressBar
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_PROGRESS_BAR_AVAILABLE = True
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except ImportError:
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_PROGRESS_BAR_AVAILABLE = False
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try:
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import folder_paths
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_FOLDER_PATHS_AVAILABLE = True
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except ImportError:
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_FOLDER_PATHS_AVAILABLE = False
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class O1keyGPTImage:
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"""
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@@ -270,3 +295,455 @@ class O1keyGPTImage:
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print(f"[o1key GPT Image] {balance_info}")
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except Exception:
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pass
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class O1keyGPTImageBatch:
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"""
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o1key GPT Image 批量节点
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复用 BatchNanoBananaPro 的批量思路：
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- 从文件夹批量加载图片
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- 按文件名同名 / 1*N / 不配对 三种模式创建任务
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- 可追加节点手动输入参考图
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- prompt 支持用独占一行 --- 展开为多提示词任务
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- 每个任务调用 GPT Image 客户端并保存到磁盘
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"""
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PAIRING_MODES = ["按相同图片命名", "1*N", "不配对"]
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IMAGE_FORMATS = ["原始", "JPEG", "PNG", "WebP"]
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MODEL_OPTIONS = ["gpt-image-2-按量", "gpt-image-2-次卡"]
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QUALITY_OPTIONS = ["高", "中", "低", "自动"]
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RESOLUTION_OPTIONS = [
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"智能",
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"1024x1024（1K 正方形 1:1）",
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"1536x1024（1K 横版 3:2）",
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"1024x1536（1K 竖版 2:3）",
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"1360x1024（1K 横版 4:3）",
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"1024x1360（1K 竖版 3:4）",
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"1824x1024（1K 横版 16:9）",
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"1024x1824（1K 竖版 9:16）",
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"2048x2048（2K 正方形 1:1）",
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"3072x2048（2K 横版 3:2）",
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"2048x3072（2K 竖版 2:3）",
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"2736x2048（2K 横版 4:3）",
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"2048x2736（2K 竖版 3:4）",
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"3648x2048（2K 横版 16:9）",
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"2048x3648（2K 竖版 9:16）",
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"2880x2880（4K 正方形 1:1）",
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"3504x2336（4K 横版 3:2）",
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"2336x3504（4K 竖版 2:3）",
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"3264x2448（4K 横版 4:3）",
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"2448x3264（4K 竖版 3:4）",
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"3840x2160（4K 横版 16:9）",
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"2160x3840（4K 竖版 9:16）",
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]
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@classmethod
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def INPUT_TYPES(cls):
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optional_inputs = {}
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for image_index in range(1, 10):
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optional_inputs[f"参考图{image_index}"] = ("IMAGE", {
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"tooltip": "追加到每个批量任务末尾的固定参考图。",
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})
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optional_inputs["遮罩"] = ("MASK", {
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"tooltip": "可选蒙版，会应用到每个任务的第一张参考图；请确保尺寸一致。",
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})
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optional_inputs["图片配对模式"] = (cls.PAIRING_MODES, {
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"default": "不配对",
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"tooltip": "文件夹图片的组合方式；手动参考图只追加，不参与配对。",
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})
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return {
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"required": {
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"prompt": ("STRING", {
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"default": "",
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"multiline": True,
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"tooltip": "提示词；可用独占一行的 --- 分隔多条批量提示词。",
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}),
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"模型": (cls.MODEL_OPTIONS, {
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"default": "gpt-image-2-次卡",
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}),
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"网络": (NETWORK_ROUTE_OPTIONS, {
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"default": "全球加速",
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}),
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"分辨率": (cls.RESOLUTION_OPTIONS, {
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"default": "智能",
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}),
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"生图数量": ("INT", {
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"default": 1,
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"min": 1,
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"max": 8,
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"step": 1,
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"display": "number",
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}),
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"质量": (cls.QUALITY_OPTIONS, {
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"default": "自动",
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}),
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"seed": ("INT", {
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"default": 0,
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"min": 0,
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"max": 2**31 - 1,
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"step": 1,
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"display": "number",
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"control_after_generate": True,
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}),
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"图片格式": (cls.IMAGE_FORMATS, {
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"default": "原始",
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}),
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"文件夹1": ("STRING", {
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"default": "",
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"multiline": False,
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}),
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"文件夹2": ("STRING", {
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"default": "",
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"multiline": False,
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}),
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"文件夹3": ("STRING", {
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"default": "",
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"multiline": False,
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}),
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"文件夹4": ("STRING", {
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"default": "",
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"multiline": False,
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}),
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"文件夹5": ("STRING", {
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"default": "",
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"multiline": False,
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}),
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"保存路径": ("STRING", {
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"default": "",
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"multiline": False,
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"tooltip": "为空时优先使用 ComfyUI 默认 output 目录。",
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}),
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},
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"optional": optional_inputs,
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("IMAGE",)
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FUNCTION = "process_batch"
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CATEGORY = "o1key/image"
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OUTPUT_NODE = False
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def _load_folders(self, folders: List[str]) -> List[List[ImageInfo]]:
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image_lists = []
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for folder_index, folder in enumerate(folders, 1):
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if not folder or not folder.strip():
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continue
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try:
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loaded_images = load_images_from_folder(folder)
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if loaded_images:
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image_lists.append(loaded_images)
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except ValueError as error:
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print(f"[o1key GPT Image Batch] 文件夹{folder_index} 加载失败 - {error}")
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return image_lists
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def _create_pairs(
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self,
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image_lists: List[List[ImageInfo]],
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pairing_mode: str,
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manual_images: Optional[List[ImageInfo]] = None,
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) -> List[Tuple[ImageInfo, ...]]:
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if pairing_mode == "不配对":
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if len(image_lists) > 1:
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raise ValueError("「不配对」模式只支持单个文件夹，请清空其他文件夹路径")
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if image_lists and manual_images:
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return [
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(folder_image,) + tuple(manual_images)
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for folder_image in image_lists[0]
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]
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if image_lists:
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return [(folder_image,) for folder_image in image_lists[0]]
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return []
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if not image_lists:
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return []
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if len(image_lists) == 1:
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base_pairs = [(folder_image,) for folder_image in image_lists[0]]
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elif pairing_mode == "按相同图片命名":
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base_pairs = list(pair_images_by_name(*image_lists))
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else:
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base_pairs = list(pair_images_cartesian(*image_lists))
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if manual_images:
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manual_tuple = tuple(manual_images)
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base_pairs = [pair + manual_tuple for pair in base_pairs]
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return base_pairs
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def _collect_manual_images(self, kwargs) -> List[ImageInfo]:
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manual_images = []
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for image_index in range(1, 10):
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key = f"参考图{image_index}"
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if key not in kwargs or kwargs[key] is None:
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continue
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for tensor_index, image in enumerate(tensor_to_pil(kwargs[key])):
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manual_images.append(ImageInfo(
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image=image,
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filename=f"manual_{image_index}_{tensor_index}",
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extension=".png",
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source_path="",
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))
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return manual_images
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@staticmethod
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def _pair_to_tensors(pair: Tuple[ImageInfo, ...]) -> List:
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return [pil_to_tensor([image_info.image]) for image_info in pair]
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@staticmethod
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def _resolve_size(分辨率: str) -> str:
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return "auto" if 分辨率 == "智能" else 分辨率.split("（")[0].strip()
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@staticmethod
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def _resolve_model(模型: str) -> str:
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model_map = {
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"gpt-image-2-次卡": "gpt-image-2-c",
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"gpt-image-2-按量": "gpt-image-2",
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}
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return model_map.get(模型, 模型)
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@staticmethod
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def _resolve_quality(质量: str) -> str:
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quality_map = {"高": "high", "中": "medium", "低": "low", "自动": "auto"}
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return quality_map.get(质量, "auto")
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@staticmethod
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def _ensure_output_folder(保存路径: str) -> str:
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output_folder = (保存路径 or "").strip()
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if not output_folder and _FOLDER_PATHS_AVAILABLE:
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output_folder = folder_paths.get_output_directory()
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print(f"[o1key GPT Image Batch] 未设置保存路径，使用 ComfyUI 默认 output 目录: {output_folder}")
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if not output_folder:
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raise ValueError("未设置保存路径，且当前环境无法获取 ComfyUI 默认 output 目录")
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os.makedirs(output_folder, exist_ok=True)
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test_path = os.path.join(output_folder, ".write_test")
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with open(test_path, "w", encoding="utf-8") as test_file:
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test_file.write("test")
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os.remove(test_path)
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return output_folder
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@staticmethod
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def _save_images(
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images: List[Image.Image],
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output_folder: str,
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image_format: str,
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base_filename: Optional[str] = None,
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) -> List[str]:
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format_ext_map = {"JPEG": ".jpg", "PNG": ".png", "WebP": ".webp"}
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save_ext = format_ext_map.get(image_format, ".png")
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saved_files = []
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for image in images:
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if base_filename:
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counter = 0
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while True:
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suffix = "" if counter == 0 else f"+{counter}"
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filename = f"{base_filename}{suffix}{save_ext}"
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output_path = os.path.join(output_folder, filename)
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if not os.path.exists(output_path):
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break
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counter += 1
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else:
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output_path = generate_timestamp_filename(
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output_folder=output_folder,
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extension=save_ext,
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)
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if image_format == "JPEG":
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if image.mode != "RGB":
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image = image.convert("RGB")
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image.save(output_path, quality=100)
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elif image_format == "WebP":
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image.save(output_path, lossless=True)
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else:
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save_image(image, output_path)
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saved_files.append(output_path)
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return saved_files
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def process_batch(
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self,
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prompt: str,
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模型: str,
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网络: str,
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分辨率: str,
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生图数量: int,
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质量: str,
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seed: int,
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图片格式: str,
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文件夹1: str,
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文件夹2: str,
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文件夹3: str,
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文件夹4: str,
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文件夹5: str,
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保存路径: str = "",
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图片配对模式: str = "不配对",
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遮罩=None,
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**kwargs,
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):
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start_time = time.time()
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client = None
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try:
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if not prompt or not prompt.strip():
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raise ValueError("提示词不能为空")
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folders = [文件夹1, 文件夹2, 文件夹3, 文件夹4, 文件夹5]
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if not any(folder and folder.strip() for folder in folders):
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raise ValueError("请至少填写一个文件夹路径，该节点专为批量文件夹处理设计")
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image_lists = self._load_folders(folders)
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total_folder_images = sum(len(image_list) for image_list in image_lists)
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if total_folder_images == 0:
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raise ValueError("文件夹中未找到任何图片，请检查文件夹路径是否正确")
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manual_images = self._collect_manual_images(kwargs)
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pairs = self._create_pairs(
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image_lists=image_lists,
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pairing_mode=图片配对模式,
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manual_images=manual_images if manual_images else None,
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)
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if not pairs:
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raise ValueError("配对结果为空，请检查输入")
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batch_prompts = parse_batch_prompts(prompt)
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prompts_per_task = None
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if batch_prompts:
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expanded_pairs = []
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expanded_prompts = []
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for pair in pairs:
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for batch_prompt in batch_prompts:
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expanded_pairs.append(pair)
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expanded_prompts.append(batch_prompt)
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pairs = expanded_pairs
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prompts_per_task = expanded_prompts
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total_tasks = len(pairs)
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if batch_prompts:
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print(
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f"[o1key GPT Image Batch] 批量任务 | {图片配对模式} × "
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f"{len(batch_prompts)} 个提示词 | 共 {total_tasks} 任务"
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)
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else:
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print(f"[o1key GPT Image Batch] 批量任务 | {图片配对模式} | 共 {total_tasks} 任务")
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output_folder = self._ensure_output_folder(保存路径)
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size = self._resolve_size(分辨率)
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model = self._resolve_model(模型)
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quality = self._resolve_quality(质量)
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client = GptImageClient()
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client.base_url = get_base_url_by_route(网络)
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progress_bar = ProgressBar(total_tasks) if _PROGRESS_BAR_AVAILABLE else None
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results = []
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all_saved_files = []
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for task_index, pair in enumerate(pairs, 1):
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if _INTERRUPT_AVAILABLE and processing_interrupted():
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print("[o1key GPT Image Batch] 用户取消，已中断批量生成")
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raise InterruptProcessingException()
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task_prompt = prompts_per_task[task_index - 1] if prompts_per_task else prompt
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base_filename = pair[0].filename if pair else None
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result = {
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"task_index": task_index,
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"success": False,
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"generated_count": 0,
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"saved_files": [],
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"error": None,
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}
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try:
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pil_images = client.run_sync(
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prompt=task_prompt,
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model=model,
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quality=quality,
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size=size,
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n=生图数量,
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seed=seed,
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image_tensor=self._pair_to_tensors(pair),
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mask_tensor=遮罩,
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)
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saved_files = self._save_images(
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images=pil_images,
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output_folder=output_folder,
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image_format=图片格式,
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base_filename=base_filename,
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)
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result["success"] = bool(pil_images)
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result["generated_count"] = len(pil_images)
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result["saved_files"] = saved_files
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all_saved_files.extend(saved_files)
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print(f"[o1key GPT Image Batch] [{task_index}/{total_tasks}] ✓ {base_filename or 'task'}")
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except InterruptProcessingException:
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raise
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except Exception as error:
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error_msg = str(error).split("\n")[0]
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result["error"] = error_msg
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print(f"[o1key GPT Image Batch] [{task_index}/{total_tasks}] ❌ {base_filename or 'task'} → {error_msg}")
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results.append(result)
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if progress_bar is not None:
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progress_bar.update(1)
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success_count = sum(1 for result in results if result.get("success", False))
|
||||
total_generated = sum(result.get("generated_count", 0) for result in results)
|
||||
if success_count == 0:
|
||||
raise RuntimeError("所有批量任务均生成失败，无可用图像输出")
|
||||
|
||||
output_images = []
|
||||
for file_path in all_saved_files[-10:]:
|
||||
try:
|
||||
loaded_image = Image.open(file_path)
|
||||
loaded_image.load()
|
||||
output_images.append(loaded_image)
|
||||
except Exception as error:
|
||||
print(f"[o1key GPT Image Batch] 无法加载输出图片 {file_path} - {error}")
|
||||
|
||||
if not output_images:
|
||||
output_images = [Image.new("RGBA", (512, 512), (128, 128, 128, 255))]
|
||||
|
||||
output_tensor = GptImageClient._pil_list_to_tensor(output_images)
|
||||
elapsed = time.time() - start_time
|
||||
print("=" * 60)
|
||||
print(
|
||||
f"[o1key GPT Image Batch] 完成！耗时 {elapsed:.1f}s | "
|
||||
f"成功 {success_count}/{total_tasks} | 生成 {total_generated} 张"
|
||||
)
|
||||
print(f"[o1key GPT Image Batch] 保存路径: {output_folder}")
|
||||
if all_saved_files:
|
||||
print(f"[o1key GPT Image Batch] 最新保存文件: {all_saved_files[-1]}")
|
||||
|
||||
failed_results = [result for result in results if not result.get("success", False)]
|
||||
if failed_results:
|
||||
print(f"[o1key GPT Image Batch] 失败任务: {len(failed_results)} 个")
|
||||
for failed_result in failed_results[:3]:
|
||||
print(
|
||||
f" - #{failed_result.get('task_index')}: "
|
||||
f"{failed_result.get('error', '未知错误')}"
|
||||
)
|
||||
|
||||
return (output_tensor,)
|
||||
|
||||
except ValueError as error:
|
||||
if str(error) == "未授权！":
|
||||
print("[o1key GPT Image Batch] 请联系作者授权后方可使用！")
|
||||
raise ValueError("未授权！") from None
|
||||
raise ValueError(str(error)) from None
|
||||
except RuntimeError as error:
|
||||
raise RuntimeError(str(error)) from None
|
||||
finally:
|
||||
if client is not None:
|
||||
try:
|
||||
balance_data = client.query_balance_sync()
|
||||
balance_info = client.format_balance_info(balance_data)
|
||||
print(f"[o1key GPT Image Batch] {balance_info}")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
Reference in New Issue
Block a user