diff --git a/__init__.py b/__init__.py index ac8a26a..2b4dae7 100644 --- a/__init__.py +++ b/__init__.py @@ -13,7 +13,7 @@ Comfyui_o1key - ComfyUI 自定义节点集合 import ssl from .nodes import NanoBananaPro, BatchNanoBananaPro, GoogleGemini, LoadFile, ImageStitchPro, SaveCleanImage, BatchCleanMetadata, VideoPreview, GoogleVeo, FluxImageEdit, UniversalLLMChat, KlingVideo, KlingFirstLastFrame, KlingMotionControlTest, AspectRatioPreset, MultiResPreview, BatchImagesO1key, Seedance, SeedanceMultiModal, StreamPreview, DoubaoImage, O1keyGPTImage, KVideo -from .nodes import K3Video, K3VideoFirstLast, K3MotionControl, K3MotionVideoCheck, AsyncImageGenerator, BatchAsyncImageGenerator +from .nodes import K3Video, K3VideoFirstLast, K3MotionControl, K3MotionVideoCheck, NanoBananaV2, NanoBananaV2Batch # 报错弹框友好文案（不修改原节点代码，仅在外层统一处理） _MSG_TIMEOUT = "API 请求超时，请稍后重试或检查网络。" @@ -47,8 +47,8 @@ def _wrap_generate_for_error_display(cls, attr="generate"): _wrap_generate_for_error_display(NanoBananaPro) _wrap_generate_for_error_display(BatchNanoBananaPro) -_wrap_generate_for_error_display(AsyncImageGenerator) -_wrap_generate_for_error_display(BatchAsyncImageGenerator) +_wrap_generate_for_error_display(NanoBananaV2) +_wrap_generate_for_error_display(NanoBananaV2Batch) # ComfyUI 节点注册 NODE_CLASS_MAPPINGS = { @@ -79,8 +79,8 @@ NODE_CLASS_MAPPINGS = { "K3VideoFirstLast": K3VideoFirstLast, "K3MotionControl": K3MotionControl, "K3MotionVideoCheck": K3MotionVideoCheck, - "AsyncImageGenerator": AsyncImageGenerator, - "BatchAsyncImageGenerator": BatchAsyncImageGenerator, + "NanoBananaV2": NanoBananaV2, + "NanoBananaV2Batch": NanoBananaV2Batch, } NODE_DISPLAY_NAME_MAPPINGS = { @@ -111,8 +111,8 @@ NODE_DISPLAY_NAME_MAPPINGS = { "K3VideoFirstLast": "首尾帧 K3 自研", "K3MotionControl": "动作控制 K3 自研", "K3MotionVideoCheck": "视频时长检测 K3", - "AsyncImageGenerator": "AI生图", - "BatchAsyncImageGenerator": "AI生图（批量版）", + "NanoBananaV2": "Nano Banana V2", + "NanoBananaV2Batch": "Nano Banana V2（批量）", } WEB_DIRECTORY = "./web" diff --git a/models_config.py b/models_config.py index db62a87..fb2bac2 100644 --- a/models_config.py +++ b/models_config.py @@ -93,31 +93,6 @@ GEMINI_MODELS = [ ], "supported_resolutions": ["512px", "1K", "2K", "4K"] }, - { - "id": "nano-banana-pro-官方", - "description": "Nano Banana Pro 官方，固定端点，适用于常规图像生成", - "enabled": True, - "provider": "gemini_async", - "endpoint_type": "standard", - "endpoint": "/v1beta/models/gemini-3-pro-image-preview:generateContent", - "supported_aspect_ratios": [ - "1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9", "21:9" - ], - "supported_resolutions": ["1K", "2K", "4K"] - }, - { - "id": "nano-banana-2-官方", - "description": "Nano Banana 2 官方，固定端点，快速图像生成模型", - "enabled": True, - "provider": "gemini_async", - "endpoint_type": "standard", - "endpoint": "/v1beta/models/gemini-3.1-flash-image-preview:generateContent", - "supported_aspect_ratios": [ - "1:1", "1:4", "1:8", "2:3", "3:2", "3:4", "4:1", "4:3", "4:5", "5:4", - "8:1", "9:16", "16:9", "21:9" - ], - "supported_resolutions": ["512px", "1K", "2K", "4K"] - } ] diff --git a/nodes/__init__.py b/nodes/__init__.py index 4540a79..23d53fa 100644 --- a/nodes/__init__.py +++ b/nodes/__init__.py @@ -18,7 +18,7 @@ from .universal_llm import UniversalLLMChat from .multi_res_preview import MultiResPreview from .batch_images_o1key import BatchImagesO1key from .seedance_video import Seedance, SeedanceMultiModal -from .async_image_generation import AsyncImageGenerator, BatchAsyncImageGenerator +from .nano_banana_v2 import NanoBananaV2, NanoBananaV2Batch, AsyncImageGenerator, BatchAsyncImageGenerator from .doubao_image import DoubaoImage from .gpt_image import O1keyGPTImage from .K_video import KVideo @@ -26,4 +26,4 @@ from .K3_video import K3Video from .K3_video_firstlast import K3VideoFirstLast from .K3_motion_control import K3MotionControl, K3MotionVideoCheck -__all__ = ['NanoBananaPro', 'BatchNanoBananaPro', 'GoogleGemini', 'LoadFile', 'ImageStitchPro', 'SaveCleanImage', 'BatchCleanMetadata', 'VideoPreview', 'KlingVideo', 'KlingFirstLastFrame', 'KlingMotionControlTest', 'AspectRatioPreset', 'GoogleVeo', 'FluxImageEdit', 'UniversalLLMChat', 'MultiResPreview', 'BatchImagesO1key', 'Seedance', 'SeedanceMultiModal', 'StreamPreview', 'DoubaoImage', 'O1keyGPTImage', 'KVideo', 'K3Video', 'K3VideoFirstLast', 'K3MotionControl', 'K3MotionVideoCheck', 'AsyncImageGenerator', 'BatchAsyncImageGenerator'] +__all__ = ['NanoBananaV2', 'NanoBananaV2Batch', 'NanoBananaPro', 'BatchNanoBananaPro', 'GoogleGemini', 'LoadFile', 'ImageStitchPro', 'SaveCleanImage', 'BatchCleanMetadata', 'VideoPreview', 'KlingVideo', 'KlingFirstLastFrame', 'KlingMotionControlTest', 'AspectRatioPreset', 'GoogleVeo', 'FluxImageEdit', 'UniversalLLMChat', 'MultiResPreview', 'BatchImagesO1key', 'Seedance', 'SeedanceMultiModal', 'StreamPreview', 'DoubaoImage', 'O1keyGPTImage', 'KVideo', 'K3Video', 'K3VideoFirstLast', 'K3MotionControl', 'K3MotionVideoCheck', 'AsyncImageGenerator', 'BatchAsyncImageGenerator'] diff --git a/nodes/batch_nano_banana_pro.py b/nodes/batch_nano_banana_pro.py index daccda1..4a9399c 100644 --- a/nodes/batch_nano_banana_pro.py +++ b/nodes/batch_nano_banana_pro.py @@ -790,7 +790,7 @@ class BatchNanoBananaPro: # 校验图片搜索（联网）与模型的兼容性 # 仅 nano-banana-2-限时特价 和 gemini-3.1-flash-image-preview 支持图片搜索 - IMAGE_SEARCH_UNSUPPORTED_MODELS = ["nano-banana-pro-限时特价", "nano-banana-pro-官方计费", "gemini-3-pro-image-preview"] + IMAGE_SEARCH_UNSUPPORTED_MODELS = ["nano-banana-pro-次卡", "nano-banana-pro-官方计费", "gemini-3-pro-image-preview"] if enable_image_search and 模型 in IMAGE_SEARCH_UNSUPPORTED_MODELS: raise ValueError( f"模型 \"{模型}\" 不支持【图片搜索（联网）】功能！" diff --git a/nodes/nano_banana_pro.py b/nodes/nano_banana_pro.py index 5a2717e..026cbef 100644 --- a/nodes/nano_banana_pro.py +++ b/nodes/nano_banana_pro.py @@ -527,7 +527,7 @@ class NanoBananaPro: # 校验图片搜索（联网）与模型的兼容性 # 仅 nano-banana-2-限时特价 和 gemini-3.1-flash-image-preview 支持图片搜索 - IMAGE_SEARCH_UNSUPPORTED_MODELS = ["nano-banana-pro-限时特价", "nano-banana-pro-官方计费", "gemini-3-pro-image-preview"] + IMAGE_SEARCH_UNSUPPORTED_MODELS = ["nano-banana-pro-次卡", "nano-banana-pro-官方计费", "gemini-3-pro-image-preview"] if enable_image_search and 模型 in IMAGE_SEARCH_UNSUPPORTED_MODELS: raise ValueError( f"模型 \"{模型}\" 不支持【图片搜索（联网）】功能！" diff --git a/nodes/async_image_generation.py b/nodes/nano_banana_v2.py similarity index 77% rename from nodes/async_image_generation.py rename to nodes/nano_banana_v2.py index 757688c..169ad75 100644 --- a/nodes/async_image_generation.py +++ b/nodes/nano_banana_v2.py @@ -11,7 +11,6 @@ ComfyUI 自定义节点，通过异步提交+轮询模式调用多种生图模 import os import time -import math import random import asyncio import aiohttp @@ -30,6 +29,7 @@ import numpy as np from PIL import Image from ..utils.image_utils import tensor_to_pil, pil_to_tensor, parse_batch_prompts +from ..utils.file_utils import load_images_from_folder, pair_images_by_name, pair_images_cartesian from ..utils.config import get_api_key_or_raise from ..models_config import ( get_enabled_async_models, @@ -60,11 +60,10 @@ except ImportError: MEMORY_MONITOR_AVAILABLE = False DEBUG_LOG_ENABLED = False -REQUEST_LOG_ENABLED = True +REQUEST_LOG_ENABLED = False _POLL_INTERVAL = 2 # 轮询间隔（秒） _MAX_WAIT_TIME = 900 # 单任务最大等待时间（秒） -_MAX_CONCURRENT = 50 # 最大并发提交数 def _images_to_tensor_safe(images: List[Image.Image], node_label: str) -> torch.Tensor: @@ -87,7 +86,7 @@ def _images_to_tensor_safe(images: List[Image.Image], node_label: str) -> torch. return pil_to_tensor(matched) -class AsyncImageGenerator: +class NanoBananaV2: """ 异步生图节点（通用） @@ -97,7 +96,7 @@ class AsyncImageGenerator: - 支持批量提示词、多参考图、代理端口 """ - NODE_LABEL = "AI生图" + NODE_LABEL = "Nano Banana V2" # Provider 注册表：provider 名称 → 类路径 PROVIDER_CLASSES = { @@ -117,8 +116,7 @@ class AsyncImageGenerator: @classmethod def INPUT_TYPES(cls): - # 模型列表（排除官方计费渠道） - models = [m for m in get_enabled_async_models() if "官方计费" not in m] + models = get_enabled_async_models() if not models: models = ["请在 models_config.py 中启用至少一个异步模型"] @@ -150,6 +148,12 @@ class AsyncImageGenerator: "max": 0xffffffffffffffff }) + optional["分组令牌"] = ("STRING", { + "default": "", + "multiline": False, + "placeholder": "手动填写分组令牌将覆盖 .config 中的默认令牌" + }) + optional["代理端口"] = ("STRING", { "default": "", "multiline": False, @@ -168,7 +172,7 @@ class AsyncImageGenerator: "生图数量": ("INT", { "default": 1, "min": 1, - "max": 1000, + "max": 9, "step": 1 }) }, @@ -185,8 +189,11 @@ class AsyncImageGenerator: # Provider 工厂 # ======================================================================== - def _get_provider(self, model_id: str, proxy_url: Optional[str] = None) -> BaseAsyncImageProvider: - """根据模型 ID 获取或创建对应的 Provider 实例""" + def _get_provider(self, model_id: str, proxy_url: Optional[str] = None, api_key_override: Optional[str] = None) -> BaseAsyncImageProvider: + """根据模型 ID 获取或创建对应的 Provider 实例 + + api_key_override: 手动填写的分组令牌，非空时优先使用，空则回退到 .config + """ provider_name = get_model_provider(model_id) if not provider_name: raise ValueError(f"模型 \"{model_id}\" 不支持异步模式") @@ -210,7 +217,9 @@ class AsyncImageGenerator: module = importlib.import_module(module_path) provider_class = getattr(module, class_name) - api_key = get_api_key_or_raise("O1KEY_API_KEY") + api_key = api_key_override.strip() if api_key_override else "" + if not api_key: + api_key = get_api_key_or_raise("O1KEY_API_KEY") self._provider = provider_class(api_key=api_key, proxy_url=proxy_url) self._provider_name = provider_name return self._provider @@ -413,7 +422,7 @@ class AsyncImageGenerator: pbar=None, **extra_kwargs, ) -> List[dict]: - """异步批量处理""" + """全并发处理（V2 节点最多 9 个任务，无需分批）""" # 构建任务定义 tasks_def = [] for p_idx, prompt in enumerate(prompts): @@ -421,7 +430,6 @@ class AsyncImageGenerator: tasks_def.append((p_idx, sub_idx, prompt)) total_tasks = len(tasks_def) - num_batches = math.ceil(total_tasks / _MAX_CONCURRENT) all_results: List[dict] = [] completed = 0 @@ -431,68 +439,57 @@ class AsyncImageGenerator: connector = aiohttp.TCPConnector(ssl=False, limit=0, limit_per_host=0) async with aiohttp.ClientSession(connector=connector) as session: - for batch_idx in range(num_batches): - self._check_interrupt() - - start_idx = batch_idx * _MAX_CONCURRENT - end_idx = min(start_idx + _MAX_CONCURRENT, total_tasks) - - batch_tasks = [] - for i in range(start_idx, end_idx): - _, _, prompt = tasks_def[i] - - task = asyncio.create_task( - self._execute_one( - session=session, - provider=provider, - prompt=prompt, - model=model, - resolution=resolution, - aspect_ratio=aspect_ratio, - input_images=input_images, - global_task_index=i, - on_progress=_on_progress, - **extra_kwargs, - ) + # 一次性全并发提交 + batch_tasks = [] + for i, (_, _, prompt) in enumerate(tasks_def): + task = asyncio.create_task( + self._execute_one( + session=session, + provider=provider, + prompt=prompt, + model=model, + resolution=resolution, + aspect_ratio=aspect_ratio, + input_images=input_images, + global_task_index=i, + on_progress=_on_progress, + **extra_kwargs, ) - batch_tasks.append(task) + ) + batch_tasks.append(task) - batch_results = [] - for coro in asyncio.as_completed(batch_tasks): - result_data = None - try: - result_data = await coro - except InterruptProcessingException: - # 用户取消：终止所有未完成任务 - for t in batch_tasks: - t.cancel() - raise - except Exception as e2: - # 意外错误（不应发生，_execute_one 内部已捕获常规异常） - result_data = { - "success": False, - "error": str(e2), - "generated_count": 0, - "output_images": [], - "prompt": "", - } + for coro in asyncio.as_completed(batch_tasks): + result_data = None + try: + result_data = await coro + except InterruptProcessingException: + # 用户取消：终止所有未完成任务 + for t in batch_tasks: + t.cancel() + raise + except Exception as e2: + # 意外错误（不应发生，_execute_one 内部已捕获常规异常） + result_data = { + "success": False, + "error": str(e2), + "generated_count": 0, + "output_images": [], + "prompt": "", + } - batch_results.append(result_data) - completed += 1 + all_results.append(result_data) + completed += 1 - prompt_snippet = (result_data.get("prompt", "") or "")[:30] - if result_data and result_data.get("success"): - count = result_data.get("generated_count", 1) - print(f"{self.NODE_LABEL}: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} -> OK({count}张)") - else: - error_msg = result_data.get("error", "未知错误") if result_data else "未知错误" - print(f"{self.NODE_LABEL}: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} -> FAIL: {error_msg}") + prompt_snippet = (result_data.get("prompt", "") or "").replace("\n", " ")[:30] + if result_data and result_data.get("success"): + count = result_data.get("generated_count", 1) + print(f"{self.NODE_LABEL}: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} -> OK({count}张)") + else: + error_msg = result_data.get("error", "未知错误") if result_data else "未知错误" + print(f"{self.NODE_LABEL}: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} -> FAIL: {error_msg}") - all_results.extend(batch_results) - - import gc - gc.collect() - await asyncio.sleep(0.1) + import gc + gc.collect() return all_results @@ -515,10 +512,11 @@ class AsyncImageGenerator: # 提取通用可选参数 seed: int = kwargs.pop("seed", 0) proxy_port: str = kwargs.pop("代理端口", "") + api_key_override: str = kwargs.pop("分组令牌", "") # 初始化 Provider proxy_url = BaseAsyncImageProvider.build_proxy_url(proxy_port) - provider = self._get_provider(模型, proxy_url=proxy_url) + provider = self._get_provider(模型, proxy_url=proxy_url, api_key_override=api_key_override) if proxy_url: print(f"{self.NODE_LABEL}: 已启用代理加速 -> {proxy_url}") @@ -578,13 +576,10 @@ class AsyncImageGenerator: images_per_prompt = 生图数量 total_tasks = 生图数量 - if total_tasks > 100: - print(f" {self.NODE_LABEL}: 警告！批量生成 {total_tasks} 张图片，内存占用可能较高") - if pbar is not None: pbar = ProgressBar(total_tasks) - # 在独立线程中运行异步批量处理 + # 在独立线程中运行异步全并发处理 def run_async(): loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) @@ -607,30 +602,26 @@ class AsyncImageGenerator: with ThreadPoolExecutor(max_workers=1) as executor: future = executor.submit(run_async) - # 总超时 = 批次数 × 单任务超时，保证每批都有完整的时间窗口 - num_batches = math.ceil(total_tasks / _MAX_CONCURRENT) - batch_timeout = num_batches * _MAX_WAIT_TIME try: - results = future.result(timeout=batch_timeout) + results = future.result(timeout=_MAX_WAIT_TIME) except TimeoutError: - raise RuntimeError(f"任务执行超时（{batch_timeout}秒），请减少数量或检查网络") + raise RuntimeError(f"任务执行超时（{_MAX_WAIT_TIME}秒），请减少数量或检查网络") # 统计结果 - success_count = sum(1 for r in results if r.get("success")) - fail_count = len(results) - success_count - elapsed = time.time() - start_time time_str = f"{elapsed:.3f}s" if elapsed < 1 else f"{elapsed:.2f}s" - print(f"{self.NODE_LABEL}: 完成！总耗时 {time_str} | 成功: {success_count}/{total_tasks} | 失败: {fail_count}") - # 打印失败详情 failed = [r for r in results if not r.get("success")] if failed: + reason = failed[0].get("error", "未知错误") + print(f"{self.NODE_LABEL}: 失败！原因：{reason}") for fr in failed: idx = fr.get("global_task_index", -1) + 1 - prompt_snippet = (fr.get("prompt", "") or "")[:30] + prompt_snippet = (fr.get("prompt", "") or "").replace("\n", " ")[:30] error_msg = fr.get("error", "未知错误") print(f" FAIL #{idx}: {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} -> {error_msg}") + else: + print(f"{self.NODE_LABEL}: 完成！总耗时 {time_str}") # 收集输出图像 output_images = [] @@ -641,10 +632,10 @@ class AsyncImageGenerator: # 收集所有错误原因 error_details = "\n".join( f" - {r.get('prompt', '未知提示词')[:40]}: {r.get('error', '未知错误')}" - for r in results if not r.get("success") + for r in failed ) raise RuntimeError( - f"所有任务均失败 ({fail_count}/{total_tasks})：\n{error_details}" + f"所有任务均失败 ({len(failed)}/{len(results)})：\n{error_details}" ) output_tensor = _images_to_tensor_safe(output_images, self.NODE_LABEL) @@ -681,9 +672,9 @@ class AsyncImageGenerator: gc.collect() -class BatchAsyncImageGenerator(AsyncImageGenerator): +class NanoBananaV2Batch(NanoBananaV2): """ - AI生图（批量版）- 全并发提交 + 即时落盘 + Nano Banana V2（批量）- 全并发提交 + 即时落盘 与原版区别： - 所有任务一次性全并发提交，不分批次 @@ -691,9 +682,35 @@ class BatchAsyncImageGenerator(AsyncImageGenerator): - 最终从磁盘加载所有已保存的图像输出 """ - NODE_LABEL = "AI生图（批量版）" + NODE_LABEL = "Nano Banana V2（批量）" RETURN_NAMES = ("输出图像",) + @classmethod + def INPUT_TYPES(cls): + types = super().INPUT_TYPES() + + # 批量专用：文件夹图片路径 + for i in range(1, 6): + types["optional"][f"图片路径（图{i}）"] = ("STRING", { + "default": "", + "multiline": False, + "placeholder": f"填写后将加载文件夹中的图片作为第{i}组参考图" + }) + + types["optional"]["图片配对模式"] = (["不配对", "按相同图片命名", "1*N"], { + "default": "不配对" + }) + + # 批量版恢复较大的生图数量上限 + types["required"]["生图数量"] = ("INT", { + "default": 1, + "min": 1, + "max": 1000, + "step": 1 + }) + + return types + def __init__(self): super().__init__() self._output_file_paths: List[str] = [] @@ -720,9 +737,13 @@ class BatchAsyncImageGenerator(AsyncImageGenerator): images_per_prompt: int, input_images: List[Image.Image], pbar=None, + per_task_images: Optional[List[List[Image.Image]]] = None, **extra_kwargs, ) -> List[dict]: - """全并发处理：所有任务一次性提交，谁先完成谁先落盘""" + """全并发处理：所有任务一次性提交，谁先完成谁先落盘 + + per_task_images: 可选，每个任务专属的图片列表，与 input_images 合并 + """ # 构建任务定义 tasks_def = [] for p_idx, prompt in enumerate(prompts): @@ -746,6 +767,9 @@ class BatchAsyncImageGenerator(AsyncImageGenerator): # 一次性提交所有任务（全并发） batch_tasks = [] for i, (_, _, prompt) in enumerate(tasks_def): + task_imgs = list(input_images) + if per_task_images and i < len(per_task_images) and per_task_images[i]: + task_imgs = per_task_images[i] + task_imgs task = asyncio.create_task( self._execute_one( session=session, @@ -754,7 +778,7 @@ class BatchAsyncImageGenerator(AsyncImageGenerator): model=model, resolution=resolution, aspect_ratio=aspect_ratio, - input_images=input_images, + input_images=task_imgs, global_task_index=i, on_progress=_on_progress, **extra_kwargs, @@ -796,7 +820,7 @@ class BatchAsyncImageGenerator(AsyncImageGenerator): all_results.append(result_data) completed += 1 - prompt_snippet = (result_data.get("prompt", "") or "")[:30] + prompt_snippet = (result_data.get("prompt", "") or "").replace("\n", " ")[:30] if result_data and result_data.get("success"): count = result_data.get("saved_count", result_data.get("generated_count", 1)) print(f"{self.NODE_LABEL}: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} -> OK({count}张) [已落盘]") @@ -823,9 +847,10 @@ class BatchAsyncImageGenerator(AsyncImageGenerator): seed: int = kwargs.pop("seed", 0) proxy_port: str = kwargs.pop("代理端口", "") + api_key_override: str = kwargs.pop("分组令牌", "") proxy_url = BaseAsyncImageProvider.build_proxy_url(proxy_port) - provider = self._get_provider(模型, proxy_url=proxy_url) + provider = self._get_provider(模型, proxy_url=proxy_url, api_key_override=api_key_override) if proxy_url: print(f"{self.NODE_LABEL}: 已启用代理加速 -> {proxy_url}") @@ -859,6 +884,7 @@ class BatchAsyncImageGenerator(AsyncImageGenerator): f"该模型支持的宽高比：{', '.join(supported_ratios)}" ) + # 收集参考图 input_images = [] for i in range(1, 10): key = f"参考图{i}" @@ -869,24 +895,80 @@ class BatchAsyncImageGenerator(AsyncImageGenerator): if input_images and len(input_images) > 14: raise ValueError(f"输入图像数量 {len(input_images)} 超过限制 14 张") - batch_prompts = parse_batch_prompts(prompt) + # 加载文件夹图片 + folder_paths = [] + for i in range(1, 6): + fp = kwargs.pop(f"图片路径（图{i}）", "").strip() + if fp: + folder_paths.append(fp) - if batch_prompts: - num_prompts = len(batch_prompts) - total_images = num_prompts * 生图数量 - mode_str = f"批量提示词模式 ({num_prompts}个提示词)" - if input_images: - mode_str += f" (输入{len(input_images)}张)" - print(f"{self.NODE_LABEL}: {mode_str} | {分辨率} {宽高比} | 共{total_images}张") - prompts_list = batch_prompts - images_per_prompt = 生图数量 - total_tasks = total_images - else: - mode_str = f"图生图模式 (输入{len(input_images)}张)" if input_images else "文生图模式" - print(f"{self.NODE_LABEL}: {mode_str} | {分辨率} {宽高比} | {生图数量}张") - prompts_list = [prompt] - images_per_prompt = 生图数量 - total_tasks = 生图数量 + pairing_mode = kwargs.pop("图片配对模式", "不配对") + per_task_images = None + if folder_paths: + # 按文件夹分组加载 + folder_image_lists = [] # List[List[ImageInfo]] + for fp in folder_paths: + try: + infos = load_images_from_folder(fp) + if infos: + folder_image_lists.append(infos) + except ValueError as e: + print(f"{self.NODE_LABEL}: {e}") + + if folder_image_lists: + # 根据配对模式生成配对 + if pairing_mode == "不配对": + if len(folder_image_lists) > 1: + 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") + pairs = [(info,) for info in folder_image_lists[0]] + elif pairing_mode == "按相同图片命名": + pairs = list(pair_images_by_name(*folder_image_lists)) + else: # 1*N + pairs = list(pair_images_cartesian(*folder_image_lists)) + + if pairs: + batch_prompts = parse_batch_prompts(prompt) + per_task_images = [] + prompts_list = [] + for pair in pairs: + task_imgs = [info.image for info in pair] + list(input_images) + if batch_prompts: + for bp in batch_prompts: + per_task_images.append(list(task_imgs)) + prompts_list.append(bp) + else: + per_task_images.append(list(task_imgs)) + prompts_list.append(prompt) + + images_per_prompt = 1 + total_tasks = len(prompts_list) + folder_count = len(folder_paths) + total_folder_imgs = sum(len(lst) for lst in folder_image_lists) + mode_str = f"文件夹批量模式 ({pairing_mode}, {folder_count}个文件夹, {total_folder_imgs}张图片→{len(pairs)}组)" + if batch_prompts: + mode_str += f" × {len(batch_prompts)}个提示词" + if input_images: + mode_str += f" (+{len(input_images)}张参考图)" + print(f"{self.NODE_LABEL}: {mode_str} | {分辨率} {宽高比} | 共{total_tasks}张") + + if per_task_images is None: + batch_prompts = parse_batch_prompts(prompt) + if batch_prompts: + num_prompts = len(batch_prompts) + total_images = num_prompts * 生图数量 + mode_str = f"批量提示词模式 ({num_prompts}个提示词)" + if input_images: + mode_str += f" (输入{len(input_images)}张)" + print(f"{self.NODE_LABEL}: {mode_str} | {分辨率} {宽高比} | 共{total_images}张") + prompts_list = batch_prompts + images_per_prompt = 生图数量 + total_tasks = total_images + else: + mode_str = f"图生图模式 (输入{len(input_images)}张)" if input_images else "文生图模式" + print(f"{self.NODE_LABEL}: {mode_str} | {分辨率} {宽高比} | {生图数量}张") + prompts_list = [prompt] + images_per_prompt = 生图数量 + total_tasks = 生图数量 if total_tasks > 100: print(f" {self.NODE_LABEL}: 全并发模式，{total_tasks} 张图片将同时提交") @@ -908,6 +990,7 @@ class BatchAsyncImageGenerator(AsyncImageGenerator): images_per_prompt=images_per_prompt, input_images=input_images, pbar=pbar, + per_task_images=per_task_images, **extra_kwargs, ) ) @@ -927,7 +1010,9 @@ class BatchAsyncImageGenerator(AsyncImageGenerator): elapsed = time.time() - start_time time_str = f"{elapsed:.3f}s" if elapsed < 1 else f"{elapsed:.2f}s" - print(f"{self.NODE_LABEL}: 完成！总耗时 {time_str} | 成功: {success_count}/{total_tasks} | 失败: {fail_count} | 已落盘: {len(self._output_file_paths)} 张") + avg_time = elapsed / success_count if success_count > 0 else 0 + avg_str = f"{avg_time:.2f}s/张" if avg_time >= 1 else f"{avg_time:.3f}s/张" + print(f"{self.NODE_LABEL}: 完成！总耗时 {time_str} ({avg_str}) | 成功: {success_count}/{total_tasks} | 失败: {fail_count} | 已落盘: {len(self._output_file_paths)} 张") failed = [r for r in results if not r.get("success")] if failed: @@ -990,3 +1075,8 @@ class BatchAsyncImageGenerator(AsyncImageGenerator): import gc gc.collect() + + +# 向后兼容别名（旧工作流中使用旧类名仍可正常加载） +AsyncImageGenerator = NanoBananaV2 +BatchAsyncImageGenerator = NanoBananaV2Batch diff --git a/nodes/universal_llm.py b/nodes/universal_llm.py index 674153d..5d1a24d 100644 --- a/nodes/universal_llm.py +++ b/nodes/universal_llm.py @@ -26,7 +26,6 @@ from ..utils.file_types import FileList SUPPORTED_MODELS = [ "gpt-5.5", - "gpt-5.4", "gemini-3.1-flash-lite-preview", "gemini-3.1-pro-preview", "deepseek-v4-pro",