From 1f3c9db7430636111581ef5279eceeb104063404 Mon Sep 17 00:00:00 2001 From: "kyj@bowong.ai" Date: Thu, 10 Jul 2025 15:32:26 +0800 Subject: [PATCH] =?UTF-8?q?PERF=20=E9=87=8D=E6=9E=84=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E4=BC=98=E5=8C=96=E5=91=BD=E5=90=8D=E5=92=8C=E7=BB=93?= =?UTF-8?q?=E6=9E=84=20FIX=20=E8=A1=A5=E9=BD=90requirements=20FIX=20?= =?UTF-8?q?=E5=89=94=E9=99=A4=E7=A1=AC=E7=BC=96=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- __init__.py | 66 +- comfyui_v2.py => ext/comfyui_modal_deploy.py | 5 +- ext/cos_utils.py | 98 -- ext/image.py | 71 -- ext/load_image_pro.py | 62 - ext/nodes_bfl.py | 3 +- ext/prompt_id_generator.py | 40 - pyproject.toml => ext/pyproject.toml | 8 +- ext/random_line.py | 77 -- ext/webhook_forward.py | 187 --- nodes/compute_video_point.py | 50 - nodes/cos.py | 132 -- nodes/cos_utils.py | 98 -- nodes/face_detect.py | 76 -- nodes/image.py | 71 -- .../{face_extract.py => image_face_nodes.py} | 79 +- nodes/image_gesture_nodes.py | 317 +++++ nodes/image_nodes.py | 188 +++ nodes/{llm_api.py => llm_nodes.py} | 11 +- nodes/load_image_pro.py | 62 - nodes/log2db.py | 66 - nodes/nodes_bfl.py | 1093 ----------------- nodes/object_storage_nodes.py | 269 ++++ nodes/prompt_id_generator.py | 40 - nodes/random_line.py | 77 -- nodes/s3.py | 146 --- nodes/string_empty_judgement.py | 21 - nodes/text.py | 127 -- nodes/text_nodes.py | 216 ++++ nodes/traverse_folder.py | 39 - nodes/unload_all_models.py | 32 - nodes/util_nodes.py | 339 +++++ nodes/{heygem.py => video_lipsync_nodes.py} | 6 +- nodes/{video.py => video_nodes.py} | 84 +- nodes/vod2local.py | 112 -- nodes/webhook_forward.py | 187 --- pyproject_comfyui.toml | 28 - requirements.txt | 21 +- user/text_file_dirs.json | 5 - .../face_occu_detect.py | 10 +- nodes/utils.py => utils/modal_utils.py | 0 nodes/model.py => utils/model_module.py | 2 +- nodes/table.py => utils/task_table.py | 0 43 files changed, 1522 insertions(+), 3099 deletions(-) rename comfyui_v2.py => ext/comfyui_modal_deploy.py (91%) delete mode 100644 ext/cos_utils.py delete mode 100644 ext/image.py delete mode 100644 ext/load_image_pro.py delete mode 100644 ext/prompt_id_generator.py rename pyproject.toml => ext/pyproject.toml (86%) delete mode 100644 ext/random_line.py delete mode 100644 ext/webhook_forward.py delete mode 100644 nodes/compute_video_point.py delete mode 100644 nodes/cos.py delete mode 100644 nodes/cos_utils.py delete mode 100644 nodes/face_detect.py delete mode 100644 nodes/image.py rename nodes/{face_extract.py => image_face_nodes.py} (55%) create mode 100644 nodes/image_gesture_nodes.py create mode 100644 nodes/image_nodes.py rename nodes/{llm_api.py => llm_nodes.py} (98%) delete mode 100644 nodes/load_image_pro.py delete mode 100644 nodes/log2db.py delete mode 100644 nodes/nodes_bfl.py create mode 100644 nodes/object_storage_nodes.py delete mode 100644 nodes/prompt_id_generator.py delete mode 100644 nodes/random_line.py delete mode 100644 nodes/s3.py delete mode 100644 nodes/string_empty_judgement.py delete mode 100644 nodes/text.py create mode 100644 nodes/text_nodes.py delete mode 100644 nodes/traverse_folder.py delete mode 100644 nodes/unload_all_models.py create mode 100644 nodes/util_nodes.py rename nodes/{heygem.py => video_lipsync_nodes.py} (98%) rename nodes/{video.py => video_nodes.py} (80%) delete mode 100644 nodes/vod2local.py delete mode 100644 nodes/webhook_forward.py delete mode 100644 pyproject_comfyui.toml delete mode 100644 user/text_file_dirs.json rename nodes/test_single_image.py => utils/face_occu_detect.py (93%) rename nodes/utils.py => utils/modal_utils.py (100%) rename nodes/model.py => utils/model_module.py (96%) rename nodes/table.py => utils/task_table.py (100%) diff --git a/__init__.py b/__init__.py index d3f1b89..8a3485b 100644 --- a/__init__.py +++ b/__init__.py @@ -1,35 +1,14 @@ -from .nodes.llm_api import LLMChat, LLMChatMultiModalImageUpload, LLMChatMultiModalImageTensor, Jinja2RenderTemplate -from .nodes.compute_video_point import VideoStartPointDurationCompute -from .nodes.cos import COSUpload, COSDownload -from .nodes.face_detect import FaceDetect -from .nodes.face_extract import FaceExtract -from .nodes.heygem import HeyGemF2F, HeyGemF2FFromFile -from .nodes.image import SaveImagePath -from .nodes.load_image_pro import LoadNetImg -from .nodes.log2db import LogToDB -from .nodes.nodes_bfl import ( - FluxProUltraImageNode, - FluxKontextProImageNode, - FluxKontextMaxImageNode, - FluxProExpandNode, - FluxProFillNode, - FluxProCannyNode, - FluxProDepthNode +from .nodes.image_face_nodes import FaceDetect, FaceExtract +from .nodes.image_gesture_nodes import JMGestureCorrect +from .nodes.image_nodes import SaveImagePath, LoadNetImg, SaveImageWithOutput +from .nodes.llm_nodes import LLMChat, LLMChatMultiModalImageUpload, LLMChatMultiModalImageTensor, Jinja2RenderTemplate +from .nodes.object_storage_nodes import COSUpload, COSDownload, S3Download, S3Upload, S3UploadURL +from .nodes.text_nodes import StringEmptyJudgement, LoadTextLocal, LoadTextOnline, RandomLineSelector +from .nodes.util_nodes import LogToDB, TaskIdGenerate, TraverseFolder, UnloadAllModels, VodToLocalNode, \ + PlugAndPlayWebhook +from .nodes.video_lipsync_nodes import HeyGemF2F, HeyGemF2FFromFile +from .nodes.video_nodes import VideoCut, VideoCutByFramePoint, VideoChangeFPS, VideoStartPointDurationCompute -) -from .nodes.s3 import S3Download, S3Upload, S3UploadURL -from .nodes.string_empty_judgement import StringEmptyJudgement -from .nodes.text import * -from .nodes.traverse_folder import TraverseFolder -from .nodes.unload_all_models import UnloadAllModels -from .nodes.video import VideoCut, VideoCutByFramePoint, VideoChangeFPS -from .nodes.vod2local import VodToLocalNode -from .nodes.prompt_id_generator import TaskIdGenerate -from .nodes.random_line import RandomLineSelector -from .nodes.webhook_forward import PlugAndPlayWebhook, SaveImageWithOutput - -# A dictionary that contains all nodes you want to export with their names -# NOTE: names should be globally unique NODE_CLASS_MAPPINGS = { "FaceOccDetect": FaceDetect, "FaceExtract": FaceExtract, @@ -53,14 +32,6 @@ NODE_CLASS_MAPPINGS = { "HeyGemF2FFromFile": HeyGemF2FFromFile, "SaveImagePath": SaveImagePath, "LoadNetImg": LoadNetImg, - "FluxProUltraImageNode": FluxProUltraImageNode, - # "FluxProImageNode": FluxProImageNode, - "FluxKontextProImageNode": FluxKontextProImageNode, - "FluxKontextMaxImageNode": FluxKontextMaxImageNode, - "FluxProExpandNode": FluxProExpandNode, - "FluxProFillNode": FluxProFillNode, - "FluxProCannyNode": FluxProCannyNode, - "FluxProDepthNode": FluxProDepthNode, "TaskIdGenerate": TaskIdGenerate, "RandomLineSelector": RandomLineSelector, "PlugAndPlayWebhook": PlugAndPlayWebhook, @@ -68,10 +39,10 @@ NODE_CLASS_MAPPINGS = { "LLMChat": LLMChat, "LLMChatMultiModalImageUpload": LLMChatMultiModalImageUpload, "LLMChatMultiModalImageTensor": LLMChatMultiModalImageTensor, - "Jinja2RenderTemplate": Jinja2RenderTemplate + "Jinja2RenderTemplate": Jinja2RenderTemplate, + "JMGestureCorrect": JMGestureCorrect } -# A dictionary that contains the friendly/humanly readable titles for the nodes NODE_DISPLAY_NAME_MAPPINGS = { "FaceOccDetect": "面部遮挡检测", "FaceExtract": "面部提取", @@ -94,15 +65,7 @@ NODE_DISPLAY_NAME_MAPPINGS = { "HeyGemF2F": "HeyGem口型同步(API, 传入文件Tensor)", "HeyGemF2FFromFile": "HeyGem口型同步(API, 传入文件路径)", "SaveImagePath": "保存图片", - "LoadNetImg": "load_net_image", - "FluxProUltraImageNode": "Flux 1.1 [pro] Ultra Image", - # "FluxProImageNode": "Flux 1.1 [pro] Image", - "FluxKontextProImageNode": "Flux.1 Kontext [pro] Image", - "FluxKontextMaxImageNode": "Flux.1 Kontext [max] Image", - "FluxProExpandNode": "Flux.1 Expand Image", - "FluxProFillNode": "Flux.1 Fill Image", - "FluxProCannyNode": "Flux.1 Canny Control Image", - "FluxProDepthNode": "Flux.1 Depth Control Image", + "LoadNetImg": "加载网络图片", "TaskIdGenerate": "TaskID生成器", "RandomLineSelector": "随机选择一行内容", "PlugAndPlayWebhook": "Webhook转发器", @@ -110,5 +73,6 @@ NODE_DISPLAY_NAME_MAPPINGS = { "LLMChat": "LLM调用", "LLMChatMultiModalImageUpload": "多模态LLM调用-图片Path", "LLMChatMultiModalImageTensor": "多模态LLM调用-图片Tensor", - "Jinja2RenderTemplate": "Jinja2格式Prompt模板渲染" + "Jinja2RenderTemplate": "Jinja2格式Prompt模板渲染", + "JMGestureCorrect": "人物侧身图片转为正面图-即梦" } diff --git a/comfyui_v2.py b/ext/comfyui_modal_deploy.py similarity index 91% rename from comfyui_v2.py rename to ext/comfyui_modal_deploy.py index d9dfe0d..e86590f 100644 --- a/comfyui_v2.py +++ b/ext/comfyui_modal_deploy.py @@ -8,14 +8,13 @@ image = ( python_version="3.10" ) .apt_install("git", "gcc", "libportaudio2", "ffmpeg") - .pip_install("fastapi[standard]==0.115.4") # install web dependencies .pip_install("comfy_cli==0.0.0", index_url="https://packages-1747622887395:0ee15474ccd7b27b57ca63a9306327678e6c2631@g-ldyi2063-pypi.pkg.coding.net/dev/packages/simple") .run_commands( "comfy --skip-prompt install --fast-deps --nvidia --version 0.3.40" ) - .pip_install_from_pyproject("./pyproject_comfyui.toml") - .run_commands("comfy node install https://e.coding.net/g-ldyi2063/dev/ComfyUI-CustomNode.git") + .pip_install_from_pyproject("./pyproject.toml") + .run_commands("comfy node install https://e.coding.net/g-ldyi2063/dev/ComfyUI-CustomNode.git", force_build=True) .run_commands("comfy node install https://github.com/yolain/ComfyUI-Easy-Use.git") .run_commands("cp -f /root/comfy/ComfyUI/custom_nodes/ComfyUI-CustomNode/ext/nodes_bfl.py /root/comfy/ComfyUI/comfy_api_nodes/nodes_bfl.py") ) diff --git a/ext/cos_utils.py b/ext/cos_utils.py deleted file mode 100644 index e7e0caa..0000000 --- a/ext/cos_utils.py +++ /dev/null @@ -1,98 +0,0 @@ -# -*- coding:utf-8 -*- -""" -File cos_utils.py -Author silence -Date 2025/6/11 20:37 -""" -import mimetypes -import os -from loguru import logger -from qcloud_cos import CosConfig -from qcloud_cos import CosS3Client - -# COS配置 -COS_BUCKET_NAME = 'sucai-1324682537' -COS_SECRET_ID = 'AKIDsrihIyjZOBsjimt8TsN8yvv1AMh5dB44' -COS_SECRET_KEY = 'CPZcxdk6W39Jd4cGY95wvupoyMd0YFqW' -COS_REGION = 'ap-shanghai' - - -class CosUploadNode: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "file_path": ("STRING", {"forceInput": True}), - }, - "optional": { - "remove_source": ("BOOLEAN", {"default": False}), - } - } - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("upload_url",) - FUNCTION = "upload_to_cos" - CATEGORY = "不忘科技-自定义节点🚩" - - def upload_to_cos(self, file_path, remove_source=False): - try: - # 检查文件是否存在 - if not os.path.isfile(file_path): - raise Exception(f"文件不存在: {file_path}") - - # 获取文件MIME类型和分类 - mime_type, _ = mimetypes.guess_type(file_path) - if mime_type: - category = mime_type.split('/')[0] - else: - category = 'unknown' - - file_name = os.path.basename(file_path) - object_key = f'tk/{category}/{file_name}' - - # 配置COS客户端 - config = CosConfig( - Region=COS_REGION, - SecretId=COS_SECRET_ID, - SecretKey=COS_SECRET_KEY - ) - client = CosS3Client(config) - - # 上传文件 - client.upload_file( - Bucket=COS_BUCKET_NAME, - Key=object_key, - LocalFilePath=file_path, - EnableMD5=False - ) - - # 生成访问URL - upload_url = f'https://{COS_BUCKET_NAME}.cos.{COS_REGION}.myqcloud.com/{object_key}' - - logger.info(f'文件上传成功: {upload_url}') - - # 如果需要删除源文件 - if remove_source and os.path.exists(file_path): - try: - os.remove(file_path) - logger.info(f'源文件已删除: {file_path}') - except Exception as e: - logger.warning(f'删除源文件失败: {e}') - - return (upload_url,) - - except Exception as e: - error_msg = f'上传失败: {str(e)}' - logger.error(error_msg) - # 返回错误信息而不是抛出异常，这样ComfyUI不会中断流程 - return (f"ERROR: {error_msg}",) - - -# 节点注册 -NODE_CLASS_MAPPINGS = { - "CosUploadNode": CosUploadNode -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "CosUploadNode": "腾讯COS上传" -} \ No newline at end of file diff --git a/ext/image.py b/ext/image.py deleted file mode 100644 index 2acdd5d..0000000 --- a/ext/image.py +++ /dev/null @@ -1,71 +0,0 @@ -import os -import uuid -from PIL import Image -import numpy as np -import torch - - -class SaveImagePath: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image_path": ("IMAGE", {"forceInput": True}), - } - } - RETURN_TYPES = ("STRING",) - FUNCTION = "load" - CATEGORY = "不忘科技-自定义节点🚩" - - def load(self, image_path): - # 如果是torch.Tensor类型，转换为numpy数组 - if isinstance(image_path, torch.Tensor): - image_path = image_path.cpu().numpy() - - # 去除多余的维度，如果形状是(1, 1, height, width, channels)或(1, height, width, channels)等情况 - while len(image_path.shape) > 3: - image_path = image_path.squeeze(0) - - # 如果是通道优先格式 (C, H, W)，转换为通道最后格式 (H, W, C) - if len(image_path.shape) == 3 and image_path.shape[0] <= 4: - image_path = np.transpose(image_path, (1, 2, 0)) - - # 如果是单通道图像，转换为3通道 - if len(image_path.shape) == 2: - image_path = np.stack([image_path] * 3, axis=-1) - - # 数据范围和类型转换 - 这是关键修复 - if image_path.dtype == np.float32 or image_path.dtype == np.float64: - # ComfyUI图像数据通常是0-1范围的浮点数 - if image_path.max() <= 1.0: - # 从0-1范围转换到0-255范围 - image_path = (image_path * 255.0).astype(np.uint8) - else: - # 如果已经是0-255范围，直接转换类型 - image_path = np.clip(image_path, 0, 255).astype(np.uint8) - elif image_path.dtype != np.uint8: - # 其他数据类型，确保在0-255范围内 - image_path = np.clip(image_path, 0, 255).astype(np.uint8) - - pil_image = Image.fromarray(image_path) - - output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "output") - # base_dir = os.path.dirname(os.path.abspath(__file__)) - # output_dir = '/root/comfy/ComfyUI/output' - if not os.path.exists(output_dir): - os.makedirs(output_dir) - - file_name = "%s.png" % str(uuid.uuid4()) - p = os.path.join(output_dir, file_name) - pil_image.save(p) - - return (p,) - - -# 节点类定义结束，以下是用于注册节点的字典结构（通常在实际使用中由ComfyUI等框架来解析和注册） -NODE_CLASS_MAPPINGS = { - "SaveImagePath": SaveImagePath -} -NODE_DISPLAY_NAME_MAPPINGS = { - "SaveImagePath": "保存图片路径" -} diff --git a/ext/load_image_pro.py b/ext/load_image_pro.py deleted file mode 100644 index 9d2e4d8..0000000 --- a/ext/load_image_pro.py +++ /dev/null @@ -1,62 +0,0 @@ -from io import BytesIO - -import numpy as np -import requests -import torch -from PIL import Image - - -# 定义节点类 -class LoadNetImg: - # 定义节点输入类型 - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "image_url": ("STRING", { - "default": "https://example.com/sample.jpg", - "multiline": False - }), - } - } - - # 定义节点输出类型 - RETURN_TYPES = ("IMAGE",) # 返回图像数据 - RETURN_NAMES = ("image",) # 命名返回值 - FUNCTION = "load_image_task" # 函数标识符，方便注册多个节点功能 - OUTPUT_NODE = False # 不允许该节点直接作为最终输出节点 - CATEGORY = "image" # 节点所属类别（在 ComfyUI 界面中分类） - - def load_image_task(self, image_url): - try: - if not image_url or not image_url.strip(): - raise ValueError("需要提供图片URL") - - # 下载网络图片 - response = requests.get(image_url) - response.raise_for_status() # 请求失败时抛出异常 - image = Image.open(BytesIO(response.content)).convert("RGB") - - # 按照官方格式转换图像数据 - # Convert to numpy array and normalize to 0-1 - image_array = np.array(image).astype(np.float32) / 255.0 - # Convert to torch tensor and add batch dimension - image_tensor = torch.from_numpy(image_array)[None,] - - return (image_tensor,) # 返回torch张量 - except Exception as e: - print(f"Error loading image: {e}") - # 返回一个空的黑色图片作为错误处理 - empty_image = torch.zeros((1, 512, 512, 3), dtype=torch.float32) - return (empty_image,) - - - - -# 映射节点类和名称 -NODE_CLASS_MAPPINGS = { - "LoadNetImg": LoadNetImg, # 将类映射到节点名称 -} -NODE_DISPLAY_NAME_MAPPINGS = { - "LoadNetImg": "load_net_image", # 节点显示名称 -} \ No newline at end of file diff --git a/ext/nodes_bfl.py b/ext/nodes_bfl.py index d971677..935edce 100644 --- a/ext/nodes_bfl.py +++ b/ext/nodes_bfl.py @@ -1,3 +1,4 @@ +# 用于替换comfy内置api节点 import io from inspect import cleandoc from typing import Union, Optional @@ -1071,7 +1072,6 @@ class FluxProDepthNode(ComfyNodeABC): # NOTE: names should be globally unique NODE_CLASS_MAPPINGS = { "FluxProUltraImageNode": FluxProUltraImageNode, - # "FluxProImageNode": FluxProImageNode, "FluxKontextProImageNode": FluxKontextProImageNode, "FluxKontextMaxImageNode": FluxKontextMaxImageNode, "FluxProExpandNode": FluxProExpandNode, @@ -1083,7 +1083,6 @@ NODE_CLASS_MAPPINGS = { # A dictionary that contains the friendly/humanly readable titles for the nodes NODE_DISPLAY_NAME_MAPPINGS = { "FluxProUltraImageNode": "Flux 1.1 [pro] Ultra Image", - # "FluxProImageNode": "Flux 1.1 [pro] Image", "FluxKontextProImageNode": "Flux.1 Kontext [pro] Image", "FluxKontextMaxImageNode": "Flux.1 Kontext [max] Image", "FluxProExpandNode": "Flux.1 Expand Image", diff --git a/ext/prompt_id_generator.py b/ext/prompt_id_generator.py deleted file mode 100644 index 95972ed..0000000 --- a/ext/prompt_id_generator.py +++ /dev/null @@ -1,40 +0,0 @@ -import uuid - - -class TaskIdGenerate: - """TaskID生成器：用户可传入或自动生成TaskID""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": {}, - "optional": { - "custom_task_id": ("STRING", {"default": "", "placeholder": "留空则自动生成"}), - }, - } - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("task_id",) - FUNCTION = "generate_task_id" - OUTPUT_NODE = False - CATEGORY = "utils" - - def generate_task_id(self, custom_task_id=""): - if custom_task_id and custom_task_id.strip(): - # 用户输入了自定义ID - task_id = custom_task_id.strip() - print(f"📝 使用自定义TaskID: {task_id}") - else: - # 自动生成UUID - task_id = str(uuid.uuid4()) - print(f"🎲 自动生成TaskID: {task_id}") - - return (task_id,) - - -NODE_CLASS_MAPPINGS = { - "TaskIdGenerate": TaskIdGenerate -} -NODE_DISPLAY_NAME_MAPPINGS = { - "TaskIdGenerate": "TaskID生成器" -} \ No newline at end of file diff --git a/pyproject.toml b/ext/pyproject.toml similarity index 86% rename from pyproject.toml rename to ext/pyproject.toml index 5bec768..ff58052 100644 --- a/pyproject.toml +++ b/ext/pyproject.toml @@ -19,10 +19,12 @@ dependencies = [ "av", "imageio", "loguru", - "openai-whisper", - "sentry-sdk", "pydantic", "pydantic_settings", "conformer==0.3.2", - "einops>0.6.1" + "einops>0.6.1", + "tqdm", + "retry", + "pyYAML", + "boto3" ] \ No newline at end of file diff --git a/ext/random_line.py b/ext/random_line.py deleted file mode 100644 index b8c9ec8..0000000 --- a/ext/random_line.py +++ /dev/null @@ -1,77 +0,0 @@ -import random -import time - - -class RandomLineSelector: - """ - ComfyUI自定义节点：随机行选择器 - 从输入的多行文本中随机选择一行输出 - """ - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "text": ("STRING", { - "multiline": True, - "default": """ -保持人物样貌和衣服不变，改变人物的光影效果。更换背景: 图像呈现出一个温馨的维多利亚复古风格房间，墙面被多样化的艺术画框与照片装饰，展现出浓郁的怀旧氛围。中心的金框全身镜是视觉焦点，上面缠绕着精致花卉装饰，增强了空间的艺术感。左侧靠墙摆放花瓶与烛台，白色地板映衬着跳动的暖黄色光线，营造出柔和而梦幻的氛围。右侧床铺整洁柔软，与一旁的小抽屉柜和台灯形成和谐的组合。整体画面色调以暖黄色为主，搭配田园风格装饰元素，构图均衡，注重细节，体现了舒适浪漫的生活场景。人物重新打光融合到场景中。 -保持人物形象和衣服不变，改变人物的光影效果。更换背景: 衣帽间的开放式悬挂区搭配浅色木地板，衣杆上挂满中性色长裙，旁边搁板整齐叠放针织毛衣与围巾。 -保持人物形象和衣服不变，改变人物的光影效果。更换背景: 白色衣柜门打开后，分层挂满长裙和连体裤，底部抽屉收纳着色彩鲜艳的运动服与家居裤。 -保持人物形象和衣服不变，改变人物的光影效果。更换背景: 玻璃门的衣柜设计体现精致生活，内部悬挂精致衬衫和镂空裙摆，饰品抽屉随时为配件搭配提供轻便选择。 -""" - }), - "seed": ("INT", { - "default": 0, - "min": -1, - "max": 0xffffffffffffffff - }), - }, - } - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("selected_line",) - FUNCTION = "select_random_line" - CATEGORY = "text" - - def select_random_line(self, text, seed): - """ - 从输入文本中随机选择一行 - - Args: - text (str): 输入的多行文本 - seed (int): 随机种子 - - Returns: - tuple: 包含选中行的元组 - """ - # 设置随机种子（-1表示使用当前时间戳） - if seed == -1: - random.seed(int(time.time() * 1000000)) # 使用微秒级时间戳 - else: - random.seed(seed) - - # 过滤掉空行 - non_empty_lines = [line.strip() for line in text.split('\n') if line.strip()] - - # 如果没有非空行，返回空字符串 - if not non_empty_lines: - return ("",) - - # 随机选择一行 - selected_line = random.choice(non_empty_lines) - - return (selected_line,) - - -# ComfyUI节点映射 -NODE_CLASS_MAPPINGS = { - "RandomLineSelector": RandomLineSelector -} - -# 节点显示名称映射 -NODE_DISPLAY_NAME_MAPPINGS = { - "RandomLineSelector": "随机选择一行内容" -} -# https://bowongai--dev-ui.modal.run/ -# https://bowongai--dev-ui.modal.run diff --git a/ext/webhook_forward.py b/ext/webhook_forward.py deleted file mode 100644 index afcaca8..0000000 --- a/ext/webhook_forward.py +++ /dev/null @@ -1,187 +0,0 @@ -# -*- coding:utf-8 -*- -""" -File webhook_forward.py -Author silence -Date 2025/6/13 13:42 -""" -import base64 -import io -import os -import random -import string -import time - -import numpy as np -import requests -from PIL import Image - - -class PromptIDGenerator: - """PromptID生成器：用户可传入或自动生成PromptID""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": {}, - "optional": { - "custom_prompt_id": ("STRING", {"default": "", "placeholder": "留空则自动生成"}), - }, - } - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("prompt_id",) - FUNCTION = "generate_prompt_id" - OUTPUT_NODE = False - CATEGORY = "utils" - - def generate_prompt_id(self, custom_prompt_id=""): - if custom_prompt_id and custom_prompt_id.strip(): - # 用户输入了自定义ID - prompt_id = custom_prompt_id.strip() - print(f"📝 使用自定义PromptID: {prompt_id}") - else: - # 自动生成ID：时间戳 + 随机字符 - timestamp = str(int(time.time())) - random_str = ''.join(random.choices(string.ascii_lowercase + string.digits, k=6)) - prompt_id = f"prompt_{timestamp}_{random_str}" - print(f"🎲 自动生成PromptID: {prompt_id}") - - return (prompt_id,) - - -class PlugAndPlayWebhook: - """即插即用Webhook节点：连上线就能转发数据""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "webhook_url": ("STRING", {"default": "http://127.0.0.1:8010/handler/webhook", - "placeholder": "https://your-api.com/webhook"}), - "image_url": ("STRING", {"default": "", - "placeholder": "图片的url"}), - }, - "optional": { - "prompt_id": ("STRING", {"default": ""}), - }, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"}, - } - - RETURN_TYPES = () - FUNCTION = "send" - OUTPUT_NODE = True - CATEGORY = "utils" - - def send(self, webhook_url, image_url, prompt_id="", prompt=None, extra_pnginfo=None, unique_id=None): - if not webhook_url: - raise ValueError("❌ 请填写Webhook URL！") - - # 使用传入的prompt_id，如果没有则用unique_id - final_prompt_id = prompt_id or unique_id or "unknown" - - # 准备发送的数据 - data = { - "img_base64": image_url, - "format": "png", - "image_url": image_url, - "prompt_id": final_prompt_id, - "timestamp": time.time() - } - - # 发送Webhook - try: - response = requests.post(webhook_url, json=data) - response.raise_for_status() - print(f'发送的数据:{data}') - except Exception as e: - print(f"❌ 发送失败: {str(e)}") - - # 终端节点，无需返回 - return () - - -class SaveImageWithOutput: - """保存图片并输出的节点：既保存图片又能继续传递数据""" - - def __init__(self): - self.output_dir = "output" - self.type = "output" - self.prefix_append = "" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "images": ("IMAGE",), - "filename_prefix": ("STRING", {"default": "ComfyUI"}), - }, - } - - RETURN_TYPES = ("IMAGE", "STRING") - RETURN_NAMES = ("images", "file_path") - FUNCTION = "save_image" - OUTPUT_NODE = False - CATEGORY = "image" - - def save_image(self, images, filename_prefix="ComfyUI"): - filename_prefix += self.prefix_append - full_output_folder, filename, counter, subfolder, filename_prefix = self.get_save_image_path(filename_prefix, - self.output_dir) - - file_paths = [] - - for i, image in enumerate(images): - # 转换图片数据 - img_array = 255. * image.cpu().numpy() - img = Image.fromarray(np.clip(img_array, 0, 255).astype(np.uint8)) - - # 生成文件名 - file = f"{filename_prefix}_{counter + i:05}_.png" - file_path = os.path.join(full_output_folder, file) - - # 保存图片 - img.save(file_path, compress_level=4) - file_paths.append(file_path) - - print(f"✅ 图片已保存到: {file_paths[0] if file_paths else '未知路径'}") - - # 直接返回元组：(原始图片数据, 第一个文件路径) - return (images, file_paths[0] if file_paths else "") - - def get_save_image_path(self, filename_prefix, output_dir): - def map_filename(filename): - prefix_len = len(os.path.basename(filename_prefix)) - prefix = filename[:prefix_len + 1] - try: - digits = int(filename[prefix_len + 1:].split('_')[0]) - except: - digits = 0 - return (digits, prefix) - - subfolder = "" - full_output_folder = os.path.join(output_dir, subfolder) - - if os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) != output_dir: - print("Saving image outside the output folder is not allowed.") - return {} - - try: - counter = max(filter(lambda a: a[1][:-1] == filename_prefix and a[1][-1] == "_", - map(map_filename, os.listdir(full_output_folder))))[0] + 1 - except ValueError: - counter = 1 - except FileNotFoundError: - os.makedirs(full_output_folder, exist_ok=True) - counter = 1 - - return full_output_folder, filename_prefix, counter, subfolder, filename_prefix - - -NODE_CLASS_MAPPINGS = { - "PlugAndPlayWebhook": PlugAndPlayWebhook, - "SaveImageWithOutput": SaveImageWithOutput -} -NODE_DISPLAY_NAME_MAPPINGS = { - "PlugAndPlayWebhook": "Webhook转发器", - "SaveImageWithOutput": "保存图片(带输出)" -} diff --git a/nodes/compute_video_point.py b/nodes/compute_video_point.py deleted file mode 100644 index c9cbd66..0000000 --- a/nodes/compute_video_point.py +++ /dev/null @@ -1,50 +0,0 @@ -import re -from datetime import datetime - -import loguru - - -def validate_time_format(time_str): - pattern = r'^([0-1][0-9]|2[0-3]):([0-5][0-9]):([0-5][0-9]|\d{1,2}).(\d{3})$' - return bool(re.match(pattern, time_str)) - - -def get_duration_wave(audio): - waveform, sample_rate = audio["waveform"], audio["sample_rate"] - # 防止话说不完 - return waveform.shape[2] / sample_rate - - -class VideoStartPointDurationCompute: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "start_time": ("STRING", {"forceInput": True}), - "audio": ("AUDIO", {"forceInput": True}), - "end_padding": ("FLOAT", {"forceInput": True, "default": 0.4}), - "fps": ("INT", {"default": 25, "step": 1}), - }, - } - - RETURN_TYPES = ("FLOAT", "FLOAT",) - RETURN_NAMES = ("起始帧位", "帧数") - - FUNCTION = "compute" - - CATEGORY = "不忘科技-自定义节点🚩" - - def compute(self, start_time, audio, fps, end_padding): - if not validate_time_format(start_time): - raise ValueError("start_time或者end_time时间格式不对（start_time or end_time is not in time format）") - - time_format = "%H:%M:%S.%f" - start_dt = datetime.strptime(start_time, time_format) - start_sec = (start_dt - datetime(1900, 1, 1)).total_seconds() - start_point = start_sec * fps - duration = get_duration_wave(audio) - loguru.logger.info("audio duration %.3f s" % duration) - duration = duration + end_padding - loguru.logger.info("audio duration with padding %.3f s" % duration) - return (start_point, duration*fps,) - diff --git a/nodes/cos.py b/nodes/cos.py deleted file mode 100644 index 837ca36..0000000 --- a/nodes/cos.py +++ /dev/null @@ -1,132 +0,0 @@ -import json -import os -import urllib - -import loguru -import yaml -from qcloud_cos import CosConfig, CosS3Client, CosClientError, CosServiceError - - -class COSDownload: - """腾讯云COS下载""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "cos_bucket": ("STRING", {"default": "bwkj-cos-1324682537"}), - "cos_key": ("STRING", {"multiline": True}), - } - } - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("视频存储路径",) - FUNCTION = "download" - CATEGORY = "不忘科技-自定义节点🚩" - - def download(self, cos_bucket, cos_key): - cos_key_in = cos_key.replace("/",os.sep) - destination = os.path.join( - os.path.dirname(os.path.dirname(os.path.abspath(__file__))), - "download", - os.path.dirname(cos_key_in), - os.path.basename(cos_key_in) - ) - loguru.logger.info(f"COS DOWNLOAD to {destination}") - os.makedirs( - os.path.dirname(destination), - exist_ok=True, - ) - for i in range(0, 10): - try: - if "aws_key_id" in list(os.environ.keys()): - yaml_config = os.environ - else: - with open( - os.path.join( - os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "config.yaml" - ), - encoding="utf-8", - mode="r+", - ) as f: - yaml_config = yaml.load(f, Loader=yaml.FullLoader) - config = CosConfig( - Region=yaml_config["cos_region"], - SecretId=yaml_config["cos_secret_id"], - SecretKey=yaml_config["cos_secret_key"], - ) - client = CosS3Client(config) - response = client.download_file( - Bucket=cos_bucket, - Key=cos_key, - DestFilePath=destination - ) - break - except CosClientError or CosServiceError as e: - raise Exception(f"COS下载失败！ bucket {cos_bucket}; key {cos_key}") - return ( - destination, - ) - - -class COSUpload: - """腾讯云COS上传""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "cos_bucket": ("STRING", {"default": "bwkj-cos-1324682537"}), - "path": ("STRING", {"multiline": True}), - "subfolder": ("STRING", {"default": "test"}), - } - } - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("COS文件Key",) - - FUNCTION = "upload" - CATEGORY = "不忘科技-自定义节点🚩" - - def upload(self, cos_bucket, path, subfolder): - dest_key = "/".join( - [ - subfolder, - ( - path.split("/")[-1] - if "/" in path - else path.split("\\")[-1] - ), - ] - ) - loguru.logger.info(f"COS UPLOAD {path} to {cos_bucket}/{subfolder}") - for i in range(0, 10): - try: - if "aws_key_id" in list(os.environ.keys()): - yaml_config = os.environ - else: - with open( - os.path.join( - os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "config.yaml" - ), - encoding="utf-8", - mode="r+", - ) as f: - yaml_config = yaml.load(f, Loader=yaml.FullLoader) - config = CosConfig( - Region=yaml_config["cos_region"], - SecretId=yaml_config["cos_secret_id"], - SecretKey=yaml_config["cos_secret_key"], - ) - client = CosS3Client(config) - response = client.upload_file( - Bucket=cos_bucket, - Key=dest_key, - LocalFilePath=path, - ) - break - except CosClientError or CosServiceError as e: - raise Exception(f"COS上传失败 bucket {cos_bucket}; local_path {path}; subfolder {subfolder}") - return ( - dest_key, - ) diff --git a/nodes/cos_utils.py b/nodes/cos_utils.py deleted file mode 100644 index e7e0caa..0000000 --- a/nodes/cos_utils.py +++ /dev/null @@ -1,98 +0,0 @@ -# -*- coding:utf-8 -*- -""" -File cos_utils.py -Author silence -Date 2025/6/11 20:37 -""" -import mimetypes -import os -from loguru import logger -from qcloud_cos import CosConfig -from qcloud_cos import CosS3Client - -# COS配置 -COS_BUCKET_NAME = 'sucai-1324682537' -COS_SECRET_ID = 'AKIDsrihIyjZOBsjimt8TsN8yvv1AMh5dB44' -COS_SECRET_KEY = 'CPZcxdk6W39Jd4cGY95wvupoyMd0YFqW' -COS_REGION = 'ap-shanghai' - - -class CosUploadNode: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "file_path": ("STRING", {"forceInput": True}), - }, - "optional": { - "remove_source": ("BOOLEAN", {"default": False}), - } - } - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("upload_url",) - FUNCTION = "upload_to_cos" - CATEGORY = "不忘科技-自定义节点🚩" - - def upload_to_cos(self, file_path, remove_source=False): - try: - # 检查文件是否存在 - if not os.path.isfile(file_path): - raise Exception(f"文件不存在: {file_path}") - - # 获取文件MIME类型和分类 - mime_type, _ = mimetypes.guess_type(file_path) - if mime_type: - category = mime_type.split('/')[0] - else: - category = 'unknown' - - file_name = os.path.basename(file_path) - object_key = f'tk/{category}/{file_name}' - - # 配置COS客户端 - config = CosConfig( - Region=COS_REGION, - SecretId=COS_SECRET_ID, - SecretKey=COS_SECRET_KEY - ) - client = CosS3Client(config) - - # 上传文件 - client.upload_file( - Bucket=COS_BUCKET_NAME, - Key=object_key, - LocalFilePath=file_path, - EnableMD5=False - ) - - # 生成访问URL - upload_url = f'https://{COS_BUCKET_NAME}.cos.{COS_REGION}.myqcloud.com/{object_key}' - - logger.info(f'文件上传成功: {upload_url}') - - # 如果需要删除源文件 - if remove_source and os.path.exists(file_path): - try: - os.remove(file_path) - logger.info(f'源文件已删除: {file_path}') - except Exception as e: - logger.warning(f'删除源文件失败: {e}') - - return (upload_url,) - - except Exception as e: - error_msg = f'上传失败: {str(e)}' - logger.error(error_msg) - # 返回错误信息而不是抛出异常，这样ComfyUI不会中断流程 - return (f"ERROR: {error_msg}",) - - -# 节点注册 -NODE_CLASS_MAPPINGS = { - "CosUploadNode": CosUploadNode -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "CosUploadNode": "腾讯COS上传" -} \ No newline at end of file diff --git a/nodes/face_detect.py b/nodes/face_detect.py deleted file mode 100644 index 8f3ea66..0000000 --- a/nodes/face_detect.py +++ /dev/null @@ -1,76 +0,0 @@ -import json - -from .test_single_image import test_node - - -class FaceDetect: - """ - 人脸遮挡检测 - """ - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "main_seed": ( - "INT:seed", - {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}, - ), - "model": (["convnext_tiny", "convnext_base"],), - "length": ("INT", {"default": 10, "min": 3, "max": 60, "step": 1}), - "threshold": ( - "FLOAT", - {"default": 94, "min": 55, "max": 99, "step": 0.1}, - ), - }, - } - - RETURN_TYPES = ( - "IMAGE", - "IMAGE", - "STRING", - "STRING", - "STRING", - "STRING", - "STRING", - "INT", - "INT", - ) - RETURN_NAMES = ( - "图像", - "选中人脸", - "分类", - "概率", - "采用帧序号", - "全部帧序列", - "剪辑配置", - "起始帧序号", - "帧数量", - ) - - FUNCTION = "predict" - - CATEGORY = "不忘科技-自定义节点🚩/面部" - - def predict(self, image, main_seed, model, length, threshold): - image, image_selected, cls, prob, nums, period = test_node( - image, length=length, thres=threshold, model_name=model - ) - print("全部帧序列", period) - if len(period) > 0: - start, end = period[main_seed % len(period)] - config = {"start": start, "end": end} - else: - raise RuntimeError("未找到符合要求的视频片段") - return ( - image, - image_selected, - cls, - prob, - nums, - str(period), - json.dumps(config), - start, - end - start + 1, - ) diff --git a/nodes/image.py b/nodes/image.py deleted file mode 100644 index 2acdd5d..0000000 --- a/nodes/image.py +++ /dev/null @@ -1,71 +0,0 @@ -import os -import uuid -from PIL import Image -import numpy as np -import torch - - -class SaveImagePath: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image_path": ("IMAGE", {"forceInput": True}), - } - } - RETURN_TYPES = ("STRING",) - FUNCTION = "load" - CATEGORY = "不忘科技-自定义节点🚩" - - def load(self, image_path): - # 如果是torch.Tensor类型，转换为numpy数组 - if isinstance(image_path, torch.Tensor): - image_path = image_path.cpu().numpy() - - # 去除多余的维度，如果形状是(1, 1, height, width, channels)或(1, height, width, channels)等情况 - while len(image_path.shape) > 3: - image_path = image_path.squeeze(0) - - # 如果是通道优先格式 (C, H, W)，转换为通道最后格式 (H, W, C) - if len(image_path.shape) == 3 and image_path.shape[0] <= 4: - image_path = np.transpose(image_path, (1, 2, 0)) - - # 如果是单通道图像，转换为3通道 - if len(image_path.shape) == 2: - image_path = np.stack([image_path] * 3, axis=-1) - - # 数据范围和类型转换 - 这是关键修复 - if image_path.dtype == np.float32 or image_path.dtype == np.float64: - # ComfyUI图像数据通常是0-1范围的浮点数 - if image_path.max() <= 1.0: - # 从0-1范围转换到0-255范围 - image_path = (image_path * 255.0).astype(np.uint8) - else: - # 如果已经是0-255范围，直接转换类型 - image_path = np.clip(image_path, 0, 255).astype(np.uint8) - elif image_path.dtype != np.uint8: - # 其他数据类型，确保在0-255范围内 - image_path = np.clip(image_path, 0, 255).astype(np.uint8) - - pil_image = Image.fromarray(image_path) - - output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "output") - # base_dir = os.path.dirname(os.path.abspath(__file__)) - # output_dir = '/root/comfy/ComfyUI/output' - if not os.path.exists(output_dir): - os.makedirs(output_dir) - - file_name = "%s.png" % str(uuid.uuid4()) - p = os.path.join(output_dir, file_name) - pil_image.save(p) - - return (p,) - - -# 节点类定义结束，以下是用于注册节点的字典结构（通常在实际使用中由ComfyUI等框架来解析和注册） -NODE_CLASS_MAPPINGS = { - "SaveImagePath": SaveImagePath -} -NODE_DISPLAY_NAME_MAPPINGS = { - "SaveImagePath": "保存图片路径" -} diff --git a/nodes/face_extract.py b/nodes/image_face_nodes.py similarity index 55% rename from nodes/face_extract.py rename to nodes/image_face_nodes.py index ccc65cf..2e379fc 100644 --- a/nodes/face_extract.py +++ b/nodes/image_face_nodes.py @@ -1,3 +1,4 @@ +import json import os import cv2 @@ -6,6 +7,81 @@ import torch from comfy import model_management from ultralytics import YOLO +from ..utils.face_occu_detect import face_occu_detect + + +class FaceDetect: + """ + 人脸遮挡检测 + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "main_seed": ( + "INT:seed", + {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}, + ), + "model": (["convnext_tiny", "convnext_base"],), + "length": ("INT", {"default": 10, "min": 3, "max": 60, "step": 1}), + "threshold": ( + "FLOAT", + {"default": 94, "min": 55, "max": 99, "step": 0.1}, + ), + }, + } + + RETURN_TYPES = ( + "IMAGE", + "IMAGE", + "STRING", + "STRING", + "STRING", + "STRING", + "STRING", + "INT", + "INT", + ) + RETURN_NAMES = ( + "图像", + "选中人脸", + "分类", + "概率", + "采用帧序号", + "全部帧序列", + "剪辑配置", + "起始帧序号", + "帧数量", + ) + + FUNCTION = "predict" + + CATEGORY = "不忘科技-自定义节点🚩/图片/人脸" + + def predict(self, image, main_seed, model, length, threshold): + image, image_selected, cls, prob, nums, period = face_occu_detect( + image, length=length, thres=threshold, model_name=model + ) + print("全部帧序列", period) + if len(period) > 0: + start, end = period[main_seed % len(period)] + config = {"start": start, "end": end} + else: + raise RuntimeError("未找到符合要求的视频片段") + return ( + image, + image_selected, + cls, + prob, + nums, + str(period), + json.dumps(config), + start, + end - start + 1, + ) + class FaceExtract: """人脸提取 By YOLO""" @@ -23,7 +99,7 @@ class FaceExtract: FUNCTION = "crop" - CATEGORY = "不忘科技-自定义节点🚩/面部" + CATEGORY = "不忘科技-自定义节点🚩/图片/人脸" def crop(self, image): device = model_management.get_torch_device() @@ -38,7 +114,6 @@ class FaceExtract: total_images = image_np.shape[0] out_images = np.ndarray(shape=(total_images, 512, 512, 3)) print("shape", image_np.shape) - print("aaaaa") idx = 0 for image_item in image_np: results = model.predict( diff --git a/nodes/image_gesture_nodes.py b/nodes/image_gesture_nodes.py new file mode 100644 index 0000000..66467be --- /dev/null +++ b/nodes/image_gesture_nodes.py @@ -0,0 +1,317 @@ +import io +import json +import os +import subprocess +import tempfile +import time +import uuid +from time import sleep + +import numpy as np +import requests +import torch +import yaml +from PIL import Image +from loguru import logger +from qcloud_cos import CosConfig, CosS3Client +from tqdm import tqdm + +class JMUtils: + def __init__(self): + if "aws_key_id" in list(os.environ.keys()): + yaml_config = os.environ + else: + with open( + os.path.join( + os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "config.yaml" + ), + encoding="utf-8", + mode="r+", + ) as f: + yaml_config = yaml.load(f, Loader=yaml.FullLoader) + + self.api_key = yaml_config["jm_api_key"] + self.cos_region = yaml_config["cos_region"] + self.cos_secret_id = yaml_config["cos_secret_id"] + self.cos_secret_key = yaml_config["cos_secret_key"] + self.cos_bucket_name = yaml_config["cos_sucai_bucket_name"] + + def submit_task(self, prompt: str, img_url: str, duration: str = "5"): + try: + headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {self.api_key}", + } + + json_data = { + "model": "doubao-seedance-1-0-lite-i2v-250428", + "content": [ + { + "type": "text", + "text": f"{prompt} --resolution 720p --dur {duration} --camerafixed false", + }, + { + "type": "image_url", + "image_url": { + "url": img_url, + }, + }, + ], + } + + response = requests.post("https://ark.cn-beijing.volces.com/api/v3/contents/generations/tasks", headers=headers, json=json_data) + logger.info(f"submit task: {json.dumps(response.json())}") + resp_json = response.json() + if "id" not in resp_json: + return {"status": False, "data": img_url, "msg": resp_json["error"]["message"]} + else: + job_id = resp_json["id"] + return {"data": job_id, "status": True, "msg": "任务提交成功"} + except Exception as e: + logger.error(e) + return {"data": None, "status": False, "msg": str(e)} + + def query_status(self, job_id: str): + resp_dict = {"status": False, "data": None, "msg": ""} + try: + headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {self.api_key}", + } + response = requests.get(f"https://ark.cn-beijing.volces.com/api/v3/contents/generations/tasks/{job_id}", headers=headers) + resp_json = response.json() + resp_dict["status"] = resp_json["status"] == "succeeded" + resp_dict["msg"] = resp_json["status"] + resp_dict["data"] = resp_json["content"]["video_url"] if "content" in resp_json else None + except Exception as e: + logger.error(f"error:{str(e)}") + resp_dict["msg"] = str(e) + finally: + return resp_dict + + + def upload_io_to_cos(self, file: io.IOBase, mime_type: str = "image/png"): + resp_data = {'status': True, 'data': '', 'msg': ''} + category = mime_type.split('/')[0] + suffix = mime_type.split('/')[1] + try: + object_key = f'tk/{category}/{uuid.uuid4()}.{suffix}' + config = CosConfig(Region=self.cos_region, SecretId=self.cos_secret_id, SecretKey=self.cos_secret_key) + client = CosS3Client(config) + _ = client.upload_file_from_buffer( + Bucket=self.cos_bucket_name, + Key=object_key, + Body=file + ) + url = f'https://{self.cos_bucket_name}.cos.{self.cos_region}.myqcloud.com/{object_key}' + resp_data['data'] = url + resp_data['msg'] = '上传成功' + + except Exception as e: + logger.error(e) + resp_data['status'] = False + resp_data['msg'] = str(e) + + return resp_data + + def tensor_to_io(srlf, tensor: torch.Tensor): + # 转换为PIL图像 + img = Image.fromarray(np.clip(255. * tensor.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) + image_data = io.BytesIO() + img.save(image_data, format='PNG') + image_data.seek(0) + return image_data + + + def download_video(self, url, timeout=30, retries=3): + """下载视频到临时文件并返回文件路径""" + for attempt in range(retries): + try: + # 创建临时文件 + temp_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) + temp_path = temp_file.name + temp_file.close() + + # 下载视频 + print(f"开始下载视频 (尝试 {attempt + 1}/{retries})...") + response = requests.get(url, stream=True, timeout=timeout) + response.raise_for_status() + + # 获取文件大小 + total_size = int(response.headers.get('content-length', 0)) + block_size = 1024 # 1 KB + + # 使用tqdm显示下载进度 + with open(temp_path, 'wb') as f, tqdm( + desc=url.split('/')[-1], + total=total_size, + unit='B', + unit_scale=True, + unit_divisor=1024 + ) as bar: + for data in response.iter_content(block_size): + size = f.write(data) + bar.update(size) + + print(f"视频下载完成: {temp_path}") + return temp_path + + except Exception as e: + print(f"下载错误 (尝试 {attempt + 1}/{retries}): {str(e)}") + if attempt < retries - 1: + time.sleep((attempt + 1) * 2) + else: + raise + + + def jpg_to_tensor(self, image_path, channel_first=False): + """ + 将JPG图像转换为PyTorch张量 + + 参数: + - image_path: JPG图像文件路径 + - normalize: 是否将像素值归一化到[0.0, 1.0] + - channel_first: 是否将通道维度放在前面 (C, H, W) + + 返回: + - tensor: PyTorch张量 + """ + try: + # 打开图像文件 + image = Image.open(image_path).convert('RGB') + + # 转换为张量 + tensor = torch.from_numpy(np.array(image).astype(np.float32)/255.0)[None,] + + return tensor + + except Exception as e: + print(f"转换失败: {str(e)}") + raise + + def get_last_15th_frame_tensor(self, video_url, cleanup=True): + """ + 从视频URL截取倒数第15帧并转换为Tensor + 先下载视频到本地临时文件再处理 + """ + try: + # 下载视频 + video_path = self.download_video(video_url) + + # 获取视频总帧数 + cmd_frames = [ + 'ffprobe', '-v', 'error', + '-select_streams', 'v:0', + '-show_entries', 'stream=nb_frames', + '-of', 'default=nokey=1:noprint_wrappers=1', + video_path + ] + + result = subprocess.run( + cmd_frames, + capture_output=True, + text=True, + check=True + ) + + # 处理可能的非数字输出 + frame_count = result.stdout.strip() + if not frame_count.isdigit(): + # 备选方案：通过解码获取帧数 + print("无法获取准确帧数，尝试直接解码...") + cmd_decode = [ + 'ffmpeg', '-i', video_path, + '-f', 'null', '-' + ] + decode_result = subprocess.run( + cmd_decode, + capture_output=True, + text=True + ) + + for line in decode_result.stderr.split('\n'): + if 'frame=' in line: + parts = line.split('frame=')[-1].split()[0] + if parts.isdigit(): + frame_count = int(parts) + break + else: + raise ValueError("无法确定视频帧数") + else: + frame_count = int(frame_count) + + # 计算目标帧 + target_frame = max(0, frame_count - 15) + print(f"视频总帧数: {frame_count}, 目标帧: {target_frame}") + + # 截取指定帧 + with tempfile.NamedTemporaryFile(suffix='%03d.jpg', delete=True) as frame_file: + frame_path = frame_file.name + + cmd_extract = [ + 'ffmpeg', + '-ss', f'00:00:00', + '-i', video_path, + '-vframes', '1', + '-vf', f'select=eq(n\,{target_frame})', + '-vsync', '0', + '-an', '-y', + frame_path + ] + + subprocess.run( + cmd_extract, + capture_output=True, + check=True + ) + + # 转换为Tensor + tensor = self.jpg_to_tensor(frame_path.replace("%03d","001")) + except Exception as e: + raise e + return tensor + +class JMGestureCorrect: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",) + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("正面图",) + FUNCTION = "gen" + CATEGORY = "不忘科技-自定义节点🚩/图片/姿态" + + def gen(self, image:torch.Tensor): + wait_time = 120 + interval = 2 + client = JMUtils() + image_io = client.tensor_to_io(image) + upload_data = client.upload_io_to_cos(image_io) + if upload_data["status"]: + image_url = upload_data["data"] + else: + raise Exception("上传失败") + prompt = "Stand straight ahead, facing the camera, showing your full body, maintaining a proper posture, keeping the camera still, and ensuring that your head and feet are all within the frame" + submit_data = client.submit_task(prompt, image_url) + if submit_data["status"]: + job_id = submit_data["data"] + else: + raise Exception("即梦任务提交失败") + job_data = None + for idx, _ in enumerate(range(0, wait_time, interval)): + logger.info(f"查询即梦结果 {idx+1}") + query = client.query_status(job_id) + if query["status"]: + job_data = query["data"] + break + else: + if "error" in query["msg"] or "失败" in query["msg"] or "fail" in query["msg"]: + raise Exception("即梦任务失败 {}".format(query["msg"])) + sleep(interval) + if not job_data: + raise Exception("即梦任务等待超时") + return (client.get_last_15th_frame_tensor(job_data),) diff --git a/nodes/image_nodes.py b/nodes/image_nodes.py new file mode 100644 index 0000000..5f41b4c --- /dev/null +++ b/nodes/image_nodes.py @@ -0,0 +1,188 @@ +import os +import uuid +from io import BytesIO + +import numpy as np +import requests +import torch +from PIL import Image + + +# 定义节点类 +class LoadNetImg: + # 定义节点输入类型 + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image_url": ("STRING", { + "default": "https://example.com/sample.jpg", + "multiline": False + }), + } + } + + # 定义节点输出类型 + RETURN_TYPES = ("IMAGE",) # 返回图像数据 + RETURN_NAMES = ("image",) # 命名返回值 + FUNCTION = "load_image_task" # 函数标识符，方便注册多个节点功能 + OUTPUT_NODE = False # 不允许该节点直接作为最终输出节点 + CATEGORY = "不忘科技-自定义节点🚩/图片" # 节点所属类别（在 ComfyUI 界面中分类） + + def load_image_task(self, image_url): + try: + if not image_url or not image_url.strip(): + raise ValueError("需要提供图片URL") + + # 下载网络图片 + response = requests.get(image_url) + response.raise_for_status() # 请求失败时抛出异常 + image = Image.open(BytesIO(response.content)).convert("RGB") + + # 按照官方格式转换图像数据 + # Convert to numpy array and normalize to 0-1 + image_array = np.array(image).astype(np.float32) / 255.0 + # Convert to torch tensor and add batch dimension + image_tensor = torch.from_numpy(image_array)[None,] + + return (image_tensor,) # 返回torch张量 + except Exception as e: + print(f"Error loading image: {e}") + # 返回一个空的黑色图片作为错误处理 + empty_image = torch.zeros((1, 512, 512, 3), dtype=torch.float32) + return (empty_image,) + + +class SaveImagePath: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image_path": ("IMAGE", {"forceInput": True}), + } + } + + RETURN_TYPES = ("STRING",) + FUNCTION = "load" + CATEGORY = "不忘科技-自定义节点🚩/图片" + + def load(self, image_path): + # 如果是torch.Tensor类型，转换为numpy数组 + if isinstance(image_path, torch.Tensor): + image_path = image_path.cpu().numpy() + + # 去除多余的维度，如果形状是(1, 1, height, width, channels)或(1, height, width, channels)等情况 + while len(image_path.shape) > 3: + image_path = image_path.squeeze(0) + + # 如果是通道优先格式 (C, H, W)，转换为通道最后格式 (H, W, C) + if len(image_path.shape) == 3 and image_path.shape[0] <= 4: + image_path = np.transpose(image_path, (1, 2, 0)) + + # 如果是单通道图像，转换为3通道 + if len(image_path.shape) == 2: + image_path = np.stack([image_path] * 3, axis=-1) + + # 数据范围和类型转换 - 这是关键修复 + if image_path.dtype == np.float32 or image_path.dtype == np.float64: + # ComfyUI图像数据通常是0-1范围的浮点数 + if image_path.max() <= 1.0: + # 从0-1范围转换到0-255范围 + image_path = (image_path * 255.0).astype(np.uint8) + else: + # 如果已经是0-255范围，直接转换类型 + image_path = np.clip(image_path, 0, 255).astype(np.uint8) + elif image_path.dtype != np.uint8: + # 其他数据类型，确保在0-255范围内 + image_path = np.clip(image_path, 0, 255).astype(np.uint8) + + pil_image = Image.fromarray(image_path) + + output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "output") + # base_dir = os.path.dirname(os.path.abspath(__file__)) + # output_dir = '/root/comfy/ComfyUI/output' + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + file_name = "%s.png" % str(uuid.uuid4()) + p = os.path.join(output_dir, file_name) + pil_image.save(p) + + return (p,) + + +class SaveImageWithOutput: + """保存图片并输出的节点：既保存图片又能继续传递数据""" + + def __init__(self): + self.output_dir = "output" + self.type = "output" + self.prefix_append = "" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "filename_prefix": ("STRING", {"default": "ComfyUI"}), + }, + } + + RETURN_TYPES = ("IMAGE", "STRING") + RETURN_NAMES = ("images", "file_path") + FUNCTION = "save_image" + OUTPUT_NODE = False + CATEGORY = "不忘科技-自定义节点🚩/图片" + + def save_image(self, images, filename_prefix="ComfyUI"): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = self.get_save_image_path(filename_prefix, + self.output_dir) + + file_paths = [] + + for i, image in enumerate(images): + # 转换图片数据 + img_array = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(img_array, 0, 255).astype(np.uint8)) + + # 生成文件名 + file = f"{filename_prefix}_{counter + i:05}_.png" + file_path = os.path.join(full_output_folder, file) + + # 保存图片 + img.save(file_path, compress_level=4) + file_paths.append(file_path) + + print(f"✅ 图片已保存到: {file_paths[0] if file_paths else '未知路径'}") + + # 直接返回元组：(原始图片数据, 第一个文件路径) + return (images, file_paths[0] if file_paths else "") + + def get_save_image_path(self, filename_prefix, output_dir): + def map_filename(filename): + prefix_len = len(os.path.basename(filename_prefix)) + prefix = filename[:prefix_len + 1] + try: + digits = int(filename[prefix_len + 1:].split('_')[0]) + except: + digits = 0 + return (digits, prefix) + + subfolder = "" + full_output_folder = os.path.join(output_dir, subfolder) + + if os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) != output_dir: + print("Saving image outside the output folder is not allowed.") + return {} + + try: + counter = max(filter(lambda a: a[1][:-1] == filename_prefix and a[1][-1] == "_", + map(map_filename, os.listdir(full_output_folder))))[0] + 1 + except ValueError: + counter = 1 + except FileNotFoundError: + os.makedirs(full_output_folder, exist_ok=True) + counter = 1 + + return full_output_folder, filename_prefix, counter, subfolder, filename_prefix diff --git a/nodes/llm_api.py b/nodes/llm_nodes.py similarity index 98% rename from nodes/llm_api.py rename to nodes/llm_nodes.py index 10ce680..c748cf8 100644 --- a/nodes/llm_api.py +++ b/nodes/llm_nodes.py @@ -7,6 +7,7 @@ import re from mimetypes import guess_type from typing import Any, Union +import folder_paths import httpx import numpy as np import torch @@ -14,8 +15,6 @@ from PIL import Image from jinja2 import Template, StrictUndefined from retry import retry -import folder_paths - def find_value_recursive(key:str, data:Union[dict, list]) -> str | None | Any: if isinstance(data, dict): @@ -73,7 +72,7 @@ class LLMChat: RETURN_TYPES = ("STRING",) RETURN_NAMES = ("llm输出",) FUNCTION = "chat" - CATEGORY = "不忘科技-自定义节点🚩/llm" + CATEGORY = "不忘科技-自定义节点🚩/LLM" def chat(self, llm_provider:str, prompt:str, temperature:float, max_tokens:int, timeout:int): @retry(Exception, tries=3, delay=1) @@ -130,7 +129,7 @@ class LLMChatMultiModalImageUpload: RETURN_TYPES = ("STRING",) RETURN_NAMES = ("llm输出",) FUNCTION = "chat" - CATEGORY = "不忘科技-自定义节点🚩/llm" + CATEGORY = "不忘科技-自定义节点🚩/LLM" def chat(self, llm_provider:str, prompt:str, image, temperature:float, max_tokens:int, timeout:int): @retry(Exception, tries=3, delay=1) @@ -194,7 +193,7 @@ class LLMChatMultiModalImageTensor: RETURN_TYPES = ("STRING",) RETURN_NAMES = ("llm输出",) FUNCTION = "chat" - CATEGORY = "不忘科技-自定义节点🚩/llm" + CATEGORY = "不忘科技-自定义节点🚩/LLM" def chat(self, llm_provider:str, prompt:str, image:torch.Tensor, temperature:float, max_tokens:int, timeout:int): @retry(Exception, tries=3, delay=1) @@ -247,7 +246,7 @@ class Jinja2RenderTemplate: RETURN_TYPES = ("STRING",) RETURN_NAMES = ("prompt",) FUNCTION = "render_prompt" - CATEGORY = "不忘科技-自定义节点🚩/llm" + CATEGORY = "不忘科技-自定义节点🚩/LLM" def render_prompt(self, template: str, kv_map: str) -> tuple: """ diff --git a/nodes/load_image_pro.py b/nodes/load_image_pro.py deleted file mode 100644 index 9d2e4d8..0000000 --- a/nodes/load_image_pro.py +++ /dev/null @@ -1,62 +0,0 @@ -from io import BytesIO - -import numpy as np -import requests -import torch -from PIL import Image - - -# 定义节点类 -class LoadNetImg: - # 定义节点输入类型 - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "image_url": ("STRING", { - "default": "https://example.com/sample.jpg", - "multiline": False - }), - } - } - - # 定义节点输出类型 - RETURN_TYPES = ("IMAGE",) # 返回图像数据 - RETURN_NAMES = ("image",) # 命名返回值 - FUNCTION = "load_image_task" # 函数标识符，方便注册多个节点功能 - OUTPUT_NODE = False # 不允许该节点直接作为最终输出节点 - CATEGORY = "image" # 节点所属类别（在 ComfyUI 界面中分类） - - def load_image_task(self, image_url): - try: - if not image_url or not image_url.strip(): - raise ValueError("需要提供图片URL") - - # 下载网络图片 - response = requests.get(image_url) - response.raise_for_status() # 请求失败时抛出异常 - image = Image.open(BytesIO(response.content)).convert("RGB") - - # 按照官方格式转换图像数据 - # Convert to numpy array and normalize to 0-1 - image_array = np.array(image).astype(np.float32) / 255.0 - # Convert to torch tensor and add batch dimension - image_tensor = torch.from_numpy(image_array)[None,] - - return (image_tensor,) # 返回torch张量 - except Exception as e: - print(f"Error loading image: {e}") - # 返回一个空的黑色图片作为错误处理 - empty_image = torch.zeros((1, 512, 512, 3), dtype=torch.float32) - return (empty_image,) - - - - -# 映射节点类和名称 -NODE_CLASS_MAPPINGS = { - "LoadNetImg": LoadNetImg, # 将类映射到节点名称 -} -NODE_DISPLAY_NAME_MAPPINGS = { - "LoadNetImg": "load_net_image", # 节点显示名称 -} \ No newline at end of file diff --git a/nodes/log2db.py b/nodes/log2db.py deleted file mode 100644 index e54c62e..0000000 --- a/nodes/log2db.py +++ /dev/null @@ -1,66 +0,0 @@ -import json - -from sqlalchemy import create_engine -from sqlalchemy.orm import sessionmaker -import server - -from .table import Task - - -class LogToDB: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "job_id": ("STRING", {"forceInput": True}), - "log": ("STRING", {"forceInput": True}), - "status": ("INT", {"default": 1, "max": 1}), - }, - "hidden": { - "unique_id": "UNIQUE_ID", - } - } - - RETURN_TYPES = ("STRING",) - - FUNCTION = "log2db" - - OUTPUT_NODE = True - OUTPUT_IS_LIST = (True,) - - # OUTPUT_NODE = False - - CATEGORY = "不忘科技-自定义节点🚩" - - def log2db(self, log, status, unique_id): - # 获取comfy服务器队列信息 - (_, prompt_id, prompt, extra_data, outputs_to_execute) = next( - iter(server.PromptServer.instance.prompt_queue.currently_running.values())) - job_id = extra_data["client_id"] - engine = create_engine( - "mysql+pymysql://root:*k3&5xxG6oqHJM@sh-cdb-1xspb808.sql.tencentcdb.com:28795/comfy", - echo=True - ) - # Base.metadata.create_all(engine) - session = sessionmaker(bind=engine)() - # 查询 - tasks = session.query(Task).filter(Task.prompt_id == prompt_id).all() - print(prompt) - result = { - "curr_node_id": str(unique_id), - "last_node_id": list(prompt.keys())[-1], - "node_output": str(log) - } - if len(tasks) == 0: - # 不存在插入 - task = Task(prompt_id=prompt_id, job_id=job_id, result=json.dumps(result), status=status) - session.add(task) - elif len(tasks) == 1: - # 存在更新 - session.query(Task).filter(Task.prompt_id == prompt_id).update({"result": json.dumps(result), - "status": status}) - else: - # 异常报错 - raise RuntimeError("状态数据库prompt_id不唯一, 无法记录状态！") - session.commit() - return {"ui": {"text": json.dumps(result)}, "result": (json.dumps(result),)} diff --git a/nodes/nodes_bfl.py b/nodes/nodes_bfl.py deleted file mode 100644 index d971677..0000000 --- a/nodes/nodes_bfl.py +++ /dev/null @@ -1,1093 +0,0 @@ -import io -from inspect import cleandoc -from typing import Union, Optional -from comfy.comfy_types.node_typing import IO, ComfyNodeABC -from comfy_api_nodes.apis.bfl_api import ( - BFLStatus, - BFLFluxExpandImageRequest, - BFLFluxFillImageRequest, - BFLFluxCannyImageRequest, - BFLFluxDepthImageRequest, - BFLFluxProGenerateRequest, - BFLFluxKontextProGenerateRequest, - BFLFluxProUltraGenerateRequest, - BFLFluxProGenerateResponse, -) -from comfy_api_nodes.apis.client import ( - ApiEndpoint, - HttpMethod, - SynchronousOperation, -) -from comfy_api_nodes.apinode_utils import ( - downscale_image_tensor, - validate_aspect_ratio, - process_image_response, - resize_mask_to_image, - validate_string, -) - -import numpy as np -from PIL import Image -import requests -import torch -import base64 -import time -from server import PromptServer - - -def convert_mask_to_image(mask: torch.Tensor): - """ - Make mask have the expected amount of dims (4) and channels (3) to be recognized as an image. - """ - mask = mask.unsqueeze(-1) - mask = torch.cat([mask]*3, dim=-1) - return mask - - -def handle_bfl_synchronous_operation( - operation: SynchronousOperation, - timeout_bfl_calls=360, - node_id: Union[str, None] = None, -): - response_api: BFLFluxProGenerateResponse = operation.execute() - return _poll_until_generated( - response_api.polling_url, timeout=timeout_bfl_calls, node_id=node_id - ) - - -def _poll_until_generated( - polling_url: str, timeout=360, node_id: Union[str, None] = None -): - # used bfl-comfy-nodes to verify code implementation: - # https://github.com/black-forest-labs/bfl-comfy-nodes/tree/main - start_time = time.time() - retries_404 = 0 - max_retries_404 = 5 - retry_404_seconds = 2 - retry_202_seconds = 2 - retry_pending_seconds = 1 - request = requests.Request(method=HttpMethod.GET, url=polling_url) - # NOTE: should True loop be replaced with checking if workflow has been interrupted? - while True: - if node_id: - time_elapsed = time.time() - start_time - PromptServer.instance.send_progress_text( - f"Generating ({time_elapsed:.0f}s)", node_id - ) - - response = requests.Session().send(request.prepare()) - if response.status_code == 200: - result = response.json() - if result["status"] == BFLStatus.ready: - img_url = result["result"]["sample"] - if node_id: - PromptServer.instance.send_progress_text( - f"Result URL: {img_url}", node_id - ) - img_response = requests.get(img_url) - return process_image_response(img_response), img_url - elif result["status"] in [ - BFLStatus.request_moderated, - BFLStatus.content_moderated, - ]: - status = result["status"] - raise Exception( - f"BFL API did not return an image due to: {status}." - ) - elif result["status"] == BFLStatus.error: - raise Exception(f"BFL API encountered an error: {result}.") - elif result["status"] == BFLStatus.pending: - time.sleep(retry_pending_seconds) - continue - elif response.status_code == 404: - if retries_404 < max_retries_404: - retries_404 += 1 - time.sleep(retry_404_seconds) - continue - raise Exception( - f"BFL API could not find task after {max_retries_404} tries." - ) - elif response.status_code == 202: - time.sleep(retry_202_seconds) - elif time.time() - start_time > timeout: - raise Exception( - f"BFL API experienced a timeout; could not return request under {timeout} seconds." - ) - else: - raise Exception(f"BFL API encountered an error: {response.json()}") - -def convert_image_to_base64(image: torch.Tensor): - scaled_image = downscale_image_tensor(image, total_pixels=2048 * 2048) - # remove batch dimension if present - if len(scaled_image.shape) > 3: - scaled_image = scaled_image[0] - image_np = (scaled_image.numpy() * 255).astype(np.uint8) - img = Image.fromarray(image_np) - img_byte_arr = io.BytesIO() - img.save(img_byte_arr, format="PNG") - return base64.b64encode(img_byte_arr.getvalue()).decode() - - -class FluxProUltraImageNode(ComfyNodeABC): - """ - Generates images using Flux Pro 1.1 Ultra via api based on prompt and resolution. - """ - - MINIMUM_RATIO = 1 / 4 - MAXIMUM_RATIO = 4 / 1 - MINIMUM_RATIO_STR = "1:4" - MAXIMUM_RATIO_STR = "4:1" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation", - }, - ), - "prompt_upsampling": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - "aspect_ratio": ( - IO.STRING, - { - "default": "16:9", - "tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.", - }, - ), - "raw": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "When True, generate less processed, more natural-looking images.", - }, - ), - }, - "optional": { - "image_prompt": (IO.IMAGE,), - "image_prompt_strength": ( - IO.FLOAT, - { - "default": 0.1, - "min": 0.0, - "max": 1.0, - "step": 0.01, - "tooltip": "Blend between the prompt and the image prompt.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - @classmethod - def VALIDATE_INPUTS(cls, aspect_ratio: str): - try: - validate_aspect_ratio( - aspect_ratio, - minimum_ratio=cls.MINIMUM_RATIO, - maximum_ratio=cls.MAXIMUM_RATIO, - minimum_ratio_str=cls.MINIMUM_RATIO_STR, - maximum_ratio_str=cls.MAXIMUM_RATIO_STR, - ) - except Exception as e: - return str(e) - return True - - RETURN_TYPES = (IO.IMAGE, IO.STRING) - RETURN_NAMES = ("image", "url") - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/BFL" - - def api_call( - self, - prompt: str, - aspect_ratio: str, - prompt_upsampling=False, - raw=False, - seed=0, - image_prompt=None, - image_prompt_strength=0.1, - unique_id: Union[str, None] = None, - **kwargs, - ): - if image_prompt is None: - validate_string(prompt, strip_whitespace=False) - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/bfl/flux-pro-1.1-ultra/generate", - method=HttpMethod.POST, - request_model=BFLFluxProUltraGenerateRequest, - response_model=BFLFluxProGenerateResponse, - ), - request=BFLFluxProUltraGenerateRequest( - prompt=prompt, - prompt_upsampling=prompt_upsampling, - seed=seed, - aspect_ratio=validate_aspect_ratio( - aspect_ratio, - minimum_ratio=self.MINIMUM_RATIO, - maximum_ratio=self.MAXIMUM_RATIO, - minimum_ratio_str=self.MINIMUM_RATIO_STR, - maximum_ratio_str=self.MAXIMUM_RATIO_STR, - ), - raw=raw, - image_prompt=( - image_prompt - if image_prompt is None - else convert_image_to_base64(image_prompt) - ), - image_prompt_strength=( - None if image_prompt is None else round(image_prompt_strength, 2) - ), - ), - auth_kwargs=kwargs, - ) - output_image, img_url = handle_bfl_synchronous_operation(operation, node_id=unique_id) - return (output_image, img_url) - - -class FluxKontextProImageNode(ComfyNodeABC): - """ - Edits images using Flux.1 Kontext [pro] via api based on prompt and aspect ratio. - """ - - MINIMUM_RATIO = 1 / 4 - MAXIMUM_RATIO = 4 / 1 - MINIMUM_RATIO_STR = "1:4" - MAXIMUM_RATIO_STR = "4:1" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation - specify what and how to edit.", - }, - ), - "aspect_ratio": ( - IO.STRING, - { - "default": "16:9", - "tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.", - }, - ), - "guidance": ( - IO.FLOAT, - { - "default": 3.0, - "min": 0.1, - "max": 99.0, - "step": 0.1, - "tooltip": "Guidance strength for the image generation process" - }, - ), - "steps": ( - IO.INT, - { - "default": 50, - "min": 1, - "max": 150, - "tooltip": "Number of steps for the image generation process" - }, - ), - "seed": ( - IO.INT, - { - "default": 1234, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - "prompt_upsampling": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", - }, - ), - }, - "optional": { - "input_image": (IO.IMAGE,), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - @classmethod - def VALIDATE_INPUTS(cls, aspect_ratio: str): - try: - validate_aspect_ratio( - aspect_ratio, - minimum_ratio=cls.MINIMUM_RATIO, - maximum_ratio=cls.MAXIMUM_RATIO, - minimum_ratio_str=cls.MINIMUM_RATIO_STR, - maximum_ratio_str=cls.MAXIMUM_RATIO_STR, - ) - except Exception as e: - return str(e) - return True - - RETURN_TYPES = (IO.IMAGE, IO.STRING) - RETURN_NAMES = ("image", "url") - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/BFL" - - BFL_PATH = "/proxy/bfl/flux-kontext-pro/generate" - - def api_call( - self, - prompt: str, - aspect_ratio: str, - guidance: float, - steps: int, - input_image: Optional[torch.Tensor]=None, - seed=0, - prompt_upsampling=False, - unique_id: Union[str, None] = None, - **kwargs, - ): - if input_image is None: - validate_string(prompt, strip_whitespace=False) - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=self.BFL_PATH, - method=HttpMethod.POST, - request_model=BFLFluxKontextProGenerateRequest, - response_model=BFLFluxProGenerateResponse, - ), - request=BFLFluxKontextProGenerateRequest( - prompt=prompt, - prompt_upsampling=prompt_upsampling, - guidance=round(guidance, 1), - steps=steps, - seed=seed, - aspect_ratio=validate_aspect_ratio( - aspect_ratio, - minimum_ratio=self.MINIMUM_RATIO, - maximum_ratio=self.MAXIMUM_RATIO, - minimum_ratio_str=self.MINIMUM_RATIO_STR, - maximum_ratio_str=self.MAXIMUM_RATIO_STR, - ), - input_image=( - input_image - if input_image is None - else convert_image_to_base64(input_image) - ) - ), - auth_kwargs=kwargs, - ) - output_image, img_url = handle_bfl_synchronous_operation(operation, node_id=unique_id) - return (output_image, img_url) - - -class FluxKontextMaxImageNode(FluxKontextProImageNode): - """ - Edits images using Flux.1 Kontext [max] via api based on prompt and aspect ratio. - """ - - DESCRIPTION = cleandoc(__doc__ or "") - BFL_PATH = "/proxy/bfl/flux-kontext-max/generate" - - -class FluxProImageNode(ComfyNodeABC): - """ - Generates images synchronously based on prompt and resolution. - """ - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation", - }, - ), - "prompt_upsampling": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", - }, - ), - "width": ( - IO.INT, - { - "default": 1024, - "min": 256, - "max": 1440, - "step": 32, - }, - ), - "height": ( - IO.INT, - { - "default": 768, - "min": 256, - "max": 1440, - "step": 32, - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "optional": { - "image_prompt": (IO.IMAGE,), - # "image_prompt_strength": ( - # IO.FLOAT, - # { - # "default": 0.1, - # "min": 0.0, - # "max": 1.0, - # "step": 0.01, - # "tooltip": "Blend between the prompt and the image prompt.", - # }, - # ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/BFL" - - def api_call( - self, - prompt: str, - prompt_upsampling, - width: int, - height: int, - seed=0, - image_prompt=None, - # image_prompt_strength=0.1, - unique_id: Union[str, None] = None, - **kwargs, - ): - image_prompt = ( - image_prompt - if image_prompt is None - else convert_image_to_base64(image_prompt) - ) - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/bfl/flux-pro-1.1/generate", - method=HttpMethod.POST, - request_model=BFLFluxProGenerateRequest, - response_model=BFLFluxProGenerateResponse, - ), - request=BFLFluxProGenerateRequest( - prompt=prompt, - prompt_upsampling=prompt_upsampling, - width=width, - height=height, - seed=seed, - image_prompt=image_prompt, - ), - auth_kwargs=kwargs, - ) - output_image, img_url = handle_bfl_synchronous_operation(operation, node_id=unique_id) - return (output_image,) - - -class FluxProExpandNode(ComfyNodeABC): - """ - Outpaints image based on prompt. - """ - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": (IO.IMAGE,), - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation", - }, - ), - "prompt_upsampling": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", - }, - ), - "top": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 2048, - "tooltip": "Number of pixels to expand at the top of the image" - }, - ), - "bottom": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 2048, - "tooltip": "Number of pixels to expand at the bottom of the image" - }, - ), - "left": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 2048, - "tooltip": "Number of pixels to expand at the left side of the image" - }, - ), - "right": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 2048, - "tooltip": "Number of pixels to expand at the right side of the image" - }, - ), - "guidance": ( - IO.FLOAT, - { - "default": 60, - "min": 1.5, - "max": 100, - "tooltip": "Guidance strength for the image generation process" - }, - ), - "steps": ( - IO.INT, - { - "default": 50, - "min": 15, - "max": 50, - "tooltip": "Number of steps for the image generation process" - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "optional": {}, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = (IO.IMAGE, IO.STRING) - RETURN_NAMES = ("image", "url") - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/BFL" - - def api_call( - self, - image: torch.Tensor, - prompt: str, - prompt_upsampling: bool, - top: int, - bottom: int, - left: int, - right: int, - steps: int, - guidance: float, - seed=0, - unique_id: Union[str, None] = None, - **kwargs, - ): - image = convert_image_to_base64(image) - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/bfl/flux-pro-1.0-expand/generate", - method=HttpMethod.POST, - request_model=BFLFluxExpandImageRequest, - response_model=BFLFluxProGenerateResponse, - ), - request=BFLFluxExpandImageRequest( - prompt=prompt, - prompt_upsampling=prompt_upsampling, - top=top, - bottom=bottom, - left=left, - right=right, - steps=steps, - guidance=guidance, - seed=seed, - image=image, - ), - auth_kwargs=kwargs, - ) - output_image, img_url = handle_bfl_synchronous_operation(operation, node_id=unique_id) - return (output_image, img_url) - - -class FluxProFillNode(ComfyNodeABC): - """ - Inpaints image based on mask and prompt. - """ - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": (IO.IMAGE,), - "mask": (IO.MASK,), - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation", - }, - ), - "prompt_upsampling": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", - }, - ), - "guidance": ( - IO.FLOAT, - { - "default": 60, - "min": 1.5, - "max": 100, - "tooltip": "Guidance strength for the image generation process" - }, - ), - "steps": ( - IO.INT, - { - "default": 50, - "min": 15, - "max": 50, - "tooltip": "Number of steps for the image generation process" - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "optional": {}, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = (IO.IMAGE, IO.STRING) - RETURN_NAMES = ("image", "url") - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/BFL" - - def api_call( - self, - image: torch.Tensor, - mask: torch.Tensor, - prompt: str, - prompt_upsampling: bool, - steps: int, - guidance: float, - seed=0, - unique_id: Union[str, None] = None, - **kwargs, - ): - # prepare mask - mask = resize_mask_to_image(mask, image) - mask = convert_image_to_base64(convert_mask_to_image(mask)) - # make sure image will have alpha channel removed - image = convert_image_to_base64(image[:, :, :, :3]) - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/bfl/flux-pro-1.0-fill/generate", - method=HttpMethod.POST, - request_model=BFLFluxFillImageRequest, - response_model=BFLFluxProGenerateResponse, - ), - request=BFLFluxFillImageRequest( - prompt=prompt, - prompt_upsampling=prompt_upsampling, - steps=steps, - guidance=guidance, - seed=seed, - image=image, - mask=mask, - ), - auth_kwargs=kwargs, - ) - output_image, img_url = handle_bfl_synchronous_operation(operation, node_id=unique_id) - return (output_image, img_url) - - -class FluxProCannyNode(ComfyNodeABC): - """ - Generate image using a control image (canny). - """ - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "control_image": (IO.IMAGE,), - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation", - }, - ), - "prompt_upsampling": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", - }, - ), - "canny_low_threshold": ( - IO.FLOAT, - { - "default": 0.1, - "min": 0.01, - "max": 0.99, - "step": 0.01, - "tooltip": "Low threshold for Canny edge detection; ignored if skip_processing is True" - }, - ), - "canny_high_threshold": ( - IO.FLOAT, - { - "default": 0.4, - "min": 0.01, - "max": 0.99, - "step": 0.01, - "tooltip": "High threshold for Canny edge detection; ignored if skip_processing is True" - }, - ), - "skip_preprocessing": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to skip preprocessing; set to True if control_image already is canny-fied, False if it is a raw image.", - }, - ), - "guidance": ( - IO.FLOAT, - { - "default": 30, - "min": 1, - "max": 100, - "tooltip": "Guidance strength for the image generation process" - }, - ), - "steps": ( - IO.INT, - { - "default": 50, - "min": 15, - "max": 50, - "tooltip": "Number of steps for the image generation process" - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "optional": {}, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = (IO.IMAGE, IO.STRING) - RETURN_NAMES = ("image", "url") - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/BFL" - - def api_call( - self, - control_image: torch.Tensor, - prompt: str, - prompt_upsampling: bool, - canny_low_threshold: float, - canny_high_threshold: float, - skip_preprocessing: bool, - steps: int, - guidance: float, - seed=0, - unique_id: Union[str, None] = None, - **kwargs, - ): - control_image = convert_image_to_base64(control_image[:, :, :, :3]) - preprocessed_image = None - - # scale canny threshold between 0-500, to match BFL's API - def scale_value(value: float, min_val=0, max_val=500): - return min_val + value * (max_val - min_val) - canny_low_threshold = int(round(scale_value(canny_low_threshold))) - canny_high_threshold = int(round(scale_value(canny_high_threshold))) - - - if skip_preprocessing: - preprocessed_image = control_image - control_image = None - canny_low_threshold = None - canny_high_threshold = None - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/bfl/flux-pro-1.0-canny/generate", - method=HttpMethod.POST, - request_model=BFLFluxCannyImageRequest, - response_model=BFLFluxProGenerateResponse, - ), - request=BFLFluxCannyImageRequest( - prompt=prompt, - prompt_upsampling=prompt_upsampling, - steps=steps, - guidance=guidance, - seed=seed, - control_image=control_image, - canny_low_threshold=canny_low_threshold, - canny_high_threshold=canny_high_threshold, - preprocessed_image=preprocessed_image, - ), - auth_kwargs=kwargs, - ) - output_image, img_url = handle_bfl_synchronous_operation(operation, node_id=unique_id) - return (output_image, img_url) - - -class FluxProDepthNode(ComfyNodeABC): - """ - Generate image using a control image (depth). - """ - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "control_image": (IO.IMAGE,), - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation", - }, - ), - "prompt_upsampling": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", - }, - ), - "skip_preprocessing": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to skip preprocessing; set to True if control_image already is depth-ified, False if it is a raw image.", - }, - ), - "guidance": ( - IO.FLOAT, - { - "default": 15, - "min": 1, - "max": 100, - "tooltip": "Guidance strength for the image generation process" - }, - ), - "steps": ( - IO.INT, - { - "default": 50, - "min": 15, - "max": 50, - "tooltip": "Number of steps for the image generation process" - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "optional": {}, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = (IO.IMAGE, IO.STRING) - RETURN_NAMES = ("image", "url") - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/BFL" - - def api_call( - self, - control_image: torch.Tensor, - prompt: str, - prompt_upsampling: bool, - skip_preprocessing: bool, - steps: int, - guidance: float, - seed=0, - unique_id: Union[str, None] = None, - **kwargs, - ): - control_image = convert_image_to_base64(control_image[:,:,:,:3]) - preprocessed_image = None - - if skip_preprocessing: - preprocessed_image = control_image - control_image = None - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/bfl/flux-pro-1.0-depth/generate", - method=HttpMethod.POST, - request_model=BFLFluxDepthImageRequest, - response_model=BFLFluxProGenerateResponse, - ), - request=BFLFluxDepthImageRequest( - prompt=prompt, - prompt_upsampling=prompt_upsampling, - steps=steps, - guidance=guidance, - seed=seed, - control_image=control_image, - preprocessed_image=preprocessed_image, - ), - auth_kwargs=kwargs, - ) - output_image, img_url = handle_bfl_synchronous_operation(operation, node_id=unique_id) - return (output_image, img_url) - - -# A dictionary that contains all nodes you want to export with their names -# NOTE: names should be globally unique -NODE_CLASS_MAPPINGS = { - "FluxProUltraImageNode": FluxProUltraImageNode, - # "FluxProImageNode": FluxProImageNode, - "FluxKontextProImageNode": FluxKontextProImageNode, - "FluxKontextMaxImageNode": FluxKontextMaxImageNode, - "FluxProExpandNode": FluxProExpandNode, - "FluxProFillNode": FluxProFillNode, - "FluxProCannyNode": FluxProCannyNode, - "FluxProDepthNode": FluxProDepthNode, -} - -# A dictionary that contains the friendly/humanly readable titles for the nodes -NODE_DISPLAY_NAME_MAPPINGS = { - "FluxProUltraImageNode": "Flux 1.1 [pro] Ultra Image", - # "FluxProImageNode": "Flux 1.1 [pro] Image", - "FluxKontextProImageNode": "Flux.1 Kontext [pro] Image", - "FluxKontextMaxImageNode": "Flux.1 Kontext [max] Image", - "FluxProExpandNode": "Flux.1 Expand Image", - "FluxProFillNode": "Flux.1 Fill Image", - "FluxProCannyNode": "Flux.1 Canny Control Image", - "FluxProDepthNode": "Flux.1 Depth Control Image", -} diff --git a/nodes/object_storage_nodes.py b/nodes/object_storage_nodes.py new file mode 100644 index 0000000..93af8fd --- /dev/null +++ b/nodes/object_storage_nodes.py @@ -0,0 +1,269 @@ +import os + +import boto3 +import loguru +import yaml +from qcloud_cos import CosConfig, CosS3Client, CosClientError, CosServiceError + + +class COSDownload: + """腾讯云COS下载""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "cos_bucket": ("STRING", {"default": "bwkj-cos-1324682537"}), + "cos_key": ("STRING", {"multiline": True}), + } + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("视频存储路径",) + FUNCTION = "download" + CATEGORY = "不忘科技-自定义节点🚩/对象存储/COS" + + def download(self, cos_bucket, cos_key): + cos_key_in = cos_key.replace("/", os.sep) + destination = os.path.join( + os.path.dirname(os.path.dirname(os.path.abspath(__file__))), + "download", + os.path.dirname(cos_key_in), + os.path.basename(cos_key_in) + ) + loguru.logger.info(f"COS DOWNLOAD to {destination}") + os.makedirs( + os.path.dirname(destination), + exist_ok=True, + ) + for i in range(0, 10): + try: + if "aws_key_id" in list(os.environ.keys()): + yaml_config = os.environ + else: + with open( + os.path.join( + os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "config.yaml" + ), + encoding="utf-8", + mode="r+", + ) as f: + yaml_config = yaml.load(f, Loader=yaml.FullLoader) + config = CosConfig( + Region=yaml_config["cos_region"], + SecretId=yaml_config["cos_secret_id"], + SecretKey=yaml_config["cos_secret_key"], + ) + client = CosS3Client(config) + response = client.download_file( + Bucket=cos_bucket, + Key=cos_key, + DestFilePath=destination + ) + break + except CosClientError or CosServiceError as e: + raise Exception(f"COS下载失败！ bucket {cos_bucket}; key {cos_key}") + return ( + destination, + ) + + +class COSUpload: + """腾讯云COS上传""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "cos_bucket": ("STRING", {"default": "bwkj-cos-1324682537"}), + "path": ("STRING", {"multiline": True}), + "subfolder": ("STRING", {"default": "test"}), + } + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("COS文件Key",) + + FUNCTION = "upload" + CATEGORY = "不忘科技-自定义节点🚩/对象存储/COS" + + def upload(self, cos_bucket, path, subfolder): + dest_key = "/".join( + [ + subfolder, + ( + path.split("/")[-1] + if "/" in path + else path.split("\\")[-1] + ), + ] + ) + loguru.logger.info(f"COS UPLOAD {path} to {cos_bucket}/{subfolder}") + for i in range(0, 10): + try: + if "aws_key_id" in list(os.environ.keys()): + yaml_config = os.environ + else: + with open( + os.path.join( + os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "config.yaml" + ), + encoding="utf-8", + mode="r+", + ) as f: + yaml_config = yaml.load(f, Loader=yaml.FullLoader) + config = CosConfig( + Region=yaml_config["cos_region"], + SecretId=yaml_config["cos_secret_id"], + SecretKey=yaml_config["cos_secret_key"], + ) + client = CosS3Client(config) + response = client.upload_file( + Bucket=cos_bucket, + Key=dest_key, + LocalFilePath=path, + ) + break + except CosClientError or CosServiceError as e: + raise Exception(f"COS上传失败 bucket {cos_bucket}; local_path {path}; subfolder {subfolder}") + return ( + dest_key, + ) + + +class S3Download: + """AWS S3下载""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "s3_bucket": ("STRING", {"default": "bw-comfyui-input"}), + "s3_key": ("STRING", {"multiline": True}), + } + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("视频存储路径",) + FUNCTION = "download" + CATEGORY = "不忘科技-自定义节点🚩/对象存储/S3" + + def download(self, s3_bucket, s3_key): + s3_key_in = s3_key.replace("/", os.sep) + destination = os.path.join( + os.path.dirname(os.path.dirname(os.path.abspath(__file__))), + "download", + os.path.dirname(s3_key_in), + os.path.basename(s3_key_in), + ) + loguru.logger.info(f"S3 DOWNLOAD to {destination}") + os.makedirs( + os.path.dirname(destination), + exist_ok=True, + ) + try: + if "aws_key_id" in list(os.environ.keys()): + yaml_config = os.environ + else: + with open(os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "config.yaml"), + encoding="utf-8", mode="r+") as f: + yaml_config = yaml.load(f, Loader=yaml.FullLoader) + client = boto3.client("s3", aws_access_key_id=yaml_config["aws_key_id"], + aws_secret_access_key=yaml_config["aws_access_key"]) + client.download_file(s3_bucket, s3_key, destination) + except Exception as e: + raise Exception(f"S3下载失败！ bucket {s3_bucket}; key {s3_key}") + return (destination,) + + +class S3Upload: + """AWS S3上传""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "s3_bucket": ("STRING", {"default": "bw-comfyui-output"}), + "path": ("STRING", {"multiline": True}), + "subfolder": ("STRING", {"default": "test"}), + } + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("S3文件Key",) + + FUNCTION = "upload" + CATEGORY = "不忘科技-自定义节点🚩/对象存储/S3" + + def upload(self, s3_bucket, path, subfolder): + loguru.logger.info(f"S3 UPLOAD {path} to {s3_bucket}/{subfolder}") + try: + if "aws_key_id" in list(os.environ.keys()): + yaml_config = os.environ + else: + with open(os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "config.yaml"), + encoding="utf-8", mode="r+") as f: + yaml_config = yaml.load(f, Loader=yaml.FullLoader) + client = boto3.client("s3", aws_access_key_id=yaml_config["aws_key_id"], + aws_secret_access_key=yaml_config["aws_access_key"]) + dest_key = "/".join( + [ + subfolder, + ( + path.split("/")[-1] + if "/" in path + else path.split("\\")[-1] + ), + ] + ) + client.upload_file(path, s3_bucket, dest_key) + except Exception as e: + raise Exception(f"S3上传失败！ bucket {s3_bucket}; local_path {path}; subfolder {subfolder}") + return (dest_key,) + + +class S3UploadURL: + """AWS S3上传 返回URL""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "path": ("STRING", {"multiline": True}), + "subfolder": ("STRING", {"default": "test"}), + } + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("S3文件Key",) + + FUNCTION = "upload" + CATEGORY = "不忘科技-自定义节点🚩/对象存储/S3" + + def upload(self, path, subfolder): + s3_bucket = "modal-media-cache" + loguru.logger.info(f"S3 UPLOAD {path} to {s3_bucket}/{subfolder}") + try: + if "aws_key_id" in list(os.environ.keys()): + yaml_config = os.environ + else: + with open(os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "config.yaml"), + encoding="utf-8", mode="r+") as f: + yaml_config = yaml.load(f, Loader=yaml.FullLoader) + client = boto3.client("s3", aws_access_key_id=yaml_config["aws_key_id"], + aws_secret_access_key=yaml_config["aws_access_key"]) + dest_key = "/".join( + [ + subfolder, + ( + path.split("/")[-1] + if "/" in path + else path.split("\\")[-1] + ), + ] + ) + client.upload_file(path, s3_bucket, dest_key) + except Exception as e: + raise Exception(f"S3上传失败！ bucket {s3_bucket}; local_path {path}; subfolder {subfolder}") + url = f"https://cdn.roasmax.cn/{dest_key}" + return (url,) diff --git a/nodes/prompt_id_generator.py b/nodes/prompt_id_generator.py deleted file mode 100644 index 95972ed..0000000 --- a/nodes/prompt_id_generator.py +++ /dev/null @@ -1,40 +0,0 @@ -import uuid - - -class TaskIdGenerate: - """TaskID生成器：用户可传入或自动生成TaskID""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": {}, - "optional": { - "custom_task_id": ("STRING", {"default": "", "placeholder": "留空则自动生成"}), - }, - } - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("task_id",) - FUNCTION = "generate_task_id" - OUTPUT_NODE = False - CATEGORY = "utils" - - def generate_task_id(self, custom_task_id=""): - if custom_task_id and custom_task_id.strip(): - # 用户输入了自定义ID - task_id = custom_task_id.strip() - print(f"📝 使用自定义TaskID: {task_id}") - else: - # 自动生成UUID - task_id = str(uuid.uuid4()) - print(f"🎲 自动生成TaskID: {task_id}") - - return (task_id,) - - -NODE_CLASS_MAPPINGS = { - "TaskIdGenerate": TaskIdGenerate -} -NODE_DISPLAY_NAME_MAPPINGS = { - "TaskIdGenerate": "TaskID生成器" -} \ No newline at end of file diff --git a/nodes/random_line.py b/nodes/random_line.py deleted file mode 100644 index b8c9ec8..0000000 --- a/nodes/random_line.py +++ /dev/null @@ -1,77 +0,0 @@ -import random -import time - - -class RandomLineSelector: - """ - ComfyUI自定义节点：随机行选择器 - 从输入的多行文本中随机选择一行输出 - """ - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "text": ("STRING", { - "multiline": True, - "default": """ -保持人物样貌和衣服不变，改变人物的光影效果。更换背景: 图像呈现出一个温馨的维多利亚复古风格房间，墙面被多样化的艺术画框与照片装饰，展现出浓郁的怀旧氛围。中心的金框全身镜是视觉焦点，上面缠绕着精致花卉装饰，增强了空间的艺术感。左侧靠墙摆放花瓶与烛台，白色地板映衬着跳动的暖黄色光线，营造出柔和而梦幻的氛围。右侧床铺整洁柔软，与一旁的小抽屉柜和台灯形成和谐的组合。整体画面色调以暖黄色为主，搭配田园风格装饰元素，构图均衡，注重细节，体现了舒适浪漫的生活场景。人物重新打光融合到场景中。 -保持人物形象和衣服不变，改变人物的光影效果。更换背景: 衣帽间的开放式悬挂区搭配浅色木地板，衣杆上挂满中性色长裙，旁边搁板整齐叠放针织毛衣与围巾。 -保持人物形象和衣服不变，改变人物的光影效果。更换背景: 白色衣柜门打开后，分层挂满长裙和连体裤，底部抽屉收纳着色彩鲜艳的运动服与家居裤。 -保持人物形象和衣服不变，改变人物的光影效果。更换背景: 玻璃门的衣柜设计体现精致生活，内部悬挂精致衬衫和镂空裙摆，饰品抽屉随时为配件搭配提供轻便选择。 -""" - }), - "seed": ("INT", { - "default": 0, - "min": -1, - "max": 0xffffffffffffffff - }), - }, - } - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("selected_line",) - FUNCTION = "select_random_line" - CATEGORY = "text" - - def select_random_line(self, text, seed): - """ - 从输入文本中随机选择一行 - - Args: - text (str): 输入的多行文本 - seed (int): 随机种子 - - Returns: - tuple: 包含选中行的元组 - """ - # 设置随机种子（-1表示使用当前时间戳） - if seed == -1: - random.seed(int(time.time() * 1000000)) # 使用微秒级时间戳 - else: - random.seed(seed) - - # 过滤掉空行 - non_empty_lines = [line.strip() for line in text.split('\n') if line.strip()] - - # 如果没有非空行，返回空字符串 - if not non_empty_lines: - return ("",) - - # 随机选择一行 - selected_line = random.choice(non_empty_lines) - - return (selected_line,) - - -# ComfyUI节点映射 -NODE_CLASS_MAPPINGS = { - "RandomLineSelector": RandomLineSelector -} - -# 节点显示名称映射 -NODE_DISPLAY_NAME_MAPPINGS = { - "RandomLineSelector": "随机选择一行内容" -} -# https://bowongai--dev-ui.modal.run/ -# https://bowongai--dev-ui.modal.run diff --git a/nodes/s3.py b/nodes/s3.py deleted file mode 100644 index bfc7429..0000000 --- a/nodes/s3.py +++ /dev/null @@ -1,146 +0,0 @@ -import os - -import boto3 -import loguru -import yaml - - - -class S3Download: - """AWS S3下载""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "s3_bucket": ("STRING", {"default": "bw-comfyui-input"}), - "s3_key": ("STRING", {"multiline": True}), - } - } - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("视频存储路径",) - FUNCTION = "download" - CATEGORY = "不忘科技-自定义节点🚩/S3" - - def download(self, s3_bucket, s3_key): - s3_key_in = s3_key.replace("/",os.sep) - destination = os.path.join( - os.path.dirname(os.path.dirname(os.path.abspath(__file__))), - "download", - os.path.dirname(s3_key_in), - os.path.basename(s3_key_in), - ) - loguru.logger.info(f"S3 DOWNLOAD to {destination}") - os.makedirs( - os.path.dirname(destination), - exist_ok=True, - ) - try: - if "aws_key_id" in list(os.environ.keys()): - yaml_config = os.environ - else: - with open(os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "config.yaml"),encoding="utf-8",mode="r+") as f: - yaml_config = yaml.load(f, Loader=yaml.FullLoader) - client = boto3.client("s3",aws_access_key_id=yaml_config["aws_key_id"],aws_secret_access_key=yaml_config["aws_access_key"]) - client.download_file(s3_bucket, s3_key, destination) - except Exception as e: - raise Exception(f"S3下载失败！ bucket {s3_bucket}; key {s3_key}") - return (destination,) - - -class S3Upload: - """AWS S3上传""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "s3_bucket": ("STRING", {"default": "bw-comfyui-output"}), - "path": ("STRING", {"multiline": True}), - "subfolder": ("STRING", {"default": "test"}), - } - } - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("S3文件Key",) - - FUNCTION = "upload" - CATEGORY = "不忘科技-自定义节点🚩/S3" - - def upload(self, s3_bucket, path, subfolder): - loguru.logger.info(f"S3 UPLOAD {path} to {s3_bucket}/{subfolder}") - try: - if "aws_key_id" in list(os.environ.keys()): - yaml_config = os.environ - else: - with open(os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "config.yaml"), - encoding="utf-8", mode="r+") as f: - yaml_config = yaml.load(f, Loader=yaml.FullLoader) - client = boto3.client("s3", aws_access_key_id=yaml_config["aws_key_id"], - aws_secret_access_key=yaml_config["aws_access_key"]) - dest_key = "/".join( - [ - subfolder, - ( - path.split("/")[-1] - if "/" in path - else path.split("\\")[-1] - ), - ] - ) - client.upload_file(path, s3_bucket, dest_key) - except Exception as e: - raise Exception(f"S3上传失败！ bucket {s3_bucket}; local_path {path}; subfolder {subfolder}") - return (dest_key,) - - -class S3UploadURL: - """AWS S3上传 返回URL""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "path": ("STRING", {"multiline": True}), - "subfolder": ("STRING", {"default": "test"}), - } - } - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("S3文件Key",) - - FUNCTION = "upload" - CATEGORY = "不忘科技-自定义节点🚩/S3" - - def upload(self, path, subfolder): - s3_bucket = "modal-media-cache" - loguru.logger.info(f"S3 UPLOAD {path} to {s3_bucket}/{subfolder}") - try: - if "aws_key_id" in list(os.environ.keys()): - yaml_config = os.environ - else: - with open(os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "config.yaml"), - encoding="utf-8", mode="r+") as f: - yaml_config = yaml.load(f, Loader=yaml.FullLoader) - client = boto3.client("s3", aws_access_key_id=yaml_config["aws_key_id"], - aws_secret_access_key=yaml_config["aws_access_key"]) - dest_key = "/".join( - [ - subfolder, - ( - path.split("/")[-1] - if "/" in path - else path.split("\\")[-1] - ), - ] - ) - client.upload_file(path, s3_bucket, dest_key) - except Exception as e: - raise Exception(f"S3上传失败！ bucket {s3_bucket}; local_path {path}; subfolder {subfolder}") - url = f"https://cdn.roasmax.cn/{dest_key}" - return (url,) - - - - diff --git a/nodes/string_empty_judgement.py b/nodes/string_empty_judgement.py deleted file mode 100644 index 3310f70..0000000 --- a/nodes/string_empty_judgement.py +++ /dev/null @@ -1,21 +0,0 @@ -class StringEmptyJudgement: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "input": ("STRING", {"forceInput": True}), - }, - } - - RETURN_TYPES = ("BOOLEAN", ) - RETURN_NAMES = ("是否为空", ) - - FUNCTION = "compute" - - CATEGORY = "不忘科技-自定义节点🚩" - - def compute(self, input): - if len(input) == 0: - return (True, ) - else: - return (False, ) diff --git a/nodes/text.py b/nodes/text.py deleted file mode 100644 index f2ef923..0000000 --- a/nodes/text.py +++ /dev/null @@ -1,127 +0,0 @@ -import json -import os -import folder_paths - - -def get_allowed_dirs(): - dir = os.path.abspath(os.path.join(__file__, "../../user")) - file = os.path.join(dir, "text_file_dirs.json") - with open(file, "r") as f: - return json.loads(f.read()) - - -def get_valid_dirs(): - return get_allowed_dirs().keys() - - -def get_dir_from_name(name): - dirs = get_allowed_dirs() - if name not in dirs: - raise KeyError(name + " dir not found") - - path = dirs[name] - path = path.replace("$input", folder_paths.get_input_directory()) - path = path.replace("$output", folder_paths.get_output_directory()) - path = path.replace("$temp", folder_paths.get_temp_directory()) - return path - - -def is_child_dir(parent_path, child_path): - parent_path = os.path.abspath(parent_path) - child_path = os.path.abspath(child_path) - return os.path.commonpath([parent_path]) == os.path.commonpath([parent_path, child_path]) - - -def get_real_path(dir): - dir = dir.replace("/**/", "/") - dir = os.path.abspath(dir) - dir = os.path.split(dir)[0] - return dir - - -def get_file(root_dir, file): - if file == "[none]" or not file or not file.strip(): - raise ValueError("No file") - - root_dir = get_dir_from_name(root_dir) - root_dir = get_real_path(root_dir) - if not os.path.exists(root_dir): - os.mkdir(root_dir) - full_path = os.path.join(root_dir, file) - - if not is_child_dir(root_dir, full_path): - raise ReferenceError() - - return full_path - - -class LoadTextLocal: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "root_dir": (list(get_valid_dirs()), {}), - "file": (["[none]"], { - "pysssss.binding": [{ - "source": "root_dir", - "callback": [{ - "type": "set", - "target": "$this.disabled", - "value": True - }, { - "type": "fetch", - "url": "/pysssss/text-file/{$source.value}", - "then": [{ - "type": "set", - "target": "$this.options.values", - "value": "$result" - }, { - "type": "validate-combo" - }, { - "type": "set", - "target": "$this.disabled", - "value": False - }] - }], - }] - }), - "encoding": ("STRING", {"default": "utf-8"}), - }, - } - - RETURN_TYPES = ("STRING",) - - FUNCTION = "load" - - CATEGORY = "不忘科技-自定义节点🚩" - - @classmethod - def VALIDATE_INPUTS(self, root_dir, file, **kwargs): - if file == "[none]" or not file or not file.strip(): - return True - get_file(root_dir, file) - return True - - def load(self, root_dir, file, encoding): - with open(get_file(root_dir,file), "r", encoding=encoding) as f: - return (f.read(),) - -class LoadTextOnline: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "file_path":("STRING", {"default": "input/"}), - "encoding": ("STRING", {"default": "utf-8"}), - } - } - - RETURN_TYPES = ("STRING",) - - FUNCTION = "load" - - CATEGORY = "不忘科技-自定义节点🚩" - - def load(self, file_path, encoding): - with open(file_path, "r", encoding=encoding) as f: - return (f.read(),) diff --git a/nodes/text_nodes.py b/nodes/text_nodes.py new file mode 100644 index 0000000..6875c18 --- /dev/null +++ b/nodes/text_nodes.py @@ -0,0 +1,216 @@ +import os +import random +import time + +import folder_paths + + +def get_allowed_dirs(): + return { + "input": "$input/**/*.txt", + "output": "$output/**/*.txt", + "temp": "$temp/**/*.txt" + } + + +def get_valid_dirs(): + return get_allowed_dirs().keys() + + +def get_dir_from_name(name): + dirs = get_allowed_dirs() + if name not in dirs: + raise KeyError(name + " dir not found") + + path = dirs[name] + path = path.replace("$input", folder_paths.get_input_directory()) + path = path.replace("$output", folder_paths.get_output_directory()) + path = path.replace("$temp", folder_paths.get_temp_directory()) + return path + + +def is_child_dir(parent_path, child_path): + parent_path = os.path.abspath(parent_path) + child_path = os.path.abspath(child_path) + return os.path.commonpath([parent_path]) == os.path.commonpath([parent_path, child_path]) + + +def get_real_path(dir): + dir = dir.replace("/**/", "/") + dir = os.path.abspath(dir) + dir = os.path.split(dir)[0] + return dir + + +def get_file(root_dir, file): + if file == "[none]" or not file or not file.strip(): + raise ValueError("No file") + + root_dir = get_dir_from_name(root_dir) + root_dir = get_real_path(root_dir) + if not os.path.exists(root_dir): + os.mkdir(root_dir) + full_path = os.path.join(root_dir, file) + + if not is_child_dir(root_dir, full_path): + raise ReferenceError() + + return full_path + + +class LoadTextLocal: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "root_dir": (list(get_valid_dirs()), {}), + "file": (["[none]"], { + "pysssss.binding": [{ + "source": "root_dir", + "callback": [{ + "type": "set", + "target": "$this.disabled", + "value": True + }, { + "type": "fetch", + "url": "/pysssss/text-file/{$source.value}", + "then": [{ + "type": "set", + "target": "$this.options.values", + "value": "$result" + }, { + "type": "validate-combo" + }, { + "type": "set", + "target": "$this.disabled", + "value": False + }] + }], + }] + }), + "encoding": ("STRING", {"default": "utf-8"}), + }, + } + + RETURN_TYPES = ("STRING",) + + FUNCTION = "load" + + CATEGORY = "不忘科技-自定义节点🚩/文本" + + @classmethod + def VALIDATE_INPUTS(self, root_dir, file, **kwargs): + if file == "[none]" or not file or not file.strip(): + return True + get_file(root_dir, file) + return True + + def load(self, root_dir, file, encoding): + with open(get_file(root_dir, file), "r", encoding=encoding) as f: + return (f.read(),) + + +class LoadTextOnline: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "file_path": ("STRING", {"default": "input/"}), + "encoding": ("STRING", {"default": "utf-8"}), + } + } + + RETURN_TYPES = ("STRING",) + + FUNCTION = "load" + + CATEGORY = "不忘科技-自定义节点🚩/文本" + + def load(self, file_path, encoding): + with open(file_path, "r", encoding=encoding) as f: + return (f.read(),) + + +class StringEmptyJudgement: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "input": ("STRING", {"forceInput": True}), + }, + } + + RETURN_TYPES = ("BOOLEAN",) + RETURN_NAMES = ("是否为空",) + + FUNCTION = "compute" + + CATEGORY = "不忘科技-自定义节点🚩/文本" + + def compute(self, input): + if len(input) == 0: + return (True,) + else: + return (False,) + + +class RandomLineSelector: + """ + ComfyUI自定义节点：随机行选择器 + 从输入的多行文本中随机选择一行输出 + """ + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text": ("STRING", { + "multiline": True, + "default": """ +保持人物样貌和衣服不变，改变人物的光影效果。更换背景: 图像呈现出一个温馨的维多利亚复古风格房间，墙面被多样化的艺术画框与照片装饰，展现出浓郁的怀旧氛围。中心的金框全身镜是视觉焦点，上面缠绕着精致花卉装饰，增强了空间的艺术感。左侧靠墙摆放花瓶与烛台，白色地板映衬着跳动的暖黄色光线，营造出柔和而梦幻的氛围。右侧床铺整洁柔软，与一旁的小抽屉柜和台灯形成和谐的组合。整体画面色调以暖黄色为主，搭配田园风格装饰元素，构图均衡，注重细节，体现了舒适浪漫的生活场景。人物重新打光融合到场景中。 +保持人物形象和衣服不变，改变人物的光影效果。更换背景: 衣帽间的开放式悬挂区搭配浅色木地板，衣杆上挂满中性色长裙，旁边搁板整齐叠放针织毛衣与围巾。 +保持人物形象和衣服不变，改变人物的光影效果。更换背景: 白色衣柜门打开后，分层挂满长裙和连体裤，底部抽屉收纳着色彩鲜艳的运动服与家居裤。 +保持人物形象和衣服不变，改变人物的光影效果。更换背景: 玻璃门的衣柜设计体现精致生活，内部悬挂精致衬衫和镂空裙摆，饰品抽屉随时为配件搭配提供轻便选择。 +""" + }), + "seed": ("INT", { + "default": 0, + "min": -1, + "max": 0xffffffffffffffff + }), + }, + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("selected_line",) + FUNCTION = "select_random_line" + CATEGORY = "不忘科技-自定义节点🚩/文本" + + def select_random_line(self, text, seed): + """ + 从输入文本中随机选择一行 + + Args: + text (str): 输入的多行文本 + seed (int): 随机种子 + + Returns: + tuple: 包含选中行的元组 + """ + # 设置随机种子（-1表示使用当前时间戳） + if seed == -1: + random.seed(int(time.time() * 1000000)) # 使用微秒级时间戳 + else: + random.seed(seed) + + # 过滤掉空行 + non_empty_lines = [line.strip() for line in text.split('\n') if line.strip()] + + # 如果没有非空行，返回空字符串 + if not non_empty_lines: + return ("",) + + # 随机选择一行 + selected_line = random.choice(non_empty_lines) + + return (selected_line,) diff --git a/nodes/traverse_folder.py b/nodes/traverse_folder.py deleted file mode 100644 index b2cda48..0000000 --- a/nodes/traverse_folder.py +++ /dev/null @@ -1,39 +0,0 @@ -import glob -import os - -class AnyType(str): - """A special class that is always equal in not equal comparisons. Credit to pythongosssss""" - - def __ne__(self, __value: object) -> bool: - return False - - -any = AnyType("*") - -class TraverseFolder: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "folder": ("STRING", {"default": r"E:\comfy\ComfyUI\input\s3", "required": True}), - "subfix": ("STRING", {"default":".mp4", "required": True}), - "recursive": ("BOOLEAN", {"default": True, "required": True}), - "idx": ( - "INT", - {"default": 0, "min": 0, "max": 0xFFFFFF}, - ), - }, - } - - RETURN_TYPES = ("STRING", ) - RETURN_NAMES = ("文件路径",) - - FUNCTION = "compute" - - CATEGORY = "不忘科技-自定义节点🚩" - - def compute(self, folder, subfix, recursive, idx): - files = glob.glob(os.path.join(folder, r"**\*%s" % subfix), recursive=recursive) - if len(files) == 0: - raise RuntimeError("No Files Found") - return (str(files[idx % len(files)]),) \ No newline at end of file diff --git a/nodes/unload_all_models.py b/nodes/unload_all_models.py deleted file mode 100644 index b70ba7c..0000000 --- a/nodes/unload_all_models.py +++ /dev/null @@ -1,32 +0,0 @@ -import comfy.model_management - -class AnyType(str): - """A special class that is always equal in not equal comparisons. Credit to pythongosssss""" - - def __ne__(self, __value: object) -> bool: - return False - - -any = AnyType("*") - -class UnloadAllModels: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "any":(any,{"forceInput": True}) - }, - "optional": {}, - } - - RETURN_TYPES = () - FUNCTION = "unload_models" - CATEGORY = "不忘科技-自定义节点🚩" - OUTPUT_NODE = True - - def unload_models(self,any=None): - # 卸载所有已加载的模型 - comfy.model_management.soft_empty_cache() - comfy.model_management.unload_all_models() - comfy.model_management.soft_empty_cache() - return () \ No newline at end of file diff --git a/nodes/util_nodes.py b/nodes/util_nodes.py new file mode 100644 index 0000000..2299273 --- /dev/null +++ b/nodes/util_nodes.py @@ -0,0 +1,339 @@ +import glob +import json +import os +import time +from pathlib import Path + +import comfy.model_management +import requests +import server +import yaml +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from tencentcloud.common import credential +from tencentcloud.common.profile.client_profile import ClientProfile +from tencentcloud.common.profile.http_profile import HttpProfile +from tencentcloud.vod.v20180717 import vod_client, models + +from ..utils.task_table import Task + + +class LogToDB: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "job_id": ("STRING", {"forceInput": True}), + "log": ("STRING", {"forceInput": True}), + "status": ("INT", {"default": 1, "max": 1}), + "sql_url": ("STRING", { + "default": "mysql+pymysql://root:root@example.com:3306/test"}), + }, + "hidden": { + "unique_id": "UNIQUE_ID", + } + } + + RETURN_TYPES = ("STRING",) + + FUNCTION = "log2db" + + OUTPUT_NODE = True + OUTPUT_IS_LIST = (True,) + + # OUTPUT_NODE = False + + CATEGORY = "不忘科技-自定义节点🚩/工具" + + def log2db(self, log, status, sql_url, unique_id): + # 获取comfy服务器队列信息 + (_, prompt_id, prompt, extra_data, outputs_to_execute) = next( + iter(server.PromptServer.instance.prompt_queue.currently_running.values())) + job_id = extra_data["client_id"] + engine = create_engine( + sql_url, + echo=True + ) + # Base.metadata.create_all(engine) + session = sessionmaker(bind=engine)() + # 查询 + tasks = session.query(Task).filter(Task.prompt_id == prompt_id).all() + print(prompt) + result = { + "curr_node_id": str(unique_id), + "last_node_id": list(prompt.keys())[-1], + "node_output": str(log) + } + if len(tasks) == 0: + # 不存在插入 + task = Task(prompt_id=prompt_id, job_id=job_id, result=json.dumps(result), status=status) + session.add(task) + elif len(tasks) == 1: + # 存在更新 + session.query(Task).filter(Task.prompt_id == prompt_id).update({"result": json.dumps(result), + "status": status}) + else: + # 异常报错 + raise RuntimeError("状态数据库prompt_id不唯一, 无法记录状态！") + session.commit() + return {"ui": {"text": json.dumps(result)}, "result": (json.dumps(result),)} + + +class VodToLocalNode: + + def __init__(self): + if "aws_key_id" in list(os.environ.keys()): + yaml_config = os.environ + else: + with open( + os.path.join( + os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "config.yaml" + ), + encoding="utf-8", + mode="r+", + ) as f: + yaml_config = yaml.load(f, Loader=yaml.FullLoader) + self.secret_id = yaml_config["cos_secret_id"] + self.secret_key = yaml_config["cos_secret_key"] + self.region = yaml_config["cos_region"] + self.vod_client = self.init_vod_client() + + def init_vod_client(self): + """初始化VOD客户端""" + try: + http_profile = HttpProfile(endpoint="vod.tencentcloudapi.com") + client_profile = ClientProfile(httpProfile=http_profile) + cred = credential.Credential(self.secret_id, self.secret_key) + return vod_client.VodClient(cred, self.region, client_profile) + except Exception as e: + raise RuntimeError(f"VOD client initialization failed: {e}") + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "file_id": ("STRING", {"default": ""}), + "sub_app_id": ("STRING", {"default": ""}), + } + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("local_path",) + FUNCTION = "execute" + CATEGORY = "不忘科技-自定义节点🚩/工具" + + def execute(self, file_id, sub_app_id): + # 调用下载逻辑 + local_path = self.download_vod(file_id, sub_app_id) + print(f"下载成功: {local_path}") + return (local_path,) + + def _get_download_url(self, file_id, sub_app_id): + """获取媒体文件下载地址""" + try: + req = models.DescribeMediaInfosRequest() + req.FileIds = [file_id] + req.SubAppId = int(sub_app_id) + + resp = self.vod_client.DescribeMediaInfos(req) + if not resp.MediaInfoSet: + raise ValueError("File not found") + + media_info = resp.MediaInfoSet[0] + if not media_info.BasicInfo.MediaUrl: + raise ValueError("No download URL available") + + return media_info.BasicInfo.MediaUrl + except Exception as e: + raise RuntimeError(f"Tencent API error: {e}") + + def create_directory(self, path): + p = Path(path) + if not p.exists(): + p.mkdir( + parents=True, exist_ok=True + ) # parents=True会自动创建所有必需的父目录，exist_ok=True表示如果目录已存在则不会引发异常 + print(f"目录已创建: {path}") + else: + print(f"目录已存在: {path}") + + def download_vod(self, file_id, sub_app_id): + """ + 需要补充腾讯云VOD SDK调用逻辑 + 返回本地文件路径 + """ + media_url = self._get_download_url(file_id=file_id, sub_app_id=sub_app_id) + print(f"download from url: {media_url}") + # 生成一个临时目录路径名并创建该目录 + self.create_directory( + os.path.join( + os.path.dirname(os.path.abspath(__file__)), "download", f"{sub_app_id}" + ) + ) + output_dir = os.path.join( + os.path.dirname(os.path.abspath(__file__)), + "download", + f"{sub_app_id}", + f"{file_id}.mp4", + ) + # 判断文件是否存在 + if os.path.exists(output_dir): + return output_dir + return self._download_file(url=media_url, save_path=output_dir, timeout=60 * 10) + + def _download_file(self, url: str, save_path: str, timeout: int = 30): + """下载文件到本地""" + try: + with requests.get(url, stream=True, timeout=timeout) as response: + response.raise_for_status() + with open(save_path, "wb") as f: + for chunk in response.iter_content(chunk_size=8192): + if chunk: + f.write(chunk) + return save_path + except Exception as e: + raise RuntimeError(f"Download error: {e}") + + +class AnyType(str): + """A special class that is always equal in not equal comparisons. Credit to pythongosssss""" + + def __ne__(self, __value: object) -> bool: + return False + + +any = AnyType("*") + + +class UnloadAllModels: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "any": (any, {"forceInput": True}) + }, + "optional": {}, + } + + RETURN_TYPES = () + FUNCTION = "unload_models" + CATEGORY = "不忘科技-自定义节点🚩/工具" + OUTPUT_NODE = True + + def unload_models(self, any=None): + # 卸载所有已加载的模型 + comfy.model_management.soft_empty_cache() + comfy.model_management.unload_all_models() + comfy.model_management.soft_empty_cache() + return () + + +class TraverseFolder: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "folder": ("STRING", {"default": r"E:\comfy\ComfyUI\input\s3", "required": True}), + "subfix": ("STRING", {"default": ".mp4", "required": True}), + "recursive": ("BOOLEAN", {"default": True, "required": True}), + "idx": ( + "INT", + {"default": 0, "min": 0, "max": 0xFFFFFF}, + ), + }, + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("文件路径",) + + FUNCTION = "compute" + + CATEGORY = "不忘科技-自定义节点🚩/工具" + + def compute(self, folder, subfix, recursive, idx): + files = glob.glob(os.path.join(folder, r"**\*%s" % subfix), recursive=recursive) + if len(files) == 0: + raise RuntimeError("No Files Found") + return (str(files[idx % len(files)]),) + + +class PlugAndPlayWebhook: + """即插即用Webhook节点：连上线就能转发数据""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "webhook_url": ("STRING", {"default": "http://127.0.0.1:8010/handler/webhook", + "placeholder": "https://your-api.com/webhook"}), + "image_url": ("STRING", {"default": "", + "placeholder": "图片的url"}), + }, + "optional": { + "prompt_id": ("STRING", {"default": ""}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = () + FUNCTION = "send" + OUTPUT_NODE = True + CATEGORY = "不忘科技-自定义节点🚩/工具" + + def send(self, webhook_url, image_url, prompt_id="", prompt=None, extra_pnginfo=None, unique_id=None): + if not webhook_url: + raise ValueError("❌ 请填写Webhook URL！") + + # 使用传入的prompt_id，如果没有则用unique_id + final_prompt_id = prompt_id or unique_id or "unknown" + + # 准备发送的数据 + data = { + "img_base64": image_url, + "format": "png", + "image_url": image_url, + "prompt_id": final_prompt_id, + "timestamp": time.time() + } + + # 发送Webhook + try: + response = requests.post(webhook_url, json=data) + response.raise_for_status() + print(f'发送的数据:{data}') + except Exception as e: + print(f"❌ 发送失败: {str(e)}") + + # 终端节点，无需返回 + return () + + +class TaskIdGenerate: + """TaskID生成器：用户可传入或自动生成TaskID""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": {}, + "optional": { + "custom_task_id": ("STRING", {"default": "", "placeholder": "留空则自动生成"}), + }, + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("task_id",) + FUNCTION = "generate_task_id" + OUTPUT_NODE = False + CATEGORY = "不忘科技-自定义节点🚩/工具" + + def generate_task_id(self, custom_task_id=""): + if custom_task_id and custom_task_id.strip(): + # 用户输入了自定义ID + task_id = custom_task_id.strip() + print(f"📝 使用自定义TaskID: {task_id}") + else: + # 自动生成UUID + task_id = str(uuid.uuid4()) + print(f"🎲 自动生成TaskID: {task_id}") + + return (task_id,) diff --git a/nodes/heygem.py b/nodes/video_lipsync_nodes.py similarity index 98% rename from nodes/heygem.py rename to nodes/video_lipsync_nodes.py index 6321982..549bcfe 100644 --- a/nodes/heygem.py +++ b/nodes/video_lipsync_nodes.py @@ -1,6 +1,7 @@ import json import os import time +import traceback import uuid import httpx @@ -8,7 +9,6 @@ import loguru import torchaudio import torchvision from torch import Tensor -import traceback def task_submit(uid, video, audio, heygem_url): @@ -140,7 +140,7 @@ class HeyGemF2F: RETURN_TYPES = ("STRING",) RETURN_NAMES = ("视频存储路径",) FUNCTION = "f2f" - CATEGORY = "不忘科技-自定义节点🚩/口型同步" + CATEGORY = "不忘科技-自定义节点🚩/视频/口型" def f2f(self, video:Tensor, audio:dict, heygem_url:str, heygem_temp_path:str, is_Windows:bool): uid = str(uuid.uuid4()) @@ -206,7 +206,7 @@ class HeyGemF2FFromFile: RETURN_TYPES = ("STRING",) RETURN_NAMES = ("视频存储路径",) FUNCTION = "f2f" - CATEGORY = "不忘科技-自定义节点🚩/口型同步" + CATEGORY = "不忘科技-自定义节点🚩/视频/口型" def f2f(self, video:str, audio:str, heygem_url:str, heygem_temp_path:str, is_Windows:bool): uid = str(uuid.uuid4()) diff --git a/nodes/video.py b/nodes/video_nodes.py similarity index 80% rename from nodes/video.py rename to nodes/video_nodes.py index f5cff41..428a108 100644 --- a/nodes/video.py +++ b/nodes/video_nodes.py @@ -1,9 +1,8 @@ -import errno import glob import os +import re import shutil import subprocess -import sys import traceback import uuid from datetime import datetime @@ -11,12 +10,10 @@ from datetime import datetime import ffmpy import loguru import torchvision.io -from ffmpy import FFExecutableNotFoundError, FFRuntimeError video_extensions = ['webm', 'mp4', 'mkv', 'gif', 'mov'] - class VideoCut: """FFMPEG视频剪辑""" @@ -24,14 +21,15 @@ class VideoCut: def INPUT_TYPES(s): return { "required": { - "video_path": ("STRING",{"placeholder": "X://insert/path/here.mp4", "vhs_path_extensions": video_extensions}), + "video_path": ( + "STRING", {"placeholder": "X://insert/path/here.mp4", "vhs_path_extensions": video_extensions}), "start": ("STRING", {"default": "00:00:00.000"}), "end": ("STRING", {"default": "00:00:10.000"}), }, } - RETURN_TYPES = ("IMAGE","AUDIO") - RETURN_NAMES = ("视频帧","音频") + RETURN_TYPES = ("IMAGE", "AUDIO") + RETURN_NAMES = ("视频帧", "音频") FUNCTION = "cut" @@ -121,11 +119,12 @@ class VideoCut: os.remove(files[0]) except: pass - return (video, {"waveform":audio,"sample_rate":info["audio_fps"]},) + return (video, {"waveform": audio, "sample_rate": info["audio_fps"]},) except: traceback.print_exc() raise Exception("Cut Failed") + class VideoCutByFramePoint: """FFMPEG视频剪辑-帧位""" @@ -133,7 +132,8 @@ class VideoCutByFramePoint: def INPUT_TYPES(s): return { "required": { - "video_path": ("STRING",{"placeholder": "X://insert/path/here.mp4", "vhs_path_extensions": video_extensions}), + "video_path": ( + "STRING", {"placeholder": "X://insert/path/here.mp4", "vhs_path_extensions": video_extensions}), "start_point": ("FLOAT", {"default": "0.0"}), "duration": ("FLOAT", {"default": "10.0"}), "fps": ("INT", {"default": "25"}), @@ -141,8 +141,8 @@ class VideoCutByFramePoint: }, } - RETURN_TYPES = ("IMAGE","AUDIO") - RETURN_NAMES = ("视频帧","音频") + RETURN_TYPES = ("IMAGE", "AUDIO") + RETURN_NAMES = ("视频帧", "音频") FUNCTION = "cut" @@ -189,9 +189,9 @@ class VideoCutByFramePoint: outputs={ output: [ "-ss", - "%.3f" % (start_point/fps), + "%.3f" % (start_point / fps), "-t", - "%.3f" % (duration/fps), + "%.3f" % (duration / fps), "-c:v", "libx264", "-c:a", @@ -234,11 +234,12 @@ class VideoCutByFramePoint: os.remove(output) except: pass - return (video, {"waveform":audio,"sample_rate":info["audio_fps"]},) + return (video, {"waveform": audio, "sample_rate": info["audio_fps"]},) except: traceback.print_exc() raise Exception("Cut Failed") + class VideoChangeFPS: """FFMPEG视频FPS转换""" @@ -246,7 +247,8 @@ class VideoChangeFPS: def INPUT_TYPES(s): return { "required": { - "video_path": ("STRING",{"placeholder": "X://insert/path/here.mp4", "vhs_path_extensions": video_extensions}), + "video_path": ( + "STRING", {"placeholder": "X://insert/path/here.mp4", "vhs_path_extensions": video_extensions}), "fps": ("INT", {"default": 30}), }, } @@ -323,7 +325,7 @@ class VideoChangeFPS: video_path = "output/" + video_path loguru.logger.info("Processing video: %s" % video_path) output_temp = ".".join([video_path.split(".")[-2] + "-%dfps-temp" % fps, video_path.split(".")[-1]]) - output = ".".join([video_path.split(".")[-2]+"-%dfps" % fps,video_path.split(".")[-1]]) + output = ".".join([video_path.split(".")[-2] + "-%dfps" % fps, video_path.split(".")[-1]]) # 分步执行 self.adjust_video_fps(video_path, output_temp, fps) # 第一步：调整FPS self.align_audio_to_video(output_temp, output) # 第二步：对齐音频 @@ -333,7 +335,8 @@ class VideoChangeFPS: final_audio_dur = self.get_media_duration(output, "a") print("video_duration:", final_video_dur, "audio_duration:", final_audio_dur) if abs(final_video_dur - final_audio_dur) > 0.01: - loguru.logger.warning(f"音视频长度未对齐！视频长度: {final_video_dur:.3f}s, 音频长度: {final_audio_dur:.3f}s") + loguru.logger.warning( + f"音视频长度未对齐！视频长度: {final_video_dur:.3f}s, 音频长度: {final_audio_dur:.3f}s") else: loguru.logger.success(f"处理成功！视频长度: {final_video_dur:.3f}s, 音频长度: {final_audio_dur:.3f}s") try: @@ -343,4 +346,49 @@ class VideoChangeFPS: return (output,) except: traceback.print_exc() - raise Exception("ChangeFPS Failed") \ No newline at end of file + raise Exception("ChangeFPS Failed") + + +def validate_time_format(time_str): + pattern = r'^([0-1][0-9]|2[0-3]):([0-5][0-9]):([0-5][0-9]|\d{1,2}).(\d{3})$' + return bool(re.match(pattern, time_str)) + + +def get_duration_wave(audio): + waveform, sample_rate = audio["waveform"], audio["sample_rate"] + # 防止话说不完 + return waveform.shape[2] / sample_rate + + +class VideoStartPointDurationCompute: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "start_time": ("STRING", {"forceInput": True}), + "audio": ("AUDIO", {"forceInput": True}), + "end_padding": ("FLOAT", {"forceInput": True, "default": 0.4}), + "fps": ("INT", {"default": 25, "step": 1}), + }, + } + + RETURN_TYPES = ("FLOAT", "FLOAT",) + RETURN_NAMES = ("起始帧位", "帧数") + + FUNCTION = "compute" + + CATEGORY = "不忘科技-自定义节点🚩/视频" + + def compute(self, start_time, audio, fps, end_padding): + if not validate_time_format(start_time): + raise ValueError("start_time或者end_time时间格式不对（start_time or end_time is not in time format）") + + time_format = "%H:%M:%S.%f" + start_dt = datetime.strptime(start_time, time_format) + start_sec = (start_dt - datetime(1900, 1, 1)).total_seconds() + start_point = start_sec * fps + duration = get_duration_wave(audio) + loguru.logger.info("audio duration %.3f s" % duration) + duration = duration + end_padding + loguru.logger.info("audio duration with padding %.3f s" % duration) + return (start_point, duration * fps,) diff --git a/nodes/vod2local.py b/nodes/vod2local.py deleted file mode 100644 index d5ffd76..0000000 --- a/nodes/vod2local.py +++ /dev/null @@ -1,112 +0,0 @@ -import os -from pathlib import Path - -import requests -from tencentcloud.common import credential -from tencentcloud.common.profile.client_profile import ClientProfile -from tencentcloud.common.profile.http_profile import HttpProfile -from tencentcloud.vod.v20180717 import vod_client, models - - -class VodToLocalNode: - - def __init__(self): - self.secret_id = "AKIDsrihIyjZOBsjimt8TsN8yvv1AMh5dB44" - self.secret_key = "CPZcxdk6W39Jd4cGY95wvupoyMd0YFqW" - self.vod_client = self.init_vod_client() - - def init_vod_client(self): - """初始化VOD客户端""" - try: - http_profile = HttpProfile(endpoint="vod.tencentcloudapi.com") - client_profile = ClientProfile(httpProfile=http_profile) - cred = credential.Credential(self.secret_id, self.secret_key) - return vod_client.VodClient(cred, "ap-shanghai", client_profile) - except Exception as e: - raise RuntimeError(f"VOD client initialization failed: {e}") - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "file_id": ("STRING", {"default": ""}), - "sub_app_id": ("STRING", {"default": ""}), - } - } - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("local_path",) - FUNCTION = "execute" - CATEGORY = "video" - - def execute(self, file_id, sub_app_id): - # 调用下载逻辑 - local_path = self.download_vod(file_id, sub_app_id) - print(f"下载成功: {local_path}") - return (local_path,) - - def _get_download_url(self, file_id, sub_app_id): - """获取媒体文件下载地址""" - try: - req = models.DescribeMediaInfosRequest() - req.FileIds = [file_id] - req.SubAppId = int(sub_app_id) - - resp = self.vod_client.DescribeMediaInfos(req) - if not resp.MediaInfoSet: - raise ValueError("File not found") - - media_info = resp.MediaInfoSet[0] - if not media_info.BasicInfo.MediaUrl: - raise ValueError("No download URL available") - - return media_info.BasicInfo.MediaUrl - except Exception as e: - raise RuntimeError(f"Tencent API error: {e}") - - def create_directory(self, path): - p = Path(path) - if not p.exists(): - p.mkdir( - parents=True, exist_ok=True - ) # parents=True会自动创建所有必需的父目录，exist_ok=True表示如果目录已存在则不会引发异常 - print(f"目录已创建: {path}") - else: - print(f"目录已存在: {path}") - - def download_vod(self, file_id, sub_app_id): - """ - 需要补充腾讯云VOD SDK调用逻辑 - 返回本地文件路径 - """ - media_url = self._get_download_url(file_id=file_id, sub_app_id=sub_app_id) - print(f"download from url: {media_url}") - # 生成一个临时目录路径名并创建该目录 - self.create_directory( - os.path.join( - os.path.dirname(os.path.abspath(__file__)), "download", f"{sub_app_id}" - ) - ) - output_dir = os.path.join( - os.path.dirname(os.path.abspath(__file__)), - "download", - f"{sub_app_id}", - f"{file_id}.mp4", - ) - # 判断文件是否存在 - if os.path.exists(output_dir): - return output_dir - return self._download_file(url=media_url, save_path=output_dir, timeout=60 * 10) - - def _download_file(self, url: str, save_path: str, timeout: int = 30): - """下载文件到本地""" - try: - with requests.get(url, stream=True, timeout=timeout) as response: - response.raise_for_status() - with open(save_path, "wb") as f: - for chunk in response.iter_content(chunk_size=8192): - if chunk: - f.write(chunk) - return save_path - except Exception as e: - raise RuntimeError(f"Download error: {e}") diff --git a/nodes/webhook_forward.py b/nodes/webhook_forward.py deleted file mode 100644 index afcaca8..0000000 --- a/nodes/webhook_forward.py +++ /dev/null @@ -1,187 +0,0 @@ -# -*- coding:utf-8 -*- -""" -File webhook_forward.py -Author silence -Date 2025/6/13 13:42 -""" -import base64 -import io -import os -import random -import string -import time - -import numpy as np -import requests -from PIL import Image - - -class PromptIDGenerator: - """PromptID生成器：用户可传入或自动生成PromptID""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": {}, - "optional": { - "custom_prompt_id": ("STRING", {"default": "", "placeholder": "留空则自动生成"}), - }, - } - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("prompt_id",) - FUNCTION = "generate_prompt_id" - OUTPUT_NODE = False - CATEGORY = "utils" - - def generate_prompt_id(self, custom_prompt_id=""): - if custom_prompt_id and custom_prompt_id.strip(): - # 用户输入了自定义ID - prompt_id = custom_prompt_id.strip() - print(f"📝 使用自定义PromptID: {prompt_id}") - else: - # 自动生成ID：时间戳 + 随机字符 - timestamp = str(int(time.time())) - random_str = ''.join(random.choices(string.ascii_lowercase + string.digits, k=6)) - prompt_id = f"prompt_{timestamp}_{random_str}" - print(f"🎲 自动生成PromptID: {prompt_id}") - - return (prompt_id,) - - -class PlugAndPlayWebhook: - """即插即用Webhook节点：连上线就能转发数据""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "webhook_url": ("STRING", {"default": "http://127.0.0.1:8010/handler/webhook", - "placeholder": "https://your-api.com/webhook"}), - "image_url": ("STRING", {"default": "", - "placeholder": "图片的url"}), - }, - "optional": { - "prompt_id": ("STRING", {"default": ""}), - }, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"}, - } - - RETURN_TYPES = () - FUNCTION = "send" - OUTPUT_NODE = True - CATEGORY = "utils" - - def send(self, webhook_url, image_url, prompt_id="", prompt=None, extra_pnginfo=None, unique_id=None): - if not webhook_url: - raise ValueError("❌ 请填写Webhook URL！") - - # 使用传入的prompt_id，如果没有则用unique_id - final_prompt_id = prompt_id or unique_id or "unknown" - - # 准备发送的数据 - data = { - "img_base64": image_url, - "format": "png", - "image_url": image_url, - "prompt_id": final_prompt_id, - "timestamp": time.time() - } - - # 发送Webhook - try: - response = requests.post(webhook_url, json=data) - response.raise_for_status() - print(f'发送的数据:{data}') - except Exception as e: - print(f"❌ 发送失败: {str(e)}") - - # 终端节点，无需返回 - return () - - -class SaveImageWithOutput: - """保存图片并输出的节点：既保存图片又能继续传递数据""" - - def __init__(self): - self.output_dir = "output" - self.type = "output" - self.prefix_append = "" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "images": ("IMAGE",), - "filename_prefix": ("STRING", {"default": "ComfyUI"}), - }, - } - - RETURN_TYPES = ("IMAGE", "STRING") - RETURN_NAMES = ("images", "file_path") - FUNCTION = "save_image" - OUTPUT_NODE = False - CATEGORY = "image" - - def save_image(self, images, filename_prefix="ComfyUI"): - filename_prefix += self.prefix_append - full_output_folder, filename, counter, subfolder, filename_prefix = self.get_save_image_path(filename_prefix, - self.output_dir) - - file_paths = [] - - for i, image in enumerate(images): - # 转换图片数据 - img_array = 255. * image.cpu().numpy() - img = Image.fromarray(np.clip(img_array, 0, 255).astype(np.uint8)) - - # 生成文件名 - file = f"{filename_prefix}_{counter + i:05}_.png" - file_path = os.path.join(full_output_folder, file) - - # 保存图片 - img.save(file_path, compress_level=4) - file_paths.append(file_path) - - print(f"✅ 图片已保存到: {file_paths[0] if file_paths else '未知路径'}") - - # 直接返回元组：(原始图片数据, 第一个文件路径) - return (images, file_paths[0] if file_paths else "") - - def get_save_image_path(self, filename_prefix, output_dir): - def map_filename(filename): - prefix_len = len(os.path.basename(filename_prefix)) - prefix = filename[:prefix_len + 1] - try: - digits = int(filename[prefix_len + 1:].split('_')[0]) - except: - digits = 0 - return (digits, prefix) - - subfolder = "" - full_output_folder = os.path.join(output_dir, subfolder) - - if os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) != output_dir: - print("Saving image outside the output folder is not allowed.") - return {} - - try: - counter = max(filter(lambda a: a[1][:-1] == filename_prefix and a[1][-1] == "_", - map(map_filename, os.listdir(full_output_folder))))[0] + 1 - except ValueError: - counter = 1 - except FileNotFoundError: - os.makedirs(full_output_folder, exist_ok=True) - counter = 1 - - return full_output_folder, filename_prefix, counter, subfolder, filename_prefix - - -NODE_CLASS_MAPPINGS = { - "PlugAndPlayWebhook": PlugAndPlayWebhook, - "SaveImageWithOutput": SaveImageWithOutput -} -NODE_DISPLAY_NAME_MAPPINGS = { - "PlugAndPlayWebhook": "Webhook转发器", - "SaveImageWithOutput": "保存图片(带输出)" -} diff --git a/pyproject_comfyui.toml b/pyproject_comfyui.toml deleted file mode 100644 index 5bec768..0000000 --- a/pyproject_comfyui.toml +++ /dev/null @@ -1,28 +0,0 @@ -[build-system] -requires = ["setuptools"] -build-backend = "setuptools.build_meta" - -[project] -name = "modal_comfyui" -version = "1.0.0" -requires-python = ">=3.10" -dependencies = [ - "fastapi[standard]==0.115.4", - "cos-python-sdk-v5", - "sqlalchemy", - "ultralytics", - "tencentcloud-sdk-python", - "pymysql", - "Pillow", - "ffmpy", - "opencv-python", - "av", - "imageio", - "loguru", - "openai-whisper", - "sentry-sdk", - "pydantic", - "pydantic_settings", - "conformer==0.3.2", - "einops>0.6.1" -] \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 06c2e11..aa1abb6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,13 +1,18 @@ -ffmpy -torch -torchvision -Pillow -opencv-python -ultralytics cos-python-sdk-v5 sqlalchemy +ultralytics tencentcloud-sdk-python -boto3 +pymysql +Pillow +ffmpy +opencv-python +av +imageio loguru +conformer==0.3.2 +einops>0.6.1 +tqdm +retry pyYAML -retry \ No newline at end of file +boto3 +Jinja2 \ No newline at end of file diff --git a/user/text_file_dirs.json b/user/text_file_dirs.json deleted file mode 100644 index 15adc22..0000000 --- a/user/text_file_dirs.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "input": "$input/**/*.txt", - "output": "$output/**/*.txt", - "temp": "$temp/**/*.txt" -} diff --git a/nodes/test_single_image.py b/utils/face_occu_detect.py similarity index 93% rename from nodes/test_single_image.py rename to utils/face_occu_detect.py index 44882f9..67446ad 100644 --- a/nodes/test_single_image.py +++ b/utils/face_occu_detect.py @@ -7,8 +7,8 @@ import torch from torchvision import transforms from torchvision.transforms import Resize -from .utils import load_weight -from .model import Model +from ..utils.modal_utils import load_weight +from ..utils.model_module import Model # CONSTANT MEAN = [0.485, 0.456, 0.406] @@ -18,7 +18,7 @@ CLASSES = {0: "non-occluded", 1: "occluded"} -def test_image(opt): +def face_occu_detect_single(opt): device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = Model(opt.model, 2, False).to(device) model = load_weight(model, opt.weight) @@ -56,7 +56,7 @@ def test_image(opt): )) -def test_node(image: torch.Tensor, length=10, thres=95, model_name="convnext_tiny"): +def face_occu_detect(image: torch.Tensor, length=10, thres=95, model_name="convnext_tiny"): weight_dic = { "convnext_tiny": "best_convnext_tiny.pth", "convnext_base": "best_convnext_base.pth" @@ -115,4 +115,4 @@ if __name__ == "__main__": parser.add_argument("--image", type=str, help="Image path") args = parser.parse_args() - test_image(args) + face_occu_detect_single(args) diff --git a/nodes/utils.py b/utils/modal_utils.py similarity index 100% rename from nodes/utils.py rename to utils/modal_utils.py diff --git a/nodes/model.py b/utils/model_module.py similarity index 96% rename from nodes/model.py rename to utils/model_module.py index 1762f37..f75e148 100644 --- a/nodes/model.py +++ b/utils/model_module.py @@ -1,7 +1,7 @@ from torch import nn from PIL import ImageFile -from .utils import get_model +from ..utils.modal_utils import get_model ImageFile.LOAD_TRUNCATED_IMAGES = True diff --git a/nodes/table.py b/utils/task_table.py similarity index 100% rename from nodes/table.py rename to utils/task_table.py