AI Rank
EN
← 开源
IuvenisSapiens

ComfyUI_Qwen3-VL-Instruct

The successful integration of Qwen3-VL-Instruct series into the ComfyUI platform has enabled a smooth operation, supporting (but not limited to) text-based queries, video queries, single-image queries, and multi-image queries for generating captions or responses.

AI EngineeringGive agent toolsPython
在 GitHub 打开
增长势头
+024 小时新增 Star0.0%
584
Star
66
Fork
+-1
本周
3
贡献者
创建于 2024-08-31 · 更新于 2026-10-10 · 今日第 16562 名
主要开发者
IuvenisSapiensedwiosMakkiShizu

IuvenisSapiens/ComfyUI_Qwen3-VL-Instruct 排名第 16,562，有 584 个星标。

同类别比较

IuvenisSapiens/ComfyUI_Qwen3-VL-Instruct 排名第 16,562，此处有 8 个同类条目；HansZ8/RoboJuDo 排名第 16,561。

同类别比较
名称排名指标
neo4j-labs/agent-memory16,539
语言:
Python
星标数:
586
分叉数:
109
JasonLovesDoggo/caddy-defender16,545
语言:
Go
星标数:
585
分叉数:
26
简介:
Caddy module to block or manipulate requests originating from AIs or cloud services trying to train on your websites
openteams-lab/openteams16,554
语言:
TypeScript
星标数:
585
分叉数:
78
HansZ8/RoboJuDo16,561
语言:
Python
星标数:
584
分叉数:
84
简介:
A plug-and-play deploy framework for robots. Just deploy, just do.
ZubeidHendricks/youtube-mcp-server16,566
语言:
TypeScript
星标数:
584
分叉数:
141
简介:
MCP Server for YouTube API, enabling video management, Shorts creation, and advanced analytics
christopherkarani/Swarm16,571
语言:
Swift
星标数:
584
分叉数:
44
简介:
Type-safe tools, on-device inference, multi-agent workflows, memory, and guardrails, in one Swift-native runtime
KeyValueSoftwareSystems/agent-opfor16,579
语言:
TypeScript
星标数:
583
分叉数:
35
简介:
Open-source adversary emulation for AI agents and MCP servers.
vivekVells/mcp-pandoc16,592
语言:
Python
星标数:
583
分叉数:
62
简介:
MCP server for document format conversion using pandoc.
关键参数与规格
关键参数与规格
参数值
语言Python
类别AI Engineering
子类别Give agent tools
关键词Multimodal, Visual understanding
星标数584
分叉数66
贡献者3
创建日期2024-08-31
更新日期2026-10-10
1 天星标数0
7 天星标数-1
排名16,562
上次排名16,348
排名变化-214
7 天星标排名17,910
上次 7 天星标排名17,856
7 天星标排名变化-54
总星标排名17,141
上次总星标排名17,110
总星标排名变化-31
国家China
README 摘录

Comfyui_Qwen3-VL-Instruct This is an implementation of Qwen3-VL-Instruct by ComfyUI, which includes, but is not limited to, support for text-based queries, video queries, single-image queries, and multi-image queries to generate captions or responses. For instance, a user might input a description like "What is the meaning of life? " **Video Query**: When a user uploads a video, the system can analyze the content and generate a detailed caption for each frame or a summary

of the entire video. For example, "Generate a caption for the given video. " **Single-Image Query**: This workflow supports generating a caption for an individual image. A user could upload a photo and ask, "What does this image show? " resulting in a caption such as "A majestic lion pride relaxing on the savannah. " **Multi-Image Query**: For multiple images, the system can provide a collective description or a narrative that ties the images together. For example, "Create a story from

the following series of images: one of a couple at a beach, another at a wedding ceremony, and the last one at a baby's christening. IMPORTANT] Important Notes for Using the Workflow - Please ensure that you have the "Display Text node" available in your ComfyUI setup. If you encounter any issues with this node being missing, you can find it in the ComfyUI_MiniCPM-V-4_5 repository. Installing this additional addon will make the "Display Text node" available for

use. Installation Install from ComfyUI Manager (search for ) Download or git clone this repository into the directory and run: Download Models All the models will be downloaded automatically when running the workflow if they are not found in the directory.

常见问题

记录了多少星标和分叉？

IuvenisSapiens/ComfyUI_Qwen3-VL-Instruct 有 584 个星标和 66 个分叉。

仓库排名是多少？

IuvenisSapiens/ComfyUI_Qwen3-VL-Instruct 排名第 16,562。

记录的语言和类别是什么？

IuvenisSapiens/ComfyUI_Qwen3-VL-Instruct 有 584 个星标，语言为 Python，类别为 AI Engineering。

哪个仓库排名最接近？

IuvenisSapiens/ComfyUI_Qwen3-VL-Instruct 与 HansZ8/RoboJuDo 比较，后者排名第 16,561，有 584 个星标。

README

Comfyui_Qwen3-VL-Instruct

This is an implementation of Qwen3-VL-Instruct by ComfyUI, which includes, but is not limited to, support for text-based queries, video queries, single-image queries, and multi-image queries to generate captions or responses.

Basic Workflow

  • Text-based Query: Users can submit textual queries to request information or generate descriptions. For instance, a user might input a description like "What is the meaning of life?"

  • Video Query: When a user uploads a video, the system can analyze the content and generate a detailed caption for each frame or a summary of the entire video. For example, "Generate a caption for the given video."

  • Single-Image Query: This workflow supports generating a caption for an individual image. A user could upload a photo and ask, "What does this image show?" resulting in a caption such as "A majestic lion pride relaxing on the savannah."

  • Multi-Image Query: For multiple images, the system can provide a collective description or a narrative that ties the images together. For example, "Create a story from the following series of images: one of a couple at a beach, another at a wedding ceremony, and the last one at a baby's christening."

[!IMPORTANT] Important Notes for Using the Workflow

  • Please ensure that you have the "Display Text node" available in your ComfyUI setup. If you encounter any issues with this node being missing, you can find it in the ComfyUI_MiniCPM-V-4_5 repository. Installing this additional addon will make the "Display Text node" available for use.

Installation

  • Install from ComfyUI Manager (search for Qwen3)

  • Download or git clone this repository into the ComfyUI\custom_nodes\ directory and run:

pip install -r requirements.txt

Download Models

All the models will be downloaded automatically when running the workflow if they are not found in the ComfyUI\models\prompt_generator\ directory.

排名相邻
同类项目