ComfyUI-Ollama-Describer
🚀 News
[17-03-2025] New Autonomous Agent & Tool Calling 🤖🔍
- Autonomous Agent Node: Added the
OllamaAgentnode that can perform iterative reasoning (ReAct) and call tools until it finds the answer. - Web Search Tool: New node to allow models to search the internet (DuckDuckGo or Ollama API).
- Thinking Support: Added a 'Think' toggle for models like Qwen 3.5, DeepSeek-R1, and GPT-OSS.
- UI Enhancements: Added password-style masking for API keys and improved model selection lists.
Introduction
This extension for ComfyUI enables the use of Ollama LLM models, such as Qwen 3.5, DeepSeek-R1, Llama 3.1/3.2, and Mistral.
📌 Features:
- Autonomous Agent 🤖: An intelligent agent that can use tools, think, and search the web to answer complex queries.
- Web Search Tool 🔍: Connect your Agent to the internet via DuckDuckGo (free) or the Ollama Search API.
- Support for 'Thinking' Models 🧠: Full support for reasoning chains in models like Qwen 3.5 and DeepSeek.
- Ollama Image Describer 🖼️: Generate structured descriptions of images.
- Ollama Text Describer 📝: Extract meaningful insights from text.
- Ollama Image Captioner 📷: Create automatic captions for images.
- Text Transformer 🔄: Prepend, append, or modify text dynamically.
- JSON Property Extractor 📑: Extract specific values from structured outputs.
Installation
1️⃣ Install Ollama
Follow the official Ollama installation guide.
2️⃣ Install via ComfyUI Manager (Recommended)
The easiest way to install this extension is through ComfyUI Manager:
- Open ComfyUI Manager.
- Search for ComfyUI-Ollama-Describer.
- Click Install and restart ComfyUI.
3️⃣ Install Manually
git clone https://github.com/alisson-anjos/ComfyUI-Ollama-Describer.git
Path should be custom_nodes\ComfyUI-Ollama-Describer.
4️⃣ Install Dependencies
Windows:
Run install.bat
Linux/Mac/Windows:
pip install -r requirements.txt
Usage
Ollama Agent & Tools 🤖🛠️
The Ollama Agent is an autonomous node that can use connected tools to answer questions. It doesn't just generate text; it enters a reasoning loop (ReAct) where it can call tools, analyze results, and "think" before giving a final answer.
Key Parameters:
model: Select models optimized for tool calling (e.g., Llama 3.1, Qwen 3.5).tools: ConnectOLLAMA_TOOLnodes (like Web Search).think: Enable reasoning chains for compatible models (Qwen 3.5, DeepSeek-R1).system_context: Default instructions that force the model to use tools for real-time data.max_tokens: Limit the response length (default 2048).
Ollama Video Describer 🎥🔍
Similar to the Image Describer, but optimized for processing video frames or sequences. It allows for detailed temporal analysis using vision-enabled models.
Key Parameters:
num_ctx: Context window size (default 4096, increase for longer descriptions).max_tokens: Maximum length of the video description.keep_model_alive: Manage VRAM by deciding how long to keep the model loaded.
Web Search Tool 🌐🔍
tool_name: Custom name for the tool (e.g., "google_search"). This is how the Agent will refer to it in its thinking process.- DuckDuckGo (free): No setup needed, search the web for free.
- Ollama API: Highly accurate search results, requires a free API key from ollama.com.
- Max Results: Control how many snippets are fed back to the Agent.
Ollama Image Describer 📷🔍
- Extracts structured descriptions from images using vision-enabled LLMs.
- Useful for analyzing images and generating detailed captions, including objects, actions, and surroundings.
Key Parameters:
model: Select LLaVa models (7B, 13B, etc.).custom_model: Specify a custom model from Ollama's library.api_host: Define the API address (e.g.,http://localhost:11434).timeout: Max response time before canceling the request.temperature: Controls randomness (0 = factual, 1 = creative).top_k,top_p,repeat_penalty: Fine-tune text generation.max_tokens: Maximum response length in tokens.seed_number: Set seed for reproducibility (-1 for random).keep_model_alive: Defines how long the model stays loaded after execution.prompt: The main instruction for the model.system_context: Provide additional context for better responses.structured_output_format: Accepts either a Python dictionary or a valid JSON string to define the expected response structure.
JSON Property Extractor 📑
- Used to extract specific values from structured JSON outputs returned by Ollama Image Describer or Ollama Text Describer.
- Works by selecting a key (or path) inside a JSON structure and outputting only the requested data.
- Useful for filtering, extracting key insights, or formatting responses for further processing.
- Compatible with
structured_output_format, which allows defining structured outputs via a Python dictionary or a valid JSON string.
Ollama Text Describer 📝🔎
- Processes text inputs to generate structured descriptions or summaries.
- Ideal for refining text-based outputs and enhancing context understanding.
Ollama Image Captioner 🖼️📖
- Automatically generates concise and relevant captions for images.
- Processes images from a specified folder, iterates through each file, and generates
.txtcaption files saved in the output directory. - Useful for bulk image captioning, dataset preparation, and AI-assisted annotation.
- Useful for image-to-text applications, content tagging, and accessibility.
Ollama Captioner Extra Options 🎛️
-
Works in conjunction with Ollama Image Captioner to provide additional customization for captions.
-
Allows fine-tuning of captions by enabling or disabling specific details like lighting, camera angle, composition, and aesthetic quality.
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Useful for controlling caption verbosity, accuracy, and inclusion of metadata like camera settings or image quality.
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Helps tailor the output for different applications such as dataset labeling, content creation, and accessibility enhancements.
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Provides additional customization settings for generated captions.
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Helps refine style, verbosity, and accuracy based on user preferences.
Text Transformer ✏️
- Allows users to modify, append, prepend, or replace text dynamically.
- Useful for formatting, restructuring, and enhancing text-based outputs.
🛠️ Technical Details
Understanding Model Suffixes & Quantization
| Suffix | Meaning |
|---|---|
| Q | Quantized model (smaller, faster) |
| 4, 8, etc. | Number of bits used (lower = smaller & faster) |
| K | K-means quantization (more efficient) |
| M | Medium-sized model |
| F16 / F32 | Floating-point precision (higher = more accurate) |
More details on quantization: Medium Article.
Perplexity Explained 🧠
- Measures how well a model predicts text.
- Lower perplexity = better predictions.