ComfyUI Extension: ComfyUI-Ollama-Describer
This is an extension for ComfyUI that makes it possible to use some LLM models provided by Ollama, such as Gemma, Llava (multimodal), Llama2, Llama3 or Mistral. Speaking specifically of the LLaVa - Large Language and Vision Assistant model, although trained on a relatively small dataset, it demonstrates exceptional capabilities in understanding images and answering questions about them. This model presents similar behaviors to multimodal models such as GPT-4, even when presented with invisible images and instructions.
Custom Nodes (0)
README
ComfyUI-Ollama-Describer
🚀 News
[22-02-2025] Added Support for Pydantic Schema Input
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Now, nodes can accept Pydantic Schemas as input, making it easier to define structured outputs.
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Example use case:
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To generate Pydantic schemas, you can use the Python Interpreter Node by Christian Byrne.
Introduction
This extension for ComfyUI enables the use of Ollama LLM models, such as Gemma, Llava (multimodal), Llama2, Llama3, and Mistral.
📌 Features:
- Ollama Image Describer 🖼️: Generate structured descriptions of images.
- Ollama Text Describer 📝: Extract meaningful insights from text.
- Ollama Image Captioner 📷: Create automatic captions for images.
- Ollama Captioner Extra Options ⚙️: Advanced customization for captions.
- Text Transformer 🔄: Prepend, append, or modify text dynamically.
- JSON Property Extractor 📑: Extract specific values from JSON 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:
pip install -r requirements.txt
Usage
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:0: Unloads immediately.-1: Stays loaded indefinitely.- Any other value (e.g.,
10) keeps it in memory for that number of seconds.
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 🎛️
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Works in conjunction with Ollama Image Captioner to provide additional customization for captions.
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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.