ComfyUI-EACloudNodes

A collection of ComfyUI custom nodes for interacting with various cloud services, such as LLM providers Groq and OpenRouter. These nodes are designed to work with any ComfyUI instance, including cloud-hosted environments where users may have limited system access.

Installation

Use ComfyUI-Manager or to manually install:

1. Clone this repository into your ComfyUI custom_nodes folder:

cd ComfyUI/custom_nodes
git clone https://github.com/EnragedAntelope/ComfyUI-EACloudNodes

2. Install required packages:

cd ComfyUI-EACloudNodes
pip install -r requirements.txt

3. Restart ComfyUI

Current Nodes

Common Features Across LLM Nodes

The following parameters are available in both OpenRouter and Groq nodes:

Common Parameters:

• api_key: ⚠️ Your API key (Note: key will be visible in workflows)
• model: Model selection (dropdown or identifier)
• system_prompt: Optional system context setting
• user_prompt: Main prompt/question for the model
• temperature: Controls response randomness (0.0-2.0)
• top_p: Nucleus sampling threshold (0.0-1.0)
• frequency_penalty: Token frequency penalty (-2.0 to 2.0)
• presence_penalty: Token presence penalty (-2.0 to 2.0)
• response_format: Choose between text or JSON object output
• seed_mode: Control reproducibility (Fixed, Random, Increment, Decrement)
• max_retries: Maximum retry attempts (0-5) for recoverable errors
• image_input: Optional image for vision-capable models
• additional_params: Optional JSON object for extra model parameters

Common Outputs:

• response: The model's generated text or JSON response
• status: Detailed information about the request, including model used and token counts
• help: Static help text with usage information and repository URL

OpenRouter Chat

Interact with OpenRouter's API to access various AI models for text and vision tasks.

Model Selection

• Choose from a curated list of free models in the dropdown
• Select "Manual Input" to use any OpenRouter-supported model
• When using Manual Input, enter the full model identifier in the manual_model field
• Supports both text and vision models (vision-capable models indicated in name)

Available Free Models

• Google Gemini models (various versions)
• Meta Llama models (including vision-capable versions)
• Microsoft Phi models
• Mistral, Qwen, and other open models
• Full list viewable in node dropdown

Parameters:

• api_key: ⚠️ Your OpenRouter API key (Get from https://openrouter.ai/keys)
• model: Select from available models or choose "Manual Input"
• manual_model: Enter custom model name (only used when "Manual Input" is selected)
• base_url: OpenRouter API endpoint URL (default: https://openrouter.ai/api/v1/chat/completions)
• system_prompt: Optional system context setting
• user_prompt: Main prompt/question for the model
• temperature: Controls response randomness (0.0-2.0)
• top_p: Nucleus sampling threshold (0.0-1.0)
• top_k: Vocabulary limit (1-1000)
• max_tokens: Maximum number of tokens to generate
• frequency_penalty: Token frequency penalty (-2.0 to 2.0)
• presence_penalty: Token presence penalty (-2.0 to 2.0)
• repetition_penalty: Repetition penalty (1.0-2.0)
• response_format: Choose between text or JSON object output
• seed_mode: Control reproducibility (Fixed, Random, Increment, Decrement)
• max_retries: Number of retry attempts for recoverable errors (0-5)
• image_input: Optional image for vision-capable models
• additional_params: Optional JSON object for extra model parameters

Outputs:

• response: The model's generated text or JSON response
• status: Detailed information about the request, including model used and token counts
• help: Static help text with usage information and repository URL

OpenRouter Models Node

Query and filter available models from OpenRouter's API.

Features:

• Retrieve complete list of available models
• Filter models using custom search terms (e.g., 'free', 'gpt', 'claude')
• Sort models by name, pricing, or context length
• Detailed model information including pricing and context length
• Easy-to-read formatted output

Parameters:

• api_key: ⚠️ Your OpenRouter API key (Note: key will be visible in workflows)
• filter_text: Text to filter models
• sort_by: Sort models by name, pricing, or context length
• sort_order: Choose ascending or descending sort order

Groq Chat

Interact with Groq's API for ultra-fast inference with various LLM models.

Features:

• High-speed inference with Groq's optimized hardware
• Dropdown selection of current Groq models
• Manual model input option for future or custom models
• Support for vision-capable models (models with 'vision' in their name)
• Real-time token usage tracking
• Automatic retry mechanism for recoverable errors
• Toggle for system prompt sending (required for vision models)

Usage:

1. Get your API key from console.groq.com/keys
2. Add the Groq node to your workflow
3. Enter your API key
4. Select a model from the dropdown or choose "Manual Input" to specify a custom model
5. Configure your prompts and parameters
6. Connect outputs to view responses, status, and help information

Parameters:

• model: Select from available Groq models or choose "Manual Input"
• manual_model: Enter custom model name (only used when "Manual Input" is selected)
• user_prompt: Main prompt/question for the model
• system_prompt: Optional system context setting
• send_system: Toggle system prompt sending (must be off for vision models)
• temperature: Controls response randomness (0.0-2.0)
• top_p: Nucleus sampling threshold (0.0-1.0)
• max_completion_tokens: Maximum number of tokens to generate
• frequency_penalty: Token frequency penalty (-2.0 to 2.0)
• presence_penalty: Token presence penalty (-2.0 to 2.0)
• response_format: Choose between text or JSON object output
• seed_mode: Control reproducibility (Fixed, Random, Increment, Decrement)
• max_retries: Number of retry attempts for recoverable errors (0-5)
• image_input: Optional image for vision-capable models
• additional_params: Optional JSON object for extra model parameters

Outputs:

• response: The model's generated text or JSON response
• status: Detailed information about the request, including model used and token counts
• help: Static help text with usage information and repository URL

Vision Analysis

1. Add an LLM node to your workflow
2. Choose a vision-capable model
3. Connect an image output to the image_input
4. For Groq vision models, toggle 'send_system' to 'no'
5. Add your prompt about the image
6. Connect outputs to view response, status, and help information

Usage Guide

Basic Text Generation

1. Add an LLM node (OpenRouter or Groq) to your workflow
2. Set your API key
3. Choose a model
4. (Optional) Set system prompt for context/behavior
5. Enter your prompt in the user_prompt field
6. Connect the node's output to a Text node to display results

Vision Analysis

1. Add an LLM node to your workflow
2. Choose a vision-capable model
3. Connect an image output to the image_input
4. Add your prompt about the image
5. Connect outputs to view both the response and status

Advanced Usage

• Use system_prompt to set context or behavior
• Adjust temperature and other parameters to control response style
• Select json_object format for structured outputs
• Monitor token usage via the status output
• Chain multiple nodes for complex workflows
• Use seed_mode for reproducible outputs (Fixed) or controlled variation (Increment/Decrement)
• Use additional_params to set model-specific parameters in JSON format:

  {
    "min_p": 0.1,
    "top_a": 0.8
  }
  

Parameter Optimization Tips

• Lower temperature (0.1-0.3) for more focused responses
• Higher temperature (0.7-1.0) for more creative outputs
• Lower top_p (0.1-0.3) for more predictable word choices
• Higher top_p (0.7-1.0) for more diverse vocabulary
• Use presence_penalty to reduce repetition
• Monitor token usage to optimize costs
• Save API key in the node for reuse in workflows
• Use seed_mode for reproducible outputs (Fixed) or controlled variation (Increment/Decrement)

Error Handling

Both nodes provide detailed error messages for common issues:

• Missing or invalid API keys
• Model compatibility issues
• Image size and format requirements
• JSON format validation
• Token limits and usage
• API rate limits and retries

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

License