ComfyUI Extension: ComfyUI-EACloudNodes

Authored by EnragedAntelope

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

Custom Nodes (3)

README

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.

Note: All nodes have been updated to ComfyUI v3 spec for enhanced reliability, validation, and features while maintaining backward compatibility with v1.

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
  1. Install required packages:
cd ComfyUI-EACloudNodes
pip install -r requirements.txt
  1. 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

Groq Chat (v3)

Interact with Groq's API for ultra-fast inference with various LLM models. Now fully compatible with ComfyUI v3 spec!

Features:

  • ComfyUI v3 compatible - Enhanced reliability and validation
  • High-speed inference with Groq's optimized hardware
  • Comprehensive model selection including production and preview models
  • Support for vision-capable models (Llama-4 Maverick and Scout)
  • Real-time token usage tracking
  • Automatic retry mechanism with exponential backoff
  • Enhanced input validation
  • Detailed tooltips for all parameters
  • Debug mode for troubleshooting

Available Models:

Production Models (Stable, recommended for production use):

  • llama-3.1-8b-instant - Fast 8B parameter model (560 T/sec)
  • llama-3.3-70b-versatile - Default - Powerful 70B model (280 T/sec)
  • meta-llama/llama-guard-4-12b - Safety and moderation model (1200 T/sec)
  • openai/gpt-oss-120b - Large open-source GPT (500 T/sec)
  • openai/gpt-oss-20b - Efficient open-source GPT (1000 T/sec)
  • whisper-large-v3 - Speech recognition model
  • whisper-large-v3-turbo - Faster speech recognition

Production Systems (Agentic systems with tools):

  • groq/compound - Multi-model system with tools
  • groq/compound-mini - Lightweight agentic system

Preview Models (Experimental, for evaluation only):

  • meta-llama/llama-4-maverick-17b-128e-instruct - Vision (600 T/sec)
  • meta-llama/llama-4-scout-17b-16e-instruct - Vision (750 T/sec)
  • meta-llama/llama-prompt-guard-2-22m - Prompt injection detection
  • meta-llama/llama-prompt-guard-2-86m - Enhanced prompt guard
  • moonshotai/kimi-k2-instruct-0905 - 262K context window (200 T/sec)
  • openai/gpt-oss-safeguard-20b - Safety-focused model (1000 T/sec)
  • playai-tts - Text-to-speech model
  • playai-tts-arabic - Arabic text-to-speech
  • qwen/qwen3-32b - Qwen 32B model (400 T/sec)

Parameters:

  • api_key: ⚠️ Your Groq API key (Get from console.groq.com/keys)
  • model: Select from available models or choose "Manual Input" for custom models
  • manual_model: Enter custom model identifier (only used when "Manual Input" is selected)
  • system_prompt: Optional system context (disable for vision models)
  • user_prompt: Main prompt/question for the model
  • send_system: Toggle system prompt sending (must be 'no' for vision models)
  • temperature: Controls response randomness (0.0-2.0)
    • Lower (0.0-0.3): More focused and deterministic
    • Higher (0.7-2.0): More creative and varied
  • top_p: Nucleus sampling threshold (0.0-1.0)
    • Lower (0.0-0.3): More focused vocabulary
    • Higher (0.7-1.0): More diverse word selection
  • max_completion_tokens: Maximum tokens to generate (1-131,072, varies by model)
  • frequency_penalty: Reduce token frequency repetition (-2.0 to 2.0)
  • presence_penalty: Encourage topic diversity (-2.0 to 2.0)
  • response_format: Choose between "text" or "json_object" output
  • seed_mode: Control reproducibility
    • fixed: Use seed_value for consistent outputs
    • random: New random seed each time
    • increment: Increase seed by 1 each run
    • decrement: Decrease seed by 1 each run
  • seed_value: Seed for 'fixed' mode (0-9007199254740991)
  • max_retries: Auto-retry attempts for recoverable errors (0-5)
  • debug_mode: Enable detailed error messages and request debugging
  • image_input: Optional image for vision models (Llama-4 only)
  • additional_params: Extra model parameters in JSON format

Outputs:

  • response: The model's generated text or JSON response
  • status: Detailed request information including model, seed, and token counts
  • help: Comprehensive help text with usage information

Vision Model Usage:

  1. Select a vision-capable model:
    • meta-llama/llama-4-maverick-17b-128e-instruct
    • meta-llama/llama-4-scout-17b-16e-instruct
  2. Connect an image to the image_input parameter
  3. Set send_system to "no" (vision models don't accept system prompts)
  4. Describe what you want to know about the image in user_prompt

Production vs Preview Models:

  • Production Models: Stable, reliable, meet high standards for speed/quality. Recommended for production use.
  • Preview Models: Experimental, intended for evaluation only. May be deprecated with short notice.

OpenRouter Chat (v3)

Interact with OpenRouter's API to access various AI models for text and vision tasks. Now fully compatible with ComfyUI v3 spec!

Features:

  • ComfyUI v3 compatible - Enhanced reliability and validation
  • Access to multiple AI providers through a single API
  • Comprehensive free model selection
  • Vision model support (Llama 3.2, Llama 4 variants)
  • JSON output support
  • Automatic retry mechanism with exponential backoff
  • Enhanced input validation
  • Detailed tooltips for all parameters
  • Debug mode for troubleshooting

Available Free Models:

Meta Llama Models:

  • meta-llama/llama-3.3-70b-instruct:free - Default
  • meta-llama/llama-3.3-8b-instruct:free
  • meta-llama/llama-3.2-3b-instruct:free
  • meta-llama/llama-3.2-1b-instruct:free
  • meta-llama/llama-3.1-8b-instruct:free
  • meta-llama/llama-4-maverick:free (Vision)
  • meta-llama/llama-4-scout:free (Vision)
  • meta-llama/llama-3.2-90b-vision-instruct:free (Vision)

Google Models:

  • google/gemini-2.0-flash-exp:free
  • google/gemma-3-27b-it:free
  • google/gemma-2-27b-it:free
  • google/gemma-2-9b-it:free
  • google/gemma-2-2b-it:free
  • google/gemini-flash-1.5-8b-exp:free

Mistral Models:

  • mistralai/mistral-small-3.1:free
  • mistralai/ministral-8b:free
  • mistralai/ministral-3b:free
  • mistralai/mistral-saba-24b:free
  • mistralai/mistral-nemo:free
  • mistralai/mistral-7b-instruct:free

Qwen Models:

  • qwen/qwen3-72b:free
  • qwen/qwen-2.5-72b-instruct:free
  • qwen/qwen-2.5-coder-32b-instruct:free
  • qwen/qwen-2.5-7b-instruct:free
  • qwen/qwen-2-7b-instruct:free
  • qwen/qwen2.5-vl-32b-instruct:free (Vision)
  • qwen/qwen2-vl-7b-instruct:free (Vision)
  • qwen/qvq-72b-preview:free (Vision)

Microsoft Models:

  • microsoft/phi-4:free
  • microsoft/phi-3.5-mini-128k-instruct:free
  • microsoft/phi-3-medium-128k-instruct:free

DeepSeek Models:

  • deepseek/deepseek-r1-zero:free
  • deepseek/deepseek-r1-distill-llama-70b:free
  • deepseek/deepseek-r1-distill-llama-8b:free
  • deepseek/deepseek-r1-distill-qwen-32b:free
  • deepseek/deepseek-r1-distill-qwen-14b:free
  • deepseek/deepseek-r1-distill-qwen-7b:free
  • deepseek/deepseek-r1-distill-qwen-1.5b:free
  • deepseek/deepseek-chat:free
  • deepseek/deepseek-reasoner:free
  • deepseek/deepseek-coder:free
  • sophosympatheia/deephermes-3-405b:free

Nvidia Models:

  • nvidia/llama-3.1-nemotron-70b-instruct:free
  • nvidia/nemotron-nano-12b-v2-vl:free (Vision)

Other Models:

  • openchat/openchat-8b:free
  • openchat/openchat-7b:free
  • anthropic/claude-sonnet-4.5:free
  • sophosympatheia/rogue-rose-103b-v0.6.0:free
  • sophosympatheia/midnight-rose-70b-v1.0.5:free
  • huggingfaceh4/zephyr-7b-beta:free

Parameters:

  • api_key: ⚠️ Your OpenRouter API key (Get from https://openrouter.ai/keys)
  • model: Select from free models or choose "Manual Input" for custom models
  • manual_model: Enter custom model identifier (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 (required)
  • send_system: Toggle system prompt on/off
  • temperature: Controls response randomness (0.0-2.0)
    • Lower (0.0-0.3): More focused and deterministic
    • Higher (0.7-2.0): More creative and varied
  • top_p: Nucleus sampling threshold (0.0-1.0)
  • top_k: Vocabulary limit (1-1000)
  • max_tokens: Maximum tokens to generate (1-32,768)
  • frequency_penalty: Reduce token frequency repetition (-2.0 to 2.0)
  • presence_penalty: Encourage topic diversity (-2.0 to 2.0)
  • repetition_penalty: OpenRouter-specific repetition penalty (1.0-2.0, 1.0=off)
  • response_format: Choose between "text" or "json_object" output
  • seed_mode: Control reproducibility (Fixed, Random, Increment, Decrement)
  • seed_value: Seed for 'fixed' mode (0-9007199254740991)
  • max_retries: Auto-retry attempts for recoverable errors (0-5)
  • debug_mode: Enable detailed error messages and request debugging
  • image_input: Optional image for vision models (max 2048x2048)
  • additional_params: Extra model parameters in JSON format

Outputs:

  • response: The model's generated text or JSON response
  • status: Detailed request information including model, seed, and token counts
  • help: Comprehensive help text with usage information

Vision Model Usage:

  1. Select a vision-capable model (marked with Vision above)
  2. Connect an image to the image_input parameter
  3. Describe what you want to know about the image in user_prompt
  4. Vision-capable models include:
    • Meta Llama: llama-4-maverick, llama-4-scout, llama-3.2-90b-vision
    • Qwen: qwen2.5-vl-32b, qwen2-vl-7b, qvq-72b-preview
    • Nvidia: nemotron-nano-12b-v2-vl

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

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 view 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. For Groq vision models, set 'send_system' to 'no'
  5. Add your prompt about the image in user_prompt
  6. Connect outputs to view 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,
  "stop": ["\n\n"]
}

Parameter Optimization Tips

  • Temperature:
    • Lower (0.1-0.3): More focused, deterministic responses
    • Higher (0.7-1.0): More creative outputs
  • Top-p:
    • Lower (0.1-0.3): More predictable word choices
    • Higher (0.7-1.0): More diverse vocabulary
  • Penalties:
    • Use presence_penalty to reduce topic repetition
    • Use frequency_penalty to reduce word repetition
  • Seed Mode:
    • fixed: Use for reproducible outputs (same seed + params = same output)
    • random: Use for varied responses each time
    • increment/decrement: Use for controlled variation across runs
  • Token Management:
    • Monitor token usage in status output to optimize costs
    • Adjust max_completion_tokens to control response length

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 automatic retries
  • Parameter validation errors

Enable debug_mode in the Groq node for detailed troubleshooting information.

Version History

v2.0.0 (Current)

  • MAJOR UPDATE: All nodes converted to ComfyUI v3 spec
  • Groq Node v3:
    • Updated models list to latest production and preview models
    • Added new production models: groq/compound, groq/compound-mini
    • Added new preview models: qwen/qwen3-32b
    • Set llama-3.3-70b-versatile as default model
    • Enhanced input validation with validate_inputs method
    • Improved tooltips with detailed explanations for all parameters
    • Better error messages and debug mode support
    • Fixed output labels to use proper display_name syntax
  • OpenRouter Node v3:
    • Converted to v3 spec with enhanced validation
    • Updated free models list to current 50+ offerings (January 2025)
    • Organized models by provider: Meta, Google, Mistral, Qwen, Microsoft, DeepSeek, Nvidia, Others
    • Set meta-llama/llama-3.3-70b-instruct:free as default model
    • Added comprehensive tooltips for all parameters
    • Enhanced error handling and debug mode
    • Better vision model detection and validation
    • Updated vision models list with all current vision-capable models
    • Fixed output labels to use proper display_name syntax
  • OpenRouter Models Node v3:
    • Converted to v3 spec
    • Enhanced validation and error handling
    • Improved tooltip documentation
    • Fixed output labels to use proper display_name syntax
  • Architecture:
    • All nodes use stateless design with class methods
    • Class-level seed tracking for reproducibility
    • Maintained full backward compatibility with v1 API
    • Combined v3 entry point for all nodes
    • Corrected combo input syntax (removed invalid enum classes)
    • Proper output definition using display_name parameter
  • Documentation:
    • Comprehensive README updates for all v3 nodes
    • Updated OpenRouter model list with all 50+ current free models
    • Production vs preview model guidance
    • Enhanced parameter optimization tips
    • Detailed vision model usage instructions with current models

v1.3.0

  • Groq node v3 conversion (initial v3 work)

Previous Versions

  • See git history for earlier changes

Technical Details

ComfyUI v3 Compatibility

All nodes have been fully migrated to ComfyUI v3 spec:

  • Uses comfy_api.latest for enhanced reliability
  • Implements define_schema() with comprehensive input/output definitions
  • Stateless design with class methods (execute(), validate_inputs())
  • Proper comfy_entrypoint() function for v3 registration
  • Combined extension class that registers all nodes
  • Maintains full v1 compatibility through legacy NODE_CLASS_MAPPINGS
  • Graceful fallback when v3 API is unavailable

API Compatibility

  • Groq: OpenAI-compatible API endpoint
  • OpenRouter: Multi-provider aggregation API
  • Both support standard OpenAI message format
  • Vision models use base64-encoded images in message content

Contributing

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

Support

For issues, questions, or feature requests:

  • Open an issue on GitHub
  • Check existing issues for solutions
  • Enable debug mode for detailed error information

License

License