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:
Clone this repository into your ComfyUI custom_nodes folder:
“`bash
cd ComfyUI/custom_nodes
git clone https://github.com/EnragedAntelope/ComfyUI-EACloudNodes
“`
Install required packages:
“`bash
cd ComfyUI-EACloudNodes
pip install -r requirements.txt
“`
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:
Select a vision-capable model:
meta-llama/llama-4-maverick-17b-128e-instruct
meta-llama/llama-4-scout-17b-16e-instruct
Connect an image to the image_input parameter
Set send_system to “no” (vision models don’t accept system prompts)
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 2048×2048)
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:
Select a vision-capable model (marked with Vision above)
Connect an image to the image_input parameter
Describe what you want to know about the image in user_prompt
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
Add an LLM node (OpenRouter or Groq) to your workflow
Set your API key
Choose a model
(Optional) Set system prompt for context/behavior
Enter your prompt in the user_prompt field
Connect the node’s output to view results
Vision Analysis
Add an LLM node to your workflow
Choose a vision-capable model
Connect an image output to the image_input
For Groq vision models, set ‘send_system’ to ‘no’
Add your prompt about the image in user_prompt
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:
“`json
{
“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