https://github.com/EnragedAntelope/ComfyUI-EACloudNodes
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.
https://github.com/EnragedAntelope/ComfyUI-EACloudNodes
Last synced: 5 months ago
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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.
- Host: GitHub
- URL: https://github.com/EnragedAntelope/ComfyUI-EACloudNodes
- Owner: EnragedAntelope
- License: apache-2.0
- Created: 2025-01-25T23:41:02.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-01-25T23:45:22.000Z (5 months ago)
- Last Synced: 2025-01-26T00:29:44.696Z (5 months ago)
- Language: Python
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-comfyui - **ComfyUI-EACloudNodes** - hosted environments (such as MimicPC) where users may have limited system access. (All Workflows Sorted by GitHub Stars)
README
# ComfyUI-EACloudNodes
A collection of [ComfyUI](https://github.com/comfyanonymous/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](https://github.com/ltdrdata/ComfyUI-Manager) or to manually install:
1. Clone this repository into your ComfyUI custom_nodes folder:
```bash
cd ComfyUI/custom_nodes
git clone https://github.com/EnragedAntelope/ComfyUI-EACloudNodes
```
2. Install required packages:
```bash
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](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](https://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:
```json
{
"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](https://github.com/EnragedAntelope/ComfyUI-EACloudNodes/blob/main/LICENSE)