This plugin extends ComfyUI with advanced prompt generation capabilities and image analysis using GPT-4 Vision. It includes the following components:
A versatile prompt generator for text-to-image AI systems.
Features:
- Generates prompts based on various customizable parameters
- Supports different art forms, photography styles, and digital art
- Allows for random selection or specific choices for each parameter
- Outputs separate prompts for different model components (e.g., CLIP, T5)
"subject" input field can be changed to support your style or lora, just add your subject and it will overwrite "man, woman ..." etc.
Analyzes images using OpenAI's GPT-4 Vision model.
Features:
- Accepts image input and generates detailed descriptions
- Supports custom base prompts
- Offers options for "happy talk" (detailed descriptions) or simple outputs
- Includes compression options to limit output length
- Ability to create posters
There is a toggle to create movie posters (08/04/24)
Generates text using OpenAI's GPT-4 model based on input text.
Features:
- Accepts text input and generates enhanced descriptions
- Supports custom base prompts
- Offers options for "happy talk" (detailed descriptions) or simple outputs
- Includes compression options to limit output length
Generates text using custom Ollama based on input text.
Features:
- Accepts text input and generates enhanced descriptions
- Supports custom base prompts
- Offers options for "happy talk" (detailed descriptions) or simple outputs
- Includes compression options to limit output length
- You can also use a pure local Florence workflow without any of the others. The prompt will have some bloat, but works fine with Flux
Generates latent representations for use in Stable Diffusion 3 pipelines.
Features:
- Creates latent tensors with specified dimensions
- Supports batch processing
- Automatically adjusts dimensions to maintain a consistent megapixel count
These classes can be integrated into ComfyUI workflows to enhance prompt generation, image analysis, and latent space manipulation for advanced AI image generation pipelines.
- OpenAI API key (for GPT4VisionNode and GPT4MiniNode)
- ComfyUI environment
- Additional dependencies as specified in the import statements
- Ensure that your OpenAI API key is set in the environment variables
- Some classes may require additional data files (JSON) for their functionality
- Refer to the individual class documentation for specific usage instructions and input types
The APNextNode is a custom node class designed for processing and enhancing prompts with additional contextual information. It's particularly useful for generating creative content by incorporating random elements from predefined categories.
- Processes input prompts and adds context from various categories
- Supports multiple input types including required and optional parameters
- Dynamically loads category data from JSON files
- Provides options for random selection of items within categories
- Allows for attribute addition to selected items
The function supports multiple categories, which are loaded from JSON files in a specific directory structure. Based on the provided image, the supported categories include:
- architecture
- art
- artist
- brands
- character
- cinematic
- fashion
- feelings
- geography
- human
- interaction
- keywords
- people
- photography
- plots
- poses
- scene
- science
- stuff
- time
- typography
- vehicle
- video_game
Each category can contain its own set of items and attributes, which are used to enhance the input prompt.
The APNextNode class is designed to be used within a larger system, likely a node-based content generation pipeline. It processes input prompts and optional category selections to produce an enhanced prompt and a random output.
-
Required:
prompt: A multiline string input for the base promptseparator: A string to separate added elements (default: ",")
-
Optional:
string: An additional string input (default: "")seed: An integer seed for random operations (default: 0)attributes: A boolean to toggle attribute inclusion (default: False)- Category-specific inputs: Dynamically generated based on available JSON files
The function returns two strings:
- An enhanced prompt incorporating selected category items
- A random output string with additional category items
The function expects a specific file structure for category data:
data/
└── next/
└── [CATEGORY_NAME]/
└── [field_name].json
Each JSON file should contain either an array of items or a dictionary with "items", "preprompt", "separator", "endprompt", and "attributes" keys.
This README provides an overview of the APNextNode function based on the given code snippet. For full implementation details and integration instructions, please refer to the complete source code and any additional documentation provided with the system where this node is used.
The APNextNode function is designed to be flexible and allow users to add their own categories and fields. This guide will explain how to do this and how to structure the JSON files for new categories.
-
Create a new folder in the
data/next/directory. The folder name should be lowercase and represent your new category (e.g.,data/next/mycategory/). -
Inside this new folder, create one or more JSON files. Each JSON file represents a field within your category. The file name (without the .json extension) will be used as the field name in the
APNextNodefunction.
The JSON file for each field can have two different structures:
A simple array of items:
[
"item1",
"item2",
"item3"
]A more detailed structure with additional properties:
{
"preprompt": "Optional text to appear before the selected items",
"separator": ", ",
"endprompt": "Optional text to appear after the selected items",
"items": [
"item1",
"item2",
"item3"
],
"attributes": {
"item1": ["attribute1", "attribute2"],
"item2": ["attribute3", "attribute4"]
}
}preprompt: (Optional) Text that appears before the selected items.separator: (Optional) String used to separate multiple selected items. Default is ", ".endprompt: (Optional) Text that appears after the selected items.items: (Required) Array of items that can be selected for this field.attributes: (Optional) Object where keys are item names and values are arrays of attributes for that item.
- Create a new folder:
data/next/visual_effects/ - Create a JSON file in this folder, e.g.,
effects.json:
{
"preprompt": "with",
"separator": " and ",
"endprompt": "visual effects",
"items": [
"motion blur",
"lens flare",
"particle effects",
"color grading",
"depth of field"
],
"attributes": {
"motion blur": ["dynamic", "speed-enhancing", "cinematic"],
"lens flare": ["bright", "atmospheric", "sci-fi-inspired"],
"particle effects": ["intricate", "flowing", "ethereal"],
"color grading": ["vibrant", "mood-setting", "stylized"],
"depth of field": ["focused", "bokeh-rich", "photorealistic"]
}
}After adding your new category and JSON file(s), the APNextNode function will automatically detect and include it as an optional input. Users can then select items from your new category when using the function for image generation prompts.
For example, using the "Visual Effects" category we just created:
- If a user selects "lens flare", the output might be: "with lens flare visual effects"
- If "attributes" is set to True and "lens flare" is selected, the output could be: "with lens flare (bright, atmospheric, sci-fi-inspired) visual effects"
Remember that the APNextNode function will handle the random selection and formatting based on the JSON structure you provide. This can greatly enhance the variety and specificity of prompts for AI image generation.
