PipeImgGen
The PipeImgGen operator is used to generate images from a text prompt using a specified image generation model.
How it works
PipeImgGen takes a text prompt and a set of parameters, then calls an underlying image generation service (like GPT Image or Flux) to create one or more images.
The prompt can be provided in two ways:
1. As a static string directly in the pipe's PLX definition using the img_gen_prompt parameter.
2. As a dynamic input by connecting a concept that is or refines native.Text.
The pipe can be configured to generate a single image or a list of images.
Configuration
PipeImgGen is configured in your pipeline's .plx file.
Image Generation Models and Backend System
PipeImgGen uses the unified inference backend system to manage image generation models. This means you can:
- Use different image generation providers (OpenAI GPT Image, FAL models like Flux, etc.)
- Configure image generation models through the same backend system as LLMs and OCR models
- Use image generation presets for consistent configurations across your pipelines
- Route image generation requests to different backends based on your routing profile
Common image generation model handles:
base-img-gen: Base image generation model (alias for flux-pro/v1.1)best-img-gen: Best quality image generation model (alias for flux-pro/v1.1-ultra)fast-img-gen: Fast image generation model (alias for fast-lightning-sdxl)
Image generation presets are defined in your model deck configuration and can include parameters like quality, guidance_scale, and safety_tolerance.
PLX Parameters
| Parameter | Type | Description | Required |
|---|---|---|---|
type |
string | The type of the pipe: PipeImgGen |
Yes |
description |
string | A description of the image generation operation. | Yes |
inputs |
dictionary | The input concept(s) for the image generation operation, as a dictionary mapping input names to concept codes. | Yes |
output |
string | The output concept produced by the image generation operation. Use bracket notation for multiple images: "Image[3]" generates exactly 3 images. |
Yes |
img_gen_prompt |
string | A static text prompt for image generation. Use this or input. |
No |
model |
string | The choice of image generation model name, setting, or preset to use (e.g., "gpt-image-1"). Defaults to the model specified in the global config. |
No |
aspect_ratio |
string | The desired aspect ratio of the image (e.g., "16:9", "1:1"). |
No |
seed |
integer or "auto" | A seed for the random number generator to ensure reproducibility. "auto" uses a random seed. |
No |
Example: Generating a single image from a static prompt
This pipe generates one image of a futuristic car without requiring any input.
[pipe.generate_car_image]
type = "PipeImgGen"
description = "Generate a futuristic car image"
output = "Image"
img_gen_prompt = "A sleek, futuristic sports car driving on a neon-lit highway at night."
model = "gen_image_basic"
aspect_ratio = "16:9"
quality = "hd"
Example: Generating multiple images from a dynamic prompt
This pipe takes a text prompt as input and generates three variations of the image using bracket notation.
[concept]
ImagePrompt = "A text prompt for generating an image"
[pipe.generate_logo_variations]
type = "PipeImgGen"
description = "Generate three logo variations from a prompt"
inputs = { prompt = "ImgGenPrompt" }
output = "Image[3]"
model = "gen_image_basic"
aspect_ratio = "1:1"
The output of the PipeImgGen has to be a concept compatible with the native Image concept. A concept is compatible with the Image concept if it refines the Image concept.
To use this pipe, you would first load a text prompt (e.g., "A minimalist logo for a coffee shop, featuring a stylized mountain and a coffee bean") into the "prompt" stuff (ImgGenPrompt concept). After running, the output will be a list containing three generated ImageContent objects.