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Image Generation

Text-to-image generation integrated directly into your pipelines.

Overview

PipeImgGen generates images from text prompts using state-of-the-art models. Generated images can be stored locally or in cloud storage with public or signed URLs. Image generation supports both text-to-image and image-to-image workflows.

Supported Models

Via Pipelex Gateway:

  • GPT-Image-2 / GPT-Image-1.5 / GPT-Image-1 / GPT-Image-1-mini — OpenAI image generation models
  • Nano Banana / Nano Banana Pro / Nano Banana 2 / Nano Banana 2 Lite — Google Gemini-based image generation

Via direct provider SDKs:

  • OpenAI — Direct OpenAI API for GPT Image models
  • Google Gemini — Native Google image generation
  • fal — FLUX models (Black Forest Labs) and others via the fal platform
  • Hugging Face Inference — Open-source models like qwen-image
  • BlackboxAI — Via completions-based image generation
  • Azure REST — Azure-hosted image generation
  • OpenRouter — Multi-provider image generation access

Cloud Storage Integration

Generated images can be automatically uploaded to AWS S3 or Google Cloud Storage with configurable URL signing. See Cloud Storage for details.

Usage in Pipelines

Use PipeImgGen in your .mthds files to generate images as part of a pipeline. The operator takes a prompt string template and outputs an Image concept. Declared inputs are injected into the template — Text variables as interpolated text, and Image variables as reference images for image-to-image generation. Model presets let you configure quality levels and default models in 2_img_gen_deck.toml.

See PipeImgGen reference.