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Bildgenerierung

Bildgenerierung is a web application for generating and editing images from natural-language descriptions. The interface combines text-to-image and image-to-image generation in a single input form and accesses an inference service connected via the OpenAI API and operated as part of the local infrastructure. Two interchangeable image models are currently supported.

At a glance

  • Generate images from text descriptions — from short keywords to detailed scenes, including text rendered within the image
  • Edit existing images — provide reference images and have them modified in a targeted way
  • Produce multiple variants in parallel — compare up to four images per run
  • Work iteratively — adopt a result directly as a reference image for the next run
  • Reach a result quickly via presets — set resolution, image count, level of detail, and prompt adherence through two preset selectors
  • Achieve reproducible results — using a fixed seed reproduces identical images
  • Download generated images — directly from the gallery as PNG files with descriptive filenames

Highlights

The application encapsulates the call to an image model — which would otherwise require a non-trivial direct API request — behind an interface tailored to typical tasks. The following properties go beyond a simple call and have direct effects on result quality and on the interchangeability of models:

  • Connection via the OpenAI API — Communication with the inference service uses the standard OpenAI endpoints for image generation and chat completion. As a result, models and servers can be swapped without changes to the application or the workflow.
  • Two generation modes in a single interface — Depending on whether reference images are present, the appropriate API path is selected automatically (text-to-image or image-to-image). Switching between modes is transparent to the user and happens in the same input form.
  • Connection to the local infrastructure — Inference runs on a server operated as part of the local infrastructure. Inputs, reference images, and generated images do not leave it.
  • Choice between multiple image models — Currently supported are Qwen-Image-2512 and black-forest-labs/FLUX.2-dev through the same interface. The active model is configured centrally.
  • Preset system for common cases — Presets for mode (resolution and image count) and quality (inference steps and guidance scale) allow a quick start without requiring familiarity with the underlying parameters.
  • Manual parameters override presets — Resolution, image count, inference steps, guidance scale, and seed can be set individually through an accordion and complement the presets without hiding them.
  • Iterative image editing — A generated image can be adopted as a reference for the next run with a single click, allowing gradual refinement towards a desired result.
  • Negative prompts — Unwanted image elements can be excluded explicitly, reducing the share of unusable generations.
  • Reproducibility via seed — With a fixed seed, identical inputs yield identical outputs. This supports systematic variation of individual parameters and the reconstruction of earlier results.