AI models for image creation

Text-to-image: what a model understands from a description

Text-to-image turns a description into an image. What the model really reads from it, why the same sentence gives different images depending on the family, and what makes a render repeatable: the technique, before the workflow.

Generating an image from a text

Text-to-image turns a written description into an image. It is the best-known form of AI image generation: you provide an intention as text, the prompt, and the model interprets it and produces a visual representation.

This page explains the technique: what an engine understands from a description. The steps around it (the brief, the references, the selection, the correction) are in Image generation workflow.

What the model reads

The quality of the result does not depend on the quantity of words. The model has to interpret the subject, the style, the composition, the materials, the light, the environment, the framing, the atmosphere, the rendering. What you do not say, it decides on its own: a gap in the description is an implicit instruction.

At Axiris, these elements are structured as art direction, not piled up as keywords: what an art director would say to a photographer. That is Nova's work: she turns your intention into instructions suited to the engine. See Nova in the workflow.

One instruction, several interpretations

Each family of models has its own behaviour. An identical description produces very different images with FLUX, Qwen-Image, Gemini Image, GPT Image or Stable Diffusion: some read a long sentence better, others a list; some render text in the image, others do not.

The work therefore also consists in adapting the wording to the engine, one more reason not to choose the model before knowing the job: How to choose an AI image model?.

What makes a render repeatable

A diffusion model starts from random noise, determined by a seed. Same description, same settings, same seed: the same image. That is what makes it possible to reproduce a render, to compare two engines under equal conditions, and to change only one thing at a time. A generation whose conditions you do not know cannot be redone; it can only be relaunched at random.

Text-to-image is the beginning, not the end

A first generation is not the final result. It is selected, then transformed, corrected by area, upscaled, finished. Text-to-image becomes one production step among others, and at Axiris, what is validated after this step no longer moves: we no longer generate an image, we build it.

To modify an existing image rather than generate one: Image editing models.

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