Chinese AI Image Prompt Builder

Structure is what makes a Chinese image prompt reliable

When you prompt a Chinese text-to-image model — Jimeng (即梦), Tongyi Wanxiang (通义万相), Hunyuan (混元), ERNIE ViLG (文心一格) or Kolors (可图) — the quality of the picture depends far more on how you structure the description than on any single magic phrase. A structured image prompt names the subject, sets the scene, fixes the style, chooses the camera and composition, defines the lighting, states the quality, and lists what to exclude. This builder keeps that structure for you: fill the fields, and it joins them into a clean prompt that leads with the subject, follows with clearly headed sections — each prefixed with a heading the model can read at a glance — and ends with the negative terms on their own line, ready to paste into any model. The result is the kind of prompt a careful prompt artist would write by hand, only assembled in seconds.

The single highest-leverage element is the subject. "A young woman in Hanfu holding an oil-paper umbrella" steers the whole composition in one line — far more efficiently than a paragraph of loose adjectives. After the subject, the scene and style do the heavy lifting: the scene places the subject in an environment, and the style ("photorealistic", "guofeng ink-wash", "cyberpunk neon") unifies the entire look so the image does not come out as a muddle of clashing references. Naming the camera and composition then fixes the framing — close-up, wide angle, top-down, centred or rule-of-thirds — which often changes the final picture more than re-editing the subject ever does. A good rule of thumb is to make each field concrete: instead of "nice background", write "a misty Jiangnan water town at dawn, stone bridges and willow".

Negatives, lighting and camera separate a lucky render from a repeatable one

The fields people skip and regret are lighting, quality and the negative terms. Lighting and colour grade ("soft side light", "warm dusk tones", "cool high-contrast") are what give an image mood and texture rather than a flat, default look. Quality and parameter terms ("high definition", "8K", "cinematic", "9:16 portrait") sharpen detail and fit the picture to its use. And the negative terms — "no extra fingers", "no text watermark", "not blurry" — are the most underrated field of all: placing them on their own at the end of the prompt is consistently the cheapest way to cut artefacts, distortions and clutter from your renders. None of these limits the model; each one focuses it.

Because the output is structured plain text, the same prompt is portable across every major Chinese image model and works just as well on Midjourney, Stable Diffusion or DALL·E. Write it in Chinese when you want faithful renderings of culturally specific concepts — guofeng, ink-wash, Hanfu — that translate stiffly into English; the structure travels regardless of language. And because the whole tool runs locally in your browser, you can iterate freely — tweak one field, copy again, and re-render — without anything you type ever leaving your device, being sent to a model, or being stored. Treat the first prompt as a draft: render it, see where the image drifts, and tighten the matching field. Two or three rounds of that usually turn a near-miss into exactly the picture you wanted, and you keep a clean, reusable prompt at the end.