Ancient Book Rubbing Style Poetry & Motto Poster

At a glance

Model

GPT-image-2

Use cases

RED covers, event posters, WeChat public account headers

Audience

Social media creators, graphic designers, content creators

Problem solved

Having a text theme but not knowing how to turn it into an engaging, memorable main visual

Subject

Text, abstract elements

Core style

Vintage, ink-wash

Aspect ratio

Choose 1:1, 4:5, 16:9, or 9:16 depending on the platform

Difficulty

Beginner. Keep the original prompt structure and fine-tune the subject.

Variable replacement guide

Best use cases

This prompt generates Chinese-style visual designs with the aesthetic of ancient books, rubbings, and old receipts. It's perfect for making poetry/motto cards, culture cards, and quote posters, complete with realistic ink stains, paper wear, and red cinnabar stamps. Ideal for RED covers, event posters, and WeChat headers. Designed for social media creators, graphic designers, and content creators to solve the problem of turning theme text into a highly memorable visual identity.

Visual style

The fundamental style of 'Ancient Book Rubbing Style Poetry & Motto Poster' is vintage and ink-wash. Focus first on the texture and retro paper aesthetic, then the relationship between text, abstract layout, and background; negative space and hierarchy serve this core structure.

How to generate a new image

1. 1Copy the entire 'Ancient Book Rubbing Style Poetry & Motto Poster' prompt, keeping the style, subject, composition, material texture, and ratio details.
2. 2Change only the theme or core subject first, keeping the style, texture, and lighting descriptions intact.
3. 3Generate 2-4 images in GPT-image-2 or other image tools with default parameters, setting the aspect ratio to 1:1, 4:5, 16:9, or 9:16 based on the platform.
4. 4Compare with the target poster visual: Is the subject clear? Is the information hierarchy readable? Are the colors and style close to the reference?
5. 5If it diverges, reduce background noise first, then reinforce core style keywords or adjust the ratio. Do not change too many variables at once.

Prompt remix example

Theme: Ancient Book Rubbing Style Poetry & Motto Poster Ratio: Original ratio

Theme: New Product Launch Ratio: 4:5

By updating these parameters, the image preserves the retro style and poster layout logic while adapting the subject, text, or aspect ratio for a brand new publishing scenario.

A sheet of ancient book rubbing style Xuan paper receipt, printed with a traditional Chinese poetry and motto poster. Features pine smoke black ink rubbing, a mottled texture, weathered worn edges, and red cinnabar stamps. The layout has the aesthetic of classical books and old receipts, set against a beige antiqued Xuan paper background. Theme: New Product Launch Ratio: 4:5

Model and parameter tips

• GPT-image-2：Excellent for Chinese understanding, text layout, and complex graphic relations. Specify the subject, frame, and intent directly.
• Midjourney：Great for artistic style and composition. Add aspect ratio parameters, but note that Chinese text generation can be unstable.
• FLUX：Perfect for clear subjects, photographic textures, and clean structures. Use natural language descriptions and avoid command-based parameters.

How to improve weak results

• If the main subject is not prominent, add keywords like 'centered main visual', 'clear outline', 'generous negative space', or explicitly describe the core item.
• If the style drifts, add 'vintage', 'high contrast hierarchy', or 'clear textures' instead of just generic aesthetic adjectives.
• If the image looks messy, remove background elements, edge annotations, decorative symbols, and subtitles to keep only the most crucial layer of information.
• For blurry or chaotic text, shorten the copy and emphasize letter spacing, negative space, hierarchy, and legibility.
• If colors are off, describe the main colors, background, brightness, and saturation clearly, and avoid abstract terms like 'premium feel'.
• Adjust aspect ratios (1:1, 4:5, 16:9, 9:16) and reduce small text density accordingly.