IP Article Visuals & Slides
IP Article Visuals & Slides
Showcase
Description
Based on the user-provided IP character and logo, automatically generate: ① 16:9 miniature logic-diorama illustrations and a cover for long-form articles; ② a 21:9 minimalist article signature card with large type (bottom CTA: Follow me ↗, supporting generation with a 4-version theme matrix); ③ Xiaohongshu 3:4 vertical viral image-and-text posts (a complete 6–8-page carousel with accompanying copy); ④ a YouMind Skill Store showcase image. Supports pure white or pure black backgrounds, abstract miniature diorama setups, strict logo placement on the hat only, and generous ultra-wide negative space.
Related Skills
View allImage Cover | Envision Style
Automatically extract engaging questions or insights from articles or text content, and generate Envision-style vertical image covers (3:4 ratio). Suitable for vertical platforms like Xiaohongshu, WeChat articles, Instagram, etc. Supports automatic multilingual adaptation while maintaining a minimalist hand-drawn illustration style with organic text integration.
Article Illustration Master
Generate fun hand-drawn style illustrations based on Chinese article content. Supports three modes: no IP, custom IP, and auto-generated IP. You can use commercial IPs, customer-provided character reference images, or automatically create entirely new original characters. Trigger commands: 配图, 给文章配图, 帮我配图, illustration
ImageMagic Cover Designer Pro
Turn an article title into a magical cover that makes its ideas stand out. “Magic Cover Designer Pro” is not a random image generator. It is a visual content packaging workflow: “content understanding → title confirmation → visualizing ideas → platform adaptation → cover generation.” It helps preserve the real title and subtitle while transforming an article’s core ideas, emotions, and narrative relationships into vivid, engaging magical visuals with a distinct identity. It supports Twitter long-form posts or Threads, WeChat Official Account articles, Xiaohongshu Notes, product introductions, knowledge sharing, growth retrospectives, and personal brand content. The Skill first identifies the article’s original title, original subtitle, and core idea. The original title is used as the default main cover title, while the original subtitle is prioritized as the cover subtitle. The core idea does not directly replace the cover text; instead, it guides the choice of subject, symbols, composition, lighting, and visual metaphors. It matches visual strategies to different content types: practical breakdowns use magical blueprints or step-by-step scrolls; growth retrospectives use journeys and illuminated paths; contrarian ideas use light-and-shadow conflicts or broken seals; product introductions use magical workshops and core devices; knowledge sharing uses academy archives and spellbooks; and case studies use before-and-after changes and narrative scenes. When a title is too long, it provides both the original-title version and a concise version for you to confirm, without rewriting it on its own. When there is no subtitle, it first offers candidate copy instead of placing an unconfirmed idea directly into the image. Its default visual language combines retro fantasy with magical journaling: deep purple, midnight blue, black-brown, antique bronze, and warm gold, along with parchment, quill pens, spellbooks, sealing wax, stardust, and other tactile elements. It can also draw shared colors, materials, lighting, and narrative qualities from reference images, but it does not copy the specific composition, people, logos, or distinctive elements of any single image. For each platform, it adapts the aspect ratio, text density, subject size, whitespace, and thumbnail legibility to real reading contexts. It is suitable for knowledge creators, personal brands, product authors, and content creators who want to build a consistent visual identity. It does not fabricate numbers, results, identities, or brand endorsements, and it does not promise viral performance. It simply helps the visuals support real content more accurately and vividly.
Information
- Version
- v7
- Last updated
- Runtime credits
- Usage-based
- Models
- Auto
