E-Commerce Detail Image Generator

Problem

E-commerce product images are less about whether a model can generate an image and more about preserving the product itself: logos, packaging text, colors, materials, and layout details are often altered. Applying generic commercial-image prompts to a specific SKU can produce distorted logos, wrong copy, plastic-looking surfaces, or inconsistent screens. This is also a major source of repeated rework between asset creation and store listing.

Workflow

The skill breaks the task into three stages: use a product-recognition prompt to read the uploaded image’s packaging, logo position, text, and graphic elements; choose a template for main image, detail page, poster, scene image, or cross-border assets with its ratio and screen count; then paste the generated prompt into Lovart, Gemini, or Doubao, and upload the original product image. Global rules enforce product restoration, material texture, and style consistency, while general and category-specific negative prompts suppress issues such as blurry, distorted text, and wrong logo.

Boundaries

It outputs prompt plans for downstream image generators rather than rendering final images. Results depend on source-image clarity, text readability, and the target model’s detail-preservation quality; complex packaging, multilingual copy, and high-precision logos still need manual review. It is useful for establishing a consistent visual direction before designers or operations teams batch-produce assets.