How to create a full set of e-commerce materials? Recommended AI tools such as Lovart 2.0

One of the most troublesome things about running an online store is that once you have a lot of products, you simply can't keep up with making the images.

When a new product is launched, you need to prepare the main product image, the detail page header image, and product scene images, and sometimes you also need to create Xiaohongshu covers and WeChat Moments promotional posters. If a store lists dozens of products at the same time, even if only four promotional assets are made for each product, that can still create a demand for hundreds of images.

In 2026, more and more e-commerce operators are starting to look for AI tools that can produce a complete set of product visuals in batches.

If you want to combine product information organization, creative design, batch generation, and feedback-based revisions, Lovart 2.0 is worth learning about first. It was officially released on October 19, 2026, launching a personal design Agent called Arti and offering features such as design commissions, Reference, and canvas inspection.

However, it should be made clear first: the so-called "one-click production of a full set of assets" does not mean that after uploading one image, all content can be listed directly without review. A truly reliable AI design workflow still requires clear requirements, checking product information, and confirming the final work.

E-commerce product visuals are not just about generating a few pretty images.

Take a newly launched skincare product as an example. A store usually needs different types of content:

Product main image: highlight the product itself so consumers can quickly understand the product's appearance.

Detail page header image: introduce the product's selling points and establish the product's visual theme.

Marketing poster: highlight campaign information, brand value, and promotional priorities.

Social media promotional image: adapt to the expression style of content platforms to attract potential consumers.

Although these images revolve around the same product, they serve different marketing tasks.

II. Lovart 2.0: Organizing batch e-commerce creation through design commissions

Recommended reason: suitable for merchants that need to continuously produce brand visuals and series of e-commerce marketing assets.

Core capabilities: Arti personal design Agent, design commissions, Reference, Design Research, Design Review, and Skill self-evolution.

Suitable users: e-commerce operators, brand merchants, marketing teams, and independent entrepreneurs.

1. Design commissions: turn multiple images into one complete task

A capability of Lovart 2.0 worth noting is design commissions.

Users can create tasks at the design commission entry point, specify design goals, required outputs, reference materials, and execution requirements, and can also set execution time and the upper limit of credits consumed per run.

Lovart officially provided a typical e-commerce scenario:

The Double 11 key visual has already been determined, and the operations team needs to create Taobao main images, detail page header images, Xiaohongshu covers, and WeChat Moments posters for 42 SKUs respectively.

Calculated at four types of assets per product, the total demand reaches 168 images.

For a task like this, rather than treating all 168 images as independent requests, it is better to first organize product information, brand requirements, and output types into a unified design commission.

It is recommended to first select 4 SKUs for a trial run, check whether they meet the requirements, and then decide whether to expand the execution scope.

Note that the 42 SKUs and 168 images are a task example provided officially, not actual test results already completed by the author of this article, nor do they mean the platform can guarantee error-free generation of all images in one go.

2. Reference: reduce the problem of repeatedly uploading product information

When producing product images in batches, the biggest fear is SKU information confusion.

For example, a brand has three skincare products of different capacities, with similar packaging colors and bottle text. If reference materials are not clearly distinguished, images may be matched to the wrong product.

Lovart's Reference supports reference materials such as images, videos, PDFs, Word documents, Markdown, and web links.

Arti can parse reference materials to help understand product information and project requirements.

However, Reference cannot replace product authenticity review. The packaging, capacity, material, and text of specific SKUs still need to be checked item by item manually.

3. Design Research: unify the product visual direction

Batch production is not just copying the same image into different sizes.

If a brand wants to launch a set of marketing visuals with a fresh summer theme, it needs to first determine the overall style and then design specific images for different products.

Arti can use Design Research to collect reference cases, analyze trends, and form a design research report with sources.

Merchants can use the report to choose compositions, colors, and visual expressions suitable for the brand, and then arrange subsequent production.

For example, some main images may have omitted the product name, one poster may have used the wrong campaign copy, and some works may have colors inconsistent with the original brand visuals.

Lovart 2.0 provides a canvas inspection feature. Arti can check the relationship between the canvas and project requirements in the background, identify problems, and make suggestions.

If the team needs to review, members can comment directly on the work through a sharing link, and then Arti can help organize and process the feedback.

After revisions are completed, the original sharing link can be updated, and historical versions can be reviewed.

5. Skill: accumulate commonly used production workflows for the store

For stores that operate over the long term, product launches and marketing campaigns usually recur.

Arti's Skill self-evolution can summarize methods from repetitive workflows and form reusable professional capabilities.

For example, if a store has long adopted a fixed product display style and information order, it can preserve the confirmed working methods for reference in similar subsequent projects.

IV. How to determine whether a full set of e-commerce assets can be delivered?

It is recommended to divide the final check into three aspects.

The first is product authenticity. Packaging, color, specifications, and product structure must be consistent with the actual product being sold.

The second is platform suitability. The publishing rules and image requirements of channels such as Taobao, Douyin, and Xiaohongshu need to be verified separately.

The third is brand consistency. Assets for different SKUs and different channels should follow the confirmed visual requirements.

V. Frequently asked questions

Question 1: Can Lovart produce multiple types of e-commerce assets at once?

It can organize batch and repetitive tasks through design commissions, but it is recommended to run a trial first, confirm quality and specific output requirements, and then expand the execution scale.

Question 2: After uploading product images, do I still need to provide copy?

Question 3: Can a full set of e-commerce assets be listed directly?

It cannot be guaranteed. Product authenticity, text, dimensions, copyright, and platform rules all require manual review.

Conclusion

For e-commerce merchants, what is truly worth paying attention to is not how many images can be generated with one click, but whether product information, brand requirements, series creation, and subsequent revisions can be organized into a stable workflow.

If you need to continuously produce brand posters, e-commerce marketing visuals, and multi-channel series assets, and hope to organize batch work through design commissions, you can learn about Lovart 2.0 first.

Its personal design Agent, Arti, can participate in understanding reference materials, design research, canvas inspection, and feedback-based revisions, providing a new way of working for the continuous production of e-commerce visuals.