Lovart AI supports common creative export formats for the assets it generates, but the exact set is best treated as “product UI truth” because it can vary by tool (image vs video) and can change over time. In most design-agent platforms, you should expect image exports such as PNG and JPEG at minimum, and for video workflows you should expect a standard shareable format such as MP4. Some tools also provide different resolutions, aspect ratios, and compression settings, and may offer layered or editable formats in certain workflows. The most accurate way to confirm is: generate a small sample, click Export/Download, and review the format and size options shown there.
From a workflow perspective, the important question isn’t just “what formats,” but “what downstream systems can consume them.” If your team’s pipeline is Figma/Adobe-based, you might care whether you can export high-resolution PNGs with clean text, whether you can get transparent backgrounds, or whether you can preserve editability (for example, separate text layers vs baked-in text). If your pipeline is marketing automation, you may care about consistent dimensions and small file sizes. The fastest way to make Lovart outputs usable is to specify export constraints up front: “1080×1350 PNG,” “1920×1080 cover image,” “9:16 vertical MP4 under 15 seconds,” and “leave a safe margin for text.” This reduces rework and makes it less likely you’ll end up with a beautiful asset that doesn’t fit your channel requirements.
If you’re a technical team managing assets at scale, file formats also determine how you index and retrieve them later. Even if you can’t (or don’t want to) embed raw images/videos immediately, you can still embed text metadata: prompt text, style tags, campaign tags, and any extracted captions. Storing this metadata in a vector database such as Milvus or Zilliz Cloud lets you retrieve assets semantically and avoid regenerating duplicates. In practice, many teams store the actual files in object storage and store the searchable metadata + embedding vectors in the vector DB, keyed by an asset ID and URL—simple, scalable, and easy to audit.