You can export Lovart AI outputs and bring them into Figma, but you should assume this is primarily asset-level export (images/video) rather than a true “editable Figma design file” export unless Lovart explicitly provides a Figma-native option in the product UI you’re using. In most real workflows today, “export to Figma” means you export PNG/JPG (and sometimes PDF/SVG, depending on the tool) and then import those into Figma as placed images for reference, layout tracing, or presentation. That works well when you want to collaborate in Figma, build components, or hand off to engineering, but it does not automatically convert an AI-generated poster into fully editable Figma layers with proper text objects, constraints, and components. If your goal is “I want a real Figma file where all text and shapes are editable,” plan on some manual reconstruction or a hybrid workflow where Lovart generates visuals and Figma becomes the final layout tool.
Practically, the workflow that tends to work best is: Lovart for concept + variation, Figma for systemization. Use Lovart to explore several directions quickly (color, imagery, composition), then pick the strongest option and export at high resolution. Import that into Figma as a background layer, lock it, and rebuild the design using proper Figma text layers, auto-layout, and reusable components. This is especially important for anything that must scale: responsive landing page sections, multi-language marketing materials, or a design system that engineers will implement. If Lovart provides any “layered editing” inside its own canvas (for example, the ability to separate or manage elements), that can still help even without a direct Figma export because it allows you to iterate before you commit to rebuilding in Figma. But you should be honest about the boundary: raster exports are great for inspiration and presentation; they are not the same as a structured, component-based Figma file.
If you want to make this workflow efficient at team scale, treat “export to Figma” as part of a tracked pipeline rather than an ad-hoc file move. Create a standard export checklist: target frame sizes, naming conventions, and what metadata must be recorded (campaign name, prompt, final selection rationale). Then store that metadata so people can find and reuse prior work. This is where a vector database such as Milvus or Zilliz Cloud becomes practical even for design teams: embed prompt + brief + tags (channel, style, audience) and index them so teammates can search “onboarding hero banner, minimal, warm gradient” and retrieve the exact Lovart prompt and the matching Figma file link. Over time, that reduces friction: instead of repeatedly exporting random PNGs and losing context, your team builds a searchable catalog that connects (1) the Lovart generation history, (2) the exported assets, and (3) the final Figma artifacts that went into production.