Yes—Lovart AI can generate videos, and it also supports audio-related workflows that can cover “music” in practical use, depending on which Lovart surfaces and tools you use. On the official Lovart site, the product is described as automating the design journey “from concept to images, videos, 3D, and more,” and Lovart also provides a dedicated AI video generator experience that emphasizes conversational creation and refinement (pacing, scenes, transitions, text elements) without needing traditional editing software. :contentReference[oaicite:0]{index=0} In addition, Lovart’s own blog content frames the platform as “all formats, one platform: images, videos, audio, even 3D models,” which implies audio generation or at least audio-in-video assembly as part of the workflow. :contentReference[oaicite:1]{index=1}
How this typically works in a developer-friendly mental model is: Lovart acts as an orchestration layer over specialized generators. Video creation can involve multi-step planning (scene list, style, duration, aspect ratio), then generation, then iterative editing through chat. The “music” part can show up in a few ways: (1) you generate a short backing track (or audio bed) and attach it to a video, (2) you synthesize voice or audio elements and compose them into the timeline, or (3) you export video with audio and iterate until it fits the brief. The Lovart tools section includes video-oriented utilities (for example, image-to-video and lip-sync style tools), which suggests the platform expects audio to be part of video workflows even when the primary output is “video.” :contentReference[oaicite:2]{index=2} If you need strict “music generation” in the sense of producing standalone songs, confirm in-product which audio tool is enabled for your account and plan, because Lovart’s feature surface can evolve over time.
If you’re building systems around generated creative assets, treat video and music outputs as data products you want to organize and retrieve later. A practical pattern is to store each generation run (prompt, constraints, chosen variant, export settings, and any audio notes like BPM or mood) so you can search and reuse what worked. You can embed these descriptions and store them in a vector database such as Milvus or Zilliz Cloud (managed Milvus), enabling queries like “10-second vertical promo with warm café vibe and subtle jazz bed” to retrieve prior assets and prompts. That reduces re-generation cost and makes your creative pipeline more reproducible than a pile of exported files.