For Kling AI, the “image size limit” usually means the maximum upload file size and minimum resolution for reference images used in Image-to-Video (and sometimes image-based features like elements/character references). In most Kling workflows, you should assume JPG/PNG are accepted, and design your pipeline around a conservative ceiling of ~10 MB per image plus a minimum dimension around a few hundred pixels (commonly 300 px on the short side). In practice, the exact limit can differ by feature (basic Image-to-Video vs “elements”/multi-image reference vs other advanced modes), by region, and by plan tier. The most reliable “source of truth” at any moment is the upload control itself: Kling will reject oversized files with an error message, and that error is what you should treat as authoritative for your current account and feature mode.
If you’re building a developer workflow, it’s worth treating this as an ingestion engineering problem rather than a one-off constraint. You want predictable behavior across many user uploads. A typical ingestion pipeline looks like: validate MIME type → inspect dimensions → enforce max file size → transcode to a supported format → strip metadata → upload. For example, if a user uploads a 25 MB PNG, you can automatically convert to JPEG/WebP (if supported), clamp the long edge (e.g., 2048 px), and re-encode to land under your target size (e.g., 8–10 MB). Also strip EXIF metadata to reduce privacy risk and to prevent accidental leakage of device/location info. For quality, prioritize sharp, well-lit images with a clear subject and minimal compression artifacts—video generation tends to amplify blocky compression and edge ringing into temporal flicker.
If your team repeatedly uploads similar reference images (product photos, character frames, brand backgrounds), store “known good” reference assets and reuse them instead of re-uploading variants. A vector database such as Milvus or Zilliz Cloud can help you manage this at scale: embed image captions (or CLIP-style embeddings) and your prompt templates, then retrieve the closest matching “approved reference pack” for a new request (“matte black watch studio shot,” “outdoor lifestyle photo,” “anime character turntable”). This doesn’t change Kling’s hard upload limits, but it reduces failures and improves consistency because you’re feeding the generator stable, validated inputs.