Introduction & Product Philosophy
← User Guide · Next: Getting Started →Mago Studio is an AI video transformation and stylization platform built for professional studios. Mago is not a video generation product. It does not create videos from scratch or from text alone. Mago transforms and stylizes existing footage. That single fact shapes every choice in the product — and understanding it will make everything else click.
The five core principles
These principles inform how the product is built and how you get the best results from it.1. Work shot by shot
A project is a collection of shots. Each shot is rendered separately, with its own settings, iterations, and review history. Divide sequences and films into shots and process each in isolation — it’s faster than working on long videos, easier to iterate, and produces more controllable results.💡 Tip — Bulk shot processing is planned for a future release. For now, copy settings from one shot to another to speed up consistent work.
2. Iterate at the image level first
The Modify Frame workspace is the recommended starting point for most workflows. Before launching a video render, generate a target frame with an image model and validate the look. Videos are expensive; images are cheap. Failing fast at the image level saves credits, time, and frustration. Then use the validated frame as a keyframe or reference for the video model.3. Build up in passes
Complex effects work best split into stages. The Edit this Render button on each track lets you chain operations: replace the character on pass one, stylize on pass two, upscale on pass three. Each pass is more controllable than trying to do everything at once. See the multi-pass recipe for the flagship example.4. Frame-perfect output
Mago video models guarantee that a 171-frame input produces a 171-frame output that matches the source frame for frame. Every output frame corresponds to a specific source frame. This is a deliberate contrast with closed-source models, which apply hidden optimizations and may alter timing, frame count, composition, or character identity. For production work where pacing, lip sync, and continuity matter, this is decisive.5. Descriptive prompts for Mago, instruction prompts for closed-source
Mago models expect a description of the desired result — write what the output should look like. Closed-source models (Kling, Seedance, Happy Horse) expect instructions — tell them what to do.⚠️ Warning — This is the single biggest source of bad results when switching between model families. See the full Prompting guide.
Privacy & confidentiality
Mago does not train on user content. Every project is confidential. For Enterprise customers, deployment options include private cloud and on-premise installation, with full control over where renders happen and where data is stored. Mago models can be deployed in specific territories, in a client’s own infrastructure, or with NDA-grade IP terms.Mago models vs. closed-source models
Mago integrates two model families. Picking the right tool starts with understanding the trade-offs:| Property | Mago models | Closed-source models |
|---|---|---|
| Prompt style | Descriptive (describe the result) | Instruction (tell it what to do) |
| Frame correspondence | Frame-perfect: N in = N out | Approximate, may drift |
| Character identity preservation | Strong with Style Transfer or Inpainting | Variable |
| Lip sync & micro-expression | Strong with Style Transfer | Strong (especially Kling Motion Control) |
| Settings exposure | High (more control, steeper curve) | Low (easier to start) |
| Cost per render | Generally lower | Generally higher |
| Content moderation | Moderated, more permissive for artistic use | Stricter; may block VFX content like blood |
| Deployment flexibility | Mago infra, private cloud, or on-premise | Provider infra only |
| Best fit | Long-form, precision, production pipelines | Quick shots, prototypes, simple transforms |
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