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Python
Hybrid

image_to_video_create_video

Create videos from still images through API automation. Generate image-to-video clips for apps, pipelines, and batch processing workflows.

Instructions

What this API does

Create the same Image To Video you can make in the browser, but programmatically, so you can automate it, run it at scale, or connect it to your own app or workflow.

Good for

  • Automation and batch processing

  • Adding image to video into apps, pipelines, or tools

How it works (3 steps)

  1. Upload your inputs (video, image, or audio) with Generate Upload URLs and copy the file_path.

  2. Send a request to create a image to video job with the basic fields.

  3. Check the job status until it's complete, then download the result from downloads.

Key options

  • Inputs: usually a file, sometimes a YouTube link, depending on project type

  • Resolution: free users are limited to 576px; higher plans unlock HD and larger sizes

  • Extra fields: e.g. face_swap_mode, start_seconds/end_seconds, or a text prompt

Cost
Credits are only charged for the frames that actually render. You'll see an estimate when the job is queued, and the final total after it's done.

For detailed examples, see the product page.

MCP guidance:

  • This starts an async video generation job and returns id plus credits_charged immediately. If the user wants the finished result, call the wait_for_video_project helper with the returned id, or poll the matching GET /v1/video-projects/{id} endpoint until status is complete, error, or canceled. Completed projects include downloads with direct URLs. The custom wait helper also returns exact_download_urls separately from expiration metadata.

  • For *_file_path values, prefer an existing Magic Hour file path or a file_path returned by the upload-URL endpoint after the file bytes are uploaded. Direct public media URLs may work when they are stable, fetchable, and return raw file bytes, but hotlinked URLs can fail; when in doubt, use the presigned upload flow first and pass the returned file_path.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoGive your video a custom name for easy identification.Image To Video - dateTime
audioNoWhether to include audio in the video. Defaults to `false` if not specified. Audio support varies by model: * **`gemini-omni-1.1`**: Not supported * **`kling-2.6`**: Not supported * **`kling-3.0`**: Toggle-able: audio adds extra credits when enabled * **`ltx-2.3`**: Toggle-able: no additional credits for audio * **`ltx-2.5`**: Toggle-able: no additional credits for audio * **`minimax-h3`**: Toggle-able: no additional credits for audio * **`seedance-1.5`**: Toggle-able: audio adds extra credits when enabled * **`seedance-2.0`**: Toggle-able: no additional credits for audio * **`seedance-2.0-mini`**: Toggle-able: no additional credits for audio * **`seedance-2.5`**: Toggle-able: no additional credits for audio * **`sora-2`**: Toggle-able: no additional credits for audio * **`veo3.1`**: Toggle-able: audio adds extra credits when enabled * **`veo3.1-lite`**: Toggle-able: audio adds extra credits when enabled * **`wan-2.2`**: Not supported * **`wan-3.0`**: Toggle-able: no additional credits for audio
modelNoThe AI model to use for video generation. * `default`: uses our currently recommended model for general use. For paid tiers, defaults to `kling-3.0`. For free tiers, it defaults to `ltx-2.5`. * `gemini-omni-1.1`: Best for precise short clips, first/last frames, and high-resolution output. * `kling-2.6`: Best for action, motion blur, and controlled camera moves. * `kling-3.0`: Best for cinematic stories, references, and optional audio. * `ltx-2.3`: Fastest for general scenes, long clips, audio, and rapid iteration. * `ltx-2.5`: Fastest for general scenes, long clips, audio, and rapid iteration. * `minimax-h3`: Great for reference-driven clips with native audio and longer durations. * `seedance-1.5`: Best for smooth, consistent motion with an end frame. * `seedance-2.0`: Best for reference-led clips with precise subject control. * `seedance-2.0-mini`: Faster reference-led clips with consistent motion and audio. * `seedance-2.5`: Best for premium realism, detail, and natural motion. * `sora-2`: Best for creative concepts and longer clips with audio. * `veo3.1`: Best for romantic interactions and expressive action, with realistic detail. * `veo3.1-lite`: Balanced realism and audio at a lower cost than Veo 3.1. * `wan-2.2`: Best for physical motion, action, and camera movement. * `wan-3.0`: High-quality video with native audio, long clips, and end-frame control. If you specify the deprecated model value that includes the `-audio` suffix, this will be the same as included `audio` as `true`.default
styleNoAttributed used to dictate the style of the output
assetsYesProvide the assets for image-to-video. Sora 2 only supports images with an aspect ratio of `9:16` or `16:9`.
resolutionNoControls the output video resolution. Defaults to `720p` on paid tiers and `480p` on free tiers. * **`gemini-omni-1.1`**: Supports 360p, 720p, 1080p, 4k. * **`kling-2.6`**: Supports 720p, 1080p. * **`kling-3.0`**: Supports 720p, 1080p, 4k. * **`ltx-2.3`**: Supports 480p, 720p, 1080p. * **`ltx-2.5`**: Supports 480p, 720p, 1080p. * **`minimax-h3`**: Supports 480p, 720p, 1080p. * **`seedance-1.5`**: Supports 480p, 720p, 1080p. * **`seedance-2.0`**: Supports 480p, 720p, 1080p, 4k. * **`seedance-2.0-mini`**: Supports 480p, 720p. * **`seedance-2.5`**: Supports 480p, 720p. * **`sora-2`**: Supports 720p. * **`veo3.1`**: Supports 720p, 1080p. * **`veo3.1-lite`**: Supports 720p, 1080p. * **`wan-2.2`**: Supports 480p, 720p, 1080p. * **`wan-3.0`**: Supports 480p, 720p, 1080p.
end_secondsYesThe total duration of the output video in seconds. Supported durations depend on the chosen model: * **`gemini-omni-1.1`**: 3, 4, 5, 6, 7, 8, 9, 10 * **`kling-2.6`**: 5, 10 * **`kling-3.0`**: 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 * **`ltx-2.3`**: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30 * **`ltx-2.5`**: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60 * **`minimax-h3`**: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30 * **`seedance-1.5`**: 4, 5, 6, 7, 8, 9, 10, 11, 12 * **`seedance-2.0`**: 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 * **`seedance-2.0-mini`**: 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 * **`seedance-2.5`**: 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 * **`sora-2`**: 4, 8, 12, 24, 36, 48, 60 * **`veo3.1`**: 4, 6, 8, 16, 24, 32, 40, 48, 56 * **`veo3.1-lite`**: 4, 6, 8, 16, 24, 32, 40, 48, 56 * **`wan-2.2`**: 3, 4, 5, 6, 7, 8, 9, 10, 15 * **`wan-3.0`**: 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesUnique ID of the video. Use it with the [Get video Project API](https://docs.magichour.ai/api-reference/video-projects/get-video-details) to fetch status and downloads.
credits_chargedYesThe amount of credits deducted from your account to generate the video. If the status is not 'complete', this value is an estimate and may be adjusted upon completion based on the actual FPS of the output video. If video generation fails, credits will be refunded, and this field will be updated to include the refund.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description fully carries the behavioral burden. It discloses async job submission, immediate return of id and credits_charged, required polling until complete/error/canceled, download URLs, credit charging behavior, resolution tier limits, and file_path/hotlink pitfalls. This is comprehensive and operationally useful.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-structured with headers, front-loaded key facts, and action-oriented steps. The MCP guidance section is dense but earns its place because it tells the agent exactly how to handle the async lifecycle and file path inputs.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the nested 7-parameter schema and available output schema, the description covers the complete async workflow, polling, downloading, cost model, and file upload caveats. It does not enumerate every parameter, but the schema already provides those details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and each parameter already has rich documentation, so the baseline is 3. The description adds helpful practical context around file_path handling and mentions resolution limits, but it does not need to duplicate schema-level parameter details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it 'Create[s] the same Image To Video you can make in the browser, but programmatically', identifying a specific verb (create), resource (image-to-video job), and use case. It is unambiguous and easily distinguished from sibling creation tools like text_to_video_create_video or audio_to_video_create_video.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides strong contextual guidance: automation/batch processing, a 3-step workflow, and explicit MCP guidance to call wait_for_video_project or poll the GET endpoint when the finished result is needed. It does not explicitly contrast with alternative video-creation siblings, but the resource-specific wording makes the intended use clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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