MODEL IDtopazlabs:proteus@4
coming-soon

Topaz Labs Proteus 4

Topaz Labs
by Topaz Labs

Proteus 4 is Topaz Labs' general-purpose video enhancement model for upscaling and restoring a wide range of footage. It improves handling of noise, compression artifacts, faces, interlaced material, and scene transitions while maintaining stronger temporal consistency than earlier Proteus versions. Use it for broad video cleanup and enlargement workflows where the source does not call for a more specialized model.

Topaz Labs Proteus 4

API Options

Platform-level options for task execution and delivery.

taskType

stringrequiredvalue: upscale

Identifier for the type of task being performed

taskUUID

stringrequiredUUID v4

UUID v4 identifier for tracking tasks and matching async responses. Must be unique per task.

outputType

stringdefault: URL

Video output type.

Allowed values1 value

outputFormat

stringdefault: MP4

Specifies the file format of the generated output. The available values depend on the task type and the specific model's capabilities.

  • `MP4`: Widely supported video container (H.264), recommended for general use.
  • `WEBM`: Optimized for web delivery.
  • `MOV`: QuickTime format, common in professional workflows (Apple ecosystem).
Allowed values3 values

outputQuality

integermin: 20max: 99default: 95

Compression quality of the output. Higher values preserve quality but increase file size.

webhookURL

stringuri

Specifies a webhook URL where JSON responses will be sent via HTTP POST when generation tasks complete. For batch requests with multiple results, each completed item triggers a separate webhook call as it becomes available.

Learn more1 resource

deliveryMethod

stringdefault: async

Determines how the API delivers task results.

Allowed values1 value
Returns an immediate acknowledgment with the task UUID. Poll for results using getResponse. Required for long-running tasks like video generation.
Learn more1 resource

Specifies a URL where the generated content will be automatically uploaded using the HTTP PUT method. The raw binary data of the media file is sent directly as the request body. For secure uploads to cloud storage, use presigned URLs that include temporary authentication credentials.

Common use cases:

  • Cloud storage: Upload directly to S3 buckets, Google Cloud Storage, or Azure Blob Storage using presigned URLs.
  • CDN integration: Upload to content delivery networks for immediate distribution.
// S3 presigned URL for secure upload
https://your-bucket.s3.amazonaws.com/generated/content.mp4?X-Amz-Signature=abc123&X-Amz-Expires=3600

// Google Cloud Storage presigned URL
https://storage.googleapis.com/your-bucket/content.jpg?X-Goog-Signature=xyz789

// Custom storage endpoint
https://storage.example.com/uploads/generated-image.jpg

The content data will be sent as the request body to the specified URL when generation is complete.

ttl

integermin: 60

Time-to-live (TTL) in seconds for generated content. Only applies when outputType is URL.

Include task cost in the response.

Inputs

Input resources for the task (images, audio, etc). These must be nested inside the inputs object.

inputs » video

video

stringrequired

Video input (UUID or URL).

Core Parameters

Primary parameters that define the task output.

model

stringrequiredvalue: topazlabs:proteus@4

Identifier of the model to use for generation.

Learn more3 resources

width

integerrequiredmin: 16max: 8192

Width of the generated media in pixels. Acts as an envelope: the source aspect ratio is preserved, so the output can come back narrower, and the value must exceed a fifth of the source width.

Learn more2 resources

height

integerrequiredmin: 16max: 8192

Height of the generated media in pixels. Acts as an envelope: the source aspect ratio is preserved, so the output can come back shorter, and the value must exceed a fifth of the source height.

Learn more2 resources

fps

floatmin: 1max: 120

Frames per second for video generation. Defaults to the source frame rate, which is also the lower bound, so a lower value is rejected.

Settings

Technical parameters to fine-tune the inference process. These must be nested inside the settings object.

settings » enhancement

Tuning weights applied during enhancement. Any weight left out keeps the model's own value.

Properties11 properties
settings » enhancement » blur

blur

floatmin: -1max: 1

Amount of sharpness applied.

settings » enhancement » compression

compression

floatmin: -1max: 1

Strength of compression recovery.

settings » enhancement » details

details

floatmin: -1max: 1

Amount of detail reconstruction.

settings » enhancement » grain

grain

floatmin: 0max: 0.1

Adds grain after model processing.

settings » enhancement » grainSize

grainSize

floatmin: 0max: 5

Size of the generated grain.

settings » enhancement » halo

halo

floatmin: -1max: 1

Amount of halo reduction.

settings » enhancement » mode

mode

string

How the tuning weights are applied.

Allowed values3 values
The model estimates every value and no weight can be supplied.
Supplied weights are added to the estimated values.
Supplied weights are used as given.
settings » enhancement » noise

noise

floatmin: -1max: 1

Amount of noise reduction.

settings » enhancement » preblur

preblur

floatmin: -1max: 1

Anti-aliasing and deblurring strength.

settings » enhancement » prenoise

prenoise

floatmin: 0max: 0.1

Adds noise to the input to reduce over-smoothing.

settings » enhancement » recoverOriginalDetail

recoverOriginalDetail

floatmin: 0max: 1

Reintroduces source details into the output video.

settings » fieldOrder

Field order for interlaced sources.

Allowed values3 values
settings » focusFixLevel

Downscales the input for stronger correction of blurred subjects.

Allowed values3 values
settings » videoType

videoType

string

Frame or field type of the source video.

Allowed values3 values