Topaz Labs Proteus 4

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.

Complete technical specification for integration
API Options
Platform-level options for task execution and delivery.
taskType
stringrequiredvalue: upscaleIdentifier for the type of task being performed
taskUUID
stringrequiredUUID v4UUID v4 identifier for tracking tasks and matching async responses. Must be unique per task.
outputType
stringdefault: URLVideo output type.
Allowed values1 value
outputFormat
stringdefault: MP4Specifies 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: 95Compression quality of the output. Higher values preserve quality but increase file size.
webhookURL
stringuriSpecifies 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
- WebhooksPLATFORM
- Webhooks
deliveryMethod
stringdefault: asyncDetermines 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
- Task PollingPLATFORM
uploadEndpoint
stringuriSpecifies 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.jpgThe content data will be sent as the request body to the specified URL when generation is complete.
ttl
integermin: 60Time-to-live (TTL) in seconds for generated content. Only applies when
outputTypeisURL.
includeCost
booleanInclude task cost in the response.
Inputs
Input resources for the task (images, audio, etc). These must be nested inside the inputs object.
inputs object.inputs»videovideo
stringrequiredVideo input (UUID or URL).
Core Parameters
Primary parameters that define the task output.
model
stringrequiredvalue: topazlabs:proteus@4Identifier of the model to use for generation.
Learn more3 resources
width
integerrequiredmin: 16max: 8192Width 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: 8192Height 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: 120Frames 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 object.settings»enhancementenhancement
objectTuning weights applied during enhancement. Any weight left out keeps the model's own value.
Properties11 properties
settings»enhancement»blurblur
floatmin: -1max: 1Amount of sharpness applied.
settings»enhancement»compressioncompression
floatmin: -1max: 1Strength of compression recovery.
settings»enhancement»detailsdetails
floatmin: -1max: 1Amount of detail reconstruction.
settings»enhancement»graingrain
floatmin: 0max: 0.1Adds grain after model processing.
settings»enhancement»grainSizegrainSize
floatmin: 0max: 5Size of the generated grain.
settings»enhancement»halohalo
floatmin: -1max: 1Amount of halo reduction.
settings»enhancement»modemode
stringHow 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»noisenoise
floatmin: -1max: 1Amount of noise reduction.
settings»enhancement»preblurpreblur
floatmin: -1max: 1Anti-aliasing and deblurring strength.
settings»enhancement»prenoiseprenoise
floatmin: 0max: 0.1Adds noise to the input to reduce over-smoothing.
settings»enhancement»recoverOriginalDetailrecoverOriginalDetail
floatmin: 0max: 1Reintroduces source details into the output video.
settings»fieldOrderfieldOrder
stringField order for interlaced sources.
Allowed values3 values
settings»focusFixLevelfocusFixLevel
stringDownscales the input for stronger correction of blurred subjects.
Allowed values3 values
settings»videoTypevideoType
stringFrame or field type of the source video.
Allowed values3 values