Browse ModelsWavespeed AIPatina Material Extract

Patina Material Extract

Patina Material Extract

Playground

Try it on WavespeedAI!

PATINA Material Extract turns any photograph or reference image into a complete seamlessly tiling PBR material set (basecolor, normal, roughness, metalness, height), guided by a text prompt. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

Patina Material Extract

Patina Material Extract extracts a seamlessly tiling PBR material from any reference image. Upload a photo of a real-world surface, describe which texture to extract, and the model produces a complete tileable material map set — isolating the target surface from complex scenes and converting it into production-ready PBR maps.


Why Choose This?

  • Surface extraction from complex scenes Isolate and extract a specific material from photos that contain multiple surfaces, objects, or backgrounds — guided by your text description.

  • Seamlessly tiling output Produces tileable material maps ready for use on any geometry without visible seams.

  • Tiling direction control Choose omnidirectional tiling (both), horizontal-only, or vertical-only to match your UV mapping needs.

  • Custom output size Specify any output resolution to match your target quality and performance requirements.

  • Production-ready PBR maps Output is formatted for direct use in game engines (Unreal, Unity), 3D tools (Blender, Maya), and real-time rendering pipelines.


Parameters

ParameterRequiredDescription
imageYesReference image URL to extract a tiling material from.
promptYesText description guiding which texture to extract (e.g. “the stone wall surface”, “the wood grain pattern”).
sizeNoOutput dimensions in width×height pixels. Default: 1024×1024.
tiling_modeNoSeamless tiling direction: both (default), horizontal, or vertical.

How to Use

  1. Upload your reference image — provide a photo containing the surface you want to extract.
  2. Write your prompt — describe which specific texture to extract from the image (e.g. “the brick wall in the background”, “the leather seat material”).
  3. Set size (optional) — specify output dimensions to match your target resolution.
  4. Choose tiling_mode (optional) — select both for omnidirectional tiling, or horizontal/vertical for axis-specific tiling.
  5. Submit — the model extracts and generates all PBR material maps in a single run.
  6. Download your complete tileable map set ready for use in your 3D pipeline.

Pricing

Just $1.02 per run (6 images).


Best Use Cases

  • Game development — Extract tileable PBR materials directly from photo references for environment and prop texturing.
  • Architectural visualization — Digitize real-world surface materials from photography for accurate scene reproduction.
  • Asset digitization — Convert complex real-world scene photos into individual, reusable tileable material assets.
  • 3D environment art — Extract specific surface materials from concept art, mood boards, or reference photography.
  • Texture library building — Rapidly generate large libraries of tileable PBR materials from photo collections.

Pro Tips

  • Be specific in your prompt about which surface to extract — include location, color, or texture characteristics (e.g. “the dark grey concrete floor” rather than just “concrete”).
  • Use images where the target surface occupies a clearly visible area for the most accurate extraction.
  • Avoid heavily shadowed or overexposed areas of the target surface for cleaner albedo map results.
  • Use tiling_mode = both for floor, wall, and terrain materials; use horizontal or vertical for directional surfaces like planks or fabric.

Notes

  • Both image and prompt are required fields.
  • Each run generates a full set of PBR map types as output.
  • Ensure image URLs are publicly accessible if using a link rather than a direct upload.
  • Please ensure your content complies with WaveSpeed AI’s usage policies.

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result


# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/patina/material-extract" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "size": "1024*1024",
    "tiling_mode": "both"
}'

# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
imagestringYes-URL of the reference image to extract a tiling material from.
promptstringYes-Text description guiding which texture to extract (e.g., 'the stone wall surface', 'the wood grain pattern').
sizestringNo1024*1024256 ~ 1536 per dimensionThe size of the generated material maps in pixels (width*height).
tiling_modestringNobothboth, horizontal, verticalSeamless tiling direction. 'both' tiles in all directions; 'horizontal' or 'vertical' tiles only along one axis.

Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
data.idstringUnique identifier for the prediction, Task Id
data.modelstringModel ID used for the prediction
data.outputsarrayArray of URLs to the generated content (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.has_nsfw_contentsarrayArray of boolean values indicating NSFW detection for each output
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds

Result Request Parameters

ParameterTypeRequiredDefaultDescription
idstringYes-Task ID

Result Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
dataobjectThe prediction data object containing all details
data.idstringUnique identifier for the prediction, the ID of the prediction to get
data.modelstringModel ID used for the prediction
data.outputsstringArray of URLs to the generated PBR material maps.
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds
© 2025 WaveSpeedAI. All rights reserved.