fal-ai/patina/material/extract

PATINA Material Extract - Photo to PBR Material

Endpoint: fal-ai/patina/material/extract

Category: Image-to-image

Pricing: $0.10 base + $0.02 per megapixel + $0.01 per megapixel per map. Upscaling adds $0.004 (2x) or $0.016 (4x) per megapixel per map.

Point at a material in a photo and PATINA will extract it into a clean, seamlessly tiling PBR texture. It first flattens and normalizes the material from the image, then generates a tileable texture with full PBR maps.

When to use this

Use this endpoint when you have a photo that contains a material you want, but the photo itself isn't a clean texture. For example, a photo of a brick wall taken at an angle, with shadows and perspective. Extract will isolate the material, flatten it, make it seamless, and generate PBR maps.

Good for:

• Extracting a material from a real-world photo (a wall, a floor, a surface)
• Turning reference photos into production-ready seamless textures
• Using the prompt to tell the model which material in the image to focus on

Use a different endpoint if:

• You already have a clean, flat texture and just want PBR maps - use /patina
• You want to generate a material from text only, with no reference image - use /patina/material
• You want to create variations of an existing texture - use /patina/material with image_url

How it works

1. An editing model extracts the chosen material from the image and renders it flat and uniform
2. The flattened result is run through image-to-image to make it seamlessly tileable
3. PBR maps are predicted from the final texture

This is different from /patina (which does not modify the image) and /patina/material with image_url (which does pure image-to-image without the extraction step).

Quick start

javascript
import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/patina/material/extract", {
  input: {
    prompt: "the brick wall",
    image_url: "https://example.com/photo-of-building.jpg"
  }
});

// result.data.images - extracted seamless texture + PBR maps

Input

Example request

json
{
  "prompt": "the brick wall",
  "image_url": "https://example.com/photo-of-building.jpg",
  "image_size": "square_hd",
  "maps": ["basecolor", "normal", "roughness", "metalness", "height"]
}

Output

Example response

json
{
  "images": [
    { "url": "https://fal.media/files/..." },
    { "url": "https://fal.media/files/...", "map_type": "basecolor" },
    { "url": "https://fal.media/files/...", "map_type": "height" },
    { "url": "https://fal.media/files/...", "map_type": "normal" },
    { "url": "https://fal.media/files/...", "map_type": "roughness" },
    { "url": "https://fal.media/files/...", "map_type": "metalness" }
  ],
  "seed": 42,
  "prompt": "the brick wall"
}

The first image (without map_type) is the extracted seamless texture. The rest are the PBR maps.

Output maps explained

Code examples

JavaScript

javascript
import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/patina/material/extract", {
  input: {
    prompt: "the brick wall",
    image_url: "https://example.com/photo-of-building.jpg"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);

Python

python
import fal_client

result = fal_client.subscribe(
    "fal-ai/patina/material/extract",
    arguments={
        "prompt": "the brick wall",
        "image_url": "https://example.com/photo-of-building.jpg"
    },
    with_logs=True,
)
print(result)

cURL

bash
curl -X POST https://fal.run/fal-ai/patina/material/extract \
  -H "Authorization: Key $FAL_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "the brick wall",
    "image_url": "https://example.com/photo-of-building.jpg"
  }'