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Marigold V2 Depth

Integrate

Model id marigold-v2
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/marigold-v2?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  }'

# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY"

This request: $0.03 30 API credits Run it on this page

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Open model Image Editing #depth#utility

Image analysis model that outputs depth, see-through depth, normals or albedo maps from one image

Open model · Image

Your first result goes here

Build a request below and run it with your API key. Outputs are copied to our storage, kept forever or 7 days for free.

Build a request

Controls are generated from the model schema. The code and the price update as you change them.

•required image_url
Drop a file, or

URL of the input image.

image_size

Inference dimensions; omitting them uses the input image's size after rounding each dimension to the closest multiple of 16. Specify a preset such as `"square_hd"` or `"landscape_4_3"`, or supply a `{width, height}` object. Width and height must each be divisible by 16, neither may exceed 2048 pixels, and their ratio must lie within 1:16 to 16:1.

Advanced prediction_type · seed · enable_safety_checker
prediction_type default depth

Type of prediction map to produce.

Random seed that makes the output reproducible when the same image is reused. Its influence is limited to VAE-encoder latent sampling, since the forward pass is itself one deterministic step.

POST /run/marigold-v2?wait=20 quote → $0.03
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/marigold-v2?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  }'

# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY"

Run this request

Your key stays in this browser: it is sent only to the Doitong API and saved (localStorage) only if you tick Remember. Get an API key

About Marigold V2 Depth

Estimates a map from a single image. `prediction_type` selects depth, see-through-depth, normals or albedo. The result is a PNG visualization rather than a numeric tensor. `image_size` can be a preset or a width/height object with multiples of 16 up to 2048, and `seed` makes results repeatable.

Parameters

Send either the canonical name or the native name, not both. Canonical names

NameType DefaultAllowed Description
prediction_typeoptional
Typeenum
Defaultdepth
depthsee-through-depthnormalsalbedo
Type of prediction map to produce.
image_urlrequired image
Typeurl · image
URL of the input image.
image_sizeoptional
Typeobject
square_hdsquareportrait_4_3portrait_16_9landscape_4_3landscape_16_9
Inference dimensions; omitting them uses the input image's size after rounding each dimension to the closest multiple of 16. Specify a preset such as `"square_hd"` or `"landscape_4_3"`, or supply a `{width, height}` object. Width and height must each be divisible by 16, neither may exceed 2048 pixels, and their ratio must lie within 1:16 to 16:1.
seedoptional
Typeinteger | null
Random seed that makes the output reproducible when the same image is reused. Its influence is limited to VAE-encoder latent sampling, since the forward pass is itself one deterministic step.
enable_safety_checkeroptional
Typeboolean
Defaulttrue
Screening can be relaxed by setting false, provided the caller is authorized.
required Canonical name, also accepted

File parameters take a public https:// URL, a data: URI up to 10 MB, or the file_url of an upload. Files

Output

GET /requests/req_8k2m4q7x1z9c3v5b6n0p2r4t
{
  "id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
  "object": "request",
  "model": "marigold-v2",
  "status": "succeeded",
  "created_at": "2026-10-20T12:00:00Z",
  "started_at": "2026-10-20T12:00:02Z",
  "completed_at": "2026-10-20T12:00:09Z",
  "queue_position": null,
  "cost": {
    "credits": 30,
    "usd": "0.03",
    "charged": 30,
    "refunded": 0
  },
  "input": {
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  },
  "output": {
    "image": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_name": "0-output.jpeg",
      "file_size": 1843200,
      "width": 1024,
      "height": 768
    },
    "seed": 42
  },
  "outputs": [
    {
      "type": "image",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_size": 1843200,
      "width": 1024,
      "height": 768,
      "stored": true
    }
  ],
  "error": null,
  "metrics": {
    "inference_seconds": 6.8
  },
  "urls": {
    "self": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
  }
}
Your files, your choiceEvery outputs[].url is a copy on our storage: kept forever (paid per GB-month) or 7 days for free, by your account setting or ?storage=.
Same shape on every modeltype, url, content_type, width, height and duration, for images, video and audio alike. output is the model’s own result object.
Fields of output
image
object {url, content_type, file_name, file_size, width, height}Prediction rendered as a PNG for viewing; the response does not provide a lossless numerical tensor through this image.
seed
integerSeed applied when sampling latents in the VAE encoder.

For AI agents: this page as Markdown is at https://doitong.com/en-US/developers/models/marigold-v2.md. All models and guides: https://doitong.com/developers/llms.txt