# Pixal3d API

> Ginagawang textured GLB 3D modelo ang isang larawan, na may detalyadong control sa bawat yugto ng pagbuo

Tumatanggap ng isang larawan at nagbabalik ng GLB modelo kasama ang ginamit na seed. May kanya-kanyang guidance, rescale, step-count at noise-schedule setting ang mga yugto ng structure, shape at texture. Ang output na `resolution` ay 1024 o 1536, ang `texture_size` ay 1024, 2048 o 4096, at itinatakda ng `decimation_target` ang bilang ng vertex mula 5000 hanggang 1586021. Binubuo muli ng `remesh` ang topology.

- Model id: `pixal3d`
- Made by: Open model
- Category: Image to 3D
- Output: 3d
- Price: from $0.30 / request (cheapest run $0.30 (300 API credits))
- Run: `POST https://api.doitong.ru/v1/run/pixal3d`
- Quote: `POST https://api.doitong.ru/v1/quote/pixal3d`
- Docs page: https://doitong.com/fil/developers/models/pixal3d

## Pricing

Price: **from $0.30 / request** (the lowest tier; the quote prices your exact input). Billed once per request.

Price rules (the first rule that matches the input wins; a parameter you leave out counts as its default):

- When `resolution` = 1536: $0.42 (420 API credits) / request

The full example input below costs $0.30 = 300 API credits (1 request × 300 API credits).

Prices are the upstream price in USD, paid from the API balance in API credits (1000 API credits = 1 USD). `POST https://api.doitong.ru/v1/quote/pixal3d` with the same JSON body returns the exact price for that input; the quote is exactly what a run charges. Invalid input is never charged and failed or canceled requests are refunded automatically.

## Input parameters

Send either the canonical name or the native field name (not both). Canonical names are the same across models.

| Parameter | Canonical name | Type | Required | Default | Allowed values / range | Description |
|---|---|---|---|---|---|---|
| `image_url` | `image` | string (image URL) | yes |  |  | URL na tumuturo sa source larawan para sa 3D conversion. |
| `seed` |  | integer \| null | no |  |  | Seed na ginagamit para sa reproducible na random pagbuo. |
| `resolution` |  | enum (integer) | no | `1024` | `1024`, `1536` | Resolution ng output; nagdaragdag ng detalye ang pagtaas nito ngunit mas tumatagal. |
| `ss_guidance_strength` |  | number | no | `7.5` | 0 – 10 | Kontrol sa fidelity ng paunang 3D structure: mas pinapaboran ng pagtaas nito ang mas malapit na pagtutugma sa input larawan kapalit ng posibilidad ng dagdag na noise. |
| `ss_guidance_rescale` |  | number | no | `0.7` | 0 – 1 | Kontrol sa guidance artifact ng Stage 1, kung saan mas malakas ang epekto ng guidance sa mababang setting at mas stable ang output sa mataas na setting. |
| `ss_sampling_steps` |  | integer | no | `12` | 1 – 50 | Bilang ng denoising para sa paunang structure: maaaring tumaas ang kalidad sa dagdag na step ngunit mas mabagal ang processing. |
| `ss_rescale_t` |  | number | no | `5` | 1 – 6 | Itakda ang sharpness ng transition sa noise schedule ng structure pagbuo: mas nagiging matalas ang transition kapag tinaasan ang value na ito. |
| `shape_slat_guidance_strength` |  | number | no | `7.5` | 0 – 10 | Kontrol sa fidelity ng detalyadong geometry: mas maraming detalye ang nagmumula sa mas mahigpit na pagsunod sa input larawan, ngunit may panganib ng dagdag na noise. |
| `shape_slat_guidance_rescale` |  | number | no | `0.5` | 0 – 1 | Taasan ang setting na ito kapag maingay ang geometry: binabawasan nito ang mga artifact na dulot ng malakas na guidance sa shape stage. |
| `shape_slat_sampling_steps` |  | integer | no | `12` | 1 – 50 | Bilang ng denoising para sa shape refinement: maaaring maging mas makinis ang geometry ngunit bumabagal kapag nagdagdag ng step. |
| `shape_slat_rescale_t` |  | number | no | `3` | 1 – 6 | Itakda ang sharpness ng geometric detail sa noise schedule ng shape refinement; mas nagiging matalas ang mga detalye kapag tinaasan ang value na ito. |
| `tex_slat_guidance_strength` |  | number | no | `1` | 0 – 10 | Kontrol sa fidelity ng kulay ng texture: mas nagiging matingkad ang mas mahigpit na pagsunod sa mga kulay ng input larawan sa mataas na setting, ngunit maaaring magdulot ng oversaturation. |
| `tex_slat_guidance_rescale` |  | number | no | `0` | 0 – 1 | Taasan ang setting na ito para tugunan ang maingay na texture o color banding: binabawasan nito ang mga artifact na dulot ng malakas na guidance sa texture stage. |
| `tex_slat_sampling_steps` |  | integer | no | `12` | 1 – 50 | Bilang ng denoising step na ginagamit sa pagbuo ng mga texture. Mas tumatagal kapag tinaasan ang bilang ngunit maaaring maging mas malinis ang mga texture. |
| `tex_slat_rescale_t` |  | number | no | `3` | 1 – 6 | Itakda ang sharpness ng texture detail sa noise schedule ng texture pagbuo; mas nagiging matalas ang mga detalye kapag tinaasan ang value na ito. |
| `mesh_scale` |  | number | no | `1` | 0.1 – 4 | I-scale ang inferred mesh upang maimpluwensyahan ang estimate ng layo ng camera na ginagamit ng modelo. |
| `max_num_tokens` |  | integer | no | `49152` | 4,096 – 131,072 | HR token ceiling para sa cascaded shape sampler, na nagbabalanse sa mas pinong geometry sa mataas na setting laban sa pagtitipid sa memory at processing time sa mababang setting. |
| `decimation_target` |  | integer | no | `200000` | 5,000 – 1,586,021 | Target na complexity ng mesh, ipinapahayag bilang bilang ng vertex para sa final result. Nababawasan ang laki ng file kapag ibinaba ang target, ngunit nababawasan din ang detalye. |
| `texture_size` |  | enum (integer) | no | `2048` | `1024`, `2048`, `4096` | Resolution ng mesh texture para sa baking. Mas malalaki ang file kapag tinaasan ang resolution kapalit ng mas pinong detalye sa surface. |
| `remesh` |  | boolean | no | `true` |  | Muling pagbuo ng mesh topology na karaniwang nakatutulong sa downstream use sa pamamagitan ng paglikha ng mas malilinis na triangle, kapalit ng mas mahabang processing. |
| `enable_safety_checker` |  | boolean | no | `true` |  | Maaaring luwagan ang screening sa pamamagitan ng pagtatakda ng false, kung awtorisado ang tumatawag. |

File parameters (`image`) take a public `https://` URL, a `data:` URI up to 10 MB, or the `file_url` of an upload from `POST /files`.

## Example input

Required parameters only:

```json
{
  "image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
```

A fuller example:

```json
{
  "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
```

## Run it

Set `DOITONG_API_KEY` to a key from https://doitong.com/developers/dashboard#keys. `?wait=20` returns the finished request in the same call when it is ready within 20 seconds; otherwise poll `GET /requests/{id}` (it also accepts `?wait=`), or pass `webhook_url`.

### curl

```bash
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.ru/v1/run/pixal3d?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "image_url": "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.ru/v1/requests/REQUEST_ID?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY"
```

### JavaScript

```javascript
const API = "https://api.doitong.ru/v1";
const headers = {
  "Authorization": "Bearer " + process.env.DOITONG_API_KEY,
  "Content-Type": "application/json",
};

// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/pixal3d?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  }),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);

// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
  res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
  request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
```

### Python

```python
import os
import requests

API = "https://api.doitong.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}

payload = {
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/pixal3d", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
    raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])

# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
    request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
    raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
```

### MCP

The MCP server at `https://api.doitong.ru/v1/mcp` exposes the tools `search_models`, `get_model`, `quote`, `run`, `get_request`, `cancel_request`, `list_requests`, `create_upload` and `get_account`. A `run` call for this model:

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "run",
    "arguments": {
      "model_id": "pixal3d",
      "input": {
        "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
      },
      "wait_seconds": 20
    }
  }
}
```

## Response

Every call returns a request object. `status` is one of `queued`, `running`, `succeeded`, `failed`, `canceled`. `outputs` lists the generated files with URLs on our storage (kept forever, or 7 days with `?storage=temporary`; `storage` says which); `output` is the model's own result object with the same URLs.

```json
{
  "id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
  "object": "request",
  "model": "pixal3d",
  "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": 300,
    "usd": "0.30",
    "charged": 300,
    "refunded": 0
  },
  "input": {
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  },
  "output": {
    "model_glb": {
      "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
    },
    "seed": 42
  },
  "outputs": [
    {
      "type": "file",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_size": 1843200,
      "stored": true
    }
  ],
  "error": null,
  "metrics": {
    "inference_seconds": 6.8
  },
  "urls": {
    "self": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
  }
}
```

### Output fields (`output`)

| Field | Type | Description |
|---|---|---|
| `model_glb` | object {url, content_type, file_name, file_size} | 3D modelo na nabuo sa GLB format. |
| `seed` | integer | Aktuwal na ginamit na seed value. |

## Related models

- [ReconViaGen 0.5](https://doitong.com/developers/models/reconviagen-0.5.md) (`reconviagen-0.5`): from $0.25 / request
- [Hi3D Image to 3D](https://doitong.com/developers/models/hitem3d/hi3d/image-to-3d.md) (`hitem3d/hi3d/image-to-3d`): $0.50 / run
- [Hi3D Image to 3D · v3.0](https://doitong.com/developers/models/hitem3d/hi3d/v3.0/image-to-3d.md) (`hitem3d/hi3d/v3.0/image-to-3d`): from $1.80 / run
- [Hi3D Multiview to 3D · Multi View to 3D](https://doitong.com/developers/models/hitem3d/hi3d/multi-view-to-3d.md) (`hitem3d/hi3d/multi-view-to-3d`): $0.50 / run
- [Hi3D Multiview to 3D · v3.0 · Multi View to 3D](https://doitong.com/developers/models/hitem3d/hi3d/v3.0/multi-view-to-3d.md) (`hitem3d/hi3d/v3.0/multi-view-to-3d`): from $1.80 / run
- [Hunyuan 3D Pro Image to 3D](https://doitong.com/developers/models/hunyuan-3d/v3.1/pro/image-to-3d.md) (`hunyuan-3d/v3.1/pro/image-to-3d`): $0.375 / run
- [Hunyuan 3D Rapid Image to 3D](https://doitong.com/developers/models/hunyuan-3d/v3.1/rapid/image-to-3d.md) (`hunyuan-3d/v3.1/rapid/image-to-3d`): from $0.225 / request
- [Hunyuan World · Image to World](https://doitong.com/developers/models/hunyuan_world/image-to-world.md) (`hunyuan_world/image-to-world`): $0.30 / request

## Links

- HTML page: https://doitong.com/developers/models/pixal3d
- All models and guides for AI agents: https://doitong.com/developers/llms.txt
- OpenAPI 3.1 with this model's typed request body: https://doitong.com/developers/openapi.json?model=pixal3d
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.ru/v1/models/pixal3d