Pixal3d
Integrar
pixal3d
# 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/pixal3d?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 $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/pixal3d?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"image": "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"])
# 1. Connect once (Claude Code; Cursor, VS Code, Claude Desktop: https://doitong.com/rm/developers/docs/mcp)
claude mcp add --transport http doitong-models https://api.doitong.com/v1/mcp \
--header "Authorization: Bearer $DOITONG_API_KEY"
# 2. Your agent calls the "run" tool with:
{
"model_id": "pixal3d",
"input": {
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
}
Cloma il model Pixal3d (pixal3d) tras la Doitong API. Documentaziun per agents: https://doitong.com/rm/developers/models/pixal3d.md
POST https://api.doitong.com/v1/run/pixal3d?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/json
{
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
Sche il status da la resposta è "queued" u "running", fa polling GET https://api.doitong.com/v1/requests/REQUEST_ID?wait=20 fin ch'el è "succeeded"; las datotecas èn en outputs[].url. POST https://api.doitong.com/v1/quote/pixal3d cun il medem body returna il pretsch exact avant l'execuziun.
Questa dumonda: $0,30 300 credits API Lantschar sin questa pagina
Tia nova clav API. Quest vegn mussada mo ussa ed è gia en il code ed en il champ da la clav API sutvart. Mantegna ella secreta: na la metta mai en code che vegn exequì en in navigatur.
Tes emprim resultat vegn qua
Construis ina dumonda sutvart e l'exequescha cun tia clav API. Outputs vegnan copiads en nossa memorisaziun, conservads per adina u 7 dis gratuitamain.
Construir ina dumonda
Ils controls vegnan generads dal schema dal model. Il code e il pretsch s'actualiseschan durant che ti als midas.
/run/pixal3d?wait=20
calculaziun → $0,30
# 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/pixal3d?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 $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/pixal3d?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"image": "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"])
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "run",
"arguments": {
"model_id": "pixal3d",
"input": {
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
},
"wait_seconds": 20
}
}
}
Executar questa dumonda
Tia clav resta en quest browser: ella vegn tramessa mo al Doitong API e memorisada (localStorage) mo sch'ella scheccas Tiar a memoria. Obtegnair ina clav API
Davart Pixal3d
Repassa in maletg e restituescha in model GLB ensemen cun il seed utilisà. Las fasas da structura, furma e textura han mintgina ses agens parameters per guidance, rescale, dumber da pass e plan da noise. L'output `resolution` è 1024 u 1536, `texture_size` è 1024, 2048 u 4096, e `decimation_target` fixescha il dumber da vertizs da 5000 fin a 1586021. `remesh` reconstruescha la topologia.
Parameters
Invia u il num canonic u il num nativ, betg omaduas. Nums canonics
image_urlobligatori
image
seedopziunal
resolutionopziunal
ss_guidance_strengthopziunal
ss_guidance_rescaleopziunal
ss_sampling_stepsopziunal
ss_rescale_topziunal
shape_slat_guidance_strengthopziunal
shape_slat_guidance_rescaleopziunal
shape_slat_sampling_stepsopziunal
shape_slat_rescale_topziunal
tex_slat_guidance_strengthopziunal
tex_slat_guidance_rescaleopziunal
tex_slat_sampling_stepsopziunal
tex_slat_rescale_topziunal
mesh_scaleopziunal
max_num_tokensopziunal
decimation_targetopziunal
texture_sizeopziunal
remeshopziunal
enable_safety_checkeropziunal
Ils parameters da datotecas acceptan ina URL publica https://, ina URI data: fin a 10 MB u il file_url d'ina chargiada. Datotecas
Output
{
"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.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
"cancel": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
}
}
Champs da l'output
- model_glb
- object {url, content_type, file_name, file_size}Model 3D generà en format GLB.
- seed
- integerValur da seed utilisada effectivamain.
Dapli models: Maletg a 3D
Per agents AI: questa pagina sco Markdown è disponibla sut https://doitong.com/rm/developers/models/pixal3d.md. Tut ils models e las guidas: https://doitong.com/developers/llms.txt