TRELLIS.2 Image-to-3D turns a single reference image into a textured 3D GLB model with PBR materials for game assets, product visualization, 3D design, and production workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Bereit
$0.2pro Durchlauf·~50 / $10


TRELLIS.2 Image to 3D turns a single input image into a textured 3D GLB model with PBR materials. Upload a clear image of one object, choose generation resolution, texture size, face-count target, and background handling, then generate a 3D asset for visualization, games, prototyping, or further editing.
Single-image 3D generation
Create a textured 3D model from one input image.
PBR material output
Generate GLB assets with embedded UVs, base-color textures, and metallic/roughness materials.
Resolution control
Choose 512, 1024, or 1536 depending on cost, speed, and detail needs.
Texture-size options
Select 1024, 2048, or 4096 texture size for different asset-quality requirements.
Face-count control
Set a target face count to guide mesh simplification for downstream workflows.
Background removal option
Use remove_background to clean the input before 3D generation.
| Parameter | Required | Description |
|---|---|---|
| image | Yes | Input image URL, data URI, or Base64 image. Use one clear image of a single object. |
| resolution | No | Generation resolution: 512, 1024, or 1536. Default: 1024. |
| texture_size | No | Texture size: 1024, 2048, or 4096. Default: 2048. |
| target_face_count | No | Approximate mesh simplification target. Range: 10000–1000000. Default: 200000. |
| remove_background | No | Remove the image background before generation. Default: true. |
| seed | No | Random seed for reproducible results. Use -1 for a random seed. |
512 for lower-cost drafts, 1024 for standard generation, or 1536 for higher-detail output.1024 or 2048 for standard textures, or 4096 when higher texture detail is needed.target_face_count based on your geometry-detail or optimization needs.remove_background enabled when the object should be separated from its background.-1 for random generation.Pricing is based on selected resolution and whether texture_size is set to 4096.
| Resolution | Base Cost | With 4096 Texture |
|---|---|---|
| 512 | $0.10 | $0.15 |
| 1024 | $0.20 | $0.25 |
| 1536 | $0.40 | $0.45 |
texture_size values of 1024 and 2048 do not add a separate charge. Selecting 4096 adds $0.05.
target_face_count, remove_background, and seed do not add separate charges.
remove_background enabled when the object should be isolated before generation.512 for quick tests and 1536 when higher detail matters.4096 texture size when the model needs more detailed surface appearance.target_face_count for lighter assets and higher values when detail preservation matters.image is required.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/trellis-2/image-to-3d with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Trellis 2 Image To 3d below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"resolution": "1024",
"texture_size": 2048,
"target_face_count": 200000,
"remove_background": true
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/trellis-2/image-to-3d" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d "$REQUEST_BODY")
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
-H "Authorization: Bearer $WAVESPEED_API_KEY")
RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
case "$STATUS" in
completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout|deleted) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
*) sleep 2 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/trellis-2/image-to-3d";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
async function requestJson(url, options = {}) {
const response = await fetch(url, options);
if (!response.ok) throw new Error(await response.text());
return response.json();
}
// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"resolution": "1024",
"texture_size": 2048,
"target_face_count": 200000,
"remove_background": true
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;
// 2. Poll until the prediction finishes.
while (true) {
const resultBody = await requestJson(resultUrl, {
headers: { "Authorization": `Bearer ${apiKey}` },
});
const result = resultBody.data ?? resultBody;
if (result.status === "completed") {
console.log(result.outputs);
break;
}
if (["failed", "cancelled", "timeout", "deleted"].includes(result.status)) throw new Error(JSON.stringify(result));
await new Promise(resolve => setTimeout(resolve, 2000));
}import json
import os
import time
from urllib.request import Request, urlopen
api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"resolution": "1024",
"texture_size": 2048,
"target_face_count": 200000,
"remove_background": True
}
def request_json(url, data=None):
request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
with urlopen(request) as response:
return json.load(response)
# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/wavespeed-ai/trellis-2/image-to-3d", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
raise RuntimeError("Submission response did not contain a prediction id")
result_url = f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"
# 2. Poll until the prediction finishes.
while True:
result_body = request_json(result_url)
result = result_body.get("data", result_body)
status = result.get("status")
if status == "completed":
print(result.get("outputs", []))
break
if status in {"failed", "cancelled", "timeout", "deleted"}:
raise RuntimeError(result)
time.sleep(2)Trellis 2 Image To 3d is a WaveSpeedAI model for 3D asset generation from images, exposed as a REST API on WaveSpeedAI. TRELLIS.2 Image-to-3D turns a single reference image into a textured 3D GLB model with PBR materials for game assets, product visualization, 3D design, and production workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates Python, JavaScript, and cURL examples for submitting requests and polling results. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/trellis-2-image-to-3d.
Trellis 2 Image To 3d starts at $0.2 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `image`, `resolution`, `seed`, `remove_background`, `target_face_count`, `texture_size`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/trellis-2-image-to-3d.
Sign up for a free WaveSpeedAI account to claim starter credits, copy your API key from /accesskey, then call the endpoint shown in the API tab of the playground. The playground also auto-generates a code sample in Python, JavaScript, or cURL for the parameters you've set.
Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). Check the provider's applicable terms and WaveSpeedAI's Terms of Service before commercial use.