ai/ai-celebrity-look-alike-finder
celebrity-look-alike-finder
AI Celebrity Look-Alike Finder analyzes a portrait and finds the closest celebrity match. Upload a face photo and discover which celebrity you resemble. Ready-to-use REST inference API, no coldstarts, affordable pricing.
Examples
Parameters
| Name | Type | Default | Constraints | Description |
|---|---|---|---|---|
| image *Image | image_upload | — | image/* · 1–1 items | The URL of the input image. |
Output fields
| Field | Type | Description |
|---|---|---|
| images | array<string> | Generated image URL(s). |
API
Call this model through one unified REST API. Get a key on the API Keys page.
cURL
# 1) Submit — returns { "task_uuid": "..." }
curl -X POST "https://www.namifusion.com/api/v1/marketplace/run/celebrity-look-alike-finder" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"input": {
"image": [
"https://assets-public.namifusion.com/marketplace/images/2026-06-12/638e1ee18ac7.jpeg"
]
}
}'
# 2) Poll until status is "completed", then read the output URLs
curl "https://www.namifusion.com/api/v1/marketplace/run/tasks/TASK_UUID" \
-H "Authorization: Bearer YOUR_API_KEY"Python
import time, requests
API_KEY = "YOUR_API_KEY"
HEADERS = {"Authorization": f"Bearer {API_KEY}"}
# 1) Submit
resp = requests.post(
"https://www.namifusion.com/api/v1/marketplace/run/celebrity-look-alike-finder",
headers=HEADERS,
json={
"input": {
"image": [
"https://assets-public.namifusion.com/marketplace/images/2026-06-12/638e1ee18ac7.jpeg"
]
}
},
)
resp.raise_for_status() # 401/402/429/5xx stop here instead of polling a bad task
task = resp.json()
# 2) Poll until a terminal state (completed / failed / cancelled).
# This model is allowed up to 300s server-side.
deadline = time.time() + 360
while task.get("status") not in ("completed", "failed", "cancelled"):
if time.time() > deadline:
raise TimeoutError(f"still {task.get('status')} — keep the task_uuid and poll later")
time.sleep(3)
poll = requests.get(f"https://www.namifusion.com/api/v1/marketplace/run/tasks/{task['task_uuid']}", headers=HEADERS)
poll.raise_for_status()
task = poll.json()
print(task["status"], task.get("output"))JavaScript
const API_KEY = "YOUR_API_KEY";
const HEADERS = { Authorization: `Bearer ${API_KEY}` };
// 1) Submit
const resp = await fetch("https://www.namifusion.com/api/v1/marketplace/run/celebrity-look-alike-finder", {
method: "POST",
headers: { ...HEADERS, "Content-Type": "application/json" },
body: JSON.stringify({
"input": {
"image": [
"https://assets-public.namifusion.com/marketplace/images/2026-06-12/638e1ee18ac7.jpeg"
]
}
}),
});
if (!resp.ok) throw new Error(`submit failed: ${resp.status} ${await resp.text()}`);
let task = await resp.json();
// 2) Poll until a terminal state (completed / failed / cancelled).
// This model is allowed up to 300s server-side.
const deadline = Date.now() + 360 * 1000;
while (!["completed", "failed", "cancelled"].includes(task.status)) {
if (Date.now() > deadline) throw new Error(`still ${task.status} — keep the task_uuid and poll later`);
await new Promise((r) => setTimeout(r, 3000));
const poll = await fetch(`https://www.namifusion.com/api/v1/marketplace/run/tasks/${task.task_uuid}`, { headers: HEADERS });
if (!poll.ok) throw new Error(`poll failed: ${poll.status}`);
task = await poll.json();
}
console.log(task.status, task.output);Documentation
ai/ai-celebrity-look-alike-finder
Upload a portrait and instantly discover your closest celebrity match.
ai/ai-celebrity-look-alike-finder is an AI model designed to analyze a face photo and identify the closest celebrity look-alike. It is well suited for entertainment, social engagement, and lightweight portrait analysis workflows, with key advantages including no cold start, simple image-based input, and affordable per-request pricing.
🚀 Key Features
- Celebrity look-alike matching: Analyzes facial features in a portrait and finds the closest celebrity resemblance.
- Simple image input: Requires only an input image URL, making it easy to evaluate and deploy in user-facing experiences.
- No cold start: Delivers responsive inference for interactive applications and high-frequency usage.
- Affordable pricing: Low per-run cost makes it practical for consumer apps, campaigns, and experimentation.
- Optimized for portrait photos: Best results are achieved with clear, front-facing, single-person images.
🛠️ Technical Specifications
| Item | Details |
|---|---|
| Model architecture | AI model for facial feature analysis and celebrity similarity matching |
| Task type | Portrait-based celebrity look-alike detection |
| Input format | Image URL |
| Output Format | Structured match result |
| Input requirements | Single clear portrait image with a visible face |
| Latency | Low-latency inference with no cold start |
| Service profile | Ready-to-use REST inference service |
Sample prompts
This model is primarily image-driven and typically does not require a Prompt. For best results, use images such as:
- A clear front-facing portrait photo
- A selfie with even lighting and minimal occlusion
- A single-subject headshot with limited background distraction
💰 Pricing
| Item | Price |
|---|---|
| Base Price | $0.05 per run |
💡 Best Use Cases
- Entertainment apps: Power “Which celebrity do you look like?” experiences for end users.
- Social media campaigns: Increase engagement through shareable celebrity match interactions.
- Photo and beauty apps: Add a fun portrait analysis feature to consumer image products.
- Web and mobile experiences: Integrate lightweight face-based matching into interactive products.
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