ByteDance Seed3D 2.0 — generates a textured, PBR-shaded 3D model (glb/obj/usd/usdz) from a single input image. Returns a downloadable .zip archive containing the 3D file.
आपके अनुरोध की लागत $0.353 प्रति रन होगी। $10 के साथ आप इस मॉडल को लगभग 28 बार चला सकते हैं।
import requests
import time
# Step 1: Start image generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
"model": "bytedance/seed3d-v2.0/image-to-3d", # Required. model name
"image": "example_value", # Required. Source image (URL or base64 data URI)
"subdivision_level": "medium", # Subdivision level for the generated 3D mesh. options: high | medium | low
"file_format": "glb", # Output 3D file format. options: glb | obj | usd | usdz
"enable_base64_output": False, # If enabled, the output will be encoded into a BASE64 string instead of a URL
}
generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]
# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
def check_status():
while True:
response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
result = response.json()
if result["data"]["status"] == "completed":
print("Generated image:", result["data"]["outputs"][0])
return result["data"]["outputs"][0]
elif result["data"]["status"] == "failed":
raise Exception(result["data"]["error"] or "Generation failed")
else:
# Still processing, wait 2 seconds
time.sleep(2)
image_url = check_status()अपनी प्रोग्रामिंग भाषा के लिए आवश्यक पैकेज इंस्टॉल करें।
pip install requestsसभी API अनुरोधों के लिए API कुंजी के माध्यम से प्रमाणीकरण आवश्यक है। आप अपनी API कुंजी Atlas Cloud डैशबोर्ड से प्राप्त कर सकते हैं।
export ATLASCLOUD_API_KEY="your-api-key-here"import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}क्लाइंट-साइड कोड या सार्वजनिक रिपॉज़िटरी में अपनी API कुंजी कभी उजागर न करें। इसके बजाय एनवायरनमेंट वेरिएबल या बैकएंड प्रॉक्सी का उपयोग करें।
import requests
url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
"model": "your-model",
"prompt": "A beautiful landscape"
}
response = requests.post(url, headers=headers, json=data)
print(response.json())एक असिंक्रोनस जनरेशन अनुरोध सबमिट करें। API एक प्रेडिक्शन ID लौटाता है जिसका उपयोग आप स्थिति जाँचने और परिणाम प्राप्त करने के लिए कर सकते हैं।
/api/v1/model/generateImageimport requests
url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
"model": "bytedance/seed3d-v2.0/image-to-3d",
"prompt": "A beautiful landscape with mountains and lake"
}
response = requests.post(url, headers=headers, json=data)
result = response.json()
print(f"Prediction ID: {result['data']['id']}")
print(f"Status: {result['data']['status']}"){
"code": 200,
"data": {
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}
}अपने अनुरोध की वर्तमान स्थिति जाँचने के लिए प्रेडिक्शन एंडपॉइंट को पोल करें।
/api/v1/model/prediction/{prediction_id}import requests
import time
prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }
while True:
response = requests.get(url, headers=headers)
result = response.json()
status = result["data"]["status"]
print(f"Status: {status}")
if status in ["completed", "succeeded"]:
output_url = result["data"]["outputs"][0]
print(f"Output URL: {output_url}")
break
elif status == "failed":
print(f"Error: {result['data'].get('error', 'Unknown')}")
break
time.sleep(3)processingअनुरोध अभी भी प्रोसेस हो रहा है।completedजनरेशन पूर्ण हो गया है। आउटपुट उपलब्ध हैं।succeededजनरेशन सफल रहा। आउटपुट उपलब्ध हैं।failedजनरेशन विफल हो गया। एरर फ़ील्ड जाँचें।{
"data": {
"id": "pred_abc123",
"status": "completed",
"outputs": [
"https://storage.atlascloud.ai/outputs/result.png"
],
"metrics": {
"predict_time": 8.3
},
"created_at": "2025-01-01T00:00:00Z",
"completed_at": "2025-01-01T00:00:10Z"
}
}Atlas Cloud स्टोरेज पर फ़ाइलें अपलोड करें और एक URL प्राप्त करें जिसका उपयोग आप अपने API अनुरोधों में कर सकते हैं। अपलोड के लिए multipart/form-data का उपयोग करें।
/api/v1/model/uploadMediaimport requests
url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }
with open("image.png", "rb") as f:
files = {"file": ("image.png", f, "image/png")}
response = requests.post(url, headers=headers, files=files)
result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}"){
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}अनुरोध बॉडी में निम्नलिखित पैरामीटर स्वीकार किए जाते हैं।
{
"model": "bytedance/seed3d-v2.0/image-to-3d",
"image": "example_image",
"subdivision_level": "medium",
"file_format": "glb",
"enable_base64_output": false
}API जनरेट किए गए आउटपुट URL के साथ एक प्रेडिक्शन प्रतिक्रिया लौटाता है।
{
"id": "pred_abc123",
"status": "completed",
"model": "model-name",
"outputs": [
"https://storage.atlascloud.ai/outputs/result.png"
],
"metrics": {
"predict_time": 8.3
},
"created_at": "2025-01-01T00:00:00Z",
"completed_at": "2025-01-01T00:00:10Z"
}Atlas Cloud Skills 400+ AI मॉडल को सीधे आपके AI कोडिंग असिस्टेंट में इंटीग्रेट करता है। इंस्टॉल करने के लिए एक कमांड, फिर इमेज, वीडियो जनरेट करने और LLM के साथ चैट करने के लिए प्राकृतिक भाषा का उपयोग करें।
npx skills add AtlasCloudAI/atlas-cloud-skillsAtlas Cloud डैशबोर्ड से अपनी API कुंजी प्राप्त करें और इसे एनवायरनमेंट वेरिएबल के रूप में सेट करें।
export ATLASCLOUD_API_KEY="your-api-key-here"एक बार इंस्टॉल होने के बाद, आप सभी Atlas Cloud मॉडल तक पहुँचने के लिए अपने AI असिस्टेंट में प्राकृतिक भाषा का उपयोग कर सकते हैं।
Atlas Cloud MCP Server आपके IDE को Model Context Protocol के माध्यम से 400+ AI मॉडल से जोड़ता है। किसी भी MCP-संगत क्लाइंट के साथ काम करता है।
npx -y atlascloud-mcpअपने IDE की MCP सेटिंग्स फ़ाइल में निम्नलिखित कॉन्फ़िगरेशन जोड़ें।
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}{
"openapi": "3.0.0",
"info": {
"title": "AtlasCloud API",
"version": "1.0.0",
"description": "The AtlasCloud API."
},
"servers": [
{
"url": "https://api.atlascloud.ai"
}
],
"paths": {
"/api/v1/model/generateImage": {
"post": {
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/Input"
}
}
},
"required": true
},
"responses": {
"200": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/PredictionResponse"
}
}
},
"description": "The request status."
}
},
"x-api-name": "model_run"
}
},
"/api/v1/model/result/{request_id}": {
"get": {
"parameters": [
{
"in": "path",
"name": "request_id",
"required": true,
"schema": {
"description": "Request ID",
"type": "string"
}
}
],
"responses": {
"200": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/PredictionResponse"
}
}
},
"description": "Result of the request."
}
},
"x-api-name": "model_result"
}
}
},
"components": {
"schemas": {
"Input": {
"type": "object",
"properties": {
"model": {
"type": "string",
"description": "model name",
"default": "bytedance/seed3d-v2.0/image-to-3d"
},
"image": {
"type": "string",
"description": "Source image (URL or base64 data URI). Constraints: total pixels < 4096x4096, size <= 10 MB, aspect ratio in (0.4, 2.5), formats: jpg, jpeg, png, webp, bmp."
},
"subdivision_level": {
"type": "string",
"description": "Subdivision level for the generated 3D mesh. high=1,000,000 / medium=500,000 / low=100,000 polygon faces.",
"enum": [
"high",
"medium",
"low"
],
"default": "medium"
},
"file_format": {
"type": "string",
"description": "Output 3D file format. The result is delivered as a .zip archive containing a single file in the chosen format.",
"enum": [
"glb",
"obj",
"usd",
"usdz"
],
"default": "glb"
},
"enable_base64_output": {
"type": "boolean",
"description": "If enabled, the output will be encoded into a BASE64 string instead of a URL. This property is only available through the API.",
"default": false,
"disabled": true
}
},
"required": [
"model",
"image"
],
"x-order-properties": [
"model",
"image",
"subdivision_level",
"file_format",
"enable_base64_output"
]
},
"PredictionResponse": {
"type": "object",
"properties": {
"created_at": {
"description": "ISO timestamp of when the request was created (e.g., \"2023-04-01T12:34:56.789Z\").",
"format": "date-time",
"type": "string"
},
"id": {
"description": "Unique identifier for the prediction, the ID of the prediction to get.",
"type": "string"
},
"model": {
"description": "Model ID used for the prediction.",
"type": "string"
},
"outputs": {
"description": "Array containing one URL pointing to the generated 3D file .zip archive (empty when status is not completed). The URL is valid for 24 hours.",
"items": {
"type": "string"
},
"type": "array"
},
"status": {
"description": "Status of the task: created, processing, completed, or failed.",
"type": "string"
},
"urls": {
"description": "Object containing related API endpoints.",
"type": "object"
}
}
}
}
}
}# bytedance/seed3d-v2.0/image-to-3d
> ByteDance Seed3D 2.0 — generates a textured, PBR-shaded 3D model (glb/obj/usd/usdz) from a single input image. Returns a downloadable .zip archive containing the 3D file.
## Overview
- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateImage` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `bytedance/seed3d-v2.0/image-to-3d`
## API Information
This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.
### Input Schema
The API accepts the following input parameters:
- **`model`** (`string`, _required_):
model name
- Default: `"bytedance/seed3d-v2.0/image-to-3d"`
- **`image`** (`string`, _required_):
Source image (URL or base64 data URI). Constraints: total pixels < 4096x4096, size <= 10 MB, aspect ratio in (0.4, 2.5), formats: jpg, jpeg, png, webp, bmp.
- **`subdivision_level`** (`string`, _optional_):
Subdivision level for the generated 3D mesh. high=1,000,000 / medium=500,000 / low=100,000 polygon faces.
- Default: `"medium"`
- Options: "high", "medium", "low"
- **`file_format`** (`string`, _optional_):
Output 3D file format. The result is delivered as a .zip archive containing a single file in the chosen format.
- Default: `"glb"`
- Options: "glb", "obj", "usd", "usdz"
- **`enable_base64_output`** (`boolean`, _optional_):
If enabled, the output will be encoded into a BASE64 string instead of a URL. This property is only available through the API.
- Default: `false`
**Required Parameters Example**:
```json
{
"model": "bytedance/seed3d-v2.0/image-to-3d",
"image": ""
}
```
**Full Example**:
```json
{
"model": "bytedance/seed3d-v2.0/image-to-3d",
"image": "",
"subdivision_level": "medium",
"file_format": "glb",
"enable_base64_output": false
}
```
### Output Schema
The API returns the following output format:
- **`created_at`** (`string`, _optional_):
ISO timestamp of when the request was created (e.g., "2023-04-01T12:34:56.789Z").
- **`id`** (`string`, _optional_):
Unique identifier for the prediction, the ID of the prediction to get.
- **`model`** (`string`, _optional_):
Model ID used for the prediction.
- **`outputs`** (`array[string]`, _optional_):
Array containing one URL pointing to the generated 3D file .zip archive (empty when status is not completed). The URL is valid for 24 hours.
- **`status`** (`string`, _optional_):
Status of the task: created, processing, completed, or failed.
- **`urls`** (`object`, _optional_):
Object containing related API endpoints.
**Example Response**:
```json
{
"created_at": "",
"id": "",
"model": "",
"outputs": [
""
],
"status": "",
"urls": {}
}
```
## Usage Examples
### cURL
```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateImage" \
-H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "bytedance/seed3d-v2.0/image-to-3d",
"image": "",
"subdivision_level": "medium",
"file_format": "glb",
"enable_base64_output": false
}'
# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}
# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
-H "Authorization: Bearer $ATLASCLOUD_API_KEY"
# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```
## Additional Resources
### Documentation
- [Model Playground](https://www.atlascloud.ai/models/bytedance/seed3d-v2.0/image-to-3d)
No 3D output