A fast, low-latency version of ERNIE Image by Baidu, optimized for rapid iteration and scalable image generation.Balances speed and quality, ideal for real-time and high-throughput scenarios.
Join the Discord community for the latest model updates, prompts, and support.
كل مرة ستكلف $0.56 مع $10 يمكنك التشغيل حوالي 17 مرة
يمكنك المتابعة بـ:
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": "baidu/ERNIE-Image-Turbo/text-to-image", # Required. Model name
"prompt": "A beautiful landscape with mountains and lake", # Required. The positive prompt for the generation
"size": "1024x1024", # The size of the generated media in pixels (widthxheight)
"n": 1, # Number of images to generate. (min: 1, max: 10)
"seed": -1, # Random seed for generation
"use_pe": True, # Use PE for generation
"num_inference_steps": 8, # Number of inference steps. (min: 1, max: 100)
"guidance_scale": 1, # Guidance scale for generation. (min: 1, max: 20)
"enable_sync_mode": False, # If set to true, the function waits for final output before returning
"enable_base64_output": False, # If enabled, output can be encoded as BASE64 instead of 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()قم بتثبيت الحزمة المطلوبة للغة البرمجة الخاصة بك.
pip install requestsتتطلب جميع طلبات API المصادقة عبر مفتاح API. يمكنك الحصول على مفتاح API الخاص بك من لوحة تحكم Atlas Cloud.
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}"
}لا تكشف أبدًا مفتاح API الخاص بك في الكود من جانب العميل أو المستودعات العامة. استخدم متغيرات البيئة أو وكيل الخادم الخلفي بدلاً من ذلك.
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())أرسل طلب توليد غير متزامن. تُرجع API معرّف التنبؤ الذي يمكنك استخدامه للتحقق من الحالة واسترداد النتيجة.
/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": "baidu/ERNIE-Image-Turbo/text-to-image",
"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"
}
}استعلم عن نقطة نهاية التنبؤ للتحقق من الحالة الحالية لطلبك.
/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)processingلا يزال الطلب قيد المعالجة.completedاكتمل التوليد. المخرجات متاحة.succeededنجح التوليد. المخرجات متاحة.failedفشل التوليد. تحقق من حقل الخطأ.{
"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"
}
}ارفع الملفات إلى تخزين Atlas Cloud واحصل على URL يمكنك استخدامه في طلبات API الخاصة بك. استخدم multipart/form-data للرفع.
/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
}
}المعاملات التالية مقبولة في نص الطلب.
{
"model": "baidu/ERNIE-Image-Turbo/text-to-image",
"prompt": "A beautiful landscape",
"size": "1024x1024",
"n": 1,
"seed": -1,
"use_pe": true,
"num_inference_steps": 8,
"guidance_scale": 1,
"enable_sync_mode": false,
"enable_base64_output": false
}تُرجع API استجابة تنبؤ تحتوي على عناوين URL للمخرجات المولّدة.
{
"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"
}يدمج Atlas Cloud Skills أكثر من 400 نموذج ذكاء اصطناعي مباشرة في مساعد البرمجة بالذكاء الاصطناعي الخاص بك. أمر واحد للتثبيت، ثم استخدم اللغة الطبيعية لتوليد الصور ومقاطع الفيديو والدردشة مع LLM.
npx skills add AtlasCloudAI/atlas-cloud-skillsاحصل على مفتاح API الخاص بك من لوحة تحكم Atlas Cloud وعيّنه كمتغير بيئة.
export ATLASCLOUD_API_KEY="your-api-key-here"بمجرد التثبيت، يمكنك استخدام اللغة الطبيعية في مساعد الذكاء الاصطناعي الخاص بك للوصول إلى جميع نماذج Atlas Cloud.
يربط Atlas Cloud MCP Server بيئة التطوير الخاصة بك بأكثر من 400 نموذج ذكاء اصطناعي عبر Model Context Protocol. يعمل مع أي عميل متوافق مع MCP.
npx -y atlascloud-mcpأضف التكوين التالي إلى ملف إعدادات MCP في بيئة التطوير الخاصة بك.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}{
"info": {
"description": "The AtlasCloud API.",
"title": "AtlasCloud API",
"version": "1.0.0"
},
"paths": {
"/api/v1/model/generateImage": {
"post": {
"responses": {
"200": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/PredictionResponse"
}
}
},
"description": "The request status."
}
},
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/Input"
}
}
}
}
},
"x-api-name": "model_run"
},
"/api/v1/model/result/{request_id}": {
"get": {
"responses": {
"200": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/PredictionResponse"
}
}
},
"description": "Result of the request."
}
},
"parameters": [
{
"in": "path",
"name": "request_id",
"schema": {
"type": "string",
"description": "Request ID"
},
"required": true
}
]
},
"x-api-name": "model_result"
}
},
"openapi": "3.0.0",
"servers": [
{
"url": "https://api.atlascloud.ai"
}
],
"components": {
"schemas": {
"Input": {
"type": "object",
"required": [
"model",
"prompt"
],
"properties": {
"model": {
"type": "string",
"description": "Model name",
"default": "baidu/ERNIE-Image-Turbo/text-to-image"
},
"prompt": {
"type": "string",
"minLength": 1,
"description": "The positive prompt for the generation."
},
"size": {
"type": "string",
"default": "1024x1024",
"description": "The size of the generated media in pixels (widthxheight).",
"enum": [
"1024x1024",
"1376x768",
"1264x848",
"1200x896",
"896x1200",
"848x1264",
"768x1376"
],
"x-ui-component": "select width*height"
},
"n": {
"type": "integer",
"default": 1,
"minimum": 1,
"maximum": 10,
"description": "Number of images to generate."
},
"seed": {
"type": "integer",
"default": -1,
"description": "Random seed for generation. Use -1 for random. "
},
"use_pe": {
"type": "boolean",
"default": true,
"description": "Use PE for generation."
},
"num_inference_steps": {
"type": "integer",
"default": 8,
"minimum": 1,
"maximum": 100,
"description": "Number of inference steps."
},
"guidance_scale": {
"type": "number",
"default": 1,
"minimum": 1,
"maximum": 20,
"description": "Guidance scale for generation."
},
"enable_sync_mode": {
"type": "boolean",
"default": false,
"disabled": true,
"description": "If set to true, the function waits for final output before returning."
},
"enable_base64_output": {
"type": "boolean",
"default": false,
"disabled": true,
"description": "If enabled, output can be encoded as BASE64 instead of URL."
}
},
"x-order-properties": [
"model",
"prompt",
"size",
"n",
"seed",
"use_pe",
"num_inference_steps",
"guidance_scale",
"enable_sync_mode",
"enable_base64_output"
]
},
"PredictionResponse": {
"type": "object",
"properties": {
"created_at": {
"type": "string",
"format": "date-time",
"description": "ISO timestamp of when the request was created."
},
"has_nsfw_contents": {
"type": "array",
"description": "Array of boolean values indicating NSFW detection for each output.",
"items": {
"type": "boolean"
}
},
"id": {
"type": "string",
"description": "Unique identifier for the prediction."
},
"model": {
"type": "string",
"description": "Model ID used for the prediction."
},
"outputs": {
"type": "array",
"description": "Array of URLs to the generated content (empty when status is not completed).",
"items": {
"type": "string"
}
},
"status": {
"type": "string",
"description": "Status of the task: created, processing, completed, or failed."
},
"urls": {
"type": "object",
"description": "Object containing related API endpoints."
}
}
}
},
"securitySchemes": {
"apiKeyAuth": {
"in": "header",
"name": "Authorization",
"type": "apiKey"
}
}
}
}# baidu/ERNIE-Image-Turbo/text-to-image
> A fast, low-latency version of ERNIE Image by Baidu, optimized for rapid iteration and scalable image generation.Balances speed and quality, ideal for real-time and high-throughput scenarios.
## 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**: `baidu/ERNIE-Image-Turbo/text-to-image`
## 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: `"baidu/ERNIE-Image-Turbo/text-to-image"`
- **`prompt`** (`string`, _required_):
The positive prompt for the generation.
- **`size`** (`string`, _optional_):
The size of the generated media in pixels (widthxheight).
- Default: `"1024x1024"`
- Options: "1024x1024", "1376x768", "1264x848", "1200x896", "896x1200", "848x1264", "768x1376"
- **`n`** (`integer`, _optional_):
Number of images to generate.
- Default: `1`
- Min: 1
- Max: 10
- **`seed`** (`integer`, _optional_):
Random seed for generation. Use -1 for random.
- Default: `-1`
- **`use_pe`** (`boolean`, _optional_):
Use PE for generation.
- Default: `true`
- **`num_inference_steps`** (`integer`, _optional_):
Number of inference steps.
- Default: `8`
- Min: 1
- Max: 100
- **`guidance_scale`** (`number`, _optional_):
Guidance scale for generation.
- Default: `1`
- Min: 1
- Max: 20
- **`enable_sync_mode`** (`boolean`, _optional_):
If set to true, the function waits for final output before returning.
- Default: `false`
- **`enable_base64_output`** (`boolean`, _optional_):
If enabled, output can be encoded as BASE64 instead of URL.
- Default: `false`
**Required Parameters Example**:
```json
{
"model": "baidu/ERNIE-Image-Turbo/text-to-image",
"prompt": ""
}
```
**Full Example**:
```json
{
"model": "baidu/ERNIE-Image-Turbo/text-to-image",
"prompt": "",
"size": "1024x1024",
"n": 1,
"seed": -1,
"use_pe": true,
"num_inference_steps": 8,
"guidance_scale": 1,
"enable_sync_mode": false,
"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.
- **`has_nsfw_contents`** (`array[boolean]`, _optional_):
Array of boolean values indicating NSFW detection for each output.
- **`id`** (`string`, _optional_):
Unique identifier for the prediction.
- **`model`** (`string`, _optional_):
Model ID used for the prediction.
- **`outputs`** (`array[string]`, _optional_):
Array of URLs to the generated content (empty when status is not completed).
- **`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": "",
"has_nsfw_contents": [],
"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": "baidu/ERNIE-Image-Turbo/text-to-image",
"prompt": "",
"size": "1024x1024",
"n": 1,
"seed": -1,
"use_pe": true,
"num_inference_steps": 8,
"guidance_scale": 1,
"enable_sync_mode": false,
"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/baidu/ERNIE-Image-Turbo/text-to-image)
Cinematic night city scene, towering skyscrapers under deep blue sky, warm window lights forming patterns across the skyline, cars streaming through avenues with glowing headlights, soft fog drifting between buildings, dramatic lighting, wide angle lens, anamorphic cinematic style, ultra detailed, 8k
Cinematic night city scene, towering skyscrapers under deep blue sky, warm window lights forming patterns across the skyline, cars streaming through avenues with glowing headlights, soft fog drifting between buildings, dramatic lighting, wide angle lens, anamorphic cinematic style, ultra detailed, 8k