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.
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Her çalıştırma $0.56 maliyete sahip. 10$ ile yaklaşık 17 kez çalıştırabilirsiniz.
Şununla devam edebilirsiniz:
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()Programlama diliniz için gerekli paketi kurun.
pip install requestsTüm API istekleri, API anahtarı ile kimlik doğrulama gerektirir. API anahtarınızı Atlas Cloud kontrol panelinden alabilirsiniz.
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}"
}API anahtarınızı asla istemci tarafı kodunda veya herkese açık depolarda ifşa etmeyin. Bunun yerine ortam değişkenleri veya arka uç proxy kullanın.
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())Asenkron bir oluşturma isteği gönderin. API, durumu kontrol etmek ve sonucu almak için kullanabileceğiniz bir tahmin ID'si döndürür.
/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"
}
}İsteğinizin mevcut durumunu kontrol etmek için tahmin uç noktasını sorgulayın.
/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)processingİstek hâlâ işleniyor.completedOluşturma tamamlandı. Çıktılar kullanılabilir.succeededOluşturma başarılı oldu. Çıktılar kullanılabilir.failedOluşturma başarısız oldu. Hata alanını kontrol edin.{
"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"
}
}Dosyaları Atlas Cloud depolama alanına yükleyin ve API isteklerinizde kullanabileceğiniz bir URL alın. Yüklemek için multipart/form-data kullanın.
/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
}
}İstek gövdesinde aşağıdaki parametreler kabul edilir.
{
"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
}API, oluşturulan çıktı URL'lerini içeren bir tahmin yanıtı döndürür.
{
"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'den fazla AI modelini doğrudan AI kodlama asistanınıza entegre eder. Kurmak için tek bir komut, ardından görüntü, video oluşturmak ve LLM ile sohbet etmek için doğal dil kullanın.
npx skills add AtlasCloudAI/atlas-cloud-skillsAPI anahtarınızı Atlas Cloud kontrol panelinden alın ve ortam değişkeni olarak ayarlayın.
export ATLASCLOUD_API_KEY="your-api-key-here"Kurulduktan sonra, tüm Atlas Cloud modellerine erişmek için AI asistanınızda doğal dil kullanabilirsiniz.
Atlas Cloud MCP Server, IDE'nizi Model Context Protocol aracılığıyla 400'den fazla AI modeline bağlar. Herhangi bir MCP uyumlu istemci ile çalışır.
npx -y atlascloud-mcpAşağıdaki yapılandırmayı IDE'nizin MCP ayarları dosyasına ekleyin.
{
"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