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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Każde uruchomienie będzie kosztować $0.56. Za $10 możesz uruchomić ten model około 17 razy.
Co możesz zrobić dalej:
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()Zainstaluj wymagany pakiet dla swojego języka programowania.
pip install requestsWszystkie żądania API wymagają uwierzytelnienia za pomocą klucza API. Klucz API możesz uzyskać z panelu 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}"
}Nigdy nie ujawniaj swojego klucza API w kodzie po stronie klienta ani w publicznych repozytoriach. Zamiast tego użyj zmiennych środowiskowych lub proxy backendowego.
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())Wyślij asynchroniczne żądanie generowania. API zwróci identyfikator predykcji, którego możesz użyć do sprawdzania statusu i pobierania wyniku.
/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"
}
}Odpytuj endpoint predykcji, aby sprawdzić bieżący status żądania.
/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)processingŻądanie jest wciąż przetwarzane.completedGenerowanie zakończone. Wyniki są dostępne.succeededGenerowanie powiodło się. Wyniki są dostępne.failedGenerowanie nie powiodło się. Sprawdź pole błędu.{
"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"
}
}Prześlij pliki do magazynu Atlas Cloud i uzyskaj URL, którego możesz użyć w swoich żądaniach API. Użyj multipart/form-data do przesyłania.
/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
}
}Następujące parametry są akceptowane w treści żądania.
{
"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 zwraca odpowiedź predykcji z URL-ami wygenerowanych wyników.
{
"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 integruje ponad 400 modeli AI bezpośrednio z Twoim asystentem kodowania AI. Jedno polecenie do instalacji, a następnie używaj języka naturalnego do generowania obrazów, filmów i rozmów z LLM.
npx skills add AtlasCloudAI/atlas-cloud-skillsUzyskaj klucz API z panelu Atlas Cloud i ustaw go jako zmienną środowiskową.
export ATLASCLOUD_API_KEY="your-api-key-here"Po zainstalowaniu możesz używać języka naturalnego w swoim asystencie AI, aby uzyskać dostęp do wszystkich modeli Atlas Cloud.
Serwer MCP Atlas Cloud łączy Twoje IDE z ponad 400 modelami AI za pośrednictwem Model Context Protocol. Działa z każdym klientem kompatybilnym z MCP.
npx -y atlascloud-mcpDodaj następującą konfigurację do pliku ustawień MCP w swoim IDE.
{
"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