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.
Cada execução custará $0.56. Com $10 você pode executar aproximadamente 17 vezes.
Você pode continuar com:
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()Instale o pacote necessário para a sua linguagem de programação.
pip install requestsTodas as solicitações de API requerem autenticação por meio de uma chave de API. Você pode obter sua chave de API no painel do 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}"
}Nunca exponha sua chave de API em código do lado do cliente ou repositórios públicos. Use variáveis de ambiente ou um proxy de backend.
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())Envie uma solicitação de geração assíncrona. A API retorna um ID de predição que você pode usar para verificar o status e obter o resultado.
/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"
}
}Consulte o endpoint de predição para verificar o status atual da sua solicitação.
/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)processingA solicitação ainda está sendo processada.completedA geração está completa. As saídas estão disponíveis.succeededA geração foi bem-sucedida. As saídas estão disponíveis.failedA geração falhou. Verifique o campo de erro.{
"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"
}
}Envie arquivos para o armazenamento do Atlas Cloud e obtenha uma URL que pode ser usada nas suas solicitações de API. Use multipart/form-data para enviar.
/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
}
}Os seguintes parâmetros são aceitos no corpo da solicitação.
{
"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
}A API retorna uma resposta de predição com as URLs de saída geradas.
{
"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"
}O Atlas Cloud Skills integra mais de 400 modelos de IA diretamente no seu assistente de codificação com IA. Um comando para instalar e depois use linguagem natural para gerar imagens, vídeos e conversar com LLM.
npx skills add AtlasCloudAI/atlas-cloud-skillsObtenha sua chave de API no painel do Atlas Cloud e defina-a como variável de ambiente.
export ATLASCLOUD_API_KEY="your-api-key-here"Após a instalação, você pode usar linguagem natural no seu assistente de IA para acessar todos os modelos do Atlas Cloud.
O Atlas Cloud MCP Server conecta seu IDE com mais de 400 modelos de IA através do Model Context Protocol. Funciona com qualquer cliente compatível com MCP.
npx -y atlascloud-mcpAdicione a seguinte configuração ao arquivo de configuração de MCP do seu 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