alibaba/qwen-image/text-to-image-max

文生图

General-purpose image generation model that supports various art styles and is particularly good at rendering complex text.

输入

正在加载参数配置...

输出

空闲

生成的图片将在这里显示

配置参数后点击运行开始生成

每次运行将花费 0.052。$10 可运行约 192 次。

你可以继续：

图生视频图生图

参数

代码示例
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": "alibaba/qwen-image/text-to-image-max",
    "prompt": "A beautiful landscape with mountains and lake",
    "width": 512,
    "height": 512,
    "steps": 20,
    "guidance_scale": 7.5,
}

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()

安装

安装所需的依赖包。

bash

pip install requests

认证

所有 API 请求需要通过 API Key 进行认证。您可以在 Atlas Cloud 控制台获取 API Key。

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP 请求头

python

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

保护好您的 API Key

切勿在客户端代码或公开仓库中暴露您的 API Key。请使用环境变量或后端代理。

提交请求

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())

提交请求

提交一个异步生成请求。API 返回一个 prediction ID，您可以用它来检查状态和获取结果。

POST/api/v1/model/generateImage

请求体

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "alibaba/qwen-image/text-to-image-max",
    "input": {
        "prompt": "A beautiful landscape with mountains and lake"
    }
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['id']}")
print(f"Status: {result['status']}")

响应

{
  "id": "pred_abc123",
  "status": "processing",
  "model": "model-name",
  "created_at": "2025-01-01T00:00:00Z"
}

检查状态

轮询 prediction 端点以检查请求的当前状态。

GET/api/v1/model/prediction/{prediction_id}

轮询示例

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)

状态值

processing请求仍在处理中。

completed生成完成，输出可用。

succeeded生成成功，输出可用。

failed生成失败，请检查 error 字段。

完成响应

{
  "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"
  }
}

上传文件

将文件上传到 Atlas Cloud 存储，获取可在 API 请求中使用的 URL。使用 multipart/form-data 上传。

POST/api/v1/model/uploadMedia

上传示例

import 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}")

响应

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

以下参数在请求体中被接受。

总计: 0必填: 0可选: 0

暂无可用参数。

请求体示例

json

{
  "model": "alibaba/qwen-image/text-to-image-max"
}

Output Schema

API 返回包含生成输出 URL 的 prediction 响应。

idstringrequired

Unique identifier for the prediction.

statusstringrequired

Current status of the prediction.

processingcompletedsucceededfailed

modelstringrequired

The model used for generation.

outputsarray[string]

Array of output URLs. Available when status is "completed".

errorstring

Error message if status is "failed".

metricsobject

Performance metrics.

predict_timenumber

Time taken for image generation in seconds.

created_atstringrequired

ISO 8601 timestamp when the prediction was created.

Format: date-time

completed_atstring

ISO 8601 timestamp when the prediction was completed.

Format: date-time

响应示例

json

{
  "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

Atlas Cloud Skills 将 300+ AI 模型直接集成到您的 AI 编程助手中。一条命令安装，即可用自然语言生成图像、视频和与 LLM 对话。

支持的客户端

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ 支持的客户端

安装

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

设置 API Key

从 Atlas Cloud 控制台获取 API Key，并将其设置为环境变量。

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

功能

安装后，您可以在 AI 助手中使用自然语言访问所有 Atlas Cloud 模型。

图像生成使用 Nano Banana 2、Z-Image 等模型生成图像。

视频创作使用 Kling、Vidu、Veo 等模型从文本或图像创建视频。

LLM 对话与 Qwen、DeepSeek 等大语言模型对话。

媒体上传上传本地文件用于图像编辑和图生视频工作流。

MCP Server

Atlas Cloud MCP Server 通过 Model Context Protocol 将您的 IDE 与 300+ AI 模型连接。支持任何兼容 MCP 的客户端。

支持的客户端

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ 支持的客户端

安装

bash

npx -y atlascloud-mcp

配置

将以下配置添加到您的 IDE 的 MCP 设置文件中。

json

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

可用工具

atlas_generate_image从文本提示生成图像。

atlas_generate_video从文本或图像创建视频。

atlas_chat与大语言模型对话。

atlas_list_models浏览 300+ 可用 AI 模型。

atlas_quick_generate一步式内容创建，自动选择最佳模型。

atlas_upload_media上传本地文件用于 API 工作流。

了解更多

github.com/AtlasCloudAI/mcp-server

API Schema

Schema 不可用

请登录以查看请求历史

您需要登录才能访问模型请求历史记录。

Alibaba Qwen-Image Text-to-Image Max

The flagship text-to-image generation model from Alibaba Cloud, designed to deliver state-of-the-art visual quality, exceptional prompt adherence, and rich artistic detail. Qwen-Image Max represents the pinnacle of the Qwen-Image family, capable of transforming complex text descriptions into stunning, high-resolution visuals suitable for professional and creative workflows.

Overview

Purpose: Generate premium-quality images from natural language descriptions.
Core Capability: Industry-leading visual fidelity with deep semantic understanding of prompts.
Foundation: Built on Alibaba's advanced large-scale multi-modal architecture.
Typical Output: High-resolution, photorealistic or artistic images with precise lighting, texture, and composition.
Use Cases: Professional design, advertising creatives, concept art, marketing materials, and high-end content creation.

Key Features

Superior Visual Quality: Delivers the highest level of detail, texture, and lighting realism available in the Qwen-Image series.
Complex Prompt Understanding: Accurately interprets long, intricate prompts, including spatial relationships, artistic styles, and specific object attributes.
Text Rendering: Enhanced capability to render legible text within generated images (e.g., signboards, posters).
Style Versatility: Masterfully handles a wide range of styles, from photorealism and cinematic shots to 3D render, oil painting, and illustration.
High Resolution: Supports generation of high-definition images suitable for professional use.

Designed For

Professional Designers: Create high-quality assets, mockups, and final visuals.
Digital Artists: Explore complex concepts and generate detailed artwork.
Marketing Agencies: Produce campaign-ready visuals with specific brand requirements.
Enterprise Users: High-demand use cases requiring consistent, top-tier visual output.

Input Requirements

To achieve the best results, follow these guidelines:

Text Prompt

Content: Detailed English descriptions of the subject, setting, lighting, style, and mood.
Length: Supports long context, but concise and descriptive prompts often yield the best focus.
Negative Prompt: Optional. Specify elements to exclude (e.g., "blur, low quality, distortion").

Parameters

Aspect Ratio: Supports various standard ratios (1:1, 16:9, 9:16, 4:3, 3:4).
Resolution: Optimized for high-resolution outputs (e.g., 1024x1024 and above).
Steps/Guidance: Configurable for fine-tuning the balance between prompt adherence and image quality.

Pricing

Billing is typically based on the number of images generated and the resolution selected.

Billing Logic: Per-image generation cost.
Tier: "Max" tier commands a premium rate due to higher computational resources and output quality compared to standard models.

How to Use

Enter Prompt: Describe the image you want to generate in detail.
Set Parameters: Choose your desired aspect ratio and number of images.
Generate: Submit the request to the Qwen-Image Max model.
Refine: Use the generated image as a reference or adjust the prompt for iterations.

Best Practices

Be Specific: Instead of "a cat," try "a fluffy white Persian cat sitting on a velvet sofa, cinematic lighting, 8k resolution."
Define Style: Explicitly state the medium (e.g., "oil painting," "photograph," "3D render").
Lighting & Composition: Mention lighting conditions (e.g., "golden hour," "studio lighting") and camera angles.
Iterate: If the first result isn't perfect, tweak the prompt or use a negative prompt to remove unwanted elements.

Limitations

Text Accuracy: While improved, complex or long text strings within the image may still occasionally have minor errors.
Spatial Logic: Extremely complex spatial arrangements might sometimes require prompt tuning.

Version

Model: Alibaba Qwen-Image Text-to-Image Max
Family: Qwen-Image
Technical Context: Large-scale diffusion transformer model optimized for maximum visual fidelity.

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