qwen/qwen-image-2.0/text-to-image

文生图

Qwen Image 2.0 Text-to-Image API by Alibaba

qwen/qwen-image-2.0/text-to-image

Text-to-image

Qwen Image 2.0 is an advanced text-to-image model with enhanced image quality and improved prompt understanding. Up to 2k. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

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 $0.028。$10 可运行约 357 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": "qwen/qwen-image-2.0/text-to-image",
    "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": "qwen/qwen-image-2.0/text-to-image",
    "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": "qwen/qwen-image-2.0/text-to-image"
}

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

暂无可用示例

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。

Qwen Image 2.0 Text-to-Image

Qwen Image 2.0 is Alibaba's advanced text-to-image model that generates high-quality images from detailed text descriptions. With exceptional prompt following, flexible aspect ratios, and custom resolution support, it excels at rendering complex scenes with fine details like hair, accessories, and textures.

Why Choose This?

Strong prompt adherence
Excels at following detailed, complex prompts with multiple elements and attributes.
Fine detail rendering
Excellent at rendering intricate details like hair textures, jewelry, and clothing accessories.
Flexible aspect ratios
Multiple presets including 1:1, 16:9, 9:16, 4:3, 3:4, 3:2, and 2:3.
Custom resolution
Adjustable width and height from 512 to 2048 pixels.
Prompt Enhancer
Built-in tool to automatically improve your descriptions.

Parameters

Parameter	Required	Description
prompt	Yes	Text description of the desired image
size	No	Aspect ratio preset: 1:1, 16:9, 9:16, 4:3, 3:4, 3:2, 2:3
width	No	Custom width in pixels (range: 512–2048)
height	No	Custom height in pixels (range: 512–2048)
seed	No	Random seed for reproducibility (-1 for random)

How to Use

Write your prompt
Describe the image in detail, including specific attributes, styles, and elements.
Choose size
Select a preset aspect ratio or customize width/height.
Use Prompt Enhancer (optional)
Click to automatically refine your description.
Set seed (optional)
Use a seed for reproducible results.
Run
Submit and download your generated image.

Best Use Cases

Detailed Character Art — Generate characters with specific attributes like hair styles, clothing, and accessories
Portrait Photography — Create photorealistic portraits with fine details
Fashion & Style — Visualize outfits, hairstyles, and jewelry with precision
Concept Art — Render complex scenes with multiple elements
Cultural & Artistic — Generate images with specific cultural elements and decorations

Pro Tips

Use highly detailed prompts — the model excels at following complex descriptions with multiple attributes
Describe specific details like "waist-length loc'd hair," "gold thread," "cowrie shells," or "blue beads" for precise rendering
Include motion and pose descriptions for dynamic images (e.g., "caught mid-spin in a dance")
Match aspect ratio to your content:
- 1:1 for portraits
- 16:9 for landscapes
- 9:16 for full-body shots
Use the same seed to reproduce or iterate on specific results

Notes

prompt is the only required field
Resolution range: 512–2048 pixels for both width and height
Default size is 1:1
Ensure your prompts comply with content guidelines

Qwen Image 2.0 Pro Text-to-Image — Pro tier with enhanced quality
Qwen Image Edit Plus — Image editing with text instructions
Seedream V5.0 Lite — ByteDance's lightweight text-to-image model

探索类似模型

Wan-2.7 Pro Image-to-image

Edits and recomposes images with Wan 2.7 image pro using text instructions and multi-image references for higher quality outputs.

Wan-2.7 Pro Text-to-image

Generates images from text prompts with Wan 2.7 image pro, supporting higher fidelity outputs and 4K-ready workflows.

Wan-2.7 Image-to-image

Edits and recomposes images with Wan 2.7 image using text instructions, multi-image references, and optional interaction boxes.

Wan-2.7 Text-to-image

Generates images from text prompts with Wan 2.7 image, supporting fast iteration and strong prompt fidelity for illustration and photorealistic outputs.

From

$0.03/张

300+ 模型，即刻开启，

探索全部模型

Qwen Image 2.0 Text-to-Image API by Alibaba

输入

输出

参数

代码示例

安装

认证

HTTP 请求头

提交请求

提交请求

请求体

响应

检查状态

轮询示例

状态值

完成响应

上传文件

上传示例

响应

Input Schema

请求体示例

Output Schema

响应示例

Atlas Cloud Skills

支持的客户端

安装

设置 API Key

功能

MCP Server

支持的客户端

安装

配置

可用工具

API Schema

请登录以查看请求历史

Qwen Image 2.0 Text-to-Image

Why Choose This?

Parameters

How to Use

Best Use Cases

Pro Tips

Notes

Related Models

探索类似模型

Wan-2.7 Pro Image-to-image

Wan-2.7 Pro Text-to-image

Wan-2.7 Image-to-image

Wan-2.7 Text-to-image

300+ 模型，即刻开启，

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