z-image/turbo

文生图

TURBO

Z-Image Turbo API by Alibaba

z-image/turbo

Turbo

Z-Image-Turbo is a 6 billion parameter text-to-image model that generates photorealistic images in sub-second time. 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 $0.01。$10 可运行约 1000 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": "z-image/turbo",
    "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。

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。

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

HTTP 请求头

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": "z-image/turbo",
    "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']}")

响应

{
  "code": 200,
  "data": {
    "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

{
  "model": "z-image/turbo"
}

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

{
  "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

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。

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

npx -y atlascloud-mcp

配置

将以下配置添加到您的 IDE 的 MCP 设置文件中。

{
  "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

加载中...

Z-Image Turbo - 极速文生图模型

阿里巴巴战略模型矩阵

阿里巴巴提供三大专业 AI 图像生成系统，各自针对不同应用场景优化

速度冠军

Z-Image Turbo

通义万相团队

Best For: 速度关键的生产工作负载

⚡ 最快：8 步推理，亚秒生成
🏆 开源模型排名第一
💰 最具性价比（$0.005/张）
🎯 快速迭代优化

质量之王

Qwen-Image

通义千问团队

Best For: 最高质量的最终渲染

🎨 无与伦比的真实感和皮肤纹理
💡 卓越的光照交互效果
⏱️ 较慢（20秒 vs Z-Image 的 5-10秒）
🎯 适合高端制作工作

多功能专家

Wan 2.5/2.6

通义万相团队

Best For: 多媒体多样性

🎬 文生视频 + 图生视频
📹 多分辨率支持（480P-720P）
🔄 音视频同步
🎯 跨模态内容生成

Key Insight: Z-Image Turbo 比 Qwen-Image 每步快 1.31-1.41 倍，非常适合需要快速生成的应用场景。虽然 Qwen-Image 在最终渲染的真实感方面略胜一筹，但 Z-Image Turbo 在生产环境中提供了速度和质量的最佳平衡。

技术亮点

性能

S3-DiT 架构

采用单流扩散 Transformer（S3-DiT）架构，统一处理各种条件输入。这种 60 亿参数设计在不增加大模型计算开销的情况下实现专业级结果，同时保持最先进的质量。

速度

Decoupled-DMD 蒸馏

先进的蒸馏算法配合 CFG 增强和分布匹配机制，实现 8 步推理（竞品需 20-50 步）。在 H800 GPU 上实现亚秒级生成，在消费级 RTX 3060/4090（16GB 显存）上流畅运行。

质量

领先的开源性能

在 Artificial Analysis Image Arena 上排名第一的开源模型，超越 FLUX.2 [dev]、HunyuanImage 3.0 和 Qwen-Image。擅长中英文双语文本渲染、逼真图像生成和强大的指令遵循。采用 Apache 2.0 许可证，允许商业使用。

完美适用于

🎨

数字艺术创作

📸

产品摄影

📊

营销素材

🎬

概念设计

📱

社交媒体内容

🖼️

图库摄影

🎮

游戏资产

✨

创意原型设计

为什么选择 Z-Image Turbo

⚡

即时生成

亚秒级生成，零冷启动延迟。立即获得您的图像，无需任何等待。

💰

高性价比

实惠的价格，每张图片仅需 $0.005。轻松扩展您的创意项目，无需担心预算。

🔌

开箱即用的 API

简单的 REST API 集成。通过我们完善的文档，几分钟内即可开始生成图像。

技术规格

模型架构60 亿参数

推理步骤8 NFEs（函数评估次数）

生成速度H800 亚秒级，消费级 GPU 5-10 秒

显存要求16GB（兼容 RTX 3060/4090）

架构单流扩散 Transformer（S3-DiT）

蒸馏方法Decoupled-DMD 配合 CFG 增强

许可证Apache 2.0（允许商业使用）

排名Artificial Analysis Arena 开源第一

价格每张图片 $0.005

立即开始使用 Z-Image Turbo

体验极速、逼真的图像生成。无需设置，调用我们的 API 即可开始创作。

零冷启动 - 即时生成

实惠价格 - 每张 $0.005

专业级质量结果

Z-Image-Turbo — 6B-parameter, ultra-fast text-to-image

Z-Image-Turbo is a 6B-parameter text-to-image model from Tongyi-MAI, engineered for production workloads where latency and throughput really matter. It uses only 8 sampling steps to render a full image, achieving sub-second latency on data-center GPUs and running comfortably on many 16 GB VRAM consumer cards.

Ultra-fast generation with production-ready quality

Where many diffusion models need dozens of steps, Z-Image-Turbo is aggressively optimised around an 8-step sampler. That keeps inference extremely fast while still delivering photorealistic images and reliable on-image text, making it a strong fit for interactive products, dashboards, and large-scale backends—not just offline batch jobs.

Why it looks so good?

Photorealistic output at speed Generates high-fidelity, realistic images that work for product photos, hero banners, and UI visuals without multi-second waits.
Bilingual prompts and text Understands prompts in English and Chinese, and can render multilingual text directly in the image—helpful for cross-market campaigns, posters, and screenshots.
Low-latency, low-step design Only 8 function evaluations per image deliver extremely low latency, ideal for chatbots, configuration tools, design assistants, and any “click → image” experience.
Friendly VRAM footprint Runs well in 16 GB VRAM environments, reducing hardware costs and making local or edge deployments more realistic.
Scales for bulk generation Its efficiency makes large jobs—catalogues, continuous feed images, or auto-generated thumbnails—practical without blowing up compute budgets.
Reproducible generations A controllable seed parameter lets you recreate a previous image or generate small, controlled variations for brand safety and experimentation.

How to use

prompt – natural-language description of the scene, style, and any on-image text (English or Chinese).
size (width / height) – choose the output resolution; supports square and rectangular images up to high resolutions (for example, 1536 × 1536).
seed – set to -1 for random results, or use a fixed integer to make outputs reproducible.

Pricing

Simple per-image billing:

Without prompt rewriting (prompt_extend=false): $0.015 per generated image
With prompt rewriting (prompt_extend=true): $0.03 per generated image

Try more models and see their difference!

Nano Banana Pro – Text-to-Image – Google’s Nano Banana Pro (Gemini 3.0 Pro Image family) delivers high-quality multi-image generation with extremely low cost per image, ideal for large-scale applications.
Seedream V4 – Text-to-Image – ByteDance’s high-resolution text-to-image model with rich detail and diverse styles, well suited for creative illustration and commercial visuals.
FLUX.2 [dev] – Text-to-Image – A lightweight FLUX.2-based base model hosted by AtlasCloud, optimised for efficient inference and LoRA-friendly training.

Paper

Tongyi-MAI/Z-Image-Turbo

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Midjourney V8.1 Text-to-Image

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Hunyuan 3D Rapid Image-to-3D

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IMAGE-TO-3D

From

$0.02/张

一个 API，畅享全模态 AI。

探索全部模型

Z-Image Turbo 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

Z-Image Turbo - 极速文生图模型

阿里巴巴战略模型矩阵

Z-Image Turbo

Qwen-Image

Wan 2.5/2.6

技术亮点

完美适用于

为什么选择 Z-Image Turbo

即时生成

高性价比

开箱即用的 API

技术规格

立即开始使用 Z-Image Turbo

Z-Image-Turbo — 6B-parameter, ultra-fast text-to-image

Ultra-fast generation with production-ready quality

Why it looks so good?

How to use

Pricing

Try more models and see their difference!

Paper

探索类似模型

Openai GPT Image 2 Text-to-Image

Openai GPT Image 2 Edit

GPT Image 2 Developer Edit

GPT Image 2 Developer Text-to-Image

MAI-Image-2.5-Flash Text-to-image

MAI-Image-2.5 Edit

MAI-Image-2.5 Text-to-image

Midjourney V8.1 Remove Background

Midjourney V8.1 Style Transfer

Midjourney V8.1 Blend

Midjourney V8.1 Image-to-Image

Seed3D 2.0 Image-to-3D

Midjourney V8.1 Text-to-Image

Hunyuan 3D Rapid Image-to-3D

Hunyuan 3D Rapid Text-to-3D

Hunyuan 3D Pro Image-to-3D

一个 API，畅享全模态 AI。

Join our Discord community

输入

输出

参数

代码示例

安装

认证

HTTP 请求头