Z-Image Turbo API by Alibaba
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.
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}"
}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。
/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",
"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。
/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。
/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。
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。
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
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。
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
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
API Schema
Schema 不可用Z-Image Turbo - 极速文生图模型
最新阿里巴巴通义万相团队 60 亿参数模型
Z-Image Turbo 是排名第一的开源文生图模型，在 Artificial Analysis Image Arena 上超越了 FLUX.2 [dev]、HunyuanImage 3.0 和 Qwen-Image。由阿里巴巴通义万相团队（独立于 Qwen/Wan 团队）打造，这款 60 亿参数模型通过先进的 Decoupled-DMD 蒸馏技术实现亚秒级生成，同时保持逼真的图像质量。仅需 8 个推理步骤，适配 16GB 显存，为速度关键的生产环境提供专业级结果。
- 仅需 8 个推理步骤（竞品需 20-50 步）
- H800 GPU 上实现亚秒级生成
- 比 Qwen Image 每步快 1.31-1.41 倍
- 适配 16GB 显存（RTX 3060/4090）
- AI Arena 开源模型排名第一
- 中英文双语文本渲染
- 强大的指令遵循能力
- 全方位超越 FLUX.1 [dev] 和 Qwen
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
Z-Image Turbo
通义万相团队
- ⚡ 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
Qwen-Image
通义千问团队
- 🎨 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
Wan 2.5/2.6
通义万相团队
- 🎬 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
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。
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。
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
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
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。
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.
