Qwen-Image-Max

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Image Generation

Overview

Image Generation

The Max series of Tongyi Qwen’s image generation model excels across a wide range of generation tasks. Compared with the Plus series, it significantly reduces the “AI-like” feel in generated images, enhancing their realism. It delivers more lifelike material textures for human subjects, finer and more detailed natural textures, and more visually appealing text rendering.

Input

Text

Output

Image

Features

Prefix Completion

Function Calling

Cache

Structured Outputs

Batches

Web Search

Pricing

Image Generation
$0.075Per image

Rate Limits

RPMRequests Per Minute
2
TPMTokens Per Minute
1M

API Reference

Get API Key

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import json
import os
import dashscope
from dashscope import MultiModalConversation

dashscope.base_http_api_url = 'https://[workspace-id].ap-southeast-1.maas.aliyuncs.com/api/v1'

messages = [
    {
        "role": "user",
        "content": [
            {"text": "An elegant and solemn couplet hangs in a hall. The room features tranquil, classic Chinese decor. On the table are several blue and white porcelain items. The left scroll of the couplet reads, "Righteousness is the root of innate knowledge. Humans and machines share the same path and excel in innovative thinking (Chinese: 义本生知人机同道善思新)". The right scroll reads, "Connecting to the cloud imparts wisdom. The cosmos reveals its patterns and inspires lofty ambitions (Chinese: 通云赋智乾坤启数高志远)". The horizontal scroll reads, "Wisdom enlightens Tongyi (Chinese: 智启通义)". The calligraphy is flowing. A Chinese-style painting of Yueyang Tower hangs in the middle."}
        ]
    }
]

# If the environment variable is not set, replace it with your Model Studio API key: api_key="sk-xxx"
api_key = os.getenv("DASHSCOPE_API_KEY")

response = MultiModalConversation.call(
    api_key=api_key,
    model="qwen-image-max",
    messages=messages,
    result_format='message',
    stream=False,
    watermark=False,
    prompt_extend=True,
    negative_prompt='',
    size='1328*1328'
)

if response.status_code == 200:
    print(json.dumps(response, ensure_ascii=False))
else:
    print(f"HTTP status code: {response.status_code}")
    print(f"Error code: {response.code}")
    print(f"Error message: {response.message}")
    print("For more information, see the documentation: https://www.alibabacloud.com/help/en/model-studio/error-code")

import json
import os
import dashscope
from dashscope import MultiModalConversation

dashscope.base_http_api_url = 'https://[workspace-id].ap-southeast-1.maas.aliyuncs.com/api/v1'

messages = [
    {
        "role": "user",
        "content": [
            {"text": "An elegant and solemn couplet hangs in a hall. The room features tranquil, classic Chinese decor. On the table are several blue and white porcelain items. The left scroll of the couplet reads, "Righteousness is the root of innate knowledge. Humans and machines share the same path and excel in innovative thinking (Chinese: 义本生知人机同道善思新)". The right scroll reads, "Connecting to the cloud imparts wisdom. The cosmos reveals its patterns and inspires lofty ambitions (Chinese: 通云赋智乾坤启数高志远)". The horizontal scroll reads, "Wisdom enlightens Tongyi (Chinese: 智启通义)". The calligraphy is flowing. A Chinese-style painting of Yueyang Tower hangs in the middle."}
        ]
    }
]

# If the environment variable is not set, replace it with your Model Studio API key: api_key="sk-xxx"
api_key = os.getenv("DASHSCOPE_API_KEY")

response = MultiModalConversation.call(
    api_key=api_key,
    model="qwen-image-max",
    messages=messages,
    result_format='message',
    stream=False,
    watermark=False,
    prompt_extend=True,
    negative_prompt='',
    size='1328*1328'
)

if response.status_code == 200:
    print(json.dumps(response, ensure_ascii=False))
else:
    print(f"HTTP status code: {response.status_code}")
    print(f"Error code: {response.code}")
    print(f"Error message: {response.message}")
    print("For more information, see the documentation: https://www.alibabacloud.com/help/en/model-studio/error-code")