AI Image Generation APIs: Complete Comparison of Chinese Models (2026)

August 13, 2026 · 17 min read

Text-to-image generation has matured rapidly in China. Models from Alibaba, ByteDance, Zhipu, and DeepSeek now produce images competitive with Midjourney and DALL-E at a fraction of the cost. This guide compares the leading Chinese image generation APIs with benchmarks, pricing, and production-ready code.

The Chinese Image Generation Landscape

Price Comparison

Quick Start: Generate Images via TokenEase

import requests
import base64

TOKEN = "your_tokenease_api_key"
BASE_URL = "https://tokenease.io/v1"

def generate_image(prompt, model="wanxiang", size="1024x1024", n=1):
    """Generate images through TokenEase unified API"""
    
    response = requests.post(
        f"{BASE_URL}/images/generations",
        headers={"Authorization": f"Bearer {TOKEN}"},
        json={
            "model": model,
            "prompt": prompt,
            "n": n,
            "size": size,
            "response_format": "b64_json"
        }
    )
    
    data = response.json()
    images = []
    for item in data["data"]:
        img_data = base64.b64decode(item["b64_json"])
        images.append(img_data)
    
    return images

# Generate a product photo
images = generate_image(
    prompt="Professional product photo of a minimalist wireless earbuds case on a marble surface, soft natural lighting, commercial photography style",
    model="wanxiang",
    size="1024x1024"
)

# Save to file
with open("product_photo.png", "wb") as f:
    f.write(images[0])

print("Image generated and saved!")

Model-Specific Capabilities

Tongyi Wanxiang (Alibaba)

Best for commercial and product imagery:

# Wanxiang supports style presets
response = requests.post(
    f"{BASE_URL}/images/generations",
    headers={"Authorization": f"Bearer {TOKEN}"},
    json={
        "model": "wanxiang",
        "prompt": "E-commerce product photo, white background, studio lighting",
        "size": "1024x1024",
        "style": "photography",  # photography, illustration, 3d, anime
        "n": 4  # Generate 4 variants
    }
)

Doubao Image (ByteDance)

Optimized for social content and character consistency:

# Doubao supports face reference for consistent characters
response = requests.post(
    f"{BASE_URL}/images/generations",
    headers={"Authorization": f"Bearer {TOKEN}"},
    json={
        "model": "doubao-image",
        "prompt": "A young professional in business attire presenting in a modern office",
        "size": "1024x1024",
        "face_reference": base64.b64encode(open("ref_face.jpg", "rb").read()).decode(),
        "style": "realistic"
    }
)

CogView-3 (Zhipu)

Strongest for artistic and creative applications:

# CogView excels at complex compositions
response = requests.post(
    f"{BASE_URL}/images/generations",
    headers={"Authorization": f"Bearer {TOKEN}"},
    json={
        "model": "cogview-3",
        "prompt": "A cyberpunk cityscape at night with neon signs reflecting in rain puddles, flying vehicles, detailed architecture",
        "size": "1536x1536",
        "quality": "hd"
    }
)

Image Editing and Variation

def edit_image(image_path, mask_path, prompt):
    """Edit specific regions of an image"""
    
    with open(image_path, "rb") as img, open(mask_path, "rb") as mask:
        response = requests.post(
            f"{BASE_URL}/images/edits",
            headers={"Authorization": f"Bearer {TOKEN}"},
            files={
                "image": img,
                "mask": mask
            },
            data={
                "prompt": prompt,
                "model": "wanxiang",
                "size": "1024x1024"
            }
        )
    
    return response.json()

# Example: Change background color
edit_image(
    "product.png",
    "background_mask.png",  # White = edit, Black = keep
    "Replace background with gradient from light blue to white"
)

Batch Generation for E-commerce

import concurrent.futures

def batch_generate_product_images(products, model="wanxiang"):
    """Generate images for multiple products in parallel"""
    
    def generate_for_product(product):
        prompt = f"Professional e-commerce product photo of {product['name']}, {product['style']}, white background, studio lighting, high detail"
        
        response = requests.post(
            f"{BASE_URL}/images/generations",
            headers={"Authorization": f"Bearer {TOKEN}"},
            json={
                "model": model,
                "prompt": prompt,
                "size": "1024x1024",
                "n": 1
            }
        )
        
        return {
            "product_id": product["id"],
            "image_data": response.json()["data"][0]["b64_json"]
        }
    
    with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
        results = list(executor.map(generate_for_product, products))
    
    return results

# Generate for 20 products
products = [
    {"id": 1, "name": "wireless headphones", "style": "minimalist black"},
    {"id": 2, "name": "smart watch", "style": "sleek silver"},
    # ... more products
]

images = batch_generate_product_images(products)
print(f"Generated {len(images)} product images")

Quality Optimization

Prompt Engineering for Images

# Bad prompt
bad = "a picture of a cat"

# Good prompt
good = "A fluffy orange tabby cat sitting on a velvet armchair, golden hour sunlight streaming through a window, shallow depth of field, professional pet photography, 85mm lens"

# Structure: [Subject] + [Action/Setting] + [Lighting] + [Style] + [Technical details]

Negative Prompts

# Exclude unwanted elements
response = requests.post(
    f"{BASE_URL}/images/generations",
    headers={"Authorization": f"Bearer {TOKEN}"},
    json={
        "model": "cogview-3",
        "prompt": "A clean modern kitchen interior",
        "negative_prompt": "cluttered, dirty, people, text, watermark, blurry, low quality",
        "size": "1024x1024"
    }
)

Performance Benchmarks

Quality scores based on human evaluation (n=100) across photorealism, prompt adherence, and aesthetic appeal.

Storage and CDN Integration

import boto3

def generate_and_upload(prompt, s3_bucket, cdn_domain):
    """Generate image and upload to S3 + CloudFront"""
    
    # Generate
    images = generate_image(prompt, model="wanxiang")
    
    # Upload to S3
    s3 = boto3.client("s3")
    key = f"images/{hash(prompt)}.png"
    s3.put_object(
        Bucket=s3_bucket,
        Key=key,
        Body=images[0],
        ContentType="image/png",
        ACL="public-read"
    )
    
    # Return CDN URL
    return f"https://{cdn_domain}/{key}"

# Usage
image_url = generate_and_upload(
    "Product photo of new smartphone",
    s3_bucket="my-ecommerce-images",
    cdn_domain="images.mystore.com"
)
print(f"Image live at: {image_url}")

Use Cases by Industry

Next Steps

Start generating images with TokenEase:

1. Get your free API key ($1 credit = ~150 images)
2. Test different models with your use case
3. Build batch pipelines for e-commerce or content creation
4. Integrate with your CDN for fast delivery

For multimodal applications, combine with our RAG guide for image-to-text and text-to-image workflows.

Last updated: August 2026. Image generation capabilities evolve rapidly.