Qwen Image 2512 Text to Image Prompt Guide

Prompting for Professional Results

The December 2025 release of Qwen Image 2512 addresses three persistent weaknesses in open-source text-to-image generation: rendering complex text (especially Chinese characters), creating photorealistic human faces without the distinctive artificial appearance, and producing natural textures in landscapes and materials. This 20B MMDiT model achieved top ranking among open-source models after 10,000 blind comparison rounds on AI Arena, while remaining competitive with closed-source systems.¹

Prompt construction determines the quality differential between amateur outputs and professional results. The Multimodal Diffusion Transformer (MMDiT) architecture processes text and image tokens through bidirectional attention mechanisms, meaning the model weighs prompt information based on position and specificity.² This guide provides the prompting techniques, parameter configurations, and API implementation details needed to integrate Qwen Image 2512 on fal into production applications.

Core Capabilities

Text Rendering Precision: The model handles complex typography, multilingual text (particularly Chinese), and accurate text-image composition. For marketing materials, signage, or any content with embedded text, this capability differentiates Qwen Image 2512 from competing models like FLUX.1 [dev].

Photorealistic Human Generation: The 2512 update reduces the distinctive artificial appearance in faces through improved facial detail rendering, age-appropriate features, and natural skin textures.

Natural Texture Fidelity: Landscapes, water surfaces, animal fur, and material textures render with improved detail and realism.

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Supported Image Sizes

The fal API accepts predefined image size enums or custom dimensions. The model operates at approximately 1.6 megapixels, automatically scaling inputs to match this target.

image_size	Resolution	Use Case
square	1024x1024	Social media, avatars
square_hd	1328x1328	Native resolution, maximum detail
landscape_4_3	1472x1104	Product photography, presentations
landscape_16_9	1664x928	Widescreen, video thumbnails
portrait_4_3	1104x1472	Mobile content, portraits
portrait_16_9	928x1664	Stories, vertical video

For custom dimensions, pass width and height as an object. Native resolution (1328x1328) provides maximum detail but increases generation time by approximately 50% compared to 1024x1024.

Prompt Structure

The model weights information based on position. Front-load your primary subject to ensure it receives the most attention during generation:

Subject, Style, Details, Composition, Lighting

Effective: "Young woman with auburn hair in casual denim jacket, editorial photography style, natural makeup, soft focus background, golden hour lighting, high detail"

Ineffective: "Make me a nice picture of someone pretty with good lighting"

The first prompt provides clear subject hierarchy, specific style direction, and defined lighting. The second lacks actionable specificity.

Parameter Reference

Guidance Scale (Default: 5, Range: 0-20)

Classifier-free guidance controls prompt adherence. Higher values produce outputs matching the prompt more precisely but risk oversaturation; lower values allow creative interpretation.³

Range	Behavior	Use Case
2-4	Creative interpretation	Abstract, artistic styles
5-7	Balanced adherence	Most production use cases
8-10	Strict following	Text rendering, technical work

Inference Steps (Default: 28, Range: 1-50)

15-20 steps: Draft quality, fast iteration
25-30 steps: Production quality
35-45 steps: Maximum quality, complex compositions

Acceleration

none: Full quality, no shortcuts. Use for final renders.
regular: Balanced speed and quality. Default for most workflows.
high: Faster generation with quality trade-offs. Use for iteration.

API Implementation

The fal API supports both synchronous subscription and queue-based workflows. Pricing is $0.02 per megapixel.

import fal_client

result = fal_client.subscribe(
    "fal-ai/qwen-image-2512",
    arguments={
        "prompt": "Professional headshot of 45-year-old executive, navy blazer, neutral gray background, soft studio lighting, natural skin texture",
        "image_size": "square_hd",
        "num_inference_steps": 28,
        "guidance_scale": 5,
        "seed": 42,
        "enable_safety_checker": True
    }
)

image_url = result["images"][0]["url"]

Key Parameters

seed: Integer for reproducible generation. The same seed and prompt produce identical outputs.
negative_prompt: String describing elements to exclude (e.g., "blurry, distorted, watermark").
num_images: Generate multiple images per request (default: 1).
output_format: Options are png, jpeg, or webp.

Safety Checker Behavior

When enable_safety_checker is True (default), blocked content returns an empty images array. Check the response before accessing image URLs in production code.

Practical Examples

Product Photography

Prompt: "Single red rose in clear glass vase on white marble with black and gold veins, harsh directional shadow, high contrast, editorial style, clean negative space"

Parameters: guidance_scale: 6, num_inference_steps: 30, image_size: landscape_4_3

Text-Heavy Compositions

Prompt: "Vintage movie poster, bold red letters spelling 'REVOLUTION' across top third, art deco typography, textured paper background, 1920s aesthetic, ornate decorative borders, sharp text edges"

Parameters: guidance_scale: 7, num_inference_steps: 40, image_size: portrait_4_3

For text rendering, increase guidance scale (6-8) and inference steps (35-45) to improve legibility.

Style Consistency

For multiple images with consistent styling, combine a fixed seed with a style template:

Base style: "editorial photography, natural lighting, muted palette, film grain"

Append subject variations while maintaining seed value for consistent lighting and color treatment across generations.

Negative Prompts

Pass negative prompts as a separate parameter to exclude unwanted elements:

General quality: "blurry, low quality, distorted, deformed, oversaturated, watermark"
Portraits: "smooth skin, airbrushed, doll-like, plastic"
Landscapes: "unnatural colors, HDR artifacts, oversharpened"

Common Mistakes

Contradictory instructions: "Photorealistic oil painting" confuses the model. Choose one primary style.

Vague descriptors: Terms like "beautiful" or "amazing" add noise without direction. Specify what constitutes quality.

Guidance too low for technical work: Product shots and text-heavy designs need guidance scale 5-7 for precision.

Ignoring composition: Without spatial instructions, the model defaults to centered compositions. Specify framing for dynamic results.

Workflow Optimization

Start with a three-tier approach:

Draft prompts at 20 steps, high acceleration
Refine at 28 steps, regular acceleration
Final renders at 35-40 steps, none acceleration

The fal serverless architecture handles scaling automatically. For production integration with webhooks and queue management, see the Model Endpoints documentation.

Qwen Image 2512 Prompt Guide