GLM-Image Prompt Guide: Mastering Text-to-Image Generation

When to Choose GLM-Image

Text rendering has historically been the weak point of diffusion models. Posters emerge with garbled letterforms and infographics contain nonsensical labels. If your use case involves legible text, multilingual typography, or information-dense visuals, GLM-Image is purpose-built for these scenarios.

GLM-Image addresses this through a hybrid autoregressive-diffusion architecture. The model pairs a 9-billion parameter autoregressive generator, initialized from GLM-4-9B, with a 7-billion parameter diffusion decoder built on a single-stream DiT architecture.¹ The autoregressive component constructs a semantic encoding of your prompt before the diffusion decoder translates that understanding into visual output. On the CVTG-2K benchmark for multi-region text accuracy, GLM-Image achieves 91.16% average word accuracy, outperforming GPT Image 1 (85.69%) and FLUX.1 Dev (49.65%).²

For photorealistic imagery without text requirements, FLUX models remain faster. For maximum text accuracy with speed constraints, consider the tradeoffs in the decision framework below.

Prompt Structure

Effective prompts follow a hierarchical pattern that aligns with how the model processes information:

Subject definition: the primary element and its core characteristics
Visual details: textures, colors, spatial relationships
Style specifications: artistic approach and aesthetic references
Technical requirements: typography, text content, precision elements

Basic: "A coffee shop sign"

Optimized: "A vintage wooden coffee shop sign with 'The Daily Grind' in elegant serif typography, weathered texture with peeling paint, hanging from wrought iron brackets, warm afternoon lighting, photorealistic style"

The optimized version provides semantic anchors for the autoregressive component while giving the diffusion decoder concrete visual targets.

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Text Rendering

GLM-Image incorporates a Glyph-ByT5 encoder that performs character-level encoding for rendered text regions, enabling precise typography control. When specifying text:

Specify font characteristics: "bold sans-serif capitals" rather than "stylish text"
Define spatial placement: "prominently displayed on the top third"
Indicate text hierarchy: "headline in large brushstroke lettering, subtitle in clean lowercase"

For product packaging, a prompt like "Product packaging with 'ORGANIC' prominently displayed on the top third, ingredient list on the left panel, barcode bottom right" produces professional results because each text element has explicit positioning.

Parameter Reference

Parameter	Default	Range	Recommended Use
num_inference_steps	30	10-100	10-20 for drafts, 40-60 for production
guidance_scale	1.5	1.0-7.0	1.0-2.0 creative, 2.5-4.0 balanced, 5.0+ technical
image_size	square_hd	preset or custom	1280px minimum for text-heavy content
num_images	1	1-4	Batch variations in single request
enable_prompt_expansion	false	true/false	Enable for conceptual prompts only

GLM-Image uses a default guidance scale of 1.5, intentionally lower than many diffusion models. This reflects the strong semantic understanding from the autoregressive component. Use 2.5-4.0 for balanced control on most production work, and reserve 5.0+ only for strict adherence scenarios where text accuracy is paramount.

For text-heavy content, use at least 1280 pixels on the shortest dimension. The model requires dimensions divisible by 32.

Performance Considerations

Due to the hybrid architecture, GLM-Image has longer generation times than pure diffusion models. The autoregressive component adds computational overhead compared to pure diffusion approaches. For latency-sensitive applications, reduce inference steps to 20-25 for drafts while accepting minor quality tradeoffs.

API Integration

A minimal implementation with recommended parameters for poster generation:

const result = await fal.subscribe("fal-ai/glm-image", {
  input: {
    prompt:
      "Movie poster: 'THE LAST ALGORITHM' in bold chrome letters at top, tagline 'When code becomes conscious' below, glowing neural network forming human face, dark blue and purple, cinematic lighting",
    num_inference_steps: 50,
    guidance_scale: 3.5,
    image_size: "portrait_16_9",
  },
});

When generating variations, use the num_images parameter (1-4 per request) rather than separate API calls. The sync_mode parameter controls output format: false (default) returns URLs for asynchronous workflows, true returns base64 data URIs.

Troubleshooting

If text appears garbled or characters render incorrectly:

Increase guidance_scale above 2.5 for typography-heavy prompts
Ensure text content is isolated in the prompt with explicit font descriptions
Verify resolution is at least 1280px on the shortest dimension
Try isolating problematic text elements into separate prompt phrases

If overall image quality is poor but text is correct, increase num_inference_steps to 50-60 for production renders.

Common Mistakes

Overloading prompts with more than 3-4 disparate concepts dilutes focus
Using guidance_scale below 2.0 for text-heavy designs reduces typography accuracy
Defaulting to maximum resolution without sufficient prompt detail creates artifacts
Using identical seeds without refining problematic prompts produces repeated failures

Example Prompts

Educational Infographic

"Clean infographic explaining the water cycle: circular diagram with four labeled stages including evaporation, condensation, precipitation, and collection, clear sans-serif text, simple icons, arrows showing flow direction, blue gradient color-coding, minimal modern design, white background"

Parameters: num_inference_steps: 40, guidance_scale: 4.0, image_size: square_hd

Product Packaging

"Organic tea packaging: kraft paper box with 'MOUNTAIN MIST' in elegant serif font, 'Green Tea' subtitle, misty mountain watercolor illustration, ingredients list on side panel, 'USDA Organic' badge, earthy palette with sage green and cream, realistic product photography lighting"

Parameters: num_inference_steps: 55, guidance_scale: 3.0, image_size: portrait_4_3

Image-to-Image Capabilities

GLM-Image supports editing, style transfer, and identity-preserving generation through its image-to-image endpoint. These tasks use both semantic-VQ tokens and VAE latents from reference images as conditioning inputs, employing block-causal attention to preserve high-frequency details while allowing controlled modifications. For editing workflows, provide clear transformation instructions alongside the reference image.

Building Effective Patterns

GLM-Image treats prompts as structured information rather than vague suggestions. Start with clear prompts defining your primary subject and text requirements. Layer in style, lighting, and technical specifications progressively. Change one variable at a time when iterating, and document effective patterns for your specific use cases.

GLM-Image Prompt Guide: Mastering Text-to-Image Generation with Precision