GLM-Image User Guide: Production-Ready Integration

What GLM-Image Does Differently

GLM-Image generates images with accurate embedded text, a capability where pure diffusion models consistently struggle. The model combines a 9B-parameter autoregressive generator (initialized from GLM-4-9B) with a 7B-parameter diffusion decoder using a single-stream DiT architecture. The autoregressive component handles semantic understanding and text layout, while the diffusion decoder synthesizes visual details.

This hybrid approach addresses a documented limitation in text-to-image generation. Diffusion models face challenges with long-range spatial dependencies required for coherent text rendering¹. GLM-Image's architecture overcomes this by using the autoregressive component for global composition before passing to the diffusion decoder for visual refinement. The practical result: you can request specific text in images, such as poster headlines or product labels, and receive accurate outputs without extensive prompt engineering.

API Setup and Authentication

Install the fal client library. Note that @fal-ai/serverless-client has been deprecated in favor of @fal-ai/client.

For Python:

pip install fal-client

For JavaScript/TypeScript:

npm install --save @fal-ai/client

Configure authentication through environment variables:

export FAL_KEY="your-api-key-here"

The GLM-Image endpoints are fal-ai/glm-image for text-to-image and fal-ai/glm-image/image-to-image for image editing workflows.

Basic Integration

Here is a minimal Python implementation:

import fal_client

result = fal_client.subscribe(
    "fal-ai/glm-image",
    arguments={
        "prompt": "A conference poster with the headline 'AI Summit 2026' in bold sans-serif",
        "image_size": "landscape_16_9",
        "num_inference_steps": 30
    }
)

print(result['images'][0]['url'])

For JavaScript:

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/glm-image", {
  input: {
    prompt:
      "A conference poster with the headline 'AI Summit 2026' in bold sans-serif",
    image_size: "landscape_16_9",
    num_inference_steps: 30,
  },
});

console.log(result.data.images[0].url);

The subscribe method handles job submission, polling, and result retrieval. Generation time varies based on resolution and inference steps.

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Request Parameters

The following parameters are documented in fal's API schema:

Parameter	Type	Default	Description
prompt	string	required	Text description; supports specific text rendering requests
image_size	enum or object	square_hd	Preset sizes or custom {width, height} with values 512-2048, divisible by 32
num_inference_steps	integer	30	Denoising steps; range 10-100
guidance_scale	float	1.5	Prompt adherence; higher values follow prompts more literally
seed	integer	random	Fixed seed ensures reproducible outputs
num_images	integer	1	Batch size (1-4)
enable_safety_checker	boolean	true	NSFW content filtering
output_format	enum	jpeg	jpeg or png
sync_mode	boolean	false	Returns base64 data URI instead of URL
enable_prompt_expansion	boolean	false	LLM enhancement of prompts

Preset image sizes include:

square_hd, square (1:1)
landscape_16_9, landscape_4_3, landscape_3_2, landscape_hd
portrait_16_9, portrait_4_3, portrait_3_2, portrait_hd

The guidance_scale default of 1.5 is lower than typical diffusion models because the autoregressive component provides semantic guidance. The underlying model uses sampling with high temperature (approximately 0.9-0.95 depending on implementation), meaning outputs vary across runs even with identical prompts unless you specify a seed.

Image-to-Image Workflows

For editing and style transfer, use the fal-ai/glm-image/image-to-image endpoint with image_urls:

result = fal_client.subscribe(
    "fal-ai/glm-image/image-to-image",
    arguments={
        "prompt": "Apply watercolor style while preserving the subject",
        "image_urls": ["https://example.com/photo.jpg"],
        "num_inference_steps": 35
    }
)

The endpoint accepts up to 4 reference images. The first image typically serves as the primary subject, with additional images providing style or compositional guidance.

Error Handling

Production applications should handle three failure categories:

Validation errors (invalid parameters): Do not retry
Content policy violations (safety checker triggered): Do not retry
Transient errors (network, capacity): Retry with backoff

import time

def generate_with_retry(prompt, max_retries=3):
    for attempt in range(max_retries):
        try:
            return fal_client.subscribe(
                "fal-ai/glm-image",
                arguments={"prompt": prompt}
            )
        except Exception as e:
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)
            else:
                raise

For production systems, implement proper exception handling based on fal's client SDK error types and log failures to your monitoring system.

Response Handling

GLM-Image returns structured responses:

{
  "images": [
    {
      "url": "https://fal.media/files/...",
      "width": 1280,
      "height": 720,
      "content_type": "image/jpeg"
    }
  ],
  "seed": 42,
  "has_nsfw_concepts": [false],
  "prompt": "Original prompt text"
}

Image URLs expire after 24 hours. Download and store images immediately after generation:

import requests

def save_generated_image(result, filepath):
    image_url = result['images'][0]['url']
    response = requests.get(image_url)
    with open(filepath, 'wb') as f:
        f.write(response.content)

Cost and Performance

GLM-Image pricing on fal is $0.15 per megapixel of output. A 1280x720 image (approximately 0.92 megapixels) costs roughly $0.14.

Optimization approaches:

Use batch generation with num_images up to 4 rather than separate API calls
Choose appropriate image sizes for your use case; smaller outputs cost less
Set sync_mode to true for latency-sensitive applications to receive base64 data directly
Implement caching with prompt and seed combinations for reproducible outputs
Use webhooks for async workflows to eliminate polling overhead

The fal platform handles rate limiting through automatic request queuing.

Production Architecture

For high-volume deployments, consider these patterns:

Queue-based processing: Buffer generation requests through Redis or similar to handle traffic spikes without overwhelming your application
Webhook callbacks: Configure fal webhooks for completion notifications instead of polling, which reduces overhead and connection hold times
Monitoring: Track error rates and generation times; sudden increases in content policy violations may indicate prompt injection attempts
Fallback strategies: Implement graceful degradation when generation fails, whether showing cached alternatives or providing clear retry options

When to Use GLM-Image

GLM-Image excels in specific scenarios where text accuracy matters:

Marketing materials requiring legible headlines, taglines or CTAs
Educational diagrams with labels and annotations
Product mockups showing packaging text or UI elements
Social media graphics with integrated typography
Presentation slides combining visuals with text elements

For general image generation without text requirements, other models may offer faster generation or different aesthetic qualities. GLM-Image's strength is the combination of visual quality with reliable text rendering, a capability that has historically required post-processing or compositing workflows.

The model also supports identity-preserving generation and multi-subject consistency across images, making it suitable for creating series of related visuals that maintain character or style coherence.

Autoregressive approaches can achieve competitive image generation quality when properly scaled². GLM-Image applies this principle with a hybrid architecture optimized for text-rendering accuracy, addressing a documented weakness in pure diffusion approaches.

The complete API documentation at docs.fal.ai covers additional options including queue management, file uploads, and client-side integration patterns.