Z AI Glm Image Text To Image

Z AI Glm Image Text To Image

Playground

Z-AI GLM Image generates high-quality images from text prompts, with enhanced understanding of user descriptions, resulting in images that are more precise and personal. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.

Features

GLM-Image is Z.AI’s powerful text-to-image generation model built on the GLM architecture. It transforms natural language prompts into high-quality images with strong prompt adherence, flexible sizing, and fast generation speed.

Why Choose This?

• Strong prompt understanding Accurately interprets detailed prompts to generate images that match your description with high fidelity.

• Flexible sizing Custom width and height controls allow you to create images for any use case — social media, print, web, or mobile.

• Prompt Enhancer Built-in tool to automatically improve your prompts for better generation results.

• Multiple output formats Export as JPEG for smaller file sizes or PNG for lossless quality.

• Fast generation Optimized for quick turnaround, ideal for rapid ideation and creative iteration.

Parameters

Output Format Options

• jpeg — Smaller file size, good for photos and web use (default)
• png — Lossless quality, supports transparency, best for graphics

Prompt Expansion

When enabled, the model uses an LLM to automatically expand and enhance your prompt for better generation results. This is useful when you have a short or simple prompt and want the model to add more detail.

How to Use

1. Write your prompt — describe the image including subject, style, lighting, and mood.
2. Set size — adjust width and height for your desired dimensions.
3. Set seed — use -1 for random results, or specify a number for reproducibility.
4. Choose output format — jpeg for smaller files, png for lossless quality.
5. Enable prompt expansion (optional) — check this to let LLM enhance your prompt automatically.
6. Run — click Run, preview the result, and iterate if needed.

Pricing

Simple flat-rate pricing regardless of image size or output format.

Best Use Cases

• Social Media Content — Create engaging visuals for posts, stories, and ads.
• Marketing Materials — Generate promotional images and banner graphics.
• Concept Art — Quickly visualize ideas for creative projects.
• Product Visualization — Create mockups and product imagery.
• Presentations — Generate visuals to enhance slides and documents.

Pro Tips

• Be specific in your prompts — include subject, style, lighting, colors, and atmosphere.
• Use the same seed with the same prompt to reproduce identical outputs.
• Start with 1024x1024 for balanced quality, adjust dimensions for specific needs.
• Use JPEG for photos and web content, PNG for graphics with text or transparency needs.
• Enable prompt expansion for short prompts; disable it if you want precise control over the output.

Notes

• Please ensure your prompts comply with content guidelines.
• If an error occurs, review your prompt and try again.

Related Models

• Z.AI CogView-4 — Z.AI’s high-quality text-to-image model with flexible quality modes.
• Qwen Image 2512 — model with exceptional text rendering capabilities.
• FLUX.2 Pro — Flagship-quality generation with cinematic detail.

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result

set -euo pipefail

export WAVESPEED_API_KEY="your-api-key"

REQUEST_BODY=$(cat <<'JSON'
{
  "prompt": "A cinematic ocean wave at sunrise, highly detailed",
  "size": "1024*1024",
  "seed": -1,
  "output_format": "jpeg"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/z-ai/glm-image/text-to-image" \
  -H "Authorization: Bearer ${WAVESPEED_API_KEY}" \
  -H "Content-Type: application/json" \
  -d "${REQUEST_BODY}")

TASK=$(printf '%s' "${SUBMIT_RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "${TASK}" | jq -r '.id // empty')
if [ -z "${PREDICTION_ID}" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "${TASK}" | jq -r '.urls.get // empty')
if [ -z "${RESULT_URL}" ]; then RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/${PREDICTION_ID}/result"; fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body \
    "${RESULT_URL}" \
    -H "Authorization: Bearer ${WAVESPEED_API_KEY}")
  RESULT=$(printf '%s' "${RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
  STATUS=$(printf '%s' "${RESULT}" | jq -r '.status // empty')

  case "${STATUS}" in
    completed) printf '%s\n' "${RESULT}" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "${RESULT}" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "${STATUS}" >&2; exit 1 ;;
  esac
done

Parameters

Task Submission Parameters

Request Parameters

Response Parameters

Result Request Parameters

Result Response Parameters