Alibaba Wan 2.5 Text To Image

Alibaba Wan 2.5 Text To Image

Playground

WAN 2.5 Text-to-Image turns text prompts into AI-generated images with the WAN 2.5 model for on-demand image creation. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

WAN 2.5 is a cutting-edge text-to-image model on Cloud’s DashScope. It generates high-quality, detailed images directly from text prompts and supports multiple output resolutions.

What makes it stand out?

• High Fidelity: Wan 2.5 produces crisp, detailed images that capture complex scene descriptions and artistic styles.
• Creative Flexibility: From product design mockups to character art, Wan 2.5 supports diverse use cases and genres.
• Multiple Styles & Formats: Choose from photo-realistic, anime, sketch, or artistic rendering modes—adaptable to your creative vision.
• Customizable Size: Easily adjust width and height with simple sliders. Set the exact dimensions you need.

Designed For

• Design teams: Quick iterations on visuals, product concepts, and campaign mockups.
• Content creators: Generate unique visuals for blogs, social posts, and digital branding.
• Storytellers & artists: Visualize characters, scenes, and worlds from simple text prompts.
• Enterprises: Efficiently produce consistent visuals across marketing, training, and documentation.

Pricing

• Every image is just cost $0.03!!

Billing Rules

• Minimum charge: 1 image.
• Total cost = number of images × price per resolution.

How to Use

1. Write your prompt.
2. Submit your request.
3. Preview and download the generated image.

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",
  "enable_prompt_expansion": false,
  "seed": -1
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/alibaba/wan-2.5/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