WAN 2.7 Text-to-Image Pro generates high-quality images up to 4K from text prompts with thinking mode for enhanced image quality. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
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![Lookbook photo of a model wearing a [dark leather bomber jacket], minimalist studio, soft side light, confident pose, magazine cover aesthetic, clean backdrop, subtle film grain, high fashion. With a "Fashion Magazine" book name in art word style at the bottom](https://static.wavespeed.ai/media/images/1783683741268857029_MYXKzyla.webp)
$0.075cho mỗi lần chạy·~13 / $1

two young people eating dessert together, close-up shot, wide angle lens, exaggerated perspective, sitting at an outdoor table, feeding each other with spoons, playful expressions, summer vibe, bright sunlight, pastel umbrellas above, blue sky, casual candid moment, lifestyle photography, vibrant colors, high contrast, natural skin texture, modern editorial style, high detail
![Lookbook photo of a model wearing a [dark leather bomber jacket], minimalist studio, soft side light, confident pose, magazine cover aesthetic, clean backdrop, subtle film grain, high fashion. With a "Fashion Magazine" book name in art word style at the bottom](https://static.wavespeed.ai/media/images/1783683741268857029_MYXKzyla.webp)
Lookbook photo of a model wearing a [dark leather bomber jacket], minimalist studio, soft side light, confident pose, magazine cover aesthetic, clean backdrop, subtle film grain, high fashion. With a "Fashion Magazine" book name in art word style at the bottom
![[High-end Wireless Headphones], centered on pure white background, studio high-key lighting, crisp hard shadow, commercial packshot, 35mm perspective, ultra-sharp details, subtle floor reflection, dust-free, 8k, realistic product photography](https://static.wavespeed.ai/media/images/1783683770880507401_3RBuTgF2.webp)
[High-end Wireless Headphones], centered on pure white background, studio high-key lighting, crisp hard shadow, commercial packshot, 35mm perspective, ultra-sharp details, subtle floor reflection, dust-free, 8k, realistic product photography

cinematic fashion editorial, blonde woman leaning on a glossy red car hood, reflection on surface, golden hour sunlight, dramatic shadows, retro american street, pharmacy sign in background, palm trees, shallow depth of field, high fashion styling, fur detail on shoulder, jewelry and sunglasses on car, confident expression, slightly parted lips, moody atmosphere, film photography look, rich contrast, warm tones, ultra realistic, 35mm lens, editorial photography, vogue style, sharp focus, highly detailed
Wan 2.7 Text-to-Image Pro is the professional tier of text-to-image generation model, supporting output resolutions up to 4K (4096×4096). With built-in thinking mode for enhanced reasoning and custom size control, it delivers higher-fidelity compositions ideal for print-ready assets, large-format displays, and any workflow where resolution and quality are the priority.
Up to 4K resolution output Generate images up to 4096×4096 pixels — ideal for print, large-format displays, and high-DPI screens where standard resolution falls short.
Thinking mode for smarter generation Built-in thinking mode enables the model to reason about prompt intent before generating, producing more coherent compositions and better prompt adherence.
Custom size output Set output width and height directly (512–8192 per dimension) to match banners, thumbnails, posters, or social formats exactly.
Seeded iteration Use a fixed seed to refine style and layout with more repeatable variations.
Prompt Enhancer Built-in tool to automatically improve your text descriptions for richer results.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the image subject, scene, style, lighting, and mood. |
| size | No | Output dimensions (width × height). Range: 512–8192 per dimension. Default: 1024×1024. |
| thinking_mode | No | Enable thinking mode for enhanced reasoning and better image quality. Default: enabled. |
| seed | No | Fixed seed for repeatable iterations. Use -1 for a random seed. |
Just $0.075 per generated image.
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/alibaba/wan-2.7/text-to-image-pro with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Wan 2.7 Text To Image Pro below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1024*1024",
"thinking_mode": true,
"seed": -1
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/alibaba/wan-2.7/text-to-image-pro" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d "$REQUEST_BODY")
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; 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' "$RESPONSE" | jq 'if has("data") then .data else . end')
STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
case "$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
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/alibaba/wan-2.7/text-to-image-pro";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
async function requestJson(url, options = {}) {
const response = await fetch(url, options);
if (!response.ok) throw new Error(await response.text());
return response.json();
}
// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1024*1024",
"thinking_mode": true,
"seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;
// 2. Poll until the prediction finishes.
while (true) {
const resultBody = await requestJson(resultUrl, {
headers: { "Authorization": `Bearer ${apiKey}` },
});
const result = resultBody.data ?? resultBody;
if (result.status === "completed") {
console.log(result.outputs);
break;
}
if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
await new Promise(resolve => setTimeout(resolve, 2000));
}import json
import os
import time
from urllib.request import Request, urlopen
api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1024*1024",
"thinking_mode": True,
"seed": -1
}
def request_json(url, data=None):
request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
with urlopen(request) as response:
return json.load(response)
# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/alibaba/wan-2.7/text-to-image-pro", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"
# 2. Poll until the prediction finishes.
while True:
result_body = request_json(result_url)
result = result_body.get("data", result_body)
status = result.get("status")
if status == "completed":
print(result.get("outputs", []))
break
if status in {"failed", "cancelled", "timeout"}:
raise RuntimeError(result)
if status not in {"created", "processing"}:
raise RuntimeError(f"Unexpected status: {status}")
time.sleep(2)Wan 2.7 Text To Image Pro is a Alibaba model for image generation, exposed as a REST API on WaveSpeedAI. WAN 2.7 Text-to-Image Pro generates high-quality images up to 4K from text prompts with thinking mode for enhanced image quality. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/alibaba/alibaba-wan-2.7-text-to-image-pro.
Wan 2.7 Text To Image Pro starts at $0.075 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `prompt`, `size`, `seed`, `thinking_mode`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/alibaba/alibaba-wan-2.7-text-to-image-pro.
Median end-to-end generation time on WaveSpeedAI is around 35 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (Alibaba). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.