Z-Image-Base is a 6 billion-parameter text-to-image model with full CFG support. Supports fine-tuning capabilities for maximum control over image generation. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
待機中

$0.011回あたり·~100 / $1

Artistic double exposure portrait. Silhouette of a serene man profile combined with a misty pine forest and flying birds. The image is high-key photography, mostly white background with soft grey and muted green tones. Dreamy, ethereal, surreal composition. The tree branches seamlessly merge with his hair and beard. Fine art photography, minimalist, sharp focus on the eye

A cinematic portrait of a charismatic man in his 40s, sitting in a dimly lit jazz club. Dramatic Rembrandt lighting casts strong shadows across his face, highlighting his rugged features. Volumetric cigarette smoke hangs in the air, catching golden light rays. A warm glow from behind illuminates the edges of his ears (subsurface scattering effect). Moody atmosphere, shallow depth of field, shot on Kodak Portra 800 film, rich colors, masterpiece.

A high-fashion editorial portrait from a magazine. A striking model with unique facial features poses outdoors in a textured tweed jacket. Film photography aesthetic, natural analog grain, muted but rich color palette. The focus is razor-sharp on her eyes, with a beautiful, soft bokeh background of an autumnal street. Elegant, sophisticated, natural pose, detailed fabric texture, Hasselblad medium format camera.
Z-Image Base is a 6-billion parameter text-to-image model from Tongyi-MAI that generates photorealistic images with optional reference image guidance. Provide a text prompt alone, or add a reference image to guide the composition, style, or subject — all at an incredibly affordable price.
Reference image guidance Optionally provide a reference image to influence the generated output's composition, style, or subject matter.
Flexible output sizing Customize width and height up to 1024px for any aspect ratio you need.
Strength control Fine-tune how much the reference image influences the output with the strength parameter.
Prompt Enhancer Built-in tool to automatically improve your prompts for better results.
Ultra-affordable Just $0.01 per image — perfect for high-volume generation and experimentation.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the image you want to generate |
| image | No | Reference image to guide generation (upload or URL) |
| size | No | Preset size options |
| width | No | Output width in pixels (default: 1024) |
| height | No | Output height in pixels (default: 1024) |
| strength | No | How much the reference image influences output, 0-1 (default: 0.6) |
| seed | No | Random seed for reproducibility (default: -1 for random) |
| output_format | No | Output format: jpeg, png (default: jpeg) |
| enable_sync_mode | No | API only: wait for result before returning response |
| Output | Cost |
|---|---|
| Per image | $0.01 |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/z-image/base with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Z Image Base below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1024*1024",
"strength": 0.6,
"seed": -1,
"output_format": "jpeg"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/z-image/base" \
-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/wavespeed-ai/z-image/base";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1024*1024",
"strength": 0.6,
"seed": -1,
"output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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",
"strength": 0.6,
"seed": -1,
"output_format": "jpeg"
}
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/wavespeed-ai/z-image/base", 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)Z Image Base is a WaveSpeedAI model for image generation, exposed as a REST API on WaveSpeedAI. Z-Image-Base is a 6 billion-parameter text-to-image model with full CFG support. Supports fine-tuning capabilities for maximum control over image generation. 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/wavespeed-ai/z-image-base.
Z Image Base starts at $0.010 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`, `image`, `size`, `seed`, `enable_base64_output`, `enable_sync_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/wavespeed-ai/z-image-base.
Median end-to-end generation time on WaveSpeedAI is around 11 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 (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.