HiDream-O1-Image Dev (Edit) is the distilled, fast-inference variant of HiDream-O1-Image in edit mode — accepts a source image plus an instruction (and optional extra references for subject-driven personalization) at up to 2K resolution, at half the price of the full model. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle

$0.02per run·~50 / $1

Replace the white blouse and beige trousers with a premium black tailored blazer and matching black trousers. Preserve the model's face, body pose, hairstyle, camera angle, background, and lighting. The new outfit should fit naturally, with realistic fabric folds, seams, and shadows. Keep the image suitable for a high-end fashion e-commerce campaign.
HiDream-O1-Image Dev is the distilled variant of HiDream-O1-Image. The same single native model handles text-to-image, prompt-driven editing, and subject-driven personalization at up to 2K (≈2048×2048) — but tuned for fast, low-step inference so you pay less and wait less per edit. Provide one or more reference images plus an instruction; the model edits while keeping identity, composition, and lighting stable.
seed to recreate exact results.| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text instruction describing the edit you want to make. |
| images | Yes | Reference images for editing or subject-driven personalization. Each entry is a public URL or base64 data URI (PNG, JPEG, JPG, or WebP, up to 50MB, aspect ratio between 1:4 and 4:1). Pass one image for editing, multiple for subject-driven personalization. |
| size | No | Output image size as WIDTH*HEIGHT (default: 2048*2048). The model snaps to the closest supported resolution. |
| output_format | No | Output format: jpeg, png, or webp (default: jpeg). |
| seed | No | Random seed for reproducible generation. |
| enable_sync_mode | No | API only — wait for the result and return it inline. |
| enable_base64_output | No | API only — return the image as a base64 string instead of a URL. |
size (optional) — keep the source aspect ratio or pick a new one.output_format — JPEG for compact files, PNG for lossless, WEBP for balance.seed (optional) — pin a number to reproduce the same edit.Flat rate per edit — half the price of the full HiDream-O1-Image (Edit).
| Output | Cost |
|---|---|
| Per image edit | $0.02 |
| HiDream-O1-Image Dev (Edit) | HiDream-O1-Image (Edit, Full) | |
|---|---|---|
| Speed | Faster (distilled, fewer steps) | Standard |
| Cost / edit | $0.02 | $0.04 |
| Quality ceiling | High | Highest |
| Best for | Batches, drafts, low-cost edits | Final hero edits |
For clean results, specify both the change and what to preserve:
Template:
Keep the same person, pose, and background. Change [target edit]. Keep lighting natural and consistent.
Examples:
For subject-driven personalization, pass several reference images of the same subject (different angles / outfits / lighting) and describe the new scene; the model uses them jointly to keep identity consistent.
seed while iterating so you can compare prompt variants against a fixed baseline.png for lossless edits when output will be re-edited downstream.enable_base64_output and enable_sync_mode options are only available through the API.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/hidream-o1-image-dev/edit with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Hidream O1 Image Dev Edit below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"size": "2048*2048",
"output_format": "jpeg"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/hidream-o1-image-dev/edit" \
-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/hidream-o1-image-dev/edit";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"size": "2048*2048",
"output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"size": "2048*2048",
"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/hidream-o1-image-dev/edit", 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)Hidream O1 Image Dev Edit is a WaveSpeedAI model for image editing, exposed as a REST API on WaveSpeedAI. HiDream-O1-Image Dev (Edit) is the distilled, fast-inference variant of HiDream-O1-Image in edit mode — accepts a source image plus an instruction (and optional extra references for subject-driven personalization) at up to 2K resolution, at half the price of the full model. 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/hidream-o1-image-dev-edit.
Hidream O1 Image Dev Edit starts at $0.020 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`, `images`, `size`, `enable_base64_output`, `enable_sync_mode`, `output_format`. 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/hidream-o1-image-dev-edit.
Median end-to-end generation time on WaveSpeedAI is around 23 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.