MiniMax H3 Open Weights Image Edit re-renders a subject from up to 9 reference images into a new scene, outfit or style from a text instruction, preserving identity at 1K or 2K resolution. Loads up to 3 custom LoRA weights per request. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.
Idle

$0.03per run·~33 / $1

The same man from <Picture 1>, preserve the exact same face, hair, stubble, and skin tone. Transform him into a stylish punk mechanic wearing a sleeveless black work shirt, dark utility trousers, heavy boots, silver rings, and grease-stained gloves. Place him leaning over the engine of a classic muscle car inside a neon-lit garage, holding a wrench. Photorealistic still, cinematic automotive editorial, cool rebellious mood.

The same woman from <Picture 1>, same face and braided blonde hair, now wearing a cream knit sweater, standing on a snowy mountain terrace at sunrise holding a steaming mug, soft pink light. Photorealistic still.
Run the open-weights edition of MiniMax H3 as a reference-guided image editor on WaveSpeedAI's own GPU infrastructure, with up to 3 custom LoRA weights per request. Provide 1 to 9 reference images and an instruction; the model re-renders the subject into a new scene, outfit, pose or art style while preserving identity, at 1k or 2k resolution.
LoRA support
Load up to 3 LoRA weights per request, each with its own scale, for custom styles, characters and products.
Strong identity preservation
Faces, hairstyles, accessories and clothing details carry over from the references.
Scene, outfit and style changes
Move a person to a new location, change what they wear, or restyle the image as a painting or animation.
Multi-image references
Combine up to 9 references, addressed in the prompt as <Picture 1>, <Picture 2>, and so on.
Aspect ratio follows your input
By default the output keeps the aspect ratio of the first reference image; override it with aspect_ratio.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | The edit instruction. Refer to references as <Picture 1> .. <Picture N>. |
| images | Yes | 1 to 9 reference image URLs. |
| aspect_ratio | No | Output aspect ratio. Options: 1:1, 1:2, 2:1, 1:3, 3:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 9:21, or 21:9. Defaults to the first reference image's aspect ratio. |
| resolution | No | Output resolution: 1k (default) or 2k. |
| output_format | No | jpeg (default), png or webp. |
| loras | No | Up to 3 LoRA weights. Each item uses {path, scale}, where path is a LoRA file URL. |
| seed | No | Random seed. -1 picks a random seed. |
The same woman from <Picture 1>, now wearing a red leather jacket, standing in a neon-lit Tokyo alley at night. Photorealistic still.1k for iteration, 2k for final assets.Pricing is based on the selected resolution, plus a flat $0.015 per image for LoRA support (already included below).
| Resolution | Cost per image |
|---|---|
| 1k | $0.045 |
| 2k | $0.105 |
scale around 0.8–1.0, then adjust based on how strongly the LoRA affects the result.This is a painting, not a photograph.seed to iterate on wording without changing the composition.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/minimax-h3/image-edit-lora with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Minimax H3 Image Edit Lora below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"aspect_ratio": "1:1",
"resolution": "1k",
"output_format": "jpeg"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/minimax-h3/image-edit-lora" \
-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="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(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' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout|deleted) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
*) sleep 2 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/minimax-h3/image-edit-lora";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"aspect_ratio": "1:1",
"resolution": "1k",
"output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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", "deleted"].includes(result.status)) throw new Error(JSON.stringify(result));
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"
],
"aspect_ratio": "1:1",
"resolution": "1k",
"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/minimax-h3/image-edit-lora", 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 = 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", "deleted"}:
raise RuntimeError(result)
time.sleep(2)Minimax H3 Image Edit Lora is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. MiniMax H3 Open Weights Image Edit re-renders a subject from up to 9 reference images into a new scene, outfit or style from a text instruction, preserving identity at 1K or 2K resolution. Loads up to 3 custom LoRA weights per request. Ready-to-use REST inference API, best performance, no cold starts, 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/minimax-h3-image-edit-lora.
Minimax H3 Image Edit Lora starts at $0.030 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`, `aspect_ratio`, `resolution`, `seed`, `enable_base64_output`. 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/minimax-h3-image-edit-lora.
Sign up for a free WaveSpeedAI account to claim starter credits, copy your API key from /accesskey, then call the endpoint shown in the API tab of the playground. The playground also auto-generates a code sample in Python, JavaScript, or cURL for the parameters you've set.
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.