P-H3-Image by Pruna AI, built on the MiniMax H3 open weights, turns a text prompt into a photorealistic, editorial-quality image, at 1K to 4K resolution. Loads up to 3 custom LoRA weights per request. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.
Idle
/filters:quality(85)/examples/e38fc2cb0f67e386ce16c4b380065fe4/332af2a9-cfc4-44ba-9f99-595a4a92c97c-6736a30b97de.webp)
$0.02per run·~50 / $1
/filters:quality(82)/examples/e38fc2cb0f67e386ce16c4b380065fe4/332af2a9-cfc4-44ba-9f99-595a4a92c97c-6736a30b97de.webp)
Cinematic portrait of a young man with short dark hair and light stubble in a navy wool coat, waiting at a foggy Lisbon tram stop at dawn, warm streetlight, 50mm lens, shallow depth of field, film grain.
P-H3-Image is Pruna AI's image generation and editing model built on the MiniMax H3 open weights. This endpoint turns a text prompt into a photorealistic, editorial-quality image from 1K up to 4K. Load up to 3 custom LoRA weights per request.
Powered by MiniMax H3.
LoRA support
Load up to 3 LoRA weights per request, each with its own scale, for custom styles, characters and products.
Photorealistic by default
Natural skin, light and materials, with strong prompt adherence for documentary, editorial and cinematic looks.
Up to 4K output
Choose 1k, 1.5k, 2k, 3k or 4k; 3k sits closest to the model's sweet spot for fine detail.
Built-in prompt enhancement
Short requests are expanded into detailed prompts automatically, so a one-line idea still renders well.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the image. |
| aspect_ratio | No | Output aspect ratio: 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, 21:9. Defaults to 1:1. |
| resolution | No | Output resolution: 1k (default, ~1 MP), 1.5k (~2.4 MP), 2k (~4 MP), 3k (~9.4 MP) or 4k (~16.8 MP). |
| 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. |
1k–2k for iteration, 3k or 4k 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 | $0.035 |
| 1.5k | $0.055 |
| 2k | $0.075 |
| 3k | $0.115 |
| 4k | $0.175 |
scale around 0.8–1.0, then adjust based on how strongly the LoRA affects the result.3k or 4k for hands, small background figures and fine textures.seed to iterate on wording without changing the composition.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/p-h3-image/text-to-image-lora with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for P H3 Image Text To Image Lora below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "1:1",
"resolution": "1k",
"output_format": "jpeg"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/p-h3-image/text-to-image-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/p-h3-image/text-to-image-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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "1:1",
"resolution": "1k",
"output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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",
"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/p-h3-image/text-to-image-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)P H3 Image Text To Image Lora is a WaveSpeedAI model for image generation, exposed as a REST API on WaveSpeedAI. P-H3-Image by Pruna AI, built on the MiniMax H3 open weights, turns a text prompt into a photorealistic, editorial-quality image, at 1K to 4K 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 Python, JavaScript, and cURL examples for submitting requests and polling results. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/p-h3-image-text-to-image-lora.
P H3 Image Text To Image Lora starts at $0.02 per run or per second. 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`, `aspect_ratio`, `resolution`, `seed`, `loras`, `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/p-h3-image-text-to-image-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). Check the provider's applicable terms and WaveSpeedAI's Terms of Service before commercial use.