from openai import OpenAI
import base64
import os
# π₯ Create an OpenAI client, pointed at Muna
openai = OpenAI(
api_key=os.environ["MUNA_API_KEY"],
base_url="https://inference.muna.ai/v1"
)
# π₯ Generate an image
response = openai.images.generate(
model="@black-forest-labs/flux.2-klein-4b",
prompt="A red fox in fresh snow, golden hour",
size="1024x1024"
)
# π Save the image
with open("fox.png", "wb") as f:
f.write(base64.b64decode(response.data[0].b64_json))
import OpenAI from "openai"
import { writeFile } from "node:fs/promises"
// π₯ Create an OpenAI client, pointed at Muna
const openai = new OpenAI({
apiKey: process.env.MUNA_API_KEY,
baseURL: "https://inference.muna.ai/v1"
});
// π₯ Generate an image
const response = await openai.images.generate({
model: "@black-forest-labs/flux.2-klein-4b",
prompt: "A red fox in fresh snow, golden hour",
size: "1024x1024"
});
// π Save the image
await writeFile("fox.png", Buffer.from(response.data[0].b64_json, "base64"));
curl https://inference.muna.ai/v1/images/generations \
-H "Authorization: Bearer $MUNA_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "@black-forest-labs/flux.2-klein-4b",
"prompt": "A red fox in fresh snow, golden hour",
"size": "1024x1024"
}'
{
"created": 1791516000,
"data": [
{ "b64_json": "iVBORw0KGgoAAAANSUhEUgAA..." }
]
}
OpenAI
Generate an Image
POST
/
v1
/
images
/
generations
from openai import OpenAI
import base64
import os
# π₯ Create an OpenAI client, pointed at Muna
openai = OpenAI(
api_key=os.environ["MUNA_API_KEY"],
base_url="https://inference.muna.ai/v1"
)
# π₯ Generate an image
response = openai.images.generate(
model="@black-forest-labs/flux.2-klein-4b",
prompt="A red fox in fresh snow, golden hour",
size="1024x1024"
)
# π Save the image
with open("fox.png", "wb") as f:
f.write(base64.b64decode(response.data[0].b64_json))
import OpenAI from "openai"
import { writeFile } from "node:fs/promises"
// π₯ Create an OpenAI client, pointed at Muna
const openai = new OpenAI({
apiKey: process.env.MUNA_API_KEY,
baseURL: "https://inference.muna.ai/v1"
});
// π₯ Generate an image
const response = await openai.images.generate({
model: "@black-forest-labs/flux.2-klein-4b",
prompt: "A red fox in fresh snow, golden hour",
size: "1024x1024"
});
// π Save the image
await writeFile("fox.png", Buffer.from(response.data[0].b64_json, "base64"));
curl https://inference.muna.ai/v1/images/generations \
-H "Authorization: Bearer $MUNA_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "@black-forest-labs/flux.2-klein-4b",
"prompt": "A red fox in fresh snow, golden hour",
"size": "1024x1024"
}'
{
"created": 1791516000,
"data": [
{ "b64_json": "iVBORw0KGgoAAAANSUhEUgAA..." }
]
}
Generates images from a text prompt.
from openai import OpenAI
import base64
import os
# π₯ Create an OpenAI client, pointed at Muna
openai = OpenAI(
api_key=os.environ["MUNA_API_KEY"],
base_url="https://inference.muna.ai/v1"
)
# π₯ Generate an image
response = openai.images.generate(
model="@black-forest-labs/flux.2-klein-4b",
prompt="A red fox in fresh snow, golden hour",
size="1024x1024"
)
# π Save the image
with open("fox.png", "wb") as f:
f.write(base64.b64decode(response.data[0].b64_json))
import OpenAI from "openai"
import { writeFile } from "node:fs/promises"
// π₯ Create an OpenAI client, pointed at Muna
const openai = new OpenAI({
apiKey: process.env.MUNA_API_KEY,
baseURL: "https://inference.muna.ai/v1"
});
// π₯ Generate an image
const response = await openai.images.generate({
model: "@black-forest-labs/flux.2-klein-4b",
prompt: "A red fox in fresh snow, golden hour",
size: "1024x1024"
});
// π Save the image
await writeFile("fox.png", Buffer.from(response.data[0].b64_json, "base64"));
curl https://inference.muna.ai/v1/images/generations \
-H "Authorization: Bearer $MUNA_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "@black-forest-labs/flux.2-klein-4b",
"prompt": "A red fox in fresh snow, golden hour",
"size": "1024x1024"
}'
{
"created": 1791516000,
"data": [
{ "b64_json": "iVBORw0KGgoAAAANSUhEUgAA..." }
]
}
Body
string
required
Model tag.
string
required
Text prompt describing the image.
integer
Number of images to generate. Defaults to
1.string
Image size:
auto, 256x256, 512x512, 1024x1024, 1536x1024, 1024x1536, 1792x1024, or 1024x1792.
Defaults to auto, which uses the modelβs native size.string
Image format:
png, jpeg, or webp. Defaults to png.integer
Compression level from
0 to 100, for the jpeg and webp formats.string
Background transparency, for models that support it.
Response
integer
required
Unix timestamp, in seconds, when the images were created.