Black Forest Labs' distilled 9 billion parameter image generation model with sub-second inference and multi-reference support
flux-2-klein-9b API OpenAI-compatible
Base URL
https://api.lumenfall.ai/openai/v1
Model
flux.2-klein-9b
Code Examples
Text to Image
/v1/images/generationscurl -X POST \
https://api.lumenfall.ai/openai/v1/images/generations \
-H "Authorization: Bearer $LUMENFALL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "flux.2-klein-9b",
"prompt": "",
"size": "1024x1024"
}'
# Response:
# { "created": 1234567890, "data": [{ "url": "https://...", "revised_prompt": "..." }] }
import OpenAI from 'openai';
const client = new OpenAI({
apiKey: 'YOUR_API_KEY',
baseURL: 'https://api.lumenfall.ai/openai/v1'
});
const response = await client.images.generate({
model: 'flux.2-klein-9b',
prompt: '',
size: '1024x1024'
});
// { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }
console.log(response.data[0].url);
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://api.lumenfall.ai/openai/v1"
)
response = client.images.generate(
model="flux.2-klein-9b",
prompt="",
size="1024x1024"
)
# { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }
print(response.data[0].url)
Image Edit
/v1/images/editsParameter Normalization
How we handle parameters across different providers
Not every provider speaks the same language. When you send a parameter, we handle it in one of four ways depending on what the model supports:
| Behavior | What happens | Example |
|---|---|---|
passthrough |
Sent as-is to the provider | style, quality |
renamed |
Same value, mapped to the field name the provider expects | prompt |
converted |
Transformed to the provider's native format | size |
emulated |
Works even if the provider has no concept of it | n, response_format |
Parameters we don't recognize pass straight through to the upstream API, so provider-specific options still work.