FLUX.1 Model: `black-forest-labs/FLUX.1-schnell`

The FLUX model is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions.

We support both the -schnell and -dev versions of the model.

Memory usage

The FLUX model itself is 12 billion parameters (~24GB), and the T5 XXL encoder model it uses requires ~9GB. We support loading the models fully onto the GPU, which allows much faster inference. If you do not have enough memory, try the offloaded (-offloaded or -Offloaded) model types. These will load the model on the CPU but perform computations on the GPU.

Type	Memory requirement	Generation Time (s), A100
Normal	~33GB	9.4
Offloaded	~4GB	92.7

HTTP server

The OpenAI HTTP server provides a compatible way to easily use this implementation. As per the specification, output images can be returned as local paths to images or be encoded to base64.

cargo run --features cuda --release -- --port 1234 diffusion-plain -m black-forest-labs/FLUX.1-schnell -a flux

After this, you can send requests via the HTTP server:

from openai import OpenAI

client = OpenAI(api_key="foobar", base_url="https://localhost:1234/v1/")

result = client.images.generate(
    model="default",
    prompt="A vibrant sunset in the mountains, 4k, high quality.",
    n=1,
)
print(result.data[0].url)

Rust example

use std::time::Instant;

use anyhow::Result;
use mistralrs::{DiffusionLoaderType, DiffusionModelBuilder, ImageGenerationResponseFormat};

#[tokio::main]
async fn main() -> Result<()> {
    let model = DiffusionModelBuilder::new(
        "black-forest-labs/FLUX.1-schnell",
        DiffusionLoaderType::FluxOffloaded,
    )
    .with_logging()
    .build()
    .await?;

    let start = Instant::now();

    let response = model
        .generate_image(
            "A vibrant sunset in the mountains, 4k, high quality.".to_string(),
            ImageGenerationResponseFormat::Url,
        )
        .await?;

    let finished = Instant::now();

    println!(
        "Done! Took {} s. Image saved at: {}",
        finished.duration_since(start).as_secs_f32(),
        response.data[0].url.as_ref().unwrap()
    );

    Ok(())
}

Python example

from mistralrs import (
    Runner,
    Which,
    DiffusionArchitecture,
    ImageGenerationResponseFormat,
)

runner = Runner(
    which=Which.DiffusionPlain(
        model_id="mistralai/Mistral-7B-Instruct-v0.1",
        arch=DiffusionArchitecture.FluxOffloaded,
    ),
)

res = runner.generate_image(
    "A vibrant sunset in the mountains, 4k, high quality.",
    ImageGenerationResponseFormat.Url,
)
print(res.choices[0].url)

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