Overview

FLUX.1 schnell is a high-performance distilled text-to-image model designed for developers who prioritize inference speed and efficiency without sacrificing visual fidelity. Unlike heavier diffusion models, schnell is optimized for rapid generation, typically producing high-quality assets in just a few steps. This makes it an ideal choice for real-time applications, iterative prototyping, and cost-sensitive production environments. It excels at following complex prompts and rendering legible text—a common pain point in image generation. With an Apache-2.0 license, it offers significant flexibility for commercial integration. For developers, this means a streamlined pipeline for integrating generative art into apps via API or local deployment, bridging the gap between high-end latent diffusion quality and the latency requirements of consumer-facing products.

Highlights

Rapid inference speed optimized for real-time application deployment
High adherence to complex prompts and accurate text rendering
Permissive Apache-2.0 license for flexible commercial use
Efficient distilled architecture reducing computational overhead per image

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("black-forest-labs/FLUX.1-schnell")
tokenizer = AutoTokenizer.from_pretrained("black-forest-labs/FLUX.1-schnell")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download black-forest-labs/FLUX.1-schnell

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download black-forest-labs/FLUX.1-schnell config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('black-forest-labs/FLUX.1-schnell')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/black-forest-labs/FLUX.1-schnell

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/black-forest-labs/FLUX.1-schnell

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('black-forest-labs/FLUX.1-schnell')
tokenizer = AutoTokenizer.from_pretrained('black-forest-labs/FLUX.1-schnell')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance

pip install modelscope

CLI Download

Download the full repository

modelscope download --model black-forest-labs/FLUX.1-schnell

Download a single file to a local folder (e.g. README.md into ./dir)

modelscope download --model black-forest-labs/FLUX.1-schnell README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('black-forest-labs/FLUX.1-schnell')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/black-forest-labs/FLUX.1-schnell.git

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/black-forest-labs/FLUX.1-schnell.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'black-forest-labs/FLUX.1-schnell')