README.md

---
language:
- en
license: apache-2.0
tags:
- text-to-image
- image-generation
- flux
widget:
- text: Eiffel Tower, Paris, explosion, fallings apart, sunset
  output:
    url: images/example_jh41etfxj.png

---

![FLUX.1 [schnell] Grid](./schnell_grid.jpeg)

`FLUX.1 [schnell]` is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions.
For more information, please read our [blog post](https://blackforestlabs.ai/announcing-black-forest-labs/).

# Key Features
1. Cutting-edge output quality and competitive prompt following, matching the performance of closed source alternatives.
2. Trained using latent adversarial diffusion distillation, `FLUX.1 [schnell]` can generate high-quality images in only 1 to 4 steps.
3. Released under the `apache-2.0` licence, the model can be used for personal, scientific, and commercial purposes.

# Usage
We provide a reference implementation of `FLUX.1 [schnell]`, as well as sampling code, in a dedicated [github repository](https://github.com/black-forest-labs/flux).
Developers and creatives looking to build on top of `FLUX.1 [schnell]` are encouraged to use this as a starting point.

## API Endpoints
The FLUX.1 models are also available via API from the following sources
- [bfl.ml](https://docs.bfl.ml/) (currently `FLUX.1 [pro]`)
- [replicate.com](https://replicate.com/collections/flux)
- [fal.ai](https://fal.ai/models/fal-ai/flux/schnell)
- [mystic.ai](https://www.mystic.ai/black-forest-labs/flux1-schnell)

## ComfyUI
`FLUX.1 [schnell]` is also available in [Comfy UI](https://github.com/comfyanonymous/ComfyUI) for local inference with a node-based workflow.

## Diffusers
To use `FLUX.1 [schnell]` with the 🧨 diffusers python library, first install or upgrade diffusers

```shell
pip install -U diffusers
```

Then you can use `FluxPipeline` to run the model

```python
import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power

prompt = "A cat holding a sign that says hello world"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save("flux-schnell.png")
```

To learn more check out the [diffusers](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux) documentation

---
# Limitations
- This model is not intended or able to provide factual information.
- As a statistical model this checkpoint might amplify existing societal biases.
- The model may fail to generate output that matches the prompts.
- Prompt following is heavily influenced by the prompting-style.

# Out-of-Scope Use
The model and its derivatives may not be used

- In any way that violates any applicable national, federal, state, local or international law or regulation.
- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; including but not limited to the solicitation, creation, acquisition, or dissemination of child exploitative content.
- To generate or disseminate verifiably false information and/or content with the purpose of harming others.
- To generate or disseminate personal identifiable information that can be used to harm an individual.
- To harass, abuse, threaten, stalk, or bully individuals or groups of individuals.
- To create non-consensual nudity or illegal pornographic content.
- For fully automated decision making that adversely impacts an individual's legal rights or otherwise creates or modifies a binding, enforceable obligation.
- Generating or facilitating large-scale disinformation campaigns.