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https://github.com/black-forest-labs/flux

Official inference repo for FLUX.1 models
https://github.com/black-forest-labs/flux

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Official inference repo for FLUX.1 models

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README 

        
# FLUX

by Black Forest Labs: https://blackforestlabs.ai



![grid](assets/grid.jpg)



This repo contains minimal inference code to run text-to-image and image-to-image with our Flux latent rectified flow transformers.



### Inference partners



We are happy to partner with [Replicate](https://replicate.com/) and [FAL](https://fal.ai/). You can sample our models using their services.

Below we list relevant links.



Replicate:

- https://replicate.com/collections/flux

- https://replicate.com/black-forest-labs/flux-pro

- https://replicate.com/black-forest-labs/flux-dev

- https://replicate.com/black-forest-labs/flux-schnell



FAL:

- https://fal.ai/models/fal-ai/flux-pro

- https://fal.ai/models/fal-ai/flux/dev

- https://fal.ai/models/fal-ai/flux/schnell



## Local installation



```bash

cd $HOME && git clone https://github.com/black-forest-labs/flux

cd $HOME/flux

python3.10 -m venv .venv

source .venv/bin/activate

pip install -e '.[all]'

```



### Models



We are offering three models:

- `FLUX.1 [pro]` the base model, available via API

- `FLUX.1 [dev]` guidance-distilled variant

- `FLUX.1 [schnell]` guidance and step-distilled variant



| Name   | HuggingFace repo   | License    | md5sum    |

|-------------|-------------|-------------|-------------|

| `FLUX.1 [schnell]` | https://huggingface.co/black-forest-labs/FLUX.1-schnell | [apache-2.0](model_licenses/LICENSE-FLUX1-schnell) | a9e1e277b9b16add186f38e3f5a34044 |

| `FLUX.1 [dev]` | https://huggingface.co/black-forest-labs/FLUX.1-dev| [FLUX.1-dev Non-Commercial License](model_licenses/LICENSE-FLUX1-dev) | a6bd8c16dfc23db6aee2f63a2eba78c0  |

| `FLUX.1 [pro]` | Only available in our API. |



The weights of the autoencoder are also released under [apache-2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) and can be found in either of the two HuggingFace repos above. They are the same for both models.



## Usage



The weights will be downloaded automatically from HuggingFace once you start one of the demos. To download `FLUX.1 [dev]`, you will need to be logged in, see [here](https://huggingface.co/docs/huggingface_hub/guides/cli#huggingface-cli-login).

If you have downloaded the model weights manually, you can specify the downloaded paths via environment-variables:

```bash

export FLUX_SCHNELL=

export FLUX_DEV=

export AE=

```



For interactive sampling run

```bash

python -m flux --name  --loop

```

Or to generate a single sample run

```bash

python -m flux --name  \

  --height  --width  \

  --prompt ""

```



We also provide a streamlit demo that does both text-to-image and image-to-image. The demo can be run via

```bash

streamlit run demo_st.py

```



We also offer a Gradio-based demo for an interactive experience. To run the Gradio demo:

```bash

python demo_gr.py --name flux-schnell --device cuda

```



Options:

- `--name`: Choose the model to use (options: "flux-schnell", "flux-dev")

- `--device`: Specify the device to use (default: "cuda" if available, otherwise "cpu")

- `--offload`: Offload model to CPU when not in use

- `--share`: Create a public link to your demo



To run the demo with the dev model and create a public link:



```bash

python -m demo_gr.py --name flux-dev --share

```



## Diffusers integration



`FLUX.1 [schnell]` and `FLUX.1 [dev]` are integrated with the [🧨 diffusers](https://github.com/huggingface/diffusers) library. To use it with diffusers, install it:



```shell

pip install git+https://github.com/huggingface/diffusers.git

```



Then you can use `FluxPipeline` to run the model



```python

import torch

from diffusers import FluxPipeline



model_id = "black-forest-labs/FLUX.1-schnell" #you can also use `black-forest-labs/FLUX.1-dev`



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"

seed = 42

image = pipe(

    prompt,

    output_type="pil",

    num_inference_steps=4, #use a larger number if you are using [dev]

    generator=torch.Generator("cpu").manual_seed(seed)

).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



## API usage



Our API offers access to the pro model. It is documented here:

[docs.bfl.ml](https://docs.bfl.ml/).



In this repository we also offer an easy python interface. To use this, you

first need to register with the API on [api.bfl.ml](https://api.bfl.ml/), and

create a new API key.



To use the API key either run `export BFL_API_KEY=` or provide

it via the `api_key=` parameter. Is is also expected that you

have installed the package as above.



Usage from python:

```python

from flux.api import ImageRequest



# this will create an api request directly but not block until the generation is finished

request = ImageRequest("A beautiful beach")

# or: request = ImageRequest("A beautiful beach", api_key="your_key_here")



# any of the following will block until the generation is finished

request.url

# -> https:<...>/sample.jpg

request.bytes

# -> b"..." bytes for the generated image

request.save("outputs/api.jpg")

# saves the sample to local storage

request.image

# -> a PIL image

```



Usage from the command line:

```bash

$ python -m flux.api --prompt="A beautiful beach" url

https:<...>/sample.jpg



# generate and save the result

$ python -m flux.api --prompt="A beautiful beach" save outputs/api



# open the image directly

$ python -m flux.api --prompt="A beautiful beach" image show

```