by black-forest-labs
Open source · 743k downloads · 4759 likes
FLUX.1 [schnell] is an AI image-generation model capable of creating images from text descriptions, featuring 12 billion parameters. Through advanced latent adversarial distillation technology, it produces high-quality images in just 1 to 4 steps, rivaling closed proprietary solutions. Freely usable under the Apache 2.0 license, it serves both individuals and businesses for personal, scientific, or commercial applications. Its key strengths lie in its fast execution speed and prompt fidelity, while remaining accessible via APIs or tools like ComfyUI. Ideal for creators, developers, and researchers, it stands out for its versatility and efficiency, while requiring responsible and ethical use.
![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.
FLUX.1 [schnell] can generate high-quality images in only 1 to 4 steps.apache-2.0 licence, the model can be used for personal, scientific, and commercial purposes.We provide a reference implementation of FLUX.1 [schnell], as well as sampling code, in a dedicated github repository.
Developers and creatives looking to build on top of FLUX.1 [schnell] are encouraged to use this as a starting point.
The FLUX.1 models are also available via API from the following sources
FLUX.1 [pro])FLUX.1 [schnell] is also available in Comfy UI for local inference with a node-based workflow.
To use FLUX.1 [schnell] with the 🧨 diffusers python library, first install or upgrade diffusers
pip install -U diffusers
Then you can use FluxPipeline to run the model
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 documentation
The model and its derivatives may not be used