by lovis93
Open source · 37k downloads · 8 likes
FLUX.1 [dev] is an advanced language model specialized in generating images from text descriptions, featuring 12 billion parameters. Through its *rectified flow transformer* architecture and guidance distillation training, it delivers high-quality results that rival proprietary solutions while remaining accessible for research and creative applications. The model stands out for its efficiency, precise adherence to instructions, and openness, enabling artists and developers to explore new creative methods. It can be used for personal, scientific, or commercial purposes, provided that ethical and legal restrictions defined by its non-commercial license are respected. FLUX.1 [dev] integrates seamlessly into various environments, from cloud APIs to local tools like ComfyUI, offering flexibility tailored to user needs.
![FLUX.1 [dev] Grid](./dev_grid.jpg)
FLUX.1 [dev] 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 [pro].FLUX.1 [dev] more efficient.FLUX.1 [dev] Non-Commercial License.We provide a reference implementation of FLUX.1 [dev], as well as sampling code, in a dedicated github repository.
Developers and creatives looking to build on top of FLUX.1 [dev] 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 [dev] is also available in Comfy UI for local inference with a node-based workflow.
To use FLUX.1 [dev] 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-dev", 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,
height=1024,
width=1024,
guidance_scale=3.5,
num_inference_steps=50,
max_sequence_length=512,
generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save("flux-dev.png")
To learn more check out the diffusers documentation
The model and its derivatives may not be used
This model falls under the FLUX.1 [dev] Non-Commercial License.