FLUX.1 Fill [dev] is a 12 billion parameter rectified flow transformer capable of filling areas in existing images based on a text description.
For more information, please read our blog post.
Key Features
- Cutting-edge output quality, second only to our state-of-the-art model
FLUX.1 Fill [pro]. - Blends impressive prompt following with completing the structure of your source image.
- Trained using guidance distillation, making
FLUX.1 Fill [dev]more efficient. - Open weights to drive new scientific research, and empower artists to develop innovative workflows.
- Generated outputs can be used for personal, scientific, and commercial purposes as described in the
FLUX.1 [dev]Non-Commercial License.
Usage
We provide a reference implementation of FLUX.1 Fill [dev], as well as sampling code, in a dedicated github repository.
Developers and creatives looking to build on top of FLUX.1 Fill [dev] are encouraged to use this as a starting point.
API Endpoints
The FLUX.1 models are also available in our API bfl.ml
Diffusers
To use FLUX.1 Fill [dev] with the 🧨 diffusers python library, first install or upgrade diffusers
pip install -U diffusers
Then you can use FluxFillPipeline to run the model
import torch
from diffusers import FluxFillPipeline
from diffusers.utils import load_image
image = load_image("https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/cup.png")
mask = load_image("https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/cup_mask.png")
pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16).to("cuda")
image = pipe(
prompt="a white paper cup",
image=image,
mask_image=mask,
height=1632,
width=1232,
guidance_scale=30,
num_inference_steps=50,
max_sequence_length=512,
generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save(f"flux-fill-dev.png")
To learn more check out the diffusers 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.
- There may be slight-color shifts in areas that are not filled in
- Filling in complex textures may produce lines at the edges of the filled-area.
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, threat