Selena Gomez (2012) - Flux

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(CivitAI)

Model description

Gomez is an established actress and television personality. Her films include Another Cinderella Story (2008), Monte Carlo (2011), Spring Breakers (2012), The Fundamentals of Caring (2016), The Dead Don't Die (2019), and Emilia Pérez (2024). She voiced Mavis in the Hotel Transylvania film franchise (2012–2022). She has produced series such as 13 Reasons Why (2017–2020), Living Undocumented (2019) and Selena + Chef (2020–2023), and has played a lead role in Only Murders in the Building since 2021, for which she earned nominations at the Golden Globe and Emmy Awards. Her accolades include an AFI Award, American Music Award, a Billboard Music Award, a Cannes Film Festival Award, two MTV Video Music Awards, and 16 Guinness World Records. As a philanthropist, Gomez has worked with charitable organizations. She advocates for mental health, and gender, racial, and LGBT equality, and has been a UNICEF Goodwill Ambassador since 2009. She founded the cosmetic company Rare Beauty in 2020, valued at $2 billion in 2024, and the mental-health non-profit Rare Impact Fund. She has appeared in listicles such as the Time 100 (2020) and Forbes 30 Under 30 (2016 and 2020), was named Woman of the Year (2017) and one of the most successful artists of the 2010s decade by Billboard, and was made a member of the Order of Arts and Letters by the Government of France. As of 2024, she has an estimated net worth of $1.3 billion, being one of the wealthiest musicians, and is the most-followed woman on Instagram.

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Weights for this model are available in Safetensors format.

Download them in the Files & versions tab.

Use it with the 🧨 diffusers library

from diffusers import AutoPipelineForText2Image
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to(device)
pipeline.load_lora_weights('Keltezaa/selena-gomez-2012-flux', weight_name='SelenaGomez_v2.safetensors')
image = pipeline('Your custom prompt').images[0]

For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers

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