import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("akhmat-s/FLUX.1-dev-LoRA-Nails-Generator", dtype=torch.bfloat16, device_map="cuda")
prompt = "Turn this cat into a dog"
input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
image = pipe(image=input_image, prompt=prompt).images[0]This model is part of a project dedicated to the generation of nail designs using diffusion models.
Read more about how this model was trained in the Medium article: "Fine-tuning the Flux.1-dev model for nail generation".
This article discusses the training of a diffusion model for nail design generation. The primary objective of our experiment is to develop a highly effective tool using relatively small datasets. To accomplish this, we use Flux.1-dev, a text-to-image model capable of generating output images based on provided textual inputs. The model was trained utilizing a strategy of preprocessing text queries, enhancing the accuracy and informativeness of the generated images.
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