Instructions to use akhmat-s/FLUX.1-dev-LoRA-Nails-Generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use akhmat-s/FLUX.1-dev-LoRA-Nails-Generator with Diffusers:
pip install -U diffusers transformers accelerate
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] - Diffusion Single File
How to use akhmat-s/FLUX.1-dev-LoRA-Nails-Generator with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
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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Model tree for akhmat-s/FLUX.1-dev-LoRA-Nails-Generator
Base model
black-forest-labs/FLUX.1-dev