Instructions to use Xinxi-Zhang/Fine-Tune-Diffusion-Vivian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Xinxi-Zhang/Fine-Tune-Diffusion-Vivian with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Xinxi-Zhang/Fine-Tune-Diffusion-Vivian", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Disclaimer
This was inspired from https://github.com/YaYaB/finetune-diffusion
Model Card for Finetuning Stable Diffusion on Vivian Maier's photographs
The main goal is to fine-tune the Stable Diffusion model to generate images reflecting the distinct photographic style of Vivian Maier.
And I chose to utilize a Jupyter Notebook to make the fine-tuning process accessible and easy to understand, particularly for those new to the diffusion pipeline and hugging face API.
Requirements
To launch the finetuning with a batch_size of 1 you need to have a gpu with at least 24G VRAM (you can use accumulating gradient to simulate higher batch size)
Make sure that you have enough disk space, the model uses ~11Gb
Examples(at epoch 90)
A woman walking down a street
a group of people getting on a bus
two man working on a constructing site
Citation
If you use this dataset, please cite it as:
@misc{cqueenccc2023vivian,
author = {cQueenccc},
title = {Finetuning Stable Diffusion on Vivian Maier's photographs},
year={2023},
howpublished= {\url{https://huggingface.co/cQueenccc/Fine-Tune-Diffusion-Vivian/}}
}
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