Instructions to use gyrojeff/Hyperstroke-VQ-Illustration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use gyrojeff/Hyperstroke-VQ-Illustration with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("gyrojeff/Hyperstroke-VQ-Illustration", 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
Hyperstroke-VQ-Quickdraw
Modified VQGAN Model for Hyperstroke: A Novel High-quality Stroke Representation for Assistive Artistic Drawing
This model is trained on our custom illustration dataset (timelapse & synthetic). Please refer to our paper for more details.
Links
Paper link: https://www.arxiv.org/abs/2408.09348
Code: https://github.com/JeffersonQin/hyperstroke
Abstract
Assistive drawing aims to facilitate the creative process by providing intelligent guidance to artists. Existing solutions often fail to effectively model intricate stroke details or adequately address the temporal aspects of drawing. We introduce hyperstroke, a novel stroke representation designed to capture precise fine stroke details, including RGB appearance and alpha-channel opacity. Using a Vector Quantization approach, hyperstroke learns compact tokenized representations of strokes from real-life drawing videos of artistic drawing. With hyperstroke, we propose to model assistive drawing via a transformer-based architecture, to enable intuitive and user-friendly drawing applications, which are experimented in our exploratory evaluation.
- Downloads last month
- 21