Instructions to use Gaojunyao/StyleShot_lineart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Gaojunyao/StyleShot_lineart with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Gaojunyao/StyleShot_lineart", 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
StyleShot Model Card
Introduction
we present StyleShot, a generalized plug-to-play style transfer method, capable of generating the high-quality stylized images that match the desired style from any reference image without test-time style-tuning. To the best of our knowledge, To the best of our knowledge, StyleShot is the first work to designate a style-aware encoder based on Stable Diffusion and a content-fusion encoder for better style and content integration, achieving the state-of-the-art text and image-driven style transfer performance compared to existing methods.
Models
StyleShot for SD 1.5 on Lineart condition
- ip.bin: weights for ip-adapter and our content-retention encoder.
- style_aware_encoder.bin: weights for style-aware encoder.
Disclaimer
We develop this repository for RESEARCH purposes, so it can only be used for personal/research/non-commercial purposes.
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Paper for Gaojunyao/StyleShot_lineart
Paper β’ 2407.01414 β’ Published β’ 2
