Instructions to use primecai/dsd_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use primecai/dsd_model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("primecai/dsd_model") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
metadata
license: apache-2.0
language:
- en
pinned: true
tags:
- personalization
- dreambooth
- lora
- customized image
Diffusion Self-Distillation Model Card
This is a model card for the diffusion self-distillation model for personalized/subject-driven generation from a single image.
The model is fine-tuned from the FLUX.1-dev model as
described in our CVPR'2025 paper titled "Diffusion Self-Distillation for Zero-Shot Customized Image Generation".
Model Details
- Developed by: Shengqu Cai, Eric Ryan Chan, Yunzhi Zhang, Leonidas Guibas, Jiajun Wu, Gordon Wetzstein.
- License: Apache License License Version 2.0.
- Resources for more information: Project Website, Paper, Code.
- Cite as:
@inproceedings{cai2024dsd,
author={Cai, Shengqu and Chan, Eric and Zhang, Yunzhi and Guibas, Leonidas and Wu, Jiajun and Wetzstein, Gordon.},
title={Diffusion Self-Distillation for Zero-Shot Customized Image Generation},
booktitle={CVPR},
year={2025}
}