Instructions to use yresearch/swd-large-6-steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yresearch/swd-large-6-steps with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("yresearch/swd-large-6-steps", 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 Settings
- Draw Things
- DiffusionBee
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Download README.md from yresearch/swd-large-6-steps: direct link, hf CLI and curl.
- Browser
- Download file 991 Bytes
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https://huggingface.co/yresearch/swd-large-6-steps/resolve/refs%2Fpr%2F2/README.md
- Command line
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hf download hf://yresearch/swd-large-6-steps@refs/pr/2/README.md
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curl -L -o README.md https://huggingface.co/yresearch/swd-large-6-steps/resolve/refs%2Fpr%2F2/README.md
991 Bytes
| license: apache-2.0 | |
| library_name: diffusers | |
| pipeline_tag: text-to-image | |
| # Scale-wise Distillation 3.5 Large | |
| Scale-wise Distillation (SwD) is a novel framework for accelerating diffusion models (DMs) | |
| by progressively increasing spatial resolution during the generation process. | |
| <br>SwD achieves significant speedups (2.5× to 10×) compared to full-resolution models | |
| while maintaining or even improving image quality. | |
|  | |
| Project page: https://yandex-research.github.io/swd | |
| ## Usage | |
| To generate images using SwD, go to <a href="https://github.com/yandex-research/swd ">GitHub</a> | |
| or <a href="https://huggingface.co/spaces/dbaranchuk/Scale-wise-Distillation">Hugging Face's demo </a>. | |
| ## Citation | |
| ```bibtex | |
| @article{starodubcev2025swd, | |
| title={Scale-wise Distillation of Diffusion Models}, | |
| author={Nikita Starodubcev and Denis Kuznedelev and Artem Babenko and Dmitry Baranchuk}, | |
| journal={arXiv preprint arXiv:2503.16397}, | |
| year={2025} | |
| } | |
| ``` |