Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+library_name: diffusers
+pipeline_tag: text-to-image
+base_model:
+- stabilityai/stable-diffusion-xl-base-1.0
+---
+# Scale-wise Distillation SDXL
+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.
+![SDXL Demo Image](swd.png)
+
+Project page: https://yandex-research.github.io/swd <br>
+GitHub: https://github.com/yandex-research/swd <br>
+Demo: https://huggingface.co/spaces/dbaranchuk/Scale-wise-Distillation
+
+## Usage
+Upgrade to the latest version of the [🧨 diffusers](https://github.com/huggingface/diffusers) and [🧨 peft](https://github.com/huggingface/peft)
+```
+pip install -U diffusers
+pip install -U peft
+```
+
+and then you can run
+<br>
+```py
+import torch
+from diffusers import DDPMScheduler, StableDiffusionXLPipeline
+from peft import PeftModel
+
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    torch_dtype=torch.float16,
+    custom_pipeline="quickjkee/swd_pipeline_sdxl",
+).to("cuda")
+
+pipe.scheduler = DDPMScheduler.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    subfolder="scheduler",
+)
+
+lora_path = "yresearch/swd-sdxl"
+pipe.unet = PeftModel.from_pretrained(
+    pipe.unet,
+    lora_path,
+)
+
+prompt = "Cute winter dragon baby, kawaii, Pixar, ultra detailed, glacial background, extremely realistic."
+sigmas = [1.0000, 0.8000, 0.6000, 0.4000, 0.0000]
+scales = [64, 80, 96, 128]
+
+image = pipe(
+    prompt,
+    timesteps=torch.tensor(sigmas) * 1000,
+    scales=scales,
+    height=1024,
+    width=1024,
+    guidance_scale=1.0,
+).images[0]
+```
+<p align="center">
+<img src="sdxl.png" width="512px"/>
+</p>
+
+## Citation
+
+```bibtex
+@inproceedings{
+    starodubcev2026scalewise,
+    title={Scale-wise Distillation of Diffusion Models},
+    author={Nikita Starodubcev and Ilya Drobyshevskiy and Denis Kuznedelev and Artem Babenko and Dmitry Baranchuk},
+    booktitle={The Fourteenth International Conference on Learning Representations},
+    year={2026},
+    url={https://openreview.net/forum?id=Z06LNjqU1g}
+}
+```