Add pipeline tag, library_name and code snippet

README.md

@@ -1,6 +1,9 @@
 ---
 license: apache-2.0
+library_name: diffusers
+pipeline_tag: text-to-image
 ---
+
 # Scale-wise Distillation 3.5 Medium
 Scale-wise Distillation (SwD) is a novel framework for accelerating diffusion models (DMs) 
 by progressively increasing spatial resolution during the generation process. 
@@ -10,4 +13,54 @@ while maintaining or even improving image quality.
 
 ## 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>.
+or <a href="https://huggingface.co/spaces/dbaranchuk/Scale-wise-Distillation">Hugging Face's demo </a>.
+
+```py
+import torch
+from inference import run
+from diffusers import StableDiffusion3Pipeline
+from peft import LoraConfig, get_peft_model, PeftModel
+
+pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-large", torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+lora_path = 'yresearch/swd-large-6-steps'
+pipe.transformer = PeftModel.from_pretrained(
+    pipe.transformer,
+    lora_path,
+)
+generator = torch.Generator().manual_seed(0)
+prompt = 'cat reading a newspaper'
+sigmas = [1.0000, 0.9454, 0.8959, 0.7904, 0.7371, 0.6022]
+scales = [32, 48, 64, 80, 96, 128]
+
+images = run(
+            pipe,
+            prompt,
+            sigmas=torch.tensor(sigmas).to('cuda'),
+            timesteps=torch.tensor(sigmas[:-1]).to('cuda') * 1000,
+            scales=scales,
+
+            guidance_scale=0.0,
+            height=int(scales[0] * 8),
+            width=int(scales[0] * 8),
+            generator=generator,
+    ).images
+```
+<p align="center">
+<img src="assets/cat.png" width="512px"/>
+</p>
+
+## 🔧 Training
+
+Coming soon!
+
+## 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}
+}
+```