Update README.md

README.md

@@ -21,7 +21,6 @@ pipeline_tag: text-to-image
   <a href="https://huggingface.co/collections/Efficient-Large-Model/sana-sprint-67d6810d65235085b3b17c76"><img src="https://img.shields.io/static/v1?label=Weights&message=Huggingface&color=yellow"></a> &ensp;
   <a href="https://github.com/NVlabs/Sana"><img src="https://img.shields.io/static/v1?label=Code&message=Github&color=blue&logo=github"></a> &ensp;
   <a href="https://nvlabs.github.io/Sana/Sprint/"><img src="https://img.shields.io/static/v1?label=Project&message=Github&color=blue&logo=github-pages"></a> &ensp;
-  <!-- <a href="https://hanlab.mit.edu/projects/sana/"><img src="https://img.shields.io/static/v1?label=Page&message=MIT&color=darkred&logo=github-pages"></a> &ensp; -->
   <a href="https://arxiv.org/pdf/2503.09641"><img src="https://img.shields.io/static/v1?label=Arxiv&message=SANA-Sprint&color=red&logo=arxiv"></a> &ensp;
   <a href="https://nv-sana.mit.edu/sprint"><img src="https://img.shields.io/static/v1?label=Demo&message=MIT&color=yellow"></a> &ensp;
   <a href="https://discord.gg/rde6eaE5Ta"><img src="https://img.shields.io/static/v1?label=Discuss&message=Discord&color=purple&logo=discord"></a> &ensp;
@@ -67,7 +66,7 @@ Source code is available at https://github.com/NVlabs/Sana.
 ### Model Description
 
 - **Developed by:** NVIDIA, Sana
-- **Model type:** One-Step Diffusion with Continuous-Time Consistency Distillation
+- **Model type:** One-Step Diffusion with Continuous-Time Consistency Distillation (Teacher Model)
 - **Model size:** 1.6B parameters
 - **Model precision:** torch.bfloat16 (BF16)
 - **Model resolution:** This model is developed to generate 1024px based images with multi-scale heigh and width.
@@ -91,19 +90,19 @@ For research purposes, we recommend our `generative-models` Github repository (h
 Under construction [PR](https://github.com/huggingface/diffusers/pull/11074)
 
 ```python
-from diffusers import SanaSprintPipeline
+from diffusers import SanaPipeline
 import torch
 
-pipeline = SanaSprintPipeline.from_pretrained(
-    "Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers",
+pipeline = SanaPipeline.from_pretrained(
+    "Efficient-Large-Model/SANA_Sprint_1.6B_1024px_teacher_diffusers",
     torch_dtype=torch.bfloat16
 )
 pipeline.to("cuda:0")
 
 prompt = "a tiny astronaut hatching from an egg on the moon"
 
-image = pipeline(prompt=prompt, num_inference_steps=2).images[0]
-image.save("sana_sprint.png")
+image = pipeline(prompt=prompt, num_inference_steps=20).images[0]
+image.save("sana_sprint_teacher.png")
 ```