Update README.md

README.md

@@ -32,4 +32,31 @@ and one 32x spatial-compressed latent feature encoder ([DC-AE](https://hanlab.mi
 
 For research purposes, we recommend our `generative-models` Github repository (https://github.com/NVlabs/Sana), 
 which is more suitable for both training and inference and for which most advanced diffusion sampler like Flow-DPM-Solver is integrated.
-[MIT Han-Lab](https://nv-sana.mit.edu/) provides free Sana inference.
+[MIT Han-Lab](https://nv-sana.mit.edu/) provides free Sana inference.
+```python
+# pip install git+https://github.com/huggingface/diffusers
+# pip install transformer
+import torch
+from diffusers import SanaPAGPipeline
+
+pipe = SanaPAGPipeline.from_pretrained(
+  "kpsss34/SANA600.fp8_Realistic_SFW_V1",
+  torch_dtype=torch.float16,
+)
+pipe.to("cuda")
+
+pipe.text_encoder.to(torch.bfloat16)
+pipe.vae.to(torch.bfloat16)
+
+prompt = 'A cute 🐼 eating 🎋, ink drawing style'
+image = pipe(
+    prompt=prompt,
+    height=1024,
+    width=1024,
+    guidance_scale=5.0,
+    pag_scale=2.0,
+    num_inference_steps=20,
+    generator=torch.Generator(device="cuda").manual_seed(42),
+)[0]
+image[0].save('sana.png')
+```