Update README.md

README.md

@@ -70,12 +70,18 @@ import torch
 from torch import autocast
 from diffusers import StableDiffusionPipeline
 
-model_id = "CompVis/stable-diffusion-v1-1"
+model_id = "CompVis/stable-diffusion-v1-2"
 device = "cuda"
 
 
 pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True)
 pipe = pipe.to(device)
+
+prompt = "a photo of an astronaut riding a horse on mars"
+with autocast("cuda"):
+    image = pipe(prompt, guidance_scale=7.5)["sample"][0]  
+    
+image.save("astronaut_rides_horse.png")
 ```
 
 **Note**:
@@ -85,8 +91,14 @@ If you are limited by GPU memory and have less than 10GB of GPU RAM available, p
 ```py
 import torch
 
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, use_auth_token=True)
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16", use_auth_token=True)
 pipe = pipe.to(device)
+
+prompt = "a photo of an astronaut riding a horse on mars"
+with autocast("cuda"):
+    image = pipe(prompt, guidance_scale=7.5)["sample"][0]  
+    
+image.save("astronaut_rides_horse.png")
 ```
 
 To swap out the noise scheduler, pass it to `from_pretrained`:
@@ -99,6 +111,12 @@ model_id = "CompVis/stable-diffusion-v1-2"
 scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
 pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, use_auth_token=True)
 pipe = pipe.to("cuda")
+
+prompt = "a photo of an astronaut riding a horse on mars"
+with autocast("cuda"):
+    image = pipe(prompt, guidance_scale=7.5)["sample"][0]  
+    
+image.save("astronaut_rides_horse.png")
 ```
 
 # Uses