Fixed inference steps

app.py

@@ -8,7 +8,7 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 noise_scheduler = DDPMScheduler(num_train_timesteps=1000)
 if torch.cuda.is_available():
     torch.cuda.max_memory_allocated(device=device)
-    pipe = DDPMPipeline.from_pretrained("FrozenScar/cartoon_face", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
+    pipe = DDPMPipeline.from_pretrained("FrozenScar/cartoon_face", torch_dtype=torch.float16, variant="fp16", use_safetensors=True,scheduler=noise_scheduler)
     pipe.enable_xformers_memory_efficient_attention()
     pipe = pipe.to(device)
 else: 
@@ -25,14 +25,13 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
         
     generator = torch.Generator().manual_seed(seed)
     
-    image = pipe().images[0] 
+    image = pipe(num_inference_steps=num_inference_steps).images[0] 
     
     return image
 
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    "OK broo",
+   "Nothing brooo"
 ]
 
 css="""
@@ -119,7 +118,7 @@ with gr.Blocks(css=css) as demo:
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
-                    maximum=12,
+                    maximum=120,
                     step=1,
                     value=2,
                 )