apply omar's suggestions.

app.py

@@ -21,18 +21,17 @@ One of the applications of this Space could be to evaluate different schedulers
 
 Here's how it works:
 
-* The users provides:
-    * An input prompt.
-    * Number of images to generate with the prompt.
-    * A checkpoint path compatible with `StableDiffusionPipeline`. You can either select one from the drop-down list or provide a valid path ("valhalla/sd-pokemon-model" for example).
-    * Names of the schedulers to evaluate.
 * The evaluator first sets a seed and then generates the initial noise which is passed as the initial latent to start the image generation process. It is done to ensure fair comparison.
 * This initial latent is used every time the pipeline is run (with different schedulers).
 * To quantify the quality of the generated images we use:
     * [Inception Score](https://en.wikipedia.org/wiki/Inception_score)
     * [Clip Score](https://arxiv.org/abs/2104.08718)
 
-**Note** that the default scheduler associated with the provided checkpoint is always used for reporting the scores.
+**Notes**:
+
+* The default scheduler associated with the provided checkpoint is always used for reporting the scores.
+* Increasing both the number of images per prompt and number of inference steps could quickly build up the inference queue and thus
+resulting in slowdowns.
 """
 
 
@@ -100,7 +99,7 @@ def initialize_pipeline(checkpoint: str):
     return sd_pipe, original_scheduler_config
 
 
-def get_scheduler(scheduler_name):
+def get_scheduler(scheduler_name: str):
     schedulers_lib = importlib.import_module("diffusers", package="schedulers")
     scheduler_abs = getattr(schedulers_lib, scheduler_name)
 
@@ -162,7 +161,7 @@ def run(
             }
         }
     )
-    print("First scheduler complete.")
+    # print("First scheduler complete.")
 
     for scheduler_name in schedulers_to_test:
         if scheduler_name == original_scheduler_name:
@@ -184,13 +183,13 @@ def run(
                 }
             }
         )
-        print(f"{scheduler_name} complete.")
+        # print(f"{scheduler_name} complete.")
 
     output_str = ""
     for scheduler_name in all_images:
-        print(f"scheduler_name: {scheduler_name}")
+        # print(f"scheduler_name: {scheduler_name}")
         output_str += prepare_report(scheduler_name, all_images[scheduler_name])
-    print(output_str)
+    # print(output_str)
     return output_str
 
 
@@ -198,8 +197,8 @@ demo = gr.Interface(
     run,
     inputs=[
         gr.Text(max_lines=1, placeholder="a painting of a dog"),
-        gr.Slider(3, 10, value=3),
-        gr.Slider(10, 100, value=50),
+        gr.Slider(3, 10, value=3, step=1),
+        gr.Slider(10, 100, value=50, step=1),
         gr.Dropdown(
             [
                 "CompVis/stable-diffusion-v1-4",
@@ -222,7 +221,7 @@ demo = gr.Interface(
             multiselect=True,
         ),
     ],
-    outputs=[gr.Markdown().style()],
+    outputs=[gr.Markdown().style(height="auto")],
     title=TITLE,
     description=DESCRIPTION,
     allow_flagging=False,