Update app.py

app.py

@@ -15,10 +15,10 @@ upscaler = upscaler.to(device)
 upscaler.enable_xformers_memory_efficient_attention()
 
 
-def genie (Prompt, negative_prompt, height, width, Scale, Steps, Seed, upscale):
+def genie (Prompt, negative_prompt, height, width, Scale, steps, Seed, upscale):
     generator = torch.Generator(device=device).manual_seed(Seed)
     if upscale == "Yes":
-        low_res_latents = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=Steps, guidance_scale=Scale, generator=generator, output_type="latent").images
+        low_res_latents = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=Scale, generator=generator, output_type="latent").images
         image = upscaler(Prompt, negative_prompt=negative_prompt, image=low_res_latents, num_inference_steps=5, guidance_scale=0, generator=generator).images[0]
     else:
         image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, generator=generator).images[0]
@@ -29,7 +29,7 @@ gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generat
                                gr.Slider(512, 1024, 768, step=128, label='Height'),
                                gr.Slider(512, 1024, 768, step=128, label='Width'),
                                gr.Slider(1, maximum=15, value=7, step=.25), 
-                               gr.Slider(25, maximum=100, value=50, step=25), 
+                               gr.Slider(25, maximum=100, value=50, step=25, label='Number of Iterations'), 
                                gr.Slider(minimum=1, step=1, maximum=9999999999999999, randomize=True), 
                                gr.Radio(["Yes", "No"], label='Upscale?', value='No'),
                               ],