Update app.py

app.py

@@ -25,16 +25,16 @@ else:
     #refiner = refiner.to(device)
     #refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)
 
-def genie (prompt, height, width, steps, seed):
+def genie (prompt, steps, seed):
     generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
-    int_image = pipe(prompt, height=height, width=width, num_inference_steps=steps, num_images_per_prompt=1, generator=generator, guidance_scale=0.0).images[0] 
+    int_image = pipe(prompt, num_inference_steps=steps, generator=generator, guidance_scale=0.0).images[0] 
     #image = refiner(prompt=prompt, prompt_2=prompt_2, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, image=int_image, denoising_start=high_noise_frac).images[0]   
     return int_image
     
 gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'), 
     gr.Textbox(label='What you Do Not want the AI to generate.'), 
-    gr.Slider(512, 1024, 768, step=128, label='Height'),
-    gr.Slider(512, 1024, 768, step=128, label='Width'),
+    #gr.Slider(512, 1024, 768, step=128, label='Height'),
+    #gr.Slider(512, 1024, 768, step=128, label='Width'),
     #gr.Slider(1, 15, 10, label='Guidance Scale'), 
     gr.Slider(1, maximum=5, value=2, step=1, label='Number of Iterations'), 
     gr.Slider(minimum=1, step=1, maximum=999999999999999999, randomize=True),