:tada: change

app.py

@@ -28,16 +28,13 @@ if device == 'cuda':
     ).to(device)
 
 
+
 @spaces.GPU()
 @torch.inference_mode()
 def inference(
         model_id: str,
         prompt: str,
         negative_prompt: str = "",
-        width: int = 512,
-        height: int = 512,
-        guidance_scale: float = 7.5,
-        num_inference_steps: int | None = None,
         progress=gr.Progress(track_tqdm=True),
 ) -> Image.Image:
     global current_model_id, pipe
@@ -55,10 +52,6 @@ def inference(
     image = pipe(
         prompt,
         negative_prompt=negative_prompt,
-        width=width,
-        height=height,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
     ).images[0]
 
     return image
@@ -82,12 +75,12 @@ if __name__ == "__main__":
 
                 with gr.Accordion("Additional Settings (W.I.P)", open=False):
                     with gr.Row():
-                        width = gr.Number(label="Width", value=512, step=64, minimum=64, maximum=1024)
-                        height = gr.Number(label="Height", value=512, step=64, minimum=64, maximum=1024)
+                        width_component = gr.Number(label="Width", value=512, step=64, minimum=64, maximum=1024)
+                        height_component = gr.Number(label="Height", value=512, step=64, minimum=64, maximum=1024)
 
                     additional_inputs = [
-                        width,
-                        height,
+                        width_component,
+                        height_component,
                         gr.Number(label="Guidance Scale", value=7.5, step=0.5, minimum=0, maximum=10),
                         gr.Slider(label="Num Inference Steps", value=None, minimum=1, maximum=1000, step=1)
                     ]
@@ -105,6 +98,6 @@ if __name__ == "__main__":
         )
 
         btn = gr.Button("Generate")
-        btn.click(fn=inference, inputs=inputs + additional_inputs, outputs=outputs)
+        btn.click(fn=inference, inputs=inputs, outputs=outputs)
 
     demo.queue().launch()