Add denoise_steps to enable optimization via early stopped diffusion process

app.py

@@ -44,18 +44,19 @@ def predict(
     prompt: str,
     negative_prompt: str,
     guidance_scale: float = 5.0,
+    denoise_steps: int = 50,
     seed: int = 0,
     randomize_seed: bool = True,
 ):
     generator = torch.Generator() if randomize_seed else torch.manual_seed(seed)
     output = pipe(
-         prompt,
+        prompt,
         width=1024,
         height=512,
         negative_prompt=negative_prompt,
         guidance_scale=guidance_scale,
         generator=generator,
-        num_inference_steps=50,
+        num_inference_steps=denoise_steps,
     )  # type: ignore
     rgb_image, depth_image = output.rgb[0], output.depth[0]  # type: ignore
     with NamedTemporaryFile(suffix=".png", delete=False, dir="tmp") as rgb_file:
@@ -87,6 +88,9 @@ For better results, specify "360 view of" or "panoramic view of" in the prompt
             guidance_scale = gr.Slider(
                 label="Guidance Scale", minimum=0, maximum=10, step=0.1, value=5.0
             )
+            denoise_steps = gr.Slider(
+                label="Denoise Steps", minimum=25, maximum=250, step=25, value=50
+            )
             randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
             seed = gr.Slider(label="Seed", minimum=0,
                              maximum=2**64 - 1, step=1)
@@ -101,16 +105,16 @@ For better results, specify "360 view of" or "panoramic view of" in the prompt
                 depth = gr.Image(label="Depth Image", type="filepath")
     gr.Examples(
         examples=[
-            ["360 view of a large bedroom", "", 7.0, 42, False]],
-        inputs=[prompt, negative_prompt, guidance_scale, seed, randomize_seed],
+            ["360 view of a large bedroom", "", 7.0, 50, 42, False]],
+        inputs=[prompt, negative_prompt, guidance_scale, denoise_steps, seed, randomize_seed],
         outputs=[rgb, depth, generated_seed, html],
         fn=predict,
         cache_examples=True)
 
     new_btn.click(
         fn=predict,
-        inputs=[prompt, negative_prompt, guidance_scale, seed, randomize_seed],
+        inputs=[prompt, negative_prompt, guidance_scale, denoise_steps, seed, randomize_seed],
         outputs=[rgb, depth, generated_seed, html],
     )
     
-block.launch() 
+block.launch()