Update gradio_demo/app.py

gradio_demo/app.py

@@ -117,8 +117,43 @@ def show_final_preview(preview_row):
 @spaces.GPU(duration=70) #[uncomment to use ZeroGPU]
 @torch.no_grad()
 def instantir_restore(
-    lq, prompt="", steps=30, cfg_scale=7.0, guidance_end=1.0,
-    creative_restoration=False, seed=3407, height=1024, width=1024, preview_start=0.0, progress=gr.Progress(track_tqdm=True)):
+    lq,  # A low-quality PIL image to be restored
+    prompt="",  # Optional: A text prompt guiding creative restoration
+    steps=30,  # Number of denoising steps (controls generation detail and time)
+    cfg_scale=7.0,  # Classifier-Free Guidance scale; higher = more prompt adherence
+    guidance_end=1.0,  # When to stop guidance and allow free generation (0.0 - 1.0 or 0 - steps)
+    creative_restoration=False,  # Toggle creative mode (uses LCM adapter)
+    seed=3407,  # Seed for reproducibility
+    height=1024,  # Target height for output image
+    width=1024,  # Target width for output image
+    preview_start=0.0,  # When to start showing previews (fraction or step index)
+    progress=gr.Progress(track_tqdm=True)  # Progress tracker for Gradio
+):
+    """
+    Restore or creatively re-generate a low-quality image using the InstantIR pipeline.
+
+    This function takes a degraded image and applies a guided diffusion model to restore it.
+    Optionally, a text prompt can be provided to guide a creative re-interpretation of the image.
+
+    Args:
+        lq (PIL.Image): The input low-quality image to restore.
+        prompt (str, optional): Text description to guide restoration or creative re-generation.
+        steps (int): Number of inference steps; more steps generally yield better results.
+        cfg_scale (float): Guidance scale for prompt adherence; higher means stronger influence.
+        guidance_end (float or int): Defines when to stop using prompt guidance during diffusion.
+        creative_restoration (bool): Whether to enable imaginative regeneration via LCM adapter.
+        seed (int): Random seed for reproducible results.
+        height (int): Output image height; used if input is square.
+        width (int): Output image width; used if input is square.
+        preview_start (float or int): Step or ratio when previewing starts.
+        progress (gr.Progress): Progress tracker for UI feedback.
+
+    Returns:
+        Tuple[PIL.Image, List[List[Union[PIL.Image, str]]]]:
+            - The final restored image.
+            - A list of preview images from intermediate steps with labels.
+    """
+    
     if creative_restoration:
         if "lcm" not in pipe.unet.active_adapters():
             pipe.unet.set_adapter('lcm')
@@ -232,10 +267,10 @@ with gr.Blocks() as demo:
         ],
         outputs=[output, pipe_out], api_name="InstantIR"
     )
-    steps.change(dynamic_guidance_slider, inputs=steps, outputs=guidance_end)
-    output.change(dynamic_preview_slider, inputs=steps, outputs=index)
-    index.release(unpack_pipe_out, inputs=[pipe_out, index], outputs=preview)
-    output.change(show_final_preview, inputs=pipe_out, outputs=preview)
+    steps.change(dynamic_guidance_slider, inputs=steps, outputs=guidance_end, show_api=False)
+    output.change(dynamic_preview_slider, inputs=steps, outputs=index, show_api=False)
+    index.release(unpack_pipe_out, inputs=[pipe_out, index], outputs=preview, show_api=False)
+    output.change(show_final_preview, inputs=pipe_out, outputs=preview, show_api=False)
     gr.Markdown(
     """
     ## Advance usage:
@@ -262,4 +297,4 @@ with gr.Blocks() as demo:
     ```
     """)
 
-demo.queue().launch()
+demo.queue().launch(mcp_server=True)