Update PixelSmile demo

.gitignore

@@ -2,3 +2,4 @@ weights/*
 !weights/.gitkeep
 __pycache__/
 *.pyc
+*.log

README.md

@@ -8,7 +8,7 @@ sdk_version: 6.10.0
 app_file: app.py
 pinned: false
 license: apache-2.0
-short_description: PixelSmile Demo [comming soon]
+short_description: PixelSmile Demo [🔨Developing]
 ---
 
 We are actively working to bring this demo online soon.

gradio_app/edit.py

@@ -51,6 +51,7 @@ def run_edit(
     seed: int,
     weight_version: str,
     num_inference_steps: int,
+    progress=None,
 ) -> Image.Image:
     import torch
 
@@ -64,12 +65,18 @@ def run_edit(
         num_inference_steps,
     )
     subject = get_subject_name(data_type)
+    if progress is not None:
+        progress(0.15, desc="Loading PixelSmile weights...")
     pipe = load_lora(weight_version)
+    if progress is not None:
+        progress(0.35, desc="Preparing input image...")
     input_image = prepare_input_image(image)
     logger.info("Input image prepared at size: %s", input_image.size)
     edit_condition = build_edit_condition(subject, expression, float(scale))
     logger.info("Edit condition: %s", edit_condition)
 
+    if progress is not None:
+        progress(0.5, desc="Encoding edit condition...")
     prompt_embeds, prompt_embeds_mask = compute_text_embeddings(
         method=DEFAULT_METHOD,
         pipeline=pipe,
@@ -80,10 +87,12 @@ def run_edit(
     logger.info(
         "Prompt embeds ready: embeds_shape=%s, mask_shape=%s",
         tuple(prompt_embeds.shape),
-        tuple(prompt_embeds_mask.shape),
+        None if prompt_embeds_mask is None else tuple(prompt_embeds_mask.shape),
     )
 
     generator = torch.Generator(device=pipe.device).manual_seed(int(seed))
+    if progress is not None:
+        progress(0.7, desc=f"Running inference ({int(num_inference_steps)} steps)...")
     logger.info("Starting pipeline inference with %s steps", num_inference_steps)
     with torch.no_grad():
         output = pipe(
@@ -96,4 +105,6 @@ def run_edit(
             generator=generator,
         )
     logger.info("Pipeline inference finished")
+    if progress is not None:
+        progress(0.95, desc="Finalizing output...")
     return output.images[0]