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app.py

@@ -337,7 +337,7 @@ logger.info("Pipeline ready.")
 # ---------------------------------------------------------------------------
 
 
-@spaces.GPU(duration=120)
+@spaces.GPU(duration=180)
 def stylize(
     content_image: Image.Image,
     style_image: Image.Image,
@@ -358,10 +358,12 @@ def stylize(
     if content_image is None or style_image is None:
         return None
 
+    logger.info("stylize called: strength=%.2f, seed=%d, steps=%d", strength, seed, steps)
     content_image = content_image.convert("RGB").resize((DEFAULT_WIDTH, DEFAULT_HEIGHT))
     style_image = style_image.convert("RGB").resize((DEFAULT_WIDTH, DEFAULT_HEIGHT))
 
     # Interpolate projector states at the requested strength
+    logger.info("Interpolating projector states ...")
     interp_states = interpolate_projector_states(
         paths=_projector_paths,
         anchor_strengths=ANCHOR_STRENGTHS,
@@ -369,10 +371,11 @@ def stylize(
         method="bspline",
         order=BSPLINE_ORDER,
         spline_mode=BSPLINE_MODE,
-        device=torch.device("cuda"),
+        device="cuda",
         dtype=_dtype,
         endpoint_state=_endpoint_projector_state,
     )
+    logger.info("Loading interpolated states into transformer ...")
     _transformer.load_state_dict(interp_states, strict=False)
     for _, c in _pipe.components.items():
         if hasattr(c, "parameters"):
@@ -388,6 +391,7 @@ def stylize(
 
     generator = torch.Generator(device="cuda").manual_seed(seed)
 
+    logger.info("Running pipeline inference ...")
     output = _pipe(
         image=[content_image, style_image],
         prompt=DEFAULT_PROMPT,
@@ -395,6 +399,7 @@ def stylize(
         num_inference_steps=steps,
         generator=generator,
     )
+    logger.info("Pipeline done, returning image.")
     return output.images[0]