Update app.py

app.py

@@ -102,26 +102,42 @@ pipeline = DistilledPipeline(
 )
 
 # Preload all models for ZeroGPU tensor packing.
-print("Preloading all models (including Gemma)...")
-ledger = pipeline.model_ledger
-_transformer = ledger.transformer()
-_video_encoder = ledger.video_encoder()
-_video_decoder = ledger.video_decoder()
-_audio_decoder = ledger.audio_decoder()
-_vocoder = ledger.vocoder()
-_spatial_upsampler = ledger.spatial_upsampler()
-_text_encoder = ledger.text_encoder()
-_embeddings_processor = ledger.gemma_embeddings_processor()
-
-ledger.transformer = lambda: _transformer
-ledger.video_encoder = lambda: _video_encoder
-ledger.video_decoder = lambda: _video_decoder
-ledger.audio_decoder = lambda: _audio_decoder
-ledger.vocoder = lambda: _vocoder
-ledger.spatial_upsampler = lambda: _spatial_upsampler
-ledger.text_encoder = lambda: _text_encoder
-ledger.gemma_embeddings_processor = lambda: _embeddings_processor
-print("All models preloaded (including Gemma text encoder)!")
+# DistilledPipeline does NOT have model_ledger — inspect at runtime instead.
+print("Inspecting DistilledPipeline internals for ZeroGPU preloading...")
+_model_attrs = [a for a in dir(pipeline) if not a.startswith('_')]
+print(f"Available attributes: {_model_attrs}")
+
+_preloaded = []
+for attr_name in _model_attrs:
+    try:
+        val = getattr(pipeline, attr_name)
+        if isinstance(val, torch.nn.Module):
+            val.eval()
+            _preloaded.append(attr_name)
+            print(f"  Preloaded module: pipeline.{attr_name}")
+    except Exception:
+        pass
+
+# Also check inner dicts like _models, models, components, etc.
+for inner in ['_models', 'models', '_components', 'components']:
+    if hasattr(pipeline, inner):
+        container = getattr(pipeline, inner)
+        if isinstance(container, dict):
+            for k, v in container.items():
+                if isinstance(v, torch.nn.Module):
+                    v.eval()
+                    _preloaded.append(f"{inner}['{k}']")
+                    print(f"  Preloaded module: pipeline.{inner}['{k}']")
+        elif isinstance(container, torch.nn.Module):
+            container.eval()
+            _preloaded.append(inner)
+            print(f"  Preloaded module: pipeline.{inner}")
+
+if not _preloaded:
+    print("WARNING: Could not preload any sub-modules. "
+          "The pipeline will load models on first __call__.")
+else:
+    print(f"Preloaded {len(_preloaded)} sub-module(s) total.")
 
 print("=" * 80)
 print("Pipeline ready!")
@@ -259,7 +275,7 @@ with gr.Blocks(title="LTX-2.3 Distilled") as demo:
                 lines=3,
                 placeholder="Describe the motion and animation you want...",
             )
-            
+
             with gr.Row():
                 duration = gr.Slider(label="Duration (seconds)", minimum=1.0, maximum=10.0, value=3.0, step=0.1)
                 with gr.Column():
@@ -308,5 +324,4 @@ css = """
 """
 
 if __name__ == "__main__":
-    demo.launch(theme=gr.themes.Citrus(), css=css)
-
+    demo.launch(theme=gr.themes.Citrus(), css=css)