Update app.py

app.py

@@ -180,36 +180,69 @@ print("=" * 80)
 # =============================================================================
 print("Preloading all models for ZeroGPU tensor packing...")
 print("This may take a few minutes...")
-print("  Running dummy inference to load all model weights...")
-
-# Create a dummy inference call to force ALL models to load at startup
-# This triggers lazy loading for all components without needing internal attribute names
-try:
-    _ = pipeline(
-        prompt="init",
-        negative_prompt="",
-        seed=0,
-        height=256,
-        width=256,
-        num_frames=9,  # 8*1+1 = minimum frames
-        frame_rate=DEFAULT_FRAME_RATE,
-        num_inference_steps=1,  # Single step for dummy init
-        video_guider_params=MultiModalGuiderParams(
-            cfg_scale=1.0, stg_scale=0.0, rescale_scale=0.0,
-            modality_scale=0.0, skip_step=0, stg_blocks=[]
-        ),
-        audio_guider_params=MultiModalGuiderParams(
-            cfg_scale=1.0, stg_scale=0.0, rescale_scale=0.0,
-            modality_scale=0.0, skip_step=0, stg_blocks=[]
-        ),
-        images=[],
-        enhance_prompt=False,
-    )
-    print("All models preloaded for ZeroGPU tensor packing!")
-except Exception as e:
-    print(f"Warning: Dummy inference preload failed (will retry during generation): {e}")
-    print("Continuing startup...")
 
+# Inspect available attributes on key components
+print("  Inspecting DiffusionStage attributes...")
+stage_attrs = [a for a in dir(pipeline.stage_1) if not a.startswith('__')]
+print(f"    DiffusionStage attributes: {stage_attrs}")
+
+# For DiffusionStage, the transformer is accessed via _transformer_ctx
+# but we need to trigger actual loading by accessing the context
+if hasattr(pipeline.stage_1, '_transformer_ctx'):
+    print("  Loading stage 1 transformer via _transformer_ctx...")
+    ctx = pipeline.stage_1._transformer_ctx
+    if hasattr(ctx, '__enter__'):
+        ctx.__enter__()  # Force context entry to load transformer
+
+if hasattr(pipeline.stage_2, '_transformer_ctx'):
+    print("  Loading stage 2 transformer via _transformer_ctx...")
+    ctx = pipeline.stage_2._transformer_ctx
+    if hasattr(ctx, '__enter__'):
+        ctx.__enter__()
+
+# Inspect PromptEncoder attributes  
+print("  Inspecting PromptEncoder attributes...")
+pe_attrs = [a for a in dir(pipeline.prompt_encoder) if not a.startswith('__')]
+print(f"    PromptEncoder attributes: {pe_attrs}")
+
+# Try common names for video encoder in PromptEncoder
+for attr_name in ['video_encoder', '_video_encoder', 'enc', 'encoder', '_enc']:
+    if hasattr(pipeline.prompt_encoder, attr_name):
+        print(f"  Loading video encoder via .{attr_name}...")
+        _ = getattr(pipeline.prompt_encoder, attr_name)
+        break
+
+# Inspect and load VideoDecoder
+print("  Inspecting VideoDecoder attributes...")
+vd_attrs = [a for a in dir(pipeline.video_decoder) if not a.startswith('__')]
+print(f"    VideoDecoder attributes: {vd_attrs}")
+for attr_name in ['model', 'decoder', '_model']:
+    if hasattr(pipeline.video_decoder, attr_name):
+        print(f"  Loading video decoder via .{attr_name}...")
+        _ = getattr(pipeline.video_decoder, attr_name)
+        break
+
+# Inspect and load AudioDecoder
+print("  Inspecting AudioDecoder attributes...")
+ad_attrs = [a for a in dir(pipeline.audio_decoder) if not a.startswith('__')]
+print(f"    AudioDecoder attributes: {ad_attrs}")
+for attr_name in ['model', 'decoder', '_model']:
+    if hasattr(pipeline.audio_decoder, attr_name):
+        print(f"  Loading audio decoder via .{attr_name}...")
+        _ = getattr(pipeline.audio_decoder, attr_name)
+        break
+
+# Inspect and load VideoUpsampler
+print("  Inspecting VideoUpsampler attributes...")
+up_attrs = [a for a in dir(pipeline.upsampler) if not a.startswith('__')]
+print(f"    VideoUpsampler attributes: {up_attrs}")
+for attr_name in ['model', 'upsampler', '_model']:
+    if hasattr(pipeline.upsampler, attr_name):
+        print(f"  Loading spatial upsampler via .{attr_name}...")
+        _ = getattr(pipeline.upsampler, attr_name)
+        break
+
+print("All models preloaded for ZeroGPU tensor packing!")
 print("=" * 80)
 
 # =============================================================================