#@spaces.GPU(duration=1500)

optimization.py

@@ -1,135 +1,135 @@
-"""
-"""
-
-from typing import Any
-from typing import Callable
-from typing import ParamSpec
-
-import spaces
-import torch
-from torch.utils._pytree import tree_map_only
-from torchao.quantization import quantize_
-from torchao.quantization import Float8DynamicActivationFloat8WeightConfig
-from torchao.quantization import Int8WeightOnlyConfig
-
-from optimization_utils import capture_component_call
-from optimization_utils import aoti_compile
-from optimization_utils import drain_module_parameters
-
-
-P = ParamSpec('P')
-
-# --- CORRECTED DYNAMIC SHAPING ---
-
-# VAE temporal scale factor is 1, latent_frames = num_frames. Range is [8, 81].
-LATENT_FRAMES_DIM = torch.export.Dim('num_latent_frames', min=8, max=81)
-
-# The transformer has a patch_size of (1, 2, 2), which means the input latent height and width
-# are effectively divided by 2. This creates constraints that fail if the symbolic tracer
-# assumes odd numbers are possible.
-#
-# To solve this, we define the dynamic dimension for the *patched* (i.e., post-division) size,
-# and then express the input shape as 2 * this dimension. This mathematically guarantees
-# to the compiler that the input latent dimensions are always even, satisfying the constraints.
-
-# App range for pixel dimensions: [480, 832]. VAE scale factor is 8.
-# Latent dimension range: [480/8, 832/8] = [60, 104].
-# Patched latent dimension range: [60/2, 104/2] = [30, 52].
-LATENT_PATCHED_HEIGHT_DIM = torch.export.Dim('latent_patched_height', min=30, max=52)
-LATENT_PATCHED_WIDTH_DIM = torch.export.Dim('latent_patched_width', min=30, max=52)
-
-# Now, we define the dynamic shapes for the transformer's `hidden_states` input,
-# which has the shape (batch_size, channels, num_frames, height, width).
-TRANSFORMER_DYNAMIC_SHAPES = {
-    'hidden_states': {
-        2: LATENT_FRAMES_DIM,
-        3: 2 * LATENT_PATCHED_HEIGHT_DIM, # Guarantees even height
-        4: 2 * LATENT_PATCHED_WIDTH_DIM,  # Guarantees even width
-    },
-}
-
-# --- END OF CORRECTION ---
-
-
-INDUCTOR_CONFIGS = {
-    'conv_1x1_as_mm': True,
-    'epilogue_fusion': False,
-    'coordinate_descent_tuning': True,
-    'coordinate_descent_check_all_directions': True,
-    'max_autotune': True,
-    'triton.cudagraphs': True,
-}
-
-
-def optimize_pipeline_(pipeline: Callable[P, Any], *args: P.args, **kwargs: P.kwargs):
-
-    @spaces.GPU(duration=1500)
-    def compile_transformer():
-        print("Start compile_transformer()")
-        
-        # This LoRA fusion part remains the same
-        pipeline.load_lora_weights(
-            "Kijai/WanVideo_comfy", 
-            weight_name="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors", 
-            adapter_name="lightx2v"
-        )
-        kwargs_lora = {}
-        kwargs_lora["load_into_transformer_2"] = True
-        pipeline.load_lora_weights(
-            "Kijai/WanVideo_comfy", 
-            weight_name="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors", 
-            adapter_name="lightx2v_2", **kwargs_lora
-        )
-        pipeline.set_adapters(["lightx2v", "lightx2v_2"], adapter_weights=[1., 1.])
-        pipeline.fuse_lora(adapter_names=["lightx2v"], lora_scale=3., components=["transformer"])
-        pipeline.fuse_lora(adapter_names=["lightx2v_2"], lora_scale=1., components=["transformer_2"])
-        pipeline.unload_lora_weights()
-        
-        # Capture a single call to get the args/kwargs structure
-        with capture_component_call(pipeline, 'transformer') as call:
-            pipeline(*args, **kwargs)
-        
-        dynamic_shapes = tree_map_only((torch.Tensor, bool), lambda t: None, call.kwargs)
-        dynamic_shapes |= TRANSFORMER_DYNAMIC_SHAPES
-
-        # Quantization remains the same
-        quantize_(pipeline.transformer, Float8DynamicActivationFloat8WeightConfig())
-        quantize_(pipeline.transformer_2, Float8DynamicActivationFloat8WeightConfig())
-        
-        # --- SIMPLIFIED COMPILATION ---
-        
-        exported_1 = torch.export.export(
-            mod=pipeline.transformer,
-            args=call.args,
-            kwargs=call.kwargs,
-            dynamic_shapes=dynamic_shapes,
-        )
-        
-        exported_2 = torch.export.export(
-            mod=pipeline.transformer_2,
-            args=call.args,
-            kwargs=call.kwargs,
-            dynamic_shapes=dynamic_shapes,
-        )
-
-        compiled_1 = aoti_compile(exported_1, INDUCTOR_CONFIGS)
-        compiled_2 = aoti_compile(exported_2, INDUCTOR_CONFIGS)
-        
-        # Return the two compiled models
-        print("End compile_transformer()")
-        return compiled_1, compiled_2
-
-
-    # Quantize text encoder (same as before)
-    quantize_(pipeline.text_encoder, Int8WeightOnlyConfig())
-    
-    # Get the two dynamically-shaped compiled models
-    compiled_transformer_1, compiled_transformer_2 = compile_transformer()
-
-    # --- SIMPLIFIED ASSIGNMENT ---
-    
-    pipeline.transformer.forward = compiled_transformer_1
-    drain_module_parameters(pipeline.transformer)
-
-    pipeline.transformer_2.forward = compiled_transformer_2
+"""
+"""
+
+from typing import Any
+from typing import Callable
+from typing import ParamSpec
+
+import spaces
+import torch
+from torch.utils._pytree import tree_map_only
+from torchao.quantization import quantize_
+from torchao.quantization import Float8DynamicActivationFloat8WeightConfig
+from torchao.quantization import Int8WeightOnlyConfig
+
+from optimization_utils import capture_component_call
+from optimization_utils import aoti_compile
+from optimization_utils import drain_module_parameters
+
+
+P = ParamSpec('P')
+
+# --- CORRECTED DYNAMIC SHAPING ---
+
+# VAE temporal scale factor is 1, latent_frames = num_frames. Range is [8, 81].
+LATENT_FRAMES_DIM = torch.export.Dim('num_latent_frames', min=8, max=81)
+
+# The transformer has a patch_size of (1, 2, 2), which means the input latent height and width
+# are effectively divided by 2. This creates constraints that fail if the symbolic tracer
+# assumes odd numbers are possible.
+#
+# To solve this, we define the dynamic dimension for the *patched* (i.e., post-division) size,
+# and then express the input shape as 2 * this dimension. This mathematically guarantees
+# to the compiler that the input latent dimensions are always even, satisfying the constraints.
+
+# App range for pixel dimensions: [480, 832]. VAE scale factor is 8.
+# Latent dimension range: [480/8, 832/8] = [60, 104].
+# Patched latent dimension range: [60/2, 104/2] = [30, 52].
+LATENT_PATCHED_HEIGHT_DIM = torch.export.Dim('latent_patched_height', min=30, max=52)
+LATENT_PATCHED_WIDTH_DIM = torch.export.Dim('latent_patched_width', min=30, max=52)
+
+# Now, we define the dynamic shapes for the transformer's `hidden_states` input,
+# which has the shape (batch_size, channels, num_frames, height, width).
+TRANSFORMER_DYNAMIC_SHAPES = {
+    'hidden_states': {
+        2: LATENT_FRAMES_DIM,
+        3: 2 * LATENT_PATCHED_HEIGHT_DIM, # Guarantees even height
+        4: 2 * LATENT_PATCHED_WIDTH_DIM,  # Guarantees even width
+    },
+}
+
+# --- END OF CORRECTION ---
+
+
+INDUCTOR_CONFIGS = {
+    'conv_1x1_as_mm': True,
+    'epilogue_fusion': False,
+    'coordinate_descent_tuning': True,
+    'coordinate_descent_check_all_directions': True,
+    'max_autotune': True,
+    'triton.cudagraphs': True,
+}
+
+
+def optimize_pipeline_(pipeline: Callable[P, Any], *args: P.args, **kwargs: P.kwargs):
+
+    #@spaces.GPU(duration=1500)
+    def compile_transformer():
+        print("Start compile_transformer()")
+        
+        # This LoRA fusion part remains the same
+        pipeline.load_lora_weights(
+            "Kijai/WanVideo_comfy", 
+            weight_name="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors", 
+            adapter_name="lightx2v"
+        )
+        kwargs_lora = {}
+        kwargs_lora["load_into_transformer_2"] = True
+        pipeline.load_lora_weights(
+            "Kijai/WanVideo_comfy", 
+            weight_name="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors", 
+            adapter_name="lightx2v_2", **kwargs_lora
+        )
+        pipeline.set_adapters(["lightx2v", "lightx2v_2"], adapter_weights=[1., 1.])
+        pipeline.fuse_lora(adapter_names=["lightx2v"], lora_scale=3., components=["transformer"])
+        pipeline.fuse_lora(adapter_names=["lightx2v_2"], lora_scale=1., components=["transformer_2"])
+        pipeline.unload_lora_weights()
+        
+        # Capture a single call to get the args/kwargs structure
+        with capture_component_call(pipeline, 'transformer') as call:
+            pipeline(*args, **kwargs)
+        
+        dynamic_shapes = tree_map_only((torch.Tensor, bool), lambda t: None, call.kwargs)
+        dynamic_shapes |= TRANSFORMER_DYNAMIC_SHAPES
+
+        # Quantization remains the same
+        quantize_(pipeline.transformer, Float8DynamicActivationFloat8WeightConfig())
+        quantize_(pipeline.transformer_2, Float8DynamicActivationFloat8WeightConfig())
+        
+        # --- SIMPLIFIED COMPILATION ---
+        
+        exported_1 = torch.export.export(
+            mod=pipeline.transformer,
+            args=call.args,
+            kwargs=call.kwargs,
+            dynamic_shapes=dynamic_shapes,
+        )
+        
+        exported_2 = torch.export.export(
+            mod=pipeline.transformer_2,
+            args=call.args,
+            kwargs=call.kwargs,
+            dynamic_shapes=dynamic_shapes,
+        )
+
+        compiled_1 = aoti_compile(exported_1, INDUCTOR_CONFIGS)
+        compiled_2 = aoti_compile(exported_2, INDUCTOR_CONFIGS)
+        
+        # Return the two compiled models
+        print("End compile_transformer()")
+        return compiled_1, compiled_2
+
+
+    # Quantize text encoder (same as before)
+    quantize_(pipeline.text_encoder, Int8WeightOnlyConfig())
+    
+    # Get the two dynamically-shaped compiled models
+    compiled_transformer_1, compiled_transformer_2 = compile_transformer()
+
+    # --- SIMPLIFIED ASSIGNMENT ---
+    
+    pipeline.transformer.forward = compiled_transformer_1
+    drain_module_parameters(pipeline.transformer)
+
+    pipeline.transformer_2.forward = compiled_transformer_2
     drain_module_parameters(pipeline.transformer_2)