TorchCompileModelFluxAdvanced

__init__.py

@@ -152,6 +152,7 @@ NODE_CONFIG = {
     "FluxBlockLoraSelect": {"class": FluxBlockLoraSelect, "name": "Flux Block Lora Select"},
     "CustomControlNetWeightsFluxFromList": {"class": CustomControlNetWeightsFluxFromList, "name": "Custom ControlNet Weights Flux From List"},
     "CheckpointLoaderKJ": {"class": CheckpointLoaderKJ, "name": "CheckpointLoaderKJ"},
+    "TorchCompileModelFluxAdvanced": {"class": TorchCompileModelFluxAdvanced, "name": "TorchCompileModelFluxAdvanced"},
 
     #instance diffusion
     "CreateInstanceDiffusionTracking": {"class": CreateInstanceDiffusionTracking},

nodes/nodes.py

@@ -2238,4 +2238,62 @@ class CheckpointLoaderKJ:
         
         
         return model, clip, vae
-    
+import re
+class TorchCompileModelFluxAdvanced:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "model": ("MODEL",),
+                    "backend": (["inductor", "cudagraphs"],),
+                    "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
+                    "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
+                    "double_blocks": ("STRING", {"default": "0-18", "multiline": True}),
+                    "single_blocks": ("STRING", {"default": "0-37", "multiline": True}),
+                }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "_for_testing"
+    EXPERIMENTAL = True
+
+    def parse_blocks(self, blocks_str):
+        blocks = []
+        for part in blocks_str.split(','):
+            part = part.strip()
+            if '-' in part:
+                start, end = map(int, part.split('-'))
+                blocks.extend(range(start, end + 1))
+            else:
+                blocks.append(int(part))
+        return blocks
+
+    def compile_diffusion_model(self, diffusion_model, backend, mode, fullgraph, single_block_list, double_block_list):
+        #print("Diffusion model object before compilation:", diffusion_model)
+        for i, block in enumerate(diffusion_model.double_blocks):
+                if i in double_block_list:
+                    print("Compiling double block", i)
+                    diffusion_model.double_blocks[i] = torch.compile(block, mode=mode, fullgraph=fullgraph, backend=backend)
+        for i, block in enumerate(diffusion_model.single_blocks):
+                if i in single_block_list:
+                    print("Compiling single block", i)
+                    diffusion_model.single_blocks[i] = torch.compile(block, mode=mode, fullgraph=fullgraph, backend=backend)
+
+        # diffusion_model.final_layer = torch.compile(diffusion_model.final_layer, mode=mode, fullgraph=fullgraph, backend=backend)
+        # diffusion_model.guidance_in = torch.compile(diffusion_model.guidance_in, mode=mode, fullgraph=fullgraph, backend=backend)
+        # diffusion_model.img_in = torch.compile(diffusion_model.img_in, mode=mode, fullgraph=fullgraph, backend=backend)
+        # diffusion_model.time_in = torch.compile(diffusion_model.time_in, mode=mode, fullgraph=fullgraph, backend=backend)
+        # diffusion_model.txt_in = torch.compile(diffusion_model.txt_in, mode=mode, fullgraph=fullgraph, backend=backend)
+        # diffusion_model.vector_in = torch.compile(diffusion_model.vector_in, mode=mode, fullgraph=fullgraph, backend=backend)
+        
+        #compiled_model = torch.compile(model=diffusion_model, backend=backend)
+        #print("Compiled diffusion model object:", compiled_model)
+        return diffusion_model
+    
+    def patch(self, model, backend, mode, fullgraph, single_blocks, double_blocks):
+        single_block_list = self.parse_blocks(single_blocks)
+        double_block_list = self.parse_blocks(double_blocks)
+        m = model.clone()
+        diffusion_model = m.get_model_object("diffusion_model")
+        #self.compile_diffusion_model(diffusion_model, backend, mode, fullgraph, single_block_list, double_block_list)
+        m.add_object_patch("diffusion_model", self.compile_diffusion_model(diffusion_model, backend, mode, fullgraph, single_block_list, double_block_list))
+        return (m, )