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torch.compile for cosmos
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@ -162,6 +162,7 @@ NODE_CONFIG = {
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"TorchCompileControlNet": {"class": TorchCompileControlNet, "name": "TorchCompileControlNet"},
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"PatchModelPatcherOrder": {"class": PatchModelPatcherOrder, "name": "Patch Model Patcher Order"},
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"TorchCompileLTXModel": {"class": TorchCompileLTXModel, "name": "TorchCompileLTXModel"},
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"TorchCompileCosmosModel": {"class": TorchCompileCosmosModel, "name": "TorchCompileCosmosModel"},
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"PathchSageAttentionKJ": {"class": PathchSageAttentionKJ, "name": "Pathch Sage Attention KJ"},
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#instance diffusion
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@ -461,4 +461,51 @@ class TorchCompileLTXModel:
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except:
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raise RuntimeError("Failed to compile model")
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return (m, )
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class TorchCompileCosmosModel:
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def __init__(self):
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self._compiled = False
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"model": ("MODEL",),
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"backend": (["inductor", "cudagraphs"],),
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"fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
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"mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
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"dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "KJNodes/experimental"
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EXPERIMENTAL = True
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def patch(self, model, backend, mode, fullgraph, dynamic):
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m = model.clone()
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diffusion_model = m.get_model_object("diffusion_model")
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if not self._compiled:
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try:
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for name, block in diffusion_model.blocks.items():
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print(f"Compiling block {name}")
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compiled_block = torch.compile(block, mode=mode, dynamic=dynamic, fullgraph=fullgraph, backend=backend)
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m.add_object_patch(f"diffusion_model.blocks.{name}", compiled_block)
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#diffusion_model.blocks[name] = compiled_block
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self._compiled = True
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compile_settings = {
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"backend": backend,
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"mode": mode,
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"fullgraph": fullgraph,
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"dynamic": dynamic,
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}
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setattr(m.model, "compile_settings", compile_settings)
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print(model.model.diffusion_model.blocks)
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except:
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raise RuntimeError("Failed to compile model")
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return (m, )
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