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TorchCompileModelWanVideo

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kijai 2025-02-26 11:10:17 +02:00
parent 24bb774432
commit 1a4259f052
2 changed files with 52 additions and 6 deletions
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1
__init__.py
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@ -176,6 +176,7 @@ NODE_CONFIG = {
"PatchModelPatcherOrder": {"class": PatchModelPatcherOrder, "name": "Patch Model Patcher Order"},
"TorchCompileLTXModel": {"class": TorchCompileLTXModel, "name": "TorchCompileLTXModel"},
"TorchCompileCosmosModel": {"class": TorchCompileCosmosModel, "name": "TorchCompileCosmosModel"},
"TorchCompileModelWanVideo": {"class": TorchCompileModelWanVideo, "name": "TorchCompileModelWanVideo"},
"PathchSageAttentionKJ": {"class": PathchSageAttentionKJ, "name": "Patch Sage Attention KJ"},
"LeapfusionHunyuanI2VPatcher": {"class": LeapfusionHunyuanI2V, "name": "Leapfusion Hunyuan I2V Patcher"},
"VAELoaderKJ": {"class": VAELoaderKJ, "name": "VAELoader KJ"},

57
nodes/model_optimization_nodes.py
View File

@ -303,7 +303,7 @@ class TorchCompileModelFluxAdvanced:
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "KJNodes/experimental"
CATEGORY = "KJNodes/torchcompile"
EXPERIMENTAL = True
def parse_blocks(self, blocks_str):
@ -378,7 +378,7 @@ class TorchCompileModelHyVideo:
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "KJNodes/experimental"
CATEGORY = "KJNodes/torchcompile"
EXPERIMENTAL = True
def patch(self, model, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_single_blocks, compile_double_blocks, compile_txt_in, compile_vector_in, compile_final_layer):
@ -415,6 +415,51 @@ class TorchCompileModelHyVideo:
except:
raise RuntimeError("Failed to compile model")
return (m, )
class TorchCompileModelWanVideo:
def __init__(self):
self._compiled = False
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"backend": (["inductor","cudagraphs"], {"default": "inductor"}),
"fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
"mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
"dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}),
"dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}),
"compile_transformer_blocks": ("BOOLEAN", {"default": True, "tooltip": "Compile all transformer blocks"}),
},
}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "KJNodes/torchcompile"
EXPERIMENTAL = True
def patch(self, model, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_transformer_blocks):
m = model.clone()
diffusion_model = m.get_model_object("diffusion_model")
torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit
if not self._compiled:
try:
if compile_transformer_blocks:
for i, block in enumerate(diffusion_model.blocks):
compiled_block = torch.compile(block, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode)
m.add_object_patch(f"diffusion_model.blocks.{i}", compiled_block)
self._compiled = True
compile_settings = {
"backend": backend,
"mode": mode,
"fullgraph": fullgraph,
"dynamic": dynamic,
}
setattr(m.model, "compile_settings", compile_settings)
except:
raise RuntimeError("Failed to compile model")
return (m, )
class TorchCompileVAE:
def __init__(self):
@ -434,7 +479,7 @@ class TorchCompileVAE:
RETURN_TYPES = ("VAE",)
FUNCTION = "compile"
CATEGORY = "KJNodes/experimental"
CATEGORY = "KJNodes/torchcompile"
EXPERIMENTAL = True
def compile(self, vae, backend, mode, fullgraph, compile_encoder, compile_decoder):
@ -495,7 +540,7 @@ class TorchCompileControlNet:
RETURN_TYPES = ("CONTROL_NET",)
FUNCTION = "compile"
CATEGORY = "KJNodes/experimental"
CATEGORY = "KJNodes/torchcompile"
EXPERIMENTAL = True
def compile(self, controlnet, backend, mode, fullgraph):
@ -528,7 +573,7 @@ class TorchCompileLTXModel:
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "KJNodes/experimental"
CATEGORY = "KJNodes/torchcompile"
EXPERIMENTAL = True
def patch(self, model, backend, mode, fullgraph, dynamic):
@ -571,7 +616,7 @@ class TorchCompileCosmosModel:
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "KJNodes/experimental"
CATEGORY = "KJNodes/torchcompile"
EXPERIMENTAL = True
def patch(self, model, backend, mode, fullgraph, dynamic, dynamo_cache_size_limit):