@classmethod

def define_schema(cls):

return io.Schema(

node_id="LotusConditioning",

category="model/conditioning/lotus",

inputs=[],

outputs=[io.Conditioning.Output(display_name="conditioning")],

)

@classmethod

def execute(cls) -> io.NodeOutput:

device = mm.get_torch_device()

#lotus uses a frozen encoder and null conditioning, i'm just inlining the results of that operation since it doesn't change

#and getting parity with the reference implementation would otherwise require inference and 800mb of tensors

prompt_embeds = torch.tensor([[[-0.3134765625, -0.447509765625, -0.00823974609375, -0.22802734375, 0.1785888671875, -0.2342529296875, -0.2188720703125, -0.0089111328125, -0.31396484375, 0.196533203125, -0.055877685546875, -0.3828125, -0.0965576171875, 0.0073394775390625, -0.284423828125, 0.07470703125, -0.086181640625, -0.211181640625, 0.0599365234375, 0.10693359375, 0.0007929801940917969, -0.78076171875, -0.382568359375, -0.1851806640625, -0.140625, -0.0936279296875, -0.1229248046875, -0.152099609375, -0.203857421875, -0.2349853515625, -0.2437744140625, -0.10858154296875, -0.08990478515625, 0.08892822265625, -0.2391357421875, -0.1611328125, -0.427978515625, -0.1336669921875, -0.27685546875, -0.1781005859375, -0.3857421875, 0.251953125, -0.055999755859375, -0.0712890625, -0.00130462646484375, 0.033477783203125, -0.26416015625, 0.07171630859375, -0.0090789794921875, -0.2025146484375, -0.2763671875, -0.09869384765625, -0.45751953125, -0.23095703125, 0.004528045654296875, -0.369140625, 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cond = [[prompt_embeds, {}]]

@override

async def get_node_list(self) -> list[type[io.ComfyNode]]:

return [

LotusConditioning,