ComfyUI/comfy_extras/nodes_lotus.py

import torch
import comfy.model_management as mm

class LotusConditioning:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
            },
        }

    RETURN_TYPES = ("CONDITIONING",)
    RETURN_NAMES = ("conditioning",)
    FUNCTION = "conditioning"
    CATEGORY = "conditioning/lotus"

    def conditioning(self):
        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, -0.366943359375, -0.205322265625, -0.1505126953125, -0.45166015625, -0.2059326171875, 0.0168609619140625, -0.305419921875, -0.150634765625, 0.02685546875, -0.609375, -0.019012451171875, 0.050445556640625, -0.0084381103515625, -0.31005859375, -0.184326171875, -0.15185546875, 0.06732177734375, 0.150390625, -0.10919189453125, -0.08837890625, -0.50537109375, -0.389892578125, -0.0294342041015625, -0.10491943359375, -0.187255859375, -0.43212890625, -0.328125, -1.060546875, 0.011871337890625, 0.04730224609375, -0.09521484375, -0.07452392578125, -0.29296875, -0.109130859375, -0.250244140625, -0.3828125, -0.171875, -0.03399658203125, -0.15478515625, -0.1861572265625, -0.2398681640625, 0.1053466796875, -0.22314453125, -0.1932373046875, -0.18798828125, -0.430419921875, -0.05364990234375, -0.474609375, -0.261474609375, -0.1077880859375, -0.439208984375, 0.08966064453125, -0.185302734375, -0.338134765625, -0.297119140625, -0.298583984375, -0.175537109375, -0.373291015625, -0.1397705078125, -0.260498046875, -0.383544921875, -0.09979248046875, -0.319580078125, -0.06884765625, -0.4365234375, -0.183837890625, -0.393310546875, -0.002277374267578125, 0.11236572265625, -0.260498046875, -0.2242431640625, -0.19384765625, -0.51123046875, 0.03216552734375, -0.048004150390625, -0.279052734375, -0.2978515625, -0.255615234375, 0.115478515625, -4.08984375, -0.1668701171875, -0.278076171875, -0.5712890625, -0.1385498046875, -0.244384765625, -0.41455078125, -0.244140625, -0.0677490234375, -0.141357421875, -0.11590576171875, -0.1439208984375, -0.0185394287109375, -2.490234375, -0.1549072265625, -0.2305908203125, -0.3828125, -0.1173095703125, -0.08258056640625, -0.1719970703125, -0.325439453125, -0.292724609375, -0.08154296875, -0.412353515625, -0.3115234375, -0.00832366943359375, 0.00489044189453125, -0.2236328125, -0.151123046875, -0.457275390625, -0.135009765625, -0.163330078125, -0.0819091796875, 0.06689453125, 0.0209197998046875, -0.11907958984375, -0.10369873046875, -0.2998046875, -0.478759765625, -0.07940673828125, -0.01517486572265625, -0.3017578125, -0.343994140625, -0.258544921875, -0.44775390625, -0.392822265625, -0.0255584716796875, -0.2998046875, 0.10833740234375, -0.271728515625, -0.36181640625, -0.255859375, -0.2056884765625, -0.055450439453125, 0.060516357421875, -0.45751953125, -0.2322998046875, -0.1737060546875, -0.40576171875, -0.2286376953125, -0.053070068359375, -0.0283660888671875, -0.1898193359375, -4.291534423828125e-05, -0.6591796875, -0.1717529296875, -0.479736328125, -0.1400146484375, -0.40771484375, 0.154296875, 0.003101348876953125, 0.00661468505859375, -0.2073974609375, -0.493408203125, 2.171875, -0.45361328125, -0.283935546875, -0.302001953125, -0.25146484375, -0.207275390625, -0.1524658203125, -0.72998046875, -0.08203125, 0.053192138671875, -0.2685546875, 0.1834716796875, -0.270263671875, -0.091552734375, -0.08319091796875, -0.1297607421875, -0.453857421875, 0.0687255859375, 0.0268096923828125, -0.16552734375, -0.4208984375, -0.1552734375, -0.057373046875, -0.300537109375, -0.04541015625, -0.486083984375, -0.2205810546875, -0.3901367

        cond = [[prompt_embeds, {}]]

        return (cond,)

NODE_CLASS_MAPPINGS = {
    "LotusConditioning" : LotusConditioning,
}