Book a Demo!
CoCalc Logo Icon
StoreFeaturesDocsShareSupportNewsAboutPoliciesSign UpSign In
shivamshrirao
GitHub Repository: shivamshrirao/diffusers
 Path: blob/main/scripts/convert_models_diffuser_to_diffusers.py
1440 views
1
import json

2
import os

3


4
import torch

5


6
from diffusers import UNet1DModel

7


8


9
os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)

10
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)

11


12
os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)

13


14


15
def unet(hor):

16
    if hor == 128:

17
        down_block_types = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")

18
        block_out_channels = (32, 128, 256)

19
        up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D")

20


21
    elif hor == 32:

22
        down_block_types = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")

23
        block_out_channels = (32, 64, 128, 256)

24
        up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")

25
    model = torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch")

26
    state_dict = model.state_dict()

27
    config = dict(

28
        down_block_types=down_block_types,

29
        block_out_channels=block_out_channels,

30
        up_block_types=up_block_types,

31
        layers_per_block=1,

32
        use_timestep_embedding=True,

33
        out_block_type="OutConv1DBlock",

34
        norm_num_groups=8,

35
        downsample_each_block=False,

36
        in_channels=14,

37
        out_channels=14,

38
        extra_in_channels=0,

39
        time_embedding_type="positional",

40
        flip_sin_to_cos=False,

41
        freq_shift=1,

42
        sample_size=65536,

43
        mid_block_type="MidResTemporalBlock1D",

44
        act_fn="mish",

45
    )

46
    hf_value_function = UNet1DModel(**config)

47
    print(f"length of state dict: {len(state_dict.keys())}")

48
    print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")

49
    mapping = dict((k, hfk) for k, hfk in zip(model.state_dict().keys(), hf_value_function.state_dict().keys()))

50
    for k, v in mapping.items():

51
        state_dict[v] = state_dict.pop(k)

52
    hf_value_function.load_state_dict(state_dict)

53


54
    torch.save(hf_value_function.state_dict(), f"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin")

55
    with open(f"hub/hopper-medium-v2/unet/hor{hor}/config.json", "w") as f:

56
        json.dump(config, f)

57


58


59
def value_function():

60
    config = dict(

61
        in_channels=14,

62
        down_block_types=("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),

63
        up_block_types=(),

64
        out_block_type="ValueFunction",

65
        mid_block_type="ValueFunctionMidBlock1D",

66
        block_out_channels=(32, 64, 128, 256),

67
        layers_per_block=1,

68
        downsample_each_block=True,

69
        sample_size=65536,

70
        out_channels=14,

71
        extra_in_channels=0,

72
        time_embedding_type="positional",

73
        use_timestep_embedding=True,

74
        flip_sin_to_cos=False,

75
        freq_shift=1,

76
        norm_num_groups=8,

77
        act_fn="mish",

78
    )

79


80
    model = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch")

81
    state_dict = model

82
    hf_value_function = UNet1DModel(**config)

83
    print(f"length of state dict: {len(state_dict.keys())}")

84
    print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")

85


86
    mapping = dict((k, hfk) for k, hfk in zip(state_dict.keys(), hf_value_function.state_dict().keys()))

87
    for k, v in mapping.items():

88
        state_dict[v] = state_dict.pop(k)

89


90
    hf_value_function.load_state_dict(state_dict)

91


92
    torch.save(hf_value_function.state_dict(), "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin")

93
    with open("hub/hopper-medium-v2/value_function/config.json", "w") as f:

94
        json.dump(config, f)

95


96


97
if __name__ == "__main__":

98
    unet(32)

99
    # unet(128)

100
    value_function()

101


102