File size: 2,007 Bytes
920fd91 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | import torch
from .general_modules import TemporalTimesteps
class MultiValueEncoder(torch.nn.Module):
def __init__(self, encoders=()):
super().__init__()
if not isinstance(encoders, list):
encoders = [encoders]
self.encoders = torch.nn.ModuleList(encoders)
def __call__(self, values, dtype):
emb = []
for encoder, value in zip(self.encoders, values):
if value is not None:
value = value.unsqueeze(0)
emb.append(encoder(value, dtype))
emb = torch.concat(emb, dim=0)
return emb
class SingleValueEncoder(torch.nn.Module):
def __init__(self, dim_in=256, dim_out=4096, prefer_len=32, computation_device=None):
super().__init__()
self.prefer_len = prefer_len
self.prefer_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0, computation_device=computation_device)
self.prefer_value_embedder = torch.nn.Sequential(
torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out)
)
self.positional_embedding = torch.nn.Parameter(
torch.randn(self.prefer_len, dim_out)
)
def forward(self, value, dtype):
value = value * 1000
emb = self.prefer_proj(value).to(dtype)
emb = self.prefer_value_embedder(emb).squeeze(0)
base_embeddings = emb.expand(self.prefer_len, -1)
positional_embedding = self.positional_embedding.to(dtype=base_embeddings.dtype, device=base_embeddings.device)
learned_embeddings = base_embeddings + positional_embedding
return learned_embeddings
@staticmethod
def state_dict_converter():
return SingleValueEncoderStateDictConverter()
class SingleValueEncoderStateDictConverter:
def __init__(self):
pass
def from_diffusers(self, state_dict):
return state_dict
def from_civitai(self, state_dict):
return state_dict
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