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import torch
import torch.nn as nn
from packaging import version
OPENAIUNETWRAPPER = "sgm.modules.diffusionmodules.wrappers.OpenAIWrapper"
class IdentityWrapper(nn.Module):
def __init__(self, diffusion_model, compile_model: bool = False, dtype: torch.dtype = torch.float32):
super().__init__()
compile = (
torch.compile
if (version.parse(torch.__version__) >= version.parse("2.0.0")) and compile_model
else lambda x: x
)
self.diffusion_model = compile(diffusion_model)
self.dtype = dtype
def forward(self, *args, **kwargs):
return self.diffusion_model(*args, **kwargs)
class OpenAIWrapper(IdentityWrapper):
def forward(self, x: torch.Tensor, t: torch.Tensor, c: dict, **kwargs) -> torch.Tensor:
for key in c:
c[key] = c[key].to(self.dtype)
if x.dim() == 4:
x = torch.cat((x, c.get("concat", torch.Tensor([]).type_as(x))), dim=1)
elif x.dim() == 5:
x = torch.cat((x, c.get("concat", torch.Tensor([]).type_as(x))), dim=2)
else:
raise ValueError("Input tensor must be 4D or 5D")
return self.diffusion_model(
x,
timesteps=t,
context=c.get("crossattn", None),
y=c.get("vector", None),
**kwargs,
)
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