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): 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) 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: x = torch.cat((x, c.get("concat", torch.Tensor([]).type_as(x))), dim=1) return self.diffusion_model( x, timesteps=t, context=c.get("crossattn", None), add_context=c.get("add_crossattn", None), y=c.get("vector", None), **kwargs )