| import torch
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| import torch.nn as nn
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| from packaging import version
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| OPENAIUNETWRAPPER = "sgm.modules.diffusionmodules.wrappers.OpenAIWrapper"
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|
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|
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| class IdentityWrapper(nn.Module):
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| def __init__(self, diffusion_model, compile_model: bool = False):
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| super().__init__()
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| compile = (
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| torch.compile
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| if (version.parse(torch.__version__) >= version.parse("2.0.0"))
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| and compile_model
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| else lambda x: x
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| )
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| self.diffusion_model = compile(diffusion_model)
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|
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| def forward(self, *args, **kwargs):
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| return self.diffusion_model(*args, **kwargs)
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|
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| class OpenAIWrapper(IdentityWrapper):
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| def forward(
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| self, x: torch.Tensor, t: torch.Tensor, c: dict, **kwargs
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| ) -> torch.Tensor:
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| x = torch.cat((x, c.get("concat", torch.Tensor([]).type_as(x))), dim=1)
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| return self.diffusion_model(
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| x,
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| timesteps=t,
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| context=c.get("crossattn", None),
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| y=c.get("vector", None),
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| **kwargs,
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| )
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| class OpenAIHalfWrapper(IdentityWrapper):
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| def __init__(self, *args, **kwargs):
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| super().__init__(*args, **kwargs)
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| self.diffusion_model = self.diffusion_model.half()
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|
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| def forward(
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| self, x: torch.Tensor, t: torch.Tensor, c: dict, **kwargs
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| ) -> torch.Tensor:
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| x = torch.cat((x, c.get("concat", torch.Tensor([]).type_as(x))), dim=1)
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| _context = c.get("crossattn", None)
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| _y = c.get("vector", None)
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| if _context is not None:
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| _context = _context.half()
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| if _y is not None:
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| _y = _y.half()
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| x = x.half()
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| t = t.half()
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|
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| out = self.diffusion_model(
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| x,
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| timesteps=t,
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| context=_context,
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| y=_y,
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| **kwargs,
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| )
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| return out.float()
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|
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|
|
| class ControlWrapper(nn.Module):
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| def __init__(self, diffusion_model, compile_model: bool = False, dtype=torch.float32):
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| super().__init__()
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| self.compile = (
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| torch.compile
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| if (version.parse(torch.__version__) >= version.parse("2.0.0"))
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| and compile_model
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| else lambda x: x
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| )
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| self.diffusion_model = self.compile(diffusion_model)
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| self.control_model = None
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| self.dtype = dtype
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|
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| def load_control_model(self, control_model):
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| self.control_model = self.compile(control_model)
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|
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| def forward(
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| self, x: torch.Tensor, t: torch.Tensor, c: dict, control_scale=1, **kwargs
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| ) -> torch.Tensor:
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| with torch.autocast("cuda", dtype=self.dtype):
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| control = self.control_model(x=c.get("control", None), timesteps=t, xt=x,
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| control_vector=c.get("control_vector", None),
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| mask_x=c.get("mask_x", None),
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| context=c.get("crossattn", None),
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| y=c.get("vector", None))
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| out = self.diffusion_model(
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| x,
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| timesteps=t,
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| context=c.get("crossattn", None),
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| y=c.get("vector", None),
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| control=control,
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| control_scale=control_scale,
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| **kwargs,
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| )
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| return out.float()
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|
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|
