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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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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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def forward(self, *args, **kwargs):
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return self.diffusion_model(*args, **kwargs)
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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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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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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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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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def load_control_model(self, control_model):
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self.control_model = self.compile(control_model)
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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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