File: //opt/sd-webui/modules/sd_hijack_unet.py
# import torch
# from packaging import version
# from einops import repeat
# import math
#
# from modules import devices
# from modules.sd_hijack_utils import CondFunc
#
#
# class TorchHijackForUnet:
# """
# This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match;
# this makes it possible to create pictures with dimensions that are multiples of 8 rather than 64
# """
#
# def __getattr__(self, item):
# if item == 'cat':
# return self.cat
#
# if hasattr(torch, item):
# return getattr(torch, item)
#
# raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
#
# def cat(self, tensors, *args, **kwargs):
# if len(tensors) == 2:
# a, b = tensors
# if a.shape[-2:] != b.shape[-2:]:
# a = torch.nn.functional.interpolate(a, b.shape[-2:], mode="nearest")
#
# tensors = (a, b)
#
# return torch.cat(tensors, *args, **kwargs)
#
#
# th = TorchHijackForUnet()
#
#
# # Below are monkey patches to enable upcasting a float16 UNet for float32 sampling
# def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
# """Always make sure inputs to unet are in correct dtype."""
# if isinstance(cond, dict):
# for y in cond.keys():
# if isinstance(cond[y], list):
# cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]]
# else:
# cond[y] = cond[y].to(devices.dtype_unet) if isinstance(cond[y], torch.Tensor) else cond[y]
#
# with devices.autocast():
# result = orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs)
# if devices.unet_needs_upcast:
# return result.float()
# else:
# return result
#
#
# # Monkey patch to create timestep embed tensor on device, avoiding a block.
# def timestep_embedding(_, timesteps, dim, max_period=10000, repeat_only=False):
# """
# Create sinusoidal timestep embeddings.
# :param timesteps: a 1-D Tensor of N indices, one per batch element.
# These may be fractional.
# :param dim: the dimension of the output.
# :param max_period: controls the minimum frequency of the embeddings.
# :return: an [N x dim] Tensor of positional embeddings.
# """
# if not repeat_only:
# half = dim // 2
# freqs = torch.exp(
# -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) / half
# )
# args = timesteps[:, None].float() * freqs[None]
# embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
# if dim % 2:
# embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
# else:
# embedding = repeat(timesteps, 'b -> b d', d=dim)
# return embedding
#
#
# # Monkey patch to SpatialTransformer removing unnecessary contiguous calls.
# # Prevents a lot of unnecessary aten::copy_ calls
# def spatial_transformer_forward(_, self, x: torch.Tensor, context=None):
# # note: if no context is given, cross-attention defaults to self-attention
# if not isinstance(context, list):
# context = [context]
# b, c, h, w = x.shape
# x_in = x
# x = self.norm(x)
# if not self.use_linear:
# x = self.proj_in(x)
# x = x.permute(0, 2, 3, 1).reshape(b, h * w, c)
# if self.use_linear:
# x = self.proj_in(x)
# for i, block in enumerate(self.transformer_blocks):
# x = block(x, context=context[i])
# if self.use_linear:
# x = self.proj_out(x)
# x = x.view(b, h, w, c).permute(0, 3, 1, 2)
# if not self.use_linear:
# x = self.proj_out(x)
# return x + x_in
#
#
# class GELUHijack(torch.nn.GELU, torch.nn.Module):
# def __init__(self, *args, **kwargs):
# torch.nn.GELU.__init__(self, *args, **kwargs)
# def forward(self, x):
# if devices.unet_needs_upcast:
# return torch.nn.GELU.forward(self.float(), x.float()).to(devices.dtype_unet)
# else:
# return torch.nn.GELU.forward(self, x)
#
#
# ddpm_edit_hijack = None
# def hijack_ddpm_edit():
# global ddpm_edit_hijack
# if not ddpm_edit_hijack:
# CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
# CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
# ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model)
#
#
# unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast
# CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
# CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding)
# CondFunc('ldm.modules.attention.SpatialTransformer.forward', spatial_transformer_forward)
# CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
#
# if version.parse(torch.__version__) <= version.parse("1.13.2") or torch.cuda.is_available():
# CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
# CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
# CondFunc('open_clip.transformer.ResidualAttentionBlock.__init__', lambda orig_func, *args, **kwargs: kwargs.update({'act_layer': GELUHijack}) and False or orig_func(*args, **kwargs), lambda _, *args, **kwargs: kwargs.get('act_layer') is None or kwargs['act_layer'] == torch.nn.GELU)
#
# first_stage_cond = lambda _, self, *args, **kwargs: devices.unet_needs_upcast and self.model.diffusion_model.dtype == torch.float16
# first_stage_sub = lambda orig_func, self, x, **kwargs: orig_func(self, x.to(devices.dtype_vae), **kwargs)
# CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
# CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
# CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond)
#
# CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model)
# CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model)
#
#
# def timestep_embedding_cast_result(orig_func, timesteps, *args, **kwargs):
# if devices.unet_needs_upcast and timesteps.dtype == torch.int64:
# dtype = torch.float32
# else:
# dtype = devices.dtype_unet
# return orig_func(timesteps, *args, **kwargs).to(dtype=dtype)
#
#
# CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding_cast_result)
# CondFunc('sgm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding_cast_result)