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)