# 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)