import torch from packaging import version 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("'{}' object has no attribute '{}'".format(type(self).__name__, 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): if isinstance(cond, dict): for y in cond.keys(): cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]] with devices.autocast(): return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float() 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) 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', 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("1.13.1"): 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)