from typing import Any, Dict, Optional, Tuple import torch import torch.fft as fft from diffusers.utils import is_torch_version from diffusers.models.unet_2d_condition import logger as logger2d from diffusers.models.unet_3d_condition import logger as logger3d def isinstance_str(x: object, cls_name: str): """ Checks whether x has any class *named* cls_name in its ancestry. Doesn't require access to the class's implementation. Useful for patching! """ for _cls in x.__class__.__mro__: if _cls.__name__ == cls_name: return True return False def Fourier_filter(x_in, threshold, scale): """ Updated Fourier filter based on: https://github.com/huggingface/diffusers/pull/5164#issuecomment-1732638706 """ x = x_in B, C, H, W = x.shape # Non-power of 2 images must be float32 if (W & (W - 1)) != 0 or (H & (H - 1)) != 0: x = x.to(dtype=torch.float32) # FFT x_freq = fft.fftn(x, dim=(-2, -1)) x_freq = fft.fftshift(x_freq, dim=(-2, -1)) B, C, H, W = x_freq.shape mask = torch.ones((B, C, H, W), device=x.device) crow, ccol = H // 2, W // 2 mask[..., crow - threshold : crow + threshold, ccol - threshold : ccol + threshold] = scale x_freq = x_freq * mask # IFFT x_freq = fft.ifftshift(x_freq, dim=(-2, -1)) x_filtered = fft.ifftn(x_freq, dim=(-2, -1)).real return x_filtered.to(dtype=x_in.dtype) def register_upblock2d(model): """ Register UpBlock2D for UNet2DCondition. """ def up_forward(self): def forward( hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None ): logger2d.debug(f"in upblock2d, hidden states shape: {hidden_states.shape}") for resnet in self.resnets: # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) else: hidden_states = resnet(hidden_states, temb) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size) return hidden_states return forward for i, upsample_block in enumerate(model.unet.up_blocks): if isinstance_str(upsample_block, "UpBlock2D"): upsample_block.forward = up_forward(upsample_block) def register_free_upblock2d(model, b1=1.2, b2=1.4, s1=0.9, s2=0.2): """ Register UpBlock2D with FreeU for UNet2DCondition. """ def up_forward(self): def forward( hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None ): logger2d.debug(f"in free upblock2d, hidden states shape: {hidden_states.shape}") for resnet in self.resnets: # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] # --------------- FreeU code ----------------------- # Only operate on the first two stages if hidden_states.shape[1] == 1280: hidden_mean = hidden_states.mean(1).unsqueeze(1) B = hidden_mean.shape[0] hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True) hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True) hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3) hidden_states[:,:640] = hidden_states[:,:640] * ((self.b1 - 1 ) * hidden_mean + 1) #hidden_states[:,:640] = hidden_states[:,:640] * self.b1 res_hidden_states = Fourier_filter(res_hidden_states, threshold=1, scale=self.s1) if hidden_states.shape[1] == 640: hidden_mean = hidden_states.mean(1).unsqueeze(1) B = hidden_mean.shape[0] hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True) hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True) hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3) hidden_states[:,:320] = hidden_states[:,:320] * ((self.b2 - 1 ) * hidden_mean + 1) #hidden_states[:,:320] = hidden_states[:,:320] * self.b2 res_hidden_states = Fourier_filter(res_hidden_states, threshold=1, scale=self.s2) # --------------------------------------------------------- hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) else: hidden_states = resnet(hidden_states, temb) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size) return hidden_states return forward for i, upsample_block in enumerate(model.unet.up_blocks): if isinstance_str(upsample_block, "UpBlock2D"): upsample_block.forward = up_forward(upsample_block) setattr(upsample_block, 'b1', b1) setattr(upsample_block, 'b2', b2) setattr(upsample_block, 's1', s1) setattr(upsample_block, 's2', s2) def register_crossattn_upblock2d(model): """ Register CrossAttn UpBlock2D for UNet2DCondition. """ def up_forward(self): def forward( hidden_states: torch.FloatTensor, res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, upsample_size: Optional[int] = None, attention_mask: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, ): logger2d.debug(f"in crossatten upblock2d, hidden states shape: {hidden_states.shape}") #lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 for resnet, attn in zip(self.resnets, self.attentions): # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, **ckpt_kwargs, ) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, None, # timestep None, # class_labels cross_attention_kwargs, attention_mask, encoder_attention_mask, **ckpt_kwargs, )[0] else: hidden_states = resnet(hidden_states, temb) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size) return hidden_states return forward for i, upsample_block in enumerate(model.unet.up_blocks): if isinstance_str(upsample_block, "CrossAttnUpBlock2D"): upsample_block.forward = up_forward(upsample_block) def register_free_crossattn_upblock2d(model, b1=1.2, b2=1.4, s1=0.9, s2=0.2): """ Register CrossAttn UpBlock2D with FreeU for UNet2DCondition. """ def up_forward(self): def forward( hidden_states: torch.FloatTensor, res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, upsample_size: Optional[int] = None, attention_mask: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, ): logger2d.debug(f"in free crossatten upblock2d, hidden states shape: {hidden_states.shape}") #lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 for resnet, attn in zip(self.resnets, self.attentions): # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] # --------------- FreeU code ----------------------- # Only operate on the first two stages if hidden_states.shape[1] == 1280: hidden_mean = hidden_states.mean(1).unsqueeze(1) B = hidden_mean.shape[0] hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True) hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True) hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3) hidden_states[:,:640] = hidden_states[:,:640] * ((self.b1 - 1 ) * hidden_mean + 1) #hidden_states[:,:640] = hidden_states[:,:640] * self.b1 res_hidden_states = Fourier_filter(res_hidden_states, threshold=1, scale=self.s1) if hidden_states.shape[1] == 640: hidden_mean = hidden_states.mean(1).unsqueeze(1) B = hidden_mean.shape[0] hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True) hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True) hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3) hidden_states[:,:320] = hidden_states[:,:320] * ((self.b2 - 1 ) * hidden_mean + 1) #hidden_states[:,:320] = hidden_states[:,:320] * self.b2 res_hidden_states = Fourier_filter(res_hidden_states, threshold=1, scale=self.s2) # --------------------------------------------------------- hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, **ckpt_kwargs, ) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, None, # timestep None, # class_labels cross_attention_kwargs, attention_mask, encoder_attention_mask, **ckpt_kwargs, )[0] else: hidden_states = resnet(hidden_states, temb) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size) return hidden_states return forward for i, upsample_block in enumerate(model.unet.up_blocks): if isinstance_str(upsample_block, "CrossAttnUpBlock2D"): upsample_block.forward = up_forward(upsample_block) setattr(upsample_block, 'b1', b1) setattr(upsample_block, 'b2', b2) setattr(upsample_block, 's1', s1) setattr(upsample_block, 's2', s2) def apply_freeu(pipe, b1=1.0, b2=1.0, s1=1.0, s2=1.0): register_free_upblock2d(pipe, b1, b2, s1, s2) register_free_crossattn_upblock2d(pipe, b1, b2, s1, s2)