src/free_lunch_utils.py

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)