Repository: huggingface/diffusers
File: src/diffusers/models/unets/unet_2d_blocks.py (Python)

Code before:

        is_freeu_enabled = (
            getattr(self, "s1", None)
            and getattr(self, "s2", None)
            and getattr(self, "b1", None)
            and getattr(self, "b2", None)
        )

        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: Only operate on the first two stages
            if is_freeu_enabled:
                hidden_states, res_hidden_states = apply_freeu(
                    self.resolution_idx,
                    hidden_states,
                    res_hidden_states,
                    s1=self.s1,
                    s2=self.s2,
                    b1=self.b1,
                    b2=self.b2,
                )
            if hidden_states.device != res_hidden_states.device:
                res_hidden_states = res_hidden_states.to(hidden_states.device)
            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

            if torch.is_grad_enabled() and self.gradient_checkpointing:
                hidden_states = self._gradient_checkpointing_func(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


class UpDecoderBlock2D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        resolution_idx: Optional[int] = None,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",  # default, spatial
        resnet_act_fn: str = "swish",

Review comment (suggestion):
we shouldn't need that since both hidden_states and res_hidden_states should be on the same device no ? The pre-forward hook added by accelerate should be move all the inputs to the same device. 

Code after:

        is_freeu_enabled = (
            getattr(self, "s1", None)
            and getattr(self, "s2", None)
            and getattr(self, "b1", None)
            and getattr(self, "b2", None)
        )

        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: Only operate on the first two stages
            if is_freeu_enabled:
                hidden_states, res_hidden_states = apply_freeu(
                    self.resolution_idx,
                    hidden_states,
                    res_hidden_states,
                    s1=self.s1,
                    s2=self.s2,
                    b1=self.b1,
                    b2=self.b2,
                )

            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

            if torch.is_grad_enabled() and self.gradient_checkpointing:
                hidden_states = self._gradient_checkpointing_func(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


class UpDecoderBlock2D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        resolution_idx: Optional[int] = None,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",  # default, spatial
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,