https://github.com/huggingface/diffusers/pull/5164

src/diffusers/models/unet_2d_condition.py

@@ -52,6 +52,54 @@
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+@dataclass
+class UNetFreeUConfig:
+    enabled: bool = False
+    s1: float = 1.0
+    s2: float = 1.0
+    b1: float = 1.0
+    b2: float = 1.0
+
+    def sd21(self):
+        self.s1 = 0.9
+        self.s2 = 0.2
+        self.b1 = 1.1
+        self.b2 = 1.2
+
+    def ones(self):
+        self.s1 = 1.0
+        self.s2 = 1.0
+        self.b1 = 1.0
+        self.b2 = 1.0
+
+def fourier_filter(x_in, threshold, scale):
+    import torch
+    from torch.fft import fftn, ifftn, fftshift, ifftshift
+
+    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 = fftn(x, dim=(-2, -1))
+    x_freq = 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 = ifftshift(x_freq, dim=(-2, -1))
+    x_filtered = ifftn(x_freq, dim=(-2, -1)).real
+
+    return x_filtered.to(dtype=x_in.dtype)
+
 
 @dataclass
 class UNet2DConditionOutput(BaseOutput):
@@ -212,11 +260,12 @@ def __init__(
         mid_block_only_cross_attention: Optional[bool] = None,
         cross_attention_norm: Optional[str] = None,
         addition_embed_type_num_heads=64,
+        freeu = UNetFreeUConfig()
     ):
         super().__init__()
 
         self.sample_size = sample_size
-
+        self.freeu = freeu
         if num_attention_heads is not None:
             raise ValueError(
                 "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
@@ -1008,6 +1057,22 @@ def forward(
             res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
             down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
 
+            # FreeU: Free Lunch in Diffusion U-Net: https://arxiv.org/pdf/2309.11497.pdf
+            if self.freeu.enabled:
+                if sample.shape[1] == 1280:
+                    sample[:, :640] *= self.freeu.b1
+                    res_samples = tuple(
+                        fourier_filter(res_sample, threshold=1, scale=self.freeu.s1)
+                        for res_sample in res_samples
+                    )
+
+                if sample.shape[1] == 640:
+                    sample[:, :320] *= self.freeu.b2
+                    res_samples = tuple(
+                        fourier_filter(res_sample, threshold=1, scale=self.freeu.s2)
+                        for res_sample in res_samples
+                    )
+
             # if we have not reached the final block and need to forward the
             # upsample size, we do it here
             if not is_final_block and forward_upsample_size: