# FFT

x_freq = torch.fft.fftn(x.float(), dim=(-2, -1))

x_freq = torch.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 = torch.fft.ifftshift(x_freq, dim=(-2, -1))

x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real

@classmethod

def define_schema(cls):

return IO.Schema(

node_id="FreeU",

category="model/patch/unet",

inputs=[

IO.Model.Input("model"),

IO.Float.Input("b1", default=1.1, min=0.0, max=10.0, step=0.01, advanced=True),

IO.Float.Input("b2", default=1.2, min=0.0, max=10.0, step=0.01, advanced=True),

IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01, advanced=True),

IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01, advanced=True),

],

outputs=[

IO.Model.Output(),

],

)

@classmethod

def execute(cls, model, b1, b2, s1, s2) -> IO.NodeOutput:

model_channels = model.model.model_config.unet_config["model_channels"]

scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}

on_cpu_devices = {}

def output_block_patch(h, hsp, transformer_options):

scale = scale_dict.get(int(h.shape[1]), None)

if scale is not None:

h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * scale[0]

if hsp.device not in on_cpu_devices:

try:

hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])

except:

logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))

on_cpu_devices[hsp.device] = True

hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)

else:

hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)

return h, hsp

m = model.clone()

m.set_model_output_block_patch(output_block_patch)

return IO.NodeOutput(m)

@classmethod

def define_schema(cls):

return IO.Schema(

node_id="FreeU_V2",

category="model/patch/unet",

inputs=[

IO.Model.Input("model"),

IO.Float.Input("b1", default=1.3, min=0.0, max=10.0, step=0.01, advanced=True),

IO.Float.Input("b2", default=1.4, min=0.0, max=10.0, step=0.01, advanced=True),

IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01, advanced=True),

IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01, advanced=True),

],

outputs=[

IO.Model.Output(),

],

)

@classmethod

def execute(cls, model, b1, b2, s1, s2) -> IO.NodeOutput:

model_channels = model.model.model_config.unet_config["model_channels"]

scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}

on_cpu_devices = {}

def output_block_patch(h, hsp, transformer_options):

scale = scale_dict.get(int(h.shape[1]), None)

if scale is not None:

hidden_mean = h.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)

h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * ((scale[0] - 1 ) * hidden_mean + 1)

if hsp.device not in on_cpu_devices:

try:

hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])

except:

logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))

on_cpu_devices[hsp.device] = True

hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)

else:

hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)

return h, hsp

m = model.clone()

m.set_model_output_block_patch(output_block_patch)

return IO.NodeOutput(m)

@override

async def get_node_list(self) -> list[type[IO.ComfyNode]]:

return [

FreeU,

FreeU_V2,