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import torch |
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import logging |
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def Fourier_filter(x, threshold, scale): |
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x_freq = torch.fft.fftn(x.float(), dim=(-2, -1)) |
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x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1)) |
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B, C, H, W = x_freq.shape |
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mask = torch.ones((B, C, H, W), device=x.device) |
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crow, ccol = H // 2, W //2 |
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mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale |
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x_freq = x_freq * mask |
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x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1)) |
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x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real |
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return x_filtered.to(x.dtype) |
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class FreeU: |
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@classmethod |
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def INPUT_TYPES(s): |
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return {"required": { "model": ("MODEL",), |
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"b1": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 10.0, "step": 0.01}), |
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"b2": ("FLOAT", {"default": 1.2, "min": 0.0, "max": 10.0, "step": 0.01}), |
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"s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), |
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"s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), |
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}} |
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RETURN_TYPES = ("MODEL",) |
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FUNCTION = "patch" |
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CATEGORY = "model_patches/unet" |
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def patch(self, model, b1, b2, s1, s2): |
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model_channels = model.model.model_config.unet_config["model_channels"] |
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scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} |
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on_cpu_devices = {} |
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def output_block_patch(h, hsp, transformer_options): |
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scale = scale_dict.get(int(h.shape[1]), None) |
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if scale is not None: |
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h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * scale[0] |
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if hsp.device not in on_cpu_devices: |
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try: |
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hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) |
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except: |
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logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device)) |
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on_cpu_devices[hsp.device] = True |
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hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) |
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else: |
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hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) |
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return h, hsp |
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m = model.clone() |
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m.set_model_output_block_patch(output_block_patch) |
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return (m, ) |
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class FreeU_V2: |
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@classmethod |
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def INPUT_TYPES(s): |
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return {"required": { "model": ("MODEL",), |
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"b1": ("FLOAT", {"default": 1.3, "min": 0.0, "max": 10.0, "step": 0.01}), |
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"b2": ("FLOAT", {"default": 1.4, "min": 0.0, "max": 10.0, "step": 0.01}), |
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"s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), |
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"s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), |
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}} |
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RETURN_TYPES = ("MODEL",) |
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FUNCTION = "patch" |
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CATEGORY = "model_patches/unet" |
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def patch(self, model, b1, b2, s1, s2): |
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model_channels = model.model.model_config.unet_config["model_channels"] |
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scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} |
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on_cpu_devices = {} |
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def output_block_patch(h, hsp, transformer_options): |
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scale = scale_dict.get(int(h.shape[1]), None) |
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if scale is not None: |
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hidden_mean = h.mean(1).unsqueeze(1) |
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B = hidden_mean.shape[0] |
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hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True) |
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hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True) |
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hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3) |
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h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * ((scale[0] - 1 ) * hidden_mean + 1) |
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if hsp.device not in on_cpu_devices: |
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try: |
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hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) |
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except: |
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logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device)) |
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on_cpu_devices[hsp.device] = True |
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hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) |
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else: |
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hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) |
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return h, hsp |
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m = model.clone() |
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m.set_model_output_block_patch(output_block_patch) |
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return (m, ) |
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NODE_CLASS_MAPPINGS = { |
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"FreeU": FreeU, |
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"FreeU_V2": FreeU_V2, |
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} |
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