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| #code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License) | |
| import torch | |
| import logging | |
| def Fourier_filter(x, threshold, scale): | |
| # 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 | |
| return x_filtered.to(x.dtype) | |
| class FreeU: | |
| def INPUT_TYPES(s): | |
| return {"required": { "model": ("MODEL",), | |
| "b1": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 10.0, "step": 0.01}), | |
| "b2": ("FLOAT", {"default": 1.2, "min": 0.0, "max": 10.0, "step": 0.01}), | |
| "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), | |
| "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), | |
| }} | |
| RETURN_TYPES = ("MODEL",) | |
| FUNCTION = "patch" | |
| CATEGORY = "model_patches/unet" | |
| def patch(self, model, b1, b2, s1, s2): | |
| 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 (m, ) | |
| class FreeU_V2: | |
| def INPUT_TYPES(s): | |
| return {"required": { "model": ("MODEL",), | |
| "b1": ("FLOAT", {"default": 1.3, "min": 0.0, "max": 10.0, "step": 0.01}), | |
| "b2": ("FLOAT", {"default": 1.4, "min": 0.0, "max": 10.0, "step": 0.01}), | |
| "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), | |
| "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), | |
| }} | |
| RETURN_TYPES = ("MODEL",) | |
| FUNCTION = "patch" | |
| CATEGORY = "model_patches/unet" | |
| def patch(self, model, b1, b2, s1, s2): | |
| 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 (m, ) | |
| NODE_CLASS_MAPPINGS = { | |
| "FreeU": FreeU, | |
| "FreeU_V2": FreeU_V2, | |
| } | |