mirror of
https://github.com/comfyanonymous/ComfyUI.git
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138 lines
5.8 KiB
Python
138 lines
5.8 KiB
Python
"""
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Portions of this code are adapted from the repository
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https://github.com/ChenyangSi/FreeU
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MIT License
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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"""
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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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# FFT
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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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# IFFT
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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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