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https://github.com/lllyasviel/stable-diffusion-webui-forge.git
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Update forge_freeu.py
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@@ -1,7 +1,43 @@
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import torch
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import gradio as gr
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from modules import scripts
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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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def patch(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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def output_block_patch(h, hsp, transformer_options):
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scale = scale_dict.get(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)) / \
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(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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hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
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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 FreeUForForge(scripts.Script):
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def title(self):
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return "FreeU Integrated"
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@@ -22,7 +58,7 @@ class FreeUForForge(scripts.Script):
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def process_batch(self, p, *script_args, **kwargs):
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freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2 = script_args
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if not freeu_enabled:
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return
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