87 lines
3.3 KiB
Python
87 lines
3.3 KiB
Python
import gradio as gr
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from modules import scripts
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from ldm_patched.contrib.external_freelunch import FreeU_V2
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opFreeU_V2 = FreeU_V2()
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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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#
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#
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# def set_freeu_v2_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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#
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# def output_block_patch(h, hsp, *args, **kwargs):
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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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#
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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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def show(self, is_img2img):
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# make this extension visible in both txt2img and img2img tab.
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return scripts.AlwaysVisible
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def ui(self, *args, **kwargs):
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with gr.Accordion(open=False, label=self.title()):
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freeu_enabled = gr.Checkbox(label='Enabled', value=False)
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freeu_b1 = gr.Slider(label='B1', minimum=0, maximum=2, step=0.01, value=1.01)
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freeu_b2 = gr.Slider(label='B2', minimum=0, maximum=2, step=0.01, value=1.02)
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freeu_s1 = gr.Slider(label='S1', minimum=0, maximum=4, step=0.01, value=0.99)
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freeu_s2 = gr.Slider(label='S2', minimum=0, maximum=4, step=0.01, value=0.95)
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return freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2
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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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unet = p.sd_model.forge_objects.unet
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# unet = set_freeu_v2_patch(unet, freeu_b1, freeu_b2, freeu_s1, freeu_s2)
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unet = opFreeU_V2.patch(unet, freeu_b1, freeu_b2, freeu_s1, freeu_s2)[0]
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p.sd_model.forge_objects.unet = unet
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# Below codes will add some logs to the texts below the image outputs on UI.
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# The extra_generation_params does not influence results.
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p.extra_generation_params.update(dict(
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freeu_enabled=freeu_enabled,
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freeu_b1=freeu_b1,
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freeu_b2=freeu_b2,
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freeu_s1=freeu_s1,
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freeu_s2=freeu_s2,
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))
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return
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