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| import torch | |
| import gradio as gr | |
| from modules import scripts | |
| 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 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(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: | |
| print("Device", hsp.device, "does not support the torch.fft functions used in the FreeU node, switching to CPU.") | |
| 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 patch(self, model, b1, b2, s1, s2): | |
| model_channels = model.model.diffusion_model.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(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: | |
| print("Device", hsp.device, "does not support the torch.fft functions used in the FreeU node, switching to CPU.") | |
| 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,) | |
| opFreeU_V2 = FreeU_V2() | |
| class FreeUForForge(scripts.Script): | |
| sorting_priority = 12 | |
| def title(self): | |
| return "FreeU Integrated" | |
| def show(self, is_img2img): | |
| # make this extension visible in both txt2img and img2img tab. | |
| return scripts.AlwaysVisible | |
| def ui(self, *args, **kwargs): | |
| with gr.Accordion(open=False, label=self.title(), elem_id="extensions-freeu", | |
| elem_classes=["extensions-freeu"]): | |
| freeu_enabled = gr.Checkbox(label='Enabled', value=False) | |
| freeu_b1 = gr.Slider(label='B1', minimum=0, maximum=2, step=0.01, value=1.01) | |
| freeu_b2 = gr.Slider(label='B2', minimum=0, maximum=2, step=0.01, value=1.02) | |
| freeu_s1 = gr.Slider(label='S1', minimum=0, maximum=4, step=0.01, value=0.99) | |
| freeu_s2 = gr.Slider(label='S2', minimum=0, maximum=4, step=0.01, value=0.95) | |
| return freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2 | |
| def process_before_every_sampling(self, p, *script_args, **kwargs): | |
| # This will be called before every sampling. | |
| # If you use highres fix, this will be called twice. | |
| freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2 = script_args | |
| if not freeu_enabled: | |
| return | |
| unet = p.sd_model.forge_objects.unet | |
| # unet = set_freeu_v2_patch(unet, freeu_b1, freeu_b2, freeu_s1, freeu_s2) | |
| unet = opFreeU_V2.patch(unet, freeu_b1, freeu_b2, freeu_s1, freeu_s2)[0] | |
| p.sd_model.forge_objects.unet = unet | |
| # Below codes will add some logs to the texts below the image outputs on UI. | |
| # The extra_generation_params does not influence results. | |
| p.extra_generation_params.update(dict( | |
| freeu_enabled=freeu_enabled, | |
| freeu_b1=freeu_b1, | |
| freeu_b2=freeu_b2, | |
| freeu_s1=freeu_s1, | |
| freeu_s2=freeu_s2, | |
| )) | |
| return | |