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import gradio as gr
from modules import scripts
from ldm_patched.contrib.external_freelunch import FreeU_V2
opFreeU_V2 = FreeU_V2()
# def Fourier_filter(x, threshold, scale):
# 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
# 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)
#
#
# def set_freeu_v2_patch(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)}
#
# def output_block_patch(h, hsp, *args, **kwargs):
# 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)
# hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
# return h, hsp
#
# m = model.clone()
# m.set_model_output_block_patch(output_block_patch)
# return m
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()):
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
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