add missing infotext entry for the pad cond/uncond option
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@ -357,6 +357,7 @@ infotext_to_setting_name_mapping = [
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('Token merging ratio hr', 'token_merging_ratio_hr'),
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('RNG', 'randn_source'),
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('NGMS', 's_min_uncond'),
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('Pad conds', 'pad_cond_uncond'),
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]
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@ -69,6 +69,7 @@ class CFGDenoiser(torch.nn.Module):
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self.init_latent = None
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self.step = 0
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self.image_cfg_scale = None
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self.padded_cond_uncond = False
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def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
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denoised_uncond = x_out[-uncond.shape[0]:]
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@ -133,15 +134,17 @@ class CFGDenoiser(torch.nn.Module):
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x_in = x_in[:-batch_size]
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sigma_in = sigma_in[:-batch_size]
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# TODO add infotext entry
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self.padded_cond_uncond = False
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if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
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empty = shared.sd_model.cond_stage_model_empty_prompt
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num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
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if num_repeats < 0:
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tensor = torch.cat([tensor, empty.repeat((tensor.shape[0], -num_repeats, 1))], axis=1)
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self.padded_cond_uncond = True
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elif num_repeats > 0:
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uncond = torch.cat([uncond, empty.repeat((uncond.shape[0], num_repeats, 1))], axis=1)
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self.padded_cond_uncond = True
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if tensor.shape[1] == uncond.shape[1] or skip_uncond:
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if is_edit_model:
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@ -405,6 +408,9 @@ class KDiffusionSampler:
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samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
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if self.model_wrap_cfg.padded_cond_uncond:
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p.extra_generation_params["Pad conds"] = True
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return samples
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def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
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@ -438,5 +444,8 @@ class KDiffusionSampler:
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's_min_uncond': self.s_min_uncond
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}, disable=False, callback=self.callback_state, **extra_params_kwargs))
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if self.model_wrap_cfg.padded_cond_uncond:
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p.extra_generation_params["Pad conds"] = True
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return samples
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