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add missing infotext entry for the pad cond/uncond option

This commit is contained in:
AUTOMATIC 2023-06-27 06:18:43 +03:00
parent 3b11f17a37
commit 4bd490c28d
2 changed files with 11 additions and 1 deletions
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1
modules/generation_parameters_copypaste.py
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@ -357,6 +357,7 @@ infotext_to_setting_name_mapping = [
('Token merging ratio hr', 'token_merging_ratio_hr'), ('Token merging ratio hr', 'token_merging_ratio_hr'),
('RNG', 'randn_source'), ('RNG', 'randn_source'),
('NGMS', 's_min_uncond'), ('NGMS', 's_min_uncond'),
('Pad conds', 'pad_cond_uncond'),
] ]

11
modules/sd_samplers_kdiffusion.py
View File

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