add infotext (5a0db84b) · Commits · github_fork / Stable Diffusion Webui

modules/generation_parameters_copypaste.py

+2 −0

Original line number	Diff line number	Diff line
		@@ -344,6 +344,8 @@ infotext_to_setting_name_mapping = [
		('Pad conds', 'pad_cond_uncond'),
		('VAE Encoder', 'sd_vae_encode_method'),
		('VAE Decoder', 'sd_vae_decode_method'),
		('Refiner', 'sd_refiner_checkpoint'),
		('Refiner switch at', 'sd_refiner_switch_at'),
		]

modules/processing.py

+10 −0

Original line number	Diff line number	Diff line
		@@ -370,6 +370,9 @@ class StableDiffusionProcessing:
		self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)
		self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)

		def get_conds(self):
		return self.c, self.uc

		def parse_extra_network_prompts(self):
		self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)

		@@ -1251,6 +1254,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
		with devices.autocast():
		extra_networks.activate(self, self.extra_network_data)

		def get_conds(self):
		if self.is_hr_pass:
		return self.hr_c, self.hr_uc

		return super().get_conds()


		def parse_extra_network_prompts(self):
		res = super().parse_extra_network_prompts()

modules/sd_samplers_common.py

+20 −9

Original line number	Diff line number	Diff line
		@@ -131,16 +131,27 @@ replace_torchsde_browinan()

		def apply_refiner(sampler):
		completed_ratio = sampler.step / sampler.steps
		if completed_ratio > shared.opts.sd_refiner_switch_at and shared.sd_model.sd_checkpoint_info.title != shared.opts.sd_refiner_checkpoint:

		if completed_ratio <= shared.opts.sd_refiner_switch_at:
		return False

		if shared.sd_model.sd_checkpoint_info.title == shared.opts.sd_refiner_checkpoint:
		return False

		refiner_checkpoint_info = sd_models.get_closet_checkpoint_match(shared.opts.sd_refiner_checkpoint)
		if refiner_checkpoint_info is None:
		raise Exception(f'Could not find checkpoint with name {shared.opts.sd_refiner_checkpoint}')

		sampler.p.extra_generation_params['Refiner'] = refiner_checkpoint_info.short_title
		sampler.p.extra_generation_params['Refiner switch at'] = shared.opts.sd_refiner_switch_at

		with sd_models.SkipWritingToConfig():
		sd_models.reload_model_weights(info=refiner_checkpoint_info)

		devices.torch_gc()

		sampler.p.setup_conds()
		sampler.update_inner_model()

		sampler.p.setup_conds()
		return True

modules/sd_samplers_compvis.py

+0 −2

Original line number	Diff line number	Diff line
		@@ -71,8 +71,6 @@ class VanillaStableDiffusionSampler:
		if state.interrupted or state.skipped:
		raise sd_samplers_common.InterruptedException

		sd_samplers_common.apply_refiner(self)

		if self.stop_at is not None and self.step > self.stop_at:
		raise sd_samplers_common.InterruptedException

modules/sd_samplers_kdiffusion.py

+16 −8

Original line number	Diff line number	Diff line
		@@ -87,8 +87,9 @@ class CFGDenoiser(torch.nn.Module):
		negative prompt.
		"""

		def __init__(self):
		def __init__(self, sampler):
		super().__init__()
		self.sampler = sampler
		self.model_wrap = None
		self.mask = None
		self.nmask = None
		@@ -126,11 +127,17 @@ class CFGDenoiser(torch.nn.Module):
		def update_inner_model(self):
		self.model_wrap = None

		c, uc = self.p.get_conds()
		self.sampler.sampler_extra_args['cond'] = c
		self.sampler.sampler_extra_args['uncond'] = uc

		def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
		if state.interrupted or state.skipped:
		raise sd_samplers_common.InterruptedException

		sd_samplers_common.apply_refiner(self)
		if sd_samplers_common.apply_refiner(self):
		cond = self.sampler.sampler_extra_args['cond']
		uncond = self.sampler.sampler_extra_args['uncond']

		# at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
		# so is_edit_model is set to False to support AND composition.
		@@ -282,12 +289,12 @@ class TorchHijack:

		class KDiffusionSampler:
		def __init__(self, funcname, sd_model):

		self.p = None
		self.funcname = funcname
		self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
		self.extra_params = sampler_extra_params.get(funcname, [])
		self.model_wrap_cfg = CFGDenoiser()
		self.sampler_extra_args = {}
		self.model_wrap_cfg = CFGDenoiser(self)
		self.model_wrap = self.model_wrap_cfg.inner_model
		self.sampler_noises = None
		self.stop_at = None
		@@ -476,7 +483,7 @@ class KDiffusionSampler:

		self.model_wrap_cfg.init_latent = x
		self.last_latent = x
		extra_args = {
		self.sampler_extra_args = {
		'cond': conditioning,
		'image_cond': image_conditioning,
		'uncond': unconditional_conditioning,
		@@ -484,7 +491,7 @@ class KDiffusionSampler:
		's_min_uncond': self.s_min_uncond
		}

		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=self.sampler_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
		@@ -514,13 +521,14 @@ class KDiffusionSampler:
		extra_params_kwargs['noise_sampler'] = noise_sampler

		self.last_latent = x
		samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
		self.sampler_extra_args = {
		'cond': conditioning,
		'image_cond': image_conditioning,
		'uncond': unconditional_conditioning,
		'cond_scale': p.cfg_scale,
		's_min_uncond': self.s_min_uncond
		}, disable=False, callback=self.callback_state, **extra_params_kwargs))
		}
		samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_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

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