eliminate duplicated code (399b2297) · Commits · github_fork / Stable Diffusion Webui

modules/sd_samplers.py

+14 −17

Original line number	Diff line number	Diff line
		@@ -23,16 +23,16 @@ samplers_k_diffusion = [
		('Euler', 'sample_euler', ['k_euler'], {}),
		('LMS', 'sample_lms', ['k_lms'], {}),
		('Heun', 'sample_heun', ['k_heun'], {}),
		('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
		('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
		('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
		('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}),
		('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
		('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
		('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
		('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
		('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
		('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
		('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
		('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
		('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
		('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
		('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
		('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
		('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),
		@@ -444,9 +444,7 @@ class KDiffusionSampler:

		return extra_params_kwargs

		def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
		steps, t_enc = setup_img2img_steps(p, steps)

		def get_sigmas(self, p, steps):
		if p.sampler_noise_scheduler_override:
		sigmas = p.sampler_noise_scheduler_override(steps)
		elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
		@@ -454,9 +452,16 @@ class KDiffusionSampler:
		else:
		sigmas = self.model_wrap.get_sigmas(steps)

		if self.funcname in ['sample_dpm_2_ancestral', 'sample_dpm_2']:
		if self.config is not None and self.config.options.get('discard_next_to_last_sigma', False):
		sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])

		return sigmas

		def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
		steps, t_enc = setup_img2img_steps(p, steps)

		sigmas = self.get_sigmas(p, steps)

		sigma_sched = sigmas[steps - t_enc - 1:]
		xi = x + noise * sigma_sched[0]

		@@ -488,18 +493,10 @@ class KDiffusionSampler:
		def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):
		steps = steps or p.steps

		if p.sampler_noise_scheduler_override:
		sigmas = p.sampler_noise_scheduler_override(steps)
		elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
		sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
		else:
		sigmas = self.model_wrap.get_sigmas(steps)
		sigmas = self.get_sigmas(p, steps)

		x = x * sigmas[0]

		if self.funcname in ['sample_dpm_2_ancestral', 'sample_dpm_2']:
		sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])

		extra_params_kwargs = self.initialize(p)
		if 'sigma_min' in inspect.signature(self.func).parameters:
		extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()

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