pass extra KDiffusionSampler function parameters (2ab3d593) · Commits · github_fork / Stable Diffusion Webui

modules/sd_samplers.py

+18 −2

Original line number	Diff line number	Diff line
		@@ -37,6 +37,11 @@ samplers = [
		]
		samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']

		sampler_extra_params = {
		'sample_euler':['s_churn','s_tmin','s_noise'],
		'sample_heun' :['s_churn','s_tmin','s_noise'],
		'sample_dpm_2':['s_churn','s_tmin','s_noise'],
		}

		def setup_img2img_steps(p, steps=None):
		if opts.img2img_fix_steps or steps is not None:
		@@ -224,6 +229,7 @@ class KDiffusionSampler:
		self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)
		self.funcname = funcname
		self.func = getattr(k_diffusion.sampling, self.funcname)
		self.extra_params = sampler_extra_params.get(funcname,[])
		self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
		self.sampler_noises = None
		self.sampler_noise_index = 0
		@@ -269,7 +275,12 @@ class KDiffusionSampler:
		if self.sampler_noises is not None:
		k_diffusion.sampling.torch = TorchHijack(self)

		return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
		extra_params_kwargs = {}
		for val in self.extra_params:
		if hasattr(opts,val):
		extra_params_kwargs[val] = getattr(opts,val)

		return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)

		def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
		steps = steps or p.steps
		@@ -286,7 +297,12 @@ class KDiffusionSampler:
		if self.sampler_noises is not None:
		k_diffusion.sampling.torch = TorchHijack(self)

		samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
		extra_params_kwargs = {}
		for val in self.extra_params:
		if hasattr(opts,val):
		extra_params_kwargs[val] = getattr(opts,val)

		samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)

		return samples

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