added support for automatically installing latest k-diffusion (d64b4516) · Commits · github_fork / Stable Diffusion Webui

launch.py

+7 −0

Original line number	Diff line number	Diff line
		@@ -113,6 +113,13 @@ if not skip_torch_cuda_test:
		if not is_installed("k_diffusion.sampling"):
		run_pip(f"install {k_diffusion_package}", "k-diffusion")

		if not check_run_python("import k_diffusion; import inspect; assert 'eta' in inspect.signature(k_diffusion.sampling.sample_euler_ancestral).parameters"):
		print(f"k-diffusion does not have 'eta' parameter; reinstalling latest version")
		try:
		run_pip(f"install --upgrade --force-reinstall {k_diffusion_package}", "k-diffusion")
		except RuntimeError as e:
		print(str(e))

		if not is_installed("gfpgan"):
		run_pip(f"install {gfpgan_package}", "gfpgan")

modules/processing.py

+5 −4

Original line number	Diff line number	Diff line
		@@ -49,7 +49,7 @@ def apply_color_correction(correction, image):


		class StableDiffusionProcessing:
		def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
		def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
		self.sd_model = sd_model
		self.outpath_samples: str = outpath_samples
		self.outpath_grids: str = outpath_grids
		@@ -75,11 +75,11 @@ class StableDiffusionProcessing:
		self.do_not_save_grid: bool = do_not_save_grid
		self.extra_generation_params: dict = extra_generation_params or {}
		self.overlay_images = overlay_images
		self.eta = eta
		self.paste_to = None
		self.color_corrections = None
		self.denoising_strength: float = 0

		self.eta = opts.eta
		self.ddim_discretize = opts.ddim_discretize
		self.s_churn = opts.s_churn
		self.s_tmin = opts.s_tmin
		@@ -271,6 +271,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
		"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
		"Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
		"Denoising strength": getattr(p, 'denoising_strength', None),
		"Eta": (None if p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
		}

		generation_params.update(p.extra_generation_params)

modules/sd_samplers.py

+44 −39

Original line number	Diff line number	Diff line
		@@ -40,10 +40,8 @@ samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']

		sampler_extra_params = {
		'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
		'sample_euler_ancestral': ['eta'],
		'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
		'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
		'sample_dpm_2_ancestral': ['eta'],
		}

		def setup_img2img_steps(p, steps=None):
		@@ -101,6 +99,8 @@ class VanillaStableDiffusionSampler:
		self.init_latent = None
		self.sampler_noises = None
		self.step = 0
		self.eta = None
		self.default_eta = 0.0

		def number_of_needed_noises(self, p):
		return 0
		@@ -123,20 +123,29 @@ class VanillaStableDiffusionSampler:
		self.step += 1
		return res

		def initialize(self, p):
		self.eta = p.eta or opts.eta_ddim

		for fieldname in ['p_sample_ddim', 'p_sample_plms']:
		if hasattr(self.sampler, fieldname):
		setattr(self.sampler, fieldname, self.p_sample_ddim_hook)

		self.mask = p.mask if hasattr(p, 'mask') else None
		self.nmask = p.nmask if hasattr(p, 'nmask') else None

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

		# existing code fails with cetain step counts, like 9
		try:
		self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
		self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
		except Exception:
		self.sampler.make_schedule(ddim_num_steps=steps+1,ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
		self.sampler.make_schedule(ddim_num_steps=steps+1, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)

		x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)

		self.sampler.p_sample_ddim = self.p_sample_ddim_hook
		self.mask = p.mask if hasattr(p, 'mask') else None
		self.nmask = p.nmask if hasattr(p, 'nmask') else None
		self.initialize(p)

		self.init_latent = x
		self.step = 0

		@@ -145,11 +154,8 @@ class VanillaStableDiffusionSampler:
		return samples

		def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
		for fieldname in ['p_sample_ddim', 'p_sample_plms']:
		if hasattr(self.sampler, fieldname):
		setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
		self.mask = None
		self.nmask = None
		self.initialize(p)

		self.init_latent = None
		self.step = 0

		@@ -157,9 +163,9 @@ class VanillaStableDiffusionSampler:

		# existing code fails with cetin step counts, like 9
		try:
		samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=p.eta)
		samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
		except Exception:
		samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=p.eta)
		samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)

		return samples_ddim

		@@ -237,6 +243,8 @@ class KDiffusionSampler:
		self.sampler_noises = None
		self.sampler_noise_index = 0
		self.stop_at = None
		self.eta = None
		self.default_eta = 1.0

		def callback_state(self, d):
		store_latent(d["denoised"])
		@@ -255,22 +263,12 @@ class KDiffusionSampler:
		self.sampler_noise_index += 1
		return res

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

		sigmas = self.model_wrap.get_sigmas(steps)

		noise = noise * sigmas[steps - t_enc - 1]

		xi = x + noise

		sigma_sched = sigmas[steps - t_enc - 1:]

		def initialize(self, p):
		self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
		self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
		self.model_wrap_cfg.init_latent = x
		self.model_wrap.step = 0
		self.sampler_noise_index = 0
		self.eta = p.eta or opts.eta_ancestral

		if hasattr(k_diffusion.sampling, 'trange'):
		k_diffusion.sampling.trange = lambda args, kwargs: extended_trange(self, args, **kwargs)
		@@ -283,6 +281,25 @@ class KDiffusionSampler:
		if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
		extra_params_kwargs[param_name] = getattr(p, param_name)

		if 'eta' in inspect.signature(self.func).parameters:
		extra_params_kwargs['eta'] = self.eta

		return extra_params_kwargs

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

		sigmas = self.model_wrap.get_sigmas(steps)

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

		extra_params_kwargs = self.initialize(p)

		sigma_sched = sigmas[steps - t_enc - 1:]

		self.model_wrap_cfg.init_latent = x

		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):
		@@ -291,19 +308,7 @@ class KDiffusionSampler:
		sigmas = self.model_wrap.get_sigmas(steps)
		x = x * sigmas[0]

		self.model_wrap_cfg.step = 0
		self.sampler_noise_index = 0

		if hasattr(k_diffusion.sampling, 'trange'):
		k_diffusion.sampling.trange = lambda args, kwargs: extended_trange(self, args, **kwargs)

		if self.sampler_noises is not None:
		k_diffusion.sampling.torch = TorchHijack(self)

		extra_params_kwargs = {}
		for param_name in self.extra_params:
		if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
		extra_params_kwargs[param_name] = getattr(p, param_name)
		extra_params_kwargs = self.initialize(p)

		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)

modules/shared.py

+3 −2

Original line number	Diff line number	Diff line
		@@ -221,7 +221,8 @@ options_templates.update(options_section(('ui', "User interface"), {
		}))

		options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
		"eta": OptionInfo(0.0, "DDIM and K Ancestral eta", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
		"eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
		"eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
		"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
		's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
		's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

scripts/xy_grid.py

+6 −6

Original line number	Diff line number	Diff line
		@@ -91,7 +91,7 @@ axis_options = [
		AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
		AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
		AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
		AxisOption("DDIM Eta", float, apply_field("ddim_eta"), format_value_add_label),
		AxisOption("Eta", float, apply_field("eta"), format_value_add_label),
		AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label), # as it is now all AxisOptionImg2Img items must go after AxisOption ones
		]

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