From d64b451681bdba5453723d3fe0b0681a470d8045 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 28 Sep 2022 18:09:06 +0300 Subject: [PATCH] added support for automatically installing latest k-diffusion added eta parameter to parameters output for generated images split eta settings into ancestral and ddim (because they have different default values) --- launch.py | 7 ++++ modules/processing.py | 9 +++-- modules/sd_samplers.py | 83 ++++++++++++++++++++++-------------------- modules/shared.py | 5 ++- scripts/xy_grid.py | 12 +++--- 5 files changed, 65 insertions(+), 51 deletions(-) diff --git a/launch.py b/launch.py index 7958c6e9..c3e99afe 100644 --- a/launch.py +++ b/launch.py @@ -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") diff --git a/modules/processing.py b/modules/processing.py index e6b84684..358a1b11 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -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 @@ -75,15 +75,15 @@ 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 - self.s_tmax = float('inf') # not representable as a standard ui option + self.s_tmax = float('inf') # not representable as a standard ui option self.s_noise = opts.s_noise if not seed_enable_extras: @@ -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) diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index a1183997..3588aae6 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -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) diff --git a/modules/shared.py b/modules/shared.py index ae459e14..39cf89bc 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -221,8 +221,9 @@ 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}), - "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform','quad']}), + "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}), 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py index 7c01231f..24fa5a0a 100644 --- a/scripts/xy_grid.py +++ b/scripts/xy_grid.py @@ -87,12 +87,12 @@ axis_options = [ AxisOption("Prompt S/R", str, apply_prompt, format_value), AxisOption("Sampler", str, apply_sampler, format_value), AxisOption("Checkpoint name", str, apply_checkpoint, format_value), - AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label), - 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), - AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label),# as it is now all AxisOptionImg2Img items must go after AxisOption ones + AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label), + 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("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 ]