From 4df5009acb6832daef1ff5949404b5aadc8f8fa4 Mon Sep 17 00:00:00 2001 From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com> Date: Mon, 26 Dec 2022 20:49:13 +0000 Subject: Update sd_samplers.py --- modules/sd_samplers.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) (limited to 'modules/sd_samplers.py') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 177b5338..f4473832 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -462,6 +462,9 @@ class KDiffusionSampler: return extra_params_kwargs def get_sigmas(self, p, steps): + disc = opts.always_discard_next_to_last_sigma or (self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)) + steps += 1 if disc else 0 + 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': @@ -469,7 +472,7 @@ class KDiffusionSampler: else: sigmas = self.model_wrap.get_sigmas(steps) - if self.config is not None and self.config.options.get('discard_next_to_last_sigma', False): + if disc: sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) return sigmas -- cgit v1.2.3 From 16b9661d2741b241c3964fcbd56559c078b84822 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 1 Jan 2023 09:51:37 +0300 Subject: change karras scheduler sigmas to values recommended by SD from old 0.1 to 10 with an option to revert to old --- modules/sd_samplers.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) (limited to 'modules/sd_samplers.py') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 177b5338..e904d860 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -465,7 +465,9 @@ class KDiffusionSampler: 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) + sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item()) + + sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device) else: sigmas = self.model_wrap.get_sigmas(steps) -- cgit v1.2.3 From bc43293c640aef65df3136de9e5bd8b7e79eb3e0 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 4 Jan 2023 23:56:43 +0300 Subject: fix incorrect display/calculation for number of steps for hires fix in progress bars --- modules/sd_samplers.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) (limited to 'modules/sd_samplers.py') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index e904d860..3851a77f 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -97,8 +97,9 @@ sampler_extra_params = { def setup_img2img_steps(p, steps=None): if opts.img2img_fix_steps or steps is not None: - steps = int((steps or p.steps) / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0 - t_enc = p.steps - 1 + requested_steps = (steps or p.steps) + steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0 + t_enc = requested_steps - 1 else: steps = p.steps t_enc = int(min(p.denoising_strength, 0.999) * steps) -- cgit v1.2.3 From 42fcc79bd31e5e5485f1cf115ad505cc623d0ac9 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 5 Jan 2023 10:43:21 +0300 Subject: add Discard penultimate sigma to infotext --- modules/sd_samplers.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) (limited to 'modules/sd_samplers.py') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 31b255a3..01221b89 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -463,8 +463,12 @@ class KDiffusionSampler: return extra_params_kwargs def get_sigmas(self, p, steps): - disc = opts.always_discard_next_to_last_sigma or (self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)) - steps += 1 if disc else 0 + discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False) + if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma: + discard_next_to_last_sigma = True + p.extra_generation_params["Discard penultimate sigma"] = True + + steps += 1 if discard_next_to_last_sigma else 0 if p.sampler_noise_scheduler_override: sigmas = p.sampler_noise_scheduler_override(steps) @@ -475,7 +479,7 @@ class KDiffusionSampler: else: sigmas = self.model_wrap.get_sigmas(steps) - if disc: + if discard_next_to_last_sigma: sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) return sigmas -- cgit v1.2.3 From f94a215abed85b34ae978853078812801d3e7738 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 14 Jan 2023 16:29:23 +0300 Subject: add an option to choose what you want to see in live preview (Live preview subject) and moves live preview settings to its own tab --- modules/sd_samplers.py | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) (limited to 'modules/sd_samplers.py') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 01221b89..7616fded 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -138,7 +138,7 @@ def samples_to_image_grid(samples, approximation=None): def store_latent(decoded): state.current_latent = decoded - if opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0: + if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0: if not shared.parallel_processing_allowed: shared.state.current_image = sample_to_image(decoded) @@ -243,7 +243,7 @@ class VanillaStableDiffusionSampler: self.nmask = p.nmask if hasattr(p, 'nmask') else None def adjust_steps_if_invalid(self, p, num_steps): - if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'): + if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'): valid_step = 999 / (1000 // num_steps) if valid_step == floor(valid_step): return int(valid_step) + 1 @@ -266,8 +266,7 @@ class VanillaStableDiffusionSampler: if image_conditioning is not None: conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]} unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]} - - + samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)) return samples @@ -352,6 +351,11 @@ class CFGDenoiser(torch.nn.Module): x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]}) + if opts.live_preview_content == "Prompt": + store_latent(x_out[0:uncond.shape[0]]) + elif opts.live_preview_content == "Negative prompt": + store_latent(x_out[-uncond.shape[0]:]) + denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale) if self.mask is not None: @@ -423,7 +427,8 @@ class KDiffusionSampler: def callback_state(self, d): step = d['i'] latent = d["denoised"] - store_latent(latent) + if opts.live_preview_content == "Combined": + store_latent(latent) self.last_latent = latent if self.stop_at is not None and step > self.stop_at: -- cgit v1.2.3 From d8b90ac121cbf0c18b1dc9d56a5e1d14ca51e74e Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 15 Jan 2023 18:50:56 +0300 Subject: big rework of progressbar/preview system to allow multiple users to prompts at the same time and do not get previews of each other --- modules/sd_samplers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/sd_samplers.py') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 7616fded..76e0e0d5 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -140,7 +140,7 @@ def store_latent(decoded): if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0: if not shared.parallel_processing_allowed: - shared.state.current_image = sample_to_image(decoded) + shared.state.assign_current_image(sample_to_image(decoded)) class InterruptedException(BaseException): -- cgit v1.2.3 From 9991967f40120b88a1dc925fdf7d747d5e016888 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Mon, 16 Jan 2023 22:59:46 +0300 Subject: Add a check and explanation for tensor with all NaNs. --- modules/sd_samplers.py | 2 ++ 1 file changed, 2 insertions(+) (limited to 'modules/sd_samplers.py') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 76e0e0d5..6261d1f7 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -351,6 +351,8 @@ class CFGDenoiser(torch.nn.Module): x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]}) + devices.test_for_nans(x_out, "unet") + if opts.live_preview_content == "Prompt": store_latent(x_out[0:uncond.shape[0]]) elif opts.live_preview_content == "Negative prompt": -- cgit v1.2.3 From 6cff4401824299a983c8e13424018efc347b4a2b Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 25 Jan 2023 23:25:40 +0300 Subject: fix prompt editing break after first batch in img2img --- modules/sd_samplers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/sd_samplers.py') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 6261d1f7..a7910b56 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -454,7 +454,7 @@ class KDiffusionSampler: 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.step = 0 + self.model_wrap_cfg.step = 0 self.eta = p.eta or opts.eta_ancestral k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else []) -- cgit v1.2.3