From 9de1e56e2dbb405213da9c221e0329d27f411691 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Fri, 30 Sep 2022 01:44:38 +0100 Subject: [PATCH 1/4] add sampler_noise_scheduler_override property --- modules/processing.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/processing.py b/modules/processing.py index 7eeb5191c..1da753a2c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -79,7 +79,7 @@ class StableDiffusionProcessing: self.paste_to = None self.color_corrections = None self.denoising_strength: float = 0 - + self.sampler_noise_scheduler_override = None self.ddim_discretize = opts.ddim_discretize self.s_churn = opts.s_churn self.s_tmin = opts.s_tmin @@ -130,7 +130,7 @@ class Processed: self.s_tmin = p.s_tmin self.s_tmax = p.s_tmax self.s_noise = p.s_noise - + self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0] self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0] self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) From bc38c80cfc83d4e2fc09c02dd49355664c05d15c Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Fri, 30 Sep 2022 01:46:06 +0100 Subject: [PATCH 2/4] add sampler_noise_scheduler_override switch --- modules/sd_samplers.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index dff89c092..925222148 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -290,7 +290,10 @@ class KDiffusionSampler: 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) + if p.sampler_noise_scheduler_override: + sigmas = p.sampler_noise_scheduler_override(steps) + else: + sigmas = self.model_wrap.get_sigmas(steps) noise = noise * sigmas[steps - t_enc - 1] xi = x + noise @@ -306,7 +309,10 @@ class KDiffusionSampler: def sample(self, p, x, conditioning, unconditional_conditioning, steps=None): steps = steps or p.steps - sigmas = self.model_wrap.get_sigmas(steps) + if p.sampler_noise_scheduler_override: + sigmas = p.sampler_noise_scheduler_override(steps) + else: + sigmas = self.model_wrap.get_sigmas(steps) x = x * sigmas[0] extra_params_kwargs = self.initialize(p) From bd4fc6633f126c4e40448e36115ed79f1c2e746f Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Fri, 30 Sep 2022 02:53:30 +0100 Subject: [PATCH 3/4] add script alternate_sampler_noise_schedules --- scripts/alternate_sampler_noise_schedules.py | 53 ++++++++++++++++++++ 1 file changed, 53 insertions(+) create mode 100644 scripts/alternate_sampler_noise_schedules.py diff --git a/scripts/alternate_sampler_noise_schedules.py b/scripts/alternate_sampler_noise_schedules.py new file mode 100644 index 000000000..343dad411 --- /dev/null +++ b/scripts/alternate_sampler_noise_schedules.py @@ -0,0 +1,53 @@ +import inspect +from modules.processing import Processed, process_images +import gradio as gr +import modules.scripts as scripts +import k_diffusion.sampling +import torch + + +class Script(scripts.Script): + + def title(self): + return "Alternate Sampler Noise Schedules" + + def ui(self, is_img2img): + noise_scheduler = gr.Dropdown(label="Noise Scheduler", choices=['Default','Karras','Exponential', 'Variance Preserving'], value='Default', type="index") + sched_smin = gr.Slider(value=0.1, label="Sigma min", minimum=0.0, maximum=100.0, step=0.5,) + sched_smax = gr.Slider(value=10.0, label="Sigma max", minimum=0.0, maximum=100.0, step=0.5) + sched_rho = gr.Slider(value=7.0, label="Sigma rho (Karras only)", minimum=7.0, maximum=100.0, step=0.5) + sched_beta_d = gr.Slider(value=19.9, label="Beta distribution (VP only)",minimum=0.0, maximum=40.0, step=0.5) + sched_beta_min = gr.Slider(value=0.1, label="Beta min (VP only)", minimum=0.0, maximum=40.0, step=0.1) + sched_eps_s = gr.Slider(value=0.001, label="Epsilon (VP only)", minimum=0.001, maximum=1.0, step=0.001) + + return [noise_scheduler, sched_smin, sched_smax, sched_rho, sched_beta_d, sched_beta_min, sched_eps_s] + + def run(self, p, noise_scheduler, sched_smin, sched_smax, sched_rho, sched_beta_d, sched_beta_min, sched_eps_s): + + noise_scheduler_func_name = ['-','get_sigmas_karras','get_sigmas_exponential','get_sigmas_vp'][noise_scheduler] + + base_params = { + "sigma_min":sched_smin, + "sigma_max":sched_smax, + "rho":sched_rho, + "beta_d":sched_beta_d, + "beta_min":sched_beta_min, + "eps_s":sched_eps_s, + "device":"cuda" if torch.cuda.is_available() else "cpu" + } + + if hasattr(k_diffusion.sampling,noise_scheduler_func_name): + + sigma_func = getattr(k_diffusion.sampling,noise_scheduler_func_name) + sigma_func_kwargs = {} + + for k,v in base_params.items(): + if k in inspect.signature(sigma_func).parameters: + sigma_func_kwargs[k] = v + + def substitute_noise_scheduler(n): + return sigma_func(n,**sigma_func_kwargs) + + p.sampler_noise_scheduler_override = substitute_noise_scheduler + + return process_images(p) \ No newline at end of file From 3f417566b0bda8eab05d247567aebf001c1d1725 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sat, 1 Oct 2022 04:19:32 +0100 Subject: [PATCH 4/4] Delete alternate_sampler_noise_schedules.py --- scripts/alternate_sampler_noise_schedules.py | 53 -------------------- 1 file changed, 53 deletions(-) delete mode 100644 scripts/alternate_sampler_noise_schedules.py diff --git a/scripts/alternate_sampler_noise_schedules.py b/scripts/alternate_sampler_noise_schedules.py deleted file mode 100644 index 343dad411..000000000 --- a/scripts/alternate_sampler_noise_schedules.py +++ /dev/null @@ -1,53 +0,0 @@ -import inspect -from modules.processing import Processed, process_images -import gradio as gr -import modules.scripts as scripts -import k_diffusion.sampling -import torch - - -class Script(scripts.Script): - - def title(self): - return "Alternate Sampler Noise Schedules" - - def ui(self, is_img2img): - noise_scheduler = gr.Dropdown(label="Noise Scheduler", choices=['Default','Karras','Exponential', 'Variance Preserving'], value='Default', type="index") - sched_smin = gr.Slider(value=0.1, label="Sigma min", minimum=0.0, maximum=100.0, step=0.5,) - sched_smax = gr.Slider(value=10.0, label="Sigma max", minimum=0.0, maximum=100.0, step=0.5) - sched_rho = gr.Slider(value=7.0, label="Sigma rho (Karras only)", minimum=7.0, maximum=100.0, step=0.5) - sched_beta_d = gr.Slider(value=19.9, label="Beta distribution (VP only)",minimum=0.0, maximum=40.0, step=0.5) - sched_beta_min = gr.Slider(value=0.1, label="Beta min (VP only)", minimum=0.0, maximum=40.0, step=0.1) - sched_eps_s = gr.Slider(value=0.001, label="Epsilon (VP only)", minimum=0.001, maximum=1.0, step=0.001) - - return [noise_scheduler, sched_smin, sched_smax, sched_rho, sched_beta_d, sched_beta_min, sched_eps_s] - - def run(self, p, noise_scheduler, sched_smin, sched_smax, sched_rho, sched_beta_d, sched_beta_min, sched_eps_s): - - noise_scheduler_func_name = ['-','get_sigmas_karras','get_sigmas_exponential','get_sigmas_vp'][noise_scheduler] - - base_params = { - "sigma_min":sched_smin, - "sigma_max":sched_smax, - "rho":sched_rho, - "beta_d":sched_beta_d, - "beta_min":sched_beta_min, - "eps_s":sched_eps_s, - "device":"cuda" if torch.cuda.is_available() else "cpu" - } - - if hasattr(k_diffusion.sampling,noise_scheduler_func_name): - - sigma_func = getattr(k_diffusion.sampling,noise_scheduler_func_name) - sigma_func_kwargs = {} - - for k,v in base_params.items(): - if k in inspect.signature(sigma_func).parameters: - sigma_func_kwargs[k] = v - - def substitute_noise_scheduler(n): - return sigma_func(n,**sigma_func_kwargs) - - p.sampler_noise_scheduler_override = substitute_noise_scheduler - - return process_images(p) \ No newline at end of file