improvements

See:
https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/10649#issuecomment-1561047723

4 files changed, 39 insertions, 20 deletions

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index e98866fc..4f827a6f 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -306,6 +306,18 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     if "RNG" not in res:

         res["RNG"] = "GPU"

 

+    if "KDiff Sched Type" not in res:

+        res["KDiff Sched Type"] = "Automatic"

+

+    if "KDiff Sched max sigma" not in res:

+        res["KDiff Sched max sigma"] = 14.6

+

+    if "KDiff Sched min sigma" not in res:

+        res["KDiff Sched min sigma"] = 0.3

+

+    if "KDiff Sched rho" not in res:

+        res["KDiff Sched rho"] = 7.0

+

     return res

 

 

@@ -318,10 +330,10 @@ infotext_to_setting_name_mapping = [
     ('Conditional mask weight', 'inpainting_mask_weight'),

     ('Model hash', 'sd_model_checkpoint'),

     ('ENSD', 'eta_noise_seed_delta'),

-    ('KDiffusion Scheduler Type', 'k_sched_type'),

-    ('KDiffusion Scheduler sigma_max', 'sigma_max'),

-    ('KDiffusion Scheduler sigma_min', 'sigma_min'),

-    ('KDiffusion Scheduler rho', 'rho'),

+    ('KDiff Sched Type', 'k_sched_type'),

+    ('KDiff Sched max sigma', 'sigma_max'),

+    ('KDiff Sched min sigma', 'sigma_min'),

+    ('KDiff Sched rho', 'rho'),

     ('Noise multiplier', 'initial_noise_multiplier'),

     ('Eta', 'eta_ancestral'),

     ('Eta DDIM', 'eta_ddim'),

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index a4c797c6..d2d172e4 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -296,12 +296,6 @@ class KDiffusionSampler:
 

         k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])

 

-        if opts.k_sched_type != "Automatic":

-            p.extra_generation_params["KDiffusion Scheduler Type"] = opts.k_sched_type

-            p.extra_generation_params["KDiffusion Scheduler sigma_max"] = opts.sigma_max

-            p.extra_generation_params["KDiffusion Scheduler sigma_min"] = opts.sigma_min

-            p.extra_generation_params["KDiffusion Scheduler rho"] = opts.rho

-

         extra_params_kwargs = {}

         for param_name in self.extra_params:

             if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:

@@ -326,14 +320,27 @@ class KDiffusionSampler:
         if p.sampler_noise_scheduler_override:

             sigmas = p.sampler_noise_scheduler_override(steps)

         elif opts.k_sched_type != "Automatic":

-            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_func = k_diffusion_scheduler[opts.k_sched_type]

+            m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())

+            sigma_min, sigma_max = (0.1, 10)

             sigmas_kwargs = {

-                'sigma_min': opts.sigma_min or sigma_min,

-                'sigma_max': opts.sigma_max or sigma_max

+                'sigma_min': sigma_min if opts.use_old_karras_scheduler_sigmas else m_sigma_min,

+                'sigma_max': sigma_max if opts.use_old_karras_scheduler_sigmas else m_sigma_max

             }

+

+            sigmas_func = k_diffusion_scheduler[opts.k_sched_type]

+            p.extra_generation_params["KDiff Sched Type"] = opts.k_sched_type

+

+            if opts.sigma_min != 0.3:

+                # take 0.0 as model default

+                sigmas_kwargs['sigma_min'] = opts.sigma_min or m_sigma_min

+                p.extra_generation_params["KDiff Sched min sigma"] = opts.sigma_min

+            if opts.sigma_max != 14.6:

+                sigmas_kwargs['sigma_max'] = opts.sigma_max or m_sigma_max

+                p.extra_generation_params["KDiff Sched max sigma"] = opts.sigma_max

             if opts.k_sched_type != 'exponential':

                 sigmas_kwargs['rho'] = opts.rho

+                p.extra_generation_params["KDiff Sched rho"] = opts.rho

+

             sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)

         elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':

             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())

diff --git a/modules/shared.py b/modules/shared.py
index da7f7cfb..00fcced8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -518,8 +518,8 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     '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}),

     'k_sched_type':  OptionInfo("Automatic", "scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}),

-    'sigma_max': OptionInfo(0.0, "sigma max", gr.Number).info("the maximum noise strength for the scheduler. Set to 0 to use the same value which 'xxx karras' samplers use."),

-    'sigma_min': OptionInfo(0.0, "sigma min", gr.Number).info("the minimum noise strength for the scheduler. Set to 0 to use the same value which 'xxx karras' samplers use."),

+    'sigma_max': OptionInfo(14.6, "sigma max", gr.Number).info("the maximum noise strength for the scheduler. Set to 0 to use the same value which 'xxx karras' samplers use."),

+    'sigma_min': OptionInfo(0.3, "sigma min", gr.Number).info("the minimum noise strength for the scheduler. Set to 0 to use the same value which 'xxx karras' samplers use."),

     'rho':  OptionInfo(7.0, "rho", gr.Number).info("higher will make a more steep noise scheduler (decrease faster). default for karras is 7.0, for polyexponential is 1.0"),

     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}).info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),

     'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma").link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),

diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py
index a4126e78..41fc2107 100644
--- a/scripts/xyz_grid.py
+++ b/scripts/xyz_grid.py
@@ -220,10 +220,10 @@ axis_options = [
     AxisOption("Sigma min", float, apply_field("s_tmin")),

     AxisOption("Sigma max", float, apply_field("s_tmax")),

     AxisOption("Sigma noise", float, apply_field("s_noise")),

-    AxisOption("KDiffusion Scheduler Type", str, apply_override("k_sched_type"), choices=lambda: list(sd_samplers_kdiffusion.k_diffusion_scheduler)),

-    AxisOption("KDiffusion Scheduler Sigma Min", float, apply_override("sigma_min")),

-    AxisOption("KDiffusion Scheduler Sigma Max", float, apply_override("sigma_max")),

-    AxisOption("KDiffusion Scheduler rho", float, apply_override("rho")),

+    AxisOption("KDiff Sched Type", str, apply_override("k_sched_type"), choices=lambda: list(sd_samplers_kdiffusion.k_diffusion_scheduler)),

+    AxisOption("KDiff Sched min sigma", float, apply_override("sigma_min")),

+    AxisOption("KDiff Sched max sigma", float, apply_override("sigma_max")),

+    AxisOption("KDiff Sched rho", float, apply_override("rho")),

     AxisOption("Eta", float, apply_field("eta")),

     AxisOption("Clip skip", int, apply_clip_skip),

     AxisOption("Denoising", float, apply_field("denoising_strength")),