diff options
| -rw-r--r-- | modules/generation_parameters_copypaste.py | 20 | ||||
| -rw-r--r-- | modules/sd_samplers_kdiffusion.py | 27 | ||||
| -rw-r--r-- | modules/shared.py | 4 | ||||
| -rw-r--r-- | scripts/xyz_grid.py | 8 |
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")),
|