diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/generation_parameters_copypaste.py | 16 | ||||
| -rw-r--r-- | modules/sd_samplers_kdiffusion.py | 38 | ||||
| -rw-r--r-- | modules/shared.py | 4 |
3 files changed, 58 insertions, 0 deletions
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index d5f0a49b..1443c5cd 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 Schedule Type" not in res:
+ res["KDiff Schedule Type"] = "Automatic"
+
+ if "KDiff Schedule max sigma" not in res:
+ res["KDiff Schedule max sigma"] = 14.6
+
+ if "KDiff Schedule min sigma" not in res:
+ res["KDiff Schedule min sigma"] = 0.3
+
+ if "KDiff Schedule rho" not in res:
+ res["KDiff Schedule rho"] = 7.0
+
return res
@@ -318,6 +330,10 @@ infotext_to_setting_name_mapping = [ ('Conditional mask weight', 'inpainting_mask_weight'),
('Model hash', 'sd_model_checkpoint'),
('ENSD', 'eta_noise_seed_delta'),
+ ('KDiff Schedule Type', 'k_sched_type'),
+ ('KDiff Schedule max sigma', 'sigma_max'),
+ ('KDiff Schedule min sigma', 'sigma_min'),
+ ('KDiff Schedule 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 638e0ac9..9c9d9f17 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -44,6 +44,14 @@ sampler_extra_params = { 'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
}
+k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
+k_diffusion_scheduler = {
+ 'Automatic': None,
+ 'karras': k_diffusion.sampling.get_sigmas_karras,
+ 'exponential': k_diffusion.sampling.get_sigmas_exponential,
+ 'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
+}
+
class CFGDenoiser(torch.nn.Module):
"""
@@ -265,6 +273,13 @@ class KDiffusionSampler: try:
return func()
+ except RecursionError:
+ print(
+ 'Encountered RecursionError during sampling, returning last latent. '
+ 'rho >5 with a polyexponential scheduler may cause this error. '
+ 'You should try to use a smaller rho value instead.'
+ )
+ return self.last_latent
except sd_samplers_common.InterruptedException:
return self.last_latent
@@ -304,6 +319,29 @@ class KDiffusionSampler: if p.sampler_noise_scheduler_override:
sigmas = p.sampler_noise_scheduler_override(steps)
+ elif opts.k_sched_type != "Automatic":
+ 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': 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 Schedule 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 Schedule 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 Schedule max sigma"] = opts.sigma_max
+ if opts.k_sched_type != 'exponential':
+ sigmas_kwargs['rho'] = opts.rho
+ p.extra_generation_params["KDiff Schedule 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 a5e7824a..364a5991 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -518,6 +518,10 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters" '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}),
+ 'k_sched_type': OptionInfo("Automatic", "scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}),
+ '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"),
'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
|