diff options
Diffstat (limited to 'modules/sd_samplers_kdiffusion.py')
| -rw-r--r-- | modules/sd_samplers_kdiffusion.py | 40 |
1 files changed, 40 insertions, 0 deletions
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 638e0ac9..e9ba2c61 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 +319,31 @@ 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) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
+ sigmas_kwargs = {
+ 'sigma_min': sigma_min,
+ 'sigma_max': sigma_max,
+ }
+
+ sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
+ p.extra_generation_params["Schedule type"] = opts.k_sched_type
+
+ if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
+ sigmas_kwargs['sigma_min'] = opts.sigma_min
+ p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
+ if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
+ sigmas_kwargs['sigma_max'] = opts.sigma_max
+ p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
+
+ default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
+
+ if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho:
+ sigmas_kwargs['rho'] = opts.rho
+ p.extra_generation_params["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())
|