diff options
Diffstat (limited to 'modules/sd_samplers_kdiffusion.py')
| -rw-r--r-- | modules/sd_samplers_kdiffusion.py | 43 |
1 files changed, 38 insertions, 5 deletions
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index e0da3425..db71a549 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -4,6 +4,7 @@ import inspect import k_diffusion.sampling
from modules import prompt_parser, devices, sd_samplers_common, sd_samplers_extra
+from modules.processing import StableDiffusionProcessing
from modules.shared import opts, state
import modules.shared as shared
from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
@@ -30,6 +31,7 @@ samplers_k_diffusion = [ ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
+ ('DPM++ 2M SDE Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_exp'], {'scheduler': 'exponential', "brownian_noise": True}),
('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras'}),
]
@@ -260,10 +262,7 @@ class TorchHijack: if noise.shape == x.shape:
return noise
- if opts.randn_source == "CPU" or x.device.type == 'mps':
- return torch.randn_like(x, device=devices.cpu).to(x.device)
- else:
- return torch.randn_like(x)
+ return devices.randn_like(x)
class KDiffusionSampler:
@@ -282,6 +281,14 @@ class KDiffusionSampler: self.last_latent = None
self.s_min_uncond = None
+ # NOTE: These are also defined in the StableDiffusionProcessing class.
+ # They should have been here to begin with but we're going to
+ # leave that class __init__ signature alone.
+ self.s_churn = 0.0
+ self.s_tmin = 0.0
+ self.s_tmax = float('inf')
+ self.s_noise = 1.0
+
self.conditioning_key = sd_model.model.conditioning_key
def callback_state(self, d):
@@ -316,7 +323,7 @@ class KDiffusionSampler: def number_of_needed_noises(self, p):
return p.steps
- def initialize(self, p):
+ def initialize(self, p: StableDiffusionProcessing):
self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
self.model_wrap_cfg.step = 0
@@ -337,6 +344,29 @@ class KDiffusionSampler: extra_params_kwargs['eta'] = self.eta
+ if len(self.extra_params) > 0:
+ s_churn = getattr(opts, 's_churn', p.s_churn)
+ s_tmin = getattr(opts, 's_tmin', p.s_tmin)
+ s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf
+ s_noise = getattr(opts, 's_noise', p.s_noise)
+
+ if s_churn != self.s_churn:
+ extra_params_kwargs['s_churn'] = s_churn
+ p.s_churn = s_churn
+ p.extra_generation_params['Sigma churn'] = s_churn
+ if s_tmin != self.s_tmin:
+ extra_params_kwargs['s_tmin'] = s_tmin
+ p.s_tmin = s_tmin
+ p.extra_generation_params['Sigma tmin'] = s_tmin
+ if s_tmax != self.s_tmax:
+ extra_params_kwargs['s_tmax'] = s_tmax
+ p.s_tmax = s_tmax
+ p.extra_generation_params['Sigma tmax'] = s_tmax
+ if s_noise != self.s_noise:
+ extra_params_kwargs['s_noise'] = s_noise
+ p.s_noise = s_noise
+ p.extra_generation_params['Sigma noise'] = s_noise
+
return extra_params_kwargs
def get_sigmas(self, p, steps):
@@ -378,6 +408,9 @@ class KDiffusionSampler: 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 = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
+ elif self.config is not None and self.config.options.get('scheduler', None) == 'exponential':
+ m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+ sigmas = k_diffusion.sampling.get_sigmas_exponential(n=steps, sigma_min=m_sigma_min, sigma_max=m_sigma_max, device=shared.device)
else:
sigmas = self.model_wrap.get_sigmas(steps)
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