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take extra sampler properties from StableDiffusionProcessing

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DepFA 2022-09-26 15:43:16 +01:00 committed by AUTOMATIC1111
parent 4ea36a37d6
commit a860839f1f
1 changed files with 6 additions and 6 deletions
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12
modules/sd_samplers.py
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@ -125,9 +125,9 @@ class VanillaStableDiffusionSampler:
# existing code fails with cetain step counts, like 9 # existing code fails with cetain step counts, like 9
try: try:
self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=opts.ddim_eta, ddim_discretize=opts.ddim_discretize, verbose=False) self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
except Exception: except Exception:
self.sampler.make_schedule(ddim_num_steps=steps+1,ddim_eta=opts.ddim_eta, ddim_discretize=opts.ddim_discretize, verbose=False) self.sampler.make_schedule(ddim_num_steps=steps+1,ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise) x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
@ -277,8 +277,8 @@ class KDiffusionSampler:
extra_params_kwargs = {} extra_params_kwargs = {}
for val in self.extra_params: for val in self.extra_params:
if hasattr(opts,val): if hasattr(p,val):
extra_params_kwargs[val] = getattr(opts,val) extra_params_kwargs[val] = getattr(p,val)
return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs) return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
@ -299,8 +299,8 @@ class KDiffusionSampler:
extra_params_kwargs = {} extra_params_kwargs = {}
for val in self.extra_params: for val in self.extra_params:
if hasattr(opts,val): if hasattr(p,val):
extra_params_kwargs[val] = getattr(opts,val) extra_params_kwargs[val] = getattr(p,val)
samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs) samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)