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pass extra KDiffusionSampler function parameters

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DepFA 2022-09-26 09:56:47 +01:00 committed by AUTOMATIC1111
parent 6b78833e33
commit 2ab3d593f9
1 changed files with 18 additions and 2 deletions
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20
modules/sd_samplers.py
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@ -37,6 +37,11 @@ samplers = [
]
samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
sampler_extra_params = {
'sample_euler':['s_churn','s_tmin','s_noise'],
'sample_heun' :['s_churn','s_tmin','s_noise'],
'sample_dpm_2':['s_churn','s_tmin','s_noise'],
}
def setup_img2img_steps(p, steps=None):
if opts.img2img_fix_steps or steps is not None:
@ -224,6 +229,7 @@ class KDiffusionSampler:
self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)
self.funcname = funcname
self.func = getattr(k_diffusion.sampling, self.funcname)
self.extra_params = sampler_extra_params.get(funcname,[])
self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
self.sampler_noises = None
self.sampler_noise_index = 0
@ -269,7 +275,12 @@ class KDiffusionSampler:
if self.sampler_noises is not None:
k_diffusion.sampling.torch = TorchHijack(self)
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 = {}
for val in self.extra_params:
if hasattr(opts,val):
extra_params_kwargs[val] = getattr(opts,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)
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
steps = steps or p.steps
@ -286,7 +297,12 @@ class KDiffusionSampler:
if self.sampler_noises is not None:
k_diffusion.sampling.torch = TorchHijack(self)
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 = {}
for val in self.extra_params:
if hasattr(opts,val):
extra_params_kwargs[val] = getattr(opts,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)
return samples