diff options
| -rw-r--r-- | modules/sd_samplers.py | 16 |
1 files changed, 7 insertions, 9 deletions
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 2fb57b7d..5642b870 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -38,9 +38,7 @@ samplers = [ SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []),
SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []),
]
-samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
-samplers_for_img2img.remove(samplers_for_img2img[6])
-samplers_for_img2img.remove(samplers_for_img2img[6])
+samplers_for_img2img = [x for x in samplers if x.name not in ['PLMS', 'DPM fast', 'DPM adaptive']]
sampler_extra_params = {
'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
@@ -314,12 +312,12 @@ class KDiffusionSampler: extra_params_kwargs = self.initialize(p)
if 'sigma_min' in inspect.signature(self.func).parameters:
+ extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
+ extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
if 'n' in inspect.signature(self.func).parameters:
- samples = self.func(self.model_wrap_cfg, x, sigma_min=self.model_wrap.sigmas[0].item(), sigma_max=self.model_wrap.sigmas[-1].item(), n=steps, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
- return samples
- samples = self.func(self.model_wrap_cfg, x, sigma_min=self.model_wrap.sigmas[0].item(), sigma_max=self.model_wrap.sigmas[-1].item(), extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
- return samples
- 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)
-
+ extra_params_kwargs['n'] = steps
+ else:
+ extra_params_kwargs['sigmas'] = sigmas
+ samples = self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
return samples
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