Merge branch 'AUTOMATIC1111:master' into deepdanbooru_pre_process

1 files changed, 17 insertions, 7 deletions

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index d168b938..20309e06 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -57,7 +57,7 @@ def set_samplers():
     global samplers, samplers_for_img2img

 

     hidden = set(opts.hide_samplers)

-    hidden_img2img = set(opts.hide_samplers + ['PLMS', 'DPM fast', 'DPM adaptive'])

+    hidden_img2img = set(opts.hide_samplers + ['PLMS'])

 

     samplers = [x for x in all_samplers if x.name not in hidden]

     samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]

@@ -365,16 +365,26 @@ class KDiffusionSampler:
         else:

             sigmas = self.model_wrap.get_sigmas(steps)

 

-        noise = noise * sigmas[steps - t_enc - 1]

-        xi = x + noise

-

-        extra_params_kwargs = self.initialize(p)

-

         sigma_sched = sigmas[steps - t_enc - 1:]

+        xi = x + noise * sigma_sched[0]

+        

+        extra_params_kwargs = self.initialize(p)

+        if 'sigma_min' in inspect.signature(self.func).parameters:

+            ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last

+            extra_params_kwargs['sigma_min'] = sigma_sched[-2]

+        if 'sigma_max' in inspect.signature(self.func).parameters:

+            extra_params_kwargs['sigma_max'] = sigma_sched[0]

+        if 'n' in inspect.signature(self.func).parameters:

+            extra_params_kwargs['n'] = len(sigma_sched) - 1

+        if 'sigma_sched' in inspect.signature(self.func).parameters:

+            extra_params_kwargs['sigma_sched'] = sigma_sched

+        if 'sigmas' in inspect.signature(self.func).parameters:

+            extra_params_kwargs['sigmas'] = sigma_sched

 

         self.model_wrap_cfg.init_latent = x

 

-        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, 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