pass extra KDiffusionSampler function parameters

modules/sd_samplers.py

@@ -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