Merge remote-tracking branch 'upstream/master' into token_count

1 files changed, 22 insertions, 6 deletions

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 1fc9d18c..666ee1ee 100644
--- a/modules/sd_samplers.py
+++ b/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_tmax','s_noise'],

+    'sample_heun' :['s_churn','s_tmin','s_tmax','s_noise'],

+    'sample_dpm_2':['s_churn','s_tmin','s_tmax','s_noise'],

+}

 

 def setup_img2img_steps(p, steps=None):

     if opts.img2img_fix_steps or steps is not None:

@@ -120,9 +125,9 @@ class VanillaStableDiffusionSampler:
 

         # existing code fails with cetain step counts, like 9

         try:

-            self.sampler.make_schedule(ddim_num_steps=steps, verbose=False)

+            self.sampler.make_schedule(ddim_num_steps=steps,  ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)

         except Exception:

-            self.sampler.make_schedule(ddim_num_steps=steps+1, 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)

 

@@ -149,9 +154,9 @@ class VanillaStableDiffusionSampler:
 

         # existing code fails with cetin step counts, like 9

         try:

-            samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)

+            samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=p.ddim_eta)

         except Exception:

-            samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)

+            samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=p.ddim_eta)

 

         return samples_ddim

 

@@ -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(p,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)

 

     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(p,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)

 

         return samples