Merge branch 'master' of github.com:AUTOMATIC1111/stable-diffusion-webui into gamepad

1 files changed, 28 insertions, 11 deletions

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 177b5338..a7910b56 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -97,8 +97,9 @@ sampler_extra_params = {
 

 def setup_img2img_steps(p, steps=None):

     if opts.img2img_fix_steps or steps is not None:

-        steps = int((steps or p.steps) / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0

-        t_enc = p.steps - 1

+        requested_steps = (steps or p.steps)

+        steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0

+        t_enc = requested_steps - 1

     else:

         steps = p.steps

         t_enc = int(min(p.denoising_strength, 0.999) * steps)

@@ -137,9 +138,9 @@ def samples_to_image_grid(samples, approximation=None):
 def store_latent(decoded):

     state.current_latent = decoded

 

-    if opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:

+    if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:

         if not shared.parallel_processing_allowed:

-            shared.state.current_image = sample_to_image(decoded)

+            shared.state.assign_current_image(sample_to_image(decoded))

 

 

 class InterruptedException(BaseException):

@@ -242,7 +243,7 @@ class VanillaStableDiffusionSampler:
         self.nmask = p.nmask if hasattr(p, 'nmask') else None

 

     def adjust_steps_if_invalid(self, p, num_steps):

-        if  (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):

+        if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):

             valid_step = 999 / (1000 // num_steps)

             if valid_step == floor(valid_step):

                 return int(valid_step) + 1

@@ -265,8 +266,7 @@ class VanillaStableDiffusionSampler:
         if image_conditioning is not None:

             conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}

             unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}

-            

-            

+

         samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))

 

         return samples

@@ -351,6 +351,13 @@ class CFGDenoiser(torch.nn.Module):
 

             x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})

 

+        devices.test_for_nans(x_out, "unet")

+

+        if opts.live_preview_content == "Prompt":

+            store_latent(x_out[0:uncond.shape[0]])

+        elif opts.live_preview_content == "Negative prompt":

+            store_latent(x_out[-uncond.shape[0]:])

+

         denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)

 

         if self.mask is not None:

@@ -422,7 +429,8 @@ class KDiffusionSampler:
     def callback_state(self, d):

         step = d['i']

         latent = d["denoised"]

-        store_latent(latent)

+        if opts.live_preview_content == "Combined":

+            store_latent(latent)

         self.last_latent = latent

 

         if self.stop_at is not None and step > self.stop_at:

@@ -446,7 +454,7 @@ class KDiffusionSampler:
     def initialize(self, p):

         self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None

         self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None

-        self.model_wrap.step = 0

+        self.model_wrap_cfg.step = 0

         self.eta = p.eta or opts.eta_ancestral

 

         k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])

@@ -462,14 +470,23 @@ class KDiffusionSampler:
         return extra_params_kwargs

 

     def get_sigmas(self, p, steps):

+        discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)

+        if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:

+            discard_next_to_last_sigma = True

+            p.extra_generation_params["Discard penultimate sigma"] = True

+

+        steps += 1 if discard_next_to_last_sigma else 0

+

         if p.sampler_noise_scheduler_override:

             sigmas = p.sampler_noise_scheduler_override(steps)

         elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':

-            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)

+            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())

+

+            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)

         else:

             sigmas = self.model_wrap.get_sigmas(steps)

 

-        if self.config is not None and self.config.options.get('discard_next_to_last_sigma', False):

+        if discard_next_to_last_sigma:

             sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])

 

         return sigmas