add an option to choose what you want to see in live preview (Live preview subject) and moves live preview settings to its own tab

1 files changed, 10 insertions, 5 deletions

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 01221b89..7616fded 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -138,7 +138,7 @@ 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)

 

@@ -243,7 +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

@@ -266,8 +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

@@ -352,6 +351,11 @@ 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]:]]})

 

+        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:

@@ -423,7 +427,8 @@ 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: