rework Negative Guidance minimum sigma to work with AND, add infotext and copypaste parameters support

1 files changed, 25 insertions, 18 deletions

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index d42d5fcf..f8aaac59 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -115,20 +115,21 @@ class CFGDenoiser(torch.nn.Module):
         sigma_in = denoiser_params.sigma

         tensor = denoiser_params.text_cond

         uncond = denoiser_params.text_uncond

+        skip_uncond = False

 

-        if self.step % 2 and s_min_uncond > 0 and not is_edit_model:

-            # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it

-            sigma_threshold = s_min_uncond

-            if(torch.dot(sigma,sigma) < sigma.shape[0] * (sigma_threshold*sigma_threshold) ):

-                uncond = torch.zeros([0,0,uncond.shape[2]])

-                x_in=x_in[:x_in.shape[0]//2]

-                sigma_in=sigma_in[:sigma_in.shape[0]//2]

+        # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it

+        if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:

+            skip_uncond = True

+            x_in = x_in[:-batch_size]

+            sigma_in = sigma_in[:-batch_size]

 

-        if tensor.shape[1] == uncond.shape[1]:

-            if not is_edit_model:

-                cond_in = torch.cat([tensor, uncond])

-            else:

+        if tensor.shape[1] == uncond.shape[1] or skip_uncond:

+            if is_edit_model:

                 cond_in = torch.cat([tensor, uncond, uncond])

+            elif skip_uncond:

+                cond_in = tensor

+            else:

+                cond_in = torch.cat([tensor, uncond])

 

             if shared.batch_cond_uncond:

                 x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))

@@ -152,9 +153,15 @@ class CFGDenoiser(torch.nn.Module):
 

                 x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))

 

-            if uncond.shape[0]:

+            if not skip_uncond:

                 x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))

 

+        if skip_uncond:

+            #x_out = torch.cat([x_out, x_out[0:batch_size]])  # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be

+            denoised_image_indexes = [x[0][0] for x in conds_list]

+            fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])

+            x_out = torch.cat([x_out, fake_uncond])

+

         denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)

         cfg_denoised_callback(denoised_params)

 

@@ -165,13 +172,12 @@ class CFGDenoiser(torch.nn.Module):
         elif opts.live_preview_content == "Negative prompt":

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

 

-        if not is_edit_model:

-            if uncond.shape[0]:

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

-            else:

-                denoised = x_out

-        else:

+        if is_edit_model:

             denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)

+        elif skip_uncond:

+            denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)

+        else:

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

 

         if self.mask is not None:

             denoised = self.init_latent * self.mask + self.nmask * denoised

@@ -221,6 +227,7 @@ class KDiffusionSampler:
         self.eta = None

         self.config = None

         self.last_latent = None

+        self.s_min_uncond = None

 

         self.conditioning_key = sd_model.model.conditioning_key