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

javascript/hints.js

@@ -111,7 +111,8 @@ titles = {
     "Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.",
     "Multiplier for extra networks": "When adding extra network such as Hypernetwork or Lora to prompt, use this multiplier for it.",
     "Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.",
-    "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited."
+    "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited.",
+    "Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction."
 }
 
 

modules/generation_parameters_copypaste.py

@@ -309,6 +309,7 @@ infotext_to_setting_name_mapping = [
     ('UniPC order', 'uni_pc_order'),
     ('UniPC lower order final', 'uni_pc_lower_order_final'),
     ('RNG', 'randn_source'),
+    ('NGMS', 's_min_uncond'),
 ]
 
 

modules/processing.py

@@ -480,7 +480,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Clip skip": None if clip_skip <= 1 else clip_skip,
         "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
         "Init image hash": getattr(p, 'init_img_hash', None),
-        "RNG": (opts.randn_source if opts.randn_source != "GPU" else None)
+        "RNG": opts.randn_source if opts.randn_source != "GPU" else None,
+        "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
     }
 
     generation_params.update(p.extra_generation_params)

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
-            # 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:
-            sigma_threshold = s_min_uncond
+            skip_uncond = True
-            if(torch.dot(sigma,sigma) < sigma.shape[0] * (sigma_threshold*sigma_threshold) ):
+            x_in = x_in[:-batch_size]
-                uncond = torch.zeros([0,0,uncond.shape[2]])
+            sigma_in = sigma_in[:-batch_size]
-                x_in=x_in[:x_in.shape[0]//2]
-                sigma_in=sigma_in[:sigma_in.shape[0]//2]
 
-        if tensor.shape[1] == uncond.shape[1]:
+        if tensor.shape[1] == uncond.shape[1] or skip_uncond:
-            if not is_edit_model:
+            if is_edit_model:
-                cond_in = torch.cat([tensor, uncond])
-            else:
                 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 is_edit_model:
-            if uncond.shape[0]:
-                denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
-            else:
-                denoised = x_out
-        else:
             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