Set default latent scale mode to Latent (nearest-exact)

AUTOMATIC1111 · Jan 29, 2024 · 9141166 · 9141166
1 parent 6d5a2e5
commit 9141166
Show file tree

Hide file tree

Showing 3 changed files with 4 additions and 4 deletions.
diff --git a/modules/processing.py b/modules/processing.py
@@ -1228,7 +1228,7 @@ def init(self, all_prompts, all_seeds, all_subseeds):
             if tuple(self.hr_negative_prompt) != tuple(self.negative_prompt):
                 self.extra_generation_params["Hires negative prompt"] = self.hr_negative_prompt
 
-            self.latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
+            self.latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest-exact")
             if self.enable_hr and self.latent_scale_mode is None:
                 if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers):
                     raise Exception(f"could not find upscaler named {self.hr_upscaler}")
@@ -1654,7 +1654,7 @@ def init(self, all_prompts, all_seeds, all_subseeds):
         devices.torch_gc()
 
         if self.resize_mode == 3:
-            self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+            self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="nearest-exact", antialias=False)
 
         if image_mask is not None:
             init_mask = latent_mask

diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py
@@ -24,7 +24,7 @@ def cat(self, tensors, *args, **kwargs):
         if len(tensors) == 2:
             a, b = tensors
             if a.shape[-2:] != b.shape[-2:]:
-                a = torch.nn.functional.interpolate(a, b.shape[-2:], mode="nearest")
+                a = torch.nn.functional.interpolate(a, b.shape[-2:], mode="nearest-exact")
 
             tensors = (a, b)
 

diff --git a/modules/shared.py b/modules/shared.py
@@ -47,7 +47,7 @@
 
 tab_names = []
 
-latent_upscale_default_mode = "Latent"
+latent_upscale_default_mode = "Latent (nearest-exact)"
 latent_upscale_modes = {
     "Latent": {"mode": "bilinear", "antialias": False},
     "Latent (antialiased)": {"mode": "bilinear", "antialias": True},