fix(nodes): fix slatents and rlatents bugs

Author: psychedelicious

invokeai/app/invocations/latent.py

@@ -379,26 +379,24 @@ class ResizeLatentsInvocation(BaseInvocation):
     type: Literal["lresize"] = "lresize"
 
     # Inputs
-    latents: Optional[LatentsField] = Field(description="The latents to resize")
-    width: int = Field(ge=64, multiple_of=8, description="The width to resize to")
-    height: int = Field(ge=64, multiple_of=8, description="The height to resize to")
-    downsample: int = Field(
-        default=8, ge=1, description="The downsampling factor (leave at 8 for SD)"
-    )
-    mode: LATENTS_INTERPOLATION_MODE = Field(
-        default="bilinear", description="The interpolation mode"
-    )
+    latents: Optional[LatentsField]             = Field(description="The latents to resize")
+    width: int                                  = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
+    height: int                                 = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
+    downsample: Optional[int]                   = Field(default=8, ge=1, description="The downsampling factor (leave at 8 for SD)")
+    mode: Optional[LATENTS_INTERPOLATION_MODE]  = Field(default="bilinear", description="The interpolation mode")
+    antialias: Optional[bool]                   = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = context.services.latents.get(self.latents.latents_name)
-        # resizing
+
         resized_latents = torch.nn.functional.interpolate(
             latents,
             size=(
                 self.height // self.downsample,
                 self.width // self.downsample,
             ),
             mode=self.mode,
+            antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
         )
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
@@ -415,27 +413,20 @@ class ScaleLatentsInvocation(BaseInvocation):
     type: Literal["lscale"] = "lscale"
 
     # Inputs
-    latents: Optional[LatentsField] = Field(description="The latents to resize")
-    scale: int = Field(
-        default=2, ge=1, description="The factor by which to scale the latents"
-    )
-    mode: LATENTS_INTERPOLATION_MODE = Field(
-        default="bilinear", description="The interpolation mode"
-    )
+    latents: Optional[LatentsField]             = Field(description="The latents to scale")
+    scale_factor: float                         = Field(ge=0, description="The factor by which to scale the latents")
+    mode: Optional[LATENTS_INTERPOLATION_MODE]  = Field(default="bilinear", description="The interpolation mode")
+    antialias: Optional[bool]                   = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = context.services.latents.get(self.latents.latents_name)
 
-        (_, _, h, w) = latents.size()
-
         # resizing
         resized_latents = torch.nn.functional.interpolate(
             latents,
-            size=(
-                h * self.scale,
-                w * self.scale,
-            ),
+            scale_factor=self.scale_factor,
             mode=self.mode,
+            antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
         )
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699