Use image-space tile dimensions on the TiledMultiDiffusionDenoiseLate…

…nts invocation. This is more natural for many users.

invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py

@@ -85,13 +85,19 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
         description=FieldDescriptions.latents,
         input=Input.Connection,
     )
-    tile_height: int = InputField(default=64, gt=0, description="Height of the tiles in latent space.")
-    tile_width: int = InputField(default=64, gt=0, description="Width of the tiles in latent space.")
+    tile_height: int = InputField(
+        default=1024, gt=0, multiple_of=LATENT_SCALE_FACTOR, description="Height of the tiles in image space."
+    )
+    tile_width: int = InputField(
+        default=1024, gt=0, multiple_of=LATENT_SCALE_FACTOR, description="Width of the tiles in image space."
+    )
     tile_overlap: int = InputField(
-        default=16,
+        default=32,
+        multiple_of=LATENT_SCALE_FACTOR,
         gt=0,
-        description="The overlap between adjacent tiles in latent space. Tiles will be cropped during merging "
-        "(if necessary) to ensure that they overlap by exactly this amount.",
+        description="The overlap between adjacent tiles in pixel space. (Of course, tile merging is applied in latent "
+        "space.) Tiles will be cropped during merging (if necessary) to ensure that they overlap by exactly this "
+        "amount.",
     )
     steps: int = InputField(default=18, gt=0, description=FieldDescriptions.steps)
     cfg_scale: float | list[float] = InputField(default=6.0, description=FieldDescriptions.cfg_scale, title="CFG Scale")
@@ -159,16 +165,21 @@ def __init__(self) -> None:
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
+        # Convert tile image-space dimensions to latent-space dimensions.
+        latent_tile_height = self.tile_height // LATENT_SCALE_FACTOR
+        latent_tile_width = self.tile_width // LATENT_SCALE_FACTOR
+        latent_tile_overlap = self.tile_overlap // LATENT_SCALE_FACTOR
+
         seed, noise, latents = DenoiseLatentsInvocation.prepare_noise_and_latents(context, self.noise, self.latents)
         _, _, latent_height, latent_width = latents.shape
 
         # Calculate the tile locations to cover the latent-space image.
         tiles = calc_tiles_min_overlap(
             image_height=latent_height,
             image_width=latent_width,
-            tile_height=self.tile_height,
-            tile_width=self.tile_width,
-            min_overlap=self.tile_overlap,
+            tile_height=latent_tile_height,
+            tile_width=latent_tile_width,
+            min_overlap=latent_tile_overlap,
         )
 
         # Get the unet's config so that we can pass the base to sd_step_callback().
@@ -207,8 +218,8 @@ def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
                 positive_conditioning_field=self.positive_conditioning,
                 negative_conditioning_field=self.negative_conditioning,
                 unet=unet,
-                latent_height=self.tile_height,
-                latent_width=self.tile_width,
+                latent_height=latent_tile_height,
+                latent_width=latent_tile_width,
                 cfg_scale=self.cfg_scale,
                 steps=self.steps,
                 cfg_rescale_multiplier=self.cfg_rescale_multiplier,
@@ -253,7 +264,7 @@ def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             # Run Multi-Diffusion denoising.
             result_latents = pipeline.multi_diffusion_denoise(
                 multi_diffusion_conditioning=multi_diffusion_conditioning,
-                target_overlap=self.tile_overlap,
+                target_overlap=latent_tile_overlap,
                 latents=latents,
                 scheduler_step_kwargs=scheduler_step_kwargs,
                 noise=noise,