feat(*): add latent scale options

modules/image_tool.py

@@ -1,7 +1,15 @@
 from numpy import ndarray
 from typing import Union
 
-from .util import get_sizes
+from .util import get_latent_size, get_image_size
+
+SCALE_8X = "SD, Qwen (8x)"
+SCALE_16X = "Flux2 (16x)"
+SCALE_OPTIONS = [SCALE_8X, SCALE_16X]
+SCALE_FACTOR_MAP = {
+    SCALE_8X: 8,
+    SCALE_16X: 16,
+}
 
 
 class OFOImageFit:
@@ -11,7 +19,11 @@ class OFOImageFit:
     @classmethod
     def INPUT_TYPES(s):
         return {
-            "required": {"latent": ("LATENT",), "image": ("IMAGE",)},
+            "required": {
+                "latent": ("LATENT",),
+                "image": ("IMAGE",),
+                "latent_scale": (SCALE_OPTIONS,),
+            },
             "optional": {
                 "width_position": (
                     "FLOAT",
@@ -76,6 +88,7 @@ class OFOImageFit:
         self,
         latent: Union[dict, ndarray],
         image: ndarray,
+        latent_scale: str,
         width_position: float = 0.5,
         height_position: float = 0.5,
         scale: float = 1.0,
@@ -86,6 +99,7 @@ class OFOImageFit:
         Args:
             latent (np.ndarray or dict): Latent representation or dictionary containing latent samples.
             image (np.ndarray): Input image to be resized and padded.
+            latent_scale (str): Latent scale factor option. "SD, Qwen (8x)" or "Flux2 (16x)".
             width_position (float, optional): Horizontal position where the image will be placed within the latent space. Defaults to 0.5.
             height_position (float, optional): Vertical position where the image will be placed within the latent space. Defaults to 0.5.
             scale (float, optional): Scaling factor applied to the image before resizing. Defaults to 1.0.
@@ -93,9 +107,10 @@ class OFOImageFit:
         Returns:
             tuple: A tuple containing six integers representing the new width, height, top, right, bottom, and left padding values.
         """
-        # get sizes
-        latent_height, latent_width = get_sizes(latent)
-        image_height, image_width = get_sizes(image)
+        scale_factor = SCALE_FACTOR_MAP[latent_scale]
+        
+        latent_height, latent_width = get_latent_size(latent, scale_factor)
+        image_height, image_width = get_image_size(image)
 
         # get minimum scale
         scale_w = latent_width / image_width

modules/util.py

@@ -1,14 +1,12 @@
 from numpy import ndarray
-from typing import Union, Tuple
-
-# latent to pixel scale factor
-SCALE = 8
+from typing import Tuple
 
 
-def get_sizes(target: Union[dict, ndarray]) -> Tuple[int, int]:
-    if isinstance(target, dict) and "samples" in target:
-        # Latent height, width
-        return (target["samples"].shape[2] * SCALE, target["samples"].shape[3] * SCALE)
-    else:
-        # Image height, width
-        return (target.shape[1], target.shape[2])
+def get_latent_size(latent: dict, scale_factor: int = 8) -> Tuple[int, int]:
+    """Convert latent dimensions to pixel dimensions using scale factor."""
+    return (latent["samples"].shape[2] * scale_factor, latent["samples"].shape[3] * scale_factor)
+
+
+def get_image_size(image: ndarray) -> Tuple[int, int]:
+    """Get image height and width from numpy array."""
+    return (image.shape[1], image.shape[2])