diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index 5a551c253..b2f9241c4 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -1,241 +1,270 @@
-import cv2
-from collections import defaultdict
-from math import log, sqrt
-import numpy as np
-from PIL import Image, ImageDraw
-
-GREEN = "#0F0"
-BLUE = "#00F"
-RED = "#F00"
-
-
-def crop_image(im, settings):
-  """ Intelligently crop an image to the subject matter """
-  if im.height > im.width:
-      im = im.resize((settings.crop_width, settings.crop_height * im.height // im.width))
-  elif im.width > im.height:
-      im = im.resize((settings.crop_width * im.width // im.height, settings.crop_height))
-  else:
-      im = im.resize((settings.crop_width, settings.crop_height))
-
-  if im.height == im.width:
-    return im
-
-  focus = focal_point(im, settings)
-
-  # take the focal point and turn it into crop coordinates that try to center over the focal
-  # point but then get adjusted back into the frame
-  y_half = int(settings.crop_height / 2)
-  x_half = int(settings.crop_width / 2)
-
-  x1 = focus.x - x_half
-  if x1 < 0:
-      x1 = 0
-  elif x1 + settings.crop_width > im.width:
-      x1 = im.width - settings.crop_width
-
-  y1 = focus.y - y_half
-  if y1 < 0:
-      y1 = 0
-  elif y1 + settings.crop_height > im.height:
-      y1 = im.height - settings.crop_height
-
-  x2 = x1 + settings.crop_width
-  y2 = y1 + settings.crop_height
-
-  crop = [x1, y1, x2, y2]
-
-  if settings.annotate_image:
-    d = ImageDraw.Draw(im)
-    rect = list(crop)
-    rect[2] -= 1
-    rect[3] -= 1
-    d.rectangle(rect, outline=GREEN)
-    if settings.destop_view_image:
-      im.show()
-
-  return im.crop(tuple(crop))
-
-def focal_point(im, settings):
-    corner_points = image_corner_points(im, settings)
-    entropy_points = image_entropy_points(im, settings)
-    face_points = image_face_points(im, settings)
-
-    total_points = len(corner_points) + len(entropy_points) + len(face_points)
-
-    corner_weight = settings.corner_points_weight
-    entropy_weight = settings.entropy_points_weight
-    face_weight = settings.face_points_weight
-
-    weight_pref_total = corner_weight + entropy_weight + face_weight
-
-    # weight things
-    pois = []
-    if weight_pref_total == 0 or total_points == 0: 
-      return pois
-
-    pois.extend(
-      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (corner_weight/weight_pref_total) / (len(corner_points)/total_points) )) for p in corner_points ]
-    )
-    pois.extend(
-      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (entropy_weight/weight_pref_total) / (len(entropy_points)/total_points) )) for p in entropy_points ]
-    )
-    pois.extend(
-      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (face_weight/weight_pref_total) / (len(face_points)/total_points) )) for p in face_points ]
-    )
-
-    average_point = poi_average(pois, settings)
-
-    if settings.annotate_image:
-      d = ImageDraw.Draw(im)
-      for f in face_points:
-        d.rectangle(f.bounding(f.size), outline=RED)
-      for f in entropy_points:
-        d.rectangle(f.bounding(30), outline=BLUE)
-      for poi in pois:
-        w = max(4, 4 * 0.5 * sqrt(poi.weight))
-        d.ellipse(poi.bounding(w), fill=BLUE)
-      d.ellipse(average_point.bounding(25), outline=GREEN)
-      
-    return average_point
-
-
-def image_face_points(im, settings):
-    np_im = np.array(im)
-    gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
-
-    tries = [
-      [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
-    ]
-
-    for t in tries:
-      # print(t[0])
-      classifier = cv2.CascadeClassifier(t[0])
-      minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
-      try:
-        faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
-          minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
-      except:
-        continue
-
-      if len(faces) > 0:
-        rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
-        return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2])) for r in rects]
-    return []
-
-
-def image_corner_points(im, settings):
-    grayscale = im.convert("L")
-
-    # naive attempt at preventing focal points from collecting at watermarks near the bottom
-    gd = ImageDraw.Draw(grayscale)
-    gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
-
-    np_im = np.array(grayscale)
-
-    points = cv2.goodFeaturesToTrack(
-        np_im,
-        maxCorners=100,
-        qualityLevel=0.04,
-        minDistance=min(grayscale.width, grayscale.height)*0.07,
-        useHarrisDetector=False,
-    )
-
-    if points is None:
-        return []
-
-    focal_points = []
-    for point in points:
-      x, y = point.ravel()
-      focal_points.append(PointOfInterest(x, y, size=4))
-
-    return focal_points
-
-
-def image_entropy_points(im, settings):
-    landscape = im.height < im.width
-    portrait = im.height > im.width
-    if landscape:
-      move_idx = [0, 2]
-      move_max = im.size[0]
-    elif portrait:
-      move_idx = [1, 3]
-      move_max = im.size[1]
-    else:
-      return []
-
-    e_max = 0
-    crop_current = [0, 0, settings.crop_width, settings.crop_height]
-    crop_best = crop_current
-    while crop_current[move_idx[1]] < move_max:
-        crop = im.crop(tuple(crop_current))
-        e = image_entropy(crop)
-
-        if (e > e_max):
-          e_max = e
-          crop_best = list(crop_current)
-
-        crop_current[move_idx[0]] += 4
-        crop_current[move_idx[1]] += 4
-
-    x_mid = int(crop_best[0] + settings.crop_width/2)
-    y_mid = int(crop_best[1] + settings.crop_height/2)
-
-    return [PointOfInterest(x_mid, y_mid, size=25)]
-
-
-def image_entropy(im):
-    # greyscale image entropy
-    # band = np.asarray(im.convert("L"))
-    band = np.asarray(im.convert("1"), dtype=np.uint8)
-    hist, _ = np.histogram(band, bins=range(0, 256))
-    hist = hist[hist > 0]
-    return -np.log2(hist / hist.sum()).sum()
-
-
-def poi_average(pois, settings):
-    weight = 0.0
-    x = 0.0
-    y = 0.0
-    for poi in pois:
-        weight += poi.weight
-        x += poi.x * poi.weight
-        y += poi.y * poi.weight
-    avg_x = round(x / weight)
-    avg_y = round(y / weight)
-
-    return PointOfInterest(avg_x, avg_y)
-
-
-class PointOfInterest:
-  def __init__(self, x, y, weight=1.0, size=10):
-    self.x = x
-    self.y = y
-    self.weight = weight
-    self.size = size
-
-  def bounding(self, size):
-    return [
-      self.x - size//2,
-      self.y - size//2,
-      self.x + size//2,
-      self.y + size//2
-    ]
-
-
-class Settings:
-  def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False):
-    self.crop_width = crop_width
-    self.crop_height = crop_height
-    self.corner_points_weight = corner_points_weight
-    self.entropy_points_weight = entropy_points_weight
-    self.face_points_weight = entropy_points_weight
-    self.annotate_image = annotate_image
+import cv2
+from collections import defaultdict
+from math import log, sqrt
+import numpy as np
+from PIL import Image, ImageDraw
+
+GREEN = "#0F0"
+BLUE = "#00F"
+RED = "#F00"
+
+
+def crop_image(im, settings):
+  """ Intelligently crop an image to the subject matter """
+
+  scale_by = 1
+  if is_landscape(im.width, im.height):
+    scale_by = settings.crop_height / im.height
+  elif is_portrait(im.width, im.height):
+    scale_by = settings.crop_width / im.width
+  elif is_square(im.width, im.height):
+    if is_square(settings.crop_width, settings.crop_height):
+      scale_by = settings.crop_width / im.width
+    elif is_landscape(settings.crop_width, settings.crop_height):
+      scale_by = settings.crop_width / im.width
+    elif is_portrait(settings.crop_width, settings.crop_height):
+      scale_by = settings.crop_height / im.height
+
+  im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
+
+  if im.width == settings.crop_width and im.height == settings.crop_height:
+    if settings.annotate_image:
+      d = ImageDraw.Draw(im)
+      rect = [0, 0, im.width, im.height]
+      rect[2] -= 1
+      rect[3] -= 1
+      d.rectangle(rect, outline=GREEN)
+      if settings.destop_view_image:
+        im.show()
+    return im
+
+  focus = focal_point(im, settings)
+
+  # take the focal point and turn it into crop coordinates that try to center over the focal
+  # point but then get adjusted back into the frame
+  y_half = int(settings.crop_height / 2)
+  x_half = int(settings.crop_width / 2)
+
+  x1 = focus.x - x_half
+  if x1 < 0:
+      x1 = 0
+  elif x1 + settings.crop_width > im.width:
+      x1 = im.width - settings.crop_width
+
+  y1 = focus.y - y_half
+  if y1 < 0:
+      y1 = 0
+  elif y1 + settings.crop_height > im.height:
+      y1 = im.height - settings.crop_height
+
+  x2 = x1 + settings.crop_width
+  y2 = y1 + settings.crop_height
+
+  crop = [x1, y1, x2, y2]
+
+  if settings.annotate_image:
+    d = ImageDraw.Draw(im)
+    rect = list(crop)
+    rect[2] -= 1
+    rect[3] -= 1
+    d.rectangle(rect, outline=GREEN)
+    if settings.destop_view_image:
+      im.show()
+
+  return im.crop(tuple(crop))
+
+def focal_point(im, settings):
+    corner_points = image_corner_points(im, settings)
+    entropy_points = image_entropy_points(im, settings)
+    face_points = image_face_points(im, settings)
+
+    total_points = len(corner_points) + len(entropy_points) + len(face_points)
+
+    corner_weight = settings.corner_points_weight
+    entropy_weight = settings.entropy_points_weight
+    face_weight = settings.face_points_weight
+
+    weight_pref_total = corner_weight + entropy_weight + face_weight
+
+    # weight things
+    pois = []
+    if weight_pref_total == 0 or total_points == 0: 
+      return pois
+
+    pois.extend(
+      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (corner_weight/weight_pref_total) / (len(corner_points)/total_points) )) for p in corner_points ]
+    )
+    pois.extend(
+      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (entropy_weight/weight_pref_total) / (len(entropy_points)/total_points) )) for p in entropy_points ]
+    )
+    pois.extend(
+      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (face_weight/weight_pref_total) / (len(face_points)/total_points) )) for p in face_points ]
+    )
+
+    average_point = poi_average(pois, settings)
+
+    if settings.annotate_image:
+      d = ImageDraw.Draw(im)
+      for f in face_points:
+        d.rectangle(f.bounding(f.size), outline=RED)
+      for f in entropy_points:
+        d.rectangle(f.bounding(30), outline=BLUE)
+      for poi in pois:
+        w = max(4, 4 * 0.5 * sqrt(poi.weight))
+        d.ellipse(poi.bounding(w), fill=BLUE)
+      d.ellipse(average_point.bounding(25), outline=GREEN)
+      
+    return average_point
+
+
+def image_face_points(im, settings):
+    np_im = np.array(im)
+    gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
+
+    tries = [
+      [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
+    ]
+
+    for t in tries:
+      # print(t[0])
+      classifier = cv2.CascadeClassifier(t[0])
+      minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
+      try:
+        faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
+          minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
+      except:
+        continue
+
+      if len(faces) > 0:
+        rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
+        return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2])) for r in rects]
+    return []
+
+
+def image_corner_points(im, settings):
+    grayscale = im.convert("L")
+
+    # naive attempt at preventing focal points from collecting at watermarks near the bottom
+    gd = ImageDraw.Draw(grayscale)
+    gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
+
+    np_im = np.array(grayscale)
+
+    points = cv2.goodFeaturesToTrack(
+        np_im,
+        maxCorners=100,
+        qualityLevel=0.04,
+        minDistance=min(grayscale.width, grayscale.height)*0.07,
+        useHarrisDetector=False,
+    )
+
+    if points is None:
+        return []
+
+    focal_points = []
+    for point in points:
+      x, y = point.ravel()
+      focal_points.append(PointOfInterest(x, y, size=4))
+
+    return focal_points
+
+
+def image_entropy_points(im, settings):
+    landscape = im.height < im.width
+    portrait = im.height > im.width
+    if landscape:
+      move_idx = [0, 2]
+      move_max = im.size[0]
+    elif portrait:
+      move_idx = [1, 3]
+      move_max = im.size[1]
+    else:
+      return []
+
+    e_max = 0
+    crop_current = [0, 0, settings.crop_width, settings.crop_height]
+    crop_best = crop_current
+    while crop_current[move_idx[1]] < move_max:
+        crop = im.crop(tuple(crop_current))
+        e = image_entropy(crop)
+
+        if (e > e_max):
+          e_max = e
+          crop_best = list(crop_current)
+
+        crop_current[move_idx[0]] += 4
+        crop_current[move_idx[1]] += 4
+
+    x_mid = int(crop_best[0] + settings.crop_width/2)
+    y_mid = int(crop_best[1] + settings.crop_height/2)
+
+    return [PointOfInterest(x_mid, y_mid, size=25)]
+
+
+def image_entropy(im):
+    # greyscale image entropy
+    # band = np.asarray(im.convert("L"))
+    band = np.asarray(im.convert("1"), dtype=np.uint8)
+    hist, _ = np.histogram(band, bins=range(0, 256))
+    hist = hist[hist > 0]
+    return -np.log2(hist / hist.sum()).sum()
+
+
+def poi_average(pois, settings):
+    weight = 0.0
+    x = 0.0
+    y = 0.0
+    for poi in pois:
+        weight += poi.weight
+        x += poi.x * poi.weight
+        y += poi.y * poi.weight
+    avg_x = round(x / weight)
+    avg_y = round(y / weight)
+
+    return PointOfInterest(avg_x, avg_y)
+
+
+def is_landscape(w, h):
+  return w > h
+
+
+def is_portrait(w, h):
+  return h > w
+
+
+def is_square(w, h):
+  return w == h
+
+
+class PointOfInterest:
+  def __init__(self, x, y, weight=1.0, size=10):
+    self.x = x
+    self.y = y
+    self.weight = weight
+    self.size = size
+
+  def bounding(self, size):
+    return [
+      self.x - size//2,
+      self.y - size//2,
+      self.x + size//2,
+      self.y + size//2
+    ]
+
+
+class Settings:
+  def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False):
+    self.crop_width = crop_width
+    self.crop_height = crop_height
+    self.corner_points_weight = corner_points_weight
+    self.entropy_points_weight = entropy_points_weight
+    self.face_points_weight = entropy_points_weight
+    self.annotate_image = annotate_image
     self.destop_view_image = False
\ No newline at end of file