auto branching fix

latent_blending.py

@@ -1029,9 +1029,9 @@ def get_time(resolution=None):
 
 def get_branching(
         quality: str = 'medium',
-        depth: str = 'medium',
-        strength_injection_first: float = 0.65,
+        deepth_strength: float = 0.65,
         nmb_frames: int = 360,
+        nmb_mindist: int = 3,
     ):
     r"""
     Helper function to set up the branching structure automatically.
@@ -1040,51 +1040,38 @@ def get_branching(
         quality: str 
             Determines how many diffusion steps are being made + how many branches in total.
             Choose: fast, medium, high, ultra
-        quality: depth 
+        deepth_strength: float = 0.65,
             Determines how deep the first injection will happen. 
             Deeper injections will cause (unwanted) formation of new structures,
-            more shallow values will go into alpha-blendy land
-            Choose: verydeep, deep, medium, shallow, veryshallow
-        strength_injection_first: float = 0.65,
-            ...
+            more shallow values will go into alpha-blendy land.
         nmb_frames: int = 360,
+            total number of frames
+        nmb_mindist: int = 3 
+            minimum distance in terms of diffusion iteratinos between subsequent injections
+        
 
     """ 
-    nmb_mindist = 3 #minimum distance between injections
-    depth = 'override'
-    #FIXME: XXX nmb frames last has to be enforced. avoid weird cases where no injection...
-
-    if depth == 'verydeep':
-        strength_injection_first = 0.35
-    elif depth == 'deep':
-        strength_injection_first = 0.45
-    elif depth == 'medium':
-        strength_injection_first = 0.6
-    elif depth == 'shallow':
-        strength_injection_first = 0.8
-    elif depth == 'veryshallow':
-        strength_injection_first = 0.9
 
         
-    if quality == 'superfast':
-        num_inference_steps = 8
+    if quality == 'lowest':
+        num_inference_steps = 12
         nmb_branches_final = 5
-    elif quality == 'fast':
+    elif quality == 'low':
         num_inference_steps = 15
-        nmb_branches_final = nmb_frames//30
+        nmb_branches_final = nmb_frames//16
     elif quality == 'medium':
         num_inference_steps = 30
-        nmb_branches_final = nmb_frames//10
+        nmb_branches_final = nmb_frames//8
     elif quality == 'high':
         num_inference_steps = 60
-        nmb_branches_final = nmb_frames//3
+        nmb_branches_final = nmb_frames//4
     elif quality == 'ultra':
         num_inference_steps = 100
-        nmb_branches_final = nmb_frames
+        nmb_branches_final = nmb_frames//2
     else: 
         raise ValueError("quality = '{quality}' not supported")
     
-    idx_injection_first = int(np.round(num_inference_steps*strength_injection_first))
+    idx_injection_first = int(np.round(num_inference_steps*deepth_strength))
     idx_injection_last = num_inference_steps - 3
     nmb_injections = int(np.floor(num_inference_steps/5)) - 1
     
@@ -1110,6 +1097,7 @@ def get_branching(
     
     return num_inference_steps, list_injection_idx_clean, list_nmb_branches_clean
 
+
 #%% le main
 if __name__ == "__main__":