Improve code legibility.

comfy/samplers.py

@@ -2,6 +2,7 @@ from .k_diffusion import sampling as k_diffusion_sampling
 from .extra_samplers import uni_pc
 import torch
 import enum
+import collections
 from comfy import model_management
 import math
 from comfy import model_base
@@ -61,9 +62,7 @@ def get_area_and_mult(conds, x_in, timestep_in):
     for c in model_conds:
         conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area)
 
-    control = None
-    if 'control' in conds:
-        control = conds['control']
+    control = conds.get('control', None)
 
     patches = None
     if 'gligen' in conds:
@@ -78,7 +77,8 @@ def get_area_and_mult(conds, x_in, timestep_in):
 
         patches['middle_patch'] = [gligen_patch]
 
-    return (input_x, mult, conditioning, area, control, patches)
+    cond_obj = collections.namedtuple('cond_obj', ['input_x', 'mult', 'conditioning', 'area', 'control', 'patches'])
+    return cond_obj(input_x, mult, conditioning, area, control, patches)
 
 def cond_equal_size(c1, c2):
     if c1 is c2:
@@ -91,24 +91,24 @@ def cond_equal_size(c1, c2):
     return True
 
 def can_concat_cond(c1, c2):
-    if c1[0].shape != c2[0].shape:
+    if c1.input_x.shape != c2.input_x.shape:
         return False
 
-    #control
-    if (c1[4] is None) != (c2[4] is None):
-        return False
-    if c1[4] is not None:
-        if c1[4] is not c2[4]:
+    def objects_concatable(obj1, obj2):
+        if (obj1 is None) != (obj2 is None):
             return False
+        if obj1 is not None:
+            if obj1 is not obj2:
+                return False
+        return True
 
-    #patches
-    if (c1[5] is None) != (c2[5] is None):
+    if not objects_concatable(c1.control, c2.control):
+        return False
+
+    if not objects_concatable(c1.patches, c2.patches):
         return False
-    if (c1[5] is not None):
-        if c1[5] is not c2[5]:
-            return False
 
-    return cond_equal_size(c1[2], c2[2])
+    return cond_equal_size(c1.conditioning, c2.conditioning)
 
 def cond_cat(c_list):
     c_crossattn = []
@@ -184,13 +184,13 @@ def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
         for x in to_batch:
             o = to_run.pop(x)
             p = o[0]
-            input_x += [p[0]]
-            mult += [p[1]]
-            c += [p[2]]
-            area += [p[3]]
-            cond_or_uncond += [o[1]]
-            control = p[4]
-            patches = p[5]
+            input_x.append(p.input_x)
+            mult.append(p.mult)
+            c.append(p.conditioning)
+            area.append(p.area)
+            cond_or_uncond.append(o[1])
+            control = p.control
+            patches = p.patches
 
         batch_chunks = len(cond_or_uncond)
         input_x = torch.cat(input_x)