Auto batching improvements.

Try batching when cond sizes don't match with smart padding.

comfy/samplers.py

@@ -6,6 +6,10 @@ import contextlib
 from comfy import model_management
 from .ldm.models.diffusion.ddim import DDIMSampler
 from .ldm.modules.diffusionmodules.util import make_ddim_timesteps
+import math
+
+def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
+    return abs(a*b) // math.gcd(a, b)
 
 #The main sampling function shared by all the samplers
 #Returns predicted noise
@@ -90,7 +94,15 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
             if c1.keys() != c2.keys():
                 return False
             if 'c_crossattn' in c1:
-                if c1['c_crossattn'].shape != c2['c_crossattn'].shape:
+                s1 = c1['c_crossattn'].shape
+                s2 = c2['c_crossattn'].shape
+                if s1 != s2:
+                    if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen
+                        return False
+
+                    mult_min = lcm(s1[1], s2[1])
+                    diff = mult_min // min(s1[1], s2[1])
+                    if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
                         return False
             if 'c_concat' in c1:
                 if c1['c_concat'].shape != c2['c_concat'].shape:
@@ -124,16 +136,28 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
             c_crossattn = []
             c_concat = []
             c_adm = []
+            crossattn_max_len = 0
             for x in c_list:
                 if 'c_crossattn' in x:
-                    c_crossattn.append(x['c_crossattn'])
+                    c = x['c_crossattn']
+                    if crossattn_max_len == 0:
+                        crossattn_max_len = c.shape[1]
+                    else:
+                        crossattn_max_len = lcm(crossattn_max_len, c.shape[1])
+                    c_crossattn.append(c)
                 if 'c_concat' in x:
                     c_concat.append(x['c_concat'])
                 if 'c_adm' in x:
                     c_adm.append(x['c_adm'])
             out = {}
-            if len(c_crossattn) > 0:
+            c_crossattn_out = []
-                out['c_crossattn'] = [torch.cat(c_crossattn)]
+            for c in c_crossattn:
+                if c.shape[1] < crossattn_max_len:
+                    c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result
+                c_crossattn_out.append(c)
+
+            if len(c_crossattn_out) > 0:
+                out['c_crossattn'] = [torch.cat(c_crossattn_out)]
             if len(c_concat) > 0:
                 out['c_concat'] = [torch.cat(c_concat)]
             if len(c_adm) > 0: