Allow having a different pooled output for each image in a batch.

comfy/model_base.py

@@ -181,7 +181,7 @@ class SDXLRefiner(BaseModel):
         out.append(self.embedder(torch.Tensor([crop_h])))
         out.append(self.embedder(torch.Tensor([crop_w])))
         out.append(self.embedder(torch.Tensor([aesthetic_score])))
-        flat = torch.flatten(torch.cat(out))[None, ]
+        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
         return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
 
 class SDXL(BaseModel):
@@ -206,5 +206,5 @@ class SDXL(BaseModel):
         out.append(self.embedder(torch.Tensor([crop_w])))
         out.append(self.embedder(torch.Tensor([target_height])))
         out.append(self.embedder(torch.Tensor([target_width])))
-        flat = torch.flatten(torch.cat(out))[None, ]
+        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
         return torch.cat((clip_pooled.to(flat.device), flat), dim=1)

comfy/samplers.py

@@ -7,6 +7,7 @@ from .ldm.models.diffusion.ddim import DDIMSampler
 from .ldm.modules.diffusionmodules.util import make_ddim_timesteps
 import math
 from comfy import model_base
+import comfy.utils
 
 def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
     return abs(a*b) // math.gcd(a, b)
@@ -538,7 +539,7 @@ def encode_adm(model, conds, batch_size, width, height, device, prompt_type):
 
         if adm_out is not None:
             x[1] = x[1].copy()
-            x[1]["adm_encoded"] = torch.cat([adm_out] * batch_size).to(device)
+            x[1]["adm_encoded"] = comfy.utils.repeat_to_batch_size(adm_out, batch_size).to(device)
 
     return conds