Optimize first attention block in cosmos VAE.

comfy/ldm/cosmos/cosmos_tokenizer/layers3d.py

@@ -30,6 +30,8 @@ import torch.nn as nn
 import torch.nn.functional as F
 import logging
 
+from comfy.ldm.modules.diffusionmodules.model import vae_attention
+
 from .patching import (
     Patcher,
     Patcher3D,
@@ -400,6 +402,8 @@ class CausalAttnBlock(nn.Module):
             in_channels, in_channels, kernel_size=1, stride=1, padding=0
         )
 
+        self.optimized_attention = vae_attention()
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         h_ = x
         h_ = self.norm(h_)
@@ -413,18 +417,7 @@ class CausalAttnBlock(nn.Module):
         v, batch_size = time2batch(v)
 
         b, c, h, w = q.shape
-        q = q.reshape(b, c, h * w)
+        h_ = self.optimized_attention(q, k, v)
-        q = q.permute(0, 2, 1)
-        k = k.reshape(b, c, h * w)
-        w_ = torch.bmm(q, k)
-        w_ = w_ * (int(c) ** (-0.5))
-        w_ = F.softmax(w_, dim=2)
-
-        # attend to values
-        v = v.reshape(b, c, h * w)
-        w_ = w_.permute(0, 2, 1)
-        h_ = torch.bmm(v, w_)
-        h_ = h_.reshape(b, c, h, w)
 
         h_ = batch2time(h_, batch_size)
         h_ = self.proj_out(h_)

comfy/ldm/modules/diffusionmodules/model.py

@@ -293,6 +293,17 @@ def pytorch_attention(q, k, v):
     return out
 
 
+def vae_attention():
+    if model_management.xformers_enabled_vae():
+        logging.info("Using xformers attention in VAE")
+        return xformers_attention
+    elif model_management.pytorch_attention_enabled():
+        logging.info("Using pytorch attention in VAE")
+        return pytorch_attention
+    else:
+        logging.info("Using split attention in VAE")
+        return normal_attention
+
 class AttnBlock(nn.Module):
     def __init__(self, in_channels, conv_op=ops.Conv2d):
         super().__init__()
@@ -320,15 +331,7 @@ class AttnBlock(nn.Module):
                                         stride=1,
                                         padding=0)
 
-        if model_management.xformers_enabled_vae():
+        self.optimized_attention = vae_attention()
-            logging.info("Using xformers attention in VAE")
-            self.optimized_attention = xformers_attention
-        elif model_management.pytorch_attention_enabled():
-            logging.info("Using pytorch attention in VAE")
-            self.optimized_attention = pytorch_attention
-        else:
-            logging.info("Using split attention in VAE")
-            self.optimized_attention = normal_attention
 
     def forward(self, x):
         h_ = x