Fix sub quadratic attention for SD2 and make it the default optimization.

comfy/ldm/modules/attention.py

@@ -175,13 +175,11 @@ class CrossAttentionBirchSan(nn.Module):
         value = value.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1)
 
         dtype = query.dtype
-        # TODO: do we still need to do *everything* in float32, given how we delay the division?
-        # TODO: do we need to support upcast_softmax too? SD 2.1 seems to work without it
-        # if self.upcast_attention:
-        #     query = query.float()
-        #     key_t = key_t.float()
-
-        bytes_per_token = torch.finfo(query.dtype).bits//8
+        upcast_attention = _ATTN_PRECISION =="fp32" and query.dtype != torch.float32
+        if upcast_attention:
+            bytes_per_token = torch.finfo(torch.float32).bits//8
+        else:
+            bytes_per_token = torch.finfo(query.dtype).bits//8
         batch_x_heads, q_tokens, _ = query.shape
         _, _, k_tokens = key_t.shape
         qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
@@ -198,7 +196,7 @@ class CrossAttentionBirchSan(nn.Module):
 
         query_chunk_size_x = 1024 * 4
         kv_chunk_size_min_x = None
-        kv_chunk_size_x = (int((chunk_threshold_bytes // (batch_x_heads * bytes_per_token * query_chunk_size_x)) * 1.2) // 1024) * 1024
+        kv_chunk_size_x = (int((chunk_threshold_bytes // (batch_x_heads * bytes_per_token * query_chunk_size_x)) * 2.0) // 1024) * 1024
         if kv_chunk_size_x < 1024:
             kv_chunk_size_x = None
 
@@ -220,6 +218,7 @@ class CrossAttentionBirchSan(nn.Module):
             kv_chunk_size=kv_chunk_size,
             kv_chunk_size_min=kv_chunk_size_min,
             use_checkpoint=self.training,
+            upcast_attention=upcast_attention,
         )
 
         hidden_states = hidden_states.to(dtype)
@@ -383,8 +382,15 @@ class OriginalCrossAttention(nn.Module):
         out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
         return self.to_out(out)
 
-class CrossAttention(CrossAttentionDoggettx):
-    pass
+import sys
+if "--use-split-cross-attention" in sys.argv:
+    print("Using split optimization for cross attention")
+    class CrossAttention(CrossAttentionDoggettx):
+        pass
+else:
+    print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
+    class CrossAttention(CrossAttentionBirchSan):
+        pass
 
 class MemoryEfficientCrossAttention(nn.Module):
     # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223

comfy/ldm/modules/sub_quadratic_attention.py

@@ -53,14 +53,27 @@ def _summarize_chunk(
     key_t: Tensor,
     value: Tensor,
     scale: float,
+    upcast_attention: bool,
 ) -> AttnChunk:
-    attn_weights = torch.baddbmm(
-        torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
-        query,
-        key_t,
-        alpha=scale,
-        beta=0,
-    )
+    if upcast_attention:
+        with torch.autocast(enabled=False, device_type = 'cuda'):
+            query = query.float()
+            key_t = key_t.float()
+            attn_weights = torch.baddbmm(
+                torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
+                query,
+                key_t,
+                alpha=scale,
+                beta=0,
+            )
+    else:
+        attn_weights = torch.baddbmm(
+            torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
+            query,
+            key_t,
+            alpha=scale,
+            beta=0,
+        )
     max_score, _ = torch.max(attn_weights, -1, keepdim=True)
     max_score = max_score.detach()
     exp_weights = torch.exp(attn_weights - max_score)
@@ -112,14 +125,27 @@ def _get_attention_scores_no_kv_chunking(
     key_t: Tensor,
     value: Tensor,
     scale: float,
+    upcast_attention: bool,
 ) -> Tensor:
-    attn_scores = torch.baddbmm(
-        torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
-        query,
-        key_t,
-        alpha=scale,
-        beta=0,
-    )
+    if upcast_attention:
+        with torch.autocast(enabled=False, device_type = 'cuda'):
+            query = query.float()
+            key_t = key_t.float()
+            attn_scores = torch.baddbmm(
+                torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
+                query,
+                key_t,
+                alpha=scale,
+                beta=0,
+            )
+    else:
+        attn_scores = torch.baddbmm(
+            torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
+            query,
+            key_t,
+            alpha=scale,
+            beta=0,
+        )
     attn_probs = attn_scores.softmax(dim=-1)
     del attn_scores
     hidden_states_slice = torch.bmm(attn_probs, value)
@@ -137,6 +163,7 @@ def efficient_dot_product_attention(
     kv_chunk_size: Optional[int] = None,
     kv_chunk_size_min: Optional[int] = None,
     use_checkpoint=True,
+    upcast_attention=False,
 ):
     """Computes efficient dot-product attention given query, transposed key, and value.
       This is efficient version of attention presented in
@@ -170,11 +197,12 @@ def efficient_dot_product_attention(
             (batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head)
         )
     
-    summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale)
+    summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale, upcast_attention=upcast_attention)
     summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk
     compute_query_chunk_attn: ComputeQueryChunkAttn = partial(
         _get_attention_scores_no_kv_chunking,
-        scale=scale
+        scale=scale,
+        upcast_attention=upcast_attention
     ) if k_tokens <= kv_chunk_size else (
         # fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw)
         partial(