attention_basic now works with hypertile.

comfy/ldm/modules/attention.py

@@ -95,9 +95,19 @@ def Normalize(in_channels, dtype=None, device=None):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
 
 def attention_basic(q, k, v, heads, mask=None):
+    b, _, dim_head = q.shape
+    dim_head //= heads
+    scale = dim_head ** -0.5
+
     h = heads
-    scale = (q.shape[-1] // heads) ** -0.5
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+    q, k, v = map(
+        lambda t: t.unsqueeze(3)
+        .reshape(b, -1, heads, dim_head)
+        .permute(0, 2, 1, 3)
+        .reshape(b * heads, -1, dim_head)
+        .contiguous(),
+        (q, k, v),
+    )
 
     # force cast to fp32 to avoid overflowing
     if _ATTN_PRECISION =="fp32":
@@ -119,7 +129,12 @@ def attention_basic(q, k, v, heads, mask=None):
     sim = sim.softmax(dim=-1)
 
     out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v)
-    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+    out = (
+        out.unsqueeze(0)
+        .reshape(b, heads, -1, dim_head)
+        .permute(0, 2, 1, 3)
+        .reshape(b, -1, heads * dim_head)
+    )
     return out