from https://github.com/basujindal/stable-diffusion/pull/103/commits/47f878421c5bf97d0fff44edaa703d152cafb483

103-memory-efficient-attention.patch

diff --git a/ldm/modules/attention.py b/ldm/modules/attention.py
index f4eff39..f90c6c4 100644
--- a/ldm/modules/attention.py
+++ b/ldm/modules/attention.py
@@ -174,23 +174,27 @@ class CrossAttention(nn.Module):
         context = default(context, x)
         k = self.to_k(context)
         v = self.to_v(context)
+        del context, x
 
         q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
 
         sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+        del q, k
 
         if exists(mask):
             mask = rearrange(mask, 'b ... -> b (...)')
             max_neg_value = -torch.finfo(sim.dtype).max
             mask = repeat(mask, 'b j -> (b h) () j', h=h)
             sim.masked_fill_(~mask, max_neg_value)
+            del mask
 
         # attention, what we cannot get enough of
-        attn = sim.softmax(dim=-1)
+        sim[4:] = sim[4:].softmax(dim=-1)
+        sim[:4] = sim[:4].softmax(dim=-1)
 
-        out = einsum('b i j, b j d -> b i d', attn, v)
-        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
-        return self.to_out(out)
+        sim = einsum('b i j, b j d -> b i d', sim, v)
+        sim = rearrange(sim, '(b h) n d -> b n (h d)', h=h)
+        return self.to_out(sim)
 
 
 class BasicTransformerBlock(nn.Module):