Update to cross attention from https://github.com/Doggettx/stable-diffusion #219

README.md

@@ -279,7 +279,8 @@ After that follow the instructions in the `Manual instructions` section starting
 - k-diffusion - https://github.com/crowsonkb/k-diffusion.git
 - GFPGAN - https://github.com/TencentARC/GFPGAN.git
 - ESRGAN - https://github.com/xinntao/ESRGAN
-- Ideas for optimizations and some code (from users) - https://github.com/basujindal/stable-diffusion
+- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
+- Cross Attention layer optimization - https://github.com/Doggettx/stable-diffusion
 - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
 - (You)

modules/sd_hijack.py

@@ -1,3 +1,4 @@
+import math
 import os
 import sys
 import traceback
@@ -12,30 +13,56 @@ from einops import rearrange
 import ldm.modules.attention
 
 
-# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
+# taken from https://github.com/Doggettx/stable-diffusion
 def split_cross_attention_forward(self, x, context=None, mask=None):
     h = self.heads
 
-    q = self.to_q(x)
+    q_in = self.to_q(x)
     context = default(context, x)
-    k = self.to_k(context)
-    v = self.to_v(context)
+    k_in = self.to_k(context)
+    v_in = 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))
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+    del q_in, k_in, v_in
 
     r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
-    for i in range(0, q.shape[0], 2):
-        end = i + 2
-        s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
-        s1 *= self.scale
+
+    stats = torch.cuda.memory_stats(q.device)
+    mem_active = stats['active_bytes.all.current']
+    mem_reserved = stats['reserved_bytes.all.current']
+    mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
+    mem_free_torch = mem_reserved - mem_active
+    mem_free_total = mem_free_cuda + mem_free_torch
+
+    gb = 1024 ** 3
+    tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * 4
+    mem_required = tensor_size * 2.5
+    steps = 1
+
+    if mem_required > mem_free_total:
+        steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
+        # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
+        #       f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
+
+    if steps > 64:
+        max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
+        raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
+                           f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
+
+    slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+    for i in range(0, q.shape[1], slice_size):
+        end = i + slice_size
+        s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
 
         s2 = s1.softmax(dim=-1)
         del s1
 
-        r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
+        r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
         del s2
 
+    del q, k, v
+
     r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
     del r1