diff options
| -rw-r--r-- | modules/sd_hijack.py | 2 | ||||
| -rw-r--r-- | modules/sd_hijack_optimizations.py | 24 |
2 files changed, 26 insertions, 0 deletions
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index e98ae51a..f4bb0266 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -47,10 +47,12 @@ def apply_optimizations(): elif cmd_opts.opt_sdp_no_mem_attention and can_use_sdp:
print("Applying scaled dot product cross attention optimization (without memory efficient attention).")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_no_mem_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_no_mem_attnblock_forward
optimization_method = 'sdp-no-mem'
elif cmd_opts.opt_sdp_attention and can_use_sdp:
print("Applying scaled dot product cross attention optimization.")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_attnblock_forward
optimization_method = 'sdp'
elif cmd_opts.opt_sub_quad_attention:
print("Applying sub-quadratic cross attention optimization.")
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 68b1dd84..2e307b5d 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -473,6 +473,30 @@ def xformers_attnblock_forward(self, x): except NotImplementedError:
return cross_attention_attnblock_forward(self, x)
+def sdp_attnblock_forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+ b, c, h, w = q.shape
+ q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+ dtype = q.dtype
+ if shared.opts.upcast_attn:
+ q, k = q.float(), k.float()
+ q = q.contiguous()
+ k = k.contiguous()
+ v = v.contiguous()
+ out = torch.nn.functional.scaled_dot_product_attention(q, k, v, dropout_p=0.0, is_causal=False)
+ out = out.to(dtype)
+ out = rearrange(out, 'b (h w) c -> b c h w', h=h)
+ out = self.proj_out(out)
+ return x + out
+
+def sdp_no_mem_attnblock_forward(self, x):
+ with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
+ return sdp_attnblock_forward(self, x)
+
def sub_quad_attnblock_forward(self, x):
h_ = x
h_ = self.norm(h_)
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