revert default cross attention optimization to Doggettx

make --disable-opt-split-attention command line option work again

modules/cmd_args.py

@@ -62,7 +62,7 @@ parser.add_argument("--opt-split-attention-invokeai", action='store_true', help=
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="prefer older version of split attention optimization for automatic choice of optimization")
 parser.add_argument("--opt-sdp-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization for automatic choice of optimization; requires PyTorch 2.*")
 parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization without memory efficient attention for automatic choice of optimization, makes image generation deterministic; requires PyTorch 2.*")
-parser.add_argument("--disable-opt-split-attention", action='store_true', help="does not do anything")
+parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")
 parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
 parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")

modules/sd_hijack.py

@@ -68,6 +68,8 @@ def apply_optimizations():
 
     if selection == "None":
         matching_optimizer = None
+    elif selection == "Automatic" and shared.cmd_opts.disable_opt_split_attention:
+        matching_optimizer = None
     elif matching_optimizer is None:
         matching_optimizer = optimizers[0]
 

modules/sd_hijack_optimizations.py

@@ -59,7 +59,7 @@ class SdOptimizationSdpNoMem(SdOptimization):
     name = "sdp-no-mem"
     label = "scaled dot product without memory efficient attention"
     cmd_opt = "opt_sdp_no_mem_attention"
-    priority = 90
+    priority = 80
 
     def is_available(self):
         return hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention)
@@ -73,7 +73,7 @@ class SdOptimizationSdp(SdOptimizationSdpNoMem):
     name = "sdp"
     label = "scaled dot product"
     cmd_opt = "opt_sdp_attention"
-    priority = 80
+    priority = 70
 
     def apply(self):
         ldm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward
@@ -116,7 +116,7 @@ class SdOptimizationInvokeAI(SdOptimization):
 class SdOptimizationDoggettx(SdOptimization):
     name = "Doggettx"
     cmd_opt = "opt_split_attention"
-    priority = 20
+    priority = 90
 
     def apply(self):
         ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward