Merge branch 'AUTOMATIC1111:master' into img2img-api-scripts

1 files changed, 12 insertions, 15 deletions

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index fa2cd4bb..cfdb09d6 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -7,8 +7,6 @@ from modules.hypernetworks import hypernetwork
 from modules.shared import cmd_opts

 from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr

 

-from modules.sd_hijack_optimizations import invokeAI_mps_available

-

 import ldm.modules.attention

 import ldm.modules.diffusionmodules.model

 import ldm.modules.diffusionmodules.openaimodel

@@ -43,20 +41,19 @@ def apply_optimizations():
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward

         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward

         optimization_method = 'xformers'

+    elif cmd_opts.opt_sub_quad_attention:

+        print("Applying sub-quadratic cross attention optimization.")

+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.sub_quad_attention_forward

+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sub_quad_attnblock_forward

+        optimization_method = 'sub-quadratic'

     elif cmd_opts.opt_split_attention_v1:

         print("Applying v1 cross attention optimization.")

         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1

         optimization_method = 'V1'

-    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):

-        if not invokeAI_mps_available and shared.device.type == 'mps':

-            print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")

-            print("Applying v1 cross attention optimization.")

-            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1

-            optimization_method = 'V1'

-        else:

-            print("Applying cross attention optimization (InvokeAI).")

-            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI

-            optimization_method = 'InvokeAI'

+    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not cmd_opts.opt_split_attention and not torch.cuda.is_available()):

+        print("Applying cross attention optimization (InvokeAI).")

+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI

+        optimization_method = 'InvokeAI'

     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):

         print("Applying cross attention optimization (Doggettx).")

         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward

@@ -150,10 +147,10 @@ class StableDiffusionModelHijack:
     def clear_comments(self):

         self.comments = []

 

-    def tokenize(self, text):

-        _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])

+    def get_prompt_lengths(self, text):

+        _, token_count = self.clip.process_texts([text])

 

-        return remade_batch_tokens[0], token_count, sd_hijack_clip.get_target_prompt_token_count(token_count)

+        return token_count, self.clip.get_target_prompt_token_count(token_count)

 

 

 class EmbeddingsWithFixes(torch.nn.Module):