Update

4 files changed, 15 insertions, 18 deletions

diff --git a/dreambooth.py b/dreambooth.py
index f3f722e..b87763e 100644
--- a/dreambooth.py
+++ b/dreambooth.py
@@ -24,6 +24,7 @@ from transformers import CLIPTextModel, CLIPTokenizer
 from slugify import slugify
 
 from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
+from pipelines.util import set_use_memory_efficient_attention_xformers
 from data.csv import CSVDataModule
 from training.optimization import get_one_cycle_schedule
 from models.clip.prompt import PromptProcessor
@@ -594,7 +595,8 @@ def main():
         args.pretrained_model_name_or_path, subfolder='scheduler')
 
     vae.enable_slicing()
-    unet.set_use_memory_efficient_attention_xformers(True)
+    set_use_memory_efficient_attention_xformers(unet, True)
+    set_use_memory_efficient_attention_xformers(vae, True)
 
     if args.gradient_checkpointing:
         unet.enable_gradient_checkpointing()
diff --git a/pipelines/stable_diffusion/vlpn_stable_diffusion.py b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
index 9b51763..f80e951 100644
--- a/pipelines/stable_diffusion/vlpn_stable_diffusion.py
+++ b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
@@ -78,22 +78,6 @@ class VlpnStableDiffusion(DiffusionPipeline):
             scheduler=scheduler,
         )
 
-    def enable_xformers_memory_efficient_attention(self):
-        r"""
-        Enable memory efficient attention as implemented in xformers.
-        When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
-        time. Speed up at training time is not guaranteed.
-        Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
-        is used.
-        """
-        self.unet.set_use_memory_efficient_attention_xformers(True)
-
-    def disable_xformers_memory_efficient_attention(self):
-        r"""
-        Disable memory efficient attention as implemented in xformers.
-        """
-        self.unet.set_use_memory_efficient_attention_xformers(False)
-
     def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
         r"""
         Enable sliced attention computation.
diff --git a/pipelines/util.py b/pipelines/util.py
new file mode 100644
index 0000000..661dbee
--- /dev/null
+++ b/pipelines/util.py
@@ -0,0 +1,9 @@
+import torch
+
+
+def set_use_memory_efficient_attention_xformers(module: torch.nn.Module, valid: bool) -> None:
+    if hasattr(module, "set_use_memory_efficient_attention_xformers"):
+        module.set_use_memory_efficient_attention_xformers(valid)
+
+    for child in module.children():
+        set_use_memory_efficient_attention_xformers(child, valid)
diff --git a/textual_inversion.py b/textual_inversion.py
index cd2d22b..1a5a8d0 100644
--- a/textual_inversion.py
+++ b/textual_inversion.py
@@ -23,6 +23,7 @@ from transformers import CLIPTextModel, CLIPTokenizer
 from slugify import slugify
 
 from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
+from pipelines.util import set_use_memory_efficient_attention_xformers
 from data.csv import CSVDataModule
 from training.optimization import get_one_cycle_schedule
 from models.clip.prompt import PromptProcessor
@@ -546,7 +547,8 @@ def main():
         args.pretrained_model_name_or_path, subfolder='scheduler')
 
     vae.enable_slicing()
-    unet.set_use_memory_efficient_attention_xformers(True)
+    set_use_memory_efficient_attention_xformers(unet, True)
+    set_use_memory_efficient_attention_xformers(vae, True)
 
     if args.gradient_checkpointing:
         text_encoder.gradient_checkpointing_enable()