Remove xformers, switch to Pytorch Nightly

4 files changed, 8 insertions, 9 deletions

diff --git a/training/functional.py b/training/functional.py
index 78a2b10..41794ea 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -12,7 +12,7 @@ from torch.utils.data import DataLoader
 
 from accelerate import Accelerator
 from transformers import CLIPTextModel
-from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel
+from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel, UniPCMultistepScheduler
 
 from tqdm.auto import tqdm
 from PIL import Image
@@ -22,7 +22,6 @@ from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
 from models.clip.embeddings import ManagedCLIPTextEmbeddings, patch_managed_embeddings
 from models.clip.util import get_extended_embeddings
 from models.clip.tokenizer import MultiCLIPTokenizer
-from schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
 from training.util import AverageMeter
 
 
diff --git a/training/strategy/dreambooth.py b/training/strategy/dreambooth.py
index 8aaed3a..d697554 100644
--- a/training/strategy/dreambooth.py
+++ b/training/strategy/dreambooth.py
@@ -144,8 +144,8 @@ def dreambooth_strategy_callbacks(
 
         print("Saving model...")
 
-        unet_ = accelerator.unwrap_model(unet)
-        text_encoder_ = accelerator.unwrap_model(text_encoder)
+        unet_ = accelerator.unwrap_model(unet, False)
+        text_encoder_ = accelerator.unwrap_model(text_encoder, False)
 
         with ema_context():
             pipeline = VlpnStableDiffusion(
@@ -167,8 +167,8 @@ def dreambooth_strategy_callbacks(
     @torch.no_grad()
     def on_sample(step):
         with ema_context():
-            unet_ = accelerator.unwrap_model(unet)
-            text_encoder_ = accelerator.unwrap_model(text_encoder)
+            unet_ = accelerator.unwrap_model(unet, False)
+            text_encoder_ = accelerator.unwrap_model(text_encoder, False)
 
             orig_unet_dtype = unet_.dtype
             orig_text_encoder_dtype = text_encoder_.dtype
diff --git a/training/strategy/lora.py b/training/strategy/lora.py
index 4dd1100..ccec215 100644
--- a/training/strategy/lora.py
+++ b/training/strategy/lora.py
@@ -90,7 +90,7 @@ def lora_strategy_callbacks(
     def on_checkpoint(step, postfix):
         print(f"Saving checkpoint for step {step}...")
 
-        unet_ = accelerator.unwrap_model(unet)
+        unet_ = accelerator.unwrap_model(unet, False)
         unet_.save_attn_procs(checkpoint_output_dir / f"{step}_{postfix}")
         del unet_
 
diff --git a/training/strategy/ti.py b/training/strategy/ti.py
index 0de3cb0..66d3129 100644
--- a/training/strategy/ti.py
+++ b/training/strategy/ti.py
@@ -144,8 +144,8 @@ def textual_inversion_strategy_callbacks(
     @torch.no_grad()
     def on_sample(step):
         with ema_context():
-            unet_ = accelerator.unwrap_model(unet)
-            text_encoder_ = accelerator.unwrap_model(text_encoder)
+            unet_ = accelerator.unwrap_model(unet, False)
+            text_encoder_ = accelerator.unwrap_model(text_encoder, False)
 
             orig_unet_dtype = unet_.dtype
             orig_text_encoder_dtype = text_encoder_.dtype