Add contextmanager to EMAModel to apply weights temporarily

1 files changed, 24 insertions, 33 deletions

diff --git a/train_ti.py b/train_ti.py
index 2f13128..aa2bf02 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -5,6 +5,7 @@ import logging
 import copy
 from pathlib import Path
 from functools import partial
+from contextlib import nullcontext
 
 import torch
 import torch.utils.checkpoint
@@ -509,20 +510,15 @@ class Checkpointer(CheckpointerBase):
 
         text_encoder = self.accelerator.unwrap_model(self.text_encoder)
 
-        if self.ema_embeddings is not None:
-            orig_weights = text_encoder.text_model.embeddings.temp_token_embedding
-            ema_weights = copy.deepcopy(text_encoder.text_model.embeddings.temp_token_embedding)
-            self.ema_embeddings.copy_to(ema_weights.parameters())
-            text_encoder.text_model.embeddings.temp_token_embedding = ema_weights
+        ema_context = self.ema_embeddings.apply_temporary(
+            text_encoder.text_model.embeddings.temp_token_embedding.parameters()) if self.ema_embeddings is not None else nullcontext()
 
-        for (token, ids) in zip(self.placeholder_token, self.new_ids):
-            text_encoder.text_model.embeddings.save_embed(
-                ids,
-                checkpoints_path.joinpath(f"{slugify(token)}_{step}_{postfix}.bin")
-            )
-
-        if self.ema_embeddings is not None:
-            text_encoder.text_model.embeddings.temp_token_embedding = orig_weights
+        with ema_context:
+            for (token, ids) in zip(self.placeholder_token, self.new_ids):
+                text_encoder.text_model.embeddings.save_embed(
+                    ids,
+                    checkpoints_path.joinpath(f"{slugify(token)}_{step}_{postfix}.bin")
+                )
 
         del text_encoder
 
@@ -530,30 +526,25 @@ class Checkpointer(CheckpointerBase):
     def save_samples(self, step, num_inference_steps, guidance_scale=7.5, eta=0.0):
         text_encoder = self.accelerator.unwrap_model(self.text_encoder)
 
-        if self.ema_embeddings is not None:
-            orig_weights = text_encoder.text_model.embeddings.temp_token_embedding
-            ema_weights = copy.deepcopy(text_encoder.text_model.embeddings.temp_token_embedding)
-            self.ema_embeddings.copy_to(ema_weights.parameters())
-            text_encoder.text_model.embeddings.temp_token_embedding = ema_weights
-
-        orig_dtype = text_encoder.dtype
-        text_encoder.to(dtype=self.weight_dtype)
+        ema_context = self.ema_embeddings.apply_temporary(
+            text_encoder.text_model.embeddings.temp_token_embedding.parameters()) if self.ema_embeddings is not None else nullcontext()
 
-        pipeline = VlpnStableDiffusion(
-            text_encoder=text_encoder,
-            vae=self.vae,
-            unet=self.unet,
-            tokenizer=self.tokenizer,
-            scheduler=self.scheduler,
-        ).to(self.accelerator.device)
-        pipeline.set_progress_bar_config(dynamic_ncols=True)
+        with ema_context:
+            orig_dtype = text_encoder.dtype
+            text_encoder.to(dtype=self.weight_dtype)
 
-        super().save_samples(pipeline, step, num_inference_steps, guidance_scale, eta)
+            pipeline = VlpnStableDiffusion(
+                text_encoder=text_encoder,
+                vae=self.vae,
+                unet=self.unet,
+                tokenizer=self.tokenizer,
+                scheduler=self.scheduler,
+            ).to(self.accelerator.device)
+            pipeline.set_progress_bar_config(dynamic_ncols=True)
 
-        text_encoder.to(dtype=orig_dtype)
+            super().save_samples(pipeline, step, num_inference_steps, guidance_scale, eta)
 
-        if self.ema_embeddings is not None:
-            text_encoder.text_model.embeddings.temp_token_embedding = orig_weights
+            text_encoder.to(dtype=orig_dtype)
 
         del text_encoder
         del pipeline