Merge pull request kohya-ss#2015 from DKnight54/uncache_vae_batch

Using --vae_batch_size to set batch size for dynamic latent generation

Author: kohya-ss

train_network.py

@@ -912,14 +912,22 @@ def remove_model(old_ckpt_name):
                     if "latents" in batch and batch["latents"] is not None:
                         latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype)
                     else:
-                        with torch.no_grad():
-                            # latentに変換
-                            latents = vae.encode(batch["images"].to(dtype=vae_dtype)).latent_dist.sample().to(dtype=weight_dtype)
-
+                        if args.vae_batch_size is None or len(batch["images"]) <= args.vae_batch_size:
+                            with torch.no_grad():
+                                # latentに変換
+                                latents = vae.encode(batch["images"].to(dtype=vae_dtype)).latent_dist.sample().to(dtype=weight_dtype)
+                        else:
+                            chunks = [batch["images"][i:i + args.vae_batch_size] for i in range(0, len(batch["images"]), args.vae_batch_size)]
+                            list_latents = []
+                            for chunk in chunks:
+                                with torch.no_grad():
+                                # latentに変換
+                                    list_latents.append(vae.encode(chunk.to(dtype=vae_dtype)).latent_dist.sample().to(dtype=weight_dtype))
+                            latents = torch.cat(list_latents, dim=0)
                             # NaNが含まれていれば警告を表示し0に置き換える
-                            if torch.any(torch.isnan(latents)):
-                                accelerator.print("NaN found in latents, replacing with zeros")
-                                latents = torch.nan_to_num(latents, 0, out=latents)
+                        if torch.any(torch.isnan(latents)):
+                            accelerator.print("NaN found in latents, replacing with zeros")
+                            latents = torch.nan_to_num(latents, 0, out=latents)
                     latents = latents * self.vae_scale_factor
 
                     # get multiplier for each sample