[textual inversion] add gradient checkpointing and small fixes. (#1848)

Co-authored-by: Henrik Forstén <[email protected]>

* update TI script

* make flake happy

* fix typo

Author: patil-suraj

examples/textual_inversion/textual_inversion.py

@@ -1,5 +1,4 @@
 import argparse
-import itertools
 import math
 import os
 import random
@@ -147,6 +146,11 @@ def parse_args():
         default=1,
         help="Number of updates steps to accumulate before performing a backward/update pass.",
     )
+    parser.add_argument(
+        "--gradient_checkpointing",
+        action="store_true",
+        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
+    )
     parser.add_argument(
         "--learning_rate",
         type=float,
@@ -383,11 +387,6 @@ def get_full_repo_name(model_id: str, organization: Optional[str] = None, token:
         return f"{organization}/{model_id}"
 
 
-def freeze_params(params):
-    for param in params:
-        param.requires_grad = False
-
-
 def main():
     args = parse_args()
     logging_dir = os.path.join(args.output_dir, args.logging_dir)
@@ -460,6 +459,10 @@ def main():
         revision=args.revision,
     )
 
+    if args.gradient_checkpointing:
+        text_encoder.gradient_checkpointing_enable()
+        unet.enable_gradient_checkpointing()
+
     if args.enable_xformers_memory_efficient_attention:
         if is_xformers_available():
             unet.enable_xformers_memory_efficient_attention()
@@ -474,15 +477,12 @@ def main():
     token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]
 
     # Freeze vae and unet
-    freeze_params(vae.parameters())
-    freeze_params(unet.parameters())
+    vae.requires_grad_(False)
+    unet.requires_grad_(False)
     # Freeze all parameters except for the token embeddings in text encoder
-    params_to_freeze = itertools.chain(
-        text_encoder.text_model.encoder.parameters(),
-        text_encoder.text_model.final_layer_norm.parameters(),
-        text_encoder.text_model.embeddings.position_embedding.parameters(),
-    )
-    freeze_params(params_to_freeze)
+    text_encoder.text_model.encoder.requires_grad_(False)
+    text_encoder.text_model.final_layer_norm.requires_grad_(False)
+    text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
 
     if args.scale_lr:
         args.learning_rate = (
@@ -541,9 +541,10 @@ def main():
     unet.to(accelerator.device, dtype=weight_dtype)
     vae.to(accelerator.device, dtype=weight_dtype)
 
-    # Keep vae and unet in eval model as we don't train these
-    vae.eval()
-    unet.eval()
+    # Keep unet in train mode if we are using gradient checkpointing to save memory.
+    # The dropout is 0 so it doesn't matter if we are in eval or train mode.
+    if args.gradient_checkpointing:
+        unet.train()
 
     # We need to recalculate our total training steps as the size of the training dataloader may have changed.
     num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
@@ -609,12 +610,11 @@ def main():
                 latents = latents * 0.18215
 
                 # Sample noise that we'll add to the latents
-                noise = torch.randn(latents.shape).to(latents.device).to(dtype=weight_dtype)
+                noise = torch.randn_like(latents)
                 bsz = latents.shape[0]
                 # Sample a random timestep for each image
-                timesteps = torch.randint(
-                    0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device
-                ).long()
+                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
+                timesteps = timesteps.long()
 
                 # Add noise to the latents according to the noise magnitude at each timestep
                 # (this is the forward diffusion process)
@@ -669,8 +669,7 @@ def main():
             if global_step >= args.max_train_steps:
                 break
 
-        accelerator.wait_for_everyone()
-
+    accelerator.wait_for_everyone()
     # Create the pipeline using using the trained modules and save it.
     if accelerator.is_main_process:
         if args.push_to_hub and args.only_save_embeds: