Incorporate upstream changes

2 files changed, 66 insertions, 8 deletions

diff --git a/dreambooth.py b/dreambooth.py
index 45a0497..c01cbe3 100644
--- a/dreambooth.py
+++ b/dreambooth.py
@@ -136,6 +136,11 @@ def parse_args():
         help="Number of steps for the warmup in the lr scheduler."
     )
     parser.add_argument(
+        "--use_8bit_adam",
+        action="store_true",
+        help="Whether or not to use 8-bit Adam from bitsandbytes."
+    )
+    parser.add_argument(
         "--adam_beta1",
         type=float,
         default=0.9,
@@ -238,12 +243,24 @@ def parse_args():
         help="The prompt to specify images in the same class as provided intance images.",
     )
     parser.add_argument(
+        "--prior_loss_weight",
+        type=float,
+        default=1.0,
+        help="The weight of prior preservation loss."
+    )
+    parser.add_argument(
         "--with_prior_preservation",
         default=False,
         action="store_true",
         help="Flag to add prior perservation loss.",
     )
     parser.add_argument(
+        "--max_grad_norm",
+        default=1.0,
+        type=float,
+        help="Max gradient norm."
+    )
+    parser.add_argument(
         "--num_class_images",
         type=int,
         default=100,
@@ -550,8 +567,19 @@ def main():
             args.train_batch_size * accelerator.num_processes
         )
 
+    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
+    if args.use_8bit_adam:
+        try:
+            import bitsandbytes as bnb
+        except ImportError:
+            raise ImportError("To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.")
+
+        optimizer_class = bnb.optim.AdamW8bit
+    else:
+        optimizer_class = torch.optim.AdamW
+
     # Initialize the optimizer
-    optimizer = torch.optim.AdamW(
+    optimizer = optimizer_class(
         unet.parameters(),  # only optimize unet
         lr=args.learning_rate,
         betas=(args.adam_beta1, args.adam_beta2),
@@ -705,8 +733,9 @@ def main():
                         noise = torch.randn(latents.shape).to(latents.device)
                         bsz = latents.shape[0]
                         # Sample a random timestep for each image
-                        timesteps = torch.randint(0, noise_scheduler.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)
@@ -719,14 +748,30 @@ def main():
                         # Predict the noise residual
                         noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
 
-                        loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
+                        if args.with_prior_preservation:
+                            # Chunk the noise and noise_pred into two parts and compute the loss on each part separately.
+                            noise_pred, noise_pred_prior = torch.chunk(noise_pred, 2, dim=0)
+                            noise, noise_prior = torch.chunk(noise, 2, dim=0)
+
+                            # Compute instance loss
+                            loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
+
+                            # Compute prior loss
+                            prior_loss = F.mse_loss(noise_pred_prior, noise_prior,
+                                                    reduction="none").mean([1, 2, 3]).mean()
+
+                            # Add the prior loss to the instance loss.
+                            loss = loss + args.prior_loss_weight * prior_loss
+                        else:
+                            loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
 
                     accelerator.backward(loss)
+                    accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)
 
                     optimizer.step()
                     if not accelerator.optimizer_step_was_skipped:
                         lr_scheduler.step()
-                    optimizer.zero_grad(set_to_none=True)
+                    optimizer.zero_grad()
 
                     loss = loss.detach().item()
                     train_loss += loss
@@ -765,8 +810,9 @@ def main():
 
                     noise = torch.randn(latents.shape).to(latents.device)
                     bsz = latents.shape[0]
-                    timesteps = torch.randint(0, noise_scheduler.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()
 
                     noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
 
@@ -776,7 +822,18 @@ def main():
 
                     noise_pred, noise = accelerator.gather_for_metrics((noise_pred, noise))
 
-                    loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
+                    if args.with_prior_preservation:
+                        noise_pred, noise_pred_prior = torch.chunk(noise_pred, 2, dim=0)
+                        noise, noise_prior = torch.chunk(noise, 2, dim=0)
+
+                        loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
+
+                        prior_loss = F.mse_loss(noise_pred_prior, noise_prior,
+                                                reduction="none").mean([1, 2, 3]).mean()
+
+                        loss = loss + args.prior_loss_weight * prior_loss
+                    else:
+                        loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
 
                     loss = loss.detach().item()
                     val_loss += loss
diff --git a/environment.yaml b/environment.yaml
index 46a4388..c9f498e 100644
--- a/environment.yaml
+++ b/environment.yaml
@@ -19,6 +19,7 @@ dependencies:
       - -e git+https://github.com/ShivamShrirao/diffusers#egg=diffusers
       - accelerate==0.12.0
       - albumentations==1.1.0
+      - bitsandbytes==0.34.0
       - einops==0.4.1
       - imageio==2.22.0
       - kornia==0.6