[bug fix] using snr gamma and prior preservation loss in the dreambooth lora...

[bug fix] using snr gamma and prior preservation loss in the dreambooth lora sdxl training scripts (#6356)

* change timesteps used to calculate snr when --with_prior_preservation is enabled

* change timesteps used to calculate snr when --with_prior_preservation is enabled (canonical script)

* style

* revert canonical script to before snr gamma change

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py

@@ -1819,9 +1819,17 @@ def main(args):
                     # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
                     # Since we predict the noise instead of x_0, the original formulation is slightly changed.
                     # This is discussed in Section 4.2 of the same paper.
-                    snr = compute_snr(noise_scheduler, timesteps)
+
+                    if args.with_prior_preservation:
+                        # if we're using prior preservation, we calc snr for instance loss only -
+                        # and hence only need timesteps corresponding to instance images
+                        snr_timesteps, _ = torch.chunk(timesteps, 2, dim=0)
+                    else:
+                        snr_timesteps = timesteps
+
+                    snr = compute_snr(noise_scheduler, snr_timesteps)
                     base_weight = (
-                        torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
+                        torch.stack([snr, args.snr_gamma * torch.ones_like(snr_timesteps)], dim=1).min(dim=1)[0] / snr
                     )
 
                     if noise_scheduler.config.prediction_type == "v_prediction":