fix: incomplete inference

When number of images to run inference for is not divisible by the batch size, the inference for the last batch failed. This is because how PNDMScheduler is implemented. Fix is achieved by calling the scheduler's  method for each batch, which resets the scheduler state before each batch.

tryoffdiff/modeling/predict.py

@@ -35,7 +35,6 @@ def inference_tryoffdiff(config: InferenceConfig):
 
     # Set up scheduler
     scheduler = PNDMScheduler.from_pretrained(config.scheduler_dir)
-    scheduler.set_timesteps(config.num_inference_steps)
 
     # Prepare dataloader
     val_set = VitonVAESigLIPDataset(root_dir=config.val_img_dir, inference=True)
@@ -61,6 +60,8 @@ def inference_tryoffdiff(config: InferenceConfig):
         for cond, img_name in tqdm(val_loader, desc="Processing batches"):
             cond = cond.to(config.device)
             batch_size = cond.size(0)  # Adjust batch size for the last batch
+            scheduler.set_timesteps(config.num_inference_steps)  # Reset scheduler for each batch
+
             # Initialize noise for this batch
             x = torch.randn(batch_size, 4, 64, 64, generator=generator, device=config.device)
             uncond = torch.zeros_like(cond) if config.guidance_scale else None