Split VAE decode batches depending on free memory.

comfy/sd.py

@@ -439,9 +439,14 @@ class VAE:
         model_management.unload_model()
         self.first_stage_model = self.first_stage_model.to(self.device)
         try:
-            samples = samples_in.to(self.device)
-            pixel_samples = self.first_stage_model.decode(1. / self.scale_factor * samples)
-            pixel_samples = torch.clamp((pixel_samples + 1.0) / 2.0, min=0.0, max=1.0)
+            free_memory = model_management.get_free_memory(self.device)
+            batch_number = int((free_memory * 0.7) / (2562 * samples_in.shape[2] * samples_in.shape[3] * 64))
+            batch_number = max(1, batch_number)
+
+            pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * 8), round(samples_in.shape[3] * 8)), device="cpu")
+            for x in range(0, samples_in.shape[0], batch_number):
+                samples = samples_in[x:x+batch_number].to(self.device)
+                pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(1. / self.scale_factor * samples) + 1.0) / 2.0, min=0.0, max=1.0).cpu()
         except model_management.OOM_EXCEPTION as e:
             print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
             pixel_samples = self.decode_tiled_(samples_in)