Auto switch to tiled VAE encode if regular one runs out of memory.

comfy/sd.py

@@ -544,6 +544,19 @@ class VAE:
             / 3.0) / 2.0, min=0.0, max=1.0)
         return output
 
+    def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
+        steps = pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
+        steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
+        steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
+        pbar = utils.ProgressBar(steps)
+
+        encode_fn = lambda a: self.first_stage_model.encode(2. * a.to(self.device) - 1.).sample() * self.scale_factor
+        samples = utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
+        samples += utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
+        samples += utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
+        samples /= 3.0
+        return samples
+
     def decode(self, samples_in):
         model_management.unload_model()
         self.first_stage_model = self.first_stage_model.to(self.device)
@@ -574,28 +587,26 @@ class VAE:
     def encode(self, pixel_samples):
         model_management.unload_model()
         self.first_stage_model = self.first_stage_model.to(self.device)
-        pixel_samples = pixel_samples.movedim(-1,1).to(self.device)
-        samples = self.first_stage_model.encode(2. * pixel_samples - 1.).sample() * self.scale_factor
+        pixel_samples = pixel_samples.movedim(-1,1)
+        try:
+            batch_number = 1
+            samples = torch.empty((pixel_samples.shape[0], 4, round(pixel_samples.shape[2] // 8), round(pixel_samples.shape[3] // 8)), device="cpu")
+            for x in range(0, pixel_samples.shape[0], batch_number):
+                pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.device)
+                samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).sample().cpu() * self.scale_factor
+        except model_management.OOM_EXCEPTION as e:
+            print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
+            samples = self.encode_tiled_(pixel_samples)
+
         self.first_stage_model = self.first_stage_model.cpu()
-        samples = samples.cpu()
         return samples
 
     def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
         model_management.unload_model()
         self.first_stage_model = self.first_stage_model.to(self.device)
-        pixel_samples = pixel_samples.movedim(-1,1).to(self.device)
-
-        steps = pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
-        steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
-        steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
-        pbar = utils.ProgressBar(steps)
-
-        samples = utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
-        samples += utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
-        samples += utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
-        samples /= 3.0
+        pixel_samples = pixel_samples.movedim(-1,1)
+        samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap)
         self.first_stage_model = self.first_stage_model.cpu()
-        samples = samples.cpu()
         return samples
 
 def broadcast_image_to(tensor, target_batch_size, batched_number):