Support tiled encode/decode for AutoencoderTiny (huggingface#4627)

Isotr0py · web-flow · commit 67ea2b7afaaa · 2023-08-18T09:12:55.000+05:30
* Impl tae slicing and tiling

* add tae tiling test

* add parameterized test

* formatted code

* fix failed test

* style docs
diff --git a/src/diffusers/models/autoencoder_tiny.py b/src/diffusers/models/autoencoder_tiny.py
@@ -137,6 +137,15 @@ def __init__(
         self.latent_shift = latent_shift
         self.scaling_factor = scaling_factor
 
+        self.use_slicing = False
+        self.use_tiling = False
+
+        # only relevant if vae tiling is enabled
+        self.spatial_scale_factor = 2**out_channels
+        self.tile_overlap_factor = 0.125
+        self.tile_sample_min_size = 512
+        self.tile_latent_min_size = self.tile_sample_min_size // self.spatial_scale_factor
+
     def _set_gradient_checkpointing(self, module, value=False):
         if isinstance(module, (EncoderTiny, DecoderTiny)):
             module.gradient_checkpointing = value
@@ -149,11 +158,147 @@ def unscale_latents(self, x):
         """[0, 1] -> raw latents"""
         return x.sub(self.latent_shift).mul(2 * self.latent_magnitude)
 
+    def enable_slicing(self):
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    def disable_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
+    def enable_tiling(self, use_tiling: bool = True):
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+        """
+        self.use_tiling = use_tiling
+
+    def disable_tiling(self):
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.enable_tiling(False)
+
+    def _tiled_encode(self, x: torch.FloatTensor) -> torch.FloatTensor:
+        r"""Encode a batch of images using a tiled encoder.
+
+        When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several
+        steps. This is useful to keep memory use constant regardless of image size. To avoid tiling artifacts, the
+        tiles overlap and are blended together to form a smooth output.
+
+        Args:
+            x (`torch.FloatTensor`): Input batch of images.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.autoencoder_tiny.AutoencoderTinyOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.autoencoder_tiny.AutoencoderTinyOutput`] or `tuple`:
+                If return_dict is True, a [`~models.autoencoder_tiny.AutoencoderTinyOutput`] is returned, otherwise a
+                plain `tuple` is returned.
+        """
+        # scale of encoder output relative to input
+        sf = self.spatial_scale_factor
+        tile_size = self.tile_sample_min_size
+
+        # number of pixels to blend and to traverse between tile
+        blend_size = int(tile_size * self.tile_overlap_factor)
+        traverse_size = tile_size - blend_size
+
+        # tiles index (up/left)
+        ti = range(0, x.shape[-2], traverse_size)
+        tj = range(0, x.shape[-1], traverse_size)
+
+        # mask for blending
+        blend_masks = torch.stack(
+            torch.meshgrid([torch.arange(tile_size / sf) / (blend_size / sf - 1)] * 2, indexing="ij")
+        )
+        blend_masks = blend_masks.clamp(0, 1).to(x.device)
+
+        # output array
+        out = torch.zeros(x.shape[0], 4, x.shape[-2] // sf, x.shape[-1] // sf, device=x.device)
+        for i in ti:
+            for j in tj:
+                tile_in = x[..., i : i + tile_size, j : j + tile_size]
+                # tile result
+                tile_out = out[..., i // sf : (i + tile_size) // sf, j // sf : (j + tile_size) // sf]
+                tile = self.encoder(tile_in)
+                h, w = tile.shape[-2], tile.shape[-1]
+                # blend tile result into output
+                blend_mask_i = torch.ones_like(blend_masks[0]) if i == 0 else blend_masks[0]
+                blend_mask_j = torch.ones_like(blend_masks[1]) if j == 0 else blend_masks[1]
+                blend_mask = blend_mask_i * blend_mask_j
+                tile, blend_mask = tile[..., :h, :w], blend_mask[..., :h, :w]
+                tile_out.copy_(blend_mask * tile + (1 - blend_mask) * tile_out)
+        return out
+
+    def _tiled_decode(self, x: torch.FloatTensor) -> torch.FloatTensor:
+        r"""Encode a batch of images using a tiled encoder.
+
+        When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several
+        steps. This is useful to keep memory use constant regardless of image size. To avoid tiling artifacts, the
+        tiles overlap and are blended together to form a smooth output.
+
+        Args:
+            x (`torch.FloatTensor`): Input batch of images.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.autoencoder_tiny.AutoencoderTinyOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.vae.DecoderOutput`] or `tuple`:
+                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
+                returned.
+        """
+        # scale of decoder output relative to input
+        sf = self.spatial_scale_factor
+        tile_size = self.tile_latent_min_size
+
+        # number of pixels to blend and to traverse between tiles
+        blend_size = int(tile_size * self.tile_overlap_factor)
+        traverse_size = tile_size - blend_size
+
+        # tiles index (up/left)
+        ti = range(0, x.shape[-2], traverse_size)
+        tj = range(0, x.shape[-1], traverse_size)
+
+        # mask for blending
+        blend_masks = torch.stack(
+            torch.meshgrid([torch.arange(tile_size * sf) / (blend_size * sf - 1)] * 2, indexing="ij")
+        )
+        blend_masks = blend_masks.clamp(0, 1).to(x.device)
+
+        # output array
+        out = torch.zeros(x.shape[0], 3, x.shape[-2] * sf, x.shape[-1] * sf, device=x.device)
+        for i in ti:
+            for j in tj:
+                tile_in = x[..., i : i + tile_size, j : j + tile_size]
+                # tile result
+                tile_out = out[..., i * sf : (i + tile_size) * sf, j * sf : (j + tile_size) * sf]
+                tile = self.decoder(tile_in)
+                h, w = tile.shape[-2], tile.shape[-1]
+                # blend tile result into output
+                blend_mask_i = torch.ones_like(blend_masks[0]) if i == 0 else blend_masks[0]
+                blend_mask_j = torch.ones_like(blend_masks[1]) if j == 0 else blend_masks[1]
+                blend_mask = (blend_mask_i * blend_mask_j)[..., :h, :w]
+                tile_out.copy_(blend_mask * tile + (1 - blend_mask) * tile_out)
+        return out
+
     @apply_forward_hook
     def encode(
         self, x: torch.FloatTensor, return_dict: bool = True
     ) -> Union[AutoencoderTinyOutput, Tuple[torch.FloatTensor]]:
-        output = self.encoder(x)
+        if self.use_slicing and x.shape[0] > 1:
+            output = [self._tiled_encode(x_slice) if self.use_tiling else self.encoder(x) for x_slice in x.split(1)]
+            output = torch.cat(output)
+        else:
+            output = self._tiled_encode(x) if self.use_tiling else self.encoder(x)
 
         if not return_dict:
             return (output,)
@@ -162,7 +307,11 @@ def encode(
 
     @apply_forward_hook
     def decode(self, x: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, Tuple[torch.FloatTensor]]:
-        output = self.decoder(x)
+        if self.use_slicing and x.shape[0] > 1:
+            output = [self._tiled_decode(x_slice) if self.use_tiling else self.decoder(x) for x_slice in x.split(1)]
+            output = torch.cat(output)
+        else:
+            output = self._tiled_decode(x) if self.use_tiling else self.decoder(x)
         # Refer to the following discussion to know why this is needed.
         # https://github.com/huggingface/diffusers/pull/4384#discussion_r1279401854
         output = output.mul_(2).sub_(1)
diff --git a/tests/models/test_models_vae.py b/tests/models/test_models_vae.py
@@ -285,6 +285,23 @@ def get_sd_vae_model(self, model_id="hf-internal-testing/taesd-diffusers", fp16=
         model.to(torch_device).eval()
         return model
 
+    @parameterized.expand(
+        [
+            [(1, 4, 73, 97), (1, 3, 584, 776)],
+            [(1, 4, 97, 73), (1, 3, 776, 584)],
+            [(1, 4, 49, 65), (1, 3, 392, 520)],
+            [(1, 4, 65, 49), (1, 3, 520, 392)],
+            [(1, 4, 49, 49), (1, 3, 392, 392)],
+        ]
+    )
+    def test_tae_tiling(self, in_shape, out_shape):
+        model = self.get_sd_vae_model()
+        model.enable_tiling()
+        with torch.no_grad():
+            zeros = torch.zeros(in_shape).to(torch_device)
+            dec = model.decode(zeros).sample
+            assert dec.shape == out_shape
+
     def test_stable_diffusion(self):
         model = self.get_sd_vae_model()
         image = self.get_sd_image(seed=33)
