Merge branch 'main' into feat/lanczos-resize-sd15-advanced

Author: yijun-lee

src/diffusers/models/autoencoders/autoencoder_kl_wan.py

@@ -730,6 +730,76 @@ def __init__(
             base_dim, z_dim, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout
         )
 
+        self.spatial_compression_ratio = 2 ** len(self.temperal_downsample)
+
+        # When decoding a batch of video latents at a time, one can save memory by slicing across the batch dimension
+        # to perform decoding of a single video latent at a time.
+        self.use_slicing = False
+
+        # When decoding spatially large video latents, the memory requirement is very high. By breaking the video latent
+        # frames spatially into smaller tiles and performing multiple forward passes for decoding, and then blending the
+        # intermediate tiles together, the memory requirement can be lowered.
+        self.use_tiling = False
+
+        # The minimal tile height and width for spatial tiling to be used
+        self.tile_sample_min_height = 256
+        self.tile_sample_min_width = 256
+
+        # The minimal distance between two spatial tiles
+        self.tile_sample_stride_height = 192
+        self.tile_sample_stride_width = 192
+
+    def enable_tiling(
+        self,
+        tile_sample_min_height: Optional[int] = None,
+        tile_sample_min_width: Optional[int] = None,
+        tile_sample_stride_height: Optional[float] = None,
+        tile_sample_stride_width: Optional[float] = None,
+    ) -> None:
+        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.
+
+        Args:
+            tile_sample_min_height (`int`, *optional*):
+                The minimum height required for a sample to be separated into tiles across the height dimension.
+            tile_sample_min_width (`int`, *optional*):
+                The minimum width required for a sample to be separated into tiles across the width dimension.
+            tile_sample_stride_height (`int`, *optional*):
+                The minimum amount of overlap between two consecutive vertical tiles. This is to ensure that there are
+                no tiling artifacts produced across the height dimension.
+            tile_sample_stride_width (`int`, *optional*):
+                The stride between two consecutive horizontal tiles. This is to ensure that there are no tiling
+                artifacts produced across the width dimension.
+        """
+        self.use_tiling = True
+        self.tile_sample_min_height = tile_sample_min_height or self.tile_sample_min_height
+        self.tile_sample_min_width = tile_sample_min_width or self.tile_sample_min_width
+        self.tile_sample_stride_height = tile_sample_stride_height or self.tile_sample_stride_height
+        self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
+
+    def disable_tiling(self) -> None:
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_tiling = False
+
+    def enable_slicing(self) -> None:
+        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) -> None:
+        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 clear_cache(self):
         def _count_conv3d(model):
             count = 0
@@ -746,11 +816,14 @@ def _count_conv3d(model):
         self._enc_conv_idx = [0]
         self._enc_feat_map = [None] * self._enc_conv_num
 
-    def _encode(self, x: torch.Tensor) -> torch.Tensor:
+    def _encode(self, x: torch.Tensor):
+        _, _, num_frame, height, width = x.shape
+
+        if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):
+            return self.tiled_encode(x)
+
         self.clear_cache()
-        ## cache
-        t = x.shape[2]
-        iter_ = 1 + (t - 1) // 4
+        iter_ = 1 + (num_frame - 1) // 4
         for i in range(iter_):
             self._enc_conv_idx = [0]
             if i == 0:
@@ -764,8 +837,6 @@ def _encode(self, x: torch.Tensor) -> torch.Tensor:
                 out = torch.cat([out, out_], 2)
 
         enc = self.quant_conv(out)
-        mu, logvar = enc[:, : self.z_dim, :, :, :], enc[:, self.z_dim :, :, :, :]
-        enc = torch.cat([mu, logvar], dim=1)
         self.clear_cache()
         return enc
 
@@ -785,18 +856,28 @@ def encode(
                 The latent representations of the encoded videos. If `return_dict` is True, a
                 [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
         """
-        h = self._encode(x)
+        if self.use_slicing and x.shape[0] > 1:
+            encoded_slices = [self._encode(x_slice) for x_slice in x.split(1)]
+            h = torch.cat(encoded_slices)
+        else:
+            h = self._encode(x)
         posterior = DiagonalGaussianDistribution(h)
+
         if not return_dict:
             return (posterior,)
         return AutoencoderKLOutput(latent_dist=posterior)
 
-    def _decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
-        self.clear_cache()
+    def _decode(self, z: torch.Tensor, return_dict: bool = True):
+        _, _, num_frame, height, width = z.shape
+        tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
+        tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
+
+        if self.use_tiling and (width > tile_latent_min_width or height > tile_latent_min_height):
+            return self.tiled_decode(z, return_dict=return_dict)
 
-        iter_ = z.shape[2]
+        self.clear_cache()
         x = self.post_quant_conv(z)
-        for i in range(iter_):
+        for i in range(num_frame):
             self._conv_idx = [0]
             if i == 0:
                 out = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx)
@@ -826,12 +907,161 @@ def decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutp
                 If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
                 returned.
         """
-        decoded = self._decode(z).sample
+        if self.use_slicing and z.shape[0] > 1:
+            decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)]
+            decoded = torch.cat(decoded_slices)
+        else:
+            decoded = self._decode(z).sample
+
         if not return_dict:
             return (decoded,)
-
         return DecoderOutput(sample=decoded)
 
+    def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        blend_extent = min(a.shape[-2], b.shape[-2], blend_extent)
+        for y in range(blend_extent):
+            b[:, :, :, y, :] = a[:, :, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, :, y, :] * (
+                y / blend_extent
+            )
+        return b
+
+    def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        blend_extent = min(a.shape[-1], b.shape[-1], blend_extent)
+        for x in range(blend_extent):
+            b[:, :, :, :, x] = a[:, :, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, :, x] * (
+                x / blend_extent
+            )
+        return b
+
+    def tiled_encode(self, x: torch.Tensor) -> AutoencoderKLOutput:
+        r"""Encode a batch of images using a tiled encoder.
+
+        Args:
+            x (`torch.Tensor`): Input batch of videos.
+
+        Returns:
+            `torch.Tensor`:
+                The latent representation of the encoded videos.
+        """
+        _, _, num_frames, height, width = x.shape
+        latent_height = height // self.spatial_compression_ratio
+        latent_width = width // self.spatial_compression_ratio
+
+        tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
+        tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
+        tile_latent_stride_height = self.tile_sample_stride_height // self.spatial_compression_ratio
+        tile_latent_stride_width = self.tile_sample_stride_width // self.spatial_compression_ratio
+
+        blend_height = tile_latent_min_height - tile_latent_stride_height
+        blend_width = tile_latent_min_width - tile_latent_stride_width
+
+        # Split x into overlapping tiles and encode them separately.
+        # The tiles have an overlap to avoid seams between tiles.
+        rows = []
+        for i in range(0, height, self.tile_sample_stride_height):
+            row = []
+            for j in range(0, width, self.tile_sample_stride_width):
+                self.clear_cache()
+                time = []
+                frame_range = 1 + (num_frames - 1) // 4
+                for k in range(frame_range):
+                    self._enc_conv_idx = [0]
+                    if k == 0:
+                        tile = x[:, :, :1, i : i + self.tile_sample_min_height, j : j + self.tile_sample_min_width]
+                    else:
+                        tile = x[
+                            :,
+                            :,
+                            1 + 4 * (k - 1) : 1 + 4 * k,
+                            i : i + self.tile_sample_min_height,
+                            j : j + self.tile_sample_min_width,
+                        ]
+                    tile = self.encoder(tile, feat_cache=self._enc_feat_map, feat_idx=self._enc_conv_idx)
+                    tile = self.quant_conv(tile)
+                    time.append(tile)
+                row.append(torch.cat(time, dim=2))
+            rows.append(row)
+        self.clear_cache()
+
+        result_rows = []
+        for i, row in enumerate(rows):
+            result_row = []
+            for j, tile in enumerate(row):
+                # blend the above tile and the left tile
+                # to the current tile and add the current tile to the result row
+                if i > 0:
+                    tile = self.blend_v(rows[i - 1][j], tile, blend_height)
+                if j > 0:
+                    tile = self.blend_h(row[j - 1], tile, blend_width)
+                result_row.append(tile[:, :, :, :tile_latent_stride_height, :tile_latent_stride_width])
+            result_rows.append(torch.cat(result_row, dim=-1))
+
+        enc = torch.cat(result_rows, dim=3)[:, :, :, :latent_height, :latent_width]
+        return enc
+
+    def tiled_decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
+        r"""
+        Decode a batch of images using a tiled decoder.
+
+        Args:
+            z (`torch.Tensor`): Input batch of latent vectors.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.vae.DecoderOutput`] 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.
+        """
+        _, _, num_frames, height, width = z.shape
+        sample_height = height * self.spatial_compression_ratio
+        sample_width = width * self.spatial_compression_ratio
+
+        tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
+        tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
+        tile_latent_stride_height = self.tile_sample_stride_height // self.spatial_compression_ratio
+        tile_latent_stride_width = self.tile_sample_stride_width // self.spatial_compression_ratio
+
+        blend_height = self.tile_sample_min_height - self.tile_sample_stride_height
+        blend_width = self.tile_sample_min_width - self.tile_sample_stride_width
+
+        # Split z into overlapping tiles and decode them separately.
+        # The tiles have an overlap to avoid seams between tiles.
+        rows = []
+        for i in range(0, height, tile_latent_stride_height):
+            row = []
+            for j in range(0, width, tile_latent_stride_width):
+                self.clear_cache()
+                time = []
+                for k in range(num_frames):
+                    self._conv_idx = [0]
+                    tile = z[:, :, k : k + 1, i : i + tile_latent_min_height, j : j + tile_latent_min_width]
+                    tile = self.post_quant_conv(tile)
+                    decoded = self.decoder(tile, feat_cache=self._feat_map, feat_idx=self._conv_idx)
+                    time.append(decoded)
+                row.append(torch.cat(time, dim=2))
+            rows.append(row)
+        self.clear_cache()
+
+        result_rows = []
+        for i, row in enumerate(rows):
+            result_row = []
+            for j, tile in enumerate(row):
+                # blend the above tile and the left tile
+                # to the current tile and add the current tile to the result row
+                if i > 0:
+                    tile = self.blend_v(rows[i - 1][j], tile, blend_height)
+                if j > 0:
+                    tile = self.blend_h(row[j - 1], tile, blend_width)
+                result_row.append(tile[:, :, :, : self.tile_sample_stride_height, : self.tile_sample_stride_width])
+            result_rows.append(torch.cat(result_row, dim=-1))
+
+        dec = torch.cat(result_rows, dim=3)[:, :, :, :sample_height, :sample_width]
+
+        if not return_dict:
+            return (dec,)
+        return DecoderOutput(sample=dec)
+
     def forward(
         self,
         sample: torch.Tensor,