Add support for Wan2.2 5B

toolkit/models/wan21/autoencoder_kl_wan.py

@@ -20,6 +20,7 @@ import torch.nn.functional as F
 import torch.utils.checkpoint
 
 from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import FromOriginalModelMixin
 from diffusers.utils import logging
 from diffusers.utils.accelerate_utils import apply_forward_hook
 from diffusers.models.activations import get_activation
@@ -34,6 +35,104 @@ logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 CACHE_T = 2
 
 
+class AvgDown3D(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        factor_t,
+        factor_s=1,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.factor_t = factor_t
+        self.factor_s = factor_s
+        self.factor = self.factor_t * self.factor_s * self.factor_s
+
+        assert in_channels * self.factor % out_channels == 0
+        self.group_size = in_channels * self.factor // out_channels
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        pad_t = (self.factor_t - x.shape[2] % self.factor_t) % self.factor_t
+        pad = (0, 0, 0, 0, pad_t, 0)
+        x = F.pad(x, pad)
+        B, C, T, H, W = x.shape
+        x = x.view(
+            B,
+            C,
+            T // self.factor_t,
+            self.factor_t,
+            H // self.factor_s,
+            self.factor_s,
+            W // self.factor_s,
+            self.factor_s,
+        )
+        x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous()
+        x = x.view(
+            B,
+            C * self.factor,
+            T // self.factor_t,
+            H // self.factor_s,
+            W // self.factor_s,
+        )
+        x = x.view(
+            B,
+            self.out_channels,
+            self.group_size,
+            T // self.factor_t,
+            H // self.factor_s,
+            W // self.factor_s,
+        )
+        x = x.mean(dim=2)
+        return x
+
+
+class DupUp3D(nn.Module):
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        factor_t,
+        factor_s=1,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        self.factor_t = factor_t
+        self.factor_s = factor_s
+        self.factor = self.factor_t * self.factor_s * self.factor_s
+
+        assert out_channels * self.factor % in_channels == 0
+        self.repeats = out_channels * self.factor // in_channels
+
+    def forward(self, x: torch.Tensor, first_chunk=False) -> torch.Tensor:
+        x = x.repeat_interleave(self.repeats, dim=1)
+        x = x.view(
+            x.size(0),
+            self.out_channels,
+            self.factor_t,
+            self.factor_s,
+            self.factor_s,
+            x.size(2),
+            x.size(3),
+            x.size(4),
+        )
+        x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous()
+        x = x.view(
+            x.size(0),
+            self.out_channels,
+            x.size(2) * self.factor_t,
+            x.size(4) * self.factor_s,
+            x.size(6) * self.factor_s,
+        )
+        if first_chunk:
+            x = x[:, :, self.factor_t - 1:, :, :]
+        return x
+
 class WanCausalConv3d(nn.Conv3d):
     r"""
     A custom 3D causal convolution layer with feature caching support.
@@ -134,19 +233,23 @@ class WanResample(nn.Module):
             - 'downsample3d': 3D downsampling with zero-padding, convolution, and causal 3D convolution.
     """
 
-    def __init__(self, dim: int, mode: str) -> None:
+    def __init__(self, dim: int, mode: str, upsample_out_dim: int = None) -> None:
         super().__init__()
         self.dim = dim
         self.mode = mode
 
+        # default to dim //2
+        if upsample_out_dim is None:
+            upsample_out_dim = dim // 2
+
         # layers
         if mode == "upsample2d":
             self.resample = nn.Sequential(
-                WanUpsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), nn.Conv2d(dim, dim // 2, 3, padding=1)
+                WanUpsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), nn.Conv2d(dim, upsample_out_dim, 3, padding=1)
             )
         elif mode == "upsample3d":
             self.resample = nn.Sequential(
-                WanUpsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), nn.Conv2d(dim, dim // 2, 3, padding=1)
+                WanUpsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), nn.Conv2d(dim, upsample_out_dim, 3, padding=1)
             )
             self.time_conv = WanCausalConv3d(dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))
 
@@ -363,6 +466,48 @@ class WanMidBlock(nn.Module):
         return x
 
 
+class WanResidualDownBlock(nn.Module):
+
+    def __init__(self,
+                 in_dim,
+                 out_dim,
+                 dropout,
+                 num_res_blocks,
+                 temperal_downsample=False,
+                 down_flag=False):
+        super().__init__()
+
+        # Shortcut path with downsample
+        self.avg_shortcut = AvgDown3D(
+            in_dim,
+            out_dim,
+            factor_t=2 if temperal_downsample else 1,
+            factor_s=2 if down_flag else 1,
+        )
+
+        # Main path with residual blocks and downsample
+        resnets = []
+        for _ in range(num_res_blocks):
+            resnets.append(WanResidualBlock(in_dim, out_dim, dropout))
+            in_dim = out_dim
+        self.resnets = nn.ModuleList(resnets)
+
+        # Add the final downsample block
+        if down_flag:
+            mode = "downsample3d" if temperal_downsample else "downsample2d"
+            self.downsampler = WanResample(out_dim, mode=mode)
+        else:
+            self.downsampler = None
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        x_copy = x.clone()
+        for resnet in self.resnets:
+            x = resnet(x, feat_cache, feat_idx)
+        if self.downsampler is not None:
+            x = self.downsampler(x, feat_cache, feat_idx)
+
+        return x + self.avg_shortcut(x_copy)
+
 class WanEncoder3d(nn.Module):
     r"""
     A 3D encoder module.
@@ -380,6 +525,7 @@ class WanEncoder3d(nn.Module):
 
     def __init__(
         self,
+        in_channels: int = 3,
         dim=128,
         z_dim=4,
         dim_mult=[1, 2, 4, 4],
@@ -388,6 +534,7 @@ class WanEncoder3d(nn.Module):
         temperal_downsample=[True, True, False],
         dropout=0.0,
         non_linearity: str = "silu",
+        is_residual: bool = False, # wan 2.2 vae use a residual downblock
     ):
         super().__init__()
         self.dim = dim
@@ -403,23 +550,35 @@ class WanEncoder3d(nn.Module):
         scale = 1.0
 
         # init block
-        self.conv_in = WanCausalConv3d(3, dims[0], 3, padding=1)
+        self.conv_in = WanCausalConv3d(in_channels, dims[0], 3, padding=1)
 
         # downsample blocks
         self.down_blocks = nn.ModuleList([])
         for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
             # residual (+attention) blocks
-            for _ in range(num_res_blocks):
-                self.down_blocks.append(WanResidualBlock(in_dim, out_dim, dropout))
-                if scale in attn_scales:
-                    self.down_blocks.append(WanAttentionBlock(out_dim))
-                in_dim = out_dim
+            if is_residual:
+                self.down_blocks.append(
+                    WanResidualDownBlock(
+                        in_dim,
+                        out_dim,
+                        dropout,
+                        num_res_blocks,
+                        temperal_downsample=temperal_downsample[i] if i != len(dim_mult) - 1 else False,
+                        down_flag=i != len(dim_mult) - 1,
+                        )
+                        )
+            else:
+                for _ in range(num_res_blocks):
+                    self.down_blocks.append(WanResidualBlock(in_dim, out_dim, dropout))
+                    if scale in attn_scales:
+                        self.down_blocks.append(WanAttentionBlock(out_dim))
+                    in_dim = out_dim
 
-            # downsample block
-            if i != len(dim_mult) - 1:
-                mode = "downsample3d" if temperal_downsample[i] else "downsample2d"
-                self.down_blocks.append(WanResample(out_dim, mode=mode))
-                scale /= 2.0
+                # downsample block
+                if i != len(dim_mult) - 1:
+                    mode = "downsample3d" if temperal_downsample[i] else "downsample2d"
+                    self.down_blocks.append(WanResample(out_dim, mode=mode))
+                    scale /= 2.0
 
         # middle blocks
         self.mid_block = WanMidBlock(out_dim, dropout, non_linearity, num_layers=1)
@@ -469,6 +628,92 @@ class WanEncoder3d(nn.Module):
             x = self.conv_out(x)
         return x
 
+class WanResidualUpBlock(nn.Module):
+    """
+    A block that handles upsampling for the WanVAE decoder.
+
+    Args:
+        in_dim (int): Input dimension
+        out_dim (int): Output dimension
+        num_res_blocks (int): Number of residual blocks
+        dropout (float): Dropout rate
+        temperal_upsample (bool): Whether to upsample on temporal dimension
+        up_flag (bool): Whether to upsample or not
+        non_linearity (str): Type of non-linearity to use
+    """
+
+    def __init__(
+        self,
+        in_dim: int,
+        out_dim: int,
+        num_res_blocks: int,
+        dropout: float = 0.0,
+        temperal_upsample: bool = False,
+        up_flag: bool = False,
+        non_linearity: str = "silu",
+    ):
+        super().__init__()
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+        if up_flag:
+            self.avg_shortcut = DupUp3D(
+                in_dim,
+                out_dim,
+                factor_t=2 if temperal_upsample else 1,
+                factor_s=2,
+            )
+        else:
+            self.avg_shortcut = None
+
+        # create residual blocks
+        resnets = []
+        current_dim = in_dim
+        for _ in range(num_res_blocks + 1):
+            resnets.append(WanResidualBlock(current_dim, out_dim, dropout, non_linearity))
+            current_dim = out_dim
+
+        self.resnets = nn.ModuleList(resnets)
+
+        # Add upsampling layer if needed
+        if up_flag:
+            upsample_mode = "upsample3d" if temperal_upsample else "upsample2d"
+            self.upsampler = WanResample(out_dim, mode=upsample_mode, upsample_out_dim=out_dim)
+        else:
+            self.upsampler = None
+
+        self.gradient_checkpointing = False
+
+    def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
+        """
+        Forward pass through the upsampling block.
+
+        Args:
+            x (torch.Tensor): Input tensor
+            feat_cache (list, optional): Feature cache for causal convolutions
+            feat_idx (list, optional): Feature index for cache management
+
+        Returns:
+            torch.Tensor: Output tensor
+        """
+        x_copy = x.clone()
+
+        for resnet in self.resnets:
+            if feat_cache is not None:
+                x = resnet(x, feat_cache, feat_idx)
+            else:
+                x = resnet(x)
+
+        if self.upsampler is not None:
+            if feat_cache is not None:
+                x = self.upsampler(x, feat_cache, feat_idx)
+            else:
+                x = self.upsampler(x)
+
+        if self.avg_shortcut is not None:
+            x = x + self.avg_shortcut(x_copy, first_chunk=first_chunk)
+
+        return x
 
 class WanUpBlock(nn.Module):
     """
@@ -513,7 +758,7 @@ class WanUpBlock(nn.Module):
 
         self.gradient_checkpointing = False
 
-    def forward(self, x, feat_cache=None, feat_idx=[0]):
+    def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=None):
         """
         Forward pass through the upsampling block.
 
@@ -564,6 +809,8 @@ class WanDecoder3d(nn.Module):
         temperal_upsample=[False, True, True],
         dropout=0.0,
         non_linearity: str = "silu",
+        out_channels: int = 3,
+        is_residual: bool = False,
     ):
         super().__init__()
         self.dim = dim
@@ -577,7 +824,6 @@ class WanDecoder3d(nn.Module):
 
         # dimensions
         dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
-        scale = 1.0 / 2 ** (len(dim_mult) - 2)
 
         # init block
         self.conv_in = WanCausalConv3d(z_dim, dims[0], 3, padding=1)
@@ -589,36 +835,47 @@ class WanDecoder3d(nn.Module):
         self.up_blocks = nn.ModuleList([])
         for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
             # residual (+attention) blocks
-            if i > 0:
+            if i > 0 and not is_residual:
+                # wan vae 2.1
                 in_dim = in_dim // 2
 
-            # Determine if we need upsampling
+            # determine if we need upsampling
+            up_flag = i != len(dim_mult) - 1
+            # determine upsampling mode, if not upsampling, set to None
             upsample_mode = None
-            if i != len(dim_mult) - 1:
-                upsample_mode = "upsample3d" if temperal_upsample[i] else "upsample2d"
-
+            if up_flag and temperal_upsample[i]:
+                upsample_mode = "upsample3d"
+            elif up_flag:
+                upsample_mode = "upsample2d"
             # Create and add the upsampling block
-            up_block = WanUpBlock(
-                in_dim=in_dim,
-                out_dim=out_dim,
-                num_res_blocks=num_res_blocks,
-                dropout=dropout,
-                upsample_mode=upsample_mode,
-                non_linearity=non_linearity,
-            )
+            if is_residual:
+                up_block = WanResidualUpBlock(
+                    in_dim=in_dim,
+                    out_dim=out_dim,
+                    num_res_blocks=num_res_blocks,
+                    dropout=dropout,
+                    temperal_upsample=temperal_upsample[i] if up_flag else False,
+                    up_flag= up_flag,
+                    non_linearity=non_linearity,
+                )
+            else:
+                up_block = WanUpBlock(
+                    in_dim=in_dim,
+                    out_dim=out_dim,
+                    num_res_blocks=num_res_blocks,
+                    dropout=dropout,
+                    upsample_mode=upsample_mode,
+                    non_linearity=non_linearity,
+                )
             self.up_blocks.append(up_block)
 
-            # Update scale for next iteration
-            if upsample_mode is not None:
-                scale *= 2.0
-
         # output blocks
         self.norm_out = WanRMS_norm(out_dim, images=False)
-        self.conv_out = WanCausalConv3d(out_dim, 3, 3, padding=1)
+        self.conv_out = WanCausalConv3d(out_dim, out_channels, 3, padding=1)
 
         self.gradient_checkpointing = False
 
-    def forward(self, x, feat_cache=None, feat_idx=[0]):
+    def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
         ## conv1
         if feat_cache is not None:
             idx = feat_idx[0]
@@ -633,20 +890,11 @@ class WanDecoder3d(nn.Module):
             x = self.conv_in(x)
 
         ## middle
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            # middle
-            x = self._gradient_checkpointing_func(self.mid_block, x, feat_cache, feat_idx)
-            
-            ## upsamples
-            for up_block in self.up_blocks:
-                x = self._gradient_checkpointing_func(up_block, x, feat_cache, feat_idx)
-                
-        else:
-            x = self.mid_block(x, feat_cache, feat_idx)
+        x = self.mid_block(x, feat_cache, feat_idx)
 
-            ## upsamples
-            for up_block in self.up_blocks:
-                x = up_block(x, feat_cache, feat_idx)
+        ## upsamples
+        for up_block in self.up_blocks:
+            x = up_block(x, feat_cache, feat_idx, first_chunk = first_chunk)
 
         ## head
         x = self.norm_out(x)
@@ -665,7 +913,46 @@ class WanDecoder3d(nn.Module):
         return x
 
 
-class AutoencoderKLWan(ModelMixin, ConfigMixin):
+def patchify(x, patch_size):
+    # YiYi TODO: refactor this
+    from einops import rearrange
+    if patch_size == 1:
+        return x
+    if x.dim() == 4:
+        x = rearrange(
+            x, "b c (h q) (w r) -> b (c r q) h w", q=patch_size, r=patch_size)
+    elif x.dim() == 5:
+        x = rearrange(
+            x,
+            "b c f (h q) (w r) -> b (c r q) f h w",
+            q=patch_size,
+            r=patch_size,
+        )
+    else:
+        raise ValueError(f"Invalid input shape: {x.shape}")
+
+    return x
+
+
+def unpatchify(x, patch_size):
+    # YiYi TODO: refactor this
+    from einops import rearrange
+    if patch_size == 1:
+        return x
+
+    if x.dim() == 4:
+        x = rearrange(
+            x, "b (c r q) h w -> b c (h q) (w r)", q=patch_size, r=patch_size)
+    elif x.dim() == 5:
+        x = rearrange(
+            x,
+            "b (c r q) f h w -> b c f (h q) (w r)",
+            q=patch_size,
+            r=patch_size,
+        )
+    return x
+
+class AutoencoderKLWan(ModelMixin, ConfigMixin, FromOriginalModelMixin):
     r"""
     A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos.
     Introduced in [Wan 2.1].
@@ -674,12 +961,13 @@ class AutoencoderKLWan(ModelMixin, ConfigMixin):
     for all models (such as downloading or saving).
     """
 
-    _supports_gradient_checkpointing = True
+    _supports_gradient_checkpointing = False
 
     @register_to_config
     def __init__(
         self,
         base_dim: int = 96,
+        decoder_base_dim: Optional[int] = None,
         z_dim: int = 16,
         dim_mult: Tuple[int] = [1, 2, 4, 4],
         num_res_blocks: int = 2,
@@ -722,6 +1010,13 @@ class AutoencoderKLWan(ModelMixin, ConfigMixin):
             2.8251,
             1.9160,
         ],
+        is_residual: bool = False,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        patch_size: Optional[int] = None,
+        scale_factor_temporal: Optional[int] = 4,
+        scale_factor_spatial: Optional[int] = 8,
+        clip_output: bool = True,
     ) -> None:
         super().__init__()
 
@@ -729,37 +1024,119 @@ class AutoencoderKLWan(ModelMixin, ConfigMixin):
         self.temperal_downsample = temperal_downsample
         self.temperal_upsample = temperal_downsample[::-1]
 
+        if decoder_base_dim is None:
+            decoder_base_dim = base_dim
+
         self.encoder = WanEncoder3d(
-            base_dim, z_dim * 2, dim_mult, num_res_blocks, attn_scales, self.temperal_downsample, dropout
+            in_channels=in_channels, dim=base_dim, z_dim=z_dim * 2, dim_mult=dim_mult, num_res_blocks=num_res_blocks, attn_scales=attn_scales, temperal_downsample=temperal_downsample, dropout=dropout, is_residual=is_residual
         )
         self.quant_conv = WanCausalConv3d(z_dim * 2, z_dim * 2, 1)
         self.post_quant_conv = WanCausalConv3d(z_dim, z_dim, 1)
 
         self.decoder = WanDecoder3d(
-            base_dim, z_dim, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout
+            dim=decoder_base_dim, z_dim=z_dim, dim_mult=dim_mult, num_res_blocks=num_res_blocks, attn_scales=attn_scales, temperal_upsample=self.temperal_upsample, dropout=dropout, out_channels=out_channels, is_residual=is_residual
         )
 
-    def clear_cache(self):
-        def _count_conv3d(model):
-            count = 0
-            for m in model.modules():
-                if isinstance(m, WanCausalConv3d):
-                    count += 1
-            return count
+        self.spatial_compression_ratio = 2 ** len(self.temperal_downsample)
 
-        self._conv_num = _count_conv3d(self.decoder)
+        # 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
+
+        # Precompute and cache conv counts for encoder and decoder for clear_cache speedup
+        self._cached_conv_counts = {
+            "decoder": sum(isinstance(m, WanCausalConv3d) for m in self.decoder.modules())
+            if self.decoder is not None
+            else 0,
+            "encoder": sum(isinstance(m, WanCausalConv3d) for m in self.encoder.modules())
+            if self.encoder is not None
+            else 0,
+        }
+
+    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):
+        # Use cached conv counts for decoder and encoder to avoid re-iterating modules each call
+        self._conv_num = self._cached_conv_counts["decoder"]
         self._conv_idx = [0]
         self._feat_map = [None] * self._conv_num
         # cache encode
-        self._enc_conv_num = _count_conv3d(self.encoder)
+        self._enc_conv_num = self._cached_conv_counts["encoder"]
         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
+        if self.config.patch_size is not None:
+            x = patchify(x, patch_size=self.config.patch_size)
+        iter_ = 1 + (num_frame - 1) // 4
         for i in range(iter_):
             self._enc_conv_idx = [0]
             if i == 0:
@@ -773,8 +1150,6 @@ class AutoencoderKLWan(ModelMixin, ConfigMixin):
                 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
 
@@ -794,27 +1169,39 @@ class AutoencoderKLWan(ModelMixin, ConfigMixin):
                 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
 
-        iter_ = z.shape[2]
+        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)
+
+        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)
+                out = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx, first_chunk=True)
             else:
                 out_ = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx)
                 out = torch.cat([out, out_], 2)
 
-        out = torch.clamp(out, min=-1.0, max=1.0)
+        if self.config.clip_output:
+            out = torch.clamp(out, min=-1.0, max=1.0)
+        if self.config.patch_size is not None:
+            out = unpatchify(out, patch_size=self.config.patch_size)
         self.clear_cache()
         if not return_dict:
             return (out,)
@@ -836,12 +1223,161 @@ class AutoencoderKLWan(ModelMixin, ConfigMixin):
                 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,
@@ -862,4 +1398,4 @@ class AutoencoderKLWan(ModelMixin, ConfigMixin):
         else:
             z = posterior.mode()
         dec = self.decode(z, return_dict=return_dict)
-        return dec
+        return dec

toolkit/models/wan21/wan21.py

@@ -89,12 +89,18 @@ class AggressiveWanUnloadPipeline(WanPipeline):
         transformer: WanTransformer3DModel,
         vae: AutoencoderKLWan,
         scheduler: FlowMatchEulerDiscreteScheduler,
+        transformer_2: Optional[WanTransformer3DModel] = None,
+        boundary_ratio: Optional[float] = None,
+        expand_timesteps: bool = False,  # Wan2.2 ti2v
         device: torch.device = torch.device("cuda"),
     ):
         super().__init__(
             tokenizer=tokenizer,
             text_encoder=text_encoder,
             transformer=transformer,
+            transformer_2=transformer_2,
+            boundary_ratio=boundary_ratio,
+            expand_timesteps=expand_timesteps,
             vae=vae,
             scheduler=scheduler,
         )
@@ -300,6 +306,8 @@ class AggressiveWanUnloadPipeline(WanPipeline):
 
 class Wan21(BaseModel):
     arch = 'wan21'
+    _wan_generation_scheduler_config = scheduler_configUniPC
+    _wan_expand_timesteps = False
     def __init__(
             self,
             device,
@@ -331,7 +339,7 @@ class Wan21(BaseModel):
         dtype = self.torch_dtype
         model_path = self.model_config.name_or_path
 
-        self.print_and_status_update("Loading Wan2.1 model")
+        self.print_and_status_update("Loading Wan model")
         subfolder = 'transformer'
         transformer_path = model_path
         if os.path.exists(transformer_path):
@@ -380,7 +388,6 @@ class Wan21(BaseModel):
             # patch the state dict method
             patch_dequantization_on_save(transformer)
             quantization_type = get_qtype(self.model_config.qtype)
-            self.print_and_status_update("Quantizing transformer")
             if self.model_config.low_vram:
                 print("Quantizing blocks")
                 orig_exclude = copy.deepcopy(quantization_args['exclude'])
@@ -474,22 +481,26 @@ class Wan21(BaseModel):
         self.tokenizer = tokenizer
 
     def get_generation_pipeline(self):
-        scheduler = UniPCMultistepScheduler(**scheduler_configUniPC)
+        scheduler = UniPCMultistepScheduler(**self._wan_generation_scheduler_config)
         if self.model_config.low_vram:
             pipeline = AggressiveWanUnloadPipeline(
                 vae=self.vae,
                 transformer=self.model,
+                transformer_2=self.model,
                 text_encoder=self.text_encoder,
                 tokenizer=self.tokenizer,
                 scheduler=scheduler,
+                expand_timesteps=self._wan_expand_timesteps,
                 device=self.device_torch
             )
         else:
             pipeline = WanPipeline(
                 vae=self.vae,
                 transformer=self.unet,
+                transformer_2=self.unet,
                 text_encoder=self.text_encoder,
                 tokenizer=self.tokenizer,
+                expand_timesteps=self._wan_expand_timesteps,
                 scheduler=scheduler,
             )
 

toolkit/models/wan21/wan21_i2v.py

@@ -48,11 +48,13 @@ class AggressiveWanI2VUnloadPipeline(WanImageToVideoPipeline):
         self,
         tokenizer: AutoTokenizer,
         text_encoder: UMT5EncoderModel,
-        image_encoder: CLIPVisionModel,
-        image_processor: CLIPImageProcessor,
         transformer: WanTransformer3DModel,
         vae: AutoencoderKLWan,
         scheduler: FlowMatchEulerDiscreteScheduler,
+        image_processor: CLIPImageProcessor = None,
+        image_encoder: CLIPVisionModel = None,
+        transformer_2: WanTransformer3DModel = None,
+        boundary_ratio: Optional[float] = None,
         device: torch.device = torch.device("cuda"),
     ):
         super().__init__(
@@ -63,6 +65,8 @@ class AggressiveWanI2VUnloadPipeline(WanImageToVideoPipeline):
             transformer=transformer,
             scheduler=scheduler,
             image_processor=image_processor,
+            transformer_2=transformer_2,
+            boundary_ratio=boundary_ratio,
         )
         self._exec_device = device
         

toolkit/models/wan21/wan_utils.py

@@ -39,7 +39,7 @@ def add_first_frame_conditioning(
         first_frame = first_frame.expand(latent_model_input.shape[0], -1, -1, -1)
         
     # resize first frame to match the latent model input
-    vae_scale_factor = 8
+    vae_scale_factor = vae.config.scale_factor_spatial
     first_frame = F.interpolate(
         first_frame,
         size=(latent_model_input.shape[3] * vae_scale_factor, latent_model_input.shape[4] * vae_scale_factor),
@@ -111,3 +111,55 @@ def add_first_frame_conditioning(
         [latent_model_input, first_frame_condition], dim=1)
 
     return conditioned_latent
+
+
+def add_first_frame_conditioning_v22(
+    latent_model_input,
+    first_frame,
+    vae
+):
+    """
+    Overwrites first few time steps in latent_model_input with VAE-encoded first_frame,
+    and returns the modified latent + binary mask (0=conditioned, 1=noise).
+
+    Args:
+        latent_model_input: torch.Tensor of shape (bs, 48, T, H, W)
+        first_frame: torch.Tensor of shape (bs, 3, H*scale, W*scale)
+        vae: VAE model with .encode() and .config.latents_mean/std
+
+    Returns:
+        latent: (bs, 48, T, H, W) - modified input latent
+        mask: (bs, 1, T, H, W) - binary mask
+    """
+    device = latent_model_input.device
+    dtype = latent_model_input.dtype
+    bs, _, T, H, W = latent_model_input.shape
+    scale = vae.config.scale_factor_spatial
+    target_h = H * scale
+    target_w = W * scale
+
+    # Ensure shape
+    if first_frame.ndim == 3:
+        first_frame = first_frame.unsqueeze(0)
+    if first_frame.shape[0] != bs:
+        first_frame = first_frame.expand(bs, -1, -1, -1)
+
+    # Resize and encode
+    first_frame_up = F.interpolate(first_frame, size=(target_h, target_w), mode="bilinear", align_corners=False)
+    first_frame_up = first_frame_up.unsqueeze(2)  # (bs, 3, 1, H, W)
+    encoded = vae.encode(first_frame_up).latent_dist.sample().to(dtype).to(device)
+
+    # Normalize
+    mean = torch.tensor(vae.config.latents_mean).view(1, -1, 1, 1, 1).to(device, dtype)
+    std = 1.0 / torch.tensor(vae.config.latents_std).view(1, -1, 1, 1, 1).to(device, dtype)
+    encoded = (encoded - mean) * std
+
+    # Replace in latent
+    latent = latent_model_input.clone()
+    latent[:, :, :encoded.shape[2]] = encoded  # typically first frame: [:, :, 0]
+
+    # Mask: 0 where conditioned, 1 otherwise
+    mask = torch.ones(bs, 1, T, H, W, device=device, dtype=dtype)
+    mask[:, :, :encoded.shape[2]] = 0.0
+
+    return latent, mask