Update text_encoder/config.json

scheduler/scheduler_config.json

@@ -1,6 +1,6 @@
 {
   "_class_name": "EulerAncestralDiscreteScheduler",
-  "_diffusers_version": "0.30.3",
+  "_diffusers_version": "0.28.0",
   "beta_end": 0.02,
   "beta_schedule": "linear",
   "beta_start": 0.0001,

text_encoder/config.json

@@ -1,9 +1,7 @@
 {
-  "_name_or_path": "/cpfs/data/user/larrytsai/Projects/Yi-VG/allegro/text_encoder",
   "architectures": [
     "T5EncoderModel"
   ],
-  "classifier_dropout": 0.0,
   "d_ff": 10240,
   "d_kv": 64,
   "d_model": 4096,
@@ -26,7 +24,7 @@
   "relative_attention_num_buckets": 32,
   "tie_word_embeddings": false,
   "torch_dtype": "float32",
-  "transformers_version": "4.40.1",
+  "transformers_version": "4.21.1",
   "use_cache": true,
   "vocab_size": 32128
 }

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-    "transformer_blocks.9.attn2.to_out.0.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
-    "transformer_blocks.9.attn2.to_q.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
-    "transformer_blocks.9.attn2.to_q.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
-    "transformer_blocks.9.attn2.to_v.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
-    "transformer_blocks.9.attn2.to_v.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
-    "transformer_blocks.9.ff.net.0.proj.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
-    "transformer_blocks.9.ff.net.0.proj.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
-    "transformer_blocks.9.ff.net.2.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
-    "transformer_blocks.9.ff.net.2.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
-    "transformer_blocks.9.scale_shift_table": "diffusion_pytorch_model-00001-of-00002.safetensors"
-  }
-}

transformer/transformer_3d_allegro.py

@@ -1,1776 +0,0 @@
-# Adapted from Open-Sora-Plan
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-# --------------------------------------------------------
-# References:
-# Open-Sora-Plan: https://github.com/PKU-YuanGroup/Open-Sora-Plan
-# --------------------------------------------------------
-
-
-import json
-import os
-from dataclasses import dataclass
-from functools import partial
-from importlib import import_module
-from typing import Any, Callable, Dict, Optional, Tuple
-
-import numpy as np
-import torch
-import collections
-import torch.nn.functional as F
-from torch.nn.attention import SDPBackend, sdpa_kernel
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.models.activations import GEGLU, GELU, ApproximateGELU
-from diffusers.models.attention_processor import (
-    AttnAddedKVProcessor,
-    AttnAddedKVProcessor2_0,
-    AttnProcessor,
-    CustomDiffusionAttnProcessor,
-    CustomDiffusionAttnProcessor2_0,
-    CustomDiffusionXFormersAttnProcessor,
-    LoRAAttnAddedKVProcessor,
-    LoRAAttnProcessor,
-    LoRAAttnProcessor2_0,
-    LoRAXFormersAttnProcessor,
-    SlicedAttnAddedKVProcessor,
-    SlicedAttnProcessor,
-    SpatialNorm,
-    XFormersAttnAddedKVProcessor,
-    XFormersAttnProcessor,
-)
-from diffusers.models.embeddings import SinusoidalPositionalEmbedding, TimestepEmbedding, Timesteps
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.models.normalization import AdaLayerNorm, AdaLayerNormZero
-from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, is_xformers_available
-from diffusers.utils.torch_utils import maybe_allow_in_graph
-from einops import rearrange, repeat
-from torch import nn
-from diffusers.models.embeddings import PixArtAlphaTextProjection
-
-
-if is_xformers_available():
-    import xformers
-    import xformers.ops
-else:
-    xformers = None
-
-from diffusers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-
-def to_2tuple(x):
-    if isinstance(x, collections.abc.Iterable):
-        return x
-    return (x, x)
-
-class CombinedTimestepSizeEmbeddings(nn.Module):
-    """
-    For PixArt-Alpha.
-
-    Reference:
-    https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29
-    """
-
-    def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False):
-        super().__init__()
-
-        self.outdim = size_emb_dim
-        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
-        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
-
-        self.use_additional_conditions = use_additional_conditions
-        if use_additional_conditions:
-            self.use_additional_conditions = True
-            self.additional_condition_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
-            self.resolution_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim)
-            self.aspect_ratio_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim)
-
-    def apply_condition(self, size: torch.Tensor, batch_size: int, embedder: nn.Module):
-        if size.ndim == 1:
-            size = size[:, None]
-
-        if size.shape[0] != batch_size:
-            size = size.repeat(batch_size // size.shape[0], 1)
-            if size.shape[0] != batch_size:
-                raise ValueError(f"`batch_size` should be {size.shape[0]} but found {batch_size}.")
-
-        current_batch_size, dims = size.shape[0], size.shape[1]
-        size = size.reshape(-1)
-        size_freq = self.additional_condition_proj(size).to(size.dtype)
-
-        size_emb = embedder(size_freq)
-        size_emb = size_emb.reshape(current_batch_size, dims * self.outdim)
-        return size_emb
-
-    def forward(self, timestep, resolution, aspect_ratio, batch_size, hidden_dtype):
-        timesteps_proj = self.time_proj(timestep)
-        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype))  # (N, D)
-
-        if self.use_additional_conditions:
-            resolution = self.apply_condition(resolution, batch_size=batch_size, embedder=self.resolution_embedder)
-            aspect_ratio = self.apply_condition(
-                aspect_ratio, batch_size=batch_size, embedder=self.aspect_ratio_embedder
-            )
-            conditioning = timesteps_emb + torch.cat([resolution, aspect_ratio], dim=1)
-        else:
-            conditioning = timesteps_emb
-
-        return conditioning
-
-
-class PositionGetter3D(object):
-    """ return positions of patches """
-
-    def __init__(self, ):
-        self.cache_positions = {}
-        
-    def __call__(self, b, t, h, w, device):
-        if not (b, t,h,w) in self.cache_positions:
-            x = torch.arange(w, device=device)
-            y = torch.arange(h, device=device)
-            z = torch.arange(t, device=device)
-            pos = torch.cartesian_prod(z, y, x)
-           
-            pos = pos.reshape(t * h * w, 3).transpose(0, 1).reshape(3, 1, -1).contiguous().expand(3, b, -1).clone()
-            poses = (pos[0].contiguous(), pos[1].contiguous(), pos[2].contiguous())
-            max_poses = (int(poses[0].max()), int(poses[1].max()), int(poses[2].max()))
-
-            self.cache_positions[b, t, h, w] = (poses, max_poses)
-        pos = self.cache_positions[b, t, h, w]
-
-        return pos
-    
-
-class RoPE3D(torch.nn.Module):
-
-    def __init__(self, freq=10000.0, F0=1.0, interpolation_scale_thw=(1, 1, 1)):
-        super().__init__()
-        self.base = freq
-        self.F0 = F0
-        self.interpolation_scale_t = interpolation_scale_thw[0]
-        self.interpolation_scale_h = interpolation_scale_thw[1]
-        self.interpolation_scale_w = interpolation_scale_thw[2]
-        self.cache = {}
-
-    def get_cos_sin(self, D, seq_len, device, dtype, interpolation_scale=1):
-        if (D, seq_len, device, dtype) not in self.cache:
-            inv_freq = 1.0 / (self.base ** (torch.arange(0, D, 2).float().to(device) / D))
-            t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) / interpolation_scale
-            freqs = torch.einsum("i,j->ij", t, inv_freq).to(dtype)
-            freqs = torch.cat((freqs, freqs), dim=-1)
-            cos = freqs.cos()  # (Seq, Dim)
-            sin = freqs.sin()
-            self.cache[D, seq_len, device, dtype] = (cos, sin)
-        return self.cache[D, seq_len, device, dtype]
-
-    @staticmethod
-    def rotate_half(x):
-        x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
-        return torch.cat((-x2, x1), dim=-1)
-
-    def apply_rope1d(self, tokens, pos1d, cos, sin):
-        assert pos1d.ndim == 2
-
-            # for (batch_size x ntokens x nheads x dim)
-        cos = torch.nn.functional.embedding(pos1d, cos)[:, None, :, :]
-        sin = torch.nn.functional.embedding(pos1d, sin)[:, None, :, :]
-        return (tokens * cos) + (self.rotate_half(tokens) * sin)
-
-    def forward(self, tokens, positions):
-        """
-        input:
-            * tokens: batch_size x nheads x ntokens x dim
-            * positions: batch_size x ntokens x 3 (t, y and x position of each token)
-        output:
-            * tokens after appplying RoPE3D (batch_size x nheads x ntokens x x dim)
-        """
-        assert tokens.size(3) % 3 == 0, "number of dimensions should be a multiple of three"
-        D = tokens.size(3) // 3
-        poses, max_poses = positions
-        assert len(poses) == 3 and poses[0].ndim == 2# Batch, Seq, 3
-        cos_t, sin_t = self.get_cos_sin(D, max_poses[0] + 1, tokens.device, tokens.dtype, self.interpolation_scale_t)
-        cos_y, sin_y = self.get_cos_sin(D, max_poses[1] + 1, tokens.device, tokens.dtype, self.interpolation_scale_h)
-        cos_x, sin_x = self.get_cos_sin(D, max_poses[2] + 1, tokens.device, tokens.dtype, self.interpolation_scale_w)
-        # split features into three along the feature dimension, and apply rope1d on each half
-        t, y, x = tokens.chunk(3, dim=-1)
-        t = self.apply_rope1d(t, poses[0], cos_t, sin_t)
-        y = self.apply_rope1d(y, poses[1], cos_y, sin_y)
-        x = self.apply_rope1d(x, poses[2], cos_x, sin_x)
-        tokens = torch.cat((t, y, x), dim=-1)
-        return tokens
-
-class PatchEmbed2D(nn.Module):
-    """2D Image to Patch Embedding"""
-
-    def __init__(
-        self,
-        num_frames=1, 
-        height=224,
-        width=224,
-        patch_size_t=1,
-        patch_size=16,
-        in_channels=3,
-        embed_dim=768,
-        layer_norm=False,
-        flatten=True,
-        bias=True,
-        interpolation_scale=(1, 1),
-        interpolation_scale_t=1,
-        use_abs_pos=False, 
-    ):
-        super().__init__()
-        self.use_abs_pos = use_abs_pos
-        self.flatten = flatten
-        self.layer_norm = layer_norm
-
-        self.proj = nn.Conv2d(
-            in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=(patch_size, patch_size), bias=bias
-        )
-        if layer_norm:
-            self.norm = nn.LayerNorm(embed_dim, elementwise_affine=False, eps=1e-6)
-        else:
-            self.norm = None
-
-        self.patch_size_t = patch_size_t
-        self.patch_size = patch_size
-
-    def forward(self, latent):
-        b, _, _, _, _ = latent.shape
-        video_latent = None
-
-        latent = rearrange(latent, 'b c t h w -> (b t) c h w')
-
-        latent = self.proj(latent)
-        if self.flatten:
-            latent = latent.flatten(2).transpose(1, 2)  # BT C H W -> BT N C
-        if self.layer_norm:
-            latent = self.norm(latent)
-
-        latent = rearrange(latent, '(b t) n c -> b (t n) c', b=b)
-        video_latent = latent
-
-        return video_latent
-
-
-@maybe_allow_in_graph
-class Attention(nn.Module):
-    r"""
-    A cross attention layer.
-
-    Parameters:
-        query_dim (`int`):
-            The number of channels in the query.
-        cross_attention_dim (`int`, *optional*):
-            The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
-        heads (`int`,  *optional*, defaults to 8):
-            The number of heads to use for multi-head attention.
-        dim_head (`int`,  *optional*, defaults to 64):
-            The number of channels in each head.
-        dropout (`float`, *optional*, defaults to 0.0):
-            The dropout probability to use.
-        bias (`bool`, *optional*, defaults to False):
-            Set to `True` for the query, key, and value linear layers to contain a bias parameter.
-        upcast_attention (`bool`, *optional*, defaults to False):
-            Set to `True` to upcast the attention computation to `float32`.
-        upcast_softmax (`bool`, *optional*, defaults to False):
-            Set to `True` to upcast the softmax computation to `float32`.
-        cross_attention_norm (`str`, *optional*, defaults to `None`):
-            The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`.
-        cross_attention_norm_num_groups (`int`, *optional*, defaults to 32):
-            The number of groups to use for the group norm in the cross attention.
-        added_kv_proj_dim (`int`, *optional*, defaults to `None`):
-            The number of channels to use for the added key and value projections. If `None`, no projection is used.
-        norm_num_groups (`int`, *optional*, defaults to `None`):
-            The number of groups to use for the group norm in the attention.
-        spatial_norm_dim (`int`, *optional*, defaults to `None`):
-            The number of channels to use for the spatial normalization.
-        out_bias (`bool`, *optional*, defaults to `True`):
-            Set to `True` to use a bias in the output linear layer.
-        scale_qk (`bool`, *optional*, defaults to `True`):
-            Set to `True` to scale the query and key by `1 / sqrt(dim_head)`.
-        only_cross_attention (`bool`, *optional*, defaults to `False`):
-            Set to `True` to only use cross attention and not added_kv_proj_dim. Can only be set to `True` if
-            `added_kv_proj_dim` is not `None`.
-        eps (`float`, *optional*, defaults to 1e-5):
-            An additional value added to the denominator in group normalization that is used for numerical stability.
-        rescale_output_factor (`float`, *optional*, defaults to 1.0):
-            A factor to rescale the output by dividing it with this value.
-        residual_connection (`bool`, *optional*, defaults to `False`):
-            Set to `True` to add the residual connection to the output.
-        _from_deprecated_attn_block (`bool`, *optional*, defaults to `False`):
-            Set to `True` if the attention block is loaded from a deprecated state dict.
-        processor (`AttnProcessor`, *optional*, defaults to `None`):
-            The attention processor to use. If `None`, defaults to `AttnProcessor2_0` if `torch 2.x` is used and
-            `AttnProcessor` otherwise.
-    """
-
-    def __init__(
-        self,
-        query_dim: int,
-        cross_attention_dim: Optional[int] = None,
-        heads: int = 8,
-        dim_head: int = 64,
-        dropout: float = 0.0,
-        bias: bool = False,
-        upcast_attention: bool = False,
-        upcast_softmax: bool = False,
-        cross_attention_norm: Optional[str] = None,
-        cross_attention_norm_num_groups: int = 32,
-        added_kv_proj_dim: Optional[int] = None,
-        norm_num_groups: Optional[int] = None,
-        spatial_norm_dim: Optional[int] = None,
-        out_bias: bool = True,
-        scale_qk: bool = True,
-        only_cross_attention: bool = False,
-        eps: float = 1e-5,
-        rescale_output_factor: float = 1.0,
-        residual_connection: bool = False,
-        _from_deprecated_attn_block: bool = False,
-        processor: Optional["AttnProcessor"] = None,
-        attention_mode: str = "xformers",
-        use_rope: bool = False,
-        interpolation_scale_thw=None, 
-    ):
-        super().__init__()
-        self.inner_dim = dim_head * heads
-        self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
-        self.upcast_attention = upcast_attention
-        self.upcast_softmax = upcast_softmax
-        self.rescale_output_factor = rescale_output_factor
-        self.residual_connection = residual_connection
-        self.dropout = dropout
-        self.use_rope = use_rope
-
-        # we make use of this private variable to know whether this class is loaded
-        # with an deprecated state dict so that we can convert it on the fly
-        self._from_deprecated_attn_block = _from_deprecated_attn_block
-
-        self.scale_qk = scale_qk
-        self.scale = dim_head**-0.5 if self.scale_qk else 1.0
-
-        self.heads = heads
-        # for slice_size > 0 the attention score computation
-        # is split across the batch axis to save memory
-        # You can set slice_size with `set_attention_slice`
-        self.sliceable_head_dim = heads
-
-        self.added_kv_proj_dim = added_kv_proj_dim
-        self.only_cross_attention = only_cross_attention
-
-        if self.added_kv_proj_dim is None and self.only_cross_attention:
-            raise ValueError(
-                "`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`."
-            )
-
-        if norm_num_groups is not None:
-            self.group_norm = nn.GroupNorm(num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True)
-        else:
-            self.group_norm = None
-
-        if spatial_norm_dim is not None:
-            self.spatial_norm = SpatialNorm(f_channels=query_dim, zq_channels=spatial_norm_dim)
-        else:
-            self.spatial_norm = None
-
-        if cross_attention_norm is None:
-            self.norm_cross = None
-        elif cross_attention_norm == "layer_norm":
-            self.norm_cross = nn.LayerNorm(self.cross_attention_dim)
-        elif cross_attention_norm == "group_norm":
-            if self.added_kv_proj_dim is not None:
-                # The given `encoder_hidden_states` are initially of shape
-                # (batch_size, seq_len, added_kv_proj_dim) before being projected
-                # to (batch_size, seq_len, cross_attention_dim). The norm is applied
-                # before the projection, so we need to use `added_kv_proj_dim` as
-                # the number of channels for the group norm.
-                norm_cross_num_channels = added_kv_proj_dim
-            else:
-                norm_cross_num_channels = self.cross_attention_dim
-
-            self.norm_cross = nn.GroupNorm(
-                num_channels=norm_cross_num_channels, num_groups=cross_attention_norm_num_groups, eps=1e-5, affine=True
-            )
-        else:
-            raise ValueError(
-                f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'"
-            )
-
-        linear_cls = nn.Linear
-
-
-        self.to_q = linear_cls(query_dim, self.inner_dim, bias=bias)
-
-        if not self.only_cross_attention:
-            # only relevant for the `AddedKVProcessor` classes
-            self.to_k = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias)
-            self.to_v = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias)
-        else:
-            self.to_k = None
-            self.to_v = None
-
-        if self.added_kv_proj_dim is not None:
-            self.add_k_proj = linear_cls(added_kv_proj_dim, self.inner_dim)
-            self.add_v_proj = linear_cls(added_kv_proj_dim, self.inner_dim)
-
-        self.to_out = nn.ModuleList([])
-        self.to_out.append(linear_cls(self.inner_dim, query_dim, bias=out_bias))
-        self.to_out.append(nn.Dropout(dropout))
-
-        # set attention processor
-        # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
-        # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
-        # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
-        if processor is None:
-            processor = (
-                AttnProcessor2_0(
-                    attention_mode,
-                    use_rope,
-                    interpolation_scale_thw=interpolation_scale_thw,
-                )
-                if hasattr(F, "scaled_dot_product_attention") and self.scale_qk
-                else AttnProcessor()
-            )
-        self.set_processor(processor)
-
-    def set_use_memory_efficient_attention_xformers(
-        self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None
-    ) -> None:
-        r"""
-        Set whether to use memory efficient attention from `xformers` or not.
-
-        Args:
-            use_memory_efficient_attention_xformers (`bool`):
-                Whether to use memory efficient attention from `xformers` or not.
-            attention_op (`Callable`, *optional*):
-                The attention operation to use. Defaults to `None` which uses the default attention operation from
-                `xformers`.
-        """
-        is_lora = hasattr(self, "processor")
-        is_custom_diffusion = hasattr(self, "processor") and isinstance(
-            self.processor,
-            (CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor, CustomDiffusionAttnProcessor2_0),
-        )
-        is_added_kv_processor = hasattr(self, "processor") and isinstance(
-            self.processor,
-            (
-                AttnAddedKVProcessor,
-                AttnAddedKVProcessor2_0,
-                SlicedAttnAddedKVProcessor,
-                XFormersAttnAddedKVProcessor,
-                LoRAAttnAddedKVProcessor,
-            ),
-        )
-
-        if use_memory_efficient_attention_xformers:
-            if is_added_kv_processor and (is_lora or is_custom_diffusion):
-                raise NotImplementedError(
-                    f"Memory efficient attention is currently not supported for LoRA or custom diffusion for attention processor type {self.processor}"
-                )
-            if not is_xformers_available():
-                raise ModuleNotFoundError(
-                    (
-                        "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
-                        " xformers"
-                    ),
-                    name="xformers",
-                )
-            elif not torch.cuda.is_available():
-                raise ValueError(
-                    "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is"
-                    " only available for GPU "
-                )
-            else:
-                try:
-                    # Make sure we can run the memory efficient attention
-                    _ = xformers.ops.memory_efficient_attention(
-                        torch.randn((1, 2, 40), device="cuda"),
-                        torch.randn((1, 2, 40), device="cuda"),
-                        torch.randn((1, 2, 40), device="cuda"),
-                    )
-                except Exception as e:
-                    raise e
-
-            if is_lora:
-                # TODO (sayakpaul): should we throw a warning if someone wants to use the xformers
-                # variant when using PT 2.0 now that we have LoRAAttnProcessor2_0?
-                processor = LoRAXFormersAttnProcessor(
-                    hidden_size=self.processor.hidden_size,
-                    cross_attention_dim=self.processor.cross_attention_dim,
-                    rank=self.processor.rank,
-                    attention_op=attention_op,
-                )
-                processor.load_state_dict(self.processor.state_dict())
-                processor.to(self.processor.to_q_lora.up.weight.device)
-            elif is_custom_diffusion:
-                processor = CustomDiffusionXFormersAttnProcessor(
-                    train_kv=self.processor.train_kv,
-                    train_q_out=self.processor.train_q_out,
-                    hidden_size=self.processor.hidden_size,
-                    cross_attention_dim=self.processor.cross_attention_dim,
-                    attention_op=attention_op,
-                )
-                processor.load_state_dict(self.processor.state_dict())
-                if hasattr(self.processor, "to_k_custom_diffusion"):
-                    processor.to(self.processor.to_k_custom_diffusion.weight.device)
-            elif is_added_kv_processor:
-                # TODO(Patrick, Suraj, William) - currently xformers doesn't work for UnCLIP
-                # which uses this type of cross attention ONLY because the attention mask of format
-                # [0, ..., -10.000, ..., 0, ...,] is not supported
-                # throw warning
-                logger.info(
-                    "Memory efficient attention with `xformers` might currently not work correctly if an attention mask is required for the attention operation."
-                )
-                processor = XFormersAttnAddedKVProcessor(attention_op=attention_op)
-            else:
-                processor = XFormersAttnProcessor(attention_op=attention_op)
-        else:
-            if is_lora:
-                attn_processor_class = (
-                    LoRAAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else LoRAAttnProcessor
-                )
-                processor = attn_processor_class(
-                    hidden_size=self.processor.hidden_size,
-                    cross_attention_dim=self.processor.cross_attention_dim,
-                    rank=self.processor.rank,
-                )
-                processor.load_state_dict(self.processor.state_dict())
-                processor.to(self.processor.to_q_lora.up.weight.device)
-            elif is_custom_diffusion:
-                attn_processor_class = (
-                    CustomDiffusionAttnProcessor2_0
-                    if hasattr(F, "scaled_dot_product_attention")
-                    else CustomDiffusionAttnProcessor
-                )
-                processor = attn_processor_class(
-                    train_kv=self.processor.train_kv,
-                    train_q_out=self.processor.train_q_out,
-                    hidden_size=self.processor.hidden_size,
-                    cross_attention_dim=self.processor.cross_attention_dim,
-                )
-                processor.load_state_dict(self.processor.state_dict())
-                if hasattr(self.processor, "to_k_custom_diffusion"):
-                    processor.to(self.processor.to_k_custom_diffusion.weight.device)
-            else:
-                # set attention processor
-                # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
-                # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
-                # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
-                processor = (
-                    AttnProcessor2_0()
-                    if hasattr(F, "scaled_dot_product_attention") and self.scale_qk
-                    else AttnProcessor()
-                )
-
-        self.set_processor(processor)
-
-    def set_attention_slice(self, slice_size: int) -> None:
-        r"""
-        Set the slice size for attention computation.
-
-        Args:
-            slice_size (`int`):
-                The slice size for attention computation.
-        """
-        if slice_size is not None and slice_size > self.sliceable_head_dim:
-            raise ValueError(f"slice_size {slice_size} has to be smaller or equal to {self.sliceable_head_dim}.")
-
-        if slice_size is not None and self.added_kv_proj_dim is not None:
-            processor = SlicedAttnAddedKVProcessor(slice_size)
-        elif slice_size is not None:
-            processor = SlicedAttnProcessor(slice_size)
-        elif self.added_kv_proj_dim is not None:
-            processor = AttnAddedKVProcessor()
-        else:
-            # set attention processor
-            # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
-            # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
-            # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
-            processor = (
-                AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
-            )
-
-        self.set_processor(processor)
-
-    def set_processor(self, processor: "AttnProcessor", _remove_lora: bool = False) -> None:
-        r"""
-        Set the attention processor to use.
-
-        Args:
-            processor (`AttnProcessor`):
-                The attention processor to use.
-            _remove_lora (`bool`, *optional*, defaults to `False`):
-                Set to `True` to remove LoRA layers from the model.
-        """
-        if not USE_PEFT_BACKEND and hasattr(self, "processor") and _remove_lora and self.to_q.lora_layer is not None:
-            deprecate(
-                "set_processor to offload LoRA",
-                "0.26.0",
-                "In detail, removing LoRA layers via calling `set_default_attn_processor` is deprecated. Please make sure to call `pipe.unload_lora_weights()` instead.",
-            )
-            # TODO(Patrick, Sayak) - this can be deprecated once PEFT LoRA integration is complete
-            # We need to remove all LoRA layers
-            # Don't forget to remove ALL `_remove_lora` from the codebase
-            for module in self.modules():
-                if hasattr(module, "set_lora_layer"):
-                    module.set_lora_layer(None)
-
-        # if current processor is in `self._modules` and if passed `processor` is not, we need to
-        # pop `processor` from `self._modules`
-        if (
-            hasattr(self, "processor")
-            and isinstance(self.processor, torch.nn.Module)
-            and not isinstance(processor, torch.nn.Module)
-        ):
-            logger.info(f"You are removing possibly trained weights of {self.processor} with {processor}")
-            self._modules.pop("processor")
-
-        self.processor = processor
-
-    def get_processor(self, return_deprecated_lora: bool = False):
-        r"""
-        Get the attention processor in use.
-
-        Args:
-            return_deprecated_lora (`bool`, *optional*, defaults to `False`):
-                Set to `True` to return the deprecated LoRA attention processor.
-
-        Returns:
-            "AttentionProcessor": The attention processor in use.
-        """
-        if not return_deprecated_lora:
-            return self.processor
-
-        # TODO(Sayak, Patrick). The rest of the function is needed to ensure backwards compatible
-        # serialization format for LoRA Attention Processors. It should be deleted once the integration
-        # with PEFT is completed.
-        is_lora_activated = {
-            name: module.lora_layer is not None
-            for name, module in self.named_modules()
-            if hasattr(module, "lora_layer")
-        }
-
-        # 1. if no layer has a LoRA activated we can return the processor as usual
-        if not any(is_lora_activated.values()):
-            return self.processor
-
-        # If doesn't apply LoRA do `add_k_proj` or `add_v_proj`
-        is_lora_activated.pop("add_k_proj", None)
-        is_lora_activated.pop("add_v_proj", None)
-        # 2. else it is not posssible that only some layers have LoRA activated
-        if not all(is_lora_activated.values()):
-            raise ValueError(
-                f"Make sure that either all layers or no layers have LoRA activated, but have {is_lora_activated}"
-            )
-
-        # 3. And we need to merge the current LoRA layers into the corresponding LoRA attention processor
-        non_lora_processor_cls_name = self.processor.__class__.__name__
-        lora_processor_cls = getattr(import_module(__name__), "LoRA" + non_lora_processor_cls_name)
-
-        hidden_size = self.inner_dim
-
-        # now create a LoRA attention processor from the LoRA layers
-        if lora_processor_cls in [LoRAAttnProcessor, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor]:
-            kwargs = {
-                "cross_attention_dim": self.cross_attention_dim,
-                "rank": self.to_q.lora_layer.rank,
-                "network_alpha": self.to_q.lora_layer.network_alpha,
-                "q_rank": self.to_q.lora_layer.rank,
-                "q_hidden_size": self.to_q.lora_layer.out_features,
-                "k_rank": self.to_k.lora_layer.rank,
-                "k_hidden_size": self.to_k.lora_layer.out_features,
-                "v_rank": self.to_v.lora_layer.rank,
-                "v_hidden_size": self.to_v.lora_layer.out_features,
-                "out_rank": self.to_out[0].lora_layer.rank,
-                "out_hidden_size": self.to_out[0].lora_layer.out_features,
-            }
-
-            if hasattr(self.processor, "attention_op"):
-                kwargs["attention_op"] = self.processor.attention_op
-
-            lora_processor = lora_processor_cls(hidden_size, **kwargs)
-            lora_processor.to_q_lora.load_state_dict(self.to_q.lora_layer.state_dict())
-            lora_processor.to_k_lora.load_state_dict(self.to_k.lora_layer.state_dict())
-            lora_processor.to_v_lora.load_state_dict(self.to_v.lora_layer.state_dict())
-            lora_processor.to_out_lora.load_state_dict(self.to_out[0].lora_layer.state_dict())
-        elif lora_processor_cls == LoRAAttnAddedKVProcessor:
-            lora_processor = lora_processor_cls(
-                hidden_size,
-                cross_attention_dim=self.add_k_proj.weight.shape[0],
-                rank=self.to_q.lora_layer.rank,
-                network_alpha=self.to_q.lora_layer.network_alpha,
-            )
-            lora_processor.to_q_lora.load_state_dict(self.to_q.lora_layer.state_dict())
-            lora_processor.to_k_lora.load_state_dict(self.to_k.lora_layer.state_dict())
-            lora_processor.to_v_lora.load_state_dict(self.to_v.lora_layer.state_dict())
-            lora_processor.to_out_lora.load_state_dict(self.to_out[0].lora_layer.state_dict())
-
-            # only save if used
-            if self.add_k_proj.lora_layer is not None:
-                lora_processor.add_k_proj_lora.load_state_dict(self.add_k_proj.lora_layer.state_dict())
-                lora_processor.add_v_proj_lora.load_state_dict(self.add_v_proj.lora_layer.state_dict())
-            else:
-                lora_processor.add_k_proj_lora = None
-                lora_processor.add_v_proj_lora = None
-        else:
-            raise ValueError(f"{lora_processor_cls} does not exist.")
-
-        return lora_processor
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        **cross_attention_kwargs,
-    ) -> torch.Tensor:
-        r"""
-        The forward method of the `Attention` class.
-
-        Args:
-            hidden_states (`torch.Tensor`):
-                The hidden states of the query.
-            encoder_hidden_states (`torch.Tensor`, *optional*):
-                The hidden states of the encoder.
-            attention_mask (`torch.Tensor`, *optional*):
-                The attention mask to use. If `None`, no mask is applied.
-            **cross_attention_kwargs:
-                Additional keyword arguments to pass along to the cross attention.
-
-        Returns:
-            `torch.Tensor`: The output of the attention layer.
-        """
-        # The `Attention` class can call different attention processors / attention functions
-        # here we simply pass along all tensors to the selected processor class
-        # For standard processors that are defined here, `**cross_attention_kwargs` is empty
-        return self.processor(
-            self,
-            hidden_states,
-            encoder_hidden_states=encoder_hidden_states,
-            attention_mask=attention_mask,
-            **cross_attention_kwargs,
-        )
-
-    def batch_to_head_dim(self, tensor: torch.Tensor) -> torch.Tensor:
-        r"""
-        Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads`
-        is the number of heads initialized while constructing the `Attention` class.
-
-        Args:
-            tensor (`torch.Tensor`): The tensor to reshape.
-
-        Returns:
-            `torch.Tensor`: The reshaped tensor.
-        """
-        head_size = self.heads
-        batch_size, seq_len, dim = tensor.shape
-        tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
-        tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
-        return tensor
-
-    def head_to_batch_dim(self, tensor: torch.Tensor, out_dim: int = 3) -> torch.Tensor:
-        r"""
-        Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is
-        the number of heads initialized while constructing the `Attention` class.
-
-        Args:
-            tensor (`torch.Tensor`): The tensor to reshape.
-            out_dim (`int`, *optional*, defaults to `3`): The output dimension of the tensor. If `3`, the tensor is
-                reshaped to `[batch_size * heads, seq_len, dim // heads]`.
-
-        Returns:
-            `torch.Tensor`: The reshaped tensor.
-        """
-        head_size = self.heads
-        batch_size, seq_len, dim = tensor.shape
-        tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
-        tensor = tensor.permute(0, 2, 1, 3)
-
-        if out_dim == 3:
-            tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size)
-
-        return tensor
-
-    def get_attention_scores(
-        self, query: torch.Tensor, key: torch.Tensor, attention_mask: torch.Tensor = None
-    ) -> torch.Tensor:
-        r"""
-        Compute the attention scores.
-
-        Args:
-            query (`torch.Tensor`): The query tensor.
-            key (`torch.Tensor`): The key tensor.
-            attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied.
-
-        Returns:
-            `torch.Tensor`: The attention probabilities/scores.
-        """
-        dtype = query.dtype
-        if self.upcast_attention:
-            query = query.float()
-            key = key.float()
-
-        if attention_mask is None:
-            baddbmm_input = torch.empty(
-                query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device
-            )
-            beta = 0
-        else:
-            baddbmm_input = attention_mask
-            beta = 1
-
-        attention_scores = torch.baddbmm(
-            baddbmm_input,
-            query,
-            key.transpose(-1, -2),
-            beta=beta,
-            alpha=self.scale,
-        )
-        del baddbmm_input
-
-        if self.upcast_softmax:
-            attention_scores = attention_scores.float()
-
-        attention_probs = attention_scores.softmax(dim=-1)
-        del attention_scores
-
-        attention_probs = attention_probs.to(dtype)
-
-        return attention_probs
-
-    def prepare_attention_mask(
-        self, attention_mask: torch.Tensor, target_length: int, batch_size: int, out_dim: int = 3, head_size = None, 
-    ) -> torch.Tensor:
-        r"""
-        Prepare the attention mask for the attention computation.
-
-        Args:
-            attention_mask (`torch.Tensor`):
-                The attention mask to prepare.
-            target_length (`int`):
-                The target length of the attention mask. This is the length of the attention mask after padding.
-            batch_size (`int`):
-                The batch size, which is used to repeat the attention mask.
-            out_dim (`int`, *optional*, defaults to `3`):
-                The output dimension of the attention mask. Can be either `3` or `4`.
-
-        Returns:
-            `torch.Tensor`: The prepared attention mask.
-        """
-        head_size = head_size if head_size is not None else self.heads
-        if attention_mask is None:
-            return attention_mask
-
-        current_length: int = attention_mask.shape[-1]
-        if current_length != target_length:
-            if attention_mask.device.type == "mps":
-                # HACK: MPS: Does not support padding by greater than dimension of input tensor.
-                # Instead, we can manually construct the padding tensor.
-                padding_shape = (attention_mask.shape[0], attention_mask.shape[1], target_length)
-                padding = torch.zeros(padding_shape, dtype=attention_mask.dtype, device=attention_mask.device)
-                attention_mask = torch.cat([attention_mask, padding], dim=2)
-            else:
-                # TODO: for pipelines such as stable-diffusion, padding cross-attn mask:
-                #       we want to instead pad by (0, remaining_length), where remaining_length is:
-                #       remaining_length: int = target_length - current_length
-                # TODO: re-enable tests/models/test_models_unet_2d_condition.py#test_model_xattn_padding
-                attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
-
-        if out_dim == 3:
-            if attention_mask.shape[0] < batch_size * head_size:
-                attention_mask = attention_mask.repeat_interleave(head_size, dim=0)
-        elif out_dim == 4:
-            attention_mask = attention_mask.unsqueeze(1)
-            attention_mask = attention_mask.repeat_interleave(head_size, dim=1)
-
-        return attention_mask
-
-    def norm_encoder_hidden_states(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
-        r"""
-        Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the
-        `Attention` class.
-
-        Args:
-            encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder.
-
-        Returns:
-            `torch.Tensor`: The normalized encoder hidden states.
-        """
-        assert self.norm_cross is not None, "self.norm_cross must be defined to call self.norm_encoder_hidden_states"
-
-        if isinstance(self.norm_cross, nn.LayerNorm):
-            encoder_hidden_states = self.norm_cross(encoder_hidden_states)
-        elif isinstance(self.norm_cross, nn.GroupNorm):
-            # Group norm norms along the channels dimension and expects
-            # input to be in the shape of (N, C, *). In this case, we want
-            # to norm along the hidden dimension, so we need to move
-            # (batch_size, sequence_length, hidden_size) ->
-            # (batch_size, hidden_size, sequence_length)
-            encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
-            encoder_hidden_states = self.norm_cross(encoder_hidden_states)
-            encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
-        else:
-            assert False
-
-        return encoder_hidden_states
-
-    def _init_compress(self):
-        self.sr.bias.data.zero_()
-        self.norm = nn.LayerNorm(self.inner_dim)
-
-
-class AttnProcessor2_0(nn.Module):
-    r"""
-    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
-    """
-
-    def __init__(self, attention_mode="xformers", use_rope=False, interpolation_scale_thw=None):
-        super().__init__()
-        self.attention_mode = attention_mode
-        self.use_rope = use_rope
-        self.interpolation_scale_thw = interpolation_scale_thw
-
-        if self.use_rope:
-            self._init_rope(interpolation_scale_thw)
-
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
-    def _init_rope(self, interpolation_scale_thw):
-        self.rope = RoPE3D(interpolation_scale_thw=interpolation_scale_thw)
-        self.position_getter = PositionGetter3D()
-        
-    def __call__(
-        self,
-        attn: Attention,
-        hidden_states: torch.FloatTensor,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        temb: Optional[torch.FloatTensor] = None,
-        frame: int = 8, 
-        height: int = 16, 
-        width: int = 16, 
-    ) -> torch.FloatTensor:
-
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-        
-        if attention_mask is not None and self.attention_mode == 'xformers':
-            attention_heads = attn.heads
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size, head_size=attention_heads)
-            attention_mask = attention_mask.view(batch_size, attention_heads, -1, attention_mask.shape[-1])
-        else:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            # scaled_dot_product_attention expects attention_mask shape to be
-            # (batch, heads, source_length, target_length)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-       
-       
-        attn_heads = attn.heads
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn_heads
-
-        query = query.view(batch_size, -1, attn_heads, head_dim).transpose(1, 2)
-        key = key.view(batch_size, -1, attn_heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn_heads, head_dim).transpose(1, 2)
-        
-
-        if self.use_rope:
-            # require the shape of (batch_size x nheads x ntokens x dim)
-            pos_thw = self.position_getter(batch_size, t=frame, h=height, w=width, device=query.device)
-            query = self.rope(query, pos_thw)
-            key = self.rope(key, pos_thw)
-
-        # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        # TODO: add support for attn.scale when we move to Torch 2.1
-        if self.attention_mode == 'flash':
-                # assert attention_mask is None, 'flash-attn do not support attention_mask'
-                with sdpa_kernel(SDPBackend.FLASH_ATTENTION):
-                    hidden_states = F.scaled_dot_product_attention(
-                        query, key, value, dropout_p=0.0, is_causal=False
-                    )
-        elif self.attention_mode == 'xformers':
-            with sdpa_kernel(SDPBackend.EFFICIENT_ATTENTION):
-                hidden_states = F.scaled_dot_product_attention(
-                    query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-                )
-        
-
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn_heads * head_dim)
-        hidden_states = hidden_states.to(query.dtype)
-        
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-class FeedForward(nn.Module):
-    r"""
-    A feed-forward layer.
-
-    Parameters:
-        dim (`int`): The number of channels in the input.
-        dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
-        mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
-        final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
-    """
-
-    def __init__(
-        self,
-        dim: int,
-        dim_out: Optional[int] = None,
-        mult: int = 4,
-        dropout: float = 0.0,
-        activation_fn: str = "geglu",
-        final_dropout: bool = False,
-    ):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        dim_out = dim_out if dim_out is not None else dim
-        linear_cls = nn.Linear
-
-        if activation_fn == "gelu":
-            act_fn = GELU(dim, inner_dim)
-        if activation_fn == "gelu-approximate":
-            act_fn = GELU(dim, inner_dim, approximate="tanh")
-        elif activation_fn == "geglu":
-            act_fn = GEGLU(dim, inner_dim)
-        elif activation_fn == "geglu-approximate":
-            act_fn = ApproximateGELU(dim, inner_dim)
-
-        self.net = nn.ModuleList([])
-        # project in
-        self.net.append(act_fn)
-        # project dropout
-        self.net.append(nn.Dropout(dropout))
-        # project out
-        self.net.append(linear_cls(inner_dim, dim_out))
-        # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
-        if final_dropout:
-            self.net.append(nn.Dropout(dropout))
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        for module in self.net:
-            hidden_states = module(hidden_states)
-        return hidden_states
-
-
-@maybe_allow_in_graph
-class BasicTransformerBlock(nn.Module):
-    r"""
-    A basic Transformer block.
-
-    Parameters:
-        dim (`int`): The number of channels in the input and output.
-        num_attention_heads (`int`): The number of heads to use for multi-head attention.
-        attention_head_dim (`int`): The number of channels in each head.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
-        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
-        num_embeds_ada_norm (:
-            obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
-        attention_bias (:
-            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
-        only_cross_attention (`bool`, *optional*):
-            Whether to use only cross-attention layers. In this case two cross attention layers are used.
-        double_self_attention (`bool`, *optional*):
-            Whether to use two self-attention layers. In this case no cross attention layers are used.
-        upcast_attention (`bool`, *optional*):
-            Whether to upcast the attention computation to float32. This is useful for mixed precision training.
-        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
-            Whether to use learnable elementwise affine parameters for normalization.
-        norm_type (`str`, *optional*, defaults to `"layer_norm"`):
-            The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
-        final_dropout (`bool` *optional*, defaults to False):
-            Whether to apply a final dropout after the last feed-forward layer.
-        positional_embeddings (`str`, *optional*, defaults to `None`):
-            The type of positional embeddings to apply to.
-        num_positional_embeddings (`int`, *optional*, defaults to `None`):
-            The maximum number of positional embeddings to apply.
-    """
-
-    def __init__(
-        self,
-        dim: int,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        dropout=0.0,
-        cross_attention_dim: Optional[int] = None,
-        activation_fn: str = "geglu",
-        num_embeds_ada_norm: Optional[int] = None,
-        attention_bias: bool = False,
-        only_cross_attention: bool = False,
-        double_self_attention: bool = False,
-        upcast_attention: bool = False,
-        norm_elementwise_affine: bool = True,
-        norm_type: str = "layer_norm",  # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single'
-        norm_eps: float = 1e-5,
-        final_dropout: bool = False,
-        positional_embeddings: Optional[str] = None,
-        num_positional_embeddings: Optional[int] = None,
-        sa_attention_mode: str = "flash", 
-        ca_attention_mode: str = "xformers", 
-        use_rope: bool = False,
-        interpolation_scale_thw: Tuple[int] = (1, 1, 1), 
-        block_idx: Optional[int] = None,
-    ):
-        super().__init__()
-        self.only_cross_attention = only_cross_attention
-
-        self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
-        self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
-        self.use_ada_layer_norm_single = norm_type == "ada_norm_single"
-        self.use_layer_norm = norm_type == "layer_norm"
-
-        if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
-            raise ValueError(
-                f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
-                f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
-            )
-
-        if positional_embeddings and (num_positional_embeddings is None):
-            raise ValueError(
-                "If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined."
-            )
-
-        if positional_embeddings == "sinusoidal":
-            self.pos_embed = SinusoidalPositionalEmbedding(dim, max_seq_length=num_positional_embeddings)
-        else:
-            self.pos_embed = None
-
-        # Define 3 blocks. Each block has its own normalization layer.
-        # 1. Self-Attn
-        if self.use_ada_layer_norm:
-            self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
-        elif self.use_ada_layer_norm_zero:
-            self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
-        else:
-            self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
-
-        self.attn1 = Attention(
-            query_dim=dim,
-            heads=num_attention_heads,
-            dim_head=attention_head_dim,
-            dropout=dropout,
-            bias=attention_bias,
-            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
-            upcast_attention=upcast_attention,
-            attention_mode=sa_attention_mode, 
-            use_rope=use_rope,
-            interpolation_scale_thw=interpolation_scale_thw, 
-        )
-
-        # 2. Cross-Attn
-        if cross_attention_dim is not None or double_self_attention:
-            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
-            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
-            # the second cross attention block.
-            self.norm2 = (
-                AdaLayerNorm(dim, num_embeds_ada_norm)
-                if self.use_ada_layer_norm
-                else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
-            )
-            self.attn2 = Attention(
-                query_dim=dim,
-                cross_attention_dim=cross_attention_dim if not double_self_attention else None,
-                heads=num_attention_heads,
-                dim_head=attention_head_dim,
-                dropout=dropout,
-                bias=attention_bias,
-                upcast_attention=upcast_attention,
-                attention_mode=ca_attention_mode,  # only xformers support attention_mask
-                use_rope=False,  # do not position in cross attention
-                interpolation_scale_thw=interpolation_scale_thw, 
-            )  # is self-attn if encoder_hidden_states is none
-        else:
-            self.norm2 = None
-            self.attn2 = None
-
-        # 3. Feed-forward
-
-        if not self.use_ada_layer_norm_single:
-            self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
-
-        self.ff = FeedForward(
-            dim,
-            dropout=dropout,
-            activation_fn=activation_fn,
-            final_dropout=final_dropout,
-        )
-
-        # 5. Scale-shift for PixArt-Alpha.
-        if self.use_ada_layer_norm_single:
-            self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)
-
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        timestep: Optional[torch.LongTensor] = None,
-        cross_attention_kwargs: Dict[str, Any] = None,
-        class_labels: Optional[torch.LongTensor] = None,
-        frame: int = None, 
-        height: int = None, 
-        width: int = None, 
-    ) -> torch.FloatTensor:
-        # Notice that normalization is always applied before the real computation in the following blocks.
-        cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
-
-        # 0. Self-Attention
-        batch_size = hidden_states.shape[0]
-
-        if self.use_ada_layer_norm:
-            norm_hidden_states = self.norm1(hidden_states, timestep)
-        elif self.use_ada_layer_norm_zero:
-            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
-                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
-            )
-        elif self.use_layer_norm:
-            norm_hidden_states = self.norm1(hidden_states)
-        elif self.use_ada_layer_norm_single:
-            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
-                self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
-            ).chunk(6, dim=1)
-            norm_hidden_states = self.norm1(hidden_states)
-            norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
-            norm_hidden_states = norm_hidden_states.squeeze(1)
-        else:
-            raise ValueError("Incorrect norm used")
-
-        if self.pos_embed is not None:
-            norm_hidden_states = self.pos_embed(norm_hidden_states)
-        
-        attn_output = self.attn1(
-            norm_hidden_states,
-            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
-            attention_mask=attention_mask, 
-            frame=frame,
-            height=height,
-            width=width, 
-            **cross_attention_kwargs,
-        )
-        if self.use_ada_layer_norm_zero:
-            attn_output = gate_msa.unsqueeze(1) * attn_output
-        elif self.use_ada_layer_norm_single:
-            attn_output = gate_msa * attn_output
-
-        hidden_states = attn_output + hidden_states
-        if hidden_states.ndim == 4:
-            hidden_states = hidden_states.squeeze(1)
-
-        # 1. Cross-Attention
-        if self.attn2 is not None:
-
-            if self.use_ada_layer_norm:
-                norm_hidden_states = self.norm2(hidden_states, timestep)
-            elif self.use_ada_layer_norm_zero or self.use_layer_norm:
-                norm_hidden_states = self.norm2(hidden_states)
-            elif self.use_ada_layer_norm_single:
-                # For PixArt norm2 isn't applied here:
-                # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103
-                norm_hidden_states = hidden_states
-            else:
-                raise ValueError("Incorrect norm")
-
-            if self.pos_embed is not None and self.use_ada_layer_norm_single is False:
-                norm_hidden_states = self.pos_embed(norm_hidden_states)
-
-            attn_output = self.attn2(
-                norm_hidden_states,
-                encoder_hidden_states=encoder_hidden_states,
-                attention_mask=encoder_attention_mask,
-                **cross_attention_kwargs,
-            )
-            hidden_states = attn_output + hidden_states
-
-
-        # 2. Feed-forward
-        if not self.use_ada_layer_norm_single:
-            norm_hidden_states = self.norm3(hidden_states)
-
-        if self.use_ada_layer_norm_zero:
-            norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
-
-        if self.use_ada_layer_norm_single:
-            norm_hidden_states = self.norm2(hidden_states)
-            norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
-
-        ff_output = self.ff(norm_hidden_states)
-
-        if self.use_ada_layer_norm_zero:
-            ff_output = gate_mlp.unsqueeze(1) * ff_output
-        elif self.use_ada_layer_norm_single:
-            ff_output = gate_mlp * ff_output
-
-
-        hidden_states = ff_output + hidden_states
-        if hidden_states.ndim == 4:
-            hidden_states = hidden_states.squeeze(1)
-
-        return hidden_states
-    
-
-class AdaLayerNormSingle(nn.Module):
-    r"""
-    Norm layer adaptive layer norm single (adaLN-single).
-
-    As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3).
-
-    Parameters:
-        embedding_dim (`int`): The size of each embedding vector.
-        use_additional_conditions (`bool`): To use additional conditions for normalization or not.
-    """
-
-    def __init__(self, embedding_dim: int, use_additional_conditions: bool = False):
-        super().__init__()
-
-        self.emb = CombinedTimestepSizeEmbeddings(
-            embedding_dim, size_emb_dim=embedding_dim // 3, use_additional_conditions=use_additional_conditions
-        )
-
-        self.silu = nn.SiLU()
-        self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True)
-
-    def forward(
-        self,
-        timestep: torch.Tensor,
-        added_cond_kwargs: Dict[str, torch.Tensor] = None,
-        batch_size: int = None,
-        hidden_dtype: Optional[torch.dtype] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-        # No modulation happening here.
-        embedded_timestep = self.emb(
-            timestep, batch_size=batch_size, hidden_dtype=hidden_dtype, resolution=None, aspect_ratio=None
-        )
-        return self.linear(self.silu(embedded_timestep)), embedded_timestep
-
-
-@dataclass
-class Transformer3DModelOutput(BaseOutput):
-    """
-    The output of [`Transformer2DModel`].
-
-    Args:
-        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
-            The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
-            distributions for the unnoised latent pixels.
-    """
-
-    sample: torch.FloatTensor
-
-
-class AllegroTransformer3DModel(ModelMixin, ConfigMixin):
-    _supports_gradient_checkpointing = True
-
-    """
-    A 2D Transformer model for image-like data.
-
-    Parameters:
-        num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
-        attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
-        in_channels (`int`, *optional*):
-            The number of channels in the input and output (specify if the input is **continuous**).
-        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
-        sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
-            This is fixed during training since it is used to learn a number of position embeddings.
-        num_vector_embeds (`int`, *optional*):
-            The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**).
-            Includes the class for the masked latent pixel.
-        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
-        num_embeds_ada_norm ( `int`, *optional*):
-            The number of diffusion steps used during training. Pass if at least one of the norm_layers is
-            `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are
-            added to the hidden states.
-
-            During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.
-        attention_bias (`bool`, *optional*):
-            Configure if the `TransformerBlocks` attention should contain a bias parameter.
-    """
-
-    @register_to_config
-    def __init__(
-        self,
-        num_attention_heads: int = 16,
-        attention_head_dim: int = 88,
-        in_channels: Optional[int] = None,
-        out_channels: Optional[int] = None,
-        num_layers: int = 1,
-        dropout: float = 0.0,
-        cross_attention_dim: Optional[int] = None,
-        attention_bias: bool = False,
-        sample_size: Optional[int] = None,
-        sample_size_t: Optional[int] = None,
-        patch_size: Optional[int] = None,
-        patch_size_t: Optional[int] = None,
-        activation_fn: str = "geglu",
-        num_embeds_ada_norm: Optional[int] = None,
-        use_linear_projection: bool = False,
-        only_cross_attention: bool = False,
-        double_self_attention: bool = False,
-        upcast_attention: bool = False,
-        norm_type: str = "ada_norm",
-        norm_elementwise_affine: bool = True,
-        norm_eps: float = 1e-5,
-        caption_channels: int = None,
-        interpolation_scale_h: float = None,
-        interpolation_scale_w: float = None,
-        interpolation_scale_t: float = None,
-        use_additional_conditions: Optional[bool] = None,
-        sa_attention_mode: str = "flash", 
-        ca_attention_mode: str = 'xformers', 
-        downsampler: str = None, 
-        use_rope: bool = False,
-        model_max_length: int = 300,
-    ):
-        super().__init__()
-        self.use_linear_projection = use_linear_projection
-        self.interpolation_scale_t = interpolation_scale_t
-        self.interpolation_scale_h = interpolation_scale_h
-        self.interpolation_scale_w = interpolation_scale_w
-        self.downsampler = downsampler
-        self.caption_channels = caption_channels
-        self.num_attention_heads = num_attention_heads
-        self.attention_head_dim = attention_head_dim
-        inner_dim = num_attention_heads * attention_head_dim
-        self.inner_dim = inner_dim
-        self.in_channels = in_channels
-        self.out_channels = in_channels if out_channels is None else out_channels
-        self.use_rope = use_rope
-        self.model_max_length = model_max_length
-        self.num_layers = num_layers
-        self.config.hidden_size = inner_dim
-
-
-        # 1. Transformer3DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
-        # Define whether input is continuous or discrete depending on configuration
-        assert in_channels is not None and patch_size is not None
-
-        # 2. Initialize the right blocks.
-        # Initialize the output blocks and other projection blocks when necessary.
-
-        assert self.config.sample_size_t is not None, "AllegroTransformer3DModel over patched input must provide sample_size_t"
-        assert self.config.sample_size is not None, "AllegroTransformer3DModel over patched input must provide sample_size"
-        #assert not (self.config.sample_size_t == 1 and self.config.patch_size_t == 2), "Image do not need patchfy in t-dim"
-
-        self.num_frames = self.config.sample_size_t
-        self.config.sample_size = to_2tuple(self.config.sample_size)
-        self.height = self.config.sample_size[0]
-        self.width = self.config.sample_size[1]
-        self.patch_size_t = self.config.patch_size_t
-        self.patch_size = self.config.patch_size
-        interpolation_scale_t = ((self.config.sample_size_t - 1) // 16 + 1) if self.config.sample_size_t % 2 == 1 else self.config.sample_size_t / 16
-        interpolation_scale_t = (
-            self.config.interpolation_scale_t if self.config.interpolation_scale_t is not None else interpolation_scale_t
-        )
-        interpolation_scale = (
-            self.config.interpolation_scale_h if self.config.interpolation_scale_h is not None else self.config.sample_size[0] / 30, 
-            self.config.interpolation_scale_w if self.config.interpolation_scale_w is not None else self.config.sample_size[1] / 40, 
-        )
-        self.pos_embed = PatchEmbed2D(
-            num_frames=self.config.sample_size_t,
-            height=self.config.sample_size[0],
-            width=self.config.sample_size[1],
-            patch_size_t=self.config.patch_size_t,
-            patch_size=self.config.patch_size,
-            in_channels=self.in_channels,
-            embed_dim=self.inner_dim,
-            interpolation_scale=interpolation_scale, 
-            interpolation_scale_t=interpolation_scale_t,
-            use_abs_pos=not self.config.use_rope, 
-        )
-        interpolation_scale_thw = (interpolation_scale_t, *interpolation_scale)
-
-        # 3. Define transformers blocks, spatial attention
-        self.transformer_blocks = nn.ModuleList(
-            [
-                BasicTransformerBlock(
-                    inner_dim,
-                    num_attention_heads,
-                    attention_head_dim,
-                    dropout=dropout,
-                    cross_attention_dim=cross_attention_dim,
-                    activation_fn=activation_fn,
-                    num_embeds_ada_norm=num_embeds_ada_norm,
-                    attention_bias=attention_bias,
-                    only_cross_attention=only_cross_attention,
-                    double_self_attention=double_self_attention,
-                    upcast_attention=upcast_attention,
-                    norm_type=norm_type,
-                    norm_elementwise_affine=norm_elementwise_affine,
-                    norm_eps=norm_eps,
-                    sa_attention_mode=sa_attention_mode,
-                    ca_attention_mode=ca_attention_mode, 
-                    use_rope=use_rope,
-                    interpolation_scale_thw=interpolation_scale_thw, 
-                    block_idx=d,
-                )
-                for d in range(num_layers)
-            ]
-        )
-
-        # 4. Define output layers
-
-        if norm_type != "ada_norm_single":
-            self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
-            self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim)
-            self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels)
-        elif norm_type == "ada_norm_single":
-            self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
-            self.scale_shift_table = nn.Parameter(torch.randn(2, inner_dim) / inner_dim**0.5)
-            self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels)
-
-        # 5. PixArt-Alpha blocks.
-        self.adaln_single = None
-        self.use_additional_conditions = False
-        if norm_type == "ada_norm_single":
-            # self.use_additional_conditions = self.config.sample_size[0] == 128  # False, 128 -> 1024
-            # TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use
-            # additional conditions until we find better name
-            self.adaln_single = AdaLayerNormSingle(inner_dim, use_additional_conditions=self.use_additional_conditions)
-
-        self.caption_projection = None
-        if caption_channels is not None:
-            self.caption_projection = PixArtAlphaTextProjection(
-                in_features=caption_channels, hidden_size=inner_dim
-            )
-        
-        self.gradient_checkpointing = False
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        self.gradient_checkpointing = value
-
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        timestep: Optional[torch.LongTensor] = None,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        added_cond_kwargs: Dict[str, torch.Tensor] = None,
-        class_labels: Optional[torch.LongTensor] = None,
-        cross_attention_kwargs: Dict[str, Any] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
-        return_dict: bool = True,
-    ):
-        """
-        The [`Transformer2DModel`] forward method.
-
-        Args:
-            hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, frame, channel, height, width)` if continuous):
-                Input `hidden_states`.
-            encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
-                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
-                self-attention.
-            timestep ( `torch.LongTensor`, *optional*):
-                Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
-            class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
-                Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
-                `AdaLayerZeroNorm`.
-            added_cond_kwargs ( `Dict[str, Any]`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AdaLayerNormSingle`
-            cross_attention_kwargs ( `Dict[str, Any]`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-            attention_mask ( `torch.Tensor`, *optional*):
-                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
-                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
-                negative values to the attention scores corresponding to "discard" tokens.
-            encoder_attention_mask ( `torch.Tensor`, *optional*):
-                Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:
-
-                    * Mask `(batch, sequence_length)` True = keep, False = discard.
-                    * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.
-
-                If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
-                above. This bias will be added to the cross-attention scores.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
-                tuple.
-
-        Returns:
-            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
-            `tuple` where the first element is the sample tensor.
-        """
-        batch_size, c, frame, h, w = hidden_states.shape
-
-        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
-        #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
-        #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
-        # expects mask of shape:
-        #   [batch, key_tokens]
-        # adds singleton query_tokens dimension:
-        #   [batch,                    1, key_tokens]
-        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
-        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
-        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)        attention_mask_vid, attention_mask_img = None, None
-        if attention_mask is not None and attention_mask.ndim == 4:
-            # assume that mask is expressed as:
-            #   (1 = keep,      0 = discard)
-            # convert mask into a bias that can be added to attention scores:
-            #   (keep = +0,     discard = -10000.0)
-            # b, frame+use_image_num, h, w -> a video with images
-            # b, 1, h, w -> only images
-            attention_mask = attention_mask.to(self.dtype)
-            attention_mask_vid = attention_mask[:, :frame]  # b, frame, h, w
-
-            if attention_mask_vid.numel() > 0:
-                attention_mask_vid = attention_mask_vid.unsqueeze(1)  # b 1 t h w
-                attention_mask_vid = F.max_pool3d(attention_mask_vid, kernel_size=(self.patch_size_t, self.patch_size, self.patch_size), 
-                                                  stride=(self.patch_size_t, self.patch_size, self.patch_size))
-                attention_mask_vid = rearrange(attention_mask_vid, 'b 1 t h w -> (b 1) 1 (t h w)') 
-
-            attention_mask_vid = (1 - attention_mask_vid.bool().to(self.dtype)) * -10000.0 if attention_mask_vid.numel() > 0 else None
-
-        # convert encoder_attention_mask to a bias the same way we do for attention_mask
-        if encoder_attention_mask is not None and encoder_attention_mask.ndim == 3:  
-            # b, 1+use_image_num, l -> a video with images
-            # b, 1, l -> only images
-            encoder_attention_mask = (1 - encoder_attention_mask.to(self.dtype)) * -10000.0
-            encoder_attention_mask_vid = rearrange(encoder_attention_mask, 'b 1 l -> (b 1) 1 l') if encoder_attention_mask.numel() > 0 else None
-
-        # 1. Input
-        frame = frame // self.patch_size_t  # patchfy
-        # print('frame', frame)
-        height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size
-
-        added_cond_kwargs = {"resolution": None, "aspect_ratio": None} if added_cond_kwargs is None else added_cond_kwargs
-        hidden_states, encoder_hidden_states_vid, \
-        timestep_vid, embedded_timestep_vid = self._operate_on_patched_inputs(
-            hidden_states, encoder_hidden_states, timestep, added_cond_kwargs, batch_size,
-        )
-
-
-        for _, block in enumerate(self.transformer_blocks):
-            hidden_states = block(
-                hidden_states,
-                attention_mask_vid,
-                encoder_hidden_states_vid,
-                encoder_attention_mask_vid,
-                timestep_vid,
-                cross_attention_kwargs,
-                class_labels,
-                frame=frame, 
-                height=height, 
-                width=width, 
-            )
-
-         # 3. Output
-        output = None 
-        if hidden_states is not None:
-            output = self._get_output_for_patched_inputs(
-                hidden_states=hidden_states,
-                timestep=timestep_vid,
-                class_labels=class_labels,
-                embedded_timestep=embedded_timestep_vid,
-                num_frames=frame, 
-                height=height,
-                width=width,
-            )  # b c t h w
-
-        if not return_dict:
-            return (output,)
-
-        return Transformer3DModelOutput(sample=output)
-
-    def _operate_on_patched_inputs(self, hidden_states, encoder_hidden_states, timestep, added_cond_kwargs, batch_size):
-            # batch_size = hidden_states.shape[0]
-            hidden_states_vid = self.pos_embed(hidden_states.to(self.dtype))
-            timestep_vid = None
-            embedded_timestep_vid = None
-            encoder_hidden_states_vid = None
-
-            if self.adaln_single is not None:
-                if self.use_additional_conditions and added_cond_kwargs is None:
-                    raise ValueError(
-                        "`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`."
-                    )
-                timestep, embedded_timestep = self.adaln_single(
-                    timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=self.dtype
-                )  # b 6d, b d
-
-                timestep_vid = timestep
-                embedded_timestep_vid = embedded_timestep
-
-            if self.caption_projection is not None:
-                encoder_hidden_states = self.caption_projection(encoder_hidden_states)  # b, 1+use_image_num, l, d or b, 1, l, d
-                encoder_hidden_states_vid = rearrange(encoder_hidden_states[:, :1], 'b 1 l d -> (b 1) l d')
-
-            return hidden_states_vid, encoder_hidden_states_vid, timestep_vid, embedded_timestep_vid
-
-    def _get_output_for_patched_inputs(
-        self, hidden_states, timestep, class_labels, embedded_timestep, num_frames, height=None, width=None
-    ):  
-        # import ipdb;ipdb.set_trace()
-        if self.config.norm_type != "ada_norm_single":
-            conditioning = self.transformer_blocks[0].norm1.emb(
-                timestep, class_labels, hidden_dtype=self.dtype
-            )
-            shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
-            hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]
-            hidden_states = self.proj_out_2(hidden_states)
-        elif self.config.norm_type == "ada_norm_single":
-            shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1)
-            hidden_states = self.norm_out(hidden_states)
-            # Modulation
-            hidden_states = hidden_states * (1 + scale) + shift
-            hidden_states = self.proj_out(hidden_states)
-            hidden_states = hidden_states.squeeze(1)
-
-        # unpatchify
-        if self.adaln_single is None:
-            height = width = int(hidden_states.shape[1] ** 0.5)
-        hidden_states = hidden_states.reshape(
-            shape=(-1, num_frames, height, width, self.patch_size_t, self.patch_size, self.patch_size, self.out_channels)
-        )
-        hidden_states = torch.einsum("nthwopqc->nctohpwq", hidden_states)
-        output = hidden_states.reshape(
-            shape=(-1, self.out_channels, num_frames * self.patch_size_t, height * self.patch_size, width * self.patch_size)
-        )
-        return output

vae/config.json

@@ -1,6 +1,6 @@
 {
   "_class_name": "AllegroAutoencoderKL3D",
-  "_diffusers_version": "0.30.3",
+  "_diffusers_version": "0.28.0",
   "act_fn": "silu",
   "block_out_channels": [
     128,

vae/vae_allegro.py

@@ -1,978 +0,0 @@
-import math
-from dataclasses import dataclass
-import os
-from typing import Dict, Optional, Tuple, Union
-from einops import rearrange
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.models.modeling_outputs import AutoencoderKLOutput
-from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
-from diffusers.models.autoencoders.vae import DecoderOutput, DiagonalGaussianDistribution
-from diffusers.models.attention_processor import Attention
-from diffusers.models.resnet import ResnetBlock2D
-from diffusers.models.upsampling import Upsample2D
-from diffusers.models.downsampling import Downsample2D
-from diffusers.models.attention_processor import SpatialNorm
-
-
-class TemporalConvBlock(nn.Module):
-    """
-    Temporal convolutional layer that can be used for video (sequence of images) input Code mostly copied from:
-    https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/models/multi_modal/video_synthesis/unet_sd.py#L1016
-    """
-
-    def __init__(self, in_dim, out_dim=None, dropout=0.0, up_sample=False, down_sample=False, spa_stride=1):
-        super().__init__()
-        out_dim = out_dim or in_dim
-        self.in_dim = in_dim
-        self.out_dim = out_dim
-        spa_pad = int((spa_stride-1)*0.5)
-        temp_pad = 0
-        self.temp_pad = temp_pad
-
-        if down_sample:
-            self.conv1 = nn.Sequential(
-                nn.GroupNorm(32, in_dim), 
-                nn.SiLU(), 
-                nn.Conv3d(in_dim, out_dim, (2, spa_stride, spa_stride), stride=(2,1,1), padding=(0, spa_pad, spa_pad))
-            )
-        elif up_sample:
-            self.conv1 = nn.Sequential(
-                nn.GroupNorm(32, in_dim), 
-                nn.SiLU(), 
-                nn.Conv3d(in_dim, out_dim*2, (1, spa_stride, spa_stride), padding=(0, spa_pad, spa_pad))
-            )
-        else:
-            self.conv1 = nn.Sequential(
-            nn.GroupNorm(32, in_dim), 
-            nn.SiLU(), 
-            nn.Conv3d(in_dim, out_dim, (3, spa_stride, spa_stride), padding=(temp_pad, spa_pad, spa_pad))
-            )
-        self.conv2 = nn.Sequential(
-            nn.GroupNorm(32, out_dim),
-            nn.SiLU(),
-            nn.Dropout(dropout),
-            nn.Conv3d(out_dim, in_dim, (3, spa_stride, spa_stride), padding=(temp_pad, spa_pad, spa_pad)),
-        )
-        self.conv3 = nn.Sequential(
-            nn.GroupNorm(32, out_dim),
-            nn.SiLU(),
-            nn.Dropout(dropout),
-            nn.Conv3d(out_dim, in_dim, (3, spa_stride, spa_stride), padding=(temp_pad, spa_pad, spa_pad)),
-        )
-        self.conv4 = nn.Sequential(
-            nn.GroupNorm(32, out_dim),
-            nn.SiLU(),
-            nn.Conv3d(out_dim, in_dim, (3, spa_stride, spa_stride), padding=(temp_pad, spa_pad, spa_pad)),
-        )
-
-        # zero out the last layer params,so the conv block is identity
-        nn.init.zeros_(self.conv4[-1].weight)
-        nn.init.zeros_(self.conv4[-1].bias)
-
-        self.down_sample = down_sample
-        self.up_sample = up_sample
-
-
-    def forward(self, hidden_states):
-        identity = hidden_states
-
-        if self.down_sample:
-            identity = identity[:,:,::2]
-        elif self.up_sample:
-            hidden_states_new = torch.cat((hidden_states,hidden_states),dim=2)
-            hidden_states_new[:, :, 0::2] = hidden_states
-            hidden_states_new[:, :, 1::2] = hidden_states
-            identity = hidden_states_new
-            del hidden_states_new
-        
-        if self.down_sample or self.up_sample:
-            hidden_states = self.conv1(hidden_states)
-        else:
-            hidden_states = torch.cat((hidden_states[:,:,0:1], hidden_states), dim=2)
-            hidden_states = torch.cat((hidden_states,hidden_states[:,:,-1:]), dim=2)
-            hidden_states = self.conv1(hidden_states)
-
-
-        if self.up_sample:
-            hidden_states = rearrange(hidden_states, 'b (d c) f h w -> b c (f d) h w', d=2)
-
-        hidden_states = torch.cat((hidden_states[:,:,0:1], hidden_states), dim=2)
-        hidden_states = torch.cat((hidden_states,hidden_states[:,:,-1:]), dim=2)
-        hidden_states = self.conv2(hidden_states)
-        hidden_states = torch.cat((hidden_states[:,:,0:1], hidden_states), dim=2)
-        hidden_states = torch.cat((hidden_states,hidden_states[:,:,-1:]), dim=2)
-        hidden_states = self.conv3(hidden_states)
-        hidden_states = torch.cat((hidden_states[:,:,0:1], hidden_states), dim=2)
-        hidden_states = torch.cat((hidden_states,hidden_states[:,:,-1:]), dim=2)
-        hidden_states = self.conv4(hidden_states)
-
-        hidden_states = identity + hidden_states
-
-        return hidden_states
-    
-
-class DownEncoderBlock3D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor=1.0,
-        add_downsample=True,
-        add_temp_downsample=False,
-        downsample_padding=1,
-    ):
-        super().__init__()
-        resnets = []
-        temp_convs = []
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=None,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-            temp_convs.append(
-                    TemporalConvBlock(
-                    out_channels,
-                    out_channels,
-                    dropout=0.1,
-                    )
-                )
-
-        self.resnets = nn.ModuleList(resnets)
-        self.temp_convs = nn.ModuleList(temp_convs)
-
-        if add_temp_downsample:
-            self.temp_convs_down = TemporalConvBlock(
-                    out_channels,
-                    out_channels,
-                    dropout=0.1,
-                    down_sample=True,
-                    spa_stride=3
-                    )
-        self.add_temp_downsample = add_temp_downsample
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    Downsample2D(
-                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-    
-    def _set_partial_grad(self):
-        for temp_conv in self.temp_convs:
-            temp_conv.requires_grad_(True)
-        if self.downsamplers:
-            for down_layer in self.downsamplers:
-                down_layer.requires_grad_(True)
-    
-    def forward(self, hidden_states):
-        bz = hidden_states.shape[0]
-        
-        for resnet, temp_conv in zip(self.resnets, self.temp_convs):
-            hidden_states = rearrange(hidden_states, 'b c n h w -> (b n) c h w')
-            hidden_states = resnet(hidden_states, temb=None)
-            hidden_states = rearrange(hidden_states, '(b n) c h w -> b c n h w', b=bz)
-            hidden_states = temp_conv(hidden_states)
-        if self.add_temp_downsample:
-            hidden_states = self.temp_convs_down(hidden_states)
-
-        if self.downsamplers is not None:
-            hidden_states = rearrange(hidden_states, 'b c n h w -> (b n) c h w')
-            for upsampler in self.downsamplers:
-                hidden_states = upsampler(hidden_states)
-            hidden_states = rearrange(hidden_states, '(b n) c h w -> b c n h w', b=bz)
-        return hidden_states
-
-
-class UpDecoderBlock3D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",  # default, spatial
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor=1.0,
-        add_upsample=True,
-        add_temp_upsample=False,
-        temb_channels=None,
-    ):
-        super().__init__()
-        self.add_upsample = add_upsample
-
-        resnets = []
-        temp_convs = []
-
-        for i in range(num_layers):
-            input_channels = in_channels if i == 0 else out_channels
-
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=input_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-            temp_convs.append(
-                    TemporalConvBlock(
-                    out_channels,
-                    out_channels,
-                    dropout=0.1,
-                    )
-                )
-        
-        self.resnets = nn.ModuleList(resnets)
-        self.temp_convs = nn.ModuleList(temp_convs)
-
-        self.add_temp_upsample = add_temp_upsample
-        if add_temp_upsample:
-            self.temp_conv_up = TemporalConvBlock(
-                    out_channels,
-                    out_channels,
-                    dropout=0.1,
-                    up_sample=True,
-                    spa_stride=3
-                    )
-
-
-        if self.add_upsample:
-            # self.upsamplers = nn.ModuleList([PSUpsample2D(out_channels, use_conv=True, use_pixel_shuffle=True, out_channels=out_channels)])
-            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-    
-    def _set_partial_grad(self):
-        for temp_conv in self.temp_convs:
-            temp_conv.requires_grad_(True)
-        if self.add_upsample:
-            self.upsamplers.requires_grad_(True)
-
-    def forward(self, hidden_states):
-        bz = hidden_states.shape[0]
-        
-        for resnet, temp_conv in zip(self.resnets, self.temp_convs):
-            hidden_states = rearrange(hidden_states, 'b c n h w -> (b n) c h w')
-            hidden_states = resnet(hidden_states, temb=None)
-            hidden_states = rearrange(hidden_states, '(b n) c h w -> b c n h w', b=bz)
-            hidden_states = temp_conv(hidden_states)
-        if self.add_temp_upsample:
-            hidden_states = self.temp_conv_up(hidden_states)
-
-        if self.upsamplers is not None:
-            hidden_states = rearrange(hidden_states, 'b c n h w -> (b n) c h w')
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states)
-            hidden_states = rearrange(hidden_states, '(b n) c h w -> b c n h w', b=bz)
-        return hidden_states
-
-    
-class UNetMidBlock3DConv(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",  # default, spatial
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        add_attention: bool = True,
-        attention_head_dim=1,
-        output_scale_factor=1.0,
-    ):
-        super().__init__()
-        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
-        self.add_attention = add_attention
-
-        # there is always at least one resnet
-        resnets = [
-            ResnetBlock2D(
-                in_channels=in_channels,
-                out_channels=in_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=resnet_groups,
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-            )
-        ]
-        temp_convs = [
-            TemporalConvBlock(
-                in_channels,
-                in_channels,
-                dropout=0.1,
-            )
-        ]
-        attentions = []
-
-        if attention_head_dim is None:
-            attention_head_dim = in_channels
-
-        for _ in range(num_layers):
-            if self.add_attention:
-                attentions.append(
-                    Attention(
-                        in_channels,
-                        heads=in_channels // attention_head_dim,
-                        dim_head=attention_head_dim,
-                        rescale_output_factor=output_scale_factor,
-                        eps=resnet_eps,
-                        norm_num_groups=resnet_groups if resnet_time_scale_shift == "default" else None,
-                        spatial_norm_dim=temb_channels if resnet_time_scale_shift == "spatial" else None,
-                        residual_connection=True,
-                        bias=True,
-                        upcast_softmax=True,
-                        _from_deprecated_attn_block=True,
-                    )
-                )
-            else:
-                attentions.append(None)
-
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=in_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-            temp_convs.append(
-                TemporalConvBlock(
-                    in_channels,
-                    in_channels,
-                    dropout=0.1,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-        self.temp_convs = nn.ModuleList(temp_convs)
-        self.attentions = nn.ModuleList(attentions)
-    
-    def _set_partial_grad(self):
-        for temp_conv in self.temp_convs:
-            temp_conv.requires_grad_(True)
-
-    def forward(
-        self, 
-        hidden_states, 
-    ):
-        bz = hidden_states.shape[0]
-        hidden_states = rearrange(hidden_states, 'b c n h w -> (b n) c h w')
-
-        hidden_states = self.resnets[0](hidden_states, temb=None)
-        hidden_states = rearrange(hidden_states, '(b n) c h w -> b c n h w', b=bz)
-        hidden_states = self.temp_convs[0](hidden_states)
-        hidden_states = rearrange(hidden_states, 'b c n h w -> (b n) c h w')
-
-        for attn, resnet, temp_conv in zip(
-            self.attentions, self.resnets[1:], self.temp_convs[1:]
-        ):
-            hidden_states = attn(hidden_states)
-            hidden_states = resnet(hidden_states, temb=None)
-            hidden_states = rearrange(hidden_states, '(b n) c h w -> b c n h w', b=bz)
-            hidden_states = temp_conv(hidden_states)
-        return hidden_states
-    
-
-class Encoder3D(nn.Module):
-    def __init__(
-        self,
-        in_channels=3,
-        out_channels=3,
-        num_blocks=4,
-        blocks_temp_li=[False, False, False, False],
-        block_out_channels=(64,),
-        layers_per_block=2,
-        norm_num_groups=32,
-        act_fn="silu",
-        double_z=True,
-    ):
-        super().__init__()
-        self.layers_per_block = layers_per_block
-        self.blocks_temp_li = blocks_temp_li
-
-        self.conv_in = nn.Conv2d(
-            in_channels,
-            block_out_channels[0],
-            kernel_size=3,
-            stride=1,
-            padding=1,
-        )
-
-        self.temp_conv_in = nn.Conv3d(
-            block_out_channels[0],
-            block_out_channels[0],
-            (3,1,1),
-            padding = (1, 0, 0)
-        )
-
-        self.mid_block = None
-        self.down_blocks = nn.ModuleList([])
-
-        # down
-        output_channel = block_out_channels[0]
-        for i in range(num_blocks):
-            input_channel = output_channel
-            output_channel = block_out_channels[i]
-            is_final_block = i == len(block_out_channels) - 1
-
-            down_block = DownEncoderBlock3D(
-                num_layers=self.layers_per_block,
-                in_channels=input_channel,
-                out_channels=output_channel,
-                add_downsample=not is_final_block,
-                add_temp_downsample=blocks_temp_li[i],
-                resnet_eps=1e-6,
-                downsample_padding=0,
-                resnet_act_fn=act_fn,
-                resnet_groups=norm_num_groups,
-            )
-            self.down_blocks.append(down_block)
-
-        # mid
-        self.mid_block = UNetMidBlock3DConv(
-            in_channels=block_out_channels[-1],
-            resnet_eps=1e-6,
-            resnet_act_fn=act_fn,
-            output_scale_factor=1,
-            resnet_time_scale_shift="default",
-            attention_head_dim=block_out_channels[-1],
-            resnet_groups=norm_num_groups,
-            temb_channels=None,
-        )
-
-        # out
-        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6)
-        self.conv_act = nn.SiLU()
-
-        conv_out_channels = 2 * out_channels if double_z else out_channels
-
-        self.temp_conv_out = nn.Conv3d(block_out_channels[-1], block_out_channels[-1], (3,1,1), padding = (1, 0, 0))
-
-        self.conv_out = nn.Conv2d(block_out_channels[-1], conv_out_channels, 3, padding=1)
-
-        nn.init.zeros_(self.temp_conv_in.weight)
-        nn.init.zeros_(self.temp_conv_in.bias)
-        nn.init.zeros_(self.temp_conv_out.weight)
-        nn.init.zeros_(self.temp_conv_out.bias)
-
-        self.gradient_checkpointing = False
-
-    def forward(self, x):
-        '''
-            x: [b, c, (tb f), h, w]
-        '''
-        bz = x.shape[0]
-        sample = rearrange(x, 'b c n h w -> (b n) c h w')
-        sample = self.conv_in(sample)
-
-        sample = rearrange(sample, '(b n) c h w -> b c n h w', b=bz)
-        temp_sample = sample
-        sample = self.temp_conv_in(sample) 
-        sample = sample+temp_sample
-        # down
-        for b_id, down_block in enumerate(self.down_blocks):
-            sample = down_block(sample)
-        # middle
-        sample = self.mid_block(sample)
-
-        # post-process
-        sample = rearrange(sample, 'b c n h w -> (b n) c h w')
-        sample = self.conv_norm_out(sample)
-        sample = self.conv_act(sample)
-        sample = rearrange(sample, '(b n) c h w -> b c n h w', b=bz)
-
-        temp_sample = sample
-        sample = self.temp_conv_out(sample) 
-        sample = sample+temp_sample
-        sample = rearrange(sample, 'b c n h w -> (b n) c h w')
-
-        sample = self.conv_out(sample)
-        sample = rearrange(sample, '(b n) c h w -> b c n h w', b=bz)
-        return sample
-    
-class Decoder3D(nn.Module):
-    def __init__(
-        self,
-        in_channels=4,
-        out_channels=3,
-        num_blocks=4,
-        blocks_temp_li=[False, False, False, False],
-        block_out_channels=(64,),
-        layers_per_block=2,
-        norm_num_groups=32,
-        act_fn="silu",
-        norm_type="group",  # group, spatial
-    ):
-        super().__init__()
-        self.layers_per_block = layers_per_block
-        self.blocks_temp_li = blocks_temp_li
-
-        self.conv_in = nn.Conv2d(
-            in_channels,
-            block_out_channels[-1],
-            kernel_size=3,
-            stride=1,
-            padding=1,
-        )
-
-        self.temp_conv_in = nn.Conv3d(
-            block_out_channels[-1],
-            block_out_channels[-1],
-            (3,1,1),
-            padding = (1, 0, 0)
-        )
-
-        self.mid_block = None
-        self.up_blocks = nn.ModuleList([])
-
-        temb_channels = in_channels if norm_type == "spatial" else None
-
-        # mid
-        self.mid_block = UNetMidBlock3DConv(
-            in_channels=block_out_channels[-1],
-            resnet_eps=1e-6,
-            resnet_act_fn=act_fn,
-            output_scale_factor=1,
-            resnet_time_scale_shift="default" if norm_type == "group" else norm_type,
-            attention_head_dim=block_out_channels[-1],
-            resnet_groups=norm_num_groups,
-            temb_channels=temb_channels,
-        )
-
-        # up
-        reversed_block_out_channels = list(reversed(block_out_channels))
-        output_channel = reversed_block_out_channels[0]
-        for i in range(num_blocks):
-            prev_output_channel = output_channel
-            output_channel = reversed_block_out_channels[i]
-
-            is_final_block = i == len(block_out_channels) - 1
-
-            up_block = UpDecoderBlock3D(
-                num_layers=self.layers_per_block + 1,
-                in_channels=prev_output_channel,
-                out_channels=output_channel,
-                add_upsample=not is_final_block,
-                add_temp_upsample=blocks_temp_li[i],
-                resnet_eps=1e-6,
-                resnet_act_fn=act_fn,
-                resnet_groups=norm_num_groups,
-                temb_channels=temb_channels,
-                resnet_time_scale_shift=norm_type,
-            )
-            self.up_blocks.append(up_block)
-            prev_output_channel = output_channel
-
-        # out
-        if norm_type == "spatial":
-            self.conv_norm_out = SpatialNorm(block_out_channels[0], temb_channels)
-        else:
-            self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6)
-        self.conv_act = nn.SiLU()
-
-        self.temp_conv_out = nn.Conv3d(block_out_channels[0], block_out_channels[0], (3,1,1), padding = (1, 0, 0))
-        self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)
-
-        nn.init.zeros_(self.temp_conv_in.weight)
-        nn.init.zeros_(self.temp_conv_in.bias)
-        nn.init.zeros_(self.temp_conv_out.weight)
-        nn.init.zeros_(self.temp_conv_out.bias)
-
-        self.gradient_checkpointing = False
-
-    def forward(self, z):
-        bz = z.shape[0]
-        sample = rearrange(z, 'b c n h w -> (b n) c h w')
-        sample = self.conv_in(sample)
-
-        sample = rearrange(sample, '(b n) c h w -> b c n h w', b=bz)
-        temp_sample = sample
-        sample = self.temp_conv_in(sample) 
-        sample = sample+temp_sample
-
-        upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
-        # middle
-        sample = self.mid_block(sample)
-        sample = sample.to(upscale_dtype)
-
-        # up
-        for b_id, up_block in enumerate(self.up_blocks):
-            sample = up_block(sample)
-
-        # post-process
-        sample = rearrange(sample, 'b c n h w -> (b n) c h w')
-        sample = self.conv_norm_out(sample)
-        sample = self.conv_act(sample)
-
-        sample = rearrange(sample, '(b n) c h w -> b c n h w', b=bz)
-        temp_sample = sample
-        sample = self.temp_conv_out(sample)
-        sample = sample+temp_sample
-        sample = rearrange(sample, 'b c n h w -> (b n) c h w')
-
-        sample = self.conv_out(sample)
-        sample = rearrange(sample, '(b n) c h w -> b c n h w', b=bz)
-        return sample
-    
-
-
-class AllegroAutoencoderKL3D(ModelMixin, ConfigMixin):
-    r"""
-    A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
-
-    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
-    for all models (such as downloading or saving).
-
-    Parameters:
-        in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
-        out_channels (int,  *optional*, defaults to 3): Number of channels in the output.
-        down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
-            Tuple of downsample block types.
-        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
-            Tuple of upsample block types.
-        block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
-            Tuple of block output channels.
-        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
-        latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.
-        sample_size (`int`, *optional*, defaults to `256`): Spatial Tiling Size.
-        tile_overlap (`tuple`, *optional*, defaults to `(120, 80`): Spatial overlapping size while tiling (height, width)
-        chunk_len (`int`, *optional*, defaults to `24`): Temporal Tiling Size.
-        t_over (`int`, *optional*, defaults to `8`): Temporal overlapping size while tiling
-        scaling_factor (`float`, *optional*, defaults to 0.13235):
-            The component-wise standard deviation of the trained latent space computed using the first batch of the
-            training set. This is used to scale the latent space to have unit variance when training the diffusion
-            model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
-            diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
-            / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
-            Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
-        force_upcast (`bool`, *optional*, default to `True`):
-            If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
-            can be fine-tuned / trained to a lower range without loosing too much precision in which case
-            `force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
-        blocks_tempdown_li (`List`, *optional*, defaults to `[True, True, False, False]`): Each item indicates whether each TemporalBlock in the Encoder performs temporal downsampling.
-        blocks_tempup_li (`List`, *optional*, defaults to `[False, True, True, False]`): Each item indicates whether each TemporalBlock in the Decoder performs temporal upsampling.
-        load_mode (`str`, *optional*, defaults to `full`): Load mode for the model. Can be one of `full`, `encoder_only`, `decoder_only`. which corresponds to loading the full model state dicts, only the encoder state dicts, or only the decoder state dicts.
-    """
-
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        in_channels: int = 3,
-        out_channels: int = 3,
-        down_block_num: int = 4,
-        up_block_num: int = 4,
-        block_out_channels: Tuple[int] = (128,256,512,512),
-        layers_per_block: int = 2,
-        act_fn: str = "silu",
-        latent_channels: int = 4,
-        norm_num_groups: int = 32,
-        sample_size: int = 320,
-        tile_overlap: tuple = (120, 80),
-        force_upcast: bool = True,
-        chunk_len: int = 24,
-        t_over: int = 8,
-        scale_factor: float = 0.13235,
-        blocks_tempdown_li=[True, True, False, False],
-        blocks_tempup_li=[False, True, True, False],
-        load_mode = 'full',
-    ):
-        super().__init__()
-
-        self.blocks_tempdown_li = blocks_tempdown_li
-        self.blocks_tempup_li = blocks_tempup_li
-        # pass init params to Encoder
-        self.load_mode = load_mode
-        if load_mode in ['full', 'encoder_only']:
-            self.encoder = Encoder3D(
-                in_channels=in_channels,
-                out_channels=latent_channels,
-                num_blocks=down_block_num,
-                blocks_temp_li=blocks_tempdown_li,
-                block_out_channels=block_out_channels,
-                layers_per_block=layers_per_block,
-                act_fn=act_fn,
-                norm_num_groups=norm_num_groups,
-                double_z=True,
-            )
-            self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
-
-        if load_mode in ['full', 'decoder_only']:
-            # pass init params to Decoder
-            self.decoder = Decoder3D(
-                in_channels=latent_channels,
-                out_channels=out_channels,
-                num_blocks=up_block_num,
-                blocks_temp_li=blocks_tempup_li,
-                block_out_channels=block_out_channels,
-                layers_per_block=layers_per_block,
-                norm_num_groups=norm_num_groups,
-                act_fn=act_fn,
-            )
-            self.post_quant_conv = nn.Conv2d(latent_channels, latent_channels, 1)
-
-
-        # only relevant if vae tiling is enabled
-        sample_size = (
-            sample_size[0]
-            if isinstance(sample_size, (list, tuple))
-            else sample_size
-        )
-        self.tile_overlap = tile_overlap
-        self.vae_scale_factor=[4, 8, 8]
-        self.scale_factor = scale_factor
-        self.sample_size = sample_size
-        self.chunk_len = chunk_len
-        self.t_over = t_over
-
-        self.latent_chunk_len = self.chunk_len//4
-        self.latent_t_over = self.t_over//4 
-        self.kernel = (self.chunk_len, self.sample_size, self.sample_size) #(24, 256, 256)
-        self.stride = (self.chunk_len - self.t_over, self.sample_size-self.tile_overlap[0], self.sample_size-self.tile_overlap[1])  # (16, 112, 192)
-
-
-    def encode(self, input_imgs: torch.Tensor, return_dict: bool = True, local_batch_size=1) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
-        KERNEL = self.kernel
-        STRIDE = self.stride
-        LOCAL_BS = local_batch_size
-        OUT_C = 8
-
-        B, C, N, H, W = input_imgs.shape
-        
-       
-        out_n = math.floor((N - KERNEL[0]) / STRIDE[0]) + 1
-        out_h = math.floor((H - KERNEL[1]) / STRIDE[1]) + 1
-        out_w = math.floor((W - KERNEL[2]) / STRIDE[2]) + 1
-        
-        ## cut video into overlapped small cubes and batch forward
-        num = 0
-
-        out_latent = torch.zeros((out_n*out_h*out_w, OUT_C, KERNEL[0]//4, KERNEL[1]//8, KERNEL[2]//8), device=input_imgs.device, dtype=input_imgs.dtype) 
-        vae_batch_input = torch.zeros((LOCAL_BS, C, KERNEL[0], KERNEL[1], KERNEL[2]), device=input_imgs.device, dtype=input_imgs.dtype)
-
-        for i in range(out_n):
-            for j in range(out_h):
-                for k in range(out_w):
-                    n_start, n_end = i * STRIDE[0], i * STRIDE[0] + KERNEL[0]
-                    h_start, h_end = j * STRIDE[1], j * STRIDE[1] + KERNEL[1]
-                    w_start, w_end = k * STRIDE[2], k * STRIDE[2] + KERNEL[2]
-                    video_cube = input_imgs[:, :, n_start:n_end, h_start:h_end, w_start:w_end]
-                    vae_batch_input[num%LOCAL_BS] = video_cube
-                    
-                    if num%LOCAL_BS == LOCAL_BS-1 or num == out_n*out_h*out_w-1:                        
-                        latent = self.encoder(vae_batch_input)
-                        
-                        if num == out_n*out_h*out_w-1 and num%LOCAL_BS != LOCAL_BS-1:
-                            out_latent[num-num%LOCAL_BS:] = latent[:num%LOCAL_BS+1]
-                        else:
-                            out_latent[num-LOCAL_BS+1:num+1] = latent
-                        vae_batch_input = torch.zeros((LOCAL_BS, C, KERNEL[0], KERNEL[1], KERNEL[2]), device=input_imgs.device, dtype=input_imgs.dtype)
-                    num+=1
-        
-        ## flatten the batched out latent to videos and supress the overlapped parts
-        B, C, N, H, W = input_imgs.shape
-
-        out_video_cube = torch.zeros((B, OUT_C, N//4, H//8, W//8), device=input_imgs.device, dtype=input_imgs.dtype)
-        OUT_KERNEL = KERNEL[0]//4, KERNEL[1]//8, KERNEL[2]//8
-        OUT_STRIDE = STRIDE[0]//4, STRIDE[1]//8, STRIDE[2]//8
-        OVERLAP = OUT_KERNEL[0]-OUT_STRIDE[0], OUT_KERNEL[1]-OUT_STRIDE[1], OUT_KERNEL[2]-OUT_STRIDE[2]
-        
-        for i in range(out_n):
-            n_start, n_end = i * OUT_STRIDE[0], i * OUT_STRIDE[0] + OUT_KERNEL[0]
-            for j in range(out_h):
-                h_start, h_end = j * OUT_STRIDE[1], j * OUT_STRIDE[1] + OUT_KERNEL[1]
-                for k in range(out_w):
-                    w_start, w_end = k * OUT_STRIDE[2], k * OUT_STRIDE[2] + OUT_KERNEL[2]
-                    latent_mean_blend = prepare_for_blend((i, out_n, OVERLAP[0]), (j, out_h, OVERLAP[1]), (k, out_w, OVERLAP[2]), out_latent[i*out_h*out_w+j*out_w+k].unsqueeze(0))
-                    out_video_cube[:, :, n_start:n_end, h_start:h_end, w_start:w_end] += latent_mean_blend
-        
-        ## final conv
-        out_video_cube = rearrange(out_video_cube, 'b c n h w -> (b n) c h w')
-        out_video_cube = self.quant_conv(out_video_cube)
-        out_video_cube = rearrange(out_video_cube, '(b n) c h w -> b c n h w', b=B)
-
-        posterior = DiagonalGaussianDistribution(out_video_cube)
-
-        if not return_dict:
-            return (posterior,)
-
-        return AutoencoderKLOutput(latent_dist=posterior)
-    
-
-    def decode(self, input_latents: torch.Tensor, return_dict: bool = True, local_batch_size=1) -> Union[DecoderOutput, torch.Tensor]:
-        KERNEL = self.kernel
-        STRIDE = self.stride
-        
-        LOCAL_BS = local_batch_size
-        OUT_C = 3
-        IN_KERNEL = KERNEL[0]//4, KERNEL[1]//8, KERNEL[2]//8
-        IN_STRIDE = STRIDE[0]//4, STRIDE[1]//8, STRIDE[2]//8
-
-        B, C, N, H, W = input_latents.shape
-
-        ## post quant conv (a mapping)
-        input_latents = rearrange(input_latents, 'b c n h w -> (b n) c h w')
-        input_latents = self.post_quant_conv(input_latents)
-        input_latents = rearrange(input_latents, '(b n) c h w -> b c n h w', b=B)
-        
-        ## out tensor shape
-        out_n = math.floor((N - IN_KERNEL[0]) / IN_STRIDE[0]) + 1
-        out_h = math.floor((H - IN_KERNEL[1]) / IN_STRIDE[1]) + 1
-        out_w = math.floor((W - IN_KERNEL[2]) / IN_STRIDE[2]) + 1
-
-        ## cut latent into overlapped small cubes and batch forward
-        num = 0
-        decoded_cube = torch.zeros((out_n*out_h*out_w, OUT_C, KERNEL[0], KERNEL[1], KERNEL[2]), device=input_latents.device, dtype=input_latents.dtype) 
-        vae_batch_input = torch.zeros((LOCAL_BS, C, IN_KERNEL[0], IN_KERNEL[1], IN_KERNEL[2]), device=input_latents.device, dtype=input_latents.dtype)
-        for i in range(out_n):
-            for j in range(out_h):
-                for k in range(out_w):
-                    n_start, n_end = i * IN_STRIDE[0], i * IN_STRIDE[0] + IN_KERNEL[0]
-                    h_start, h_end = j * IN_STRIDE[1], j * IN_STRIDE[1] + IN_KERNEL[1]
-                    w_start, w_end = k * IN_STRIDE[2], k * IN_STRIDE[2] + IN_KERNEL[2]
-                    latent_cube = input_latents[:, :, n_start:n_end, h_start:h_end, w_start:w_end]
-                    vae_batch_input[num%LOCAL_BS] = latent_cube
-                    if num%LOCAL_BS == LOCAL_BS-1 or num == out_n*out_h*out_w-1:
-                        
-                        latent = self.decoder(vae_batch_input)
-                        
-                        if num == out_n*out_h*out_w-1 and num%LOCAL_BS != LOCAL_BS-1:
-                            decoded_cube[num-num%LOCAL_BS:] = latent[:num%LOCAL_BS+1]
-                        else:
-                            decoded_cube[num-LOCAL_BS+1:num+1] = latent
-                        vae_batch_input = torch.zeros((LOCAL_BS, C, IN_KERNEL[0], IN_KERNEL[1], IN_KERNEL[2]), device=input_latents.device, dtype=input_latents.dtype)
-                    num+=1
-        B, C, N, H, W = input_latents.shape
-        
-        out_video = torch.zeros((B, OUT_C, N*4, H*8, W*8), device=input_latents.device, dtype=input_latents.dtype)
-        OVERLAP = KERNEL[0]-STRIDE[0], KERNEL[1]-STRIDE[1], KERNEL[2]-STRIDE[2]
-        for i in range(out_n):
-            n_start, n_end = i * STRIDE[0], i * STRIDE[0] + KERNEL[0]
-            for j in range(out_h):
-                h_start, h_end = j * STRIDE[1], j * STRIDE[1] + KERNEL[1]
-                for k in range(out_w):
-                    w_start, w_end = k * STRIDE[2], k * STRIDE[2] + KERNEL[2]
-                    out_video_blend = prepare_for_blend((i, out_n, OVERLAP[0]), (j, out_h, OVERLAP[1]), (k, out_w, OVERLAP[2]), decoded_cube[i*out_h*out_w+j*out_w+k].unsqueeze(0))
-                    out_video[:, :, n_start:n_end, h_start:h_end, w_start:w_end] += out_video_blend
-       
-        out_video = rearrange(out_video, 'b c t h w -> b t c h w').contiguous()
-
-        decoded = out_video
-        if not return_dict:
-            return (decoded,)
-
-        return DecoderOutput(sample=decoded)
-    
-    def forward(
-        self,
-        sample: torch.Tensor,
-        sample_posterior: bool = False,
-        return_dict: bool = True,
-        generator: Optional[torch.Generator] = None,
-        encoder_local_batch_size: int = 2,
-        decoder_local_batch_size: int = 2,
-    ) -> Union[DecoderOutput, torch.Tensor]:
-        r"""
-        Args:
-            sample (`torch.Tensor`): Input sample.
-            sample_posterior (`bool`, *optional*, defaults to `False`):
-                Whether to sample from the posterior.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
-            generator (`torch.Generator`, *optional*): 
-                PyTorch random number generator.
-            encoder_local_batch_size (`int`, *optional*, defaults to 2):
-                Local batch size for the encoder's batch inference.
-            decoder_local_batch_size (`int`, *optional*, defaults to 2):
-                Local batch size for the decoder's batch inference.
-        """
-        x = sample
-        posterior = self.encode(x, local_batch_size=encoder_local_batch_size).latent_dist
-        if sample_posterior:
-            z = posterior.sample(generator=generator)
-        else:
-            z = posterior.mode()
-        dec = self.decode(z, local_batch_size=decoder_local_batch_size).sample
-
-        if not return_dict:
-            return (dec,)
-
-        return DecoderOutput(sample=dec)
-    
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
-        kwargs["torch_type"] = torch.float32
-        return super().from_pretrained(pretrained_model_name_or_path, **kwargs)
-
-
-def prepare_for_blend(n_param, h_param, w_param, x):
-    n, n_max, overlap_n = n_param
-    h, h_max, overlap_h = h_param
-    w, w_max, overlap_w = w_param
-    if overlap_n > 0:
-        if n > 0: # the head overlap part decays from 0 to 1
-            x[:,:,0:overlap_n,:,:] = x[:,:,0:overlap_n,:,:] * (torch.arange(0, overlap_n).float().to(x.device) / overlap_n).reshape(overlap_n,1,1)
-        if n < n_max-1:  # the tail overlap part decays from 1 to 0
-            x[:,:,-overlap_n:,:,:] = x[:,:,-overlap_n:,:,:] * (1 - torch.arange(0, overlap_n).float().to(x.device) / overlap_n).reshape(overlap_n,1,1)
-    if h > 0:
-        x[:,:,:,0:overlap_h,:] = x[:,:,:,0:overlap_h,:] * (torch.arange(0, overlap_h).float().to(x.device) / overlap_h).reshape(overlap_h,1)
-    if h < h_max-1:
-        x[:,:,:,-overlap_h:,:] = x[:,:,:,-overlap_h:,:] * (1 - torch.arange(0, overlap_h).float().to(x.device) / overlap_h).reshape(overlap_h,1)
-    if w > 0:
-        x[:,:,:,:,0:overlap_w] = x[:,:,:,:,0:overlap_w] * (torch.arange(0, overlap_w).float().to(x.device) / overlap_w)
-    if w < w_max-1:
-        x[:,:,:,:,-overlap_w:] = x[:,:,:,:,-overlap_w:] * (1 - torch.arange(0, overlap_w).float().to(x.device) / overlap_w)
-    return x