528 lines
22 KiB
Python
528 lines
22 KiB
Python
# Copyright 2025 The EasyAnimate team and The HuggingFace Team.
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# All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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from torch import nn
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from ...configuration_utils import ConfigMixin, register_to_config
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from ...utils import logging
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from ...utils.torch_utils import maybe_allow_in_graph
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from ..attention import Attention, FeedForward
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from ..embeddings import TimestepEmbedding, Timesteps, get_3d_rotary_pos_embed
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from ..modeling_outputs import Transformer2DModelOutput
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from ..modeling_utils import ModelMixin
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from ..normalization import AdaLayerNorm, FP32LayerNorm, RMSNorm
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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class EasyAnimateLayerNormZero(nn.Module):
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def __init__(
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self,
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conditioning_dim: int,
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embedding_dim: int,
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elementwise_affine: bool = True,
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eps: float = 1e-5,
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bias: bool = True,
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norm_type: str = "fp32_layer_norm",
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) -> None:
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super().__init__()
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self.silu = nn.SiLU()
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self.linear = nn.Linear(conditioning_dim, 6 * embedding_dim, bias=bias)
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if norm_type == "layer_norm":
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self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=elementwise_affine, eps=eps)
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elif norm_type == "fp32_layer_norm":
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self.norm = FP32LayerNorm(embedding_dim, elementwise_affine=elementwise_affine, eps=eps)
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else:
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raise ValueError(
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f"Unsupported `norm_type` ({norm_type}) provided. Supported ones are: 'layer_norm', 'fp32_layer_norm'."
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)
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def forward(
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self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, temb: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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shift, scale, gate, enc_shift, enc_scale, enc_gate = self.linear(self.silu(temb)).chunk(6, dim=1)
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hidden_states = self.norm(hidden_states) * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
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encoder_hidden_states = self.norm(encoder_hidden_states) * (1 + enc_scale.unsqueeze(1)) + enc_shift.unsqueeze(
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1
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)
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return hidden_states, encoder_hidden_states, gate, enc_gate
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class EasyAnimateRotaryPosEmbed(nn.Module):
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def __init__(self, patch_size: int, rope_dim: List[int]) -> None:
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super().__init__()
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self.patch_size = patch_size
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self.rope_dim = rope_dim
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def get_resize_crop_region_for_grid(self, src, tgt_width, tgt_height):
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tw = tgt_width
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th = tgt_height
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h, w = src
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r = h / w
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if r > (th / tw):
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resize_height = th
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resize_width = int(round(th / h * w))
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else:
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resize_width = tw
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resize_height = int(round(tw / w * h))
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crop_top = int(round((th - resize_height) / 2.0))
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crop_left = int(round((tw - resize_width) / 2.0))
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return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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bs, c, num_frames, grid_height, grid_width = hidden_states.size()
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grid_height = grid_height // self.patch_size
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grid_width = grid_width // self.patch_size
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base_size_width = 90 // self.patch_size
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base_size_height = 60 // self.patch_size
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grid_crops_coords = self.get_resize_crop_region_for_grid(
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(grid_height, grid_width), base_size_width, base_size_height
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)
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image_rotary_emb = get_3d_rotary_pos_embed(
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self.rope_dim,
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grid_crops_coords,
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grid_size=(grid_height, grid_width),
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temporal_size=hidden_states.size(2),
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use_real=True,
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)
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return image_rotary_emb
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class EasyAnimateAttnProcessor2_0:
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r"""
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Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
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used in the EasyAnimateTransformer3DModel model.
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"""
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def __init__(self):
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if not hasattr(F, "scaled_dot_product_attention"):
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raise ImportError(
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"EasyAnimateAttnProcessor2_0 requires PyTorch 2.0 or above. To use it, please install PyTorch 2.0."
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)
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def __call__(
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self,
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attn: Attention,
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hidden_states: torch.Tensor,
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encoder_hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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image_rotary_emb: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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if attn.add_q_proj is None and encoder_hidden_states is not None:
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hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
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# 1. QKV projections
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query = attn.to_q(hidden_states)
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key = attn.to_k(hidden_states)
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value = attn.to_v(hidden_states)
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query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2)
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key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2)
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value = value.unflatten(2, (attn.heads, -1)).transpose(1, 2)
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# 2. QK normalization
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if attn.norm_q is not None:
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query = attn.norm_q(query)
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if attn.norm_k is not None:
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key = attn.norm_k(key)
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# 3. Encoder condition QKV projection and normalization
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if attn.add_q_proj is not None and encoder_hidden_states is not None:
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encoder_query = attn.add_q_proj(encoder_hidden_states)
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encoder_key = attn.add_k_proj(encoder_hidden_states)
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encoder_value = attn.add_v_proj(encoder_hidden_states)
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encoder_query = encoder_query.unflatten(2, (attn.heads, -1)).transpose(1, 2)
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encoder_key = encoder_key.unflatten(2, (attn.heads, -1)).transpose(1, 2)
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encoder_value = encoder_value.unflatten(2, (attn.heads, -1)).transpose(1, 2)
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if attn.norm_added_q is not None:
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encoder_query = attn.norm_added_q(encoder_query)
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if attn.norm_added_k is not None:
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encoder_key = attn.norm_added_k(encoder_key)
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query = torch.cat([encoder_query, query], dim=2)
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key = torch.cat([encoder_key, key], dim=2)
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value = torch.cat([encoder_value, value], dim=2)
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if image_rotary_emb is not None:
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from ..embeddings import apply_rotary_emb
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query[:, :, encoder_hidden_states.shape[1] :] = apply_rotary_emb(
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query[:, :, encoder_hidden_states.shape[1] :], image_rotary_emb
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)
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if not attn.is_cross_attention:
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key[:, :, encoder_hidden_states.shape[1] :] = apply_rotary_emb(
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key[:, :, encoder_hidden_states.shape[1] :], image_rotary_emb
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)
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# 5. Attention
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hidden_states = F.scaled_dot_product_attention(
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query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
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)
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hidden_states = hidden_states.transpose(1, 2).flatten(2, 3)
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hidden_states = hidden_states.to(query.dtype)
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# 6. Output projection
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if encoder_hidden_states is not None:
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encoder_hidden_states, hidden_states = (
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hidden_states[:, : encoder_hidden_states.shape[1]],
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hidden_states[:, encoder_hidden_states.shape[1] :],
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)
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if getattr(attn, "to_out", None) is not None:
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hidden_states = attn.to_out[0](hidden_states)
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hidden_states = attn.to_out[1](hidden_states)
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if getattr(attn, "to_add_out", None) is not None:
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encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
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else:
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if getattr(attn, "to_out", None) is not None:
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hidden_states = attn.to_out[0](hidden_states)
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hidden_states = attn.to_out[1](hidden_states)
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return hidden_states, encoder_hidden_states
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@maybe_allow_in_graph
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class EasyAnimateTransformerBlock(nn.Module):
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def __init__(
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self,
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dim: int,
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num_attention_heads: int,
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attention_head_dim: int,
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time_embed_dim: int,
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dropout: float = 0.0,
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activation_fn: str = "gelu-approximate",
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norm_elementwise_affine: bool = True,
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norm_eps: float = 1e-6,
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final_dropout: bool = True,
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ff_inner_dim: Optional[int] = None,
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ff_bias: bool = True,
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qk_norm: bool = True,
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after_norm: bool = False,
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norm_type: str = "fp32_layer_norm",
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is_mmdit_block: bool = True,
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):
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super().__init__()
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# Attention Part
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self.norm1 = EasyAnimateLayerNormZero(
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time_embed_dim, dim, norm_elementwise_affine, norm_eps, norm_type=norm_type, bias=True
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)
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self.attn1 = Attention(
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query_dim=dim,
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dim_head=attention_head_dim,
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heads=num_attention_heads,
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qk_norm="layer_norm" if qk_norm else None,
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eps=1e-6,
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bias=True,
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added_proj_bias=True,
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added_kv_proj_dim=dim if is_mmdit_block else None,
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context_pre_only=False if is_mmdit_block else None,
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processor=EasyAnimateAttnProcessor2_0(),
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)
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# FFN Part
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self.norm2 = EasyAnimateLayerNormZero(
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time_embed_dim, dim, norm_elementwise_affine, norm_eps, norm_type=norm_type, bias=True
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)
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self.ff = FeedForward(
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dim,
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dropout=dropout,
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activation_fn=activation_fn,
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final_dropout=final_dropout,
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inner_dim=ff_inner_dim,
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bias=ff_bias,
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)
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self.txt_ff = None
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if is_mmdit_block:
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self.txt_ff = FeedForward(
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dim,
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dropout=dropout,
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activation_fn=activation_fn,
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final_dropout=final_dropout,
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inner_dim=ff_inner_dim,
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bias=ff_bias,
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)
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self.norm3 = None
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if after_norm:
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self.norm3 = FP32LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
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def forward(
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self,
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hidden_states: torch.Tensor,
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encoder_hidden_states: torch.Tensor,
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temb: torch.Tensor,
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image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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# 1. Attention
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norm_hidden_states, norm_encoder_hidden_states, gate_msa, enc_gate_msa = self.norm1(
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hidden_states, encoder_hidden_states, temb
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)
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attn_hidden_states, attn_encoder_hidden_states = self.attn1(
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hidden_states=norm_hidden_states,
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encoder_hidden_states=norm_encoder_hidden_states,
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image_rotary_emb=image_rotary_emb,
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)
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hidden_states = hidden_states + gate_msa.unsqueeze(1) * attn_hidden_states
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encoder_hidden_states = encoder_hidden_states + enc_gate_msa.unsqueeze(1) * attn_encoder_hidden_states
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# 2. Feed-forward
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norm_hidden_states, norm_encoder_hidden_states, gate_ff, enc_gate_ff = self.norm2(
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hidden_states, encoder_hidden_states, temb
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)
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if self.norm3 is not None:
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norm_hidden_states = self.norm3(self.ff(norm_hidden_states))
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if self.txt_ff is not None:
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norm_encoder_hidden_states = self.norm3(self.txt_ff(norm_encoder_hidden_states))
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else:
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norm_encoder_hidden_states = self.norm3(self.ff(norm_encoder_hidden_states))
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else:
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norm_hidden_states = self.ff(norm_hidden_states)
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if self.txt_ff is not None:
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norm_encoder_hidden_states = self.txt_ff(norm_encoder_hidden_states)
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else:
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norm_encoder_hidden_states = self.ff(norm_encoder_hidden_states)
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hidden_states = hidden_states + gate_ff.unsqueeze(1) * norm_hidden_states
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encoder_hidden_states = encoder_hidden_states + enc_gate_ff.unsqueeze(1) * norm_encoder_hidden_states
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return hidden_states, encoder_hidden_states
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class EasyAnimateTransformer3DModel(ModelMixin, ConfigMixin):
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"""
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A Transformer model for video-like data in [EasyAnimate](https://github.com/aigc-apps/EasyAnimate).
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Parameters:
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num_attention_heads (`int`, defaults to `48`):
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The number of heads to use for multi-head attention.
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attention_head_dim (`int`, defaults to `64`):
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The number of channels in each head.
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in_channels (`int`, defaults to `16`):
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The number of channels in the input.
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out_channels (`int`, *optional*, defaults to `16`):
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The number of channels in the output.
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patch_size (`int`, defaults to `2`):
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The size of the patches to use in the patch embedding layer.
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sample_width (`int`, defaults to `90`):
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The width of the input latents.
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sample_height (`int`, defaults to `60`):
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The height of the input latents.
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activation_fn (`str`, defaults to `"gelu-approximate"`):
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Activation function to use in feed-forward.
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timestep_activation_fn (`str`, defaults to `"silu"`):
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Activation function to use when generating the timestep embeddings.
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num_layers (`int`, defaults to `30`):
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The number of layers of Transformer blocks to use.
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mmdit_layers (`int`, defaults to `1000`):
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The number of layers of Multi Modal Transformer blocks to use.
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dropout (`float`, defaults to `0.0`):
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The dropout probability to use.
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time_embed_dim (`int`, defaults to `512`):
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Output dimension of timestep embeddings.
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text_embed_dim (`int`, defaults to `4096`):
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Input dimension of text embeddings from the text encoder.
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norm_eps (`float`, defaults to `1e-5`):
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The epsilon value to use in normalization layers.
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norm_elementwise_affine (`bool`, defaults to `True`):
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Whether to use elementwise affine in normalization layers.
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flip_sin_to_cos (`bool`, defaults to `True`):
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Whether to flip the sin to cos in the time embedding.
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time_position_encoding_type (`str`, defaults to `3d_rope`):
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Type of time position encoding.
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after_norm (`bool`, defaults to `False`):
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Flag to apply normalization after.
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resize_inpaint_mask_directly (`bool`, defaults to `True`):
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Flag to resize inpaint mask directly.
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enable_text_attention_mask (`bool`, defaults to `True`):
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Flag to enable text attention mask.
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add_noise_in_inpaint_model (`bool`, defaults to `False`):
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Flag to add noise in inpaint model.
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"""
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_supports_gradient_checkpointing = True
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_no_split_modules = ["EasyAnimateTransformerBlock"]
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_skip_layerwise_casting_patterns = ["^proj$", "norm", "^proj_out$"]
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@register_to_config
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def __init__(
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self,
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num_attention_heads: int = 48,
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attention_head_dim: int = 64,
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in_channels: Optional[int] = None,
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out_channels: Optional[int] = None,
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patch_size: Optional[int] = None,
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sample_width: int = 90,
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sample_height: int = 60,
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activation_fn: str = "gelu-approximate",
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timestep_activation_fn: str = "silu",
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freq_shift: int = 0,
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num_layers: int = 48,
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mmdit_layers: int = 48,
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dropout: float = 0.0,
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time_embed_dim: int = 512,
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add_norm_text_encoder: bool = False,
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text_embed_dim: int = 3584,
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text_embed_dim_t5: int = None,
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norm_eps: float = 1e-5,
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norm_elementwise_affine: bool = True,
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flip_sin_to_cos: bool = True,
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time_position_encoding_type: str = "3d_rope",
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after_norm=False,
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resize_inpaint_mask_directly: bool = True,
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enable_text_attention_mask: bool = True,
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add_noise_in_inpaint_model: bool = True,
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):
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super().__init__()
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inner_dim = num_attention_heads * attention_head_dim
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# 1. Timestep embedding
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self.time_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift)
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self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, timestep_activation_fn)
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self.rope_embedding = EasyAnimateRotaryPosEmbed(patch_size, attention_head_dim)
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# 2. Patch embedding
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self.proj = nn.Conv2d(
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in_channels, inner_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=True
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)
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# 3. Text refined embedding
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self.text_proj = None
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self.text_proj_t5 = None
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if not add_norm_text_encoder:
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self.text_proj = nn.Linear(text_embed_dim, inner_dim)
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if text_embed_dim_t5 is not None:
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self.text_proj_t5 = nn.Linear(text_embed_dim_t5, inner_dim)
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else:
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self.text_proj = nn.Sequential(
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RMSNorm(text_embed_dim, 1e-6, elementwise_affine=True), nn.Linear(text_embed_dim, inner_dim)
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)
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if text_embed_dim_t5 is not None:
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self.text_proj_t5 = nn.Sequential(
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RMSNorm(text_embed_dim, 1e-6, elementwise_affine=True), nn.Linear(text_embed_dim_t5, inner_dim)
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)
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# 4. Transformer blocks
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self.transformer_blocks = nn.ModuleList(
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[
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EasyAnimateTransformerBlock(
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dim=inner_dim,
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num_attention_heads=num_attention_heads,
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attention_head_dim=attention_head_dim,
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time_embed_dim=time_embed_dim,
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dropout=dropout,
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activation_fn=activation_fn,
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norm_elementwise_affine=norm_elementwise_affine,
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norm_eps=norm_eps,
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|
after_norm=after_norm,
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|
is_mmdit_block=True if _ < mmdit_layers else False,
|
|
)
|
|
for _ in range(num_layers)
|
|
]
|
|
)
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|
self.norm_final = nn.LayerNorm(inner_dim, norm_eps, norm_elementwise_affine)
|
|
|
|
# 5. Output norm & projection
|
|
self.norm_out = AdaLayerNorm(
|
|
embedding_dim=time_embed_dim,
|
|
output_dim=2 * inner_dim,
|
|
norm_elementwise_affine=norm_elementwise_affine,
|
|
norm_eps=norm_eps,
|
|
chunk_dim=1,
|
|
)
|
|
self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * out_channels)
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
timestep: torch.Tensor,
|
|
timestep_cond: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states_t5: Optional[torch.Tensor] = None,
|
|
inpaint_latents: Optional[torch.Tensor] = None,
|
|
control_latents: Optional[torch.Tensor] = None,
|
|
return_dict: bool = True,
|
|
) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
|
|
batch_size, channels, video_length, height, width = hidden_states.size()
|
|
p = self.config.patch_size
|
|
post_patch_height = height // p
|
|
post_patch_width = width // p
|
|
|
|
# 1. Time embedding
|
|
temb = self.time_proj(timestep).to(dtype=hidden_states.dtype)
|
|
temb = self.time_embedding(temb, timestep_cond)
|
|
image_rotary_emb = self.rope_embedding(hidden_states)
|
|
|
|
# 2. Patch embedding
|
|
if inpaint_latents is not None:
|
|
hidden_states = torch.concat([hidden_states, inpaint_latents], 1)
|
|
if control_latents is not None:
|
|
hidden_states = torch.concat([hidden_states, control_latents], 1)
|
|
|
|
hidden_states = hidden_states.permute(0, 2, 1, 3, 4).flatten(0, 1) # [B, C, F, H, W] -> [BF, C, H, W]
|
|
hidden_states = self.proj(hidden_states)
|
|
hidden_states = hidden_states.unflatten(0, (batch_size, -1)).permute(
|
|
0, 2, 1, 3, 4
|
|
) # [BF, C, H, W] -> [B, F, C, H, W]
|
|
hidden_states = hidden_states.flatten(2, 4).transpose(1, 2) # [B, F, C, H, W] -> [B, FHW, C]
|
|
|
|
# 3. Text embedding
|
|
encoder_hidden_states = self.text_proj(encoder_hidden_states)
|
|
if encoder_hidden_states_t5 is not None:
|
|
encoder_hidden_states_t5 = self.text_proj_t5(encoder_hidden_states_t5)
|
|
encoder_hidden_states = torch.cat([encoder_hidden_states, encoder_hidden_states_t5], dim=1).contiguous()
|
|
|
|
# 4. Transformer blocks
|
|
for block in self.transformer_blocks:
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
hidden_states, encoder_hidden_states = self._gradient_checkpointing_func(
|
|
block, hidden_states, encoder_hidden_states, temb, image_rotary_emb
|
|
)
|
|
else:
|
|
hidden_states, encoder_hidden_states = block(
|
|
hidden_states, encoder_hidden_states, temb, image_rotary_emb
|
|
)
|
|
|
|
hidden_states = self.norm_final(hidden_states)
|
|
|
|
# 5. Output norm & projection
|
|
hidden_states = self.norm_out(hidden_states, temb=temb)
|
|
hidden_states = self.proj_out(hidden_states)
|
|
|
|
# 6. Unpatchify
|
|
p = self.config.patch_size
|
|
output = hidden_states.reshape(batch_size, video_length, post_patch_height, post_patch_width, channels, p, p)
|
|
output = output.permute(0, 4, 1, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
|
|
|
|
if not return_dict:
|
|
return (output,)
|
|
return Transformer2DModelOutput(sample=output)
|