aigc-apps-EasyAnimate/easyanimate/models/norm.py

from typing import Any, Dict, Optional, Tuple

import torch
import torch.nn.functional as F
from diffusers.models.embeddings import TimestepEmbedding, Timesteps
from torch import nn


def zero_module(module):
    # Zero out the parameters of a module and return it.
    for p in module.parameters():
        p.detach().zero_()
    return module


class FP32LayerNorm(nn.LayerNorm):
    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
        origin_dtype = inputs.dtype
        if hasattr(self, 'weight') and self.weight is not None:
            return F.layer_norm(
                inputs.float(), self.normalized_shape, self.weight.float(), self.bias.float(), self.eps
            ).to(origin_dtype)
        else:
            return F.layer_norm(
                inputs.float(), self.normalized_shape, None, None, self.eps
            ).to(origin_dtype)

class PixArtAlphaCombinedTimestepSizeEmbeddings(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.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)
            
            self.resolution_embedder.linear_2 = zero_module(self.resolution_embedder.linear_2)
            self.aspect_ratio_embedder.linear_2 = zero_module(self.aspect_ratio_embedder.linear_2)

    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_emb = self.additional_condition_proj(resolution.flatten()).to(hidden_dtype)
            resolution_emb = self.resolution_embedder(resolution_emb).reshape(batch_size, -1)
            aspect_ratio_emb = self.additional_condition_proj(aspect_ratio.flatten()).to(hidden_dtype)
            aspect_ratio_emb = self.aspect_ratio_embedder(aspect_ratio_emb).reshape(batch_size, -1)
            conditioning = timesteps_emb + torch.cat([resolution_emb, aspect_ratio_emb], dim=1)
        else:
            conditioning = timesteps_emb

        return conditioning

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 = PixArtAlphaCombinedTimestepSizeEmbeddings(
            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: Optional[Dict[str, torch.Tensor]] = None,
        batch_size: Optional[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, **added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_dtype)
        return self.linear(self.silu(embedded_timestep)), embedded_timestep

class AdaLayerNormShift(nn.Module):
    r"""
    Norm layer modified to incorporate timestep embeddings.

    Parameters:
        embedding_dim (`int`): The size of each embedding vector.
        num_embeddings (`int`): The size of the embeddings dictionary.
    """

    def __init__(self, embedding_dim: int, elementwise_affine=True, eps=1e-6):
        super().__init__()
        self.silu = nn.SiLU()
        self.linear = nn.Linear(embedding_dim, embedding_dim)
        self.norm = FP32LayerNorm(embedding_dim, elementwise_affine=elementwise_affine, eps=eps)

    def forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor:
        shift = self.linear(self.silu(emb.to(torch.float32)).to(emb.dtype))
        x = self.norm(x) + shift.unsqueeze(dim=1)
        return x