models/hyvideo/modules/upsample.py

# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/main/LICENSE
#
# Unless and only to the extent required by applicable law, the Tencent Hunyuan works and any
# output and results therefrom are provided "AS IS" without any express or implied warranties of
# any kind including any warranties of title, merchantability, noninfringement, course of dealing,
# usage of trade, or fitness for a particular purpose. You are solely responsible for determining the
# appropriateness of using, reproducing, modifying, performing, displaying or distributing any of
# the Tencent Hunyuan works or outputs and assume any and all risks associated with your or a
# third party's use or distribution of any of the Tencent Hunyuan works or outputs and your exercise
# of rights and permissions under this agreement.
# See the License for the specific language governing permissions and limitations under the License.

from collections.abc import Sequence
from dataclasses import dataclass
from enum import Enum

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from torch import Tensor
from diffusers.models import ModelMixin
from diffusers.configuration_utils import ConfigMixin, register_to_config

from ..vae.hunyuanvideo_15_vae import (
    CausalConv3d,
    ResnetBlock,
    RMS_norm,
    forward_with_checkpointing,
    swish,
)


class UpsamplerType(Enum):
    LEARNED = "learned"
    FIXED = "fixed"
    NONE = "none"
    LEARNED_FIXED = "learned_fixed"


@dataclass
class UpsamplerConfig:
    load_from: str
    enable: bool = False
    hidden_channels: int = 128
    num_blocks: int = 16
    model_type: UpsamplerType = UpsamplerType.NONE
    version: str = "720p"


class SRResidualCausalBlock3D(nn.Module):
    def __init__(self, channels: int):
        super().__init__()
        self.block = nn.Sequential(
            CausalConv3d(channels, channels, kernel_size=3),
            nn.SiLU(inplace=True),
            CausalConv3d(channels, channels, kernel_size=3),
            nn.SiLU(inplace=True),
            CausalConv3d(channels, channels, kernel_size=3),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return x + self.block(x)


class SRTo720pUpsampler(ModelMixin, ConfigMixin):

    @register_to_config
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        hidden_channels: int | None = None,
        num_blocks: int = 6,
        global_residual: bool = False,
    ):
        super().__init__()
        if hidden_channels is None:
            hidden_channels = 64
        self.in_conv = CausalConv3d(in_channels, hidden_channels, kernel_size=3)
        self.blocks = nn.ModuleList([SRResidualCausalBlock3D(hidden_channels) for _ in range(num_blocks)])
        self.out_conv = CausalConv3d(hidden_channels, out_channels, kernel_size=3)
        self.global_residual = bool(global_residual)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        residual = x
        y = self.in_conv(x)
        for blk in self.blocks:
            y = blk(y)
        y = self.out_conv(y)
        if self.global_residual and (y.shape == residual.shape):
            y +=  residual
        return y


class SRTo1080pUpsampler(ModelMixin, ConfigMixin):

    @register_to_config
    def __init__(
        self,
        z_channels: int,
        out_channels: int,
        block_out_channels: tuple[int, ...],
        num_res_blocks: int = 2,
        is_residual: bool = False,
    ):
        super().__init__()
        self.num_res_blocks = num_res_blocks
        self.block_out_channels = block_out_channels
        self.z_channels = z_channels

        block_in = block_out_channels[0]
        self.conv_in = CausalConv3d(z_channels, block_in, kernel_size=3)

        self.up = nn.ModuleList()
        for i_level, ch in enumerate(block_out_channels):
            block = nn.ModuleList()
            block_out = ch
            for _ in range(self.num_res_blocks + 1):
                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
                block_in = block_out
            up = nn.Module()
            up.block = block

            self.up.append(up)

        self.norm_out = RMS_norm(block_in, images=False)
        self.conv_out = CausalConv3d(block_in, out_channels, kernel_size=3)

        self.gradient_checkpointing = False
        self.is_residual = is_residual

    def forward(self, z: Tensor, target_shape: Sequence[int] = None) -> Tensor:
        """
        Args:
            z: (B, C, T, H, W)
            target_shape: (H, W)
        """
        use_checkpointing = bool(self.training and self.gradient_checkpointing)
        if target_shape is not None and z.shape[-2:] != target_shape:
            bsz = z.shape[0]
            z = rearrange(z, "b c f h w -> (b f) c h w")
            z = F.interpolate(z, size=target_shape, mode="bilinear", align_corners=False)
            z = rearrange(z, "(b f) c h w -> b c f h w", b=bsz)

        # z to block_in
        repeats = self.block_out_channels[0] // (self.z_channels)
        h = self.conv_in(z) + z.repeat_interleave(repeats=repeats, dim=1)

        # upsampling
        for i_level in range(len(self.block_out_channels)):
            for i_block in range(self.num_res_blocks + 1):
                x_list= [h]
                del h
                h = self.up[i_level].block[i_block](x_list)
            if hasattr(self.up[i_level], "upsample"):
                x_list= [h]
                del h
                h = self.up[i_level].upsample(x_list)

        # end
        h = self.norm_out(h).to(z.dtype)
        h = swish(h)
        h = self.conv_out(h)
        return h