341 lines
12 KiB
Python
341 lines
12 KiB
Python
# Modified from HunyuanVideo
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#
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# Copyright 2024 HunyuanVideo
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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from dataclasses import dataclass
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from typing import Optional, Tuple
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import numpy as np
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import torch
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import torch.nn as nn
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from diffusers.utils import BaseOutput
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from diffusers.utils.torch_utils import randn_tensor
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from opensora.acceleration.checkpoint import auto_grad_checkpoint, checkpoint
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from opensora.models.hunyuan_vae.unet_causal_3d_blocks import (
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CausalConv3d,
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DownEncoderBlockCausal3D,
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UNetMidBlockCausal3D,
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UpDecoderBlockCausal3D,
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prepare_causal_attention_mask,
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)
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@dataclass
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class DecoderOutput(BaseOutput):
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r"""
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Output of decoding method.
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Args:
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sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
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The decoded output sample from the last layer of the model.
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"""
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sample: torch.FloatTensor
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class EncoderCausal3D(nn.Module):
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r"""
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The `EncoderCausal3D` layer of a variational autoencoder that encodes its input into a latent representation.
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"""
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def __init__(
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self,
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in_channels: int = 3,
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out_channels: int = 3,
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block_out_channels: Tuple[int, ...] = (64,),
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layers_per_block: int = 2,
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norm_num_groups: int = 32,
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act_fn: str = "silu",
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double_z: bool = True,
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mid_block_add_attention=True,
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time_compression_ratio: int = 4,
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spatial_compression_ratio: int = 8,
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dropout: float = 0.0,
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):
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super().__init__()
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self.layers_per_block = layers_per_block
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self.conv_in = CausalConv3d(in_channels, block_out_channels[0], kernel_size=3, stride=1)
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self.mid_block = None
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self.down_blocks = nn.ModuleList([])
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# down
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output_channel = block_out_channels[0]
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for i, _ in enumerate(block_out_channels):
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input_channel = output_channel
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output_channel = block_out_channels[i]
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is_final_block = i == len(block_out_channels) - 1
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num_spatial_downsample_layers = int(np.log2(spatial_compression_ratio))
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num_time_downsample_layers = int(np.log2(time_compression_ratio))
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if time_compression_ratio == 4:
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add_spatial_downsample = bool(i < num_spatial_downsample_layers)
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add_time_downsample = bool(
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i >= (len(block_out_channels) - 1 - num_time_downsample_layers) and not is_final_block
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)
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elif time_compression_ratio == 8:
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add_spatial_downsample = bool(i < num_spatial_downsample_layers)
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add_time_downsample = bool(i < num_spatial_downsample_layers)
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else:
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raise ValueError(f"Unsupported time_compression_ratio: {time_compression_ratio}.")
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downsample_stride_HW = (2, 2) if add_spatial_downsample else (1, 1)
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downsample_stride_T = (2,) if add_time_downsample else (1,)
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downsample_stride = tuple(downsample_stride_T + downsample_stride_HW)
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down_block = DownEncoderBlockCausal3D(
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num_layers=self.layers_per_block,
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in_channels=input_channel,
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out_channels=output_channel,
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dropout=dropout,
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add_downsample=bool(add_spatial_downsample or add_time_downsample),
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downsample_stride=downsample_stride,
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resnet_eps=1e-6,
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resnet_act_fn=act_fn,
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resnet_groups=norm_num_groups,
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)
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self.down_blocks.append(down_block)
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# mid
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self.mid_block = UNetMidBlockCausal3D(
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in_channels=block_out_channels[-1],
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resnet_eps=1e-6,
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resnet_act_fn=act_fn,
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output_scale_factor=1,
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attention_head_dim=block_out_channels[-1],
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resnet_groups=norm_num_groups,
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add_attention=mid_block_add_attention,
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)
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# out
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self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6)
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self.conv_act = nn.SiLU()
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conv_out_channels = 2 * out_channels if double_z else out_channels
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self.conv_out = CausalConv3d(block_out_channels[-1], conv_out_channels, kernel_size=3)
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def prepare_attention_mask(self, hidden_states: torch.Tensor) -> torch.Tensor:
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B, C, T, H, W = hidden_states.shape
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attention_mask = prepare_causal_attention_mask(
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T, H * W, hidden_states.dtype, hidden_states.device, batch_size=B
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)
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return attention_mask
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def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor:
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r"""The forward method of the `EncoderCausal3D` class."""
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assert len(sample.shape) == 5, "The input tensor should have 5 dimensions"
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sample = self.conv_in(sample)
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# down
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for down_block in self.down_blocks:
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sample = down_block(sample)
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# middle
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if self.mid_block.add_attention:
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attention_mask = self.prepare_attention_mask(sample)
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else:
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attention_mask = None
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sample = auto_grad_checkpoint(self.mid_block, sample, attention_mask)
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# post-process
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sample = self.conv_norm_out(sample)
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sample = self.conv_act(sample)
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sample = self.conv_out(sample)
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return sample
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class DecoderCausal3D(nn.Module):
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r"""
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The `DecoderCausal3D` layer of a variational autoencoder that decodes its latent representation into an output sample.
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"""
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def __init__(
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self,
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in_channels: int = 3,
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out_channels: int = 3,
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block_out_channels: Tuple[int, ...] = (64,),
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layers_per_block: int = 2,
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norm_num_groups: int = 32,
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act_fn: str = "silu",
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mid_block_add_attention=True,
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time_compression_ratio: int = 4,
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spatial_compression_ratio: int = 8,
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dropout: float = 0.0,
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):
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super().__init__()
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self.layers_per_block = layers_per_block
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self.conv_in = CausalConv3d(in_channels, block_out_channels[-1], kernel_size=3, stride=1)
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self.mid_block = None
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self.up_blocks = nn.ModuleList([])
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# mid
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self.mid_block = UNetMidBlockCausal3D(
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in_channels=block_out_channels[-1],
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resnet_eps=1e-6,
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resnet_act_fn=act_fn,
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output_scale_factor=1,
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attention_head_dim=block_out_channels[-1],
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resnet_groups=norm_num_groups,
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add_attention=mid_block_add_attention,
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)
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# up
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reversed_block_out_channels = list(reversed(block_out_channels))
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output_channel = reversed_block_out_channels[0]
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for i, _ in enumerate(block_out_channels):
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prev_output_channel = output_channel
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output_channel = reversed_block_out_channels[i]
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is_final_block = i == len(block_out_channels) - 1
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num_spatial_upsample_layers = int(np.log2(spatial_compression_ratio))
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num_time_upsample_layers = int(np.log2(time_compression_ratio))
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if time_compression_ratio == 4:
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add_spatial_upsample = bool(i < num_spatial_upsample_layers)
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add_time_upsample = bool(
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i >= len(block_out_channels) - 1 - num_time_upsample_layers and not is_final_block
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)
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elif time_compression_ratio == 8:
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add_spatial_upsample = bool(i < num_spatial_upsample_layers)
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add_time_upsample = bool(i < num_spatial_upsample_layers)
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else:
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raise ValueError(f"Unsupported time_compression_ratio: {time_compression_ratio}.")
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upsample_scale_factor_HW = (2, 2) if add_spatial_upsample else (1, 1)
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upsample_scale_factor_T = (2,) if add_time_upsample else (1,)
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upsample_scale_factor = tuple(upsample_scale_factor_T + upsample_scale_factor_HW)
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up_block = UpDecoderBlockCausal3D(
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num_layers=self.layers_per_block + 1,
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in_channels=prev_output_channel,
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out_channels=output_channel,
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resolution_idx=None,
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dropout=dropout,
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add_upsample=bool(add_spatial_upsample or add_time_upsample),
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upsample_scale_factor=upsample_scale_factor,
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resnet_eps=1e-6,
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resnet_act_fn=act_fn,
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resnet_groups=norm_num_groups,
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)
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self.up_blocks.append(up_block)
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prev_output_channel = output_channel
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self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6)
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self.conv_act = nn.SiLU()
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self.conv_out = CausalConv3d(block_out_channels[0], out_channels, kernel_size=3)
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def post_process(self, sample: torch.Tensor) -> torch.Tensor:
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sample = self.conv_norm_out(sample)
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sample = self.conv_act(sample)
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return sample
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def prepare_attention_mask(self, hidden_states: torch.Tensor) -> torch.Tensor:
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B, C, T, H, W = hidden_states.shape
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attention_mask = prepare_causal_attention_mask(
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T, H * W, hidden_states.dtype, hidden_states.device, batch_size=B
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)
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return attention_mask
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def forward(
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self,
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sample: torch.FloatTensor,
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) -> torch.FloatTensor:
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r"""The forward method of the `DecoderCausal3D` class."""
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assert len(sample.shape) == 5, "The input tensor should have 5 dimensions."
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sample = self.conv_in(sample)
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upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
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# middle
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if self.mid_block.add_attention:
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attention_mask = self.prepare_attention_mask(sample)
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else:
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attention_mask = None
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sample = auto_grad_checkpoint(self.mid_block, sample, attention_mask)
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sample = sample.to(upscale_dtype)
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# up
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for up_block in self.up_blocks:
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sample = up_block(sample)
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# post-process
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if getattr(self, "grad_checkpointing", False):
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sample = checkpoint(self.post_process, sample, use_reentrant=True)
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else:
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sample = self.post_process(sample)
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sample = self.conv_out(sample)
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return sample
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class DiagonalGaussianDistribution(object):
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def __init__(self, parameters: torch.Tensor, deterministic: bool = False):
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if parameters.ndim == 3:
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dim = 2 # (B, L, C)
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elif parameters.ndim == 5 or parameters.ndim == 4:
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dim = 1 # (B, C, T, H ,W) / (B, C, H, W)
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else:
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raise NotImplementedError
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self.parameters = parameters
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self.mean, self.logvar = torch.chunk(parameters, 2, dim=dim)
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self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
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self.deterministic = deterministic
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self.std = torch.exp(0.5 * self.logvar)
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self.var = torch.exp(self.logvar)
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if self.deterministic:
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self.var = self.std = torch.zeros_like(
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self.mean, device=self.parameters.device, dtype=self.parameters.dtype
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)
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def sample(self, generator: Optional[torch.Generator] = None) -> torch.FloatTensor:
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# make sure sample is on the same device as the parameters and has same dtype
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sample = randn_tensor(
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self.mean.shape,
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generator=generator,
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device=self.parameters.device,
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dtype=self.parameters.dtype,
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)
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x = self.mean + self.std * sample
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return x
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def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor:
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if self.deterministic:
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return torch.Tensor([0.0])
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else:
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reduce_dim = list(range(1, self.mean.ndim))
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if other is None:
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return 0.5 * torch.sum(
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torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
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dim=reduce_dim,
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)
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else:
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return 0.5 * torch.sum(
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torch.pow(self.mean - other.mean, 2) / other.var
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+ self.var / other.var
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- 1.0
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- self.logvar
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+ other.logvar,
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dim=reduce_dim,
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)
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def nll(self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3]) -> torch.Tensor:
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if self.deterministic:
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return torch.Tensor([0.0])
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logtwopi = np.log(2.0 * np.pi)
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return 0.5 * torch.sum(
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logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
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dim=dims,
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)
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def mode(self) -> torch.Tensor:
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return self.mean
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