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https://github.com/huggingface/diffusers.git
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932 lines
33 KiB
Python
932 lines
33 KiB
Python
# Copyright 2025 The HuggingFace Team. 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 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 ...utils import BaseOutput
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from ...utils.torch_utils import randn_tensor
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from ..activations import get_activation
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from ..attention_processor import SpatialNorm
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from ..unets.unet_2d_blocks import (
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AutoencoderTinyBlock,
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UNetMidBlock2D,
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get_down_block,
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get_up_block,
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)
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@dataclass
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class EncoderOutput(BaseOutput):
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r"""
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Output of encoding method.
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Args:
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latent (`torch.Tensor` of shape `(batch_size, num_channels, latent_height, latent_width)`):
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The encoded latent.
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"""
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latent: torch.Tensor
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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.Tensor` 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.Tensor
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commit_loss: Optional[torch.FloatTensor] = None
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class Encoder(nn.Module):
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r"""
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The `Encoder` layer of a variational autoencoder that encodes its input into a latent representation.
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Args:
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in_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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out_channels (`int`, *optional*, defaults to 3):
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The number of output channels.
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down_block_types (`Tuple[str, ...]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
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The types of down blocks to use. See `~diffusers.models.unet_2d_blocks.get_down_block` for available
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options.
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block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`):
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The number of output channels for each block.
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layers_per_block (`int`, *optional*, defaults to 2):
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The number of layers per block.
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norm_num_groups (`int`, *optional*, defaults to 32):
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The number of groups for normalization.
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act_fn (`str`, *optional*, defaults to `"silu"`):
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The activation function to use. See `~diffusers.models.activations.get_activation` for available options.
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double_z (`bool`, *optional*, defaults to `True`):
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Whether to double the number of output channels for the last block.
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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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down_block_types: Tuple[str, ...] = ("DownEncoderBlock2D",),
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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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):
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super().__init__()
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self.layers_per_block = layers_per_block
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self.conv_in = nn.Conv2d(
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in_channels,
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block_out_channels[0],
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kernel_size=3,
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stride=1,
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padding=1,
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)
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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, down_block_type in enumerate(down_block_types):
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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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down_block = get_down_block(
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down_block_type,
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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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add_downsample=not is_final_block,
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resnet_eps=1e-6,
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downsample_padding=0,
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resnet_act_fn=act_fn,
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resnet_groups=norm_num_groups,
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attention_head_dim=output_channel,
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temb_channels=None,
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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 = UNetMidBlock2D(
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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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resnet_time_scale_shift="default",
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attention_head_dim=block_out_channels[-1],
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resnet_groups=norm_num_groups,
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temb_channels=None,
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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 = nn.Conv2d(block_out_channels[-1], conv_out_channels, 3, padding=1)
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self.gradient_checkpointing = False
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def forward(self, sample: torch.Tensor) -> torch.Tensor:
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r"""The forward method of the `Encoder` class."""
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sample = self.conv_in(sample)
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if torch.is_grad_enabled() and self.gradient_checkpointing:
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# down
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for down_block in self.down_blocks:
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sample = self._gradient_checkpointing_func(down_block, sample)
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# middle
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sample = self._gradient_checkpointing_func(self.mid_block, sample)
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else:
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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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sample = self.mid_block(sample)
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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 Decoder(nn.Module):
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r"""
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The `Decoder` layer of a variational autoencoder that decodes its latent representation into an output sample.
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Args:
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in_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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out_channels (`int`, *optional*, defaults to 3):
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The number of output channels.
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up_block_types (`Tuple[str, ...]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
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The types of up blocks to use. See `~diffusers.models.unet_2d_blocks.get_up_block` for available options.
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block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`):
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The number of output channels for each block.
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layers_per_block (`int`, *optional*, defaults to 2):
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The number of layers per block.
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norm_num_groups (`int`, *optional*, defaults to 32):
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The number of groups for normalization.
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act_fn (`str`, *optional*, defaults to `"silu"`):
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The activation function to use. See `~diffusers.models.activations.get_activation` for available options.
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norm_type (`str`, *optional*, defaults to `"group"`):
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The normalization type to use. Can be either `"group"` or `"spatial"`.
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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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up_block_types: Tuple[str, ...] = ("UpDecoderBlock2D",),
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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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norm_type: str = "group", # group, spatial
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mid_block_add_attention=True,
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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 = nn.Conv2d(
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in_channels,
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block_out_channels[-1],
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kernel_size=3,
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stride=1,
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padding=1,
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)
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self.up_blocks = nn.ModuleList([])
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temb_channels = in_channels if norm_type == "spatial" else None
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# mid
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self.mid_block = UNetMidBlock2D(
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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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resnet_time_scale_shift="default" if norm_type == "group" else norm_type,
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attention_head_dim=block_out_channels[-1],
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resnet_groups=norm_num_groups,
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temb_channels=temb_channels,
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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, up_block_type in enumerate(up_block_types):
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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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up_block = get_up_block(
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up_block_type,
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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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prev_output_channel=prev_output_channel,
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add_upsample=not is_final_block,
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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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attention_head_dim=output_channel,
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temb_channels=temb_channels,
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resnet_time_scale_shift=norm_type,
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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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# out
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if norm_type == "spatial":
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self.conv_norm_out = SpatialNorm(block_out_channels[0], temb_channels)
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else:
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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 = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)
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self.gradient_checkpointing = False
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def forward(
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self,
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sample: torch.Tensor,
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latent_embeds: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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r"""The forward method of the `Decoder` class."""
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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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if torch.is_grad_enabled() and self.gradient_checkpointing:
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# middle
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sample = self._gradient_checkpointing_func(self.mid_block, sample, latent_embeds)
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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 = self._gradient_checkpointing_func(up_block, sample, latent_embeds)
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else:
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# middle
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sample = self.mid_block(sample, latent_embeds)
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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, latent_embeds)
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# post-process
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if latent_embeds is None:
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sample = self.conv_norm_out(sample)
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else:
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sample = self.conv_norm_out(sample, latent_embeds)
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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 UpSample(nn.Module):
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r"""
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The `UpSample` layer of a variational autoencoder that upsamples its input.
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Args:
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in_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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out_channels (`int`, *optional*, defaults to 3):
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The number of output channels.
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"""
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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) -> None:
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.deconv = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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r"""The forward method of the `UpSample` class."""
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x = torch.relu(x)
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x = self.deconv(x)
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return x
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class MaskConditionEncoder(nn.Module):
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"""
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used in AsymmetricAutoencoderKL
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"""
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def __init__(
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self,
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in_ch: int,
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out_ch: int = 192,
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res_ch: int = 768,
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stride: int = 16,
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) -> None:
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super().__init__()
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channels = []
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while stride > 1:
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stride = stride // 2
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in_ch_ = out_ch * 2
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if out_ch > res_ch:
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out_ch = res_ch
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if stride == 1:
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in_ch_ = res_ch
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channels.append((in_ch_, out_ch))
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out_ch *= 2
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out_channels = []
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for _in_ch, _out_ch in channels:
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out_channels.append(_out_ch)
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out_channels.append(channels[-1][0])
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layers = []
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in_ch_ = in_ch
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for l in range(len(out_channels)):
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out_ch_ = out_channels[l]
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if l == 0 or l == 1:
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layers.append(nn.Conv2d(in_ch_, out_ch_, kernel_size=3, stride=1, padding=1))
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else:
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layers.append(nn.Conv2d(in_ch_, out_ch_, kernel_size=4, stride=2, padding=1))
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in_ch_ = out_ch_
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self.layers = nn.Sequential(*layers)
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def forward(self, x: torch.Tensor, mask=None) -> torch.Tensor:
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r"""The forward method of the `MaskConditionEncoder` class."""
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out = {}
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for l in range(len(self.layers)):
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layer = self.layers[l]
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x = layer(x)
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out[str(tuple(x.shape))] = x
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x = torch.relu(x)
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return out
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class MaskConditionDecoder(nn.Module):
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r"""The `MaskConditionDecoder` should be used in combination with [`AsymmetricAutoencoderKL`] to enhance the model's
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decoder with a conditioner on the mask and masked image.
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Args:
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in_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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out_channels (`int`, *optional*, defaults to 3):
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The number of output channels.
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up_block_types (`Tuple[str, ...]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
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The types of up blocks to use. See `~diffusers.models.unet_2d_blocks.get_up_block` for available options.
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block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`):
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The number of output channels for each block.
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layers_per_block (`int`, *optional*, defaults to 2):
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The number of layers per block.
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norm_num_groups (`int`, *optional*, defaults to 32):
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The number of groups for normalization.
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act_fn (`str`, *optional*, defaults to `"silu"`):
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The activation function to use. See `~diffusers.models.activations.get_activation` for available options.
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norm_type (`str`, *optional*, defaults to `"group"`):
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The normalization type to use. Can be either `"group"` or `"spatial"`.
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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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up_block_types: Tuple[str, ...] = ("UpDecoderBlock2D",),
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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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norm_type: str = "group", # group, spatial
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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 = nn.Conv2d(
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in_channels,
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block_out_channels[-1],
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kernel_size=3,
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stride=1,
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padding=1,
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)
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self.up_blocks = nn.ModuleList([])
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temb_channels = in_channels if norm_type == "spatial" else None
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# mid
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self.mid_block = UNetMidBlock2D(
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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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resnet_time_scale_shift="default" if norm_type == "group" else norm_type,
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attention_head_dim=block_out_channels[-1],
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resnet_groups=norm_num_groups,
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temb_channels=temb_channels,
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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, up_block_type in enumerate(up_block_types):
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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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up_block = get_up_block(
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up_block_type,
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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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prev_output_channel=None,
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add_upsample=not is_final_block,
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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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attention_head_dim=output_channel,
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temb_channels=temb_channels,
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resnet_time_scale_shift=norm_type,
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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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# condition encoder
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self.condition_encoder = MaskConditionEncoder(
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in_ch=out_channels,
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out_ch=block_out_channels[0],
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res_ch=block_out_channels[-1],
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)
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# out
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if norm_type == "spatial":
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self.conv_norm_out = SpatialNorm(block_out_channels[0], temb_channels)
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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.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
z: torch.Tensor,
|
|
image: Optional[torch.Tensor] = None,
|
|
mask: Optional[torch.Tensor] = None,
|
|
latent_embeds: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
r"""The forward method of the `MaskConditionDecoder` class."""
|
|
sample = z
|
|
sample = self.conv_in(sample)
|
|
|
|
upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
# middle
|
|
sample = self._gradient_checkpointing_func(self.mid_block, sample, latent_embeds)
|
|
sample = sample.to(upscale_dtype)
|
|
|
|
# condition encoder
|
|
if image is not None and mask is not None:
|
|
masked_image = (1 - mask) * image
|
|
im_x = self._gradient_checkpointing_func(
|
|
self.condition_encoder,
|
|
masked_image,
|
|
mask,
|
|
)
|
|
|
|
# up
|
|
for up_block in self.up_blocks:
|
|
if image is not None and mask is not None:
|
|
sample_ = im_x[str(tuple(sample.shape))]
|
|
mask_ = nn.functional.interpolate(mask, size=sample.shape[-2:], mode="nearest")
|
|
sample = sample * mask_ + sample_ * (1 - mask_)
|
|
sample = self._gradient_checkpointing_func(up_block, sample, latent_embeds)
|
|
if image is not None and mask is not None:
|
|
sample = sample * mask + im_x[str(tuple(sample.shape))] * (1 - mask)
|
|
else:
|
|
# middle
|
|
sample = self.mid_block(sample, latent_embeds)
|
|
sample = sample.to(upscale_dtype)
|
|
|
|
# condition encoder
|
|
if image is not None and mask is not None:
|
|
masked_image = (1 - mask) * image
|
|
im_x = self.condition_encoder(masked_image, mask)
|
|
|
|
# up
|
|
for up_block in self.up_blocks:
|
|
if image is not None and mask is not None:
|
|
sample_ = im_x[str(tuple(sample.shape))]
|
|
mask_ = nn.functional.interpolate(mask, size=sample.shape[-2:], mode="nearest")
|
|
sample = sample * mask_ + sample_ * (1 - mask_)
|
|
sample = up_block(sample, latent_embeds)
|
|
if image is not None and mask is not None:
|
|
sample = sample * mask + im_x[str(tuple(sample.shape))] * (1 - mask)
|
|
|
|
# post-process
|
|
if latent_embeds is None:
|
|
sample = self.conv_norm_out(sample)
|
|
else:
|
|
sample = self.conv_norm_out(sample, latent_embeds)
|
|
sample = self.conv_act(sample)
|
|
sample = self.conv_out(sample)
|
|
|
|
return sample
|
|
|
|
|
|
class VectorQuantizer(nn.Module):
|
|
"""
|
|
Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly avoids costly matrix
|
|
multiplications and allows for post-hoc remapping of indices.
|
|
"""
|
|
|
|
# NOTE: due to a bug the beta term was applied to the wrong term. for
|
|
# backwards compatibility we use the buggy version by default, but you can
|
|
# specify legacy=False to fix it.
|
|
def __init__(
|
|
self,
|
|
n_e: int,
|
|
vq_embed_dim: int,
|
|
beta: float,
|
|
remap=None,
|
|
unknown_index: str = "random",
|
|
sane_index_shape: bool = False,
|
|
legacy: bool = True,
|
|
):
|
|
super().__init__()
|
|
self.n_e = n_e
|
|
self.vq_embed_dim = vq_embed_dim
|
|
self.beta = beta
|
|
self.legacy = legacy
|
|
|
|
self.embedding = nn.Embedding(self.n_e, self.vq_embed_dim)
|
|
self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
|
|
|
|
self.remap = remap
|
|
if self.remap is not None:
|
|
self.register_buffer("used", torch.tensor(np.load(self.remap)))
|
|
self.used: torch.Tensor
|
|
self.re_embed = self.used.shape[0]
|
|
self.unknown_index = unknown_index # "random" or "extra" or integer
|
|
if self.unknown_index == "extra":
|
|
self.unknown_index = self.re_embed
|
|
self.re_embed = self.re_embed + 1
|
|
print(
|
|
f"Remapping {self.n_e} indices to {self.re_embed} indices. "
|
|
f"Using {self.unknown_index} for unknown indices."
|
|
)
|
|
else:
|
|
self.re_embed = n_e
|
|
|
|
self.sane_index_shape = sane_index_shape
|
|
|
|
def remap_to_used(self, inds: torch.LongTensor) -> torch.LongTensor:
|
|
ishape = inds.shape
|
|
assert len(ishape) > 1
|
|
inds = inds.reshape(ishape[0], -1)
|
|
used = self.used.to(inds)
|
|
match = (inds[:, :, None] == used[None, None, ...]).long()
|
|
new = match.argmax(-1)
|
|
unknown = match.sum(2) < 1
|
|
if self.unknown_index == "random":
|
|
new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device)
|
|
else:
|
|
new[unknown] = self.unknown_index
|
|
return new.reshape(ishape)
|
|
|
|
def unmap_to_all(self, inds: torch.LongTensor) -> torch.LongTensor:
|
|
ishape = inds.shape
|
|
assert len(ishape) > 1
|
|
inds = inds.reshape(ishape[0], -1)
|
|
used = self.used.to(inds)
|
|
if self.re_embed > self.used.shape[0]: # extra token
|
|
inds[inds >= self.used.shape[0]] = 0 # simply set to zero
|
|
back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
|
|
return back.reshape(ishape)
|
|
|
|
def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, Tuple]:
|
|
# reshape z -> (batch, height, width, channel) and flatten
|
|
z = z.permute(0, 2, 3, 1).contiguous()
|
|
z_flattened = z.view(-1, self.vq_embed_dim)
|
|
|
|
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
|
|
min_encoding_indices = torch.argmin(torch.cdist(z_flattened, self.embedding.weight), dim=1)
|
|
|
|
z_q = self.embedding(min_encoding_indices).view(z.shape)
|
|
perplexity = None
|
|
min_encodings = None
|
|
|
|
# compute loss for embedding
|
|
if not self.legacy:
|
|
loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + torch.mean((z_q - z.detach()) ** 2)
|
|
else:
|
|
loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
|
|
|
|
# preserve gradients
|
|
z_q: torch.Tensor = z + (z_q - z).detach()
|
|
|
|
# reshape back to match original input shape
|
|
z_q = z_q.permute(0, 3, 1, 2).contiguous()
|
|
|
|
if self.remap is not None:
|
|
min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis
|
|
min_encoding_indices = self.remap_to_used(min_encoding_indices)
|
|
min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten
|
|
|
|
if self.sane_index_shape:
|
|
min_encoding_indices = min_encoding_indices.reshape(z_q.shape[0], z_q.shape[2], z_q.shape[3])
|
|
|
|
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
|
|
|
|
def get_codebook_entry(self, indices: torch.LongTensor, shape: Tuple[int, ...]) -> torch.Tensor:
|
|
# shape specifying (batch, height, width, channel)
|
|
if self.remap is not None:
|
|
indices = indices.reshape(shape[0], -1) # add batch axis
|
|
indices = self.unmap_to_all(indices)
|
|
indices = indices.reshape(-1) # flatten again
|
|
|
|
# get quantized latent vectors
|
|
z_q: torch.Tensor = self.embedding(indices)
|
|
|
|
if shape is not None:
|
|
z_q = z_q.view(shape)
|
|
# reshape back to match original input shape
|
|
z_q = z_q.permute(0, 3, 1, 2).contiguous()
|
|
|
|
return z_q
|
|
|
|
|
|
class DiagonalGaussianDistribution(object):
|
|
def __init__(self, parameters: torch.Tensor, deterministic: bool = False):
|
|
self.parameters = parameters
|
|
self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
|
|
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
|
|
self.deterministic = deterministic
|
|
self.std = torch.exp(0.5 * self.logvar)
|
|
self.var = torch.exp(self.logvar)
|
|
if self.deterministic:
|
|
self.var = self.std = torch.zeros_like(
|
|
self.mean, device=self.parameters.device, dtype=self.parameters.dtype
|
|
)
|
|
|
|
def sample(self, generator: Optional[torch.Generator] = None) -> torch.Tensor:
|
|
# make sure sample is on the same device as the parameters and has same dtype
|
|
sample = randn_tensor(
|
|
self.mean.shape,
|
|
generator=generator,
|
|
device=self.parameters.device,
|
|
dtype=self.parameters.dtype,
|
|
)
|
|
x = self.mean + self.std * sample
|
|
return x
|
|
|
|
def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor:
|
|
if self.deterministic:
|
|
return torch.Tensor([0.0])
|
|
else:
|
|
if other is None:
|
|
return 0.5 * torch.sum(
|
|
torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
|
|
dim=[1, 2, 3],
|
|
)
|
|
else:
|
|
return 0.5 * torch.sum(
|
|
torch.pow(self.mean - other.mean, 2) / other.var
|
|
+ self.var / other.var
|
|
- 1.0
|
|
- self.logvar
|
|
+ other.logvar,
|
|
dim=[1, 2, 3],
|
|
)
|
|
|
|
def nll(self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3]) -> torch.Tensor:
|
|
if self.deterministic:
|
|
return torch.Tensor([0.0])
|
|
logtwopi = np.log(2.0 * np.pi)
|
|
return 0.5 * torch.sum(
|
|
logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
|
|
dim=dims,
|
|
)
|
|
|
|
def mode(self) -> torch.Tensor:
|
|
return self.mean
|
|
|
|
|
|
class IdentityDistribution(object):
|
|
def __init__(self, parameters: torch.Tensor):
|
|
self.parameters = parameters
|
|
|
|
def sample(self, generator: Optional[torch.Generator] = None) -> torch.Tensor:
|
|
return self.parameters
|
|
|
|
def mode(self) -> torch.Tensor:
|
|
return self.parameters
|
|
|
|
|
|
class EncoderTiny(nn.Module):
|
|
r"""
|
|
The `EncoderTiny` layer is a simpler version of the `Encoder` layer.
|
|
|
|
Args:
|
|
in_channels (`int`):
|
|
The number of input channels.
|
|
out_channels (`int`):
|
|
The number of output channels.
|
|
num_blocks (`Tuple[int, ...]`):
|
|
Each value of the tuple represents a Conv2d layer followed by `value` number of `AutoencoderTinyBlock`'s to
|
|
use.
|
|
block_out_channels (`Tuple[int, ...]`):
|
|
The number of output channels for each block.
|
|
act_fn (`str`):
|
|
The activation function to use. See `~diffusers.models.activations.get_activation` for available options.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
out_channels: int,
|
|
num_blocks: Tuple[int, ...],
|
|
block_out_channels: Tuple[int, ...],
|
|
act_fn: str,
|
|
):
|
|
super().__init__()
|
|
|
|
layers = []
|
|
for i, num_block in enumerate(num_blocks):
|
|
num_channels = block_out_channels[i]
|
|
|
|
if i == 0:
|
|
layers.append(nn.Conv2d(in_channels, num_channels, kernel_size=3, padding=1))
|
|
else:
|
|
layers.append(
|
|
nn.Conv2d(
|
|
num_channels,
|
|
num_channels,
|
|
kernel_size=3,
|
|
padding=1,
|
|
stride=2,
|
|
bias=False,
|
|
)
|
|
)
|
|
|
|
for _ in range(num_block):
|
|
layers.append(AutoencoderTinyBlock(num_channels, num_channels, act_fn))
|
|
|
|
layers.append(nn.Conv2d(block_out_channels[-1], out_channels, kernel_size=3, padding=1))
|
|
|
|
self.layers = nn.Sequential(*layers)
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
r"""The forward method of the `EncoderTiny` class."""
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
x = self._gradient_checkpointing_func(self.layers, x)
|
|
|
|
else:
|
|
# scale image from [-1, 1] to [0, 1] to match TAESD convention
|
|
x = self.layers(x.add(1).div(2))
|
|
|
|
return x
|
|
|
|
|
|
class DecoderTiny(nn.Module):
|
|
r"""
|
|
The `DecoderTiny` layer is a simpler version of the `Decoder` layer.
|
|
|
|
Args:
|
|
in_channels (`int`):
|
|
The number of input channels.
|
|
out_channels (`int`):
|
|
The number of output channels.
|
|
num_blocks (`Tuple[int, ...]`):
|
|
Each value of the tuple represents a Conv2d layer followed by `value` number of `AutoencoderTinyBlock`'s to
|
|
use.
|
|
block_out_channels (`Tuple[int, ...]`):
|
|
The number of output channels for each block.
|
|
upsampling_scaling_factor (`int`):
|
|
The scaling factor to use for upsampling.
|
|
act_fn (`str`):
|
|
The activation function to use. See `~diffusers.models.activations.get_activation` for available options.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
out_channels: int,
|
|
num_blocks: Tuple[int, ...],
|
|
block_out_channels: Tuple[int, ...],
|
|
upsampling_scaling_factor: int,
|
|
act_fn: str,
|
|
upsample_fn: str,
|
|
):
|
|
super().__init__()
|
|
|
|
layers = [
|
|
nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=1),
|
|
get_activation(act_fn),
|
|
]
|
|
|
|
for i, num_block in enumerate(num_blocks):
|
|
is_final_block = i == (len(num_blocks) - 1)
|
|
num_channels = block_out_channels[i]
|
|
|
|
for _ in range(num_block):
|
|
layers.append(AutoencoderTinyBlock(num_channels, num_channels, act_fn))
|
|
|
|
if not is_final_block:
|
|
layers.append(nn.Upsample(scale_factor=upsampling_scaling_factor, mode=upsample_fn))
|
|
|
|
conv_out_channel = num_channels if not is_final_block else out_channels
|
|
layers.append(
|
|
nn.Conv2d(
|
|
num_channels,
|
|
conv_out_channel,
|
|
kernel_size=3,
|
|
padding=1,
|
|
bias=is_final_block,
|
|
)
|
|
)
|
|
|
|
self.layers = nn.Sequential(*layers)
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
r"""The forward method of the `DecoderTiny` class."""
|
|
# Clamp.
|
|
x = torch.tanh(x / 3) * 3
|
|
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
x = self._gradient_checkpointing_func(self.layers, x)
|
|
else:
|
|
x = self.layers(x)
|
|
|
|
# scale image from [0, 1] to [-1, 1] to match diffusers convention
|
|
return x.mul(2).sub(1)
|
|
|
|
|
|
class AutoencoderMixin:
|
|
def enable_tiling(self):
|
|
r"""
|
|
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
|
|
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
|
|
processing larger images.
|
|
"""
|
|
if not hasattr(self, "use_tiling"):
|
|
raise NotImplementedError(f"Tiling doesn't seem to be implemented for {self.__class__.__name__}.")
|
|
self.use_tiling = True
|
|
|
|
def disable_tiling(self):
|
|
r"""
|
|
Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
|
|
decoding in one step.
|
|
"""
|
|
self.use_tiling = False
|
|
|
|
def enable_slicing(self):
|
|
r"""
|
|
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
|
|
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
|
|
"""
|
|
if not hasattr(self, "use_slicing"):
|
|
raise NotImplementedError(f"Slicing doesn't seem to be implemented for {self.__class__.__name__}.")
|
|
self.use_slicing = True
|
|
|
|
def disable_slicing(self):
|
|
r"""
|
|
Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
|
|
decoding in one step.
|
|
"""
|
|
self.use_slicing = False
|