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757 lines
24 KiB
Python
757 lines
24 KiB
Python
"""Classes for diffusion-based image generation"""
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# pytorch_diffusion + derived encoder decoder
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import logging
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import math
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from typing import Any, Callable, Optional
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import torch
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import torch.nn as nn
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from einops import rearrange
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from imaginairy.modules.attention import LinearAttention, MemoryEfficientCrossAttention
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logger = logging.getLogger(__name__)
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try:
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import xformers
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import xformers.ops
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XFORMERS_IS_AVAILABLE = True
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except ImportError:
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XFORMERS_IS_AVAILABLE = False
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logger.debug("no module 'xformers'. Processing without...")
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# from ...modules.attention import LinearAttention, MemoryEfficientCrossAttention
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def get_timestep_embedding(timesteps, embedding_dim):
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"""
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This matches the implementation in Denoising Diffusion Probabilistic Models:
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From Fairseq.
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Build sinusoidal embeddings.
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This matches the implementation in tensor2tensor, but differs slightly
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from the description in Section 3.5 of "Attention Is All You Need".
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"""
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assert len(timesteps.shape) == 1
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half_dim = embedding_dim // 2
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emb = math.log(10000) / (half_dim - 1)
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emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
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emb = emb.to(device=timesteps.device)
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emb = timesteps.float()[:, None] * emb[None, :]
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emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
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if embedding_dim % 2 == 1: # zero pad
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emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
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return emb
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def nonlinearity(x):
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# swish
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return x * torch.sigmoid(x)
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def Normalize(in_channels, num_groups=32):
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return torch.nn.GroupNorm(
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num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True
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)
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class Upsample(nn.Module):
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def __init__(self, in_channels, with_conv):
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super().__init__()
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self.with_conv = with_conv
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if self.with_conv:
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self.conv = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=3, stride=1, padding=1
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)
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def forward(self, x):
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x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
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if self.with_conv:
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x = self.conv(x)
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return x
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class Downsample(nn.Module):
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def __init__(self, in_channels, with_conv):
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super().__init__()
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self.with_conv = with_conv
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if self.with_conv:
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# no asymmetric padding in torch conv, must do it ourselves
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self.conv = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=3, stride=2, padding=0
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)
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def forward(self, x):
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if self.with_conv:
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pad = (0, 1, 0, 1)
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x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
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x = self.conv(x)
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else:
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x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
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return x
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class ResnetBlock(nn.Module):
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def __init__(
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self,
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*,
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in_channels,
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out_channels=None,
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conv_shortcut=False,
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dropout,
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temb_channels=512,
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):
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super().__init__()
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self.in_channels = in_channels
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out_channels = in_channels if out_channels is None else out_channels
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self.out_channels = out_channels
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self.use_conv_shortcut = conv_shortcut
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self.norm1 = Normalize(in_channels)
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self.conv1 = torch.nn.Conv2d(
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in_channels, out_channels, kernel_size=3, stride=1, padding=1
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)
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if temb_channels > 0:
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self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
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self.norm2 = Normalize(out_channels)
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self.dropout = torch.nn.Dropout(dropout)
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self.conv2 = torch.nn.Conv2d(
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out_channels, out_channels, kernel_size=3, stride=1, padding=1
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)
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if self.in_channels != self.out_channels:
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if self.use_conv_shortcut:
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self.conv_shortcut = torch.nn.Conv2d(
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in_channels, out_channels, kernel_size=3, stride=1, padding=1
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)
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else:
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self.nin_shortcut = torch.nn.Conv2d(
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in_channels, out_channels, kernel_size=1, stride=1, padding=0
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)
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def forward(self, x, temb):
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h = x
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h = self.norm1(h)
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h = nonlinearity(h)
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h = self.conv1(h)
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if temb is not None:
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h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]
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h = self.norm2(h)
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h = nonlinearity(h)
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h = self.dropout(h)
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h = self.conv2(h)
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if self.in_channels != self.out_channels:
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if self.use_conv_shortcut:
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x = self.conv_shortcut(x)
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else:
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x = self.nin_shortcut(x)
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return x + h
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class LinAttnBlock(LinearAttention):
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"""to match AttnBlock usage"""
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def __init__(self, in_channels):
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super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
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class AttnBlock(nn.Module):
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def __init__(self, in_channels):
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super().__init__()
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self.in_channels = in_channels
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self.norm = Normalize(in_channels)
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self.q = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.k = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.v = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.proj_out = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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def attention(self, h_: torch.Tensor) -> torch.Tensor:
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h_ = self.norm(h_)
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q = self.q(h_)
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k = self.k(h_)
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v = self.v(h_)
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b, c, h, w = q.shape
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q, k, v = (
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rearrange(x, "b c h w -> b 1 (h w) c").contiguous() for x in (q, k, v)
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)
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h_ = torch.nn.functional.scaled_dot_product_attention(
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q, k, v
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) # scale is dim ** -0.5 per default
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# compute attention
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return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)
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def forward(self, x, **kwargs):
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h_ = x
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h_ = self.attention(h_)
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h_ = self.proj_out(h_)
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return x + h_
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class MemoryEfficientAttnBlock(nn.Module):
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"""
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Uses xformers efficient implementation,
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see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
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Note: this is a single-head self-attention operation
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"""
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#
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def __init__(self, in_channels):
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super().__init__()
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self.in_channels = in_channels
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self.norm = Normalize(in_channels)
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self.q = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.k = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.v = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.proj_out = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.attention_op: Optional[Any] = None
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def attention(self, h_: torch.Tensor) -> torch.Tensor:
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h_ = self.norm(h_)
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q = self.q(h_)
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k = self.k(h_)
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v = self.v(h_)
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# compute attention
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B, C, H, W = q.shape
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q, k, v = (rearrange(x, "b c h w -> b (h w) c") for x in (q, k, v))
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q, k, v = (
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t.unsqueeze(3)
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.reshape(B, t.shape[1], 1, C)
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.permute(0, 2, 1, 3)
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.reshape(B * 1, t.shape[1], C)
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.contiguous()
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for t in (q, k, v)
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)
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out = xformers.ops.memory_efficient_attention(
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q, k, v, attn_bias=None, op=self.attention_op
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)
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out = (
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out.unsqueeze(0)
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.reshape(B, 1, out.shape[1], C)
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.permute(0, 2, 1, 3)
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.reshape(B, out.shape[1], C)
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)
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return rearrange(out, "b (h w) c -> b c h w", b=B, h=H, w=W, c=C)
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def forward(self, x, **kwargs):
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h_ = x
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h_ = self.attention(h_)
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h_ = self.proj_out(h_)
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return x + h_
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class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
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def forward(self, x, context=None, mask=None, **unused_kwargs):
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b, c, h, w = x.shape
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x = rearrange(x, "b c h w -> b (h w) c")
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out = super().forward(x, context=context, mask=mask)
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out = rearrange(out, "b (h w) c -> b c h w", h=h, w=w, c=c)
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return x + out
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def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
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assert attn_type in [
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"vanilla",
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"vanilla-xformers",
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"memory-efficient-cross-attn",
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"linear",
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"none",
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], f"attn_type {attn_type} unknown"
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if not XFORMERS_IS_AVAILABLE and attn_type == "vanilla-xformers":
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logger.debug(
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f"Attention mode '{attn_type}' is not available. Falling back to vanilla attention. "
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f"This is not a problem in Pytorch >= 2.0. FYI, you are running with PyTorch version {torch.__version__}"
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)
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attn_type = "vanilla"
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# if (
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# version.parse(torch.__version__) < version.parse("2.0.0")
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# and attn_type != "none"
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# ):
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# assert XFORMERS_IS_AVAILABLE, (
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# f"We do not support vanilla attention in {torch.__version__} anymore, "
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# f"as it is too expensive. Please install xformers via e.g. 'pip install xformers==0.0.16'"
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# )
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# attn_type = "vanilla-xformers"
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# logger.info(f"making attention of type '{attn_type}' with {in_channels} in_channels")
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if attn_type == "vanilla":
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assert attn_kwargs is None
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return AttnBlock(in_channels)
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elif attn_type == "vanilla-xformers":
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# logger.info(
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# f"building MemoryEfficientAttnBlock with {in_channels} in_channels..."
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# )
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return MemoryEfficientAttnBlock(in_channels)
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elif type == "memory-efficient-cross-attn":
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attn_kwargs["query_dim"] = in_channels
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return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
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elif attn_type == "none":
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return nn.Identity(in_channels)
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else:
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return LinAttnBlock(in_channels)
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class Model(nn.Module):
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def __init__(
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self,
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*,
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ch,
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out_ch,
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ch_mult=(1, 2, 4, 8),
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num_res_blocks,
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attn_resolutions,
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dropout=0.0,
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resamp_with_conv=True,
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in_channels,
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resolution,
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use_timestep=True,
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use_linear_attn=False,
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attn_type="vanilla",
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):
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super().__init__()
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if use_linear_attn:
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attn_type = "linear"
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self.ch = ch
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self.temb_ch = self.ch * 4
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self.num_resolutions = len(ch_mult)
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self.num_res_blocks = num_res_blocks
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self.resolution = resolution
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self.in_channels = in_channels
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self.use_timestep = use_timestep
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if self.use_timestep:
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# timestep embedding
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self.temb = nn.Module()
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self.temb.dense = nn.ModuleList(
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[
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torch.nn.Linear(self.ch, self.temb_ch),
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torch.nn.Linear(self.temb_ch, self.temb_ch),
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]
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)
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# downsampling
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self.conv_in = torch.nn.Conv2d(
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in_channels, self.ch, kernel_size=3, stride=1, padding=1
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)
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curr_res = resolution
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in_ch_mult = (1, *tuple(ch_mult))
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self.down = nn.ModuleList()
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for i_level in range(self.num_resolutions):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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block_in = ch * in_ch_mult[i_level]
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block_out = ch * ch_mult[i_level]
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for i_block in range(self.num_res_blocks):
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block.append(
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ResnetBlock(
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in_channels=block_in,
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out_channels=block_out,
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temb_channels=self.temb_ch,
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dropout=dropout,
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)
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)
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block_in = block_out
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if curr_res in attn_resolutions:
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attn.append(make_attn(block_in, attn_type=attn_type))
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down = nn.Module()
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down.block = block
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down.attn = attn
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if i_level != self.num_resolutions - 1:
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down.downsample = Downsample(block_in, resamp_with_conv)
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curr_res = curr_res // 2
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self.down.append(down)
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# middle
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self.mid = nn.Module()
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self.mid.block_1 = ResnetBlock(
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in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout,
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)
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self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
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self.mid.block_2 = ResnetBlock(
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in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout,
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)
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# upsampling
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self.up = nn.ModuleList()
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for i_level in reversed(range(self.num_resolutions)):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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block_out = ch * ch_mult[i_level]
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skip_in = ch * ch_mult[i_level]
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for i_block in range(self.num_res_blocks + 1):
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if i_block == self.num_res_blocks:
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skip_in = ch * in_ch_mult[i_level]
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block.append(
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ResnetBlock(
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in_channels=block_in + skip_in,
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out_channels=block_out,
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temb_channels=self.temb_ch,
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dropout=dropout,
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)
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)
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block_in = block_out
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if curr_res in attn_resolutions:
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attn.append(make_attn(block_in, attn_type=attn_type))
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up = nn.Module()
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up.block = block
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up.attn = attn
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if i_level != 0:
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up.upsample = Upsample(block_in, resamp_with_conv)
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curr_res = curr_res * 2
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self.up.insert(0, up) # prepend to get consistent order
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# end
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self.norm_out = Normalize(block_in)
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self.conv_out = torch.nn.Conv2d(
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block_in, out_ch, kernel_size=3, stride=1, padding=1
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)
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def forward(self, x, t=None, context=None):
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# assert x.shape[2] == x.shape[3] == self.resolution
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if context is not None:
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# assume aligned context, cat along channel axis
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x = torch.cat((x, context), dim=1)
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if self.use_timestep:
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# timestep embedding
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assert t is not None
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temb = get_timestep_embedding(t, self.ch)
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temb = self.temb.dense[0](temb)
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temb = nonlinearity(temb)
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temb = self.temb.dense[1](temb)
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else:
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temb = None
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# downsampling
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hs = [self.conv_in(x)]
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for i_level in range(self.num_resolutions):
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for i_block in range(self.num_res_blocks):
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h = self.down[i_level].block[i_block](hs[-1], temb)
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if len(self.down[i_level].attn) > 0:
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h = self.down[i_level].attn[i_block](h)
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hs.append(h)
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if i_level != self.num_resolutions - 1:
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hs.append(self.down[i_level].downsample(hs[-1]))
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# middle
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h = hs[-1]
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h = self.mid.block_1(h, temb)
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h = self.mid.attn_1(h)
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h = self.mid.block_2(h, temb)
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# upsampling
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for i_level in reversed(range(self.num_resolutions)):
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for i_block in range(self.num_res_blocks + 1):
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h = self.up[i_level].block[i_block](
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torch.cat([h, hs.pop()], dim=1), temb
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)
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if len(self.up[i_level].attn) > 0:
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h = self.up[i_level].attn[i_block](h)
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if i_level != 0:
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h = self.up[i_level].upsample(h)
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# end
|
|
h = self.norm_out(h)
|
|
h = nonlinearity(h)
|
|
h = self.conv_out(h)
|
|
return h
|
|
|
|
def get_last_layer(self):
|
|
return self.conv_out.weight
|
|
|
|
|
|
class Encoder(nn.Module):
|
|
def __init__(
|
|
self,
|
|
*,
|
|
ch,
|
|
out_ch,
|
|
ch_mult=(1, 2, 4, 8),
|
|
num_res_blocks,
|
|
attn_resolutions,
|
|
dropout=0.0,
|
|
resamp_with_conv=True,
|
|
in_channels,
|
|
resolution,
|
|
z_channels,
|
|
double_z=True,
|
|
use_linear_attn=False,
|
|
attn_type="vanilla",
|
|
**ignore_kwargs,
|
|
):
|
|
super().__init__()
|
|
if use_linear_attn:
|
|
attn_type = "linear"
|
|
self.ch = ch
|
|
self.temb_ch = 0
|
|
self.num_resolutions = len(ch_mult)
|
|
self.num_res_blocks = num_res_blocks
|
|
self.resolution = resolution
|
|
self.in_channels = in_channels
|
|
|
|
# downsampling
|
|
self.conv_in = torch.nn.Conv2d(
|
|
in_channels, self.ch, kernel_size=3, stride=1, padding=1
|
|
)
|
|
|
|
curr_res = resolution
|
|
in_ch_mult = (1, *tuple(ch_mult))
|
|
self.in_ch_mult = in_ch_mult
|
|
self.down = nn.ModuleList()
|
|
for i_level in range(self.num_resolutions):
|
|
block = nn.ModuleList()
|
|
attn = nn.ModuleList()
|
|
block_in = ch * in_ch_mult[i_level]
|
|
block_out = ch * ch_mult[i_level]
|
|
for i_block in range(self.num_res_blocks):
|
|
block.append(
|
|
ResnetBlock(
|
|
in_channels=block_in,
|
|
out_channels=block_out,
|
|
temb_channels=self.temb_ch,
|
|
dropout=dropout,
|
|
)
|
|
)
|
|
block_in = block_out
|
|
if curr_res in attn_resolutions:
|
|
attn.append(make_attn(block_in, attn_type=attn_type))
|
|
down = nn.Module()
|
|
down.block = block
|
|
down.attn = attn
|
|
if i_level != self.num_resolutions - 1:
|
|
down.downsample = Downsample(block_in, resamp_with_conv)
|
|
curr_res = curr_res // 2
|
|
self.down.append(down)
|
|
|
|
# middle
|
|
self.mid = nn.Module()
|
|
self.mid.block_1 = ResnetBlock(
|
|
in_channels=block_in,
|
|
out_channels=block_in,
|
|
temb_channels=self.temb_ch,
|
|
dropout=dropout,
|
|
)
|
|
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
|
|
self.mid.block_2 = ResnetBlock(
|
|
in_channels=block_in,
|
|
out_channels=block_in,
|
|
temb_channels=self.temb_ch,
|
|
dropout=dropout,
|
|
)
|
|
|
|
# end
|
|
self.norm_out = Normalize(block_in)
|
|
self.conv_out = torch.nn.Conv2d(
|
|
block_in,
|
|
2 * z_channels if double_z else z_channels,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1,
|
|
)
|
|
|
|
def forward(self, x):
|
|
# timestep embedding
|
|
temb = None
|
|
|
|
# downsampling
|
|
hs = [self.conv_in(x)]
|
|
for i_level in range(self.num_resolutions):
|
|
for i_block in range(self.num_res_blocks):
|
|
h = self.down[i_level].block[i_block](hs[-1], temb)
|
|
if len(self.down[i_level].attn) > 0:
|
|
h = self.down[i_level].attn[i_block](h)
|
|
hs.append(h)
|
|
if i_level != self.num_resolutions - 1:
|
|
hs.append(self.down[i_level].downsample(hs[-1]))
|
|
|
|
# middle
|
|
h = hs[-1]
|
|
h = self.mid.block_1(h, temb)
|
|
h = self.mid.attn_1(h)
|
|
h = self.mid.block_2(h, temb)
|
|
|
|
# end
|
|
h = self.norm_out(h)
|
|
h = nonlinearity(h)
|
|
h = self.conv_out(h)
|
|
return h
|
|
|
|
|
|
class Decoder(nn.Module):
|
|
def __init__(
|
|
self,
|
|
*,
|
|
ch,
|
|
out_ch,
|
|
ch_mult=(1, 2, 4, 8),
|
|
num_res_blocks,
|
|
attn_resolutions,
|
|
dropout=0.0,
|
|
resamp_with_conv=True,
|
|
in_channels,
|
|
resolution,
|
|
z_channels,
|
|
give_pre_end=False,
|
|
tanh_out=False,
|
|
use_linear_attn=False,
|
|
attn_type="vanilla",
|
|
**ignorekwargs,
|
|
):
|
|
super().__init__()
|
|
if use_linear_attn:
|
|
attn_type = "linear"
|
|
self.ch = ch
|
|
self.temb_ch = 0
|
|
self.num_resolutions = len(ch_mult)
|
|
self.num_res_blocks = num_res_blocks
|
|
self.resolution = resolution
|
|
self.in_channels = in_channels
|
|
self.give_pre_end = give_pre_end
|
|
self.tanh_out = tanh_out
|
|
|
|
# compute in_ch_mult, block_in and curr_res at lowest res
|
|
(1, *tuple(ch_mult))
|
|
block_in = ch * ch_mult[self.num_resolutions - 1]
|
|
curr_res = resolution // 2 ** (self.num_resolutions - 1)
|
|
self.z_shape = (1, z_channels, curr_res, curr_res)
|
|
# logger.debug(
|
|
# "Working with z of shape {} = {} dimensions.".format(
|
|
# self.z_shape, np.prod(self.z_shape)
|
|
# )
|
|
# )
|
|
|
|
make_attn_cls = self._make_attn()
|
|
make_resblock_cls = self._make_resblock()
|
|
make_conv_cls = self._make_conv()
|
|
# z to block_in
|
|
self.conv_in = torch.nn.Conv2d(
|
|
z_channels, block_in, kernel_size=3, stride=1, padding=1
|
|
)
|
|
|
|
# middle
|
|
self.mid = nn.Module()
|
|
self.mid.block_1 = make_resblock_cls(
|
|
in_channels=block_in,
|
|
out_channels=block_in,
|
|
temb_channels=self.temb_ch,
|
|
dropout=dropout,
|
|
)
|
|
self.mid.attn_1 = make_attn_cls(block_in, attn_type=attn_type)
|
|
self.mid.block_2 = make_resblock_cls(
|
|
in_channels=block_in,
|
|
out_channels=block_in,
|
|
temb_channels=self.temb_ch,
|
|
dropout=dropout,
|
|
)
|
|
|
|
# upsampling
|
|
self.up = nn.ModuleList()
|
|
for i_level in reversed(range(self.num_resolutions)):
|
|
block = nn.ModuleList()
|
|
attn = nn.ModuleList()
|
|
block_out = ch * ch_mult[i_level]
|
|
for i_block in range(self.num_res_blocks + 1):
|
|
block.append(
|
|
make_resblock_cls(
|
|
in_channels=block_in,
|
|
out_channels=block_out,
|
|
temb_channels=self.temb_ch,
|
|
dropout=dropout,
|
|
)
|
|
)
|
|
block_in = block_out
|
|
if curr_res in attn_resolutions:
|
|
attn.append(make_attn_cls(block_in, attn_type=attn_type))
|
|
up = nn.Module()
|
|
up.block = block
|
|
up.attn = attn
|
|
if i_level != 0:
|
|
up.upsample = Upsample(block_in, resamp_with_conv)
|
|
curr_res = curr_res * 2
|
|
self.up.insert(0, up) # prepend to get consistent order
|
|
|
|
# end
|
|
self.norm_out = Normalize(block_in)
|
|
self.conv_out = make_conv_cls(
|
|
block_in, out_ch, kernel_size=3, stride=1, padding=1
|
|
)
|
|
|
|
def _make_attn(self) -> Callable:
|
|
return make_attn
|
|
|
|
def _make_resblock(self) -> Callable:
|
|
return ResnetBlock
|
|
|
|
def _make_conv(self) -> Callable:
|
|
return torch.nn.Conv2d
|
|
|
|
def get_last_layer(self, **kwargs):
|
|
return self.conv_out.weight
|
|
|
|
def forward(self, z, **kwargs):
|
|
# assert z.shape[1:] == self.z_shape[1:]
|
|
self.last_z_shape = z.shape
|
|
|
|
# timestep embedding
|
|
temb = None
|
|
|
|
# z to block_in
|
|
h = self.conv_in(z)
|
|
|
|
# middle
|
|
h = self.mid.block_1(h, temb, **kwargs)
|
|
h = self.mid.attn_1(h, **kwargs)
|
|
h = self.mid.block_2(h, temb, **kwargs)
|
|
|
|
# upsampling
|
|
for i_level in reversed(range(self.num_resolutions)):
|
|
for i_block in range(self.num_res_blocks + 1):
|
|
h = self.up[i_level].block[i_block](h, temb, **kwargs)
|
|
if len(self.up[i_level].attn) > 0:
|
|
h = self.up[i_level].attn[i_block](h, **kwargs)
|
|
if i_level != 0:
|
|
h = self.up[i_level].upsample(h)
|
|
|
|
# end
|
|
if self.give_pre_end:
|
|
return h
|
|
|
|
h = self.norm_out(h)
|
|
h = nonlinearity(h)
|
|
h = self.conv_out(h, **kwargs)
|
|
if self.tanh_out:
|
|
h = torch.tanh(h)
|
|
return h
|