938 lines
37 KiB
Python
938 lines
37 KiB
Python
# pytorch_diffusion + derived encoder decoder
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import math
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import torch
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import torch.nn as nn
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import numpy as np
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from einops import rearrange
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from typing import Optional, Any
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from ..attention import MemoryEfficientCrossAttention
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from comfy import model_management
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if model_management.xformers_enabled_vae():
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import xformers
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import xformers.ops
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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(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
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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(in_channels,
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in_channels,
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kernel_size=3,
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stride=1,
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padding=1)
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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(in_channels,
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in_channels,
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kernel_size=3,
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stride=2,
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padding=0)
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def forward(self, x, already_padded=False):
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if self.with_conv:
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if not already_padded:
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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__(self, *, in_channels, out_channels=None, conv_shortcut=False,
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dropout, temb_channels=512):
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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.swish = torch.nn.SiLU(inplace=True)
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self.norm1 = Normalize(in_channels)
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self.conv1 = torch.nn.Conv2d(in_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=1)
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if temb_channels > 0:
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self.temb_proj = torch.nn.Linear(temb_channels,
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out_channels)
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self.norm2 = Normalize(out_channels)
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self.dropout = torch.nn.Dropout(dropout, inplace=True)
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self.conv2 = torch.nn.Conv2d(out_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=1)
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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(in_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=1)
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else:
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self.nin_shortcut = torch.nn.Conv2d(in_channels,
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out_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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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 = self.swish(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(self.swish(temb))[:,:,None,None]
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h = self.norm2(h)
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h = self.swish(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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def slice_attention(q, k, v):
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r1 = torch.zeros_like(k, device=q.device)
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scale = (int(q.shape[-1])**(-0.5))
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mem_free_total = model_management.get_free_memory(q.device)
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gb = 1024 ** 3
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tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
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modifier = 3 if q.element_size() == 2 else 2.5
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mem_required = tensor_size * modifier
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steps = 1
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if mem_required > mem_free_total:
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steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
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while True:
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try:
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slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
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for i in range(0, q.shape[1], slice_size):
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end = i + slice_size
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s1 = torch.bmm(q[:, i:end], k) * scale
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s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1)
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del s1
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r1[:, :, i:end] = torch.bmm(v, s2)
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del s2
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break
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except model_management.OOM_EXCEPTION as e:
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steps *= 2
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if steps > 128:
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raise e
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print("out of memory error, increasing steps and trying again", steps)
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return r1
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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(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.k = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.v = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.proj_out = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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def forward(self, x):
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h_ = x
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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 = q.reshape(b,c,h*w)
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q = q.permute(0,2,1) # b,hw,c
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k = k.reshape(b,c,h*w) # b,c,hw
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v = v.reshape(b,c,h*w)
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r1 = slice_attention(q, k, v)
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h_ = r1.reshape(b,c,h,w)
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del r1
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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(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.k = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.v = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.proj_out = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.attention_op: Optional[Any] = None
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def forward(self, x):
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h_ = x
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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 = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v))
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q, k, v = map(
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lambda t: 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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(q, k, v),
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)
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out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
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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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out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C)
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out = self.proj_out(out)
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return x+out
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class MemoryEfficientAttnBlockPytorch(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(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.k = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.v = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.proj_out = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.attention_op: Optional[Any] = None
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def forward(self, x):
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h_ = x
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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 = map(
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lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
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(q, k, v),
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)
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try:
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out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
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out = out.transpose(2, 3).reshape(B, C, H, W)
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except model_management.OOM_EXCEPTION as e:
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print("scaled_dot_product_attention OOMed: switched to slice attention")
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out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
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out = self.proj_out(out)
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return x+out
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class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
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def forward(self, x, context=None, mask=None):
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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 ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown'
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if model_management.xformers_enabled_vae() and attn_type == "vanilla":
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attn_type = "vanilla-xformers"
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if model_management.pytorch_attention_enabled() and attn_type == "vanilla":
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attn_type = "vanilla-pytorch"
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print(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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print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
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return MemoryEfficientAttnBlock(in_channels)
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elif attn_type == "vanilla-pytorch":
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return MemoryEfficientAttnBlockPytorch(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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raise NotImplementedError()
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class Model(nn.Module):
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def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
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attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
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resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
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super().__init__()
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if use_linear_attn: 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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torch.nn.Linear(self.ch,
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self.temb_ch),
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torch.nn.Linear(self.temb_ch,
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self.temb_ch),
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])
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# downsampling
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self.conv_in = torch.nn.Conv2d(in_channels,
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self.ch,
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kernel_size=3,
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stride=1,
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padding=1)
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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(ResnetBlock(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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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(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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self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
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self.mid.block_2 = ResnetBlock(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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# 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(ResnetBlock(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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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(block_in,
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out_ch,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1)
|
|
|
|
def forward(self, x, t=None, context=None):
|
|
#assert x.shape[2] == x.shape[3] == self.resolution
|
|
if context is not None:
|
|
# assume aligned context, cat along channel axis
|
|
x = torch.cat((x, context), dim=1)
|
|
if self.use_timestep:
|
|
# timestep embedding
|
|
assert t is not None
|
|
temb = get_timestep_embedding(t, self.ch)
|
|
temb = self.temb.dense[0](temb)
|
|
temb = nonlinearity(temb)
|
|
temb = self.temb.dense[1](temb)
|
|
else:
|
|
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)
|
|
|
|
# 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](
|
|
torch.cat([h, hs.pop()], dim=1), temb)
|
|
if len(self.up[i_level].attn) > 0:
|
|
h = self.up[i_level].attn[i_block](h)
|
|
if i_level != 0:
|
|
h = self.up[i_level].upsample(h)
|
|
|
|
# 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
|
|
pad = (0,1,0,1)
|
|
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
|
|
already_padded = True
|
|
# downsampling
|
|
h = 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](h, temb)
|
|
if len(self.down[i_level].attn) > 0:
|
|
h = self.down[i_level].attn[i_block](h)
|
|
if i_level != self.num_resolutions-1:
|
|
h = self.down[i_level].downsample(h, already_padded)
|
|
already_padded = False
|
|
|
|
# middle
|
|
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
|
|
in_ch_mult = (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)
|
|
print("Working with z of shape {} = {} dimensions.".format(
|
|
self.z_shape, np.prod(self.z_shape)))
|
|
|
|
# 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 = 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)
|
|
|
|
# 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(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))
|
|
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 = torch.nn.Conv2d(block_in,
|
|
out_ch,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1)
|
|
|
|
def forward(self, z):
|
|
#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)
|
|
h = self.mid.attn_1(h)
|
|
h = self.mid.block_2(h, temb)
|
|
|
|
# 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)
|
|
if len(self.up[i_level].attn) > 0:
|
|
h = self.up[i_level].attn[i_block](h)
|
|
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)
|
|
if self.tanh_out:
|
|
h = torch.tanh(h)
|
|
return h
|
|
|
|
|
|
class SimpleDecoder(nn.Module):
|
|
def __init__(self, in_channels, out_channels, *args, **kwargs):
|
|
super().__init__()
|
|
self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
|
|
ResnetBlock(in_channels=in_channels,
|
|
out_channels=2 * in_channels,
|
|
temb_channels=0, dropout=0.0),
|
|
ResnetBlock(in_channels=2 * in_channels,
|
|
out_channels=4 * in_channels,
|
|
temb_channels=0, dropout=0.0),
|
|
ResnetBlock(in_channels=4 * in_channels,
|
|
out_channels=2 * in_channels,
|
|
temb_channels=0, dropout=0.0),
|
|
nn.Conv2d(2*in_channels, in_channels, 1),
|
|
Upsample(in_channels, with_conv=True)])
|
|
# end
|
|
self.norm_out = Normalize(in_channels)
|
|
self.conv_out = torch.nn.Conv2d(in_channels,
|
|
out_channels,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1)
|
|
|
|
def forward(self, x):
|
|
for i, layer in enumerate(self.model):
|
|
if i in [1,2,3]:
|
|
x = layer(x, None)
|
|
else:
|
|
x = layer(x)
|
|
|
|
h = self.norm_out(x)
|
|
h = nonlinearity(h)
|
|
x = self.conv_out(h)
|
|
return x
|
|
|
|
|
|
class UpsampleDecoder(nn.Module):
|
|
def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
|
|
ch_mult=(2,2), dropout=0.0):
|
|
super().__init__()
|
|
# upsampling
|
|
self.temb_ch = 0
|
|
self.num_resolutions = len(ch_mult)
|
|
self.num_res_blocks = num_res_blocks
|
|
block_in = in_channels
|
|
curr_res = resolution // 2 ** (self.num_resolutions - 1)
|
|
self.res_blocks = nn.ModuleList()
|
|
self.upsample_blocks = nn.ModuleList()
|
|
for i_level in range(self.num_resolutions):
|
|
res_block = []
|
|
block_out = ch * ch_mult[i_level]
|
|
for i_block in range(self.num_res_blocks + 1):
|
|
res_block.append(ResnetBlock(in_channels=block_in,
|
|
out_channels=block_out,
|
|
temb_channels=self.temb_ch,
|
|
dropout=dropout))
|
|
block_in = block_out
|
|
self.res_blocks.append(nn.ModuleList(res_block))
|
|
if i_level != self.num_resolutions - 1:
|
|
self.upsample_blocks.append(Upsample(block_in, True))
|
|
curr_res = curr_res * 2
|
|
|
|
# end
|
|
self.norm_out = Normalize(block_in)
|
|
self.conv_out = torch.nn.Conv2d(block_in,
|
|
out_channels,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1)
|
|
|
|
def forward(self, x):
|
|
# upsampling
|
|
h = x
|
|
for k, i_level in enumerate(range(self.num_resolutions)):
|
|
for i_block in range(self.num_res_blocks + 1):
|
|
h = self.res_blocks[i_level][i_block](h, None)
|
|
if i_level != self.num_resolutions - 1:
|
|
h = self.upsample_blocks[k](h)
|
|
h = self.norm_out(h)
|
|
h = nonlinearity(h)
|
|
h = self.conv_out(h)
|
|
return h
|
|
|
|
|
|
class LatentRescaler(nn.Module):
|
|
def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
|
|
super().__init__()
|
|
# residual block, interpolate, residual block
|
|
self.factor = factor
|
|
self.conv_in = nn.Conv2d(in_channels,
|
|
mid_channels,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1)
|
|
self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
|
|
out_channels=mid_channels,
|
|
temb_channels=0,
|
|
dropout=0.0) for _ in range(depth)])
|
|
self.attn = AttnBlock(mid_channels)
|
|
self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
|
|
out_channels=mid_channels,
|
|
temb_channels=0,
|
|
dropout=0.0) for _ in range(depth)])
|
|
|
|
self.conv_out = nn.Conv2d(mid_channels,
|
|
out_channels,
|
|
kernel_size=1,
|
|
)
|
|
|
|
def forward(self, x):
|
|
x = self.conv_in(x)
|
|
for block in self.res_block1:
|
|
x = block(x, None)
|
|
x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
|
|
x = self.attn(x)
|
|
for block in self.res_block2:
|
|
x = block(x, None)
|
|
x = self.conv_out(x)
|
|
return x
|
|
|
|
|
|
class MergedRescaleEncoder(nn.Module):
|
|
def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
|
|
attn_resolutions, dropout=0.0, resamp_with_conv=True,
|
|
ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
|
|
super().__init__()
|
|
intermediate_chn = ch * ch_mult[-1]
|
|
self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
|
|
z_channels=intermediate_chn, double_z=False, resolution=resolution,
|
|
attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
|
|
out_ch=None)
|
|
self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
|
|
mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
|
|
|
|
def forward(self, x):
|
|
x = self.encoder(x)
|
|
x = self.rescaler(x)
|
|
return x
|
|
|
|
|
|
class MergedRescaleDecoder(nn.Module):
|
|
def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
|
|
dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
|
|
super().__init__()
|
|
tmp_chn = z_channels*ch_mult[-1]
|
|
self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
|
|
resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
|
|
ch_mult=ch_mult, resolution=resolution, ch=ch)
|
|
self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
|
|
out_channels=tmp_chn, depth=rescale_module_depth)
|
|
|
|
def forward(self, x):
|
|
x = self.rescaler(x)
|
|
x = self.decoder(x)
|
|
return x
|
|
|
|
|
|
class Upsampler(nn.Module):
|
|
def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
|
|
super().__init__()
|
|
assert out_size >= in_size
|
|
num_blocks = int(np.log2(out_size//in_size))+1
|
|
factor_up = 1.+ (out_size % in_size)
|
|
print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
|
|
self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
|
|
out_channels=in_channels)
|
|
self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
|
|
attn_resolutions=[], in_channels=None, ch=in_channels,
|
|
ch_mult=[ch_mult for _ in range(num_blocks)])
|
|
|
|
def forward(self, x):
|
|
x = self.rescaler(x)
|
|
x = self.decoder(x)
|
|
return x
|
|
|
|
|
|
class Resize(nn.Module):
|
|
def __init__(self, in_channels=None, learned=False, mode="bilinear"):
|
|
super().__init__()
|
|
self.with_conv = learned
|
|
self.mode = mode
|
|
if self.with_conv:
|
|
print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
|
|
raise NotImplementedError()
|
|
assert in_channels is not None
|
|
# no asymmetric padding in torch conv, must do it ourselves
|
|
self.conv = torch.nn.Conv2d(in_channels,
|
|
in_channels,
|
|
kernel_size=4,
|
|
stride=2,
|
|
padding=1)
|
|
|
|
def forward(self, x, scale_factor=1.0):
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|
if scale_factor==1.0:
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|
return x
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|
else:
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|
x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
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|
return x
|