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https://github.com/hpcaitech/ColossalAI.git
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add ColoDiffusion codes: /ldm/module/, /ldm/data/, /scripts/test/
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Maruyama_Aya
862
examples/images/diffusion/ldm/modules/diffusionmodules/model.py
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862
examples/images/diffusion/ldm/modules/diffusionmodules/model.py
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# 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 ldm.util import instantiate_from_config
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from ldm.modules.attention import LinearAttention
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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):
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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__(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.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)
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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 = 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(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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w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
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w_ = w_ * (int(c)**(-0.5))
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w_ = torch.nn.functional.softmax(w_, dim=2)
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# attend to values
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v = v.reshape(b,c,h*w)
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w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
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h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
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h_ = h_.reshape(b,c,h,w)
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h_ = self.proj_out(h_)
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return x+h_
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def make_attn(in_channels, attn_type="vanilla"):
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assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
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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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return AttnBlock(in_channels)
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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 temb_module(nn.Module):
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def __init__(self):
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super().__init__()
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pass
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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 = temb_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 = Down_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 = Mid_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 = Up_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,
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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, 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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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
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h = self.norm_out(h)
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h = nonlinearity(h)
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h = self.conv_out(h)
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return h
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def get_last_layer(self):
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return self.conv_out.weight
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class Down_module(nn.Module):
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def __init__(self):
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super().__init__()
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pass
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class Up_module(nn.Module):
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def __init__(self):
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super().__init__()
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pass
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class Mid_module(nn.Module):
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def __init__(self):
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super().__init__()
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pass
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class Encoder(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, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
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**ignore_kwargs):
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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 = 0
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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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# 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.in_ch_mult = in_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 = Down_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 = Mid_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,
|
||||
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
|
||||
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 = Mid_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 = Up_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):
|
||||
if scale_factor==1.0:
|
||||
return x
|
||||
else:
|
||||
x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
|
||||
return x
|
||||
|
||||
class FirstStagePostProcessor(nn.Module):
|
||||
|
||||
def __init__(self, ch_mult:list, in_channels,
|
||||
pretrained_model:nn.Module=None,
|
||||
reshape=False,
|
||||
n_channels=None,
|
||||
dropout=0.,
|
||||
pretrained_config=None):
|
||||
super().__init__()
|
||||
if pretrained_config is None:
|
||||
assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
|
||||
self.pretrained_model = pretrained_model
|
||||
else:
|
||||
assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
|
||||
self.instantiate_pretrained(pretrained_config)
|
||||
|
||||
self.do_reshape = reshape
|
||||
|
||||
if n_channels is None:
|
||||
n_channels = self.pretrained_model.encoder.ch
|
||||
|
||||
self.proj_norm = Normalize(in_channels,num_groups=in_channels//2)
|
||||
self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3,
|
||||
stride=1,padding=1)
|
||||
|
||||
blocks = []
|
||||
downs = []
|
||||
ch_in = n_channels
|
||||
for m in ch_mult:
|
||||
blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout))
|
||||
ch_in = m * n_channels
|
||||
downs.append(Downsample(ch_in, with_conv=False))
|
||||
|
||||
self.model = nn.ModuleList(blocks)
|
||||
self.downsampler = nn.ModuleList(downs)
|
||||
|
||||
|
||||
def instantiate_pretrained(self, config):
|
||||
model = instantiate_from_config(config)
|
||||
self.pretrained_model = model.eval()
|
||||
# self.pretrained_model.train = False
|
||||
for param in self.pretrained_model.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def encode_with_pretrained(self,x):
|
||||
c = self.pretrained_model.encode(x)
|
||||
if isinstance(c, DiagonalGaussianDistribution):
|
||||
c = c.mode()
|
||||
return c
|
||||
|
||||
def forward(self,x):
|
||||
z_fs = self.encode_with_pretrained(x)
|
||||
z = self.proj_norm(z_fs)
|
||||
z = self.proj(z)
|
||||
z = nonlinearity(z)
|
||||
|
||||
for submodel, downmodel in zip(self.model,self.downsampler):
|
||||
z = submodel(z,temb=None)
|
||||
z = downmodel(z)
|
||||
|
||||
if self.do_reshape:
|
||||
z = rearrange(z,'b c h w -> b (h w) c')
|
||||
return z
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
276
examples/images/diffusion/ldm/modules/diffusionmodules/util.py
Normal file
276
examples/images/diffusion/ldm/modules/diffusionmodules/util.py
Normal file
@@ -0,0 +1,276 @@
|
||||
# adopted from
|
||||
# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
|
||||
# and
|
||||
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
|
||||
# and
|
||||
# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
|
||||
#
|
||||
# thanks!
|
||||
|
||||
|
||||
import os
|
||||
import math
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from einops import repeat
|
||||
|
||||
from ldm.util import instantiate_from_config
|
||||
|
||||
|
||||
def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
|
||||
if schedule == "linear":
|
||||
betas = (
|
||||
torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
|
||||
)
|
||||
|
||||
elif schedule == "cosine":
|
||||
timesteps = (
|
||||
torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
|
||||
)
|
||||
alphas = timesteps / (1 + cosine_s) * np.pi / 2
|
||||
alphas = torch.cos(alphas).pow(2)
|
||||
alphas = alphas / alphas[0]
|
||||
betas = 1 - alphas[1:] / alphas[:-1]
|
||||
betas = np.clip(betas, a_min=0, a_max=0.999)
|
||||
|
||||
elif schedule == "sqrt_linear":
|
||||
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
|
||||
elif schedule == "sqrt":
|
||||
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
|
||||
else:
|
||||
raise ValueError(f"schedule '{schedule}' unknown.")
|
||||
return betas.numpy()
|
||||
|
||||
|
||||
def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
|
||||
if ddim_discr_method == 'uniform':
|
||||
c = num_ddpm_timesteps // num_ddim_timesteps
|
||||
ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
|
||||
elif ddim_discr_method == 'quad':
|
||||
ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
|
||||
else:
|
||||
raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
|
||||
|
||||
# assert ddim_timesteps.shape[0] == num_ddim_timesteps
|
||||
# add one to get the final alpha values right (the ones from first scale to data during sampling)
|
||||
steps_out = ddim_timesteps + 1
|
||||
if verbose:
|
||||
print(f'Selected timesteps for ddim sampler: {steps_out}')
|
||||
return steps_out
|
||||
|
||||
|
||||
def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
|
||||
# select alphas for computing the variance schedule
|
||||
alphas = alphacums[ddim_timesteps]
|
||||
alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
|
||||
|
||||
# according the the formula provided in https://arxiv.org/abs/2010.02502
|
||||
sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
|
||||
if verbose:
|
||||
print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
|
||||
print(f'For the chosen value of eta, which is {eta}, '
|
||||
f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
|
||||
return sigmas, alphas, alphas_prev
|
||||
|
||||
|
||||
def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
|
||||
"""
|
||||
Create a beta schedule that discretizes the given alpha_t_bar function,
|
||||
which defines the cumulative product of (1-beta) over time from t = [0,1].
|
||||
:param num_diffusion_timesteps: the number of betas to produce.
|
||||
:param alpha_bar: a lambda that takes an argument t from 0 to 1 and
|
||||
produces the cumulative product of (1-beta) up to that
|
||||
part of the diffusion process.
|
||||
:param max_beta: the maximum beta to use; use values lower than 1 to
|
||||
prevent singularities.
|
||||
"""
|
||||
betas = []
|
||||
for i in range(num_diffusion_timesteps):
|
||||
t1 = i / num_diffusion_timesteps
|
||||
t2 = (i + 1) / num_diffusion_timesteps
|
||||
betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
|
||||
return np.array(betas)
|
||||
|
||||
|
||||
def extract_into_tensor(a, t, x_shape):
|
||||
b, *_ = t.shape
|
||||
out = a.gather(-1, t)
|
||||
return out.reshape(b, *((1,) * (len(x_shape) - 1)))
|
||||
|
||||
|
||||
def checkpoint(func, inputs, params, flag):
|
||||
"""
|
||||
Evaluate a function without caching intermediate activations, allowing for
|
||||
reduced memory at the expense of extra compute in the backward pass.
|
||||
:param func: the function to evaluate.
|
||||
:param inputs: the argument sequence to pass to `func`.
|
||||
:param params: a sequence of parameters `func` depends on but does not
|
||||
explicitly take as arguments.
|
||||
:param flag: if False, disable gradient checkpointing.
|
||||
"""
|
||||
if flag:
|
||||
args = tuple(inputs) + tuple(params)
|
||||
return CheckpointFunction.apply(func, len(inputs), *args)
|
||||
else:
|
||||
return func(*inputs)
|
||||
|
||||
|
||||
class CheckpointFunction(torch.autograd.Function):
|
||||
@staticmethod
|
||||
def forward(ctx, run_function, length, *args):
|
||||
ctx.run_function = run_function
|
||||
ctx.input_tensors = list(args[:length])
|
||||
ctx.input_params = list(args[length:])
|
||||
|
||||
with torch.no_grad():
|
||||
output_tensors = ctx.run_function(*ctx.input_tensors)
|
||||
return output_tensors
|
||||
|
||||
@staticmethod
|
||||
def backward(ctx, *output_grads):
|
||||
ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
|
||||
with torch.enable_grad():
|
||||
# Fixes a bug where the first op in run_function modifies the
|
||||
# Tensor storage in place, which is not allowed for detach()'d
|
||||
# Tensors.
|
||||
shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
|
||||
output_tensors = ctx.run_function(*shallow_copies)
|
||||
input_grads = torch.autograd.grad(
|
||||
output_tensors,
|
||||
ctx.input_tensors + ctx.input_params,
|
||||
output_grads,
|
||||
allow_unused=True,
|
||||
)
|
||||
del ctx.input_tensors
|
||||
del ctx.input_params
|
||||
del output_tensors
|
||||
return (None, None) + input_grads
|
||||
|
||||
|
||||
def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False, use_fp16=True):
|
||||
"""
|
||||
Create sinusoidal timestep embeddings.
|
||||
:param timesteps: a 1-D Tensor of N indices, one per batch element.
|
||||
These may be fractional.
|
||||
:param dim: the dimension of the output.
|
||||
:param max_period: controls the minimum frequency of the embeddings.
|
||||
:return: an [N x dim] Tensor of positional embeddings.
|
||||
"""
|
||||
if not repeat_only:
|
||||
half = dim // 2
|
||||
freqs = torch.exp(
|
||||
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
|
||||
).to(device=timesteps.device)
|
||||
args = timesteps[:, None].float() * freqs[None]
|
||||
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
|
||||
if dim % 2:
|
||||
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
|
||||
else:
|
||||
embedding = repeat(timesteps, 'b -> b d', d=dim)
|
||||
if use_fp16:
|
||||
return embedding.half()
|
||||
else:
|
||||
return embedding
|
||||
|
||||
|
||||
def zero_module(module):
|
||||
"""
|
||||
Zero out the parameters of a module and return it.
|
||||
"""
|
||||
for p in module.parameters():
|
||||
p.detach().zero_()
|
||||
return module
|
||||
|
||||
|
||||
def scale_module(module, scale):
|
||||
"""
|
||||
Scale the parameters of a module and return it.
|
||||
"""
|
||||
for p in module.parameters():
|
||||
p.detach().mul_(scale)
|
||||
return module
|
||||
|
||||
|
||||
def mean_flat(tensor):
|
||||
"""
|
||||
Take the mean over all non-batch dimensions.
|
||||
"""
|
||||
return tensor.mean(dim=list(range(1, len(tensor.shape))))
|
||||
|
||||
|
||||
def normalization(channels, precision=16):
|
||||
"""
|
||||
Make a standard normalization layer.
|
||||
:param channels: number of input channels.
|
||||
:return: an nn.Module for normalization.
|
||||
"""
|
||||
if precision == 16:
|
||||
return GroupNorm16(16, channels)
|
||||
else:
|
||||
return GroupNorm32(32, channels)
|
||||
|
||||
|
||||
# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
|
||||
class SiLU(nn.Module):
|
||||
def forward(self, x):
|
||||
return x * torch.sigmoid(x)
|
||||
|
||||
class GroupNorm16(nn.GroupNorm):
|
||||
def forward(self, x):
|
||||
return super().forward(x.half()).type(x.dtype)
|
||||
|
||||
class GroupNorm32(nn.GroupNorm):
|
||||
def forward(self, x):
|
||||
return super().forward(x.float()).type(x.dtype)
|
||||
|
||||
def conv_nd(dims, *args, **kwargs):
|
||||
"""
|
||||
Create a 1D, 2D, or 3D convolution module.
|
||||
"""
|
||||
if dims == 1:
|
||||
return nn.Conv1d(*args, **kwargs)
|
||||
elif dims == 2:
|
||||
return nn.Conv2d(*args, **kwargs)
|
||||
elif dims == 3:
|
||||
return nn.Conv3d(*args, **kwargs)
|
||||
raise ValueError(f"unsupported dimensions: {dims}")
|
||||
|
||||
|
||||
def linear(*args, **kwargs):
|
||||
"""
|
||||
Create a linear module.
|
||||
"""
|
||||
return nn.Linear(*args, **kwargs)
|
||||
|
||||
|
||||
def avg_pool_nd(dims, *args, **kwargs):
|
||||
"""
|
||||
Create a 1D, 2D, or 3D average pooling module.
|
||||
"""
|
||||
if dims == 1:
|
||||
return nn.AvgPool1d(*args, **kwargs)
|
||||
elif dims == 2:
|
||||
return nn.AvgPool2d(*args, **kwargs)
|
||||
elif dims == 3:
|
||||
return nn.AvgPool3d(*args, **kwargs)
|
||||
raise ValueError(f"unsupported dimensions: {dims}")
|
||||
|
||||
|
||||
class HybridConditioner(nn.Module):
|
||||
|
||||
def __init__(self, c_concat_config, c_crossattn_config):
|
||||
super().__init__()
|
||||
self.concat_conditioner = instantiate_from_config(c_concat_config)
|
||||
self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
|
||||
|
||||
def forward(self, c_concat, c_crossattn):
|
||||
c_concat = self.concat_conditioner(c_concat)
|
||||
c_crossattn = self.crossattn_conditioner(c_crossattn)
|
||||
return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
|
||||
|
||||
|
||||
def noise_like(shape, device, repeat=False):
|
||||
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
|
||||
noise = lambda: torch.randn(shape, device=device)
|
||||
return repeat_noise() if repeat else noise()
|
||||
Reference in New Issue
Block a user