mirror of
https://github.com/wassname/denoising-diffusion-pytorch.git
synced 2026-10-04 12:30:22 +08:00
584 lines
20 KiB
Python
584 lines
20 KiB
Python
import math
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import copy
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import torch
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from torch import nn, einsum
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import torch.nn.functional as F
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from inspect import isfunction
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from functools import partial
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from torch.utils import data
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from torch.cuda.amp import autocast, GradScaler
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from pathlib import Path
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from torch.optim import Adam
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from torchvision import transforms, utils
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from PIL import Image
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from tqdm import tqdm
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from einops import rearrange
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# helpers functions
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def exists(x):
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return x is not None
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def default(val, d):
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if exists(val):
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return val
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return d() if isfunction(d) else d
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def cycle(dl):
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while True:
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for data in dl:
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yield data
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def num_to_groups(num, divisor):
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groups = num // divisor
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remainder = num % divisor
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arr = [divisor] * groups
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if remainder > 0:
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arr.append(remainder)
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return arr
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# small helper modules
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class EMA():
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def __init__(self, beta):
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super().__init__()
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self.beta = beta
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def update_model_average(self, ma_model, current_model):
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for current_params, ma_params in zip(current_model.parameters(), ma_model.parameters()):
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old_weight, up_weight = ma_params.data, current_params.data
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ma_params.data = self.update_average(old_weight, up_weight)
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def update_average(self, old, new):
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if old is None:
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return new
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return old * self.beta + (1 - self.beta) * new
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class Residual(nn.Module):
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def __init__(self, fn):
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super().__init__()
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self.fn = fn
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def forward(self, x, *args, **kwargs):
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return self.fn(x, *args, **kwargs) + x
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class SinusoidalPosEmb(nn.Module):
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def __init__(self, dim):
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super().__init__()
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self.dim = dim
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def forward(self, x):
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device = x.device
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half_dim = self.dim // 2
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emb = math.log(10000) / (half_dim - 1)
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emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
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emb = x[:, None] * emb[None, :]
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emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
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return emb
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def Upsample(dim):
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return nn.ConvTranspose2d(dim, dim, 4, 2, 1)
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def Downsample(dim):
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return nn.Conv2d(dim, dim, 4, 2, 1)
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class LayerNorm(nn.Module):
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def __init__(self, dim, eps = 1e-5):
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super().__init__()
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self.eps = eps
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self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
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self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
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def forward(self, x):
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var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
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mean = torch.mean(x, dim = 1, keepdim = True)
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return (x - mean) / (var + self.eps).sqrt() * self.g + self.b
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class PreNorm(nn.Module):
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def __init__(self, dim, fn):
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super().__init__()
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self.fn = fn
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self.norm = LayerNorm(dim)
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def forward(self, x):
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x = self.norm(x)
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return self.fn(x)
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# building block modules
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class ConvNextBlock(nn.Module):
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""" https://arxiv.org/abs/2201.03545 """
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def __init__(self, dim, dim_out, *, time_emb_dim = None, mult = 2, norm = True):
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super().__init__()
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self.mlp = nn.Sequential(
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nn.GELU(),
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nn.Linear(time_emb_dim, dim)
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) if exists(time_emb_dim) else None
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self.ds_conv = nn.Conv2d(dim, dim, 7, padding = 3, groups = dim)
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self.net = nn.Sequential(
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LayerNorm(dim) if norm else nn.Identity(),
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nn.Conv2d(dim, dim_out * mult, 3, padding = 1),
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nn.GELU(),
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nn.Conv2d(dim_out * mult, dim_out, 3, padding = 1)
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)
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self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
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def forward(self, x, time_emb = None):
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h = self.ds_conv(x)
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if exists(self.mlp):
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assert exists(time_emb), 'time emb must be passed in'
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condition = self.mlp(time_emb)
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h = h + rearrange(condition, 'b c -> b c 1 1')
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h = self.net(h)
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return h + self.res_conv(x)
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class LinearAttention(nn.Module):
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def __init__(self, dim, heads = 4, dim_head = 32):
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super().__init__()
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self.scale = dim_head ** -0.5
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self.heads = heads
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hidden_dim = dim_head * heads
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self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
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self.to_out = nn.Conv2d(hidden_dim, dim, 1)
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def forward(self, x):
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b, c, h, w = x.shape
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qkv = self.to_qkv(x).chunk(3, dim = 1)
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q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> b h c (x y)', h = self.heads), qkv)
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q = q * self.scale
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k = k.softmax(dim = -1)
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context = torch.einsum('b h d n, b h e n -> b h d e', k, v)
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out = torch.einsum('b h d e, b h d n -> b h e n', context, q)
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out = rearrange(out, 'b h c (x y) -> b (h c) x y', h = self.heads, x = h, y = w)
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return self.to_out(out)
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class Attention(nn.Module):
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def __init__(self, dim, heads = 4, dim_head = 32):
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super().__init__()
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self.scale = dim_head ** -0.5
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self.heads = heads
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hidden_dim = dim_head * heads
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self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
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self.to_out = nn.Conv2d(hidden_dim, dim, 1)
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def forward(self, x):
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b, c, h, w = x.shape
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qkv = self.to_qkv(x).chunk(3, dim = 1)
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q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> b h c (x y)', h = self.heads), qkv)
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q = q * self.scale
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sim = einsum('b h d i, b h d j -> b h i j', q, k)
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sim = sim - sim.amax(dim = -1, keepdim = True).detach()
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attn = sim.softmax(dim = -1)
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out = einsum('b h i j, b h d j -> b h i d', attn, v)
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out = rearrange(out, 'b h (x y) d -> b (h d) x y', x = h, y = w)
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return self.to_out(out)
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# model
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class Unet(nn.Module):
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def __init__(
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self,
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dim,
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out_dim = None,
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dim_mults=(1, 2, 4, 8),
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channels = 3,
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with_time_emb = True
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):
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super().__init__()
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self.channels = channels
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dims = [channels, *map(lambda m: dim * m, dim_mults)]
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in_out = list(zip(dims[:-1], dims[1:]))
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if with_time_emb:
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time_dim = dim
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self.time_mlp = nn.Sequential(
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SinusoidalPosEmb(dim),
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nn.Linear(dim, dim * 4),
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nn.GELU(),
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nn.Linear(dim * 4, dim)
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)
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else:
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time_dim = None
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self.time_mlp = None
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self.downs = nn.ModuleList([])
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self.ups = nn.ModuleList([])
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num_resolutions = len(in_out)
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for ind, (dim_in, dim_out) in enumerate(in_out):
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is_last = ind >= (num_resolutions - 1)
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self.downs.append(nn.ModuleList([
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ConvNextBlock(dim_in, dim_out, time_emb_dim = time_dim, norm = ind != 0),
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ConvNextBlock(dim_out, dim_out, time_emb_dim = time_dim),
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Residual(PreNorm(dim_out, LinearAttention(dim_out))),
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Downsample(dim_out) if not is_last else nn.Identity()
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]))
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mid_dim = dims[-1]
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self.mid_block1 = ConvNextBlock(mid_dim, mid_dim, time_emb_dim = time_dim)
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self.mid_attn = Residual(PreNorm(mid_dim, Attention(mid_dim)))
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self.mid_block2 = ConvNextBlock(mid_dim, mid_dim, time_emb_dim = time_dim)
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for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
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is_last = ind >= (num_resolutions - 1)
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self.ups.append(nn.ModuleList([
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ConvNextBlock(dim_out * 2, dim_in, time_emb_dim = time_dim),
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ConvNextBlock(dim_in, dim_in, time_emb_dim = time_dim),
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Residual(PreNorm(dim_in, LinearAttention(dim_in))),
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Upsample(dim_in) if not is_last else nn.Identity()
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]))
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out_dim = default(out_dim, channels)
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self.final_conv = nn.Sequential(
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ConvNextBlock(dim, dim),
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nn.Conv2d(dim, out_dim, 1)
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)
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def forward(self, x, time):
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t = self.time_mlp(time) if exists(self.time_mlp) else None
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h = []
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for convnext, convnext2, attn, downsample in self.downs:
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x = convnext(x, t)
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x = convnext2(x, t)
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x = attn(x)
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h.append(x)
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x = downsample(x)
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x = self.mid_block1(x, t)
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x = self.mid_attn(x)
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x = self.mid_block2(x, t)
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for convnext, convnext2, attn, upsample in self.ups:
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x = torch.cat((x, h.pop()), dim=1)
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x = convnext(x, t)
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x = convnext2(x, t)
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x = attn(x)
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x = upsample(x)
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return self.final_conv(x)
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# gaussian diffusion trainer class
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def extract(a, t, x_shape):
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b, *_ = t.shape
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out = a.gather(-1, t)
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return out.reshape(b, *((1,) * (len(x_shape) - 1)))
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def noise_like(shape, device, repeat=False):
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repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
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noise = lambda: torch.randn(shape, device=device)
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return repeat_noise() if repeat else noise()
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def cosine_beta_schedule(timesteps, s = 0.008):
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"""
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cosine schedule
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as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
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"""
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steps = timesteps + 1
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x = torch.linspace(0, steps, steps)
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alphas_cumprod = torch.cos(((x / steps) + s) / (1 + s) * torch.pi * 0.5) ** 2
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alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
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betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
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return torch.clip(betas, 0, 0.999)
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class GaussianDiffusion(nn.Module):
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def __init__(
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self,
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denoise_fn,
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*,
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image_size,
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channels = 3,
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timesteps = 1000,
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loss_type = 'l1'
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):
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super().__init__()
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self.channels = channels
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self.image_size = image_size
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self.denoise_fn = denoise_fn
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betas = cosine_beta_schedule(timesteps)
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alphas = 1. - betas
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alphas_cumprod = torch.cumprod(alphas, axis=0)
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alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (0, 1), value = 1.)
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timesteps, = betas.shape
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self.num_timesteps = int(timesteps)
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self.loss_type = loss_type
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self.register_buffer('betas', betas)
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self.register_buffer('alphas_cumprod', alphas_cumprod)
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self.register_buffer('alphas_cumprod_prev', alphas_cumprod_prev)
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# calculations for diffusion q(x_t | x_{t-1}) and others
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self.register_buffer('sqrt_alphas_cumprod', torch.sqrt(alphas_cumprod))
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self.register_buffer('sqrt_one_minus_alphas_cumprod', torch.sqrt(1. - alphas_cumprod))
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self.register_buffer('log_one_minus_alphas_cumprod', torch.log(1. - alphas_cumprod))
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self.register_buffer('sqrt_recip_alphas_cumprod', torch.sqrt(1. / alphas_cumprod))
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self.register_buffer('sqrt_recipm1_alphas_cumprod', torch.sqrt(1. / alphas_cumprod - 1))
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# calculations for posterior q(x_{t-1} | x_t, x_0)
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posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
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# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
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self.register_buffer('posterior_variance', posterior_variance)
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# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
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self.register_buffer('posterior_log_variance_clipped', torch.log(posterior_variance.clamp(min =1e-20)))
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self.register_buffer('posterior_mean_coef1', betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
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self.register_buffer('posterior_mean_coef2', (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod))
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def q_mean_variance(self, x_start, t):
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mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
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variance = extract(1. - self.alphas_cumprod, t, x_start.shape)
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log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
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return mean, variance, log_variance
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def predict_start_from_noise(self, x_t, t, noise):
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return (
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extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
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extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
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)
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def q_posterior(self, x_start, x_t, t):
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posterior_mean = (
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extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
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extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
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)
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posterior_variance = extract(self.posterior_variance, t, x_t.shape)
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posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
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return posterior_mean, posterior_variance, posterior_log_variance_clipped
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def p_mean_variance(self, x, t, clip_denoised: bool):
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x_recon = self.predict_start_from_noise(x, t=t, noise=self.denoise_fn(x, t))
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if clip_denoised:
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x_recon.clamp_(-1., 1.)
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model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
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return model_mean, posterior_variance, posterior_log_variance
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@torch.no_grad()
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def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
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b, *_, device = *x.shape, x.device
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model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
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noise = noise_like(x.shape, device, repeat_noise)
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# no noise when t == 0
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nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
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return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
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@torch.no_grad()
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def p_sample_loop(self, shape):
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device = self.betas.device
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b = shape[0]
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img = torch.randn(shape, device=device)
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for i in tqdm(reversed(range(0, self.num_timesteps)), desc='sampling loop time step', total=self.num_timesteps):
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img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long))
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return img
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@torch.no_grad()
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def sample(self, batch_size = 16):
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image_size = self.image_size
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channels = self.channels
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return self.p_sample_loop((batch_size, channels, image_size, image_size))
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@torch.no_grad()
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def interpolate(self, x1, x2, t = None, lam = 0.5):
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b, *_, device = *x1.shape, x1.device
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t = default(t, self.num_timesteps - 1)
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assert x1.shape == x2.shape
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t_batched = torch.stack([torch.tensor(t, device=device)] * b)
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xt1, xt2 = map(lambda x: self.q_sample(x, t=t_batched), (x1, x2))
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img = (1 - lam) * xt1 + lam * xt2
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for i in tqdm(reversed(range(0, t)), desc='interpolation sample time step', total=t):
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img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long))
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return img
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def q_sample(self, x_start, t, noise=None):
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noise = default(noise, lambda: torch.randn_like(x_start))
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return (
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extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
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extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
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)
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def p_losses(self, x_start, t, noise = None):
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b, c, h, w = x_start.shape
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noise = default(noise, lambda: torch.randn_like(x_start))
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x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
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x_recon = self.denoise_fn(x_noisy, t)
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if self.loss_type == 'l1':
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loss = (noise - x_recon).abs().mean()
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elif self.loss_type == 'l2':
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loss = F.mse_loss(noise, x_recon)
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else:
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raise NotImplementedError()
|
|
|
|
return loss
|
|
|
|
def forward(self, x, *args, **kwargs):
|
|
b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
|
|
assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
|
|
t = torch.randint(0, self.num_timesteps, (b,), device=device).long()
|
|
return self.p_losses(x, t, *args, **kwargs)
|
|
|
|
# dataset classes
|
|
|
|
class Dataset(data.Dataset):
|
|
def __init__(self, folder, image_size, exts = ['jpg', 'jpeg', 'png']):
|
|
super().__init__()
|
|
self.folder = folder
|
|
self.image_size = image_size
|
|
self.paths = [p for ext in exts for p in Path(f'{folder}').glob(f'**/*.{ext}')]
|
|
|
|
self.transform = transforms.Compose([
|
|
transforms.Resize(image_size),
|
|
transforms.RandomHorizontalFlip(),
|
|
transforms.CenterCrop(image_size),
|
|
transforms.ToTensor(),
|
|
transforms.Lambda(lambda t: (t * 2) - 1)
|
|
])
|
|
|
|
def __len__(self):
|
|
return len(self.paths)
|
|
|
|
def __getitem__(self, index):
|
|
path = self.paths[index]
|
|
img = Image.open(path)
|
|
return self.transform(img)
|
|
|
|
# trainer class
|
|
|
|
class Trainer(object):
|
|
def __init__(
|
|
self,
|
|
diffusion_model,
|
|
folder,
|
|
*,
|
|
ema_decay = 0.995,
|
|
image_size = 128,
|
|
train_batch_size = 32,
|
|
train_lr = 2e-5,
|
|
train_num_steps = 100000,
|
|
gradient_accumulate_every = 2,
|
|
amp = False,
|
|
step_start_ema = 2000,
|
|
update_ema_every = 10,
|
|
save_and_sample_every = 1000,
|
|
results_folder = './results'
|
|
):
|
|
super().__init__()
|
|
self.model = diffusion_model
|
|
self.ema = EMA(ema_decay)
|
|
self.ema_model = copy.deepcopy(self.model)
|
|
self.update_ema_every = update_ema_every
|
|
|
|
self.step_start_ema = step_start_ema
|
|
self.save_and_sample_every = save_and_sample_every
|
|
|
|
self.batch_size = train_batch_size
|
|
self.image_size = diffusion_model.image_size
|
|
self.gradient_accumulate_every = gradient_accumulate_every
|
|
self.train_num_steps = train_num_steps
|
|
|
|
self.ds = Dataset(folder, image_size)
|
|
self.dl = cycle(data.DataLoader(self.ds, batch_size = train_batch_size, shuffle=True, pin_memory=True))
|
|
self.opt = Adam(diffusion_model.parameters(), lr=train_lr)
|
|
|
|
self.step = 0
|
|
|
|
self.amp = amp
|
|
self.scaler = GradScaler(enabled = amp)
|
|
|
|
self.results_folder = Path(results_folder)
|
|
self.results_folder.mkdir(exist_ok = True)
|
|
|
|
self.reset_parameters()
|
|
|
|
def reset_parameters(self):
|
|
self.ema_model.load_state_dict(self.model.state_dict())
|
|
|
|
def step_ema(self):
|
|
if self.step < self.step_start_ema:
|
|
self.reset_parameters()
|
|
return
|
|
self.ema.update_model_average(self.ema_model, self.model)
|
|
|
|
def save(self, milestone):
|
|
data = {
|
|
'step': self.step,
|
|
'model': self.model.state_dict(),
|
|
'ema': self.ema_model.state_dict(),
|
|
'scaler': self.scaler.state_dict()
|
|
}
|
|
torch.save(data, str(self.results_folder / f'model-{milestone}.pt'))
|
|
|
|
def load(self, milestone):
|
|
data = torch.load(str(self.results_folder / f'model-{milestone}.pt'))
|
|
|
|
self.step = data['step']
|
|
self.model.load_state_dict(data['model'])
|
|
self.ema_model.load_state_dict(data['ema'])
|
|
self.scaler.load_state_dict(data['scaler'])
|
|
|
|
def train(self):
|
|
while self.step < self.train_num_steps:
|
|
for i in range(self.gradient_accumulate_every):
|
|
data = next(self.dl).cuda()
|
|
|
|
with autocast(enabled = self.amp):
|
|
loss = self.model(data)
|
|
self.scaler.scale(loss / self.gradient_accumulate_every).backward()
|
|
|
|
print(f'{self.step}: {loss.item()}')
|
|
|
|
self.scaler.step(self.opt)
|
|
self.scaler.update()
|
|
self.opt.zero_grad()
|
|
|
|
if self.step % self.update_ema_every == 0:
|
|
self.step_ema()
|
|
|
|
if self.step != 0 and self.step % self.save_and_sample_every == 0:
|
|
milestone = self.step // self.save_and_sample_every
|
|
batches = num_to_groups(36, self.batch_size)
|
|
all_images_list = list(map(lambda n: self.ema_model.sample(batch_size=n), batches))
|
|
all_images = torch.cat(all_images_list, dim=0)
|
|
all_images = (all_images + 1) * 0.5
|
|
utils.save_image(all_images, str(self.results_folder / f'sample-{milestone}.png'), nrow = 6)
|
|
self.save(milestone)
|
|
|
|
self.step += 1
|
|
|
|
print('training completed')
|