mirror of
https://github.com/wassname/denoising-diffusion-pytorch.git
synced 2026-08-30 11:22:17 +08:00
help researchers in need of 1d ddpm, ex. https://github.com/lucidrains/denoising-diffusion-pytorch/issues/116
This commit is contained in:
Phil Wang
@@ -101,6 +101,37 @@ Then, in the same directory
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$ accelerate launch train.py
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```
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## Miscellaenous
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By popular request, a 1D Unet + Gaussian Diffusion implementation. You will have to do the training code yourself
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```python
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import torch
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from denoising_diffusion_pytorch import Unet1D, GaussianDiffusion1D
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model = Unet1D(
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dim = 64,
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dim_mults = (1, 2, 4, 8),
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channels = 32
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)
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diffusion = GaussianDiffusion1D(
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model,
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seq_length = 128,
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timesteps = 1000,
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objective = 'pred_v'
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)
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training_seq = torch.randn(8, 32, 128) # features are normalized from 0 to 1
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loss = diffusion(training_seq)
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loss.backward()
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# after a lot of training
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sampled_seq = diffusion.sample(batch_size = 4)
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sampled_seq.shape # (4, 32, 128)
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```
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## Citations
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```bibtex
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@@ -5,3 +5,6 @@ from denoising_diffusion_pytorch.continuous_time_gaussian_diffusion import Conti
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from denoising_diffusion_pytorch.weighted_objective_gaussian_diffusion import WeightedObjectiveGaussianDiffusion
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from denoising_diffusion_pytorch.elucidated_diffusion import ElucidatedDiffusion
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from denoising_diffusion_pytorch.v_param_continuous_time_gaussian_diffusion import VParamContinuousTimeGaussianDiffusion
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from denoising_diffusion_pytorch.denoising_diffusion_pytorch_1d import GaussianDiffusion1D, Unet1D
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@@ -0,0 +1,695 @@
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import math
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from random import random
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from functools import partial
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from collections import namedtuple
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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 einops import rearrange, reduce
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from einops.layers.torch import Rearrange
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from tqdm.auto import tqdm
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# constants
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ModelPrediction = namedtuple('ModelPrediction', ['pred_noise', 'pred_x_start'])
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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 callable(d) else d
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def identity(t, *args, **kwargs):
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return t
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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 has_int_squareroot(num):
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return (math.sqrt(num) ** 2) == num
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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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def convert_image_to_fn(img_type, image):
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if image.mode != img_type:
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return image.convert(img_type)
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return image
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# normalization functions
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def normalize_to_neg_one_to_one(img):
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return img * 2 - 1
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def unnormalize_to_zero_to_one(t):
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return (t + 1) * 0.5
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# small helper modules
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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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def Upsample(dim, dim_out = None):
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return nn.Sequential(
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nn.Upsample(scale_factor = 2, mode = 'nearest'),
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nn.Conv1d(dim, default(dim_out, dim), 3, padding = 1)
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)
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def Downsample(dim, dim_out = None):
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return nn.Conv1d(dim, default(dim_out, dim), 4, 2, 1)
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class WeightStandardizedConv2d(nn.Conv1d):
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"""
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https://arxiv.org/abs/1903.10520
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weight standardization purportedly works synergistically with group normalization
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"""
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def forward(self, x):
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eps = 1e-5 if x.dtype == torch.float32 else 1e-3
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weight = self.weight
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mean = reduce(weight, 'o ... -> o 1 1', 'mean')
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var = reduce(weight, 'o ... -> o 1 1', partial(torch.var, unbiased = False))
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normalized_weight = (weight - mean) * (var + eps).rsqrt()
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return F.conv1d(x, normalized_weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
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class LayerNorm(nn.Module):
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def __init__(self, dim):
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super().__init__()
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self.g = nn.Parameter(torch.ones(1, dim, 1))
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def forward(self, x):
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eps = 1e-5 if x.dtype == torch.float32 else 1e-3
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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 + eps).rsqrt() * self.g
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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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# sinusoidal positional embeds
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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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class RandomOrLearnedSinusoidalPosEmb(nn.Module):
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""" following @crowsonkb 's lead with random (learned optional) sinusoidal pos emb """
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""" https://github.com/crowsonkb/v-diffusion-jax/blob/master/diffusion/models/danbooru_128.py#L8 """
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def __init__(self, dim, is_random = False):
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super().__init__()
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assert (dim % 2) == 0
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half_dim = dim // 2
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self.weights = nn.Parameter(torch.randn(half_dim), requires_grad = not is_random)
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def forward(self, x):
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x = rearrange(x, 'b -> b 1')
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freqs = x * rearrange(self.weights, 'd -> 1 d') * 2 * math.pi
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fouriered = torch.cat((freqs.sin(), freqs.cos()), dim = -1)
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fouriered = torch.cat((x, fouriered), dim = -1)
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return fouriered
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# building block modules
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class Block(nn.Module):
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def __init__(self, dim, dim_out, groups = 8):
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super().__init__()
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self.proj = WeightStandardizedConv2d(dim, dim_out, 3, padding = 1)
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self.norm = nn.GroupNorm(groups, dim_out)
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self.act = nn.SiLU()
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def forward(self, x, scale_shift = None):
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x = self.proj(x)
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x = self.norm(x)
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if exists(scale_shift):
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scale, shift = scale_shift
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x = x * (scale + 1) + shift
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x = self.act(x)
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return x
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class ResnetBlock(nn.Module):
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def __init__(self, dim, dim_out, *, time_emb_dim = None, groups = 8):
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super().__init__()
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self.mlp = nn.Sequential(
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nn.SiLU(),
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nn.Linear(time_emb_dim, dim_out * 2)
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) if exists(time_emb_dim) else None
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self.block1 = Block(dim, dim_out, groups = groups)
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self.block2 = Block(dim_out, dim_out, groups = groups)
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self.res_conv = nn.Conv1d(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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scale_shift = None
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if exists(self.mlp) and exists(time_emb):
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time_emb = self.mlp(time_emb)
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time_emb = rearrange(time_emb, 'b c -> b c 1')
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scale_shift = time_emb.chunk(2, dim = 1)
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h = self.block1(x, scale_shift = scale_shift)
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h = self.block2(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.Conv1d(dim, hidden_dim * 3, 1, bias = False)
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self.to_out = nn.Sequential(
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nn.Conv1d(hidden_dim, dim, 1),
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LayerNorm(dim)
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)
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def forward(self, x):
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b, c, n = 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) n -> b h c n', h = self.heads), qkv)
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q = q.softmax(dim = -2)
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k = k.softmax(dim = -1)
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q = q * self.scale
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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 n -> b (h c) n', h = self.heads)
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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.Conv1d(dim, hidden_dim * 3, 1, bias = False)
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self.to_out = nn.Conv1d(hidden_dim, dim, 1)
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def forward(self, x):
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b, c, n = 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) n -> b h c n', 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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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 n d -> b (h d) n')
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return self.to_out(out)
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# model
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class Unet1D(nn.Module):
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def __init__(
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self,
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dim,
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init_dim = None,
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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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self_condition = False,
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resnet_block_groups = 8,
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learned_variance = False,
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learned_sinusoidal_cond = False,
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random_fourier_features = False,
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learned_sinusoidal_dim = 16
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):
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super().__init__()
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# determine dimensions
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self.channels = channels
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self.self_condition = self_condition
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input_channels = channels * (2 if self_condition else 1)
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init_dim = default(init_dim, dim)
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self.init_conv = nn.Conv1d(input_channels, init_dim, 7, padding = 3)
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dims = [init_dim, *map(lambda m: dim * m, dim_mults)]
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in_out = list(zip(dims[:-1], dims[1:]))
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block_klass = partial(ResnetBlock, groups = resnet_block_groups)
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# time embeddings
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time_dim = dim * 4
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self.random_or_learned_sinusoidal_cond = learned_sinusoidal_cond or random_fourier_features
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if self.random_or_learned_sinusoidal_cond:
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sinu_pos_emb = RandomOrLearnedSinusoidalPosEmb(learned_sinusoidal_dim, random_fourier_features)
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fourier_dim = learned_sinusoidal_dim + 1
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else:
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sinu_pos_emb = SinusoidalPosEmb(dim)
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fourier_dim = dim
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self.time_mlp = nn.Sequential(
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sinu_pos_emb,
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nn.Linear(fourier_dim, time_dim),
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nn.GELU(),
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nn.Linear(time_dim, time_dim)
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)
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# layers
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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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block_klass(dim_in, dim_in, time_emb_dim = time_dim),
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block_klass(dim_in, dim_in, time_emb_dim = time_dim),
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Residual(PreNorm(dim_in, LinearAttention(dim_in))),
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Downsample(dim_in, dim_out) if not is_last else nn.Conv1d(dim_in, dim_out, 3, padding = 1)
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]))
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mid_dim = dims[-1]
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self.mid_block1 = block_klass(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 = block_klass(mid_dim, mid_dim, time_emb_dim = time_dim)
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for ind, (dim_in, dim_out) in enumerate(reversed(in_out)):
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is_last = ind == (len(in_out) - 1)
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self.ups.append(nn.ModuleList([
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block_klass(dim_out + dim_in, dim_out, time_emb_dim = time_dim),
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block_klass(dim_out + dim_in, dim_out, time_emb_dim = time_dim),
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Residual(PreNorm(dim_out, LinearAttention(dim_out))),
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Upsample(dim_out, dim_in) if not is_last else nn.Conv1d(dim_out, dim_in, 3, padding = 1)
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]))
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default_out_dim = channels * (1 if not learned_variance else 2)
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self.out_dim = default(out_dim, default_out_dim)
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self.final_res_block = block_klass(dim * 2, dim, time_emb_dim = time_dim)
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self.final_conv = nn.Conv1d(dim, self.out_dim, 1)
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|
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def forward(self, x, time, x_self_cond = None):
|
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if self.self_condition:
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x_self_cond = default(x_self_cond, lambda: torch.zeros_like(x))
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x = torch.cat((x_self_cond, x), dim = 1)
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x = self.init_conv(x)
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r = x.clone()
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t = self.time_mlp(time)
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|
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h = []
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|
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for block1, block2, attn, downsample in self.downs:
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x = block1(x, t)
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h.append(x)
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|
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x = block2(x, t)
|
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x = attn(x)
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h.append(x)
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|
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x = downsample(x)
|
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|
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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 block1, block2, attn, upsample in self.ups:
|
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x = torch.cat((x, h.pop()), dim = 1)
|
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x = block1(x, t)
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|
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x = torch.cat((x, h.pop()), dim = 1)
|
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x = block2(x, t)
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x = attn(x)
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x = upsample(x)
|
||||
|
||||
x = torch.cat((x, r), dim = 1)
|
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|
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x = self.final_res_block(x, t)
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return self.final_conv(x)
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||||
|
||||
# gaussian diffusion trainer class
|
||||
|
||||
def extract(a, t, x_shape):
|
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b, *_ = t.shape
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out = a.gather(-1, t)
|
||||
return out.reshape(b, *((1,) * (len(x_shape) - 1)))
|
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|
||||
def linear_beta_schedule(timesteps):
|
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scale = 1000 / timesteps
|
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beta_start = scale * 0.0001
|
||||
beta_end = scale * 0.02
|
||||
return torch.linspace(beta_start, beta_end, timesteps, dtype = torch.float64)
|
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|
||||
def cosine_beta_schedule(timesteps, s = 0.008):
|
||||
"""
|
||||
cosine schedule
|
||||
as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
|
||||
"""
|
||||
steps = timesteps + 1
|
||||
x = torch.linspace(0, timesteps, steps, dtype = torch.float64)
|
||||
alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * math.pi * 0.5) ** 2
|
||||
alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
|
||||
betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
|
||||
return torch.clip(betas, 0, 0.999)
|
||||
|
||||
class GaussianDiffusion1D(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
model,
|
||||
*,
|
||||
seq_length,
|
||||
timesteps = 1000,
|
||||
sampling_timesteps = None,
|
||||
loss_type = 'l1',
|
||||
objective = 'pred_noise',
|
||||
beta_schedule = 'cosine',
|
||||
p2_loss_weight_gamma = 0.,
|
||||
p2_loss_weight_k = 1,
|
||||
ddim_sampling_eta = 1.
|
||||
):
|
||||
super().__init__()
|
||||
self.model = model
|
||||
self.channels = self.model.channels
|
||||
self.self_condition = self.model.self_condition
|
||||
|
||||
self.seq_length = seq_length
|
||||
|
||||
self.objective = objective
|
||||
|
||||
assert objective in {'pred_noise', 'pred_x0', 'pred_v'}, 'objective must be either pred_noise (predict noise) or pred_x0 (predict image start) or pred_v (predict v [v-parameterization as defined in appendix D of progressive distillation paper, used in imagen-video successfully])'
|
||||
|
||||
if beta_schedule == 'linear':
|
||||
betas = linear_beta_schedule(timesteps)
|
||||
elif beta_schedule == 'cosine':
|
||||
betas = cosine_beta_schedule(timesteps)
|
||||
else:
|
||||
raise ValueError(f'unknown beta schedule {beta_schedule}')
|
||||
|
||||
alphas = 1. - betas
|
||||
alphas_cumprod = torch.cumprod(alphas, dim=0)
|
||||
alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (1, 0), value = 1.)
|
||||
|
||||
timesteps, = betas.shape
|
||||
self.num_timesteps = int(timesteps)
|
||||
self.loss_type = loss_type
|
||||
|
||||
# sampling related parameters
|
||||
|
||||
self.sampling_timesteps = default(sampling_timesteps, timesteps) # default num sampling timesteps to number of timesteps at training
|
||||
|
||||
assert self.sampling_timesteps <= timesteps
|
||||
self.is_ddim_sampling = self.sampling_timesteps < timesteps
|
||||
self.ddim_sampling_eta = ddim_sampling_eta
|
||||
|
||||
# helper function to register buffer from float64 to float32
|
||||
|
||||
register_buffer = lambda name, val: self.register_buffer(name, val.to(torch.float32))
|
||||
|
||||
register_buffer('betas', betas)
|
||||
register_buffer('alphas_cumprod', alphas_cumprod)
|
||||
register_buffer('alphas_cumprod_prev', alphas_cumprod_prev)
|
||||
|
||||
# calculations for diffusion q(x_t | x_{t-1}) and others
|
||||
|
||||
register_buffer('sqrt_alphas_cumprod', torch.sqrt(alphas_cumprod))
|
||||
register_buffer('sqrt_one_minus_alphas_cumprod', torch.sqrt(1. - alphas_cumprod))
|
||||
register_buffer('log_one_minus_alphas_cumprod', torch.log(1. - alphas_cumprod))
|
||||
register_buffer('sqrt_recip_alphas_cumprod', torch.sqrt(1. / alphas_cumprod))
|
||||
register_buffer('sqrt_recipm1_alphas_cumprod', torch.sqrt(1. / alphas_cumprod - 1))
|
||||
|
||||
# calculations for posterior q(x_{t-1} | x_t, x_0)
|
||||
|
||||
posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
|
||||
|
||||
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
|
||||
|
||||
register_buffer('posterior_variance', posterior_variance)
|
||||
|
||||
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
|
||||
|
||||
register_buffer('posterior_log_variance_clipped', torch.log(posterior_variance.clamp(min =1e-20)))
|
||||
register_buffer('posterior_mean_coef1', betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
|
||||
register_buffer('posterior_mean_coef2', (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod))
|
||||
|
||||
# calculate p2 reweighting
|
||||
|
||||
register_buffer('p2_loss_weight', (p2_loss_weight_k + alphas_cumprod / (1 - alphas_cumprod)) ** -p2_loss_weight_gamma)
|
||||
|
||||
def predict_start_from_noise(self, x_t, t, noise):
|
||||
return (
|
||||
extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
|
||||
extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
|
||||
)
|
||||
|
||||
def predict_noise_from_start(self, x_t, t, x0):
|
||||
return (
|
||||
(extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - x0) / \
|
||||
extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
|
||||
)
|
||||
|
||||
def predict_v(self, x_start, t, noise):
|
||||
return (
|
||||
extract(self.sqrt_alphas_cumprod, t, x_start.shape) * noise -
|
||||
extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * x_start
|
||||
)
|
||||
|
||||
def predict_start_from_v(self, x_t, t, v):
|
||||
return (
|
||||
extract(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
|
||||
extract(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
|
||||
)
|
||||
|
||||
def q_posterior(self, x_start, x_t, t):
|
||||
posterior_mean = (
|
||||
extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
|
||||
extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
|
||||
)
|
||||
posterior_variance = extract(self.posterior_variance, t, x_t.shape)
|
||||
posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
|
||||
return posterior_mean, posterior_variance, posterior_log_variance_clipped
|
||||
|
||||
def model_predictions(self, x, t, x_self_cond = None, clip_x_start = False):
|
||||
model_output = self.model(x, t, x_self_cond)
|
||||
maybe_clip = partial(torch.clamp, min = -1., max = 1.) if clip_x_start else identity
|
||||
|
||||
if self.objective == 'pred_noise':
|
||||
pred_noise = model_output
|
||||
x_start = self.predict_start_from_noise(x, t, pred_noise)
|
||||
x_start = maybe_clip(x_start)
|
||||
|
||||
elif self.objective == 'pred_x0':
|
||||
x_start = model_output
|
||||
x_start = maybe_clip(x_start)
|
||||
pred_noise = self.predict_noise_from_start(x, t, x_start)
|
||||
|
||||
elif self.objective == 'pred_v':
|
||||
v = model_output
|
||||
x_start = self.predict_start_from_v(x, t, v)
|
||||
x_start = maybe_clip(x_start)
|
||||
pred_noise = self.predict_noise_from_start(x, t, x_start)
|
||||
|
||||
return ModelPrediction(pred_noise, x_start)
|
||||
|
||||
def p_mean_variance(self, x, t, x_self_cond = None, clip_denoised = True):
|
||||
preds = self.model_predictions(x, t, x_self_cond)
|
||||
x_start = preds.pred_x_start
|
||||
|
||||
if clip_denoised:
|
||||
x_start.clamp_(-1., 1.)
|
||||
|
||||
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start = x_start, x_t = x, t = t)
|
||||
return model_mean, posterior_variance, posterior_log_variance, x_start
|
||||
|
||||
@torch.no_grad()
|
||||
def p_sample(self, x, t: int, x_self_cond = None, clip_denoised = True):
|
||||
b, *_, device = *x.shape, x.device
|
||||
batched_times = torch.full((x.shape[0],), t, device = x.device, dtype = torch.long)
|
||||
model_mean, _, model_log_variance, x_start = self.p_mean_variance(x = x, t = batched_times, x_self_cond = x_self_cond, clip_denoised = clip_denoised)
|
||||
noise = torch.randn_like(x) if t > 0 else 0. # no noise if t == 0
|
||||
pred_img = model_mean + (0.5 * model_log_variance).exp() * noise
|
||||
return pred_img, x_start
|
||||
|
||||
@torch.no_grad()
|
||||
def p_sample_loop(self, shape):
|
||||
batch, device = shape[0], self.betas.device
|
||||
|
||||
img = torch.randn(shape, device=device)
|
||||
|
||||
x_start = None
|
||||
|
||||
for t in tqdm(reversed(range(0, self.num_timesteps)), desc = 'sampling loop time step', total = self.num_timesteps):
|
||||
self_cond = x_start if self.self_condition else None
|
||||
img, x_start = self.p_sample(img, t, self_cond)
|
||||
|
||||
img = unnormalize_to_zero_to_one(img)
|
||||
return img
|
||||
|
||||
@torch.no_grad()
|
||||
def ddim_sample(self, shape, clip_denoised = True):
|
||||
batch, device, total_timesteps, sampling_timesteps, eta, objective = shape[0], self.betas.device, self.num_timesteps, self.sampling_timesteps, self.ddim_sampling_eta, self.objective
|
||||
|
||||
times = torch.linspace(-1, total_timesteps - 1, steps=sampling_timesteps + 1) # [-1, 0, 1, 2, ..., T-1] when sampling_timesteps == total_timesteps
|
||||
times = list(reversed(times.int().tolist()))
|
||||
time_pairs = list(zip(times[:-1], times[1:])) # [(T-1, T-2), (T-2, T-3), ..., (1, 0), (0, -1)]
|
||||
|
||||
img = torch.randn(shape, device = device)
|
||||
|
||||
x_start = None
|
||||
|
||||
for time, time_next in tqdm(time_pairs, desc = 'sampling loop time step'):
|
||||
time_cond = torch.full((batch,), time, device=device, dtype=torch.long)
|
||||
self_cond = x_start if self.self_condition else None
|
||||
pred_noise, x_start, *_ = self.model_predictions(img, time_cond, self_cond, clip_x_start = clip_denoised)
|
||||
|
||||
if time_next < 0:
|
||||
img = x_start
|
||||
continue
|
||||
|
||||
alpha = self.alphas_cumprod[time]
|
||||
alpha_next = self.alphas_cumprod[time_next]
|
||||
|
||||
sigma = eta * ((1 - alpha / alpha_next) * (1 - alpha_next) / (1 - alpha)).sqrt()
|
||||
c = (1 - alpha_next - sigma ** 2).sqrt()
|
||||
|
||||
noise = torch.randn_like(img)
|
||||
|
||||
img = x_start * alpha_next.sqrt() + \
|
||||
c * pred_noise + \
|
||||
sigma * noise
|
||||
|
||||
img = unnormalize_to_zero_to_one(img)
|
||||
return img
|
||||
|
||||
@torch.no_grad()
|
||||
def sample(self, batch_size = 16):
|
||||
seq_length, channels = self.seq_length, self.channels
|
||||
sample_fn = self.p_sample_loop if not self.is_ddim_sampling else self.ddim_sample
|
||||
return sample_fn((batch_size, channels, seq_length))
|
||||
|
||||
@torch.no_grad()
|
||||
def interpolate(self, x1, x2, t = None, lam = 0.5):
|
||||
b, *_, device = *x1.shape, x1.device
|
||||
t = default(t, self.num_timesteps - 1)
|
||||
|
||||
assert x1.shape == x2.shape
|
||||
|
||||
t_batched = torch.stack([torch.tensor(t, device = device)] * b)
|
||||
xt1, xt2 = map(lambda x: self.q_sample(x, t = t_batched), (x1, x2))
|
||||
|
||||
img = (1 - lam) * xt1 + lam * xt2
|
||||
for i in tqdm(reversed(range(0, t)), desc = 'interpolation sample time step', total = t):
|
||||
img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long))
|
||||
|
||||
return img
|
||||
|
||||
def q_sample(self, x_start, t, noise=None):
|
||||
noise = default(noise, lambda: torch.randn_like(x_start))
|
||||
|
||||
return (
|
||||
extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
|
||||
extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
|
||||
)
|
||||
|
||||
@property
|
||||
def loss_fn(self):
|
||||
if self.loss_type == 'l1':
|
||||
return F.l1_loss
|
||||
elif self.loss_type == 'l2':
|
||||
return F.mse_loss
|
||||
else:
|
||||
raise ValueError(f'invalid loss type {self.loss_type}')
|
||||
|
||||
def p_losses(self, x_start, t, noise = None):
|
||||
b, c, n = x_start.shape
|
||||
noise = default(noise, lambda: torch.randn_like(x_start))
|
||||
|
||||
# noise sample
|
||||
|
||||
x = self.q_sample(x_start = x_start, t = t, noise = noise)
|
||||
|
||||
# if doing self-conditioning, 50% of the time, predict x_start from current set of times
|
||||
# and condition with unet with that
|
||||
# this technique will slow down training by 25%, but seems to lower FID significantly
|
||||
|
||||
x_self_cond = None
|
||||
if self.self_condition and random() < 0.5:
|
||||
with torch.no_grad():
|
||||
x_self_cond = self.model_predictions(x, t).pred_x_start
|
||||
x_self_cond.detach_()
|
||||
|
||||
# predict and take gradient step
|
||||
|
||||
model_out = self.model(x, t, x_self_cond)
|
||||
|
||||
if self.objective == 'pred_noise':
|
||||
target = noise
|
||||
elif self.objective == 'pred_x0':
|
||||
target = x_start
|
||||
elif self.objective == 'pred_v':
|
||||
v = self.predict_v(x_start, t, noise)
|
||||
target = v
|
||||
else:
|
||||
raise ValueError(f'unknown objective {self.objective}')
|
||||
|
||||
loss = self.loss_fn(model_out, target, reduction = 'none')
|
||||
loss = reduce(loss, 'b ... -> b (...)', 'mean')
|
||||
|
||||
loss = loss * extract(self.p2_loss_weight, t, loss.shape)
|
||||
return loss.mean()
|
||||
|
||||
def forward(self, img, *args, **kwargs):
|
||||
b, c, n, device, seq_length, = *img.shape, img.device, self.seq_length
|
||||
assert n == seq_length, f'seq length must be {seq_length}'
|
||||
t = torch.randint(0, self.num_timesteps, (b,), device=device).long()
|
||||
|
||||
img = normalize_to_neg_one_to_one(img)
|
||||
return self.p_losses(img, t, *args, **kwargs)
|
||||
@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
|
||||
setup(
|
||||
name = 'denoising-diffusion-pytorch',
|
||||
packages = find_packages(),
|
||||
version = '0.30.0',
|
||||
version = '0.31.0',
|
||||
license='MIT',
|
||||
description = 'Denoising Diffusion Probabilistic Models - Pytorch',
|
||||
author = 'Phil Wang',
|
||||
|
||||
Reference in New Issue
Block a user