get working version of gaussian diffusion with continuous time (only beta linear schedule for now, but will eventually contain alpha cosine schedule as well as parameterized, learned monotonic MLP)

2026-09-10 12:01:08 +08:00 · 2022-06-07 15:49:39 -07:00
14 changed files with 231 additions and 2656 deletions
@@ -1,4 +1,4 @@
-<img src="./images/denoising-diffusion.png" width="500px"></img>
+<img src="./denoising-diffusion.png" width="500px"></img>
 ## Denoising Diffusion Probabilistic Model, in Pytorch
@@ -6,15 +6,7 @@ Implementation of <a href="https://arxiv.org/abs/2006.11239">Denoising Diffusion
 This implementation was transcribed from the official Tensorflow version <a href="https://github.com/hojonathanho/diffusion">here</a>
-Youtube AI Educators - <a href="https://www.youtube.com/watch?v=W-O7AZNzbzQ">Yannic Kilcher</a> | <a href="https://www.youtube.com/watch?v=344w5h24-h8">AI Coffeebreak with Letitia</a> | <a href="https://www.youtube.com/watch?v=HoKDTa5jHvg">Outlier</a>
+<img src="./sample.png" width="500px"><img>
 <a href="https://github.com/yiyixuxu/denoising-diffusion-flax">Flax implementation</a> from <a href="https://github.com/yiyixuxu">YiYi Xu</a>
 <a href="https://huggingface.co/blog/annotated-diffusion">Annotated code</a> by Research Scientists / Engineers from <a href="https://huggingface.co/">🤗 Huggingface</a>
 Update: Turns out none of the technicalities really matters at all | <a href="https://arxiv.org/abs/2208.09392">"Cold Diffusion" paper</a>
 <img src="./images/sample.png" width="500px"><img>
 [![PyPI version](https://badge.fury.io/py/denoising-diffusion-pytorch.svg)](https://badge.fury.io/py/denoising-diffusion-pytorch)
@@ -42,7 +34,7 @@ diffusion = GaussianDiffusion(
    loss_type = 'l1'    # L1 or L2
 )
-training_images = torch.rand(8, 3, 128, 128) # images are normalized from 0 to 1
+training_images = torch.randn(8, 3, 128, 128) # images are normalized from 0 to 1
 loss = diffusion(training_images)
 loss.backward()
 # after a lot of training
@@ -64,16 +56,15 @@ model = Unet(
 diffusion = GaussianDiffusion(
    model,
    image_size = 128,
-    timesteps = 1000,           # number of steps
+    timesteps = 1000,   # number of steps
-    sampling_timesteps = 250,   # number of sampling timesteps (using ddim for faster inference [see citation for ddim paper])
+    loss_type = 'l1'    # L1 or L2
    loss_type = 'l1'            # L1 or L2
 ).cuda()
 trainer = Trainer(
    diffusion,
    'path/to/your/images',
    train_batch_size = 32,
-    train_lr = 8e-5,
+    train_lr = 1e-4,
    train_num_steps = 700000,         # total training steps
    gradient_accumulate_every = 2,    # gradient accumulation steps
    ema_decay = 0.995,                # exponential moving average decay
@@ -85,55 +76,6 @@ trainer.train()
 Samples and model checkpoints will be logged to `./results` periodically
 ## Multi-GPU Training
 The `Trainer` class is now equipped with <a href="https://huggingface.co/docs/accelerate/accelerator">🤗 Accelerator</a>. You can easily do multi-gpu training in two steps using their `accelerate` CLI
 At the project root directory, where the training script is, run
 ```python
 $ accelerate config
 ```
 Then, in the same directory
 ```python
 $ accelerate launch train.py
 ```
 ## Miscellaneous
 ### 1D Sequence
 By popular request, a 1D Unet + Gaussian Diffusion implementation. You will have to do the training code yourself
 ```python
 import torch
 from denoising_diffusion_pytorch import Unet1D, GaussianDiffusion1D
 model = Unet1D(
    dim = 64,
    dim_mults = (1, 2, 4, 8),
    channels = 32
 )
 diffusion = GaussianDiffusion1D(
    model,
    seq_length = 128,
    timesteps = 1000,
    objective = 'pred_v'
 )
 training_seq = torch.rand(8, 32, 128) # features are normalized from 0 to 1
 loss = diffusion(training_seq)
 loss.backward()
 # after a lot of training
 sampled_seq = diffusion.sample(batch_size = 4)
 sampled_seq.shape # (4, 32, 128)
 ```
 ## Citations
 ```bibtex
@@ -177,84 +119,3 @@ sampled_seq.shape # (4, 32, 128)
    url         = {https://openreview.net/forum?id=2LdBqxc1Yv}
 }
 ```
 ```bibtex
@article{Choi2022PerceptionPT,
    title   = {Perception Prioritized Training of Diffusion Models},
    author  = {Jooyoung Choi and Jungbeom Lee and Chaehun Shin and Sungwon Kim and Hyunwoo J. Kim and Sung-Hoon Yoon},
    journal = {ArXiv},
    year    = {2022},
    volume  = {abs/2204.00227}
 }
 ```
 ```bibtex
@article{Karras2022ElucidatingTD,
    title   = {Elucidating the Design Space of Diffusion-Based Generative Models},
    author  = {Tero Karras and Miika Aittala and Timo Aila and Samuli Laine},
    journal = {ArXiv},
    year    = {2022},
    volume  = {abs/2206.00364}
 }
 ```
 ```bibtex
@article{Song2021DenoisingDI,
    title   = {Denoising Diffusion Implicit Models},
    author  = {Jiaming Song and Chenlin Meng and Stefano Ermon},
    journal = {ArXiv},
    year    = {2021},
    volume  = {abs/2010.02502}
 }
 ```
 ```bibtex
@misc{chen2022analog,
    title   = {Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning},
    author  = {Ting Chen and Ruixiang Zhang and Geoffrey Hinton},
    year    = {2022},
    eprint  = {2208.04202},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
 }
 ```
 ```bibtex
@article{Qiao2019WeightS,
    title   = {Weight Standardization},
    author  = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Loddon Yuille},
    journal = {ArXiv},
    year    = {2019},
    volume  = {abs/1903.10520}
 }
 ```
 ```bibtex
@article{Salimans2022ProgressiveDF,
    title   = {Progressive Distillation for Fast Sampling of Diffusion Models},
    author  = {Tim Salimans and Jonathan Ho},
    journal = {ArXiv},
    year    = {2022},
    volume  = {abs/2202.00512}
 }
 ```
 ```bibtex
@article{Ho2022ClassifierFreeDG,
    title   = {Classifier-Free Diffusion Guidance},
    author  = {Jonathan Ho},
    journal = {ArXiv},
    year    = {2022},
    volume  = {abs/2207.12598}
 }
 ```
 ```bibtex
@article{Sunkara2022NoMS,
    title   = {No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects},
    author  = {Raja Sunkara and Tie Luo},
    journal = {ArXiv},
    year    = {2022},
    volume  = {abs/2208.03641}
 }
 ```
@@ -3,8 +3,3 @@ from denoising_diffusion_pytorch.denoising_diffusion_pytorch import GaussianDiff
 from denoising_diffusion_pytorch.learned_gaussian_diffusion import LearnedGaussianDiffusion
 from denoising_diffusion_pytorch.continuous_time_gaussian_diffusion import ContinuousTimeGaussianDiffusion
 from denoising_diffusion_pytorch.weighted_objective_gaussian_diffusion import WeightedObjectiveGaussianDiffusion
 from denoising_diffusion_pytorch.elucidated_diffusion import ElucidatedDiffusion
 from denoising_diffusion_pytorch.v_param_continuous_time_gaussian_diffusion import VParamContinuousTimeGaussianDiffusion
 from denoising_diffusion_pytorch.denoising_diffusion_pytorch_1d import GaussianDiffusion1D, Unet1D
@@ -1,792 +0,0 @@
 import math
 import copy
 from pathlib import Path
 from random import random
 from functools import partial
 from collections import namedtuple
 from multiprocessing import cpu_count
 import torch
 from torch import nn, einsum
 import torch.nn.functional as F
 from einops import rearrange, reduce, repeat
 from einops.layers.torch import Rearrange
 from tqdm.auto import tqdm
 # constants
 ModelPrediction =  namedtuple('ModelPrediction', ['pred_noise', 'pred_x_start'])
 # helpers functions
 def exists(x):
    return x is not None
 def default(val, d):
    if exists(val):
        return val
    return d() if callable(d) else d
 def identity(t, *args, **kwargs):
    return t
 def cycle(dl):
    while True:
        for data in dl:
            yield data
 def has_int_squareroot(num):
    return (math.sqrt(num) ** 2) == num
 def num_to_groups(num, divisor):
    groups = num // divisor
    remainder = num % divisor
    arr = [divisor] * groups
    if remainder > 0:
        arr.append(remainder)
    return arr
 def convert_image_to_fn(img_type, image):
    if image.mode != img_type:
        return image.convert(img_type)
    return image
 # normalization functions
 def normalize_to_neg_one_to_one(img):
    return img * 2 - 1
 def unnormalize_to_zero_to_one(t):
    return (t + 1) * 0.5
 # classifier free guidance functions
 def uniform(shape, device):
    return torch.zeros(shape, device = device).float().uniform_(0, 1)
 def prob_mask_like(shape, prob, device):
    if prob == 1:
        return torch.ones(shape, device = device, dtype = torch.bool)
    elif prob == 0:
        return torch.zeros(shape, device = device, dtype = torch.bool)
    else:
        return torch.zeros(shape, device = device).float().uniform_(0, 1) < prob
 # small helper modules
 class Residual(nn.Module):
    def __init__(self, fn):
        super().__init__()
        self.fn = fn
    def forward(self, x, *args, **kwargs):
        return self.fn(x, *args, **kwargs) + x
 def Upsample(dim, dim_out = None):
    return nn.Sequential(
        nn.Upsample(scale_factor = 2, mode = 'nearest'),
        nn.Conv2d(dim, default(dim_out, dim), 3, padding = 1)
    )
 def Downsample(dim, dim_out = None):
    return nn.Conv2d(dim, default(dim_out, dim), 4, 2, 1)
 class WeightStandardizedConv2d(nn.Conv2d):
    """
    https://arxiv.org/abs/1903.10520
    weight standardization purportedly works synergistically with group normalization
    """
    def forward(self, x):
        eps = 1e-5 if x.dtype == torch.float32 else 1e-3
        weight = self.weight
        mean = reduce(weight, 'o ... -> o 1 1 1', 'mean')
        var = reduce(weight, 'o ... -> o 1 1 1', partial(torch.var, unbiased = False))
        normalized_weight = (weight - mean) * (var + eps).rsqrt()
        return F.conv2d(x, normalized_weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
 class LayerNorm(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
    def forward(self, x):
        eps = 1e-5 if x.dtype == torch.float32 else 1e-3
        var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
        mean = torch.mean(x, dim = 1, keepdim = True)
        return (x - mean) * (var + eps).rsqrt() * self.g
 class PreNorm(nn.Module):
    def __init__(self, dim, fn):
        super().__init__()
        self.fn = fn
        self.norm = LayerNorm(dim)
    def forward(self, x):
        x = self.norm(x)
        return self.fn(x)
 # sinusoidal positional embeds
 class SinusoidalPosEmb(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.dim = dim
    def forward(self, x):
        device = x.device
        half_dim = self.dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
        emb = x[:, None] * emb[None, :]
        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
        return emb
 class RandomOrLearnedSinusoidalPosEmb(nn.Module):
    """ following @crowsonkb 's lead with random (learned optional) sinusoidal pos emb """
    """ https://github.com/crowsonkb/v-diffusion-jax/blob/master/diffusion/models/danbooru_128.py#L8 """
    def __init__(self, dim, is_random = False):
        super().__init__()
        assert (dim % 2) == 0
        half_dim = dim // 2
        self.weights = nn.Parameter(torch.randn(half_dim), requires_grad = not is_random)
    def forward(self, x):
        x = rearrange(x, 'b -> b 1')
        freqs = x * rearrange(self.weights, 'd -> 1 d') * 2 * math.pi
        fouriered = torch.cat((freqs.sin(), freqs.cos()), dim = -1)
        fouriered = torch.cat((x, fouriered), dim = -1)
        return fouriered
 # building block modules
 class Block(nn.Module):
    def __init__(self, dim, dim_out, groups = 8):
        super().__init__()
        self.proj = WeightStandardizedConv2d(dim, dim_out, 3, padding = 1)
        self.norm = nn.GroupNorm(groups, dim_out)
        self.act = nn.SiLU()
    def forward(self, x, scale_shift = None):
        x = self.proj(x)
        x = self.norm(x)
        if exists(scale_shift):
            scale, shift = scale_shift
            x = x * (scale + 1) + shift
        x = self.act(x)
        return x
 class ResnetBlock(nn.Module):
    def __init__(self, dim, dim_out, *, time_emb_dim = None, classes_emb_dim = None, groups = 8):
        super().__init__()
        self.mlp = nn.Sequential(
            nn.SiLU(),
            nn.Linear(int(time_emb_dim) + int(classes_emb_dim), dim_out * 2)
        ) if exists(time_emb_dim) or exists(classes_emb_dim) else None
        self.block1 = Block(dim, dim_out, groups = groups)
        self.block2 = Block(dim_out, dim_out, groups = groups)
        self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
    def forward(self, x, time_emb = None, class_emb = None):
        scale_shift = None
        if exists(self.mlp) and (exists(time_emb) or exists(class_emb)):
            cond_emb = tuple(filter(exists, (time_emb, class_emb)))
            cond_emb = torch.cat(cond_emb, dim = -1)
            cond_emb = self.mlp(cond_emb)
            cond_emb = rearrange(cond_emb, 'b c -> b c 1 1')
            scale_shift = cond_emb.chunk(2, dim = 1)
        h = self.block1(x, scale_shift = scale_shift)
        h = self.block2(h)
        return h + self.res_conv(x)
 class LinearAttention(nn.Module):
    def __init__(self, dim, heads = 4, dim_head = 32):
        super().__init__()
        self.scale = dim_head ** -0.5
        self.heads = heads
        hidden_dim = dim_head * heads
        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
        self.to_out = nn.Sequential(
            nn.Conv2d(hidden_dim, dim, 1),
            LayerNorm(dim)
        )
    def forward(self, x):
        b, c, h, w = x.shape
        qkv = self.to_qkv(x).chunk(3, dim = 1)
        q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> b h c (x y)', h = self.heads), qkv)
        q = q.softmax(dim = -2)
        k = k.softmax(dim = -1)
        q = q * self.scale
        v = v / (h * w)
        context = torch.einsum('b h d n, b h e n -> b h d e', k, v)
        out = torch.einsum('b h d e, b h d n -> b h e n', context, q)
        out = rearrange(out, 'b h c (x y) -> b (h c) x y', h = self.heads, x = h, y = w)
        return self.to_out(out)
 class Attention(nn.Module):
    def __init__(self, dim, heads = 4, dim_head = 32):
        super().__init__()
        self.scale = dim_head ** -0.5
        self.heads = heads
        hidden_dim = dim_head * heads
        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
        self.to_out = nn.Conv2d(hidden_dim, dim, 1)
    def forward(self, x):
        b, c, h, w = x.shape
        qkv = self.to_qkv(x).chunk(3, dim = 1)
        q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> b h c (x y)', h = self.heads), qkv)
        q = q * self.scale
        sim = einsum('b h d i, b h d j -> b h i j', q, k)
        attn = sim.softmax(dim = -1)
        out = einsum('b h i j, b h d j -> b h i d', attn, v)
        out = rearrange(out, 'b h (x y) d -> b (h d) x y', x = h, y = w)
        return self.to_out(out)
 # model
 class Unet(nn.Module):
    def __init__(
        self,
        dim,
        num_classes,
        cond_drop_prob = 0.5,
        init_dim = None,
        out_dim = None,
        dim_mults=(1, 2, 4, 8),
        channels = 3,
        resnet_block_groups = 8,
        learned_variance = False,
        learned_sinusoidal_cond = False,
        random_fourier_features = False,
        learned_sinusoidal_dim = 16,
    ):
        super().__init__()
        # classifier free guidance stuff
        self.cond_drop_prob = cond_drop_prob
        # determine dimensions
        self.channels = channels
        input_channels = channels
        init_dim = default(init_dim, dim)
        self.init_conv = nn.Conv2d(input_channels, init_dim, 7, padding = 3)
        dims = [init_dim, *map(lambda m: dim * m, dim_mults)]
        in_out = list(zip(dims[:-1], dims[1:]))
        block_klass = partial(ResnetBlock, groups = resnet_block_groups)
        # time embeddings
        time_dim = dim * 4
        self.random_or_learned_sinusoidal_cond = learned_sinusoidal_cond or random_fourier_features
        if self.random_or_learned_sinusoidal_cond:
            sinu_pos_emb = RandomOrLearnedSinusoidalPosEmb(learned_sinusoidal_dim, random_fourier_features)
            fourier_dim = learned_sinusoidal_dim + 1
        else:
            sinu_pos_emb = SinusoidalPosEmb(dim)
            fourier_dim = dim
        self.time_mlp = nn.Sequential(
            sinu_pos_emb,
            nn.Linear(fourier_dim, time_dim),
            nn.GELU(),
            nn.Linear(time_dim, time_dim)
        )
        # class embeddings
        self.classes_emb = nn.Embedding(num_classes, dim)
        self.null_classes_emb = nn.Parameter(torch.randn(dim))
        classes_dim = dim * 4
        self.classes_mlp = nn.Sequential(
            nn.Linear(dim, classes_dim),
            nn.GELU(),
            nn.Linear(classes_dim, classes_dim)
        )
        # layers
        self.downs = nn.ModuleList([])
        self.ups = nn.ModuleList([])
        num_resolutions = len(in_out)
        for ind, (dim_in, dim_out) in enumerate(in_out):
            is_last = ind >= (num_resolutions - 1)
            self.downs.append(nn.ModuleList([
                block_klass(dim_in, dim_in, time_emb_dim = time_dim, classes_emb_dim = classes_dim),
                block_klass(dim_in, dim_in, time_emb_dim = time_dim, classes_emb_dim = classes_dim),
                Residual(PreNorm(dim_in, LinearAttention(dim_in))),
                Downsample(dim_in, dim_out) if not is_last else nn.Conv2d(dim_in, dim_out, 3, padding = 1)
            ]))
        mid_dim = dims[-1]
        self.mid_block1 = block_klass(mid_dim, mid_dim, time_emb_dim = time_dim, classes_emb_dim = classes_dim)
        self.mid_attn = Residual(PreNorm(mid_dim, Attention(mid_dim)))
        self.mid_block2 = block_klass(mid_dim, mid_dim, time_emb_dim = time_dim, classes_emb_dim = classes_dim)
        for ind, (dim_in, dim_out) in enumerate(reversed(in_out)):
            is_last = ind == (len(in_out) - 1)
            self.ups.append(nn.ModuleList([
                block_klass(dim_out + dim_in, dim_out, time_emb_dim = time_dim, classes_emb_dim = classes_dim),
                block_klass(dim_out + dim_in, dim_out, time_emb_dim = time_dim, classes_emb_dim = classes_dim),
                Residual(PreNorm(dim_out, LinearAttention(dim_out))),
                Upsample(dim_out, dim_in) if not is_last else  nn.Conv2d(dim_out, dim_in, 3, padding = 1)
            ]))
        default_out_dim = channels * (1 if not learned_variance else 2)
        self.out_dim = default(out_dim, default_out_dim)
        self.final_res_block = block_klass(dim * 2, dim, time_emb_dim = time_dim, classes_emb_dim = classes_dim)
        self.final_conv = nn.Conv2d(dim, self.out_dim, 1)
    def forward_with_cond_scale(
        self,
        *args,
        cond_scale = 1.,
        **kwargs
    ):
        logits = self.forward(*args, **kwargs)
        if cond_scale == 1:
            return logits
        null_logits = self.forward(*args, cond_drop_prob = 1., **kwargs)
        return null_logits + (logits - null_logits) * cond_scale
    def forward(
        self,
        x,
        time,
        classes,
        cond_drop_prob = None
    ):
        batch, device = x.shape[0], x.device
        cond_drop_prob = default(cond_drop_prob, self.cond_drop_prob)
        # derive condition, with condition dropout for classifier free guidance        
        classes_emb = self.classes_emb(classes)
        if cond_drop_prob > 0:
            keep_mask = prob_mask_like((batch,), 1 - cond_drop_prob, device = device)
            null_classes_emb = repeat(self.null_classes_emb, 'd -> b d', b = batch)
            classes_emb = torch.where(
                rearrange(keep_mask, 'b -> b 1'),
                classes_emb,
                null_classes_emb
            )
        c = self.classes_mlp(classes_emb)
        # unet
        x = self.init_conv(x)
        r = x.clone()
        t = self.time_mlp(time)
        h = []
        for block1, block2, attn, downsample in self.downs:
            x = block1(x, t, c)
            h.append(x)
            x = block2(x, t, c)
            x = attn(x)
            h.append(x)
            x = downsample(x)
        x = self.mid_block1(x, t, c)
        x = self.mid_attn(x)
        x = self.mid_block2(x, t, c)
        for block1, block2, attn, upsample in self.ups:
            x = torch.cat((x, h.pop()), dim = 1)
            x = block1(x, t, c)
            x = torch.cat((x, h.pop()), dim = 1)
            x = block2(x, t, c)
            x = attn(x)
            x = upsample(x)
        x = torch.cat((x, r), dim = 1)
        x = self.final_res_block(x, t, c)
        return self.final_conv(x)
 # gaussian diffusion trainer class
 def extract(a, t, x_shape):
    b, *_ = t.shape
    out = a.gather(-1, t)
    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
 def linear_beta_schedule(timesteps):
    scale = 1000 / timesteps
    beta_start = scale * 0.0001
    beta_end = scale * 0.02
    return torch.linspace(beta_start, beta_end, timesteps, dtype = torch.float64)
 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 GaussianDiffusion(nn.Module):
    def __init__(
        self,
        model,
        *,
        image_size,
        timesteps = 1000,
        sampling_timesteps = None,
        loss_type = 'l1',
        objective = 'pred_noise',
        beta_schedule = 'cosine',
        p2_loss_weight_gamma = 0., # p2 loss weight, from https://arxiv.org/abs/2204.00227 - 0 is equivalent to weight of 1 across time - 1. is recommended
        p2_loss_weight_k = 1,
        ddim_sampling_eta = 1.
    ):
        super().__init__()
        assert not (type(self) == GaussianDiffusion and model.channels != model.out_dim)
        assert not model.random_or_learned_sinusoidal_cond
        self.model = model
        self.channels = self.model.channels
        self.image_size = image_size
        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, classes, cond_scale = 3., clip_x_start = False):
        model_output = self.model.forward_with_cond_scale(x, t, classes, cond_scale = cond_scale)
        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, classes, cond_scale, clip_denoised = True):
        preds = self.model_predictions(x, t, classes, cond_scale)
        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, classes, cond_scale = 3., 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, classes = classes, cond_scale = cond_scale, 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, classes, shape, cond_scale = 3.):
        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):
            img, x_start = self.p_sample(img, t, classes, cond_scale)
        img = unnormalize_to_zero_to_one(img)
        return img
    @torch.no_grad()
    def ddim_sample(self, classes, shape, cond_scale = 3., 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)
            pred_noise, x_start, *_ = self.model_predictions(img, time_cond, classes, cond_scale = cond_scale, 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, classes, cond_scale = 3.):
        batch_size, image_size, channels = classes.shape[0], self.image_size, self.channels
        sample_fn = self.p_sample_loop if not self.is_ddim_sampling else self.ddim_sample
        return sample_fn(classes, (batch_size, channels, image_size, image_size), cond_scale)
    @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, *, classes, noise = None):
        b, c, h, w = 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)
        # predict and take gradient step
        model_out = self.model(x, t, classes)
        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, h, w, device, img_size, = *img.shape, img.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()
        img = normalize_to_neg_one_to_one(img)
        return self.p_losses(img, t, *args, **kwargs)
 # example
 if __name__ == '__main__':
    num_classes = 10
    model = Unet(
        dim = 64,
        dim_mults = (1, 2, 4, 8),
        num_classes = num_classes,
        cond_drop_prob = 0.5
    )
    diffusion = GaussianDiffusion(
        model,
        image_size = 128,
        timesteps = 1000
    ).cuda()
    training_images = torch.randn(8, 3, 128, 128).cuda() # images are normalized from 0 to 1
    image_classes = torch.randint(0, num_classes, (8,)).cuda()    # say 10 classes
    loss = diffusion(training_images, classes = image_classes)
    loss.backward()
    # do above for many steps
    sampled_images = diffusion.sample(
        classes = image_classes,
        cond_scale = 3.                # condition scaling, anything greater than 1 strengthens the classifier free guidance. reportedly 3-8 is good empirically
    )
    sampled_images.shape # (8, 3, 128, 128)
@@ -1,4 +1,3 @@
 import math
 import torch
 from torch import sqrt
 from torch import nn, einsum
@@ -6,8 +5,7 @@ import torch.nn.functional as F
 from torch.special import expm1
 from tqdm import tqdm
-from einops import rearrange, repeat, reduce
+from einops import rearrange, repeat
 from einops.layers.torch import Rearrange
 # helpers
@@ -35,24 +33,6 @@ def right_pad_dims_to(x, t):
        return t
    return t.view(*t.shape, *((1,) * padding_dims))
 # neural net helpers
 class Residual(nn.Module):
    def __init__(self, fn):
        super().__init__()
        self.fn = fn
    def forward(self, x):
        return x + self.fn(x)
 class MonotonicLinear(nn.Module):
    def __init__(self, *args, **kwargs):
        super().__init__()
        self.net = nn.Linear(*args, **kwargs)
    def forward(self, x):
        return F.linear(x, self.net.weight.abs(), self.net.bias.abs())
 # continuous schedules
 # equations are taken from https://openreview.net/attachment?id=2LdBqxc1Yv&name=supplementary_material
@@ -60,76 +40,33 @@ class MonotonicLinear(nn.Module):
 # log(snr) that approximates the original linear schedule
 def log(t, eps = 1e-20):
    return torch.log(t.clamp(min = eps))
 def beta_linear_log_snr(t):
-    return -log(expm1(1e-4 + 10 * (t ** 2)))
+    return -torch.log(expm1(1e-4 + 10 * (t ** 2)))
-def alpha_cosine_log_snr(t, s = 0.008):
+def alpha_cosine_log_snr(t):
-    return -log((torch.cos((t + s) / (1 + s) * math.pi * 0.5) ** -2) - 1, eps = 1e-5)
+    raise NotImplementedError
 class learned_noise_schedule(nn.Module):
-    """ described in section H and then I.2 of the supplementary material for variational ddpm paper """
+    def __init__(self):
    def __init__(
        self,
        *,
        log_snr_max,
        log_snr_min,
        hidden_dim = 1024,
        frac_gradient = 1.
    ):
        super().__init__()
-        self.slope = log_snr_min - log_snr_max
+        raise NotImplementedError
-        self.intercept = log_snr_max
+        # learned noise schedule, using learned monotonic MLP (weights kept positive) in the paper
        self.net = nn.Sequential(
            Rearrange('... -> ... 1'),
            MonotonicLinear(1, 1),
            Residual(nn.Sequential(
                MonotonicLinear(1, hidden_dim),
                nn.Sigmoid(),
                MonotonicLinear(hidden_dim, 1)
            )),
            Rearrange('... 1 -> ...'),
        )
        self.frac_gradient = frac_gradient
    def forward(self, x):
        frac_gradient = self.frac_gradient
        device = x.device
        out_zero = self.net(torch.zeros_like(x))
        out_one =  self.net(torch.ones_like(x))
        x = self.net(x)
        normed = self.slope * ((x - out_zero) / (out_one - out_zero)) + self.intercept
        return normed * frac_gradient + normed.detach() * (1 - frac_gradient)
 class ContinuousTimeGaussianDiffusion(nn.Module):
    def __init__(
        self,
-        model,
+        denoise_fn,
        *,
        image_size,
        channels = 3,
        cond_scale = 500,
        loss_type = 'l1',
        noise_schedule = 'linear',
-        num_sample_steps = 500,
+        num_sample_steps = 500
        clip_sample_denoised = True,
        learned_schedule_net_hidden_dim = 1024,
        learned_noise_schedule_frac_gradient = 1.,   # between 0 and 1, determines what percentage of gradients go back, so one can update the learned noise schedule more slowly
        p2_loss_weight_gamma = 0.,                   # p2 loss weight, from https://arxiv.org/abs/2204.00227 - 0 is equivalent to weight of 1 across time
        p2_loss_weight_k = 1
    ):
        super().__init__()
        assert model.random_or_learned_sinusoidal_cond
        assert not model.self_condition, 'not supported yet'
-        self.model = model
+        self.denoise_fn = denoise_fn
        # image dimensions
@@ -138,40 +75,21 @@ class ContinuousTimeGaussianDiffusion(nn.Module):
        # continuous noise schedule related stuff
        self.cond_scale = cond_scale # the log(snr) will be scaled by this value
        self.loss_type = loss_type
        if noise_schedule == 'linear':
            self.log_snr = beta_linear_log_snr
        elif noise_schedule == 'cosine':
            self.log_snr = alpha_cosine_log_snr
        elif noise_schedule == 'learned':
            log_snr_max, log_snr_min = [beta_linear_log_snr(torch.tensor([time])).item() for time in (0., 1.)]
            self.log_snr = learned_noise_schedule(
                log_snr_max = log_snr_max,
                log_snr_min = log_snr_min,
                hidden_dim = learned_schedule_net_hidden_dim,
                frac_gradient = learned_noise_schedule_frac_gradient
            )
        else:
            raise ValueError(f'unknown noise schedule {noise_schedule}')
        # sampling
        self.num_sample_steps = num_sample_steps
        self.clip_sample_denoised = clip_sample_denoised
        # p2 loss weight
        # proposed https://arxiv.org/abs/2204.00227
        assert p2_loss_weight_gamma <= 2, 'in paper, they noticed any gamma greater than 2 is harmful'
        self.p2_loss_weight_gamma = p2_loss_weight_gamma  # recommended to be 0.5 or 1
        self.p2_loss_weight_k = p2_loss_weight_k
    @property
    def device(self):
-        return next(self.model.parameters()).device
+        return next(self.denoise_fn.parameters()).device
    @property
    def loss_fn(self):
@@ -186,6 +104,14 @@ class ContinuousTimeGaussianDiffusion(nn.Module):
        # reviewer found an error in the equation in the paper (missing sigma)
        # following - https://openreview.net/forum?id=2LdBqxc1Yv&noteId=rIQgH0zKsRt
        # todo - derive x_start from the posterior mean and do dynamic thresholding
        # assumed that is what is going on in Imagen
        batch = x.shape[0]
        batch_time = repeat(time, ' -> b', b = batch)
        pred_noise = self.denoise_fn(x, batch_time * self.cond_scale)
        log_snr = self.log_snr(time)
        log_snr_next = self.log_snr(time_next)
        c = -expm1(log_snr - log_snr_next)
@@ -193,21 +119,7 @@ class ContinuousTimeGaussianDiffusion(nn.Module):
        squared_alpha, squared_alpha_next = log_snr.sigmoid(), log_snr_next.sigmoid()
        squared_sigma, squared_sigma_next = (-log_snr).sigmoid(), (-log_snr_next).sigmoid()
-        alpha, sigma, alpha_next = map(sqrt, (squared_alpha, squared_sigma, squared_alpha_next))
+        model_mean = sqrt(squared_alpha_next / squared_alpha) * (x - c * sqrt(squared_sigma) * pred_noise)
        batch_log_snr = repeat(log_snr, ' -> b', b = x.shape[0])
        pred_noise = self.model(x, batch_log_snr)
        if self.clip_sample_denoised:
            x_start = (x - sigma * pred_noise) / alpha
            # in Imagen, this was changed to dynamic thresholding
            x_start.clamp_(-1., 1.)
            model_mean = alpha_next * (x * (1 - c) / alpha + c * x_start)
        else:
            model_mean = alpha_next / alpha * (x - c * sigma * pred_noise)
        posterior_variance = squared_sigma_next * c
        return model_mean, posterior_variance
@@ -215,16 +127,18 @@ class ContinuousTimeGaussianDiffusion(nn.Module):
    # sampling related functions
    @torch.no_grad()
-    def p_sample(self, x, time, time_next):
+    def p_sample(self, x, time, time_next, eps = 2e-4):
        batch, *_, device = *x.shape, x.device
        model_mean, model_variance = self.p_mean_variance(x = x, time = time, time_next = time_next)
        if time_next == 0:
            return model_mean
        noise = torch.randn_like(x)
-        return model_mean + sqrt(model_variance) * noise
+
        # no noise when time is below some epsilon
        # not sure how important this is
        time = repeat(time, ' -> b', b = batch)
        nonzero_mask = (1 - (time < eps).float()).reshape(batch, *((1,) * (len(x.shape) - 1)))
        return model_mean + nonzero_mask * sqrt(model_variance) * noise
    @torch.no_grad()
    def p_sample_loop(self, shape):
@@ -238,7 +152,6 @@ class ContinuousTimeGaussianDiffusion(nn.Module):
            times_next = steps[i + 1]
            img = self.p_sample(img, times, times_next)
        img.clamp_(-1., 1.)
        img = unnormalize_to_zero_to_one(img)
        return img
@@ -267,17 +180,9 @@ class ContinuousTimeGaussianDiffusion(nn.Module):
        noise = default(noise, lambda: torch.randn_like(x_start))
        x, log_snr = self.q_sample(x_start = x_start, times = times, noise = noise)
        model_out = self.model(x, log_snr)
-        losses = self.loss_fn(model_out, noise, reduction = 'none')
+        model_out = self.denoise_fn(x, log_snr * self.cond_scale)
-        losses = reduce(losses, 'b ... -> b', 'mean')
+        return self.loss_fn(model_out, noise)
        if self.p2_loss_weight_gamma >= 0:
            # following eq 8. in https://arxiv.org/abs/2204.00227
            loss_weight = (self.p2_loss_weight_k + log_snr.exp()) ** -self.p2_loss_weight_gamma
            losses = losses * loss_weight
        return losses.mean()
    def forward(self, img, *args, **kwargs):
        b, c, h, w, device, img_size, = *img.shape, img.device, self.image_size
@@ -1,33 +1,21 @@
 import math
 import copy
 from pathlib import Path
 from random import random
 from functools import partial
 from collections import namedtuple
 from multiprocessing import cpu_count
 import torch
 from torch import nn, einsum
 import torch.nn.functional as F
-from torch.utils.data import Dataset, DataLoader
+from inspect import isfunction
 from functools import partial
 from torch.utils import data
 from torch.cuda.amp import autocast, GradScaler
 from pathlib import Path
 from torch.optim import Adam
-from torchvision import transforms as T, utils
+from torchvision import transforms, utils
 from einops import rearrange, reduce
 from einops.layers.torch import Rearrange
 from PIL import Image
 from tqdm.auto import tqdm
 from ema_pytorch import EMA
-from accelerate import Accelerator
+from tqdm import tqdm
-
+from einops import rearrange
 from denoising_diffusion_pytorch.version import __version__
 # constants
 ModelPrediction =  namedtuple('ModelPrediction', ['pred_noise', 'pred_x_start'])
 # helpers functions
@@ -37,19 +25,13 @@ def exists(x):
 def default(val, d):
    if exists(val):
        return val
-    return d() if callable(d) else d
+    return d() if isfunction(d) else d
 def identity(t, *args, **kwargs):
    return t
 def cycle(dl):
    while True:
        for data in dl:
            yield data
 def has_int_squareroot(num):
    return (math.sqrt(num) ** 2) == num
 def num_to_groups(num, divisor):
    groups = num // divisor
    remainder = num % divisor
@@ -58,13 +40,6 @@ def num_to_groups(num, divisor):
        arr.append(remainder)
    return arr
 def convert_image_to_fn(img_type, image):
    if image.mode != img_type:
        return image.convert(img_type)
    return image
 # normalization functions
 def normalize_to_neg_one_to_one(img):
    return img * 2 - 1
@@ -73,6 +48,21 @@ def unnormalize_to_zero_to_one(t):
 # small helper modules
 class EMA():
    def __init__(self, beta):
        super().__init__()
        self.beta = beta
    def update_model_average(self, ma_model, current_model):
        for current_params, ma_params in zip(current_model.parameters(), ma_model.parameters()):
            old_weight, up_weight = ma_params.data, current_params.data
            ma_params.data = self.update_average(old_weight, up_weight)
    def update_average(self, old, new):
        if old is None:
            return new
        return old * self.beta + (1 - self.beta) * new
 class Residual(nn.Module):
    def __init__(self, fn):
        super().__init__()
@@ -81,56 +71,6 @@ class Residual(nn.Module):
    def forward(self, x, *args, **kwargs):
        return self.fn(x, *args, **kwargs) + x
 def Upsample(dim, dim_out = None):
    return nn.Sequential(
        nn.Upsample(scale_factor = 2, mode = 'nearest'),
        nn.Conv2d(dim, default(dim_out, dim), 3, padding = 1)
    )
 def Downsample(dim, dim_out = None):
    return nn.Sequential(
        Rearrange('b c (h p1) (w p2) -> b (c p1 p2) h w', p1 = 2, p2 = 2),
        nn.Conv2d(dim * 4, default(dim_out, dim), 1)
    )
 class WeightStandardizedConv2d(nn.Conv2d):
    """
    https://arxiv.org/abs/1903.10520
    weight standardization purportedly works synergistically with group normalization
    """
    def forward(self, x):
        eps = 1e-5 if x.dtype == torch.float32 else 1e-3
        weight = self.weight
        mean = reduce(weight, 'o ... -> o 1 1 1', 'mean')
        var = reduce(weight, 'o ... -> o 1 1 1', partial(torch.var, unbiased = False))
        normalized_weight = (weight - mean) * (var + eps).rsqrt()
        return F.conv2d(x, normalized_weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
 class LayerNorm(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
    def forward(self, x):
        eps = 1e-5 if x.dtype == torch.float32 else 1e-3
        var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
        mean = torch.mean(x, dim = 1, keepdim = True)
        return (x - mean) * (var + eps).rsqrt() * self.g
 class PreNorm(nn.Module):
    def __init__(self, dim, fn):
        super().__init__()
        self.fn = fn
        self.norm = LayerNorm(dim)
    def forward(self, x):
        x = self.norm(x)
        return self.fn(x)
 # sinusoidal positional embeds
 class SinusoidalPosEmb(nn.Module):
    def __init__(self, dim):
        super().__init__()
@@ -145,29 +85,40 @@ class SinusoidalPosEmb(nn.Module):
        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
        return emb
-class RandomOrLearnedSinusoidalPosEmb(nn.Module):
+def Upsample(dim):
-    """ following @crowsonkb 's lead with random (learned optional) sinusoidal pos emb """
+    return nn.ConvTranspose2d(dim, dim, 4, 2, 1)
    """ https://github.com/crowsonkb/v-diffusion-jax/blob/master/diffusion/models/danbooru_128.py#L8 """
-    def __init__(self, dim, is_random = False):
+def Downsample(dim):
    return nn.Conv2d(dim, dim, 4, 2, 1)
 class LayerNorm(nn.Module):
    def __init__(self, dim, eps = 1e-5):
        super().__init__()
-        assert (dim % 2) == 0
+        self.eps = eps
-        half_dim = dim // 2
+        self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
-        self.weights = nn.Parameter(torch.randn(half_dim), requires_grad = not is_random)
+        self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
    def forward(self, x):
-        x = rearrange(x, 'b -> b 1')
+        var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
-        freqs = x * rearrange(self.weights, 'd -> 1 d') * 2 * math.pi
+        mean = torch.mean(x, dim = 1, keepdim = True)
-        fouriered = torch.cat((freqs.sin(), freqs.cos()), dim = -1)
+        return (x - mean) / (var + self.eps).sqrt() * self.g + self.b
-        fouriered = torch.cat((x, fouriered), dim = -1)
+
-        return fouriered
+class PreNorm(nn.Module):
    def __init__(self, dim, fn):
        super().__init__()
        self.fn = fn
        self.norm = LayerNorm(dim)
    def forward(self, x):
        x = self.norm(x)
        return self.fn(x)
 # building block modules
 class Block(nn.Module):
    def __init__(self, dim, dim_out, groups = 8):
        super().__init__()
-        self.proj = WeightStandardizedConv2d(dim, dim_out, 3, padding = 1)
+        self.proj = nn.Conv2d(dim, dim_out, 3, padding = 1)
        self.norm = nn.GroupNorm(groups, dim_out)
        self.act = nn.SiLU()
@@ -205,7 +156,6 @@ class ResnetBlock(nn.Module):
        h = self.block1(x, scale_shift = scale_shift)
        h = self.block2(h)
        return h + self.res_conv(x)
 class LinearAttention(nn.Module):
@@ -230,8 +180,6 @@ class LinearAttention(nn.Module):
        k = k.softmax(dim = -1)
        q = q * self.scale
        v = v / (h * w)
        context = torch.einsum('b h d n, b h e n -> b h d e', k, v)
        out = torch.einsum('b h d e, b h d n -> b h e n', context, q)
@@ -244,7 +192,6 @@ class Attention(nn.Module):
        self.scale = dim_head ** -0.5
        self.heads = heads
        hidden_dim = dim_head * heads
        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
        self.to_out = nn.Conv2d(hidden_dim, dim, 1)
@@ -252,13 +199,13 @@ class Attention(nn.Module):
        b, c, h, w = x.shape
        qkv = self.to_qkv(x).chunk(3, dim = 1)
        q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> b h c (x y)', h = self.heads), qkv)
        q = q * self.scale
        sim = einsum('b h d i, b h d j -> b h i j', q, k)
        sim = sim - sim.amax(dim = -1, keepdim = True).detach()
        attn = sim.softmax(dim = -1)
        out = einsum('b h i j, b h d j -> b h i d', attn, v)
        out = einsum('b h i j, b h d j -> b h i d', attn, v)
        out = rearrange(out, 'b h (x y) d -> b (h d) x y', x = h, y = w)
        return self.to_out(out)
@@ -272,23 +219,18 @@ class Unet(nn.Module):
        out_dim = None,
        dim_mults=(1, 2, 4, 8),
        channels = 3,
-        self_condition = False,
+        with_time_emb = True,
        resnet_block_groups = 8,
-        learned_variance = False,
+        learned_variance = False
        learned_sinusoidal_cond = False,
        random_fourier_features = False,
        learned_sinusoidal_dim = 16
    ):
        super().__init__()
        # determine dimensions
        self.channels = channels
        self.self_condition = self_condition
        input_channels = channels * (2 if self_condition else 1)
-        init_dim = default(init_dim, dim)
+        init_dim = default(init_dim, dim // 3 * 2)
-        self.init_conv = nn.Conv2d(input_channels, init_dim, 7, padding = 3)
+        self.init_conv = nn.Conv2d(channels, init_dim, 7, padding = 3)
        dims = [init_dim, *map(lambda m: dim * m, dim_mults)]
        in_out = list(zip(dims[:-1], dims[1:]))
@@ -297,23 +239,17 @@ class Unet(nn.Module):
        # time embeddings
-        time_dim = dim * 4
+        if with_time_emb:
-
+            time_dim = dim * 4
-        self.random_or_learned_sinusoidal_cond = learned_sinusoidal_cond or random_fourier_features
+            self.time_mlp = nn.Sequential(
-
+                SinusoidalPosEmb(dim),
-        if self.random_or_learned_sinusoidal_cond:
+                nn.Linear(dim, time_dim),
-            sinu_pos_emb = RandomOrLearnedSinusoidalPosEmb(learned_sinusoidal_dim, random_fourier_features)
+                nn.GELU(),
-            fourier_dim = learned_sinusoidal_dim + 1
+                nn.Linear(time_dim, time_dim)
            )
        else:
-            sinu_pos_emb = SinusoidalPosEmb(dim)
+            time_dim = None
-            fourier_dim = dim
+            self.time_mlp = None
        self.time_mlp = nn.Sequential(
            sinu_pos_emb,
            nn.Linear(fourier_dim, time_dim),
            nn.GELU(),
            nn.Linear(time_dim, time_dim)
        )
        # layers
@@ -325,10 +261,10 @@ class Unet(nn.Module):
            is_last = ind >= (num_resolutions - 1)
            self.downs.append(nn.ModuleList([
-                block_klass(dim_in, dim_in, time_emb_dim = time_dim),
+                block_klass(dim_in, dim_out, time_emb_dim = time_dim),
-                block_klass(dim_in, dim_in, time_emb_dim = time_dim),
+                block_klass(dim_out, dim_out, time_emb_dim = time_dim),
-                Residual(PreNorm(dim_in, LinearAttention(dim_in))),
+                Residual(PreNorm(dim_out, LinearAttention(dim_out))),
-                Downsample(dim_in, dim_out) if not is_last else nn.Conv2d(dim_in, dim_out, 3, padding = 1)
+                Downsample(dim_out) if not is_last else nn.Identity()
            ]))
        mid_dim = dims[-1]
@@ -336,42 +272,36 @@ class Unet(nn.Module):
        self.mid_attn = Residual(PreNorm(mid_dim, Attention(mid_dim)))
        self.mid_block2 = block_klass(mid_dim, mid_dim, time_emb_dim = time_dim)
-        for ind, (dim_in, dim_out) in enumerate(reversed(in_out)):
+        for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
-            is_last = ind == (len(in_out) - 1)
+            is_last = ind >= (num_resolutions - 1)
            self.ups.append(nn.ModuleList([
-                block_klass(dim_out + dim_in, dim_out, time_emb_dim = time_dim),
+                block_klass(dim_out * 2, dim_in, time_emb_dim = time_dim),
-                block_klass(dim_out + dim_in, dim_out, time_emb_dim = time_dim),
+                block_klass(dim_in, dim_in, time_emb_dim = time_dim),
-                Residual(PreNorm(dim_out, LinearAttention(dim_out))),
+                Residual(PreNorm(dim_in, LinearAttention(dim_in))),
-                Upsample(dim_out, dim_in) if not is_last else  nn.Conv2d(dim_out, dim_in, 3, padding = 1)
+                Upsample(dim_in) if not is_last else nn.Identity()
            ]))
        default_out_dim = channels * (1 if not learned_variance else 2)
        self.out_dim = default(out_dim, default_out_dim)
-        self.final_res_block = block_klass(dim * 2, dim, time_emb_dim = time_dim)
+        self.final_conv = nn.Sequential(
-        self.final_conv = nn.Conv2d(dim, self.out_dim, 1)
+            block_klass(dim, dim),
-
+            nn.Conv2d(dim, self.out_dim, 1)
-    def forward(self, x, time, x_self_cond = None):
+        )
        if self.self_condition:
            x_self_cond = default(x_self_cond, lambda: torch.zeros_like(x))
            x = torch.cat((x_self_cond, x), dim = 1)
    def forward(self, x, time):
        x = self.init_conv(x)
        r = x.clone()
-        t = self.time_mlp(time)
+        t = self.time_mlp(time) if exists(self.time_mlp) else None
        h = []
        for block1, block2, attn, downsample in self.downs:
            x = block1(x, t)
            h.append(x)
            x = block2(x, t)
            x = attn(x)
            h.append(x)
            x = downsample(x)
        x = self.mid_block1(x, t)
@@ -379,18 +309,12 @@ class Unet(nn.Module):
        x = self.mid_block2(x, t)
        for block1, block2, attn, upsample in self.ups:
-            x = torch.cat((x, h.pop()), dim = 1)
+            x = torch.cat((x, h.pop()), dim=1)
            x = block1(x, t)
            x = torch.cat((x, h.pop()), dim = 1)
            x = block2(x, t)
            x = attn(x)
            x = upsample(x)
        x = torch.cat((x, r), dim = 1)
        x = self.final_res_block(x, t)
        return self.final_conv(x)
 # gaussian diffusion trainer class
@@ -413,7 +337,7 @@ def cosine_beta_schedule(timesteps, s = 0.008):
    """
    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 = torch.cos(((x / timesteps) + s) / (1 + s) * torch.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)
@@ -421,32 +345,23 @@ def cosine_beta_schedule(timesteps, s = 0.008):
 class GaussianDiffusion(nn.Module):
    def __init__(
        self,
-        model,
+        denoise_fn,
        *,
        image_size,
        channels = 3,
        timesteps = 1000,
        sampling_timesteps = None,
        loss_type = 'l1',
        objective = 'pred_noise',
-        beta_schedule = 'cosine',
+        beta_schedule = 'cosine'
        p2_loss_weight_gamma = 0., # p2 loss weight, from https://arxiv.org/abs/2204.00227 - 0 is equivalent to weight of 1 across time - 1. is recommended
        p2_loss_weight_k = 1,
        ddim_sampling_eta = 0.
    ):
        super().__init__()
-        assert not (type(self) == GaussianDiffusion and model.channels != model.out_dim)
+        assert not (type(self) == GaussianDiffusion and denoise_fn.channels != denoise_fn.out_dim)
        assert not model.random_or_learned_sinusoidal_cond
        self.model = model
        self.channels = self.model.channels
        self.self_condition = self.model.self_condition
        self.channels = channels
        self.image_size = image_size
-
+        self.denoise_fn = denoise_fn
        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':
@@ -455,21 +370,13 @@ class GaussianDiffusion(nn.Module):
            raise ValueError(f'unknown beta schedule {beta_schedule}')
        alphas = 1. - betas
-        alphas_cumprod = torch.cumprod(alphas, dim=0)
+        alphas_cumprod = torch.cumprod(alphas, axis=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))
@@ -500,34 +407,12 @@ class GaussianDiffusion(nn.Module):
        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 +
@@ -537,103 +422,49 @@ class GaussianDiffusion(nn.Module):
        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):
+    def p_mean_variance(self, x, t, clip_denoised: bool):
-        model_output = self.model(x, t, x_self_cond)
+        model_output = self.denoise_fn(x, t)
        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 = t, 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)
+        else:
-            pred_noise = self.predict_noise_from_start(x, t, x_start)
+            raise ValueError(f'unknown objective {self.objective}')
        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
+        return model_mean, posterior_variance, posterior_log_variance
    @torch.no_grad()
-    def p_sample(self, x, t: int, x_self_cond = None, clip_denoised = True):
+    def p_sample(self, x, t, 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 = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
-        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)
-        noise = torch.randn_like(x) if t > 0 else 0. # no noise if t == 0
+        # no noise when t == 0
-        pred_img = model_mean + (0.5 * model_log_variance).exp() * noise
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-        return pred_img, x_start
+        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
    @torch.no_grad()
    def p_sample_loop(self, shape):
-        batch, device = shape[0], self.betas.device
+        device = self.betas.device
        b = shape[0]
        img = torch.randn(shape, device=device)
-        x_start = None
+        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='sampling loop time step', total=self.num_timesteps):
-
+            img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long))
        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):
-        image_size, channels = self.image_size, self.channels
+        image_size = self.image_size
-        sample_fn = self.p_sample_loop if not self.is_ddim_sampling else self.ddim_sample
+        channels = self.channels
-        return sample_fn((batch_size, channels, image_size, image_size))
+        return self.p_sample_loop((batch_size, channels, image_size, image_size))
    @torch.no_grad()
    def interpolate(self, x1, x2, t = None, lam = 0.5):
@@ -642,11 +473,11 @@ class GaussianDiffusion(nn.Module):
        assert x1.shape == x2.shape
-        t_batched = torch.stack([torch.tensor(t, device = device)] * b)
+        t_batched = torch.stack([torch.tensor(t, device=device)] * b)
-        xt1, xt2 = map(lambda x: self.q_sample(x, t = t_batched), (x1, x2))
+        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):
+        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
@@ -672,39 +503,18 @@ class GaussianDiffusion(nn.Module):
        b, c, h, w = 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)
-
+        model_out = self.denoise_fn(x, t)
        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 = self.loss_fn(model_out, target)
-        loss = reduce(loss, 'b ... -> b (...)', 'mean')
+        return loss
        loss = loss * extract(self.p2_loss_weight, t, loss.shape)
        return loss.mean()
    def forward(self, img, *args, **kwargs):
        b, c, h, w, device, img_size, = *img.shape, img.device, self.image_size
@@ -716,28 +526,18 @@ class GaussianDiffusion(nn.Module):
 # dataset classes
-class Dataset(Dataset):
+class Dataset(data.Dataset):
-    def __init__(
+    def __init__(self, folder, image_size, exts = ['jpg', 'jpeg', 'png']):
        self,
        folder,
        image_size,
        exts = ['jpg', 'jpeg', 'png', 'tiff'],
        augment_horizontal_flip = False,
        convert_image_to = None
    ):
        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}')]
-        maybe_convert_fn = partial(convert_image_to_fn, convert_image_to) if exists(convert_image_to) else nn.Identity()
+        self.transform = transforms.Compose([
-
+            transforms.Resize(image_size),
-        self.transform = T.Compose([
+            transforms.RandomHorizontalFlip(),
-            T.Lambda(maybe_convert_fn),
+            transforms.CenterCrop(image_size),
-            T.Resize(image_size),
+            transforms.ToTensor()
            T.RandomHorizontalFlip() if augment_horizontal_flip else nn.Identity(),
            T.CenterCrop(image_size),
            T.ToTensor()
        ])
    def __len__(self):
@@ -756,152 +556,103 @@ class Trainer(object):
        diffusion_model,
        folder,
        *,
-        train_batch_size = 16,
+        ema_decay = 0.995,
-        gradient_accumulate_every = 1,
+        image_size = 128,
-        augment_horizontal_flip = True,
+        train_batch_size = 32,
        train_lr = 1e-4,
        train_num_steps = 100000,
-        ema_update_every = 10,
+        gradient_accumulate_every = 2,
        ema_decay = 0.995,
        adam_betas = (0.9, 0.99),
        save_and_sample_every = 1000,
        num_samples = 25,
        results_folder = './results',
        amp = False,
-        fp16 = False,
+        step_start_ema = 2000,
-        split_batches = True,
+        update_ema_every = 10,
-        convert_image_to = None
+        save_and_sample_every = 1000,
        results_folder = './results'
    ):
        super().__init__()
        self.accelerator = Accelerator(
            split_batches = split_batches,
            mixed_precision = 'fp16' if fp16 else 'no'
        )
        self.accelerator.native_amp = amp
        self.model = diffusion_model
        self.ema = EMA(ema_decay)
        self.ema_model = copy.deepcopy(self.model)
        self.update_ema_every = update_ema_every
-        assert has_int_squareroot(num_samples), 'number of samples must have an integer square root'
+        self.step_start_ema = step_start_ema
        self.num_samples = num_samples
        self.save_and_sample_every = save_and_sample_every
        self.batch_size = train_batch_size
        self.gradient_accumulate_every = gradient_accumulate_every
        self.train_num_steps = train_num_steps
        self.image_size = diffusion_model.image_size
        self.gradient_accumulate_every = gradient_accumulate_every
        self.train_num_steps = train_num_steps
-        # dataset and dataloader
+        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.ds = Dataset(folder, self.image_size, augment_horizontal_flip = augment_horizontal_flip, convert_image_to = convert_image_to)
+        self.step = 0
        dl = DataLoader(self.ds, batch_size = train_batch_size, shuffle = True, pin_memory = True, num_workers = cpu_count())
-        dl = self.accelerator.prepare(dl)
+        self.amp = amp
-        self.dl = cycle(dl)
+        self.scaler = GradScaler(enabled = amp)
        # optimizer
        self.opt = Adam(diffusion_model.parameters(), lr = train_lr, betas = adam_betas)
        # for logging results in a folder periodically
        if self.accelerator.is_main_process:
            self.ema = EMA(diffusion_model, beta = ema_decay, update_every = ema_update_every)
        self.results_folder = Path(results_folder)
        self.results_folder.mkdir(exist_ok = True)
-        # step counter state
+        self.reset_parameters()
-        self.step = 0
+    def reset_parameters(self):
        self.ema_model.load_state_dict(self.model.state_dict())
-        # prepare model, dataloader, optimizer with accelerator
+    def step_ema(self):
-
+        if self.step < self.step_start_ema:
-        self.model, self.opt = self.accelerator.prepare(self.model, self.opt)
+            self.reset_parameters()
            return
        self.ema.update_model_average(self.ema_model, self.model)
    def save(self, milestone):
        if not self.accelerator.is_local_main_process:
            return
        data = {
            'step': self.step,
-            'model': self.accelerator.get_state_dict(self.model),
+            'model': self.model.state_dict(),
-            'opt': self.opt.state_dict(),
+            'ema': self.ema_model.state_dict(),
-            'ema': self.ema.state_dict(),
+            'scaler': self.scaler.state_dict()
            'scaler': self.accelerator.scaler.state_dict() if exists(self.accelerator.scaler) else None,
            'version': __version__
        }
        torch.save(data, str(self.results_folder / f'model-{milestone}.pt'))
    def load(self, milestone):
-        accelerator = self.accelerator
+        data = torch.load(str(self.results_folder / f'model-{milestone}.pt'))
        device = accelerator.device
        data = torch.load(str(self.results_folder / f'model-{milestone}.pt'), map_location=device)
        model = self.accelerator.unwrap_model(self.model)
        model.load_state_dict(data['model'])
        self.step = data['step']
-        self.opt.load_state_dict(data['opt'])
+        self.model.load_state_dict(data['model'])
-        self.ema.load_state_dict(data['ema'])
+        self.ema_model.load_state_dict(data['ema'])
-
+        self.scaler.load_state_dict(data['scaler'])
        if 'version' in data:
            print(f"loading from version {data['version']}")
        if exists(self.accelerator.scaler) and exists(data['scaler']):
            self.accelerator.scaler.load_state_dict(data['scaler'])
    def train(self):
-        accelerator = self.accelerator
+        with tqdm(initial = self.step, total = self.train_num_steps) as pbar:
        device = accelerator.device
        with tqdm(initial = self.step, total = self.train_num_steps, disable = not accelerator.is_main_process) as pbar:
            while self.step < self.train_num_steps:
                for i in range(self.gradient_accumulate_every):
                    data = next(self.dl).cuda()
-                total_loss = 0.
+                    with autocast(enabled = self.amp):
                for _ in range(self.gradient_accumulate_every):
                    data = next(self.dl).to(device)
                    with self.accelerator.autocast():
                        loss = self.model(data)
-                        loss = loss / self.gradient_accumulate_every
+                        self.scaler.scale(loss / self.gradient_accumulate_every).backward()
                        total_loss += loss.item()
-                    self.accelerator.backward(loss)
+                    pbar.set_description(f'loss: {loss.item():.4f}')
-                accelerator.clip_grad_norm_(self.model.parameters(), 1.0)
+                self.scaler.step(self.opt)
-                pbar.set_description(f'loss: {total_loss:.4f}')
+                self.scaler.update()
                accelerator.wait_for_everyone()
                self.opt.step()
                self.opt.zero_grad()
-                accelerator.wait_for_everyone()
+                if self.step % self.update_ema_every == 0:
                    self.step_ema()
                if self.step != 0 and self.step % self.save_and_sample_every == 0:
                    self.ema_model.eval()
                    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)
                    utils.save_image(all_images, str(self.results_folder / f'sample-{milestone}.png'), nrow = 6)
                    self.save(milestone)
                self.step += 1
                if accelerator.is_main_process:
                    self.ema.to(device)
                    self.ema.update()
                    if self.step != 0 and self.step % self.save_and_sample_every == 0:
                        self.ema.ema_model.eval()
                        with torch.no_grad():
                            milestone = self.step // self.save_and_sample_every
                            batches = num_to_groups(self.num_samples, self.batch_size)
                            all_images_list = list(map(lambda n: self.ema.ema_model.sample(batch_size=n), batches))
                        all_images = torch.cat(all_images_list, dim = 0)
                        utils.save_image(all_images, str(self.results_folder / f'sample-{milestone}.png'), nrow = int(math.sqrt(self.num_samples)))
                        self.save(milestone)
                pbar.update(1)
-        accelerator.print('training complete')
+        print('training complete')
@@ -1,695 +0,0 @@
 import math
 from random import random
 from functools import partial
 from collections import namedtuple
 import torch
 from torch import nn, einsum
 import torch.nn.functional as F
 from einops import rearrange, reduce
 from einops.layers.torch import Rearrange
 from tqdm.auto import tqdm
 # constants
 ModelPrediction =  namedtuple('ModelPrediction', ['pred_noise', 'pred_x_start'])
 # helpers functions
 def exists(x):
    return x is not None
 def default(val, d):
    if exists(val):
        return val
    return d() if callable(d) else d
 def identity(t, *args, **kwargs):
    return t
 def cycle(dl):
    while True:
        for data in dl:
            yield data
 def has_int_squareroot(num):
    return (math.sqrt(num) ** 2) == num
 def num_to_groups(num, divisor):
    groups = num // divisor
    remainder = num % divisor
    arr = [divisor] * groups
    if remainder > 0:
        arr.append(remainder)
    return arr
 def convert_image_to_fn(img_type, image):
    if image.mode != img_type:
        return image.convert(img_type)
    return image
 # normalization functions
 def normalize_to_neg_one_to_one(img):
    return img * 2 - 1
 def unnormalize_to_zero_to_one(t):
    return (t + 1) * 0.5
 # small helper modules
 class Residual(nn.Module):
    def __init__(self, fn):
        super().__init__()
        self.fn = fn
    def forward(self, x, *args, **kwargs):
        return self.fn(x, *args, **kwargs) + x
 def Upsample(dim, dim_out = None):
    return nn.Sequential(
        nn.Upsample(scale_factor = 2, mode = 'nearest'),
        nn.Conv1d(dim, default(dim_out, dim), 3, padding = 1)
    )
 def Downsample(dim, dim_out = None):
    return nn.Conv1d(dim, default(dim_out, dim), 4, 2, 1)
 class WeightStandardizedConv2d(nn.Conv1d):
    """
    https://arxiv.org/abs/1903.10520
    weight standardization purportedly works synergistically with group normalization
    """
    def forward(self, x):
        eps = 1e-5 if x.dtype == torch.float32 else 1e-3
        weight = self.weight
        mean = reduce(weight, 'o ... -> o 1 1', 'mean')
        var = reduce(weight, 'o ... -> o 1 1', partial(torch.var, unbiased = False))
        normalized_weight = (weight - mean) * (var + eps).rsqrt()
        return F.conv1d(x, normalized_weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
 class LayerNorm(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.g = nn.Parameter(torch.ones(1, dim, 1))
    def forward(self, x):
        eps = 1e-5 if x.dtype == torch.float32 else 1e-3
        var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
        mean = torch.mean(x, dim = 1, keepdim = True)
        return (x - mean) * (var + eps).rsqrt() * self.g
 class PreNorm(nn.Module):
    def __init__(self, dim, fn):
        super().__init__()
        self.fn = fn
        self.norm = LayerNorm(dim)
    def forward(self, x):
        x = self.norm(x)
        return self.fn(x)
 # sinusoidal positional embeds
 class SinusoidalPosEmb(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.dim = dim
    def forward(self, x):
        device = x.device
        half_dim = self.dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
        emb = x[:, None] * emb[None, :]
        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
        return emb
 class RandomOrLearnedSinusoidalPosEmb(nn.Module):
    """ following @crowsonkb 's lead with random (learned optional) sinusoidal pos emb """
    """ https://github.com/crowsonkb/v-diffusion-jax/blob/master/diffusion/models/danbooru_128.py#L8 """
    def __init__(self, dim, is_random = False):
        super().__init__()
        assert (dim % 2) == 0
        half_dim = dim // 2
        self.weights = nn.Parameter(torch.randn(half_dim), requires_grad = not is_random)
    def forward(self, x):
        x = rearrange(x, 'b -> b 1')
        freqs = x * rearrange(self.weights, 'd -> 1 d') * 2 * math.pi
        fouriered = torch.cat((freqs.sin(), freqs.cos()), dim = -1)
        fouriered = torch.cat((x, fouriered), dim = -1)
        return fouriered
 # building block modules
 class Block(nn.Module):
    def __init__(self, dim, dim_out, groups = 8):
        super().__init__()
        self.proj = WeightStandardizedConv2d(dim, dim_out, 3, padding = 1)
        self.norm = nn.GroupNorm(groups, dim_out)
        self.act = nn.SiLU()
    def forward(self, x, scale_shift = None):
        x = self.proj(x)
        x = self.norm(x)
        if exists(scale_shift):
            scale, shift = scale_shift
            x = x * (scale + 1) + shift
        x = self.act(x)
        return x
 class ResnetBlock(nn.Module):
    def __init__(self, dim, dim_out, *, time_emb_dim = None, groups = 8):
        super().__init__()
        self.mlp = nn.Sequential(
            nn.SiLU(),
            nn.Linear(time_emb_dim, dim_out * 2)
        ) if exists(time_emb_dim) else None
        self.block1 = Block(dim, dim_out, groups = groups)
        self.block2 = Block(dim_out, dim_out, groups = groups)
        self.res_conv = nn.Conv1d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
    def forward(self, x, time_emb = None):
        scale_shift = None
        if exists(self.mlp) and exists(time_emb):
            time_emb = self.mlp(time_emb)
            time_emb = rearrange(time_emb, 'b c -> b c 1')
            scale_shift = time_emb.chunk(2, dim = 1)
        h = self.block1(x, scale_shift = scale_shift)
        h = self.block2(h)
        return h + self.res_conv(x)
 class LinearAttention(nn.Module):
    def __init__(self, dim, heads = 4, dim_head = 32):
        super().__init__()
        self.scale = dim_head ** -0.5
        self.heads = heads
        hidden_dim = dim_head * heads
        self.to_qkv = nn.Conv1d(dim, hidden_dim * 3, 1, bias = False)
        self.to_out = nn.Sequential(
            nn.Conv1d(hidden_dim, dim, 1),
            LayerNorm(dim)
        )
    def forward(self, x):
        b, c, n = x.shape
        qkv = self.to_qkv(x).chunk(3, dim = 1)
        q, k, v = map(lambda t: rearrange(t, 'b (h c) n -> b h c n', h = self.heads), qkv)
        q = q.softmax(dim = -2)
        k = k.softmax(dim = -1)
        q = q * self.scale        
        context = torch.einsum('b h d n, b h e n -> b h d e', k, v)
        out = torch.einsum('b h d e, b h d n -> b h e n', context, q)
        out = rearrange(out, 'b h c n -> b (h c) n', h = self.heads)
        return self.to_out(out)
 class Attention(nn.Module):
    def __init__(self, dim, heads = 4, dim_head = 32):
        super().__init__()
        self.scale = dim_head ** -0.5
        self.heads = heads
        hidden_dim = dim_head * heads
        self.to_qkv = nn.Conv1d(dim, hidden_dim * 3, 1, bias = False)
        self.to_out = nn.Conv1d(hidden_dim, dim, 1)
    def forward(self, x):
        b, c, n = x.shape
        qkv = self.to_qkv(x).chunk(3, dim = 1)
        q, k, v = map(lambda t: rearrange(t, 'b (h c) n -> b h c n', h = self.heads), qkv)
        q = q * self.scale
        sim = einsum('b h d i, b h d j -> b h i j', q, k)
        attn = sim.softmax(dim = -1)
        out = einsum('b h i j, b h d j -> b h i d', attn, v)
        out = rearrange(out, 'b h n d -> b (h d) n')
        return self.to_out(out)
 # model
 class Unet1D(nn.Module):
    def __init__(
        self,
        dim,
        init_dim = None,
        out_dim = None,
        dim_mults=(1, 2, 4, 8),
        channels = 3,
        self_condition = False,
        resnet_block_groups = 8,
        learned_variance = False,
        learned_sinusoidal_cond = False,
        random_fourier_features = False,
        learned_sinusoidal_dim = 16
    ):
        super().__init__()
        # determine dimensions
        self.channels = channels
        self.self_condition = self_condition
        input_channels = channels * (2 if self_condition else 1)
        init_dim = default(init_dim, dim)
        self.init_conv = nn.Conv1d(input_channels, init_dim, 7, padding = 3)
        dims = [init_dim, *map(lambda m: dim * m, dim_mults)]
        in_out = list(zip(dims[:-1], dims[1:]))
        block_klass = partial(ResnetBlock, groups = resnet_block_groups)
        # time embeddings
        time_dim = dim * 4
        self.random_or_learned_sinusoidal_cond = learned_sinusoidal_cond or random_fourier_features
        if self.random_or_learned_sinusoidal_cond:
            sinu_pos_emb = RandomOrLearnedSinusoidalPosEmb(learned_sinusoidal_dim, random_fourier_features)
            fourier_dim = learned_sinusoidal_dim + 1
        else:
            sinu_pos_emb = SinusoidalPosEmb(dim)
            fourier_dim = dim
        self.time_mlp = nn.Sequential(
            sinu_pos_emb,
            nn.Linear(fourier_dim, time_dim),
            nn.GELU(),
            nn.Linear(time_dim, time_dim)
        )
        # layers
        self.downs = nn.ModuleList([])
        self.ups = nn.ModuleList([])
        num_resolutions = len(in_out)
        for ind, (dim_in, dim_out) in enumerate(in_out):
            is_last = ind >= (num_resolutions - 1)
            self.downs.append(nn.ModuleList([
                block_klass(dim_in, dim_in, time_emb_dim = time_dim),
                block_klass(dim_in, dim_in, time_emb_dim = time_dim),
                Residual(PreNorm(dim_in, LinearAttention(dim_in))),
                Downsample(dim_in, dim_out) if not is_last else nn.Conv1d(dim_in, dim_out, 3, padding = 1)
            ]))
        mid_dim = dims[-1]
        self.mid_block1 = block_klass(mid_dim, mid_dim, time_emb_dim = time_dim)
        self.mid_attn = Residual(PreNorm(mid_dim, Attention(mid_dim)))
        self.mid_block2 = block_klass(mid_dim, mid_dim, time_emb_dim = time_dim)
        for ind, (dim_in, dim_out) in enumerate(reversed(in_out)):
            is_last = ind == (len(in_out) - 1)
            self.ups.append(nn.ModuleList([
                block_klass(dim_out + dim_in, dim_out, time_emb_dim = time_dim),
                block_klass(dim_out + dim_in, dim_out, time_emb_dim = time_dim),
                Residual(PreNorm(dim_out, LinearAttention(dim_out))),
                Upsample(dim_out, dim_in) if not is_last else  nn.Conv1d(dim_out, dim_in, 3, padding = 1)
            ]))
        default_out_dim = channels * (1 if not learned_variance else 2)
        self.out_dim = default(out_dim, default_out_dim)
        self.final_res_block = block_klass(dim * 2, dim, time_emb_dim = time_dim)
        self.final_conv = nn.Conv1d(dim, self.out_dim, 1)
    def forward(self, x, time, x_self_cond = None):
        if self.self_condition:
            x_self_cond = default(x_self_cond, lambda: torch.zeros_like(x))
            x = torch.cat((x_self_cond, x), dim = 1)
        x = self.init_conv(x)
        r = x.clone()
        t = self.time_mlp(time)
        h = []
        for block1, block2, attn, downsample in self.downs:
            x = block1(x, t)
            h.append(x)
            x = block2(x, t)
            x = attn(x)
            h.append(x)
            x = downsample(x)
        x = self.mid_block1(x, t)
        x = self.mid_attn(x)
        x = self.mid_block2(x, t)
        for block1, block2, attn, upsample in self.ups:
            x = torch.cat((x, h.pop()), dim = 1)
            x = block1(x, t)
            x = torch.cat((x, h.pop()), dim = 1)
            x = block2(x, t)
            x = attn(x)
            x = upsample(x)
        x = torch.cat((x, r), dim = 1)
        x = self.final_res_block(x, t)
        return self.final_conv(x)
 # gaussian diffusion trainer class
 def extract(a, t, x_shape):
    b, *_ = t.shape
    out = a.gather(-1, t)
    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
 def linear_beta_schedule(timesteps):
    scale = 1000 / timesteps
    beta_start = scale * 0.0001
    beta_end = scale * 0.02
    return torch.linspace(beta_start, beta_end, timesteps, dtype = torch.float64)
 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 = 0.
    ):
        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)
@@ -1,240 +0,0 @@
 from math import sqrt
 from random import random
 import torch
 from torch import nn, einsum
 import torch.nn.functional as F
 from tqdm import tqdm
 from einops import rearrange, repeat, reduce
 # helpers
 def exists(val):
    return val is not None
 def default(val, d):
    if exists(val):
        return val
    return d() if callable(d) else d
 # tensor helpers
 def log(t, eps = 1e-20):
    return torch.log(t.clamp(min = eps))
 # normalization functions
 def normalize_to_neg_one_to_one(img):
    return img * 2 - 1
 def unnormalize_to_zero_to_one(t):
    return (t + 1) * 0.5
 # main class
 class ElucidatedDiffusion(nn.Module):
    def __init__(
        self,
        net,
        *,
        image_size,
        channels = 3,
        num_sample_steps = 32, # number of sampling steps
        sigma_min = 0.002,     # min noise level
        sigma_max = 80,        # max noise level
        sigma_data = 0.5,      # standard deviation of data distribution
        rho = 7,               # controls the sampling schedule
        P_mean = -1.2,         # mean of log-normal distribution from which noise is drawn for training
        P_std = 1.2,           # standard deviation of log-normal distribution from which noise is drawn for training
        S_churn = 80,          # parameters for stochastic sampling - depends on dataset, Table 5 in apper
        S_tmin = 0.05,
        S_tmax = 50,
        S_noise = 1.003,
    ):
        super().__init__()
        assert net.random_or_learned_sinusoidal_cond
        self.self_condition = net.self_condition
        self.net = net
        # image dimensions
        self.channels = channels
        self.image_size = image_size
        # parameters
        self.sigma_min = sigma_min
        self.sigma_max = sigma_max
        self.sigma_data = sigma_data
        self.rho = rho
        self.P_mean = P_mean
        self.P_std = P_std
        self.num_sample_steps = num_sample_steps  # otherwise known as N in the paper
        self.S_churn = S_churn
        self.S_tmin = S_tmin
        self.S_tmax = S_tmax
        self.S_noise = S_noise
    @property
    def device(self):
        return next(self.net.parameters()).device
    # derived preconditioning params - Table 1
    def c_skip(self, sigma):
        return (self.sigma_data ** 2) / (sigma ** 2 + self.sigma_data ** 2)
    def c_out(self, sigma):
        return sigma * self.sigma_data * (self.sigma_data ** 2 + sigma ** 2) ** -0.5
    def c_in(self, sigma):
        return 1 * (sigma ** 2 + self.sigma_data ** 2) ** -0.5
    def c_noise(self, sigma):
        return log(sigma) * 0.25
    # preconditioned network output
    # equation (7) in the paper
    def preconditioned_network_forward(self, noised_images, sigma, self_cond = None, clamp = False):
        batch, device = noised_images.shape[0], noised_images.device
        if isinstance(sigma, float):
            sigma = torch.full((batch,), sigma, device = device)
        padded_sigma = rearrange(sigma, 'b -> b 1 1 1')
        net_out = self.net(
            self.c_in(padded_sigma) * noised_images,
            self.c_noise(sigma),
            self_cond
        )
        out = self.c_skip(padded_sigma) * noised_images +  self.c_out(padded_sigma) * net_out
        if clamp:
            out = out.clamp(-1., 1.)
        return out
    # sampling
    # sample schedule
    # equation (5) in the paper
    def sample_schedule(self, num_sample_steps = None):
        num_sample_steps = default(num_sample_steps, self.num_sample_steps)
        N = num_sample_steps
        inv_rho = 1 / self.rho
        steps = torch.arange(num_sample_steps, device = self.device, dtype = torch.float32)
        sigmas = (self.sigma_max ** inv_rho + steps / (N - 1) * (self.sigma_min ** inv_rho - self.sigma_max ** inv_rho)) ** self.rho
        sigmas = F.pad(sigmas, (0, 1), value = 0.) # last step is sigma value of 0.
        return sigmas
    @torch.no_grad()
    def sample(self, batch_size = 16, num_sample_steps = None, clamp = True):
        num_sample_steps = default(num_sample_steps, self.num_sample_steps)
        shape = (batch_size, self.channels, self.image_size, self.image_size)
        # get the schedule, which is returned as (sigma, gamma) tuple, and pair up with the next sigma and gamma
        sigmas = self.sample_schedule(num_sample_steps)
        gammas = torch.where(
            (sigmas >= self.S_tmin) & (sigmas <= self.S_tmax),
            min(self.S_churn / num_sample_steps, sqrt(2) - 1),
            0.
        )
        sigmas_and_gammas = list(zip(sigmas[:-1], sigmas[1:], gammas[:-1]))
        # images is noise at the beginning
        init_sigma = sigmas[0]
        images = init_sigma * torch.randn(shape, device = self.device)
        # for self conditioning
        x_start = None
        # gradually denoise
        for sigma, sigma_next, gamma in tqdm(sigmas_and_gammas, desc = 'sampling time step'):
            sigma, sigma_next, gamma = map(lambda t: t.item(), (sigma, sigma_next, gamma))
            eps = self.S_noise * torch.randn(shape, device = self.device) # stochastic sampling
            sigma_hat = sigma + gamma * sigma
            images_hat = images + sqrt(sigma_hat ** 2 - sigma ** 2) * eps
            self_cond = x_start if self.self_condition else None
            model_output = self.preconditioned_network_forward(images_hat, sigma_hat, self_cond, clamp = clamp)
            denoised_over_sigma = (images_hat - model_output) / sigma_hat
            images_next = images_hat + (sigma_next - sigma_hat) * denoised_over_sigma
            # second order correction, if not the last timestep
            if sigma_next != 0:
                self_cond = model_output if self.self_condition else None
                model_output_next = self.preconditioned_network_forward(images_next, sigma_next, self_cond, clamp = clamp)
                denoised_prime_over_sigma = (images_next - model_output_next) / sigma_next
                images_next = images_hat + 0.5 * (sigma_next - sigma_hat) * (denoised_over_sigma + denoised_prime_over_sigma)
            images = images_next
            x_start = model_output
        images = images.clamp(-1., 1.)
        return unnormalize_to_zero_to_one(images)
    # training
    def loss_weight(self, sigma):
        return (sigma ** 2 + self.sigma_data ** 2) * (sigma * self.sigma_data) ** -2
    def noise_distribution(self, batch_size):
        return (self.P_mean + self.P_std * torch.randn((batch_size,), device = self.device)).exp()
    def forward(self, images):
        batch_size, c, h, w, device, image_size, channels = *images.shape, images.device, self.image_size, self.channels
        assert h == image_size and w == image_size, f'height and width of image must be {image_size}'
        assert c == channels, 'mismatch of image channels'
        images = normalize_to_neg_one_to_one(images)
        sigmas = self.noise_distribution(batch_size)
        padded_sigmas = rearrange(sigmas, 'b -> b 1 1 1')
        noise = torch.randn_like(images)
        noised_images = images + padded_sigmas * noise  # alphas are 1. in the paper
        self_cond = None
        if self.self_condition and random() < 0.5:
            # from hinton's group's bit diffusion paper
            with torch.no_grad():
                self_cond = self.preconditioned_network_forward(noised_images, sigmas)
                self_cond.detach_()
        denoised = self.preconditioned_network_forward(noised_images, sigmas, self_cond)
        losses = F.mse_loss(denoised, images, reduction = 'none')
        losses = reduce(losses, 'b ... -> b', 'mean')
        losses = losses * self.loss_weight(sigmas)
        return losses.mean()
@@ -1,5 +1,4 @@
 import torch
 from collections import namedtuple
 from math import pi, sqrt, log as ln
 from inspect import isfunction
 from torch import nn, einsum
@@ -11,8 +10,6 @@ from denoising_diffusion_pytorch.denoising_diffusion_pytorch import GaussianDiff
 NAT = 1. / ln(2)
 ModelPrediction = namedtuple('ModelPrediction', ['pred_noise', 'pred_x_start', 'pred_variance'])
 # helper functions
 def exists(x):
@@ -25,7 +22,7 @@ def default(val, d):
 # tensor helpers
-def log(t, eps = 1e-15):
+def log(t, eps = 1e-12):
    return torch.log(t.clamp(min = eps))
 def meanflat(x):
@@ -70,33 +67,17 @@ def discretized_gaussian_log_likelihood(x, *, means, log_scales, thres = 0.999):
 class LearnedGaussianDiffusion(GaussianDiffusion):
    def __init__(
        self,
-        model,
+        denoise_fn,
        vb_loss_weight = 0.001,  # lambda was 0.001 in the paper
        *args,
        **kwargs
    ):
-        super().__init__(model, *args, **kwargs)
+        super().__init__(denoise_fn, *args, **kwargs)
-        assert model.out_dim == (model.channels * 2), 'dimension out of unet must be twice the number of channels for learned variance - you can also set the `learned_variance` keyword argument on the Unet to be `True`'
+        assert denoise_fn.out_dim == (denoise_fn.channels * 2), 'dimension out of unet must be twice the number of channels for learned variance - you can also set the `learned_variance` keyword argument on the Unet to be `True`'
        assert not model.self_condition, 'not supported yet'
        self.vb_loss_weight = vb_loss_weight
    def model_predictions(self, x, t):
        model_output = self.model(x, t)
        model_output, pred_variance = model_output.chunk(2, dim = 1)
        if self.objective == 'pred_noise':
            pred_noise = model_output
            x_start = self.predict_start_from_noise(x, t, model_output)
        elif self.objective == 'pred_x0':
            pred_noise = self.predict_noise_from_start(x, t, model_output)
            x_start = model_output
        return ModelPrediction(pred_noise, x_start, pred_variance)
    def p_mean_variance(self, *, x, t, clip_denoised, model_output = None):
-        model_output = default(model_output, lambda: self.model(x, t))
+        model_output = default(model_output, lambda: self.denoise_fn(x, t))
        pred_noise, var_interp_frac_unnormalized = model_output.chunk(2, dim = 1)
        min_log = extract(self.posterior_log_variance_clipped, t, x.shape)
@@ -121,7 +102,7 @@ class LearnedGaussianDiffusion(GaussianDiffusion):
        # model output
-        model_output = self.model(x_t, t)
+        model_output = self.denoise_fn(x_t, t)
        # calculating kl loss for learned variance (interpolation)
@@ -1,184 +0,0 @@
 import math
 import torch
 from torch import sqrt
 from torch import nn, einsum
 import torch.nn.functional as F
 from torch.special import expm1
 from tqdm import tqdm
 from einops import rearrange, repeat, reduce
 from einops.layers.torch import Rearrange
 # helpers
 def exists(val):
    return val is not None
 def default(val, d):
    if exists(val):
        return val
    return d() if callable(d) else d
 # normalization functions
 def normalize_to_neg_one_to_one(img):
    return img * 2 - 1
 def unnormalize_to_zero_to_one(t):
    return (t + 1) * 0.5
 # diffusion helpers
 def right_pad_dims_to(x, t):
    padding_dims = x.ndim - t.ndim
    if padding_dims <= 0:
        return t
    return t.view(*t.shape, *((1,) * padding_dims))
 # continuous schedules
 # log(snr) that approximates the original linear schedule
 def log(t, eps = 1e-20):
    return torch.log(t.clamp(min = eps))
 def alpha_cosine_log_snr(t, s = 0.008):
    return -log((torch.cos((t + s) / (1 + s) * math.pi * 0.5) ** -2) - 1, eps = 1e-5)
 class VParamContinuousTimeGaussianDiffusion(nn.Module):
    """
    a new type of parameterization in v-space proposed in https://arxiv.org/abs/2202.00512 that
    (1) allows for improved distillation over noise prediction objective and
    (2) noted in imagen-video to improve upsampling unets by removing the color shifting artifacts
    """
    def __init__(
        self,
        model,
        *,
        image_size,
        channels = 3,
        num_sample_steps = 500,
        clip_sample_denoised = True,
    ):
        super().__init__()
        assert model.random_or_learned_sinusoidal_cond
        assert not model.self_condition, 'not supported yet'
        self.model = model
        # image dimensions
        self.channels = channels
        self.image_size = image_size
        # continuous noise schedule related stuff
        self.log_snr = alpha_cosine_log_snr
        # sampling
        self.num_sample_steps = num_sample_steps
        self.clip_sample_denoised = clip_sample_denoised        
    @property
    def device(self):
        return next(self.model.parameters()).device
    def p_mean_variance(self, x, time, time_next):
        # reviewer found an error in the equation in the paper (missing sigma)
        # following - https://openreview.net/forum?id=2LdBqxc1Yv&noteId=rIQgH0zKsRt
        log_snr = self.log_snr(time)
        log_snr_next = self.log_snr(time_next)
        c = -expm1(log_snr - log_snr_next)
        squared_alpha, squared_alpha_next = log_snr.sigmoid(), log_snr_next.sigmoid()
        squared_sigma, squared_sigma_next = (-log_snr).sigmoid(), (-log_snr_next).sigmoid()
        alpha, sigma, alpha_next = map(sqrt, (squared_alpha, squared_sigma, squared_alpha_next))
        batch_log_snr = repeat(log_snr, ' -> b', b = x.shape[0])
        pred_v = self.model(x, batch_log_snr)
        # shown in Appendix D in the paper
        x_start = alpha * x - sigma * pred_v
        if self.clip_sample_denoised:
            x_start.clamp_(-1., 1.)
        model_mean = alpha_next * (x * (1 - c) / alpha + c * x_start)
        posterior_variance = squared_sigma_next * c
        return model_mean, posterior_variance
    # sampling related functions
    @torch.no_grad()
    def p_sample(self, x, time, time_next):
        batch, *_, device = *x.shape, x.device
        model_mean, model_variance = self.p_mean_variance(x = x, time = time, time_next = time_next)
        if time_next == 0:
            return model_mean
        noise = torch.randn_like(x)
        return model_mean + sqrt(model_variance) * noise
    @torch.no_grad()
    def p_sample_loop(self, shape):
        batch = shape[0]
        img = torch.randn(shape, device = self.device)
        steps = torch.linspace(1., 0., self.num_sample_steps + 1, device = self.device)
        for i in tqdm(range(self.num_sample_steps), desc = 'sampling loop time step', total = self.num_sample_steps):
            times = steps[i]
            times_next = steps[i + 1]
            img = self.p_sample(img, times, times_next)
        img.clamp_(-1., 1.)
        img = unnormalize_to_zero_to_one(img)
        return img
    @torch.no_grad()
    def sample(self, batch_size = 16):
        return self.p_sample_loop((batch_size, self.channels, self.image_size, self.image_size))
    # training related functions - noise prediction
    def q_sample(self, x_start, times, noise = None):
        noise = default(noise, lambda: torch.randn_like(x_start))
        log_snr = self.log_snr(times)
        log_snr_padded = right_pad_dims_to(x_start, log_snr)
        alpha, sigma = sqrt(log_snr_padded.sigmoid()), sqrt((-log_snr_padded).sigmoid())
        x_noised =  x_start * alpha + noise * sigma
        return x_noised, log_snr, alpha, sigma
    def random_times(self, batch_size):
        return torch.zeros((batch_size,), device = self.device).float().uniform_(0, 1)
    def p_losses(self, x_start, times, noise = None):
        noise = default(noise, lambda: torch.randn_like(x_start))
        x, log_snr, alpha, sigma = self.q_sample(x_start = x_start, times = times, noise = noise)
        # described in section 4 as the prediction objective, with derivation in Appendix D
        v = alpha * noise - sigma * x_start
        model_out = self.model(x, log_snr)
        return F.mse_loss(model_out, v)
    def forward(self, img, *args, **kwargs):
        b, c, h, w, device, img_size, = *img.shape, img.device, self.image_size
        assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
        times = self.random_times(b)
        img = normalize_to_neg_one_to_one(img)
        return self.p_losses(img, times, *args, **kwargs)
@@ -1 +0,0 @@
 __version__ = '0.1.5'
@@ -22,24 +22,22 @@ def default(val, d):
 class WeightedObjectiveGaussianDiffusion(GaussianDiffusion):
    def __init__(
        self,
-        model,
+        denoise_fn,
        *args,
        pred_noise_loss_weight = 0.1,
        pred_x_start_loss_weight = 0.1,
        **kwargs
    ):
-        super().__init__(model, *args, **kwargs)
+        super().__init__(denoise_fn, *args, **kwargs)
-        channels = model.channels
+        channels = denoise_fn.channels
-        assert model.out_dim == (channels * 2 + 2), 'dimension out (out_dim) of unet must be twice the number of channels + 2 (for the softmax weighted sum) - for channels of 3, this should be (3 * 2) + 2 = 8'
+        assert denoise_fn.out_dim == (channels * 2 + 2), 'dimension out (out_dim) of unet must be twice the number of channels + 2 (for the softmax weighted sum) - for channels of 3, this should be (3 * 2) + 2 = 8'
        assert not model.self_condition, 'not supported yet'
        assert not self.is_ddim_sampling, 'ddim sampling cannot be used'
        self.split_dims = (channels, channels, 2)
        self.pred_noise_loss_weight = pred_noise_loss_weight
        self.pred_x_start_loss_weight = pred_x_start_loss_weight
    def p_mean_variance(self, *, x, t, clip_denoised, model_output = None):
-        model_output = self.model(x, t)
+        model_output = self.denoise_fn(x, t)
        pred_noise, pred_x_start, weights = model_output.split(self.split_dims, dim = 1)
        normalized_weights = weights.softmax(dim = 1)
@@ -60,7 +58,7 @@ class WeightedObjectiveGaussianDiffusion(GaussianDiffusion):
        noise = default(noise, lambda: torch.randn_like(x_start))
        x_t = self.q_sample(x_start = x_start, t = t, noise = noise)
-        model_output = self.model(x_t, t)
+        model_output = self.denoise_fn(x_t, t)
        pred_noise, pred_x_start, weights = model_output.split(self.split_dims, dim = 1)
        # get loss for predicted noise and x_start
@@ -1,11 +1,9 @@
 from setuptools import setup, find_packages
 exec(open('denoising_diffusion_pytorch/version.py').read())
 setup(
  name = 'denoising-diffusion-pytorch',
  packages = find_packages(),
-  version = __version__,
+  version = '0.16.2',
  license='MIT',
  description = 'Denoising Diffusion Probabilistic Models - Pytorch',
  author = 'Phil Wang',
@@ -17,9 +15,7 @@ setup(
    'generative models'
  ],
  install_requires=[
    'accelerate',
    'einops',
    'ema-pytorch',
    'pillow',
    'torch',
    'torchvision',
@@ -32,4 +28,4 @@ setup(
    'License :: OSI Approved :: MIT License',
    'Programming Language :: Python :: 3.6',
  ],
-)
+)