Upload DiffAE

DiffAE.py

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+import copy
+
+import numpy as np
+import torch
+from pytorch_lightning.callbacks import *
+from torch.optim.optimizer import Optimizer
+
+from transformers import PreTrainedModel
+
+from .DiffAEConfig import DiffAEConfig
+from .DiffAE_support import *
+
+class DiffAE(PreTrainedModel):
+    config_class = DiffAEConfig
+    def __init__(self, config):
+        super().__init__(config)
+
+        conf = ukbb_autoenc(n_latents=config.latent_dim) 
+        conf.__dict__.update(**vars(config)) #update the supplied DiffAE params 
+        
+        if config.test_with_TEval:
+            conf.T_inv = conf.T_eval
+            conf.T_step = conf.T_eval
+            
+        conf.fp16 = config.ampmode not in ["32", "32-true"]
+            
+        conf.refresh_values()
+        conf.make_model_conf()
+        
+        self.config = config
+        self.conf = conf
+        
+        self.net = conf.make_model_conf().make_model()
+        self.ema_net = copy.deepcopy(self.net)
+        self.ema_net.requires_grad_(False)
+        self.ema_net.eval()
+
+        model_size = sum(param.data.nelement() for param in self.net.parameters())
+        print('Model params: %.2f M' % (model_size / 1024 / 1024))
+
+        self.sampler = conf.make_diffusion_conf().make_sampler()
+        self.eval_sampler = conf.make_eval_diffusion_conf().make_sampler()
+
+        # this is shared for both model and latent
+        self.T_sampler = conf.make_T_sampler()
+
+        if conf.train_mode.use_latent_net():
+            self.latent_sampler = conf.make_latent_diffusion_conf(
+            ).make_sampler()
+            self.eval_latent_sampler = conf.make_latent_eval_diffusion_conf(
+            ).make_sampler()
+        else:
+            self.latent_sampler = None
+            self.eval_latent_sampler = None
+
+        # initial variables for consistent sampling
+        self.register_buffer('x_T', torch.randn(conf.sample_size, conf.in_channels, *conf.input_shape))
+
+        if conf.pretrain is not None: 
+            print(f'loading pretrain ... {conf.pretrain.name}')
+            state = torch.load(conf.pretrain.path, map_location='cpu')
+            print('step:', state['global_step'])
+            self.load_state_dict(state['state_dict'], strict=False)
+
+        if conf.latent_infer_path is not None:
+            print('loading latent stats ...')
+            state = torch.load(conf.latent_infer_path)
+            self.conds = state['conds']
+            self.register_buffer('conds_mean', state['conds_mean'][None, :])
+            self.register_buffer('conds_std', state['conds_std'][None, :])
+        else:
+            self.conds_mean = None
+            self.conds_std = None
+    
+    def normalise(self, cond):
+        cond = (cond - self.conds_mean.to(self.device)) / self.conds_std.to(
+            self.device)
+        return cond
+
+    def denormalise(self, cond):
+        cond = (cond * self.conds_std.to(self.device)) + self.conds_mean.to(
+            self.device)
+        return cond
+
+    def sample(self, N, device, T=None, T_latent=None):
+        if T is None:
+            sampler = self.eval_sampler
+            latent_sampler = self.latent_sampler
+        else:
+            sampler = self.conf._make_diffusion_conf(T).make_sampler()
+            latent_sampler = self.conf._make_latent_diffusion_conf(T_latent).make_sampler()
+
+        noise = torch.randn(N,
+                            self.conf.in_channels,
+                            *self.conf.input_shape,
+                            device=device)
+        pred_img = render_uncondition(
+            self.conf,
+            self.ema_net,
+            noise,
+            sampler=sampler,
+            latent_sampler=latent_sampler,
+            conds_mean=self.conds_mean,
+            conds_std=self.conds_std,
+        )
+        pred_img = (pred_img + 1) / 2
+        return pred_img
+
+    def render(self, noise, cond=None, T=None, use_ema=True):
+        if T is None:
+            sampler = self.eval_sampler
+        else:
+            sampler = self.conf._make_diffusion_conf(T).make_sampler()
+
+        if cond is not None:
+            pred_img = render_condition(self.conf,
+                                        self.ema_net if use_ema else self.net,
+                                        noise,
+                                        sampler=sampler,
+                                        cond=cond)
+        else:
+            pred_img = render_uncondition(self.conf,
+                                          self.ema_net if use_ema else self.net,
+                                          noise,
+                                          sampler=sampler,
+                                          latent_sampler=None)
+        pred_img = (pred_img + 1) / 2
+        return pred_img
+
+    def encode(self, x, use_ema=True):
+        assert self.conf.model_type.has_autoenc()
+        return self.ema_net.encoder.forward(x) if use_ema else self.net.encoder.forward(x)
+
+    def encode_stochastic(self, x, cond, T=None, use_ema=True):
+        if T is None:
+            sampler = self.eval_sampler
+        else:
+            sampler = self.conf._make_diffusion_conf(T).make_sampler()
+        out = sampler.ddim_reverse_sample_loop(self.ema_net if use_ema else self.net,
+                                               x,
+                                               model_kwargs={'cond': cond})
+        return out['sample']
+
+    def forward(self, x_start=None, noise=None, ema_model: bool = False):
+        with amp.autocast(False):
+            model = self.ema_net if ema_model else self.net
+            return self.eval_sampler.sample(
+                model=model,
+                noise=noise,
+                x_start=x_start,
+                shape=noise.shape if noise is not None else x_start.shape,
+            )
+    
+    def is_last_accum(self, batch_idx):
+        """
+        is it the last gradient accumulation loop? 
+        used with gradient_accum > 1 and to see if the optimizer will perform "step" in this iteration or not
+        """
+        return (batch_idx + 1) % self.conf.accum_batches == 0
+    
+    def training_step(self, batch, batch_idx):
+        """
+        given an input, calculate the loss function
+        no optimization at this stage.
+        """
+        with amp.autocast(False):
+            # forward
+            if self.conf.train_mode.require_dataset_infer():
+                # this mode as pre-calculated cond
+                cond = batch[0]
+                if self.conf.latent_znormalize:
+                    cond = (cond - self.conds_mean.to(
+                        self.device)) / self.conds_std.to(self.device)
+            else:
+                imgs, idxs = batch['inp']['data'], batch_idx
+                # print(f'(rank {self.global_rank}) batch size:', len(imgs))
+                x_start = imgs
+
+            if self.conf.train_mode == TrainMode.diffusion:
+                """
+                main training mode!!!
+                """
+                # with numpy seed we have the problem that the sample t's are related!
+                t, weight = self.T_sampler.sample(len(x_start), x_start.device)
+                losses = self.sampler.training_losses(model=self.net,
+                                                        x_start=x_start,
+                                                        t=t)
+            elif self.conf.train_mode.is_latent_diffusion():
+                """
+                training the latent variables!
+                """
+                # diffusion on the latent
+                t, weight = self.T_sampler.sample(len(cond), cond.device)
+                latent_losses = self.latent_sampler.training_losses(
+                    model=self.net.latent_net, x_start=cond, t=t)
+                # train only do the latent diffusion
+                losses = {
+                    'latent': latent_losses['loss'],
+                    'loss': latent_losses['loss']
+                }
+            else:
+                raise NotImplementedError()
+
+            loss = losses['loss'].mean()
+            loss_dict = {"train_loss": loss}
+            for key in ['vae', 'latent', 'mmd', 'chamfer', 'arg_cnt']:
+                if key in losses:
+                    loss_dict[f'train_{key}'] = losses[key].mean()
+            self.log_dict(loss_dict, on_step=True, on_epoch=True, reduce_fx="mean", sync_dist=True, batch_size=batch['inp']['data'].shape[0])
+
+        return loss
+
+    def on_train_batch_end(self, outputs, batch, batch_idx: int) -> None:
+        """
+        after each training step ...
+        """
+        if self.is_last_accum(batch_idx):
+            # only apply ema on the last gradient accumulation step,
+            # if it is the iteration that has optimizer.step()
+            if self.conf.train_mode == TrainMode.latent_diffusion:
+                # it trains only the latent hence change only the latent
+                ema(self.net.latent_net, self.ema_net.latent_net,
+                    self.conf.ema_decay)
+            else:
+                ema(self.net, self.ema_net, self.conf.ema_decay)
+
+    def on_before_optimizer_step(self, optimizer: Optimizer) -> None:
+        # fix the fp16 + clip grad norm problem with pytorch lightinng
+        # this is the currently correct way to do it
+        if self.conf.grad_clip > 0:
+            # from trainer.params_grads import grads_norm, iter_opt_params
+            params = [
+                p for group in optimizer.param_groups for p in group['params']
+            ]
+            # print('before:', grads_norm(iter_opt_params(optimizer)))
+            torch.nn.utils.clip_grad_norm_(params,
+                                           max_norm=self.conf.grad_clip)
+            # print('after:', grads_norm(iter_opt_params(optimizer)))
+    
+    #Validation  
+    def validation_step(self, batch, batch_idx):
+        _, prediction_ema = self.inference_pass(batch['inp']['data'], T_inv=self.conf.T_eval, T_step=self.conf.T_eval, use_ema=True)
+        _, prediction_base = self.inference_pass(batch['inp']['data'], T_inv=self.conf.T_eval, T_step=self.conf.T_eval, use_ema=False)        
+
+        inp = batch['inp']['data'].cpu() 
+        inp = (inp + 1) / 2
+        
+        _, val_ssim_ema = self._eval_prediction(inp, prediction_ema)
+        _, val_ssim_base = self._eval_prediction(inp, prediction_base)
+        
+        self.log_dict({"val_ssim_ema": val_ssim_ema, "val_ssim_base": val_ssim_base, "val_loss": -val_ssim_ema}, on_step=True, on_epoch=True, reduce_fx="mean", sync_dist=True, batch_size=batch['inp']['data'].shape[0])
+        self.img_logger("val_ema", batch_idx, inp, prediction_ema)
+        self.img_logger("val_base", batch_idx, inp, prediction_base)
+        
+    def _eval_prediction(self, inp, prediction):
+        prediction = prediction.detach().cpu() 
+        prediction = prediction.numpy() if prediction.dtype not in {torch.bfloat16, torch.float16} else prediction.to(dtype=torch.float32).numpy()
+        if self.config.grey2RGB in [0, 2]:
+            inp = inp[:, 1, ...].unsqueeze(1)
+            prediction = np.expand_dims(prediction[:, 1, ...], axis=1)
+        val_ssim = getSSIM(inp.numpy(), prediction, data_range=1) 
+        return prediction, val_ssim
+        
+    def inference_pass(self, inp, T_inv, T_step, use_ema=True):
+        semantic_latent = self.encode(inp, use_ema=use_ema) 
+        if self.config.test_emb_only:
+            return semantic_latent, None
+        stochastic_latent = self.encode_stochastic(inp, semantic_latent, T=T_inv) 
+        prediction = self.render(stochastic_latent, semantic_latent, T=T_step, use_ema=use_ema) 
+        return semantic_latent, prediction
+    
+    # Testing
+    def test_step(self, batch, batch_idx):
+        emb, recon = self.inference_pass(batch['inp']['data'], T_inv=self.conf.T_inv, T_step=self.conf.T_step, use_ema=self.config.test_ema)
+
+        emb = emb.detach().cpu()
+        emb = emb.numpy() if emb.dtype not in {torch.bfloat16, torch.float16} else emb.to(dtype=torch.float32).numpy()
+
+        return emb, recon
+
+    #Prediction
+    def predict_step(self, batch, batch_idx):
+        emb = self.encode(batch['inp']['data']).detach().cpu()
+        return emb.numpy() if emb.dtype not in {torch.bfloat16, torch.float16} else emb.to(dtype=torch.float32).numpy()
+
+    def configure_optimizers(self):
+        if self.conf.optimizer == OptimizerType.adam:
+            optim = torch.optim.Adam(self.net.parameters(),
+                                     lr=self.conf.lr,
+                                     weight_decay=self.conf.weight_decay)
+        elif self.conf.optimizer == OptimizerType.adamw:
+            optim = torch.optim.AdamW(self.net.parameters(),
+                                      lr=self.conf.lr,
+                                      weight_decay=self.conf.weight_decay)
+        else:
+            raise NotImplementedError()
+        out = {'optimizer': optim}
+        if self.conf.warmup > 0:
+            sched = torch.optim.lr_scheduler.LambdaLR(optim,
+                                                      lr_lambda=WarmupLR(
+                                                          self.conf.warmup))
+            out['lr_scheduler'] = {
+                'scheduler': sched,
+                'interval': 'step',
+            }
+        return out
+
+    def split_tensor(self, x):
+        """
+        extract the tensor for a corresponding "worker" in the batch dimension
+
+        Args:
+            x: (n, c)
+
+        Returns: x: (n_local, c)
+        """
+        n = len(x)
+        rank = self.global_rank
+        world_size = get_world_size()
+        # print(f'rank: {rank}/{world_size}')
+        per_rank = n // world_size
+        return x[rank * per_rank:(rank + 1) * per_rank]

DiffAEConfig.py

@@ -0,0 +1,54 @@
+from transformers import PretrainedConfig
+
+class DiffAEConfig(PretrainedConfig):
+    model_type = "DiffAE"
+    def __init__(self, 
+                 is3D=True,
+                 in_channels=1,
+                 out_channels=1,
+                 latent_dim=128,
+                 net_ch=32,
+                 sample_every_batches=1000, #log samples during training. Set it to 0 to disable
+                 sample_size=4, #Number of samples in the buffer for consistent sampling (batch size of x_T)
+                 test_with_TEval=True,
+                 ampmode="16-mixed",
+                 grey2RGB=-1,
+                 test_emb_only=True,
+                 test_ema=True,
+                 batch_size=9,
+                #  beta_scheduler='linear',
+                #  latent_beta_scheduler='linear',
+                 data_name="ukbb",
+                 diffusion_type = 'beatgans',
+                #  eval_ema_every_samples = 200_000,
+                #  eval_every_samples = 200_000,
+                 lr=0.0001,
+                #  net_beatgans_attn_head = 1,
+                #  net_beatgans_embed_channels = 128,
+                #  net_ch_mult = (1, 1, 2, 3, 4),
+                #  T_eval = 20,
+                #  latent_T_eval=1000,
+                #  group_norm_limit=32,
+                 seed=1701,
+                 input_shape=(50, 128, 128),
+                #  dropout=0.1,
+                 **kwargs):
+        self.is3D = is3D
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.latent_dim = latent_dim
+        self.net_ch = net_ch
+        self.sample_every_batches = sample_every_batches
+        self.sample_size = sample_size
+        self.test_with_TEval = test_with_TEval
+        self.ampmode = ampmode
+        self.grey2RGB = grey2RGB
+        self.test_emb_only = test_emb_only
+        self.test_ema = test_ema
+        self.batch_size = batch_size
+        self.data_name = data_name
+        self.diffusion_type = diffusion_type
+        self.lr = lr      
+        self.seed = seed
+        self.input_shape = input_shape  
+        super().__init__(**kwargs)

DiffAE_diffusion_base.py

@@ -0,0 +1,1109 @@
+"""
+This code started out as a PyTorch port of Ho et al's diffusion models:
+https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py
+
+Docstrings have been added, as well as DDIM sampling and a new collection of beta schedules.
+"""
+
+from .DiffAE_model_unet_autoenc import AutoencReturn
+from .DiffAE_support_config_base import BaseConfig
+import enum
+import math
+
+import numpy as np
+import torch as th
+from .DiffAE_model import *
+from .DiffAE_model_nn import mean_flat
+from typing import NamedTuple, Tuple
+from .DiffAE_support_choices import *
+from torch.cuda.amp import autocast
+import torch.nn.functional as F
+
+from dataclasses import dataclass
+
+
+@dataclass
+class GaussianDiffusionBeatGansConfig(BaseConfig):
+    gen_type: GenerativeType
+    betas: Tuple[float]
+    model_type: ModelType
+    model_mean_type: ModelMeanType
+    model_var_type: ModelVarType
+    loss_type: LossType
+    rescale_timesteps: bool
+    fp16: bool
+    train_pred_xstart_detach: bool = True
+
+    def make_sampler(self):
+        return GaussianDiffusionBeatGans(self)
+
+
+class GaussianDiffusionBeatGans:
+    """
+    Utilities for training and sampling diffusion models.
+
+    Ported directly from here, and then adapted over time to further experimentation.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
+
+    :param betas: a 1-D numpy array of betas for each diffusion timestep,
+                  starting at T and going to 1.
+    :param model_mean_type: a ModelMeanType determining what the model outputs.
+    :param model_var_type: a ModelVarType determining how variance is output.
+    :param loss_type: a LossType determining the loss function to use.
+    :param rescale_timesteps: if True, pass floating point timesteps into the
+                              model so that they are always scaled like in the
+                              original paper (0 to 1000).
+    """
+    def __init__(self, conf: GaussianDiffusionBeatGansConfig):
+        self.conf = conf
+        self.model_mean_type = conf.model_mean_type
+        self.model_var_type = conf.model_var_type
+        self.loss_type = conf.loss_type
+        self.rescale_timesteps = conf.rescale_timesteps
+
+        # Use float64 for accuracy.
+        betas = np.array(conf.betas, dtype=np.float64)
+        self.betas = betas
+        assert len(betas.shape) == 1, "betas must be 1-D"
+        assert (betas > 0).all() and (betas <= 1).all()
+
+        self.num_timesteps = int(betas.shape[0])
+
+        alphas = 1.0 - betas
+        self.alphas_cumprod = np.cumprod(alphas, axis=0)
+        self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1])
+        self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0)
+        assert self.alphas_cumprod_prev.shape == (self.num_timesteps, )
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod)
+        self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod)
+        self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod)
+        self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod)
+        self.sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod -
+                                                   1)
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        self.posterior_variance = (betas * (1.0 - self.alphas_cumprod_prev) /
+                                   (1.0 - self.alphas_cumprod))
+        # log calculation clipped because the posterior variance is 0 at the
+        # beginning of the diffusion chain.
+        self.posterior_log_variance_clipped = np.log(
+            np.append(self.posterior_variance[1], self.posterior_variance[1:]))
+        self.posterior_mean_coef1 = (betas *
+                                     np.sqrt(self.alphas_cumprod_prev) /
+                                     (1.0 - self.alphas_cumprod))
+        self.posterior_mean_coef2 = ((1.0 - self.alphas_cumprod_prev) *
+                                     np.sqrt(alphas) /
+                                     (1.0 - self.alphas_cumprod))
+
+    def training_losses(self,
+                        model: Model,
+                        x_start: th.Tensor,
+                        t: th.Tensor,
+                        model_kwargs=None,
+                        noise: th.Tensor = None):
+        """
+        Compute training losses for a single timestep.
+
+        :param model: the model to evaluate loss on.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :param t: a batch of timestep indices.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :param noise: if specified, the specific Gaussian noise to try to remove.
+        :return: a dict with the key "loss" containing a tensor of shape [N].
+                 Some mean or variance settings may also have other keys.
+        """
+        if model_kwargs is None:
+            model_kwargs = {}
+        if noise is None:
+            noise = th.randn_like(x_start)
+
+        x_t = self.q_sample(x_start, t, noise=noise)
+
+        terms = {'x_t': x_t}
+
+        if self.loss_type in [
+                LossType.mse,
+                LossType.l1,
+        ]:
+            with autocast(self.conf.fp16):
+                # x_t is static wrt. to the diffusion process
+                model_forward = model.forward(x=x_t.detach(),
+                                              t=self._scale_timesteps(t),
+                                              x_start=x_start.detach(),
+                                              **model_kwargs)
+            model_output = model_forward.pred
+
+            _model_output = model_output
+            if self.conf.train_pred_xstart_detach:
+                _model_output = _model_output.detach()
+            # get the pred xstart
+            p_mean_var = self.p_mean_variance(
+                model=DummyModel(pred=_model_output),
+                # gradient goes through x_t
+                x=x_t,
+                t=t,
+                clip_denoised=False)
+            terms['pred_xstart'] = p_mean_var['pred_xstart']
+
+            # model_output = model(x_t, self._scale_timesteps(t), **model_kwargs)
+
+            target_types = {
+                ModelMeanType.eps: noise,
+            }
+            target = target_types[self.model_mean_type]
+            assert model_output.shape == target.shape == x_start.shape
+
+            if self.loss_type == LossType.mse:
+                if self.model_mean_type == ModelMeanType.eps:
+                    # (n, c, h, w) => (n, )
+                    terms["mse"] = mean_flat((target - model_output)**2)
+                else:
+                    raise NotImplementedError()
+            elif self.loss_type == LossType.l1:
+                # (n, c, h, w) => (n, )
+                terms["mse"] = mean_flat((target - model_output).abs())
+            else:
+                raise NotImplementedError()
+
+            if "vb" in terms:
+                # if learning the variance also use the vlb loss
+                terms["loss"] = terms["mse"] + terms["vb"]
+            else:
+                terms["loss"] = terms["mse"]
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        return terms
+
+    def sample(self,
+               model: Model,
+               shape=None,
+               noise=None,
+               cond=None,
+               x_start=None,
+               clip_denoised=True,
+               model_kwargs=None,
+               progress=False):
+        """
+        Args:
+            x_start: given for the autoencoder
+        """
+        if model_kwargs is None:
+            model_kwargs = {}
+            if self.conf.model_type.has_autoenc():
+                model_kwargs['x_start'] = x_start
+                model_kwargs['cond'] = cond
+
+        if self.conf.gen_type == GenerativeType.ddpm:
+            return self.p_sample_loop(model,
+                                      shape=shape,
+                                      noise=noise,
+                                      clip_denoised=clip_denoised,
+                                      model_kwargs=model_kwargs,
+                                      progress=progress)
+        elif self.conf.gen_type == GenerativeType.ddim:
+            return self.ddim_sample_loop(model,
+                                         shape=shape,
+                                         noise=noise,
+                                         clip_denoised=clip_denoised,
+                                         model_kwargs=model_kwargs,
+                                         progress=progress)
+        else:
+            raise NotImplementedError()
+
+    def q_mean_variance(self, x_start, t):
+        """
+        Get the distribution q(x_t | x_0).
+
+        :param x_start: the [N x C x ...] tensor of noiseless inputs.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+        """
+        mean = (
+            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) *
+            x_start)
+        variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t,
+                                        x_start.shape)
+        log_variance = _extract_into_tensor(self.log_one_minus_alphas_cumprod,
+                                            t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data for a given number of diffusion steps.
+
+        In other words, sample from q(x_t | x_0).
+
+        :param x_start: the initial data batch.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :param noise: if specified, the split-out normal noise.
+        :return: A noisy version of x_start.
+        """
+        if noise is None:
+            noise = th.randn_like(x_start)
+        assert noise.shape == x_start.shape
+        return (
+            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) *
+            x_start + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod,
+                                           t, x_start.shape) * noise)
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior:
+
+            q(x_{t-1} | x_t, x_0)
+
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+            _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) *
+            x_start +
+            _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) *
+            x_t)
+        posterior_variance = _extract_into_tensor(self.posterior_variance, t,
+                                                  x_t.shape)
+        posterior_log_variance_clipped = _extract_into_tensor(
+            self.posterior_log_variance_clipped, t, x_t.shape)
+        assert (posterior_mean.shape[0] == posterior_variance.shape[0] ==
+                posterior_log_variance_clipped.shape[0] == x_start.shape[0])
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(self,
+                        model: Model,
+                        x,
+                        t,
+                        clip_denoised=True,
+                        denoised_fn=None,
+                        model_kwargs=None):
+        """
+        Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
+        the initial x, x_0.
+
+        :param model: the model, which takes a signal and a batch of timesteps
+                      as input.
+        :param x: the [N x C x ...] tensor at time t.
+        :param t: a 1-D Tensor of timesteps.
+        :param clip_denoised: if True, clip the denoised signal into [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample. Applies before
+            clip_denoised.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict with the following keys:
+                 - 'mean': the model mean output.
+                 - 'variance': the model variance output.
+                 - 'log_variance': the log of 'variance'.
+                 - 'pred_xstart': the prediction for x_0.
+        """
+        if model_kwargs is None:
+            model_kwargs = {}
+
+        B, C = x.shape[:2]
+        assert t.shape == (B, )
+        with autocast(self.conf.fp16):
+            model_forward = model.forward(x=x,
+                                          t=self._scale_timesteps(t),
+                                          **model_kwargs)
+        model_output = model_forward.pred
+
+        if self.model_var_type in [
+                ModelVarType.fixed_large, ModelVarType.fixed_small
+        ]:
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so
+                # to get a better decoder log likelihood.
+                ModelVarType.fixed_large: (
+                    np.append(self.posterior_variance[1], self.betas[1:]),
+                    np.log(
+                        np.append(self.posterior_variance[1], self.betas[1:])),
+                ),
+                ModelVarType.fixed_small: (
+                    self.posterior_variance,
+                    self.posterior_log_variance_clipped,
+                ),
+            }[self.model_var_type]
+            model_variance = _extract_into_tensor(model_variance, t, x.shape)
+            model_log_variance = _extract_into_tensor(model_log_variance, t,
+                                                      x.shape)
+
+        def process_xstart(x):
+            if denoised_fn is not None:
+                x = denoised_fn(x)
+            if clip_denoised:
+                return x.clamp(-1, 1)
+            return x
+
+        if self.model_mean_type in [
+                ModelMeanType.eps,
+        ]:
+            if self.model_mean_type == ModelMeanType.eps:
+                pred_xstart = process_xstart(
+                    self._predict_xstart_from_eps(x_t=x, t=t,
+                                                  eps=model_output))
+            else:
+                raise NotImplementedError()
+            model_mean, _, _ = self.q_posterior_mean_variance(
+                x_start=pred_xstart, x_t=x, t=t)
+        else:
+            raise NotImplementedError(self.model_mean_type)
+
+        assert (model_mean.shape == model_log_variance.shape ==
+                pred_xstart.shape == x.shape)
+        return {
+            "mean": model_mean,
+            "variance": model_variance,
+            "log_variance": model_log_variance,
+            "pred_xstart": pred_xstart,
+            'model_forward': model_forward,
+        }
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (_extract_into_tensor(self.sqrt_recip_alphas_cumprod, t,
+                                     x_t.shape) * x_t -
+                _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t,
+                                     x_t.shape) * eps)
+
+    def _predict_xstart_from_xprev(self, x_t, t, xprev):
+        assert x_t.shape == xprev.shape
+        return (  # (xprev - coef2*x_t) / coef1
+            _extract_into_tensor(1.0 / self.posterior_mean_coef1, t, x_t.shape)
+            * xprev - _extract_into_tensor(
+                self.posterior_mean_coef2 / self.posterior_mean_coef1, t,
+                x_t.shape) * x_t)
+
+    def _predict_xstart_from_scaled_xstart(self, t, scaled_xstart):
+        return scaled_xstart * _extract_into_tensor(
+            self.sqrt_recip_alphas_cumprod, t, scaled_xstart.shape)
+
+    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+        return (_extract_into_tensor(self.sqrt_recip_alphas_cumprod, t,
+                                     x_t.shape) * x_t -
+                pred_xstart) / _extract_into_tensor(
+                    self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+    def _predict_eps_from_scaled_xstart(self, x_t, t, scaled_xstart):
+        """
+        Args:
+            scaled_xstart: is supposed to be sqrt(alphacum) * x_0
+        """
+        # 1 / sqrt(1-alphabar) * (x_t - scaled xstart)
+        return (x_t - scaled_xstart) / _extract_into_tensor(
+            self.sqrt_one_minus_alphas_cumprod, t, x_t.shape)
+
+    def _scale_timesteps(self, t):
+        if self.rescale_timesteps:
+            # scale t to be maxed out at 1000 steps
+            return t.float() * (1000.0 / self.num_timesteps)
+        return t
+
+    def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
+        """
+        Compute the mean for the previous step, given a function cond_fn that
+        computes the gradient of a conditional log probability with respect to
+        x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
+        condition on y.
+
+        This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
+        """
+        gradient = cond_fn(x, self._scale_timesteps(t), **model_kwargs)
+        new_mean = (p_mean_var["mean"].float() +
+                    p_mean_var["variance"] * gradient.float())
+        return new_mean
+
+    def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
+        """
+        Compute what the p_mean_variance output would have been, should the
+        model's score function be conditioned by cond_fn.
+
+        See condition_mean() for details on cond_fn.
+
+        Unlike condition_mean(), this instead uses the conditioning strategy
+        from Song et al (2020).
+        """
+        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
+
+        eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
+        eps = eps - (1 - alpha_bar).sqrt() * cond_fn(
+            x, self._scale_timesteps(t), **model_kwargs)
+
+        out = p_mean_var.copy()
+        out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
+        out["mean"], _, _ = self.q_posterior_mean_variance(
+            x_start=out["pred_xstart"], x_t=x, t=t)
+        return out
+
+    def p_sample(
+        self,
+        model: Model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+    ):
+        """
+        Sample x_{t-1} from the model at the given timestep.
+
+        :param model: the model to sample from.
+        :param x: the current tensor at x_{t-1}.
+        :param t: the value of t, starting at 0 for the first diffusion step.
+        :param clip_denoised: if True, clip the x_start prediction to [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample.
+        :param cond_fn: if not None, this is a gradient function that acts
+                        similarly to the model.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict containing the following keys:
+                 - 'sample': a random sample from the model.
+                 - 'pred_xstart': a prediction of x_0.
+        """
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        noise = th.randn_like(x)
+        nonzero_mask = ((t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+                        )  # no noise when t == 0
+        if cond_fn is not None:
+            out["mean"] = self.condition_mean(cond_fn,
+                                              out,
+                                              x,
+                                              t,
+                                              model_kwargs=model_kwargs)
+        sample = out["mean"] + nonzero_mask * th.exp(
+            0.5 * out["log_variance"]) * noise
+        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+
+    def p_sample_loop(
+        self,
+        model: Model,
+        shape=None,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+    ):
+        """
+        Generate samples from the model.
+
+        :param model: the model module.
+        :param shape: the shape of the samples, (N, C, H, W).
+        :param noise: if specified, the noise from the encoder to sample.
+                      Should be of the same shape as `shape`.
+        :param clip_denoised: if True, clip x_start predictions to [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample.
+        :param cond_fn: if not None, this is a gradient function that acts
+                        similarly to the model.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :param device: if specified, the device to create the samples on.
+                       If not specified, use a model parameter's device.
+        :param progress: if True, show a tqdm progress bar.
+        :return: a non-differentiable batch of samples.
+        """
+        final = None
+        for sample in self.p_sample_loop_progressive(
+                model,
+                shape,
+                noise=noise,
+                clip_denoised=clip_denoised,
+                denoised_fn=denoised_fn,
+                cond_fn=cond_fn,
+                model_kwargs=model_kwargs,
+                device=device,
+                progress=progress,
+        ):
+            final = sample
+        return final["sample"]
+
+    def p_sample_loop_progressive(
+        self,
+        model: Model,
+        shape=None,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+    ):
+        """
+        Generate samples from the model and yield intermediate samples from
+        each timestep of diffusion.
+
+        Arguments are the same as p_sample_loop().
+        Returns a generator over dicts, where each dict is the return value of
+        p_sample().
+        """
+        if device is None:
+            device = next(model.parameters()).device
+        if noise is not None:
+            img = noise
+        else:
+            assert isinstance(shape, (tuple, list))
+            img = th.randn(*shape, device=device)
+        indices = list(range(self.num_timesteps))[::-1]
+
+        if progress:
+            # Lazy import so that we don't depend on tqdm.
+            from tqdm.auto import tqdm
+
+            indices = tqdm(indices)
+
+        for i in indices:
+            # t = th.tensor([i] * shape[0], device=device)
+            t = th.tensor([i] * len(img), device=device)
+            with th.no_grad():
+                out = self.p_sample(
+                    model,
+                    img,
+                    t,
+                    clip_denoised=clip_denoised,
+                    denoised_fn=denoised_fn,
+                    cond_fn=cond_fn,
+                    model_kwargs=model_kwargs,
+                )
+                yield out
+                img = out["sample"]
+
+    def ddim_sample(
+        self,
+        model: Model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        eta=0.0,
+    ):
+        """
+        Sample x_{t-1} from the model using DDIM.
+
+        Same usage as p_sample().
+        """
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        if cond_fn is not None:
+            out = self.condition_score(cond_fn,
+                                       out,
+                                       x,
+                                       t,
+                                       model_kwargs=model_kwargs)
+
+        # Usually our model outputs epsilon, but we re-derive it
+        # in case we used x_start or x_prev prediction.
+        eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
+
+        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
+        alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t,
+                                              x.shape)
+        sigma = (eta * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar)) *
+                 th.sqrt(1 - alpha_bar / alpha_bar_prev))
+        # Equation 12.
+        noise = th.randn_like(x)
+        mean_pred = (out["pred_xstart"] * th.sqrt(alpha_bar_prev) +
+                     th.sqrt(1 - alpha_bar_prev - sigma**2) * eps)
+        nonzero_mask = ((t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+                        )  # no noise when t == 0
+        sample = mean_pred + nonzero_mask * sigma * noise
+        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+
+    def ddim_reverse_sample(
+        self,
+        model: Model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        model_kwargs=None,
+        eta=0.0,
+    ):
+        """
+        Sample x_{t+1} from the model using DDIM reverse ODE.
+        NOTE: never used ? 
+        """
+        assert eta == 0.0, "Reverse ODE only for deterministic path"
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        # Usually our model outputs epsilon, but we re-derive it
+        # in case we used x_start or x_prev prediction.
+        eps = (_extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape)
+               * x - out["pred_xstart"]) / _extract_into_tensor(
+                   self.sqrt_recipm1_alphas_cumprod, t, x.shape)
+        alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t,
+                                              x.shape)
+
+        # Equation 12. reversed  (DDIM paper)  (th.sqrt == torch.sqrt)
+        mean_pred = (out["pred_xstart"] * th.sqrt(alpha_bar_next) +
+                     th.sqrt(1 - alpha_bar_next) * eps)
+
+        return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}
+
+    def ddim_reverse_sample_loop(
+        self,
+        model: Model,
+        x,
+        clip_denoised=True,
+        denoised_fn=None,
+        model_kwargs=None,
+        eta=0.0,
+        device=None,
+    ):
+        if device is None:
+            device = next(model.parameters()).device
+        sample_t = []
+        xstart_t = []
+        T = []
+        indices = list(range(self.num_timesteps))
+        sample = x
+        for i in indices:
+            t = th.tensor([i] * len(sample), device=device)
+            with th.no_grad():
+                out = self.ddim_reverse_sample(model,
+                                               sample,
+                                               t=t,
+                                               clip_denoised=clip_denoised,
+                                               denoised_fn=denoised_fn,
+                                               model_kwargs=model_kwargs,
+                                               eta=eta)
+                sample = out['sample']
+                # [1, ..., T]
+                sample_t.append(sample)
+                # [0, ...., T-1]
+                xstart_t.append(out['pred_xstart'])
+                # [0, ..., T-1] ready to use
+                T.append(t)
+
+        return {
+            #  xT "
+            'sample': sample,
+            # (1, ..., T)
+            'sample_t': sample_t,
+            # xstart here is a bit different from sampling from T = T-1 to T = 0
+            # may not be exact
+            'xstart_t': xstart_t,
+            'T': T,
+        }
+
+    def ddim_sample_loop(
+        self,
+        model: Model,
+        shape=None,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        eta=0.0,
+    ):
+        """
+        Generate samples from the model using DDIM.
+
+        Same usage as p_sample_loop().
+        """
+        final = None
+        for sample in self.ddim_sample_loop_progressive(
+                model,
+                shape,
+                noise=noise,
+                clip_denoised=clip_denoised,
+                denoised_fn=denoised_fn,
+                cond_fn=cond_fn,
+                model_kwargs=model_kwargs,
+                device=device,
+                progress=progress,
+                eta=eta,
+        ):
+            final = sample
+        return final["sample"]
+
+    def ddim_sample_loop_progressive(
+        self,
+        model: Model,
+        shape=None,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        eta=0.0,
+    ):
+        """
+        Use DDIM to sample from the model and yield intermediate samples from
+        each timestep of DDIM.
+
+        Same usage as p_sample_loop_progressive().
+        """
+        if device is None:
+            device = next(model.parameters()).device
+        if noise is not None:
+            img = noise
+        else:
+            assert isinstance(shape, (tuple, list))
+            img = th.randn(*shape, device=device)
+        indices = list(range(self.num_timesteps))[::-1]
+
+        if progress:
+            # Lazy import so that we don't depend on tqdm.
+            from tqdm.auto import tqdm
+
+            indices = tqdm(indices)
+
+        for i in indices:
+
+            if isinstance(model_kwargs, list):
+                # index dependent model kwargs
+                # (T-1, ..., 0)
+                _kwargs = model_kwargs[i]
+            else:
+                _kwargs = model_kwargs
+
+            t = th.tensor([i] * len(img), device=device)
+            with th.no_grad():
+                out = self.ddim_sample(
+                    model,
+                    img,
+                    t,
+                    clip_denoised=clip_denoised,
+                    denoised_fn=denoised_fn,
+                    cond_fn=cond_fn,
+                    model_kwargs=_kwargs,
+                    eta=eta,
+                )
+                out['t'] = t
+                yield out
+                img = out["sample"]
+
+    def _vb_terms_bpd(self,
+                      model: Model,
+                      x_start,
+                      x_t,
+                      t,
+                      clip_denoised=True,
+                      model_kwargs=None):
+        """
+        Get a term for the variational lower-bound.
+
+        The resulting units are bits (rather than nats, as one might expect).
+        This allows for comparison to other papers.
+
+        :return: a dict with the following keys:
+                 - 'output': a shape [N] tensor of NLLs or KLs.
+                 - 'pred_xstart': the x_0 predictions.
+        """
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
+            x_start=x_start, x_t=x_t, t=t)
+        out = self.p_mean_variance(model,
+                                   x_t,
+                                   t,
+                                   clip_denoised=clip_denoised,
+                                   model_kwargs=model_kwargs)
+        kl = normal_kl(true_mean, true_log_variance_clipped, out["mean"],
+                       out["log_variance"])
+        kl = mean_flat(kl) / np.log(2.0)
+
+        decoder_nll = -discretized_gaussian_log_likelihood(
+            x_start, means=out["mean"], log_scales=0.5 * out["log_variance"])
+        assert decoder_nll.shape == x_start.shape
+        decoder_nll = mean_flat(decoder_nll) / np.log(2.0)
+
+        # At the first timestep return the decoder NLL,
+        # otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t))
+        output = th.where((t == 0), decoder_nll, kl)
+        return {
+            "output": output,
+            "pred_xstart": out["pred_xstart"],
+            'model_forward': out['model_forward'],
+        }
+
+    def _prior_bpd(self, x_start):
+        """
+        Get the prior KL term for the variational lower-bound, measured in
+        bits-per-dim.
+
+        This term can't be optimized, as it only depends on the encoder.
+
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :return: a batch of [N] KL values (in bits), one per batch element.
+        """
+        batch_size = x_start.shape[0]
+        t = th.tensor([self.num_timesteps - 1] * batch_size,
+                      device=x_start.device)
+        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+        kl_prior = normal_kl(mean1=qt_mean,
+                             logvar1=qt_log_variance,
+                             mean2=0.0,
+                             logvar2=0.0)
+        return mean_flat(kl_prior) / np.log(2.0)
+
+    def calc_bpd_loop(self,
+                      model: Model,
+                      x_start,
+                      clip_denoised=True,
+                      model_kwargs=None):
+        """
+        Compute the entire variational lower-bound, measured in bits-per-dim,
+        as well as other related quantities.
+
+        :param model: the model to evaluate loss on.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :param clip_denoised: if True, clip denoised samples.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+
+        :return: a dict containing the following keys:
+                 - total_bpd: the total variational lower-bound, per batch element.
+                 - prior_bpd: the prior term in the lower-bound.
+                 - vb: an [N x T] tensor of terms in the lower-bound.
+                 - xstart_mse: an [N x T] tensor of x_0 MSEs for each timestep.
+                 - mse: an [N x T] tensor of epsilon MSEs for each timestep.
+        """
+        device = x_start.device
+        batch_size = x_start.shape[0]
+
+        vb = []
+        xstart_mse = []
+        mse = []
+        for t in list(range(self.num_timesteps))[::-1]:
+            t_batch = th.tensor([t] * batch_size, device=device)
+            noise = th.randn_like(x_start)
+            x_t = self.q_sample(x_start=x_start, t=t_batch, noise=noise)
+            # Calculate VLB term at the current timestep
+            with th.no_grad():
+                out = self._vb_terms_bpd(
+                    model,
+                    x_start=x_start,
+                    x_t=x_t,
+                    t=t_batch,
+                    clip_denoised=clip_denoised,
+                    model_kwargs=model_kwargs,
+                )
+            vb.append(out["output"])
+            xstart_mse.append(mean_flat((out["pred_xstart"] - x_start)**2))
+            eps = self._predict_eps_from_xstart(x_t, t_batch,
+                                                out["pred_xstart"])
+            mse.append(mean_flat((eps - noise)**2))
+
+        vb = th.stack(vb, dim=1)
+        xstart_mse = th.stack(xstart_mse, dim=1)
+        mse = th.stack(mse, dim=1)
+
+        prior_bpd = self._prior_bpd(x_start)
+        total_bpd = vb.sum(dim=1) + prior_bpd
+        return {
+            "total_bpd": total_bpd,
+            "prior_bpd": prior_bpd,
+            "vb": vb,
+            "xstart_mse": xstart_mse,
+            "mse": mse,
+        }
+
+
+def _extract_into_tensor(arr, timesteps, broadcast_shape):
+    """
+    Extract values from a 1-D numpy array for a batch of indices.
+
+    :param arr: the 1-D numpy array.
+    :param timesteps: a tensor of indices into the array to extract.
+    :param broadcast_shape: a larger shape of K dimensions with the batch
+                            dimension equal to the length of timesteps.
+    :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
+    """
+    res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
+    while len(res.shape) < len(broadcast_shape):
+        res = res[..., None]
+    return res.expand(broadcast_shape)
+
+
+def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
+    """
+    Get a pre-defined beta schedule for the given name.
+
+    The beta schedule library consists of beta schedules which remain similar
+    in the limit of num_diffusion_timesteps.
+    Beta schedules may be added, but should not be removed or changed once
+    they are committed to maintain backwards compatibility.
+    """
+    if schedule_name == "linear":
+        # Linear schedule from Ho et al, extended to work for any number of
+        # diffusion steps.
+        scale = 1000 / num_diffusion_timesteps
+        beta_start = scale * 0.0001
+        beta_end = scale * 0.02
+        return np.linspace(beta_start,
+                           beta_end,
+                           num_diffusion_timesteps,
+                           dtype=np.float64)
+    elif schedule_name == "cosine":
+        return betas_for_alpha_bar(
+            num_diffusion_timesteps,
+            lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2)**2,
+        )
+    elif schedule_name == "const0.01":
+        scale = 1000 / num_diffusion_timesteps
+        return np.array([scale * 0.01] * num_diffusion_timesteps,
+                        dtype=np.float64)
+    elif schedule_name == "const0.015":
+        scale = 1000 / num_diffusion_timesteps
+        return np.array([scale * 0.015] * num_diffusion_timesteps,
+                        dtype=np.float64)
+    elif schedule_name == "const0.008":
+        scale = 1000 / num_diffusion_timesteps
+        return np.array([scale * 0.008] * num_diffusion_timesteps,
+                        dtype=np.float64)
+    elif schedule_name == "const0.0065":
+        scale = 1000 / num_diffusion_timesteps
+        return np.array([scale * 0.0065] * num_diffusion_timesteps,
+                        dtype=np.float64)
+    elif schedule_name == "const0.0055":
+        scale = 1000 / num_diffusion_timesteps
+        return np.array([scale * 0.0055] * num_diffusion_timesteps,
+                        dtype=np.float64)
+    elif schedule_name == "const0.0045":
+        scale = 1000 / num_diffusion_timesteps
+        return np.array([scale * 0.0045] * num_diffusion_timesteps,
+                        dtype=np.float64)
+    elif schedule_name == "const0.0035":
+        scale = 1000 / num_diffusion_timesteps
+        return np.array([scale * 0.0035] * num_diffusion_timesteps,
+                        dtype=np.float64)
+    elif schedule_name == "const0.0025":
+        scale = 1000 / num_diffusion_timesteps
+        return np.array([scale * 0.0025] * num_diffusion_timesteps,
+                        dtype=np.float64)
+    elif schedule_name == "const0.0015":
+        scale = 1000 / num_diffusion_timesteps
+        return np.array([scale * 0.0015] * num_diffusion_timesteps,
+                        dtype=np.float64)
+    else:
+        raise NotImplementedError(f"unknown beta schedule: {schedule_name}")
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function,
+    which defines the cumulative product of (1-beta) over time from t = [0,1].
+
+    :param num_diffusion_timesteps: the number of betas to produce.
+    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+                      produces the cumulative product of (1-beta) up to that
+                      part of the diffusion process.
+    :param max_beta: the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+    """
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+    return np.array(betas)
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    Compute the KL divergence between two gaussians.
+
+    Shapes are automatically broadcasted, so batches can be compared to
+    scalars, among other use cases.
+    """
+    tensor = None
+    for obj in (mean1, logvar1, mean2, logvar2):
+        if isinstance(obj, th.Tensor):
+            tensor = obj
+            break
+    assert tensor is not None, "at least one argument must be a Tensor"
+
+    # Force variances to be Tensors. Broadcasting helps convert scalars to
+    # Tensors, but it does not work for th.exp().
+    logvar1, logvar2 = [
+        x if isinstance(x, th.Tensor) else th.tensor(x).to(tensor)
+        for x in (logvar1, logvar2)
+    ]
+
+    return 0.5 * (-1.0 + logvar2 - logvar1 + th.exp(logvar1 - logvar2) +
+                  ((mean1 - mean2)**2) * th.exp(-logvar2))
+
+
+def approx_standard_normal_cdf(x):
+    """
+    A fast approximation of the cumulative distribution function of the
+    standard normal.
+    """
+    return 0.5 * (
+        1.0 + th.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * th.pow(x, 3))))
+
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+    """
+    Compute the log-likelihood of a Gaussian distribution discretizing to a
+    given image.
+
+    :param x: the target images. It is assumed that this was uint8 values,
+              rescaled to the range [-1, 1].
+    :param means: the Gaussian mean Tensor.
+    :param log_scales: the Gaussian log stddev Tensor.
+    :return: a tensor like x of log probabilities (in nats).
+    """
+    assert x.shape == means.shape == log_scales.shape
+    centered_x = x - means
+    inv_stdv = th.exp(-log_scales)
+    plus_in = inv_stdv * (centered_x + 1.0 / 255.0)
+    cdf_plus = approx_standard_normal_cdf(plus_in)
+    min_in = inv_stdv * (centered_x - 1.0 / 255.0)
+    cdf_min = approx_standard_normal_cdf(min_in)
+    log_cdf_plus = th.log(cdf_plus.clamp(min=1e-12))
+    log_one_minus_cdf_min = th.log((1.0 - cdf_min).clamp(min=1e-12))
+    cdf_delta = cdf_plus - cdf_min
+    log_probs = th.where(
+        x < -0.999,
+        log_cdf_plus,
+        th.where(x > 0.999, log_one_minus_cdf_min,
+                 th.log(cdf_delta.clamp(min=1e-12))),
+    )
+    assert log_probs.shape == x.shape
+    return log_probs
+
+
+class DummyModel(th.nn.Module):
+    def __init__(self, pred):
+        super().__init__()
+        self.pred = pred
+
+    def forward(self, *args, **kwargs):
+        return DummyReturn(pred=self.pred)
+
+
+class DummyReturn(NamedTuple):
+    pred: th.Tensor

DiffAE_diffusion_diffusion.py

@@ -0,0 +1,160 @@
+from .DiffAE_diffusion_base import *
+from dataclasses import dataclass
+
+
+def space_timesteps(num_timesteps, section_counts):
+    """
+    Create a list of timesteps to use from an original diffusion process,
+    given the number of timesteps we want to take from equally-sized portions
+    of the original process.
+
+    For example, if there's 300 timesteps and the section counts are [10,15,20]
+    then the first 100 timesteps are strided to be 10 timesteps, the second 100
+    are strided to be 15 timesteps, and the final 100 are strided to be 20.
+
+    If the stride is a string starting with "ddim", then the fixed striding
+    from the DDIM paper is used, and only one section is allowed.
+
+    :param num_timesteps: the number of diffusion steps in the original
+                          process to divide up.
+    :param section_counts: either a list of numbers, or a string containing
+                           comma-separated numbers, indicating the step count
+                           per section. As a special case, use "ddimN" where N
+                           is a number of steps to use the striding from the
+                           DDIM paper.
+    :return: a set of diffusion steps from the original process to use.
+    """
+    if isinstance(section_counts, str):
+        if section_counts.startswith("ddim"):
+            desired_count = int(section_counts[len("ddim"):])
+            for i in range(1, num_timesteps):
+                if len(range(0, num_timesteps, i)) == desired_count:
+                    return set(range(0, num_timesteps, i))
+            raise ValueError(
+                f"cannot create exactly {num_timesteps} steps with an integer stride"
+            )
+        section_counts = [int(x) for x in section_counts.split(",")]
+    size_per = num_timesteps // len(section_counts)
+    extra = num_timesteps % len(section_counts)
+    start_idx = 0
+    all_steps = []
+    for i, section_count in enumerate(section_counts):
+        size = size_per + (1 if i < extra else 0)
+        if size < section_count:
+            raise ValueError(
+                f"cannot divide section of {size} steps into {section_count}")
+        if section_count <= 1:
+            frac_stride = 1
+        else:
+            frac_stride = (size - 1) / (section_count - 1)
+        cur_idx = 0.0
+        taken_steps = []
+        for _ in range(section_count):
+            taken_steps.append(start_idx + round(cur_idx))
+            cur_idx += frac_stride
+        all_steps += taken_steps
+        start_idx += size
+    return set(all_steps)
+
+
+@dataclass
+class SpacedDiffusionBeatGansConfig(GaussianDiffusionBeatGansConfig):
+    use_timesteps: Tuple[int] = None
+
+    def make_sampler(self):
+        return SpacedDiffusionBeatGans(self)
+
+
+class SpacedDiffusionBeatGans(GaussianDiffusionBeatGans):
+    """
+    A diffusion process which can skip steps in a base diffusion process.
+
+    :param use_timesteps: a collection (sequence or set) of timesteps from the
+                          original diffusion process to retain.
+    :param kwargs: the kwargs to create the base diffusion process.
+    """
+    def __init__(self, conf: SpacedDiffusionBeatGansConfig):
+        self.conf = conf
+        self.use_timesteps = set(conf.use_timesteps)
+        # how the new t's mapped to the old t's
+        self.timestep_map = []
+        self.original_num_steps = len(conf.betas)
+
+        base_diffusion = GaussianDiffusionBeatGans(conf)  # pylint: disable=missing-kwoa
+        last_alpha_cumprod = 1.0
+        new_betas = []
+        for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod):
+            if i in self.use_timesteps:
+                # getting the new betas of the new timesteps
+                new_betas.append(1 - alpha_cumprod / last_alpha_cumprod)
+                last_alpha_cumprod = alpha_cumprod
+                self.timestep_map.append(i)
+        conf.betas = np.array(new_betas)
+        super().__init__(conf)
+
+    def p_mean_variance(self, model: Model, *args, **kwargs):  # pylint: disable=signature-differs
+        return super().p_mean_variance(self._wrap_model(model), *args,
+                                       **kwargs)
+
+    def training_losses(self, model: Model, *args, **kwargs):  # pylint: disable=signature-differs
+        return super().training_losses(self._wrap_model(model), *args,
+                                       **kwargs)
+
+    def condition_mean(self, cond_fn, *args, **kwargs):
+        return super().condition_mean(self._wrap_model(cond_fn), *args,
+                                      **kwargs)
+
+    def condition_score(self, cond_fn, *args, **kwargs):
+        return super().condition_score(self._wrap_model(cond_fn), *args,
+                                       **kwargs)
+
+    def _wrap_model(self, model: Model):
+        if isinstance(model, _WrappedModel):
+            return model
+        return _WrappedModel(model, self.timestep_map, self.rescale_timesteps,
+                             self.original_num_steps)
+
+    def _scale_timesteps(self, t):
+        # Scaling is done by the wrapped model.
+        return t
+
+
+class _WrappedModel:
+    """
+    converting the supplied t's to the old t's scales.
+    """
+    def __init__(self, model, timestep_map, rescale_timesteps,
+                 original_num_steps):
+        self.model = model
+        self.timestep_map = timestep_map
+        self.rescale_timesteps = rescale_timesteps
+        self.original_num_steps = original_num_steps
+
+    def forward(self, x, t, t_cond=None, **kwargs):
+        """
+        Args:
+            t: t's with differrent ranges (can be << T due to smaller eval T) need to be converted to the original t's
+            t_cond: the same as t but can be of different values
+        """
+        map_tensor = th.tensor(self.timestep_map,
+                               device=t.device,
+                               dtype=t.dtype)
+
+        def do(t):
+            new_ts = map_tensor[t]
+            if self.rescale_timesteps:
+                new_ts = new_ts.float() * (1000.0 / self.original_num_steps)
+            return new_ts
+
+        if t_cond is not None:
+            # support t_cond
+            t_cond = do(t_cond)
+
+        return self.model(x=x, t=do(t), t_cond=t_cond, **kwargs)
+
+    def __getattr__(self, name):
+        # allow for calling the model's methods
+        if hasattr(self.model, name):
+            func = getattr(self.model, name)
+            return func
+        raise AttributeError(name)

DiffAE_diffusion_resample.py

@@ -0,0 +1,63 @@
+from abc import ABC, abstractmethod
+
+import numpy as np
+import torch as th
+import torch.distributed as dist
+
+
+def create_named_schedule_sampler(name, diffusion):
+    """
+    Create a ScheduleSampler from a library of pre-defined samplers.
+
+    :param name: the name of the sampler.
+    :param diffusion: the diffusion object to sample for.
+    """
+    if name == "uniform":
+        return UniformSampler(diffusion)
+    else:
+        raise NotImplementedError(f"unknown schedule sampler: {name}")
+
+
+class ScheduleSampler(ABC):
+    """
+    A distribution over timesteps in the diffusion process, intended to reduce
+    variance of the objective.
+
+    By default, samplers perform unbiased importance sampling, in which the
+    objective's mean is unchanged.
+    However, subclasses may override sample() to change how the resampled
+    terms are reweighted, allowing for actual changes in the objective.
+    """
+    @abstractmethod
+    def weights(self):
+        """
+        Get a numpy array of weights, one per diffusion step.
+
+        The weights needn't be normalized, but must be positive.
+        """
+
+    def sample(self, batch_size, device):
+        """
+        Importance-sample timesteps for a batch.
+
+        :param batch_size: the number of timesteps.
+        :param device: the torch device to save to.
+        :return: a tuple (timesteps, weights):
+                 - timesteps: a tensor of timestep indices.
+                 - weights: a tensor of weights to scale the resulting losses.
+        """
+        w = self.weights()
+        p = w / np.sum(w)
+        indices_np = np.random.choice(len(p), size=(batch_size, ), p=p)
+        indices = th.from_numpy(indices_np).long().to(device)
+        weights_np = 1 / (len(p) * p[indices_np])
+        weights = th.from_numpy(weights_np).float().to(device)
+        return indices, weights
+
+
+class UniformSampler(ScheduleSampler):
+    def __init__(self, num_timesteps):
+        self._weights = np.ones([num_timesteps])
+
+    def weights(self):
+        return self._weights

DiffAE_model.py

@@ -0,0 +1,7 @@
+from typing import Union
+from .DiffAE_model_unet import BeatGANsUNetModel, BeatGANsUNetConfig
+from .DiffAE_model_unet_autoenc import BeatGANsAutoencConfig, BeatGANsAutoencModel
+from .DiffAE_model_latentnet import *
+
+Model = Union[BeatGANsUNetModel, BeatGANsAutoencModel]
+ModelConfig = Union[BeatGANsUNetConfig, BeatGANsAutoencConfig]

DiffAE_model_blocks.py

@@ -0,0 +1,569 @@
+import math
+import numpy as np
+from abc import abstractmethod
+from dataclasses import dataclass
+from numbers import Number
+
+import torch as th
+import torch.nn.functional as F
+from .DiffAE_support_choices import *
+from .DiffAE_support_config_base import BaseConfig
+from torch import nn
+
+from .DiffAE_model_nn import (avg_pool_nd, conv_nd, linear, normalization,
+                 timestep_embedding, torch_checkpoint, zero_module)
+
+class ScaleAt(Enum):
+    after_norm = 'afternorm'
+
+
+class TimestepBlock(nn.Module):
+    """
+    Any module where forward() takes timestep embeddings as a second argument.
+    """
+    @abstractmethod
+    def forward(self, x, emb=None, cond=None, lateral=None):
+        """
+        Apply the module to `x` given `emb` timestep embeddings.
+        """
+
+
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+    """
+    A sequential module that passes timestep embeddings to the children that
+    support it as an extra input.
+    """
+    def forward(self, x, emb=None, cond=None, lateral=None):
+        for layer in self:
+            if isinstance(layer, TimestepBlock):
+                x = layer(x, emb=emb, cond=cond, lateral=lateral)
+            else:
+                x = layer(x)
+        return x
+
+
+@dataclass
+class ResBlockConfig(BaseConfig):
+    channels: int
+    emb_channels: int
+    dropout: float
+    out_channels: int = None
+    # condition the resblock with time (and encoder's output)
+    use_condition: bool = True
+    # whether to use 3x3 conv for skip path when the channels aren't matched
+    use_conv: bool = False
+    group_norm_limit: int = 32
+    # dimension of conv (always 2 = 2d)
+    dims: int = 2
+    # gradient checkpoint
+    use_checkpoint: bool = False
+    up: bool = False
+    down: bool = False
+    # whether to condition with both time & encoder's output
+    two_cond: bool = False
+    # number of encoders' output channels
+    cond_emb_channels: int = None
+    # suggest: False
+    has_lateral: bool = False
+    lateral_channels: int = None
+    # whether to init the convolution with zero weights
+    # this is default from BeatGANs and seems to help learning
+    use_zero_module: bool = True
+
+    def __post_init__(self):
+        self.out_channels = self.out_channels or self.channels
+        self.cond_emb_channels = self.cond_emb_channels or self.emb_channels
+
+    def make_model(self):
+        return ResBlock(self)
+
+
+class ResBlock(TimestepBlock):
+    """
+    A residual block that can optionally change the number of channels.
+
+    total layers:
+        in_layers
+        - norm
+        - act
+        - conv
+        out_layers
+        - norm
+        - (modulation)
+        - act
+        - conv
+    """
+    def __init__(self, conf: ResBlockConfig):
+        super().__init__()
+        self.conf = conf
+
+        #############################
+        # IN LAYERS
+        #############################
+        assert conf.lateral_channels is None
+        layers = [
+            normalization(conf.channels, limit=conf.group_norm_limit if "group_norm_limit" in conf.__dict__ else 32),
+            nn.SiLU(),
+            conv_nd(conf.dims, conf.channels, conf.out_channels, 3, padding=1)
+        ]
+        self.in_layers = nn.Sequential(*layers)
+
+        self.updown = conf.up or conf.down
+
+        if conf.up:
+            self.h_upd = Upsample(conf.channels, False, conf.dims)
+            self.x_upd = Upsample(conf.channels, False, conf.dims)
+        elif conf.down:
+            self.h_upd = Downsample(conf.channels, False, conf.dims)
+            self.x_upd = Downsample(conf.channels, False, conf.dims)
+        else:
+            self.h_upd = self.x_upd = nn.Identity()
+
+        #############################
+        # OUT LAYERS CONDITIONS
+        #############################
+        if conf.use_condition:
+            # condition layers for the out_layers
+            self.emb_layers = nn.Sequential(
+                nn.SiLU(),
+                linear(conf.emb_channels, 2 * conf.out_channels),
+            )
+
+            if conf.two_cond:
+                self.cond_emb_layers = nn.Sequential(
+                    nn.SiLU(),
+                    linear(conf.cond_emb_channels, conf.out_channels),
+                )
+            #############################
+            # OUT LAYERS (ignored when there is no condition)
+            #############################
+            # original version
+            conv = conv_nd(conf.dims,
+                           conf.out_channels,
+                           conf.out_channels,
+                           3,
+                           padding=1)
+            if conf.use_zero_module:
+                # zere out the weights
+                # it seems to help training
+                conv = zero_module(conv)
+
+            # construct the layers
+            # - norm
+            # - (modulation)
+            # - act
+            # - dropout
+            # - conv
+            layers = []
+            layers += [
+                normalization(conf.out_channels, limit=conf.group_norm_limit if "group_norm_limit" in conf.__dict__ else 32),
+                nn.SiLU(),
+                nn.Dropout(p=conf.dropout),
+                conv,
+            ]
+            self.out_layers = nn.Sequential(*layers)
+
+        #############################
+        # SKIP LAYERS
+        #############################
+        if conf.out_channels == conf.channels:
+            # cannot be used with gatedconv, also gatedconv is alsways used as the first block
+            self.skip_connection = nn.Identity()
+        else:
+            if conf.use_conv:
+                kernel_size = 3
+                padding = 1
+            else:
+                kernel_size = 1
+                padding = 0
+
+            self.skip_connection = conv_nd(conf.dims,
+                                           conf.channels,
+                                           conf.out_channels,
+                                           kernel_size,
+                                           padding=padding)
+
+    def forward(self, x, emb=None, cond=None, lateral=None):
+        """
+        Apply the block to a Tensor, conditioned on a timestep embedding.
+
+        Args:
+            x: input
+            lateral: lateral connection from the encoder
+        """
+        return torch_checkpoint(self._forward, (x, emb, cond, lateral),
+                                self.conf.use_checkpoint)
+
+    def _forward(
+        self,
+        x,
+        emb=None,
+        cond=None,
+        lateral=None,
+    ):
+        """
+        Args:
+            lateral: required if "has_lateral" and non-gated, with gated, it can be supplied optionally    
+        """
+        if self.conf.has_lateral:
+            # lateral may be supplied even if it doesn't require
+            # the model will take the lateral only if "has_lateral"
+            assert lateral is not None
+            x = th.cat([x, lateral], dim=1)
+
+        if self.updown:
+            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+            h = in_rest(x)
+            h = self.h_upd(h)
+            x = self.x_upd(x)
+            h = in_conv(h)
+        else:
+            h = self.in_layers(x)
+
+        if self.conf.use_condition:
+            # it's possible that the network may not receieve the time emb
+            # this happens with autoenc and setting the time_at
+            if emb is not None:
+                emb_out = self.emb_layers(emb).type(h.dtype)
+            else:
+                emb_out = None
+
+            if self.conf.two_cond:
+                # it's possible that the network is two_cond
+                # but it doesn't get the second condition
+                # in which case, we ignore the second condition
+                # and treat as if the network has one condition
+                if cond is None:
+                    cond_out = None
+                else:
+                    cond_out = self.cond_emb_layers(cond).type(h.dtype)
+
+                if cond_out is not None:
+                    while len(cond_out.shape) < len(h.shape):
+                        cond_out = cond_out[..., None]
+            else:
+                cond_out = None
+
+            # this is the new refactored code
+            h = apply_conditions(
+                h=h,
+                emb=emb_out,
+                cond=cond_out,
+                layers=self.out_layers,
+                scale_bias=1,
+                in_channels=self.conf.out_channels,
+                up_down_layer=None,
+            )
+
+        return self.skip_connection(x) + h
+
+
+def apply_conditions(
+    h,
+    emb=None,
+    cond=None,
+    layers: nn.Sequential = None,
+    scale_bias: float = 1,
+    in_channels: int = 512,
+    up_down_layer: nn.Module = None,
+):
+    """
+    apply conditions on the feature maps
+
+    Args:
+        emb: time conditional (ready to scale + shift)
+        cond: encoder's conditional (read to scale + shift)
+    """
+    two_cond = emb is not None and cond is not None
+
+    if emb is not None:
+        # adjusting shapes
+        while len(emb.shape) < len(h.shape):
+            emb = emb[..., None]
+
+    if two_cond:
+        # adjusting shapes
+        while len(cond.shape) < len(h.shape):
+            cond = cond[..., None]
+        # time first
+        scale_shifts = [emb, cond]
+    else:
+        # "cond" is not used with single cond mode
+        scale_shifts = [emb]
+
+    # support scale, shift or shift only
+    for i, each in enumerate(scale_shifts):
+        if each is None:
+            # special case: the condition is not provided
+            a = None
+            b = None
+        else:
+            if each.shape[1] == in_channels * 2:
+                a, b = th.chunk(each, 2, dim=1)
+            else:
+                a = each
+                b = None
+        scale_shifts[i] = (a, b)
+
+    # condition scale bias could be a list
+    if isinstance(scale_bias, Number):
+        biases = [scale_bias] * len(scale_shifts)
+    else:
+        # a list
+        biases = scale_bias
+
+    # default, the scale & shift are applied after the group norm but BEFORE SiLU
+    pre_layers, post_layers = layers[0], layers[1:]
+
+    # spilt the post layer to be able to scale up or down before conv
+    # post layers will contain only the conv
+    mid_layers, post_layers = post_layers[:-2], post_layers[-2:]
+
+    h = pre_layers(h)
+    # scale and shift for each condition
+    for i, (scale, shift) in enumerate(scale_shifts):
+        # if scale is None, it indicates that the condition is not provided
+        if scale is not None:
+            h = h * (biases[i] + scale)
+            if shift is not None:
+                h = h + shift
+    h = mid_layers(h)
+
+    # upscale or downscale if any just before the last conv
+    if up_down_layer is not None:
+        h = up_down_layer(h)
+    h = post_layers(h)
+    return h
+
+
+class Upsample(nn.Module):
+    """
+    An upsampling layer with an optional convolution.
+
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 upsampling occurs in the inner-two dimensions.
+    """
+    def __init__(self, channels, use_conv, dims=2, out_channels=None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        if use_conv:
+            self.conv = conv_nd(dims,
+                                self.channels,
+                                self.out_channels,
+                                3,
+                                padding=1)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        if self.dims == 3:
+            x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2),
+                              mode="nearest")
+        else:
+            x = F.interpolate(x, scale_factor=2, mode="nearest")
+        if self.use_conv:
+            x = self.conv(x)
+        return x
+
+
+class Downsample(nn.Module):
+    """
+    A downsampling layer with an optional convolution.
+
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 downsampling occurs in the inner-two dimensions.
+    """
+    def __init__(self, channels, use_conv, dims=2, out_channels=None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        stride = 2 if dims != 3 else (1, 2, 2)
+        if use_conv:
+            self.op = conv_nd(dims,
+                              self.channels,
+                              self.out_channels,
+                              3,
+                              stride=stride,
+                              padding=1)
+        else:
+            assert self.channels == self.out_channels
+            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        return self.op(x)
+
+
+class AttentionBlock(nn.Module):
+    """
+    An attention block that allows spatial positions to attend to each other.
+
+    Originally ported from here, but adapted to the N-d case.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+    """
+    def __init__(
+        self,
+        channels,
+        num_heads=1,
+        num_head_channels=-1,
+        group_norm_limit=32,
+        use_checkpoint=False,
+        use_new_attention_order=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        if num_head_channels == -1:
+            self.num_heads = num_heads
+        else:
+            assert (
+                channels % num_head_channels == 0
+            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            self.num_heads = channels // num_head_channels
+        self.use_checkpoint = use_checkpoint
+        self.norm = normalization(channels, limit=group_norm_limit)
+        self.qkv = conv_nd(1, channels, channels * 3, 1)
+        if use_new_attention_order:
+            # split qkv before split heads
+            self.attention = QKVAttention(self.num_heads)
+        else:
+            # split heads before split qkv
+            self.attention = QKVAttentionLegacy(self.num_heads)
+
+        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
+
+    def forward(self, x):
+        return torch_checkpoint(self._forward, (x, ), self.use_checkpoint)
+
+    def _forward(self, x):
+        b, c, *spatial = x.shape
+        x = x.reshape(b, c, -1)
+        qkv = self.qkv(self.norm(x))
+        h = self.attention(qkv)
+        h = self.proj_out(h)
+        return (x + h).reshape(b, c, *spatial)
+
+
+def count_flops_attn(model, _x, y):
+    """
+    A counter for the `thop` package to count the operations in an
+    attention operation.
+    Meant to be used like:
+        macs, params = thop.profile(
+            model,
+            inputs=(inputs, timestamps),
+            custom_ops={QKVAttention: QKVAttention.count_flops},
+        )
+    """
+    b, c, *spatial = y[0].shape
+    num_spatial = int(np.prod(spatial))
+    # We perform two matmuls with the same number of ops.
+    # The first computes the weight matrix, the second computes
+    # the combination of the value vectors.
+    matmul_ops = 2 * b * (num_spatial**2) * c
+    model.total_ops += th.DoubleTensor([matmul_ops])
+
+
+class QKVAttentionLegacy(nn.Module):
+    """
+    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
+    """
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv):
+        """
+        Apply QKV attention.
+
+        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch,
+                                                                       dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = th.einsum(
+            "bct,bcs->bts", q * scale,
+            k * scale)  # More stable with f16 than dividing afterwards
+        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = th.einsum("bts,bcs->bct", weight, v)
+        return a.reshape(bs, -1, length)
+
+    @staticmethod
+    def count_flops(model, _x, y):
+        return count_flops_attn(model, _x, y)
+
+
+class QKVAttention(nn.Module):
+    """
+    A module which performs QKV attention and splits in a different order.
+    """
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv):
+        """
+        Apply QKV attention.
+
+        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.chunk(3, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = th.einsum(
+            "bct,bcs->bts",
+            (q * scale).view(bs * self.n_heads, ch, length),
+            (k * scale).view(bs * self.n_heads, ch, length),
+        )  # More stable with f16 than dividing afterwards
+        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = th.einsum("bts,bcs->bct", weight,
+                      v.reshape(bs * self.n_heads, ch, length))
+        return a.reshape(bs, -1, length)
+
+    @staticmethod
+    def count_flops(model, _x, y):
+        return count_flops_attn(model, _x, y)
+
+
+class AttentionPool2d(nn.Module):
+    """
+    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
+    """
+    def __init__(
+        self,
+        spacial_dim: int,
+        embed_dim: int,
+        num_heads_channels: int,
+        output_dim: int = None,
+    ):
+        super().__init__()
+        self.positional_embedding = nn.Parameter(
+            th.randn(embed_dim, spacial_dim**2 + 1) / embed_dim**0.5)
+        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
+        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
+        self.num_heads = embed_dim // num_heads_channels
+        self.attention = QKVAttention(self.num_heads)
+
+    def forward(self, x):
+        b, c, *_spatial = x.shape
+        x = x.reshape(b, c, -1)  # NC(HW)
+        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
+        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
+        x = self.qkv_proj(x)
+        x = self.attention(x)
+        x = self.c_proj(x)
+        return x[:, :, 0]

DiffAE_model_latentnet.py

@@ -0,0 +1,193 @@
+import math
+from dataclasses import dataclass
+from enum import Enum
+from typing import NamedTuple, Tuple
+
+import torch
+from .DiffAE_support_choices import *
+from .DiffAE_support_config_base import BaseConfig
+from torch import nn
+from torch.nn import init
+
+from .DiffAE_model_blocks import *
+from .DiffAE_model_nn import timestep_embedding
+from .DiffAE_model_unet import *
+
+
+class LatentNetType(Enum):
+    none = 'none'
+    # injecting inputs into the hidden layers
+    skip = 'skip'
+
+
+class LatentNetReturn(NamedTuple):
+    pred: torch.Tensor = None
+
+
+@dataclass
+class MLPSkipNetConfig(BaseConfig):
+    """
+    default MLP for the latent DPM in the paper!
+    """
+    num_channels: int
+    skip_layers: Tuple[int]
+    num_hid_channels: int
+    num_layers: int
+    num_time_emb_channels: int = 64
+    activation: Activation = Activation.silu
+    use_norm: bool = True
+    condition_bias: float = 1
+    dropout: float = 0
+    last_act: Activation = Activation.none
+    num_time_layers: int = 2
+    time_last_act: bool = False
+
+    def make_model(self):
+        return MLPSkipNet(self)
+
+
+class MLPSkipNet(nn.Module):
+    """
+    concat x to hidden layers
+
+    default MLP for the latent DPM in the paper!
+    """
+    def __init__(self, conf: MLPSkipNetConfig):
+        super().__init__()
+        self.conf = conf
+
+        layers = []
+        for i in range(conf.num_time_layers):
+            if i == 0:
+                a = conf.num_time_emb_channels
+                b = conf.num_channels
+            else:
+                a = conf.num_channels
+                b = conf.num_channels
+            layers.append(nn.Linear(a, b))
+            if i < conf.num_time_layers - 1 or conf.time_last_act:
+                layers.append(conf.activation.get_act())
+        self.time_embed = nn.Sequential(*layers)
+
+        self.layers = nn.ModuleList([])
+        for i in range(conf.num_layers):
+            if i == 0:
+                act = conf.activation
+                norm = conf.use_norm
+                cond = True
+                a, b = conf.num_channels, conf.num_hid_channels
+                dropout = conf.dropout
+            elif i == conf.num_layers - 1:
+                act = Activation.none
+                norm = False
+                cond = False
+                a, b = conf.num_hid_channels, conf.num_channels
+                dropout = 0
+            else:
+                act = conf.activation
+                norm = conf.use_norm
+                cond = True
+                a, b = conf.num_hid_channels, conf.num_hid_channels
+                dropout = conf.dropout
+
+            if i in conf.skip_layers:
+                a += conf.num_channels
+
+            self.layers.append(
+                MLPLNAct(
+                    a,
+                    b,
+                    norm=norm,
+                    activation=act,
+                    cond_channels=conf.num_channels,
+                    use_cond=cond,
+                    condition_bias=conf.condition_bias,
+                    dropout=dropout,
+                ))
+        self.last_act = conf.last_act.get_act()
+
+    def forward(self, x, t, **kwargs):
+        t = timestep_embedding(t, self.conf.num_time_emb_channels)
+        cond = self.time_embed(t)
+        h = x
+        for i in range(len(self.layers)):
+            if i in self.conf.skip_layers:
+                # injecting input into the hidden layers
+                h = torch.cat([h, x], dim=1)
+            h = self.layers[i].forward(x=h, cond=cond)
+        h = self.last_act(h)
+        return LatentNetReturn(h)
+
+
+class MLPLNAct(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        norm: bool,
+        use_cond: bool,
+        activation: Activation,
+        cond_channels: int,
+        condition_bias: float = 0,
+        dropout: float = 0,
+    ):
+        super().__init__()
+        self.activation = activation
+        self.condition_bias = condition_bias
+        self.use_cond = use_cond
+
+        self.linear = nn.Linear(in_channels, out_channels)
+        self.act = activation.get_act()
+        if self.use_cond:
+            self.linear_emb = nn.Linear(cond_channels, out_channels)
+            self.cond_layers = nn.Sequential(self.act, self.linear_emb)
+        if norm:
+            self.norm = nn.LayerNorm(out_channels)
+        else:
+            self.norm = nn.Identity()
+
+        if dropout > 0:
+            self.dropout = nn.Dropout(p=dropout)
+        else:
+            self.dropout = nn.Identity()
+
+        self.init_weights()
+
+    def init_weights(self):
+        for module in self.modules():
+            if isinstance(module, nn.Linear):
+                if self.activation == Activation.relu:
+                    init.kaiming_normal_(module.weight,
+                                         a=0,
+                                         nonlinearity='relu')
+                elif self.activation == Activation.lrelu:
+                    init.kaiming_normal_(module.weight,
+                                         a=0.2,
+                                         nonlinearity='leaky_relu')
+                elif self.activation == Activation.silu:
+                    init.kaiming_normal_(module.weight,
+                                         a=0,
+                                         nonlinearity='relu')
+                else:
+                    # leave it as default
+                    pass
+
+    def forward(self, x, cond=None):
+        x = self.linear(x)
+        if self.use_cond:
+            # (n, c) or (n, c * 2)
+            cond = self.cond_layers(cond)
+            cond = (cond, None)
+
+            # scale shift first
+            x = x * (self.condition_bias + cond[0])
+            if cond[1] is not None:
+                x = x + cond[1]
+            # then norm
+            x = self.norm(x)
+        else:
+            # no condition
+            x = self.norm(x)
+        x = self.act(x)
+        x = self.dropout(x)
+        return x

DiffAE_model_nn.py

@@ -0,0 +1,138 @@
+"""
+Various utilities for neural networks.
+"""
+
+from enum import Enum
+import math
+from typing import Optional
+
+import torch as th
+import torch.nn as nn
+import torch.utils.checkpoint
+
+import torch.nn.functional as F
+
+
+# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
+class SiLU(nn.Module):
+    # @th.jit.script
+    def forward(self, x):
+        return x * th.sigmoid(x)
+
+
+class GroupNorm32(nn.GroupNorm):
+    def forward(self, x):
+        return super().forward(x.float()).type(x.dtype)
+
+
+def conv_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D convolution module.
+    """
+    if dims == 1:
+        return nn.Conv1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.Conv2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.Conv3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def linear(*args, **kwargs):
+    """
+    Create a linear module.
+    """
+    return nn.Linear(*args, **kwargs)
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D average pooling module.
+    """
+    if dims == 1:
+        return nn.AvgPool1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.AvgPool2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.AvgPool3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def update_ema(target_params, source_params, rate=0.99):
+    """
+    Update target parameters to be closer to those of source parameters using
+    an exponential moving average.
+
+    :param target_params: the target parameter sequence.
+    :param source_params: the source parameter sequence.
+    :param rate: the EMA rate (closer to 1 means slower).
+    """
+    for targ, src in zip(target_params, source_params):
+        targ.detach().mul_(rate).add_(src, alpha=1 - rate)
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+def scale_module(module, scale):
+    """
+    Scale the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().mul_(scale)
+    return module
+
+
+def mean_flat(tensor):
+    """
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def normalization(channels, limit=32):
+    """
+    Make a standard normalization layer.
+
+    :param channels: number of input channels.
+    :param limit: the maximum number of groups. It's required if the number of net_channel is too small. Default: 32 (Added by Soumick, default from original)
+    :return: an nn.Module for normalization.
+    """
+    return GroupNorm32(min(limit, channels), channels)
+
+
+def timestep_embedding(timesteps, dim, max_period=10000):
+    """
+    Create sinusoidal timestep embeddings.
+
+    :param timesteps: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an [N x dim] Tensor of positional embeddings.
+    """
+    half = dim // 2
+    freqs = th.exp(-math.log(max_period) *
+                   th.arange(start=0, end=half, dtype=th.float32) /
+                   half).to(device=timesteps.device)
+    args = timesteps[:, None].float() * freqs[None]
+    embedding = th.cat([th.cos(args), th.sin(args)], dim=-1)
+    if dim % 2:
+        embedding = th.cat(
+            [embedding, th.zeros_like(embedding[:, :1])], dim=-1)
+    return embedding
+
+
+def torch_checkpoint(func, args, flag, preserve_rng_state=False):
+    # torch's gradient checkpoint works with automatic mixed precision, given torch >= 1.8
+    if flag:
+        return torch.utils.checkpoint.checkpoint(
+            func, *args, preserve_rng_state=preserve_rng_state)
+    else:
+        return func(*args)

DiffAE_model_unet.py

@@ -0,0 +1,569 @@
+import math
+from dataclasses import dataclass
+from numbers import Number
+from typing import NamedTuple, Tuple, Union
+
+import numpy as np
+import torch as th
+from torch import nn
+import torch.nn.functional as F
+from .DiffAE_support_choices import *
+from .DiffAE_support_config_base import BaseConfig
+from .DiffAE_model_blocks import *
+
+from .DiffAE_model_nn import (conv_nd, linear, normalization, timestep_embedding,
+                 torch_checkpoint, zero_module)
+
+
+@dataclass
+class BeatGANsUNetConfig(BaseConfig):
+    image_size: int = 64
+    in_channels: int = 3
+    # base channels, will be multiplied
+    model_channels: int = 64
+    # output of the unet
+    # suggest: 3
+    # you only need 6 if you also model the variance of the noise prediction (usually we use an analytical variance hence 3)
+    out_channels: int = 3
+    # how many repeating resblocks per resolution
+    # the decoding side would have "one more" resblock
+    # default: 2
+    num_res_blocks: int = 2
+    # you can also set the number of resblocks specifically for the input blocks
+    # default: None = above
+    num_input_res_blocks: int = None
+    # number of time embed channels and style channels
+    embed_channels: int = 512
+    # at what resolutions you want to do self-attention of the feature maps
+    # attentions generally improve performance
+    # default: [16]
+    # beatgans: [32, 16, 8]
+    attention_resolutions: Tuple[int] = (16, )
+    # number of time embed channels
+    time_embed_channels: int = None
+    # dropout applies to the resblocks (on feature maps)
+    dropout: float = 0.1
+    channel_mult: Tuple[int] = (1, 2, 4, 8)
+    input_channel_mult: Tuple[int] = None
+    conv_resample: bool = True
+    group_norm_limit: int = 32
+    # always 2 = 2d conv
+    dims: int = 2
+    # don't use this, legacy from BeatGANs
+    num_classes: int = None
+    use_checkpoint: bool = False
+    # number of attention heads
+    num_heads: int = 1
+    # or specify the number of channels per attention head
+    num_head_channels: int = -1
+    # what's this?
+    num_heads_upsample: int = -1
+    # use resblock for upscale/downscale blocks (expensive)
+    # default: True (BeatGANs)
+    resblock_updown: bool = True
+    # never tried
+    use_new_attention_order: bool = False
+    resnet_two_cond: bool = False
+    resnet_cond_channels: int = None
+    # init the decoding conv layers with zero weights, this speeds up training
+    # default: True (BeattGANs)
+    resnet_use_zero_module: bool = True
+    # gradient checkpoint the attention operation
+    attn_checkpoint: bool = False
+
+    def make_model(self):
+        return BeatGANsUNetModel(self)
+
+
+class BeatGANsUNetModel(nn.Module):
+    def __init__(self, conf: BeatGANsUNetConfig):
+        super().__init__()
+        self.conf = conf
+
+        if conf.num_heads_upsample == -1:
+            self.num_heads_upsample = conf.num_heads
+
+        self.dtype = th.float32
+
+        self.time_emb_channels = conf.time_embed_channels or conf.model_channels
+        self.time_embed = nn.Sequential(
+            linear(self.time_emb_channels, conf.embed_channels),
+            nn.SiLU(),
+            linear(conf.embed_channels, conf.embed_channels),
+        )
+
+        if conf.num_classes is not None:
+            self.label_emb = nn.Embedding(conf.num_classes,
+                                          conf.embed_channels)
+
+        ch = input_ch = int(conf.channel_mult[0] * conf.model_channels)
+        self.input_blocks = nn.ModuleList([
+            TimestepEmbedSequential(
+                conv_nd(conf.dims, conf.in_channels, ch, 3, padding=1))
+        ])
+
+        kwargs = dict(
+            use_condition=True,
+            two_cond=conf.resnet_two_cond,
+            use_zero_module=conf.resnet_use_zero_module,
+            # style channels for the resnet block
+            cond_emb_channels=conf.resnet_cond_channels,
+        )
+
+        self._feature_size = ch
+
+        # input_block_chans = [ch]
+        input_block_chans = [[] for _ in range(len(conf.channel_mult))]
+        input_block_chans[0].append(ch)
+
+        # number of blocks at each resolution
+        self.input_num_blocks = [0 for _ in range(len(conf.channel_mult))]
+        self.input_num_blocks[0] = 1
+        self.output_num_blocks = [0 for _ in range(len(conf.channel_mult))]
+
+        ds = 1
+        resolution = conf.image_size
+        for level, mult in enumerate(conf.input_channel_mult
+                                     or conf.channel_mult):
+            for _ in range(conf.num_input_res_blocks or conf.num_res_blocks):
+                layers = [
+                    ResBlockConfig(
+                        ch,
+                        conf.embed_channels,
+                        conf.dropout,
+                        out_channels=int(mult * conf.model_channels),
+                        group_norm_limit=conf.group_norm_limit,
+                        dims=conf.dims,
+                        use_checkpoint=conf.use_checkpoint,
+                        **kwargs,
+                    ).make_model()
+                ]
+                ch = int(mult * conf.model_channels)
+                if resolution in conf.attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=conf.use_checkpoint
+                            or conf.attn_checkpoint,
+                            num_heads=conf.num_heads,
+                            num_head_channels=conf.num_head_channels,
+                            group_norm_limit=conf.group_norm_limit,
+                            use_new_attention_order=conf.
+                            use_new_attention_order,
+                        ))
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                # input_block_chans.append(ch)
+                input_block_chans[level].append(ch)
+                self.input_num_blocks[level] += 1
+                # print(input_block_chans)
+            if level != len(conf.channel_mult) - 1:
+                resolution //= 2
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlockConfig(
+                            ch,
+                            conf.embed_channels,
+                            conf.dropout,
+                            out_channels=out_ch,
+                            group_norm_limit=conf.group_norm_limit,
+                            dims=conf.dims,
+                            use_checkpoint=conf.use_checkpoint,
+                            down=True,
+                            **kwargs,
+                        ).make_model() if conf.
+                        resblock_updown else Downsample(ch,
+                                                        conf.conv_resample,
+                                                        dims=conf.dims,
+                                                        out_channels=out_ch)))
+                ch = out_ch
+                # input_block_chans.append(ch)
+                input_block_chans[level + 1].append(ch)
+                self.input_num_blocks[level + 1] += 1
+                ds *= 2
+                self._feature_size += ch
+
+        self.middle_block = TimestepEmbedSequential(
+            ResBlockConfig(
+                ch,
+                conf.embed_channels,
+                conf.dropout,
+                group_norm_limit=conf.group_norm_limit,
+                dims=conf.dims,
+                use_checkpoint=conf.use_checkpoint,
+                **kwargs,
+            ).make_model(),
+            AttentionBlock(
+                ch,
+                use_checkpoint=conf.use_checkpoint or conf.attn_checkpoint,
+                num_heads=conf.num_heads,
+                num_head_channels=conf.num_head_channels,
+                group_norm_limit=conf.group_norm_limit,
+                use_new_attention_order=conf.use_new_attention_order,
+            ),
+            ResBlockConfig(
+                ch,
+                conf.embed_channels,
+                conf.dropout,
+                group_norm_limit=conf.group_norm_limit,
+                dims=conf.dims,
+                use_checkpoint=conf.use_checkpoint,
+                **kwargs,
+            ).make_model(),
+        )
+        self._feature_size += ch
+
+        self.output_blocks = nn.ModuleList([])
+        for level, mult in list(enumerate(conf.channel_mult))[::-1]:
+            for i in range(conf.num_res_blocks + 1):
+                # print(input_block_chans)
+                # ich = input_block_chans.pop()
+                try:
+                    ich = input_block_chans[level].pop()
+                except IndexError:
+                    # this happens only when num_res_block > num_enc_res_block
+                    # we will not have enough lateral (skip) connecions for all decoder blocks
+                    ich = 0
+                # print('pop:', ich)
+                layers = [
+                    ResBlockConfig(
+                        # only direct channels when gated
+                        channels=ch + ich,
+                        emb_channels=conf.embed_channels,
+                        dropout=conf.dropout,
+                        out_channels=int(conf.model_channels * mult),                        
+                        group_norm_limit=conf.group_norm_limit,
+                        dims=conf.dims,
+                        use_checkpoint=conf.use_checkpoint,
+                        # lateral channels are described here when gated
+                        has_lateral=True if ich > 0 else False,
+                        lateral_channels=None,
+                        **kwargs,
+                    ).make_model()
+                ]
+                ch = int(conf.model_channels * mult)
+                if resolution in conf.attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=conf.use_checkpoint
+                            or conf.attn_checkpoint,
+                            num_heads=self.num_heads_upsample,
+                            num_head_channels=conf.num_head_channels,
+                            group_norm_limit=conf.group_norm_limit,
+                            use_new_attention_order=conf.
+                            use_new_attention_order,
+                        ))
+                if level and i == conf.num_res_blocks:
+                    resolution *= 2
+                    out_ch = ch
+                    layers.append(
+                        ResBlockConfig(
+                            ch,
+                            conf.embed_channels,
+                            conf.dropout,
+                            out_channels=out_ch,
+                            group_norm_limit=conf.group_norm_limit,
+                            dims=conf.dims,
+                            use_checkpoint=conf.use_checkpoint,
+                            up=True,
+                            **kwargs,
+                        ).make_model() if (
+                            conf.resblock_updown
+                        ) else Upsample(ch,
+                                        conf.conv_resample,
+                                        dims=conf.dims,
+                                        out_channels=out_ch))
+                    ds //= 2
+                self.output_blocks.append(TimestepEmbedSequential(*layers))
+                self.output_num_blocks[level] += 1
+                self._feature_size += ch
+
+        # print(input_block_chans)
+        # print('inputs:', self.input_num_blocks)
+        # print('outputs:', self.output_num_blocks)
+
+        if conf.resnet_use_zero_module:
+            self.out = nn.Sequential(
+                normalization(ch, limit=conf.group_norm_limit if "group_norm_limit" in conf.__dict__ else 32),
+                nn.SiLU(),
+                zero_module(
+                    conv_nd(conf.dims,
+                            input_ch,
+                            conf.out_channels,
+                            3,
+                            padding=1)),
+            )
+        else:
+            self.out = nn.Sequential(
+                normalization(ch, limit=conf.group_norm_limit if "group_norm_limit" in conf.__dict__ else 32),
+                nn.SiLU(),
+                conv_nd(conf.dims, input_ch, conf.out_channels, 3, padding=1),
+            )
+
+    def forward(self, x, t, y=None, **kwargs):
+        """
+        Apply the model to an input batch.
+
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :param y: an [N] Tensor of labels, if class-conditional.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        assert (y is not None) == (
+            self.conf.num_classes is not None
+        ), "must specify y if and only if the model is class-conditional"
+
+        # hs = []
+        hs = [[] for _ in range(len(self.conf.channel_mult))]
+        emb = self.time_embed(timestep_embedding(t, self.time_emb_channels))
+
+        if self.conf.num_classes is not None:
+            raise NotImplementedError()
+            # assert y.shape == (x.shape[0], )
+            # emb = emb + self.label_emb(y)
+
+        # new code supports input_num_blocks != output_num_blocks
+        h = x.type(self.dtype)
+        k = 0
+        for i in range(len(self.input_num_blocks)):
+            for j in range(self.input_num_blocks[i]):
+                h = self.input_blocks[k](h, emb=emb)
+                # print(i, j, h.shape)
+                hs[i].append(h)
+                k += 1
+        assert k == len(self.input_blocks)
+
+        h = self.middle_block(h, emb=emb)
+        k = 0
+        for i in range(len(self.output_num_blocks)):
+            for j in range(self.output_num_blocks[i]):
+                # take the lateral connection from the same layer (in reserve)
+                # until there is no more, use None
+                try:
+                    lateral = hs[-i - 1].pop()
+                    # print(i, j, lateral.shape)
+                except IndexError:
+                    lateral = None
+                    # print(i, j, lateral)
+                h = self.output_blocks[k](h, emb=emb, lateral=lateral)
+                k += 1
+
+        h = h.type(x.dtype)
+        pred = self.out(h)
+        return Return(pred=pred)
+
+
+class Return(NamedTuple):
+    pred: th.Tensor
+
+
+@dataclass
+class BeatGANsEncoderConfig(BaseConfig):
+    image_size: int
+    in_channels: int
+    model_channels: int
+    out_hid_channels: int
+    out_channels: int
+    num_res_blocks: int
+    attention_resolutions: Tuple[int]
+    dropout: float = 0
+    channel_mult: Tuple[int] = (1, 2, 4, 8)
+    use_time_condition: bool = True
+    conv_resample: bool = True
+    group_norm_limit: int = 32
+    dims: int = 2
+    use_checkpoint: bool = False
+    num_heads: int = 1
+    num_head_channels: int = -1
+    resblock_updown: bool = False
+    use_new_attention_order: bool = False
+    pool: str = 'adaptivenonzero'
+
+    def make_model(self):
+        return BeatGANsEncoderModel(self)
+
+
+class BeatGANsEncoderModel(nn.Module):
+    """
+    The half UNet model with attention and timestep embedding.
+
+    For usage, see UNet.
+    """
+    def __init__(self, conf: BeatGANsEncoderConfig):
+        super().__init__()
+        self.conf = conf
+        self.dtype = th.float32
+
+        if conf.use_time_condition:
+            time_embed_dim = conf.model_channels * 4
+            self.time_embed = nn.Sequential(
+                linear(conf.model_channels, time_embed_dim),
+                nn.SiLU(),
+                linear(time_embed_dim, time_embed_dim),
+            )
+        else:
+            time_embed_dim = None
+
+        ch = int(conf.channel_mult[0] * conf.model_channels)
+        self.input_blocks = nn.ModuleList([
+            TimestepEmbedSequential(
+                conv_nd(conf.dims, conf.in_channels, ch, 3, padding=1))
+        ])
+        self._feature_size = ch
+        input_block_chans = [ch]
+        ds = 1
+        resolution = conf.image_size
+        for level, mult in enumerate(conf.channel_mult):
+            for _ in range(conf.num_res_blocks):
+                layers = [
+                    ResBlockConfig(
+                        ch,
+                        time_embed_dim,
+                        conf.dropout,
+                        out_channels=int(mult * conf.model_channels),
+                        group_norm_limit=conf.group_norm_limit,
+                        dims=conf.dims,
+                        use_condition=conf.use_time_condition,
+                        use_checkpoint=conf.use_checkpoint,
+                    ).make_model()
+                ]
+                ch = int(mult * conf.model_channels)
+                if resolution in conf.attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=conf.use_checkpoint,
+                            num_heads=conf.num_heads,
+                            num_head_channels=conf.num_head_channels,
+                            group_norm_limit=conf.group_norm_limit,
+                            use_new_attention_order=conf.
+                            use_new_attention_order,
+                        ))
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(conf.channel_mult) - 1:
+                resolution //= 2
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlockConfig(
+                            ch,
+                            time_embed_dim,
+                            conf.dropout,
+                            out_channels=out_ch,                            
+                            group_norm_limit=conf.group_norm_limit,
+                            dims=conf.dims,
+                            use_condition=conf.use_time_condition,
+                            use_checkpoint=conf.use_checkpoint,
+                            down=True,
+                        ).make_model() if (
+                            conf.resblock_updown
+                        ) else Downsample(ch,
+                                          conf.conv_resample,
+                                          dims=conf.dims,
+                                          out_channels=out_ch)))
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        self.middle_block = TimestepEmbedSequential(
+            ResBlockConfig(
+                ch,
+                time_embed_dim,
+                conf.dropout,
+                group_norm_limit=conf.group_norm_limit,
+                dims=conf.dims,
+                use_condition=conf.use_time_condition,
+                use_checkpoint=conf.use_checkpoint,
+            ).make_model(),
+            AttentionBlock(
+                ch,
+                use_checkpoint=conf.use_checkpoint,
+                num_heads=conf.num_heads,
+                num_head_channels=conf.num_head_channels,
+                group_norm_limit=conf.group_norm_limit,
+                use_new_attention_order=conf.use_new_attention_order,
+            ),
+            ResBlockConfig(
+                ch,
+                time_embed_dim,
+                conf.dropout,
+                group_norm_limit=conf.group_norm_limit,
+                dims=conf.dims,
+                use_condition=conf.use_time_condition,
+                use_checkpoint=conf.use_checkpoint,
+            ).make_model(),
+        )
+        self._feature_size += ch
+        if conf.pool == "adaptivenonzero":
+            self.out = nn.Sequential(
+                normalization(ch, limit=conf.group_norm_limit if "group_norm_limit" in conf.__dict__ else 32),
+                nn.SiLU(),
+                nn.AdaptiveAvgPool2d((1, 1)) if conf.dims == 2 else nn.AdaptiveAvgPool3d((1, 1, 1)),
+                conv_nd(conf.dims, ch, conf.out_channels, 1),
+                nn.Flatten(),
+            )
+        else:
+            raise NotImplementedError(f"Unexpected {conf.pool} pooling")
+
+    def forward(self, x, t=None, return_Nd_feature=False):
+        """
+        Apply the model to an input batch.
+
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :return: an [N x K] Tensor of outputs.
+        """
+        if self.conf.use_time_condition:
+            emb = self.time_embed(timestep_embedding(t, self.model_channels))
+        else:
+            emb = None
+
+        results = []
+        h = x.type(self.dtype)
+        for module in self.input_blocks:
+            h = module(h, emb=emb)
+            if self.conf.pool.startswith("spatial"):
+                results.append(h.type(x.dtype).mean(dim=(2, 3) if self.conf.dims == 2 else (2, 3, 4)))
+        h = self.middle_block(h, emb=emb)
+        if self.conf.pool.startswith("spatial"):
+            results.append(h.type(x.dtype).mean(dim=(2, 3) if self.conf.dims == 2 else (2, 3, 4)))
+            h = th.cat(results, axis=-1)
+        else:
+            h = h.type(x.dtype)
+
+        h_Nd = h
+        h = self.out(h)
+
+        if return_Nd_feature:
+            return h, h_Nd
+        else:
+            return h
+
+    def forward_flatten(self, x):
+        """
+        transform the last Nd feature into a flatten vector
+        """
+        h = self.out(x)
+        return h
+
+
+class SuperResModel(BeatGANsUNetModel):
+    """
+    A UNetModel that performs super-resolution.
+
+    Expects an extra kwarg `low_res` to condition on a low-resolution image.
+    """
+    def __init__(self, image_size, in_channels, *args, **kwargs):
+        super().__init__(image_size, in_channels * 2, *args, **kwargs)
+
+    def forward(self, x, timesteps, low_res=None, **kwargs):
+        _, _, new_height, new_width = x.shape
+        upsampled = F.interpolate(low_res, (new_height, new_width),
+                                  mode="bilinear")
+        x = th.cat([x, upsampled], dim=1)
+        return super().forward(x, timesteps, **kwargs)

DiffAE_model_unet_autoenc.py

@@ -0,0 +1,284 @@
+from enum import Enum
+
+import torch
+from torch import Tensor
+from torch.nn.functional import silu
+
+from .DiffAE_model_latentnet import *
+from .DiffAE_model_unet import *
+from .DiffAE_support_choices import *
+
+
+@dataclass
+class BeatGANsAutoencConfig(BeatGANsUNetConfig):
+    # number of style channels
+    enc_out_channels: int = 512
+    enc_attn_resolutions: Tuple[int] = None
+    enc_pool: str = 'depthconv'
+    enc_num_res_block: int = 2
+    enc_channel_mult: Tuple[int] = None
+    enc_grad_checkpoint: bool = False
+    latent_net_conf: MLPSkipNetConfig = None
+
+    def make_model(self):
+        return BeatGANsAutoencModel(self)
+
+
+class BeatGANsAutoencModel(BeatGANsUNetModel):
+    def __init__(self, conf: BeatGANsAutoencConfig):
+        super().__init__(conf)
+        self.conf = conf
+
+        # having only time, cond
+        self.time_embed = TimeStyleSeperateEmbed(
+            time_channels=conf.model_channels,
+            time_out_channels=conf.embed_channels,
+        )
+
+        self.encoder = BeatGANsEncoderConfig(
+            image_size=conf.image_size,
+            in_channels=conf.in_channels,
+            model_channels=conf.model_channels,
+            out_hid_channels=conf.enc_out_channels,
+            out_channels=conf.enc_out_channels,
+            num_res_blocks=conf.enc_num_res_block,
+            attention_resolutions=(conf.enc_attn_resolutions
+                                   or conf.attention_resolutions),
+            dropout=conf.dropout,
+            channel_mult=conf.enc_channel_mult or conf.channel_mult,
+            use_time_condition=False,
+            conv_resample=conf.conv_resample,
+            group_norm_limit=conf.group_norm_limit,
+            dims=conf.dims,
+            use_checkpoint=conf.use_checkpoint or conf.enc_grad_checkpoint,
+            num_heads=conf.num_heads,
+            num_head_channels=conf.num_head_channels,
+            resblock_updown=conf.resblock_updown,
+            use_new_attention_order=conf.use_new_attention_order,
+            pool=conf.enc_pool,
+        ).make_model()
+
+        if conf.latent_net_conf is not None:
+            self.latent_net = conf.latent_net_conf.make_model()
+
+    def reparameterize(self, mu: Tensor, logvar: Tensor) -> Tensor:
+        """
+        Reparameterization trick to sample from N(mu, var) from
+        N(0,1).
+        :param mu: (Tensor) Mean of the latent Gaussian [B x D]
+        :param logvar: (Tensor) Standard deviation of the latent Gaussian [B x D]
+        :return: (Tensor) [B x D]
+        """
+        assert self.conf.is_stochastic
+        std = torch.exp(0.5 * logvar)
+        eps = torch.randn_like(std)
+        return eps * std + mu
+
+    def sample_z(self, n: int, device):
+        assert self.conf.is_stochastic
+        return torch.randn(n, self.conf.enc_out_channels, device=device)
+
+    def noise_to_cond(self, noise: Tensor):
+        raise NotImplementedError()
+        assert self.conf.noise_net_conf is not None
+        return self.noise_net.forward(noise)
+
+    def encode(self, x):
+        cond = self.encoder.forward(x)
+        return {'cond': cond}
+
+    @property
+    def stylespace_sizes(self):
+        modules = list(self.input_blocks.modules()) + list(
+            self.middle_block.modules()) + list(self.output_blocks.modules())
+        sizes = []
+        for module in modules:
+            if isinstance(module, ResBlock):
+                linear = module.cond_emb_layers[-1]
+                sizes.append(linear.weight.shape[0])
+        return sizes
+
+    def encode_stylespace(self, x, return_vector: bool = True):
+        """
+        encode to style space
+        """
+        modules = list(self.input_blocks.modules()) + list(
+            self.middle_block.modules()) + list(self.output_blocks.modules())
+        # (n, c)
+        cond = self.encoder.forward(x)
+        S = []
+        for module in modules:
+            if isinstance(module, ResBlock):
+                # (n, c')
+                s = module.cond_emb_layers.forward(cond)
+                S.append(s)
+
+        if return_vector:
+            # (n, sum_c)
+            return torch.cat(S, dim=1)
+        else:
+            return S
+
+    def forward(self,
+                x,
+                t,
+                y=None,
+                x_start=None,
+                cond=None,
+                style=None,
+                noise=None,
+                t_cond=None,
+                **kwargs):
+        """
+        Apply the model to an input batch.
+
+        Args:
+            x_start: the original image to encode
+            cond: output of the encoder
+            noise: random noise (to predict the cond)
+        """
+
+        if t_cond is None:
+            t_cond = t
+
+        if noise is not None:
+            # if the noise is given, we predict the cond from noise
+            cond = self.noise_to_cond(noise)
+
+        if cond is None:
+            if x is not None:
+                assert len(x) == len(x_start), f'{len(x)} != {len(x_start)}'
+
+            tmp = self.encode(x_start)
+            cond = tmp['cond']
+
+        if t is not None:
+            _t_emb = timestep_embedding(t, self.conf.model_channels)
+            _t_cond_emb = timestep_embedding(t_cond, self.conf.model_channels)
+        else:
+            # this happens when training only autoenc
+            _t_emb = None
+            _t_cond_emb = None
+
+        if self.conf.resnet_two_cond:
+            res = self.time_embed.forward(
+                time_emb=_t_emb,
+                cond=cond,
+                time_cond_emb=_t_cond_emb,
+            )
+        else:
+            raise NotImplementedError()
+
+        if self.conf.resnet_two_cond:
+            # two cond: first = time emb, second = cond_emb
+            emb = res.time_emb
+            cond_emb = res.emb
+        else:
+            # one cond = combined of both time and cond
+            emb = res.emb
+            cond_emb = None
+
+        # override the style if given
+        style = style or res.style
+
+        assert (y is not None) == (
+            self.conf.num_classes is not None
+        ), "must specify y if and only if the model is class-conditional"
+
+        if self.conf.num_classes is not None:
+            raise NotImplementedError()
+            # assert y.shape == (x.shape[0], )
+            # emb = emb + self.label_emb(y)
+
+        # where in the model to supply time conditions
+        enc_time_emb = emb
+        mid_time_emb = emb
+        dec_time_emb = emb
+        # where in the model to supply style conditions
+        enc_cond_emb = cond_emb
+        mid_cond_emb = cond_emb
+        dec_cond_emb = cond_emb
+
+        # hs = []
+        hs = [[] for _ in range(len(self.conf.channel_mult))]
+
+        if x is not None:
+            h = x.type(self.dtype)
+
+            # input blocks
+            k = 0
+            for i in range(len(self.input_num_blocks)):
+                for j in range(self.input_num_blocks[i]):
+                    h = self.input_blocks[k](h,
+                                             emb=enc_time_emb,
+                                             cond=enc_cond_emb)
+
+                    # print(i, j, h.shape)
+                    hs[i].append(h)
+                    k += 1
+            assert k == len(self.input_blocks)
+
+            # middle blocks
+            h = self.middle_block(h, emb=mid_time_emb, cond=mid_cond_emb)
+        else:
+            # no lateral connections
+            # happens when training only the autonecoder
+            h = None
+            hs = [[] for _ in range(len(self.conf.channel_mult))]
+
+        # output blocks
+        k = 0
+        for i in range(len(self.output_num_blocks)):
+            for j in range(self.output_num_blocks[i]):
+                # take the lateral connection from the same layer (in reserve)
+                # until there is no more, use None
+                try:
+                    lateral = hs[-i - 1].pop()
+                    # print(i, j, lateral.shape)
+                except IndexError:
+                    lateral = None
+                    # print(i, j, lateral)
+
+                h = self.output_blocks[k](h,
+                                          emb=dec_time_emb,
+                                          cond=dec_cond_emb,
+                                          lateral=lateral)
+                k += 1
+
+        pred = self.out(h)
+        return AutoencReturn(pred=pred, cond=cond)
+
+
+class AutoencReturn(NamedTuple):
+    pred: Tensor
+    cond: Tensor = None
+
+
+class EmbedReturn(NamedTuple):
+    # style and time
+    emb: Tensor = None
+    # time only
+    time_emb: Tensor = None
+    # style only (but could depend on time)
+    style: Tensor = None
+
+
+class TimeStyleSeperateEmbed(nn.Module):
+    # embed only style
+    def __init__(self, time_channels, time_out_channels):
+        super().__init__()
+        self.time_embed = nn.Sequential(
+            linear(time_channels, time_out_channels),
+            nn.SiLU(),
+            linear(time_out_channels, time_out_channels),
+        )
+        self.style = nn.Identity()
+
+    def forward(self, time_emb=None, cond=None, **kwargs):
+        if time_emb is None:
+            # happens with autoenc training mode
+            time_emb = None
+        else:
+            time_emb = self.time_embed(time_emb)
+        style = self.style(cond)
+        return EmbedReturn(emb=style, time_emb=time_emb, style=style)

DiffAE_support.py

@@ -0,0 +1,9 @@
+from .DiffAE_support_choices import *
+from .DiffAE_support_config_base import *
+from .DiffAE_support_config import *
+from .DiffAE_support_dist_utils import *
+from .DiffAE_support_metrics import *
+from .DiffAE_support_renderer import *
+from .DiffAE_support_templates_latent import *
+from .DiffAE_support_templates import *
+from .DiffAE_support_utils import *

DiffAE_support_choices.py

@@ -0,0 +1,179 @@
+from enum import Enum
+from torch import nn
+
+
+class TrainMode(Enum):
+    # manipulate mode = training the classifier
+    manipulate = 'manipulate'
+    # default trainin mode!
+    diffusion = 'diffusion'
+    # default latent training mode!
+    # fitting the a DDPM to a given latent
+    latent_diffusion = 'latentdiffusion'
+
+    def is_manipulate(self):
+        return self in [
+            TrainMode.manipulate,
+        ]
+
+    def is_diffusion(self):
+        return self in [
+            TrainMode.diffusion,
+            TrainMode.latent_diffusion,
+        ]
+
+    def is_autoenc(self):
+        # the network possibly does autoencoding
+        return self in [
+            TrainMode.diffusion,
+        ]
+
+    def is_latent_diffusion(self):
+        return self in [
+            TrainMode.latent_diffusion,
+        ]
+
+    def use_latent_net(self):
+        return self.is_latent_diffusion()
+
+    def require_dataset_infer(self):
+        """
+        whether training in this mode requires the latent variables to be available?
+        """
+        # this will precalculate all the latents before hand
+        # and the dataset will be all the predicted latents
+        return self in [
+            TrainMode.latent_diffusion,
+            TrainMode.manipulate,
+        ]
+
+
+class ManipulateMode(Enum):
+    """
+    how to train the classifier to manipulate
+    """
+    # train on whole celeba attr dataset
+    celebahq_all = 'celebahq_all'
+    # celeba with D2C's crop
+    d2c_fewshot = 'd2cfewshot'
+    d2c_fewshot_allneg = 'd2cfewshotallneg'
+
+    def is_celeba_attr(self):
+        return self in [
+            ManipulateMode.d2c_fewshot,
+            ManipulateMode.d2c_fewshot_allneg,
+            ManipulateMode.celebahq_all,
+        ]
+
+    def is_single_class(self):
+        return self in [
+            ManipulateMode.d2c_fewshot,
+            ManipulateMode.d2c_fewshot_allneg,
+        ]
+
+    def is_fewshot(self):
+        return self in [
+            ManipulateMode.d2c_fewshot,
+            ManipulateMode.d2c_fewshot_allneg,
+        ]
+
+    def is_fewshot_allneg(self):
+        return self in [
+            ManipulateMode.d2c_fewshot_allneg,
+        ]
+
+
+class ModelType(Enum):
+    """
+    Kinds of the backbone models
+    """
+
+    # unconditional ddpm
+    ddpm = 'ddpm'
+    # autoencoding ddpm cannot do unconditional generation
+    autoencoder = 'autoencoder'
+
+    def has_autoenc(self):
+        return self in [
+            ModelType.autoencoder,
+        ]
+
+    def can_sample(self):
+        return self in [ModelType.ddpm]
+
+
+class ModelName(Enum):
+    """
+    List of all supported model classes
+    """
+
+    beatgans_ddpm = 'beatgans_ddpm'
+    beatgans_autoenc = 'beatgans_autoenc'
+
+
+class ModelMeanType(Enum):
+    """
+    Which type of output the model predicts.
+    """
+
+    eps = 'eps'  # the model predicts epsilon
+
+
+class ModelVarType(Enum):
+    """
+    What is used as the model's output variance.
+
+    The LEARNED_RANGE option has been added to allow the model to predict
+    values between FIXED_SMALL and FIXED_LARGE, making its job easier.
+    """
+
+    # posterior beta_t
+    fixed_small = 'fixed_small'
+    # beta_t
+    fixed_large = 'fixed_large'
+
+
+class LossType(Enum):
+    mse = 'mse'  # use raw MSE loss (and KL when learning variances)
+    l1 = 'l1'
+
+
+class GenerativeType(Enum):
+    """
+    How's a sample generated
+    """
+
+    ddpm = 'ddpm'
+    ddim = 'ddim'
+
+
+class OptimizerType(Enum):
+    adam = 'adam'
+    adamw = 'adamw'
+
+
+class Activation(Enum):
+    none = 'none'
+    relu = 'relu'
+    lrelu = 'lrelu'
+    silu = 'silu'
+    tanh = 'tanh'
+
+    def get_act(self):
+        if self == Activation.none:
+            return nn.Identity()
+        elif self == Activation.relu:
+            return nn.ReLU()
+        elif self == Activation.lrelu:
+            return nn.LeakyReLU(negative_slope=0.2)
+        elif self == Activation.silu:
+            return nn.SiLU()
+        elif self == Activation.tanh:
+            return nn.Tanh()
+        else:
+            raise NotImplementedError()
+
+
+class ManipulateLossType(Enum):
+    bce = 'bce'
+    mse = 'mse'

DiffAE_support_config.py

@@ -0,0 +1,438 @@
+from .DiffAE_model_blocks import ScaleAt
+from .DiffAE_model import *
+from .DiffAE_diffusion_resample import UniformSampler
+from .DiffAE_diffusion_diffusion import space_timesteps
+from typing import Tuple
+
+from torch.utils.data import DataLoader
+
+from .DiffAE_support_config_base import BaseConfig
+from .DiffAE_support_choices import GenerativeType, LossType, ModelMeanType, ModelVarType
+from .DiffAE_diffusion_base import get_named_beta_schedule
+from .DiffAE_support_choices import *
+from .DiffAE_diffusion_diffusion import SpacedDiffusionBeatGansConfig
+from multiprocessing import get_context
+import os
+from torch.utils.data.distributed import DistributedSampler
+
+from dataclasses import dataclass
+
+data_paths = {
+    'ffhqlmdb256':
+    os.path.expanduser('datasets/ffhq256.lmdb'),
+    # used for training a classifier
+    'celeba':
+    os.path.expanduser('datasets/celeba'),
+    # used for training DPM models
+    'celebalmdb':
+    os.path.expanduser('datasets/celeba.lmdb'),
+    'celebahq':
+    os.path.expanduser('datasets/celebahq256.lmdb'),
+    'horse256':
+    os.path.expanduser('datasets/horse256.lmdb'),
+    'bedroom256':
+    os.path.expanduser('datasets/bedroom256.lmdb'),
+    'celeba_anno':
+    os.path.expanduser('datasets/celeba_anno/list_attr_celeba.txt'),
+    'celebahq_anno':
+    os.path.expanduser(
+        'datasets/celeba_anno/CelebAMask-HQ-attribute-anno.txt'),
+    'celeba_relight':
+    os.path.expanduser('datasets/celeba_hq_light/celeba_light.txt'),
+}
+
+
+@dataclass
+class PretrainConfig(BaseConfig):
+    name: str
+    path: str
+
+
+@dataclass
+class TrainConfig(BaseConfig):
+    #new params added (Soumick)
+    n_dims: int = 2
+    in_channels: int = 3
+    out_channels: int = 3
+    group_norm_limit: int = 32
+
+    # random seed
+    seed: int = 0
+    train_mode: TrainMode = TrainMode.diffusion
+    train_cond0_prob: float = 0
+    train_pred_xstart_detach: bool = True
+    train_interpolate_prob: float = 0
+    train_interpolate_img: bool = False
+    manipulate_mode: ManipulateMode = ManipulateMode.celebahq_all
+    manipulate_cls: str = None
+    manipulate_shots: int = None
+    manipulate_loss: ManipulateLossType = ManipulateLossType.bce
+    manipulate_znormalize: bool = False
+    manipulate_seed: int = 0
+    accum_batches: int = 1
+    autoenc_mid_attn: bool = True
+    batch_size: int = 16
+    batch_size_eval: int = None
+    beatgans_gen_type: GenerativeType = GenerativeType.ddim
+    beatgans_loss_type: LossType = LossType.mse
+    beatgans_model_mean_type: ModelMeanType = ModelMeanType.eps
+    beatgans_model_var_type: ModelVarType = ModelVarType.fixed_large
+    beatgans_rescale_timesteps: bool = False
+    latent_infer_path: str = None
+    latent_znormalize: bool = False
+    latent_gen_type: GenerativeType = GenerativeType.ddim
+    latent_loss_type: LossType = LossType.mse
+    latent_model_mean_type: ModelMeanType = ModelMeanType.eps
+    latent_model_var_type: ModelVarType = ModelVarType.fixed_large
+    latent_rescale_timesteps: bool = False
+    latent_T_eval: int = 1_000
+    latent_clip_sample: bool = False
+    latent_beta_scheduler: str = 'linear'
+    beta_scheduler: str = 'linear'
+    data_name: str = ''
+    data_val_name: str = None
+    diffusion_type: str = None
+    dropout: float = 0.1
+    ema_decay: float = 0.9999
+    eval_num_images: int = 5_000
+    eval_every_samples: int = 200_000
+    eval_ema_every_samples: int = 200_000
+    fid_use_torch: bool = True
+    fp16: bool = False
+    grad_clip: float = 1
+    img_size: int = 64
+    lr: float = 0.0001
+    optimizer: OptimizerType = OptimizerType.adam
+    weight_decay: float = 0
+    model_conf: ModelConfig = None
+    model_name: ModelName = None
+    model_type: ModelType = None
+    net_attn: Tuple[int] = None
+    net_beatgans_attn_head: int = 1
+    # not necessarily the same as the the number of style channels
+    net_beatgans_embed_channels: int = 512
+    net_resblock_updown: bool = True
+    net_enc_use_time: bool = False
+    net_enc_pool: str = 'adaptivenonzero'
+    net_beatgans_gradient_checkpoint: bool = False
+    net_beatgans_resnet_two_cond: bool = False
+    net_beatgans_resnet_use_zero_module: bool = True
+    net_beatgans_resnet_scale_at: ScaleAt = ScaleAt.after_norm
+    net_beatgans_resnet_cond_channels: int = None
+    net_ch_mult: Tuple[int] = None
+    net_ch: int = 64
+    net_enc_attn: Tuple[int] = None
+    net_enc_k: int = None
+    # number of resblocks for the encoder (half-unet)
+    net_enc_num_res_blocks: int = 2
+    net_enc_channel_mult: Tuple[int] = None
+    net_enc_grad_checkpoint: bool = False
+    net_autoenc_stochastic: bool = False
+    net_latent_activation: Activation = Activation.silu
+    net_latent_channel_mult: Tuple[int] = (1, 2, 4)
+    net_latent_condition_bias: float = 0
+    net_latent_dropout: float = 0
+    net_latent_layers: int = None
+    net_latent_net_last_act: Activation = Activation.none
+    net_latent_net_type: LatentNetType = LatentNetType.none
+    net_latent_num_hid_channels: int = 1024
+    net_latent_num_time_layers: int = 2
+    net_latent_skip_layers: Tuple[int] = None
+    net_latent_time_emb_channels: int = 64
+    net_latent_use_norm: bool = False
+    net_latent_time_last_act: bool = False
+    net_num_res_blocks: int = 2
+    # number of resblocks for the UNET
+    net_num_input_res_blocks: int = None
+    net_enc_num_cls: int = None
+    num_workers: int = 4
+    parallel: bool = False
+    postfix: str = ''
+    sample_size: int = 64
+    sample_every_samples: int = 20_000
+    save_every_samples: int = 100_000
+    style_ch: int = 512
+    T_eval: int = 1_000
+    T_sampler: str = 'uniform'
+    T: int = 1_000
+    total_samples: int = 10_000_000
+    warmup: int = 0
+    pretrain: PretrainConfig = None
+    continue_from: PretrainConfig = None
+    eval_programs: Tuple[str] = None
+    # if present load the checkpoint from this path instead
+    eval_path: str = None
+    base_dir: str = 'checkpoints'
+    use_cache_dataset: bool = False
+    data_cache_dir: str = os.path.expanduser('~/cache')
+    work_cache_dir: str = os.path.expanduser('~/mycache')
+    # to be overridden
+    name: str = ''
+
+    def refresh_values(self):
+        self.img_size = max(self.input_shape)
+        self.n_dims = 3 if self.is3D else 2
+        self.group_norm_limit = min(32, self.net_ch)
+
+    def __post_init__(self):
+        self.batch_size_eval = self.batch_size_eval or self.batch_size
+        self.data_val_name = self.data_val_name or self.data_name
+
+    def scale_up_gpus(self, num_gpus, num_nodes=1):
+        self.eval_ema_every_samples *= num_gpus * num_nodes
+        self.eval_every_samples *= num_gpus * num_nodes
+        self.sample_every_samples *= num_gpus * num_nodes
+        self.batch_size *= num_gpus * num_nodes
+        self.batch_size_eval *= num_gpus * num_nodes
+        return self
+
+    @property
+    def batch_size_effective(self):
+        return self.batch_size * self.accum_batches
+
+    @property
+    def fid_cache(self):
+        # we try to use the local dirs to reduce the load over network drives
+        # hopefully, this would reduce the disconnection problems with sshfs
+        return f'{self.work_cache_dir}/eval_images/{self.data_name}_size{self.img_size}_{self.eval_num_images}'
+
+    @property
+    def data_path(self):
+        # may use the cache dir
+        path = data_paths[self.data_name]
+        if self.use_cache_dataset and path is not None:
+            path = use_cached_dataset_path(
+                path, f'{self.data_cache_dir}/{self.data_name}')
+        return path
+
+    @property
+    def logdir(self):
+        return f'{self.base_dir}/{self.name}'
+
+    @property
+    def generate_dir(self):
+        # we try to use the local dirs to reduce the load over network drives
+        # hopefully, this would reduce the disconnection problems with sshfs
+        return f'{self.work_cache_dir}/gen_images/{self.name}'
+
+    def _make_diffusion_conf(self, T=None):
+        if self.diffusion_type == 'beatgans':
+            # can use T < self.T for evaluation
+            # follows the guided-diffusion repo conventions
+            # t's are evenly spaced
+            if self.beatgans_gen_type == GenerativeType.ddpm:
+                section_counts = [T]
+            elif self.beatgans_gen_type == GenerativeType.ddim:
+                section_counts = f'ddim{T}'
+            else:
+                raise NotImplementedError()
+
+            return SpacedDiffusionBeatGansConfig(
+                gen_type=self.beatgans_gen_type,
+                model_type=self.model_type,
+                betas=get_named_beta_schedule(self.beta_scheduler, self.T),
+                model_mean_type=self.beatgans_model_mean_type,
+                model_var_type=self.beatgans_model_var_type,
+                loss_type=self.beatgans_loss_type,
+                rescale_timesteps=self.beatgans_rescale_timesteps,
+                use_timesteps=space_timesteps(num_timesteps=self.T,
+                                              section_counts=section_counts),
+                fp16=self.fp16,
+            )
+        else:
+            raise NotImplementedError()
+
+    def _make_latent_diffusion_conf(self, T=None):
+        # can use T < self.T for evaluation
+        # follows the guided-diffusion repo conventions
+        # t's are evenly spaced
+        if self.latent_gen_type == GenerativeType.ddpm:
+            section_counts = [T]
+        elif self.latent_gen_type == GenerativeType.ddim:
+            section_counts = f'ddim{T}'
+        else:
+            raise NotImplementedError()
+
+        return SpacedDiffusionBeatGansConfig(
+            train_pred_xstart_detach=self.train_pred_xstart_detach,
+            gen_type=self.latent_gen_type,
+            # latent's model is always ddpm
+            model_type=ModelType.ddpm,
+            # latent shares the beta scheduler and full T
+            betas=get_named_beta_schedule(self.latent_beta_scheduler, self.T),
+            model_mean_type=self.latent_model_mean_type,
+            model_var_type=self.latent_model_var_type,
+            loss_type=self.latent_loss_type,
+            rescale_timesteps=self.latent_rescale_timesteps,
+            use_timesteps=space_timesteps(num_timesteps=self.T,
+                                          section_counts=section_counts),
+            fp16=self.fp16,
+        )
+
+    @property
+    def model_out_channels(self):
+        return self.out_channels
+
+    def make_T_sampler(self):
+        if self.T_sampler == 'uniform':
+            return UniformSampler(self.T)
+        else:
+            raise NotImplementedError()
+
+    def make_diffusion_conf(self):
+        return self._make_diffusion_conf(self.T)
+
+    def make_eval_diffusion_conf(self):
+        return self._make_diffusion_conf(T=self.T_eval)
+
+    def make_latent_diffusion_conf(self):
+        return self._make_latent_diffusion_conf(T=self.T)
+
+    def make_latent_eval_diffusion_conf(self):
+        # latent can have different eval T
+        return self._make_latent_diffusion_conf(T=self.latent_T_eval)
+
+    def make_dataset(self, path=None, **kwargs):
+        if self.data_name == 'ffhqlmdb256':
+            return FFHQlmdb(path=path or self.data_path,
+                            image_size=self.img_size,
+                            **kwargs)
+        elif self.data_name == 'horse256':
+            return Horse_lmdb(path=path or self.data_path,
+                              image_size=self.img_size,
+                              **kwargs)
+        elif self.data_name == 'bedroom256':
+            return Horse_lmdb(path=path or self.data_path,
+                              image_size=self.img_size,
+                              **kwargs)
+        elif self.data_name == 'celebalmdb':
+            # always use d2c crop
+            return CelebAlmdb(path=path or self.data_path,
+                              image_size=self.img_size,
+                              original_resolution=None,
+                              crop_d2c=True,
+                              **kwargs)
+        else:
+            raise NotImplementedError()
+
+    def make_loader(self,
+                    dataset,
+                    shuffle: bool,
+                    num_worker: bool = None,
+                    drop_last: bool = True,
+                    batch_size: int = None,
+                    parallel: bool = False):
+        if parallel and distributed.is_initialized():
+            # drop last to make sure that there is no added special indexes
+            sampler = DistributedSampler(dataset,
+                                         shuffle=shuffle,
+                                         drop_last=True)
+        else:
+            sampler = None
+        return DataLoader(
+            dataset,
+            batch_size=batch_size or self.batch_size,
+            sampler=sampler,
+            # with sampler, use the sample instead of this option
+            shuffle=False if sampler else shuffle,
+            num_workers=num_worker or self.num_workers,
+            pin_memory=True,
+            drop_last=drop_last,
+            multiprocessing_context=get_context('fork'),
+        )
+
+    def make_model_conf(self):
+        if self.model_name == ModelName.beatgans_ddpm:
+            self.model_type = ModelType.ddpm
+            self.model_conf = BeatGANsUNetConfig(
+                attention_resolutions=self.net_attn,
+                channel_mult=self.net_ch_mult,
+                conv_resample=True,
+                group_norm_limit=self.group_norm_limit,
+                dims=self.n_dims,
+                dropout=self.dropout,
+                embed_channels=self.net_beatgans_embed_channels,
+                image_size=self.img_size,
+                in_channels=self.in_channels,
+                model_channels=self.net_ch,
+                num_classes=None,
+                num_head_channels=-1,
+                num_heads_upsample=-1,
+                num_heads=self.net_beatgans_attn_head,
+                num_res_blocks=self.net_num_res_blocks,
+                num_input_res_blocks=self.net_num_input_res_blocks,
+                out_channels=self.model_out_channels,
+                resblock_updown=self.net_resblock_updown,
+                use_checkpoint=self.net_beatgans_gradient_checkpoint,
+                use_new_attention_order=False,
+                resnet_two_cond=self.net_beatgans_resnet_two_cond,
+                resnet_use_zero_module=self.
+                net_beatgans_resnet_use_zero_module,
+            )
+        elif self.model_name in [
+                ModelName.beatgans_autoenc,
+        ]:
+            cls = BeatGANsAutoencConfig
+            # supports both autoenc and vaeddpm
+            if self.model_name == ModelName.beatgans_autoenc:
+                self.model_type = ModelType.autoencoder
+            else:
+                raise NotImplementedError()
+
+            if self.net_latent_net_type == LatentNetType.none:
+                latent_net_conf = None
+            elif self.net_latent_net_type == LatentNetType.skip:
+                latent_net_conf = MLPSkipNetConfig(
+                    num_channels=self.style_ch,
+                    skip_layers=self.net_latent_skip_layers,
+                    num_hid_channels=self.net_latent_num_hid_channels,
+                    num_layers=self.net_latent_layers,
+                    num_time_emb_channels=self.net_latent_time_emb_channels,
+                    activation=self.net_latent_activation,
+                    use_norm=self.net_latent_use_norm,
+                    condition_bias=self.net_latent_condition_bias,
+                    dropout=self.net_latent_dropout,
+                    last_act=self.net_latent_net_last_act,
+                    num_time_layers=self.net_latent_num_time_layers,
+                    time_last_act=self.net_latent_time_last_act,
+                )
+            else:
+                raise NotImplementedError()
+
+            self.model_conf = cls(
+                attention_resolutions=self.net_attn,
+                channel_mult=self.net_ch_mult,
+                conv_resample=True,
+                group_norm_limit=self.group_norm_limit,
+                dims=self.n_dims,
+                dropout=self.dropout,
+                embed_channels=self.net_beatgans_embed_channels,
+                enc_out_channels=self.style_ch,
+                enc_pool=self.net_enc_pool,
+                enc_num_res_block=self.net_enc_num_res_blocks,
+                enc_channel_mult=self.net_enc_channel_mult,
+                enc_grad_checkpoint=self.net_enc_grad_checkpoint,
+                enc_attn_resolutions=self.net_enc_attn,
+                image_size=self.img_size,
+                in_channels=self.in_channels,
+                model_channels=self.net_ch,
+                num_classes=None,
+                num_head_channels=-1,
+                num_heads_upsample=-1,
+                num_heads=self.net_beatgans_attn_head,
+                num_res_blocks=self.net_num_res_blocks,
+                num_input_res_blocks=self.net_num_input_res_blocks,
+                out_channels=self.model_out_channels,
+                resblock_updown=self.net_resblock_updown,
+                use_checkpoint=self.net_beatgans_gradient_checkpoint,
+                use_new_attention_order=False,
+                resnet_two_cond=self.net_beatgans_resnet_two_cond,
+                resnet_use_zero_module=self.
+                net_beatgans_resnet_use_zero_module,
+                latent_net_conf=latent_net_conf,
+                resnet_cond_channels=self.net_beatgans_resnet_cond_channels,
+            )
+        else:
+            raise NotImplementedError(self.model_name)
+
+        return self.model_conf

DiffAE_support_config_base.py

@@ -0,0 +1,72 @@
+import json
+import os
+from copy import deepcopy
+from dataclasses import dataclass
+
+
+@dataclass
+class BaseConfig:
+    def clone(self):
+        return deepcopy(self)
+
+    def inherit(self, another):
+        """inherit common keys from a given config"""
+        common_keys = set(self.__dict__.keys()) & set(another.__dict__.keys())
+        for k in common_keys:
+            setattr(self, k, getattr(another, k))
+
+    def propagate(self):
+        """push down the configuration to all members"""
+        for k, v in self.__dict__.items():
+            if isinstance(v, BaseConfig):
+                v.inherit(self)
+                v.propagate()
+
+    def save(self, save_path):
+        """save config to json file"""
+        dirname = os.path.dirname(save_path)
+        if not os.path.exists(dirname):
+            os.makedirs(dirname)
+        conf = self.as_dict_jsonable()
+        with open(save_path, 'w') as f:
+            json.dump(conf, f)
+
+    def load(self, load_path):
+        """load json config"""
+        with open(load_path) as f:
+            conf = json.load(f)
+        self.from_dict(conf)
+
+    def from_dict(self, dict, strict=False):
+        for k, v in dict.items():
+            if not hasattr(self, k):
+                if strict:
+                    raise ValueError(f"loading extra '{k}'")
+                else:
+                    print(f"loading extra '{k}'")
+                    continue
+            if isinstance(self.__dict__[k], BaseConfig):
+                self.__dict__[k].from_dict(v)
+            else:
+                self.__dict__[k] = v
+
+    def as_dict_jsonable(self):
+        conf = {}
+        for k, v in self.__dict__.items():
+            if isinstance(v, BaseConfig):
+                conf[k] = v.as_dict_jsonable()
+            else:
+                if jsonable(v):
+                    conf[k] = v
+                else:
+                    # ignore not jsonable
+                    pass
+        return conf
+
+
+def jsonable(x):
+    try:
+        json.dumps(x)
+        return True
+    except TypeError:
+        return False

DiffAE_support_dist_utils.py

@@ -0,0 +1,42 @@
+from typing import List
+from torch import distributed
+
+
+def barrier():
+    if distributed.is_initialized():
+        distributed.barrier()
+    else:
+        pass
+
+
+def broadcast(data, src):
+    if distributed.is_initialized():
+        distributed.broadcast(data, src)
+    else:
+        pass
+
+
+def all_gather(data: List, src):
+    if distributed.is_initialized():
+        distributed.all_gather(data, src)
+    else:
+        data[0] = src
+
+
+def get_rank():
+    if distributed.is_initialized():
+        return distributed.get_rank()
+    else:
+        return 0
+
+
+def get_world_size():
+    if distributed.is_initialized():
+        return distributed.get_world_size()
+    else:
+        return 1
+
+
+def chunk_size(size, rank, world_size):
+    extra = rank < size % world_size
+    return size // world_size + extra

DiffAE_support_metrics.py

@@ -0,0 +1,357 @@
+import os
+import shutil
+
+import torch
+import torchvision
+from pytorch_fid import fid_score
+from torch import distributed
+from torch.utils.data import DataLoader
+from torch.utils.data.distributed import DistributedSampler
+from tqdm.autonotebook import tqdm, trange
+
+from .DiffAE_support_renderer import *
+from .DiffAE_support_config import *
+from .DiffAE_diffusion_diffusion import SpacedDiffusionBeatGans as Sampler
+import lpips
+from ssim import compute_ssim as ssim
+
+
+def make_subset_loader(conf: TrainConfig,
+                       dataset,
+                       batch_size: int,
+                       shuffle: bool,
+                       parallel: bool,
+                       drop_last=True):
+    dataset = SubsetDataset(dataset, size=conf.eval_num_images)
+    if parallel and distributed.is_initialized():
+        sampler = DistributedSampler(dataset, shuffle=shuffle)
+    else:
+        sampler = None
+    return DataLoader(
+        dataset,
+        batch_size=batch_size,
+        sampler=sampler,
+        # with sampler, use the sample instead of this option
+        shuffle=False if sampler else shuffle,
+        num_workers=conf.num_workers,
+        pin_memory=True,
+        drop_last=drop_last,
+        multiprocessing_context=get_context('fork'),
+    )
+
+
+def evaluate_lpips(
+    sampler: Sampler,
+    model: Model,
+    conf: TrainConfig,
+    device,
+    val_data,
+    latent_sampler: Sampler = None,
+    use_inverted_noise: bool = False,
+):
+    """
+    compare the generated images from autoencoder on validation dataset
+
+    Args:
+        use_inversed_noise: the noise is also inverted from DDIM
+    """
+    lpips_fn = lpips.LPIPS(net='alex').to(device)
+    val_loader = make_subset_loader(conf,
+                                    dataset=val_data,
+                                    batch_size=conf.batch_size_eval,
+                                    shuffle=False,
+                                    parallel=True)
+
+    model.eval()
+    with torch.no_grad():
+        scores = {
+            'lpips': [],
+            'mse': [],
+            'ssim': [],
+            'psnr': [],
+        }
+        for batch in tqdm(val_loader, desc='lpips'):
+            imgs = batch['img'].to(device)
+
+            if use_inverted_noise:
+                # inverse the noise
+                # with condition from the encoder
+                model_kwargs = {}
+                if conf.model_type.has_autoenc():
+                    with torch.no_grad():
+                        model_kwargs = model.encode(imgs)
+                x_T = sampler.ddim_reverse_sample_loop(
+                    model=model,
+                    x=imgs,
+                    clip_denoised=True,
+                    model_kwargs=model_kwargs)
+                x_T = x_T['sample']
+            else:
+                x_T = torch.randn((len(imgs), 3, conf.img_size, conf.img_size),
+                                  device=device)
+
+            if conf.model_type == ModelType.ddpm:
+                # the case where you want to calculate the inversion capability of the DDIM model
+                assert use_inverted_noise
+                pred_imgs = render_uncondition(
+                    conf=conf,
+                    model=model,
+                    x_T=x_T,
+                    sampler=sampler,
+                    latent_sampler=latent_sampler,
+                )
+            else:
+                pred_imgs = render_condition(conf=conf,
+                                             model=model,
+                                             x_T=x_T,
+                                             x_start=imgs,
+                                             cond=None,
+                                             sampler=sampler)
+            # # returns {'cond', 'cond2'}
+            # conds = model.encode(imgs)
+            # pred_imgs = sampler.sample(model=model,
+            #                            noise=x_T,
+            #                            model_kwargs=conds)
+
+            # (n, 1, 1, 1) => (n, )
+            scores['lpips'].append(lpips_fn.forward(imgs, pred_imgs).view(-1))
+
+            # need to normalize into [0, 1]
+            norm_imgs = (imgs + 1) / 2
+            norm_pred_imgs = (pred_imgs + 1) / 2
+            # (n, )
+            scores['ssim'].append(
+                ssim(norm_imgs, norm_pred_imgs, size_average=False))
+            # (n, )
+            scores['mse'].append(
+                (norm_imgs - norm_pred_imgs).pow(2).mean(dim=[1, 2, 3]))
+            # (n, )
+            scores['psnr'].append(psnr(norm_imgs, norm_pred_imgs))
+        # (N, )
+        for key in scores.keys():
+            scores[key] = torch.cat(scores[key]).float()
+    model.train()
+
+    barrier()
+
+    # support multi-gpu
+    outs = {
+        key: [
+            torch.zeros(len(scores[key]), device=device)
+            for i in range(get_world_size())
+        ]
+        for key in scores.keys()
+    }
+    for key in scores.keys():
+        all_gather(outs[key], scores[key])
+
+    # final scores
+    for key in scores.keys():
+        scores[key] = torch.cat(outs[key]).mean().item()
+
+    # {'lpips', 'mse', 'ssim'}
+    return scores
+
+
+def psnr(img1, img2):
+    """
+    Args:
+        img1: (n, c, h, w)
+    """
+    v_max = 1.
+    # (n,)
+    mse = torch.mean((img1 - img2)**2, dim=[1, 2, 3])
+    return 20 * torch.log10(v_max / torch.sqrt(mse))
+
+
+def evaluate_fid(
+    sampler: Sampler,
+    model: Model,
+    conf: TrainConfig,
+    device,
+    train_data,
+    val_data,
+    latent_sampler: Sampler = None,
+    conds_mean=None,
+    conds_std=None,
+    remove_cache: bool = True,
+    clip_latent_noise: bool = False,
+):
+    assert conf.fid_cache is not None
+    if get_rank() == 0:
+        # no parallel
+        # validation data for a comparing FID
+        val_loader = make_subset_loader(conf,
+                                        dataset=val_data,
+                                        batch_size=conf.batch_size_eval,
+                                        shuffle=False,
+                                        parallel=False)
+
+        # put the val images to a directory
+        cache_dir = f'{conf.fid_cache}_{conf.eval_num_images}'
+        if (os.path.exists(cache_dir)
+                and len(os.listdir(cache_dir)) < conf.eval_num_images):
+            shutil.rmtree(cache_dir)
+
+        if not os.path.exists(cache_dir):
+            # write files to the cache
+            # the images are normalized, hence need to denormalize first
+            loader_to_path(val_loader, cache_dir, denormalize=True)
+
+        # create the generate dir
+        if os.path.exists(conf.generate_dir):
+            shutil.rmtree(conf.generate_dir)
+        os.makedirs(conf.generate_dir)
+
+    barrier()
+
+    world_size = get_world_size()
+    rank = get_rank()
+    batch_size = chunk_size(conf.batch_size_eval, rank, world_size)
+
+    def filename(idx):
+        return world_size * idx + rank
+
+    model.eval()
+    with torch.no_grad():
+        if conf.model_type.can_sample():
+            eval_num_images = chunk_size(conf.eval_num_images, rank,
+                                         world_size)
+            desc = "generating images"
+            for i in trange(0, eval_num_images, batch_size, desc=desc):
+                batch_size = min(batch_size, eval_num_images - i)
+                x_T = torch.randn(
+                    (batch_size, 3, conf.img_size, conf.img_size),
+                    device=device)
+                batch_images = render_uncondition(
+                    conf=conf,
+                    model=model,
+                    x_T=x_T,
+                    sampler=sampler,
+                    latent_sampler=latent_sampler,
+                    conds_mean=conds_mean,
+                    conds_std=conds_std).cpu()
+
+                batch_images = (batch_images + 1) / 2
+                # keep the generated images
+                for j in range(len(batch_images)):
+                    img_name = filename(i + j)
+                    torchvision.utils.save_image(
+                        batch_images[j],
+                        os.path.join(conf.generate_dir, f'{img_name}.png'))
+        elif conf.model_type == ModelType.autoencoder:
+            if conf.train_mode.is_latent_diffusion():
+                # evaluate autoencoder + latent diffusion (doesn't give the images)
+                model: BeatGANsAutoencModel
+                eval_num_images = chunk_size(conf.eval_num_images, rank,
+                                             world_size)
+                desc = "generating images"
+                for i in trange(0, eval_num_images, batch_size, desc=desc):
+                    batch_size = min(batch_size, eval_num_images - i)
+                    x_T = torch.randn(
+                        (batch_size, 3, conf.img_size, conf.img_size),
+                        device=device)
+                    batch_images = render_uncondition(
+                        conf=conf,
+                        model=model,
+                        x_T=x_T,
+                        sampler=sampler,
+                        latent_sampler=latent_sampler,
+                        conds_mean=conds_mean,
+                        conds_std=conds_std,
+                        clip_latent_noise=clip_latent_noise,
+                    ).cpu()
+                    batch_images = (batch_images + 1) / 2
+                    # keep the generated images
+                    for j in range(len(batch_images)):
+                        img_name = filename(i + j)
+                        torchvision.utils.save_image(
+                            batch_images[j],
+                            os.path.join(conf.generate_dir, f'{img_name}.png'))
+            else:
+                # evaulate autoencoder (given the images)
+                # to make the FID fair, autoencoder must not see the validation dataset
+                # also shuffle to make it closer to unconditional generation
+                train_loader = make_subset_loader(conf,
+                                                  dataset=train_data,
+                                                  batch_size=batch_size,
+                                                  shuffle=True,
+                                                  parallel=True)
+
+                i = 0
+                for batch in tqdm(train_loader, desc='generating images'):
+                    imgs = batch['img'].to(device)
+                    x_T = torch.randn(
+                        (len(imgs), 3, conf.img_size, conf.img_size),
+                        device=device)
+                    batch_images = render_condition(
+                        conf=conf,
+                        model=model,
+                        x_T=x_T,
+                        x_start=imgs,
+                        cond=None,
+                        sampler=sampler,
+                        latent_sampler=latent_sampler).cpu()
+                    # model: BeatGANsAutoencModel
+                    # # returns {'cond', 'cond2'}
+                    # conds = model.encode(imgs)
+                    # batch_images = sampler.sample(model=model,
+                    #                               noise=x_T,
+                    #                               model_kwargs=conds).cpu()
+                    # denormalize the images
+                    batch_images = (batch_images + 1) / 2
+                    # keep the generated images
+                    for j in range(len(batch_images)):
+                        img_name = filename(i + j)
+                        torchvision.utils.save_image(
+                            batch_images[j],
+                            os.path.join(conf.generate_dir, f'{img_name}.png'))
+                    i += len(imgs)
+        else:
+            raise NotImplementedError()
+    model.train()
+
+    barrier()
+
+    if get_rank() == 0:
+        fid = fid_score.calculate_fid_given_paths(
+            [cache_dir, conf.generate_dir],
+            batch_size,
+            device=device,
+            dims=2048)
+
+        # remove the cache
+        if remove_cache and os.path.exists(conf.generate_dir):
+            shutil.rmtree(conf.generate_dir)
+
+    barrier()
+
+    if get_rank() == 0:
+        # need to float it! unless the broadcasted value is wrong
+        fid = torch.tensor(float(fid), device=device)
+        broadcast(fid, 0)
+    else:
+        fid = torch.tensor(0., device=device)
+        broadcast(fid, 0)
+    fid = fid.item()
+    print(f'fid ({get_rank()}):', fid)
+
+    return fid
+
+
+def loader_to_path(loader: DataLoader, path: str, denormalize: bool):
+    # not process safe!
+
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+    # write the loader to files
+    i = 0
+    for batch in tqdm(loader, desc='copy images'):
+        imgs = batch['img']
+        if denormalize:
+            imgs = (imgs + 1) / 2
+        for j in range(len(imgs)):
+            torchvision.utils.save_image(imgs[j],
+                                         os.path.join(path, f'{i+j}.png'))
+        i += len(imgs)

DiffAE_support_renderer.py

@@ -0,0 +1,62 @@
+from .DiffAE_support_config import *
+from .DiffAE_diffusion_diffusion import SpacedDiffusionBeatGans as Sampler
+from .DiffAE_model_unet_autoenc import BeatGANsAutoencModel
+
+from torch.cuda import amp
+
+
+def render_uncondition(conf: TrainConfig,
+                       model: BeatGANsAutoencModel,
+                       x_T,
+                       sampler: Sampler,
+                       latent_sampler: Sampler,
+                       conds_mean=None,
+                       conds_std=None,
+                       clip_latent_noise: bool = False):
+    device = x_T.device
+    if conf.train_mode == TrainMode.diffusion:
+        assert conf.model_type.can_sample()
+        return sampler.sample(model=model, noise=x_T)
+    elif conf.train_mode.is_latent_diffusion():
+        model: BeatGANsAutoencModel
+        if conf.train_mode == TrainMode.latent_diffusion:
+            latent_noise = torch.randn(len(x_T), conf.style_ch, device=device)
+        else:
+            raise NotImplementedError()
+
+        if clip_latent_noise:
+            latent_noise = latent_noise.clip(-1, 1)
+
+        cond = latent_sampler.sample(
+            model=model.latent_net,
+            noise=latent_noise,
+            clip_denoised=conf.latent_clip_sample,
+        )
+
+        if conf.latent_znormalize:
+            cond = cond * conds_std.to(device) + conds_mean.to(device)
+
+        # the diffusion on the model
+        return sampler.sample(model=model, noise=x_T, cond=cond)
+    else:
+        raise NotImplementedError()
+
+
+def render_condition(
+    conf: TrainConfig,
+    model: BeatGANsAutoencModel,
+    x_T,
+    sampler: Sampler,
+    x_start=None,
+    cond=None,
+):
+    if conf.train_mode == TrainMode.diffusion:
+        assert conf.model_type.has_autoenc()
+        # returns {'cond', 'cond2'}
+        if cond is None:
+            cond = model.encode(x_start)
+        return sampler.sample(model=model,
+                              noise=x_T,
+                              model_kwargs={'cond': cond})
+    else:
+        raise NotImplementedError()

DiffAE_support_templates.py

@@ -0,0 +1,327 @@
+from .DiffAE_support_config import *
+
+
+def ddpm():
+    """
+    base configuration for all DDIM-based models.
+    """
+    conf = TrainConfig()
+    conf.batch_size = 32
+    conf.beatgans_gen_type = GenerativeType.ddim
+    conf.beta_scheduler = 'linear'
+    conf.data_name = 'ffhq'
+    conf.diffusion_type = 'beatgans'
+    conf.eval_ema_every_samples = 200_000
+    conf.eval_every_samples = 200_000
+    conf.fp16 = True
+    conf.lr = 1e-4
+    conf.model_name = ModelName.beatgans_ddpm
+    conf.net_attn = (16, )
+    conf.net_beatgans_attn_head = 1
+    conf.net_beatgans_embed_channels = 512
+    conf.net_ch_mult = (1, 2, 4, 8)
+    conf.net_ch = 64
+    conf.sample_size = 32
+    conf.T_eval = 20
+    conf.T = 1000
+    conf.make_model_conf()
+    return conf
+
+
+def autoenc_base():
+    """
+    base configuration for all Diff-AE models.
+    """
+    conf = TrainConfig()
+    conf.batch_size = 32
+    conf.beatgans_gen_type = GenerativeType.ddim
+    conf.beta_scheduler = 'linear'
+    conf.data_name = 'ffhq'
+    conf.diffusion_type = 'beatgans'
+    conf.eval_ema_every_samples = 200_000
+    conf.eval_every_samples = 200_000
+    conf.fp16 = True
+    conf.lr = 1e-4
+    conf.model_name = ModelName.beatgans_autoenc
+    conf.net_attn = (16, )
+    conf.net_beatgans_attn_head = 1
+    conf.net_beatgans_embed_channels = 512
+    conf.net_beatgans_resnet_two_cond = True
+    conf.net_ch_mult = (1, 2, 4, 8)
+    conf.net_ch = 64
+    conf.net_enc_channel_mult = (1, 2, 4, 8, 8)
+    conf.net_enc_pool = 'adaptivenonzero'
+    conf.sample_size = 32
+    conf.T_eval = 20
+    conf.T = 1000
+    conf.make_model_conf()
+    return conf
+
+def ffhq64_ddpm():
+    conf = ddpm()
+    conf.data_name = 'ffhqlmdb256'
+    conf.warmup = 0
+    conf.total_samples = 72_000_000
+    conf.scale_up_gpus(4)
+    return conf
+
+
+def ffhq64_autoenc():
+    conf = autoenc_base()
+    conf.data_name = 'ffhqlmdb256'
+    conf.warmup = 0
+    conf.total_samples = 72_000_000
+    conf.net_ch_mult = (1, 2, 4, 8)
+    conf.net_enc_channel_mult = (1, 2, 4, 8, 8)
+    conf.eval_every_samples = 1_000_000
+    conf.eval_ema_every_samples = 1_000_000
+    conf.scale_up_gpus(4)
+    conf.make_model_conf()
+    return conf
+
+
+def celeba64d2c_ddpm():
+    conf = ffhq128_ddpm()
+    conf.data_name = 'celebalmdb'
+    conf.eval_every_samples = 10_000_000
+    conf.eval_ema_every_samples = 10_000_000
+    conf.total_samples = 72_000_000
+    conf.name = 'celeba64d2c_ddpm'
+    return conf
+
+
+def celeba64d2c_autoenc():
+    conf = ffhq64_autoenc()
+    conf.data_name = 'celebalmdb'
+    conf.eval_every_samples = 10_000_000
+    conf.eval_ema_every_samples = 10_000_000
+    conf.total_samples = 72_000_000
+    conf.name = 'celeba64d2c_autoenc'
+    return conf
+
+
+def ffhq128_ddpm():
+    conf = ddpm()
+    conf.data_name = 'ffhqlmdb256'
+    conf.warmup = 0
+    conf.total_samples = 48_000_000
+    conf.img_size = 128
+    conf.net_ch = 128
+    # channels:
+    # 3 => 128 * 1 => 128 * 1 => 128 * 2 => 128 * 3 => 128 * 4
+    # sizes:
+    # 128 => 128 => 64 => 32 => 16 => 8
+    conf.net_ch_mult = (1, 1, 2, 3, 4)
+    conf.eval_every_samples = 1_000_000
+    conf.eval_ema_every_samples = 1_000_000
+    conf.scale_up_gpus(4)
+    conf.eval_ema_every_samples = 10_000_000
+    conf.eval_every_samples = 10_000_000
+    conf.make_model_conf()
+    return conf
+
+
+def ffhq128_autoenc_base():
+    conf = autoenc_base()
+    conf.data_name = 'ffhqlmdb256'
+    conf.scale_up_gpus(4)
+    conf.img_size = 128
+    conf.net_ch = 128
+    # final resolution = 8x8
+    conf.net_ch_mult = (1, 1, 2, 3, 4)
+    # final resolution = 4x4
+    conf.net_enc_channel_mult = (1, 1, 2, 3, 4, 4)
+    conf.eval_ema_every_samples = 10_000_000
+    conf.eval_every_samples = 10_000_000
+    conf.make_model_conf()
+    return conf
+
+def ffhq256_autoenc():
+    conf = ffhq128_autoenc_base()
+    conf.img_size = 256
+    conf.net_ch = 128
+    conf.net_ch_mult = (1, 1, 2, 2, 4, 4)
+    conf.net_enc_channel_mult = (1, 1, 2, 2, 4, 4, 4)
+    conf.eval_every_samples = 10_000_000
+    conf.eval_ema_every_samples = 10_000_000
+    conf.total_samples = 200_000_000
+    conf.batch_size = 64
+    conf.make_model_conf()
+    conf.name = 'ffhq256_autoenc'
+    return conf
+
+
+def ffhq256_autoenc_eco():
+    conf = ffhq128_autoenc_base()
+    conf.img_size = 256
+    conf.net_ch = 128
+    conf.net_ch_mult = (1, 1, 2, 2, 4, 4)
+    conf.net_enc_channel_mult = (1, 1, 2, 2, 4, 4, 4)
+    conf.eval_every_samples = 10_000_000
+    conf.eval_ema_every_samples = 10_000_000
+    conf.total_samples = 200_000_000
+    conf.batch_size = 64
+    conf.make_model_conf()
+    conf.name = 'ffhq256_autoenc_eco'
+    return conf
+
+
+def ffhq128_ddpm_72M():
+    conf = ffhq128_ddpm()
+    conf.total_samples = 72_000_000
+    conf.name = 'ffhq128_ddpm_72M'
+    return conf
+
+
+def ffhq128_autoenc_72M():
+    conf = ffhq128_autoenc_base()
+    conf.total_samples = 72_000_000
+    conf.name = 'ffhq128_autoenc_72M'
+    return conf
+
+
+def ffhq128_ddpm_130M():
+    conf = ffhq128_ddpm()
+    conf.total_samples = 130_000_000
+    conf.eval_ema_every_samples = 10_000_000
+    conf.eval_every_samples = 10_000_000
+    conf.name = 'ffhq128_ddpm_130M'
+    return conf
+
+
+def ffhq128_autoenc_130M():
+    conf = ffhq128_autoenc_base()
+    conf.total_samples = 130_000_000
+    conf.eval_ema_every_samples = 10_000_000
+    conf.eval_every_samples = 10_000_000
+    conf.name = 'ffhq128_autoenc_130M'
+    return conf
+
+#created from ffhq128_autoenc_130M
+def ukbb_autoenc(ds_name="ukbb", n_latents=128): 
+    conf = TrainConfig()
+    conf.beatgans_gen_type = GenerativeType.ddim
+    conf.beta_scheduler = 'linear'
+    conf.diffusion_type = 'beatgans'
+    conf.fp16 = True
+    conf.model_name = ModelName.beatgans_autoenc
+    conf.net_attn = (16, )
+    conf.net_beatgans_attn_head = 1
+    conf.net_beatgans_embed_channels = n_latents 
+    conf.style_ch = n_latents
+    conf.net_beatgans_resnet_two_cond = True
+    conf.net_enc_pool = 'adaptivenonzero'
+    conf.sample_size = 32
+    conf.T_eval = 20 
+    conf.T = 1000
+    
+    conf.T_inv = 200
+    conf.T_step = 100
+    
+    conf.data_name = ds_name
+    conf.net_ch_mult = (1, 1, 2, 3, 4)
+    conf.net_enc_channel_mult = (1, 1, 2, 3, 4, 4)
+
+    conf.name = 'ukbb_ffhq128_autoenc'
+    return conf
+
+
+def horse128_ddpm():
+    conf = ffhq128_ddpm()
+    conf.data_name = 'horse256'
+    conf.total_samples = 130_000_000
+    conf.eval_ema_every_samples = 10_000_000
+    conf.eval_every_samples = 10_000_000
+    conf.name = 'horse128_ddpm'
+    return conf
+
+
+def horse128_autoenc():
+    conf = ffhq128_autoenc_base()
+    conf.data_name = 'horse256'
+    conf.total_samples = 130_000_000
+    conf.eval_ema_every_samples = 10_000_000
+    conf.eval_every_samples = 10_000_000
+    conf.name = 'horse128_autoenc'
+    return conf
+
+
+def bedroom128_ddpm():
+    conf = ffhq128_ddpm()
+    conf.data_name = 'bedroom256'
+    conf.eval_ema_every_samples = 10_000_000
+    conf.eval_every_samples = 10_000_000
+    conf.total_samples = 120_000_000
+    conf.name = 'bedroom128_ddpm'
+    return conf
+
+
+def bedroom128_autoenc():
+    conf = ffhq128_autoenc_base()
+    conf.data_name = 'bedroom256'
+    conf.eval_ema_every_samples = 10_000_000
+    conf.eval_every_samples = 10_000_000
+    conf.total_samples = 120_000_000
+    conf.name = 'bedroom128_autoenc'
+    return conf
+
+
+def pretrain_celeba64d2c_72M():
+    conf = celeba64d2c_autoenc()
+    conf.pretrain = PretrainConfig(
+        name='72M',
+        path=f'checkpoints/{celeba64d2c_autoenc().name}/last.ckpt',
+    )
+    conf.latent_infer_path = f'checkpoints/{celeba64d2c_autoenc().name}/latent.pkl'
+    return conf
+
+
+def pretrain_ffhq128_autoenc72M():
+    conf = ffhq128_autoenc_base()
+    conf.postfix = ''
+    conf.pretrain = PretrainConfig(
+        name='72M',
+        path=f'checkpoints/{ffhq128_autoenc_72M().name}/last.ckpt',
+    )
+    conf.latent_infer_path = f'checkpoints/{ffhq128_autoenc_72M().name}/latent.pkl'
+    return conf
+
+
+def pretrain_ffhq128_autoenc130M():
+    conf = ffhq128_autoenc_base()
+    conf.pretrain = PretrainConfig(
+        name='130M',
+        path=f'checkpoints/{ffhq128_autoenc_130M().name}/last.ckpt',
+    )
+    conf.latent_infer_path = f'checkpoints/{ffhq128_autoenc_130M().name}/latent.pkl'
+    return conf
+
+
+def pretrain_ffhq256_autoenc():
+    conf = ffhq256_autoenc()
+    conf.pretrain = PretrainConfig(
+        name='90M',
+        path=f'checkpoints/{ffhq256_autoenc().name}/last.ckpt',
+    )
+    conf.latent_infer_path = f'checkpoints/{ffhq256_autoenc().name}/latent.pkl'
+    return conf
+
+
+def pretrain_horse128():
+    conf = horse128_autoenc()
+    conf.pretrain = PretrainConfig(
+        name='82M',
+        path=f'checkpoints/{horse128_autoenc().name}/last.ckpt',
+    )
+    conf.latent_infer_path = f'checkpoints/{horse128_autoenc().name}/latent.pkl'
+    return conf
+
+
+def pretrain_bedroom128():
+    conf = bedroom128_autoenc()
+    conf.pretrain = PretrainConfig(
+        name='120M',
+        path=f'checkpoints/{bedroom128_autoenc().name}/last.ckpt',
+    )
+    conf.latent_infer_path = f'checkpoints/{bedroom128_autoenc().name}/latent.pkl'
+    return conf

DiffAE_support_templates_latent.py

@@ -0,0 +1,150 @@
+from .DiffAE_support_templates import *
+
+
+def latent_diffusion_config(conf: TrainConfig):
+    conf.batch_size = 128
+    conf.train_mode = TrainMode.latent_diffusion
+    conf.latent_gen_type = GenerativeType.ddim
+    conf.latent_loss_type = LossType.mse
+    conf.latent_model_mean_type = ModelMeanType.eps
+    conf.latent_model_var_type = ModelVarType.fixed_large
+    conf.latent_rescale_timesteps = False
+    conf.latent_clip_sample = False
+    conf.latent_T_eval = 20
+    conf.latent_znormalize = True
+    conf.total_samples = 96_000_000
+    conf.sample_every_samples = 400_000
+    conf.eval_every_samples = 20_000_000
+    conf.eval_ema_every_samples = 20_000_000
+    conf.save_every_samples = 2_000_000
+    return conf
+
+
+def latent_diffusion128_config(conf: TrainConfig):
+    conf = latent_diffusion_config(conf)
+    conf.batch_size_eval = 32
+    return conf
+
+
+def latent_mlp_2048_norm_10layers(conf: TrainConfig):
+    conf.net_latent_net_type = LatentNetType.skip
+    conf.net_latent_layers = 10
+    conf.net_latent_skip_layers = list(range(1, conf.net_latent_layers))
+    conf.net_latent_activation = Activation.silu
+    conf.net_latent_num_hid_channels = 2048
+    conf.net_latent_use_norm = True
+    conf.net_latent_condition_bias = 1
+    return conf
+
+
+def latent_mlp_2048_norm_20layers(conf: TrainConfig):
+    conf = latent_mlp_2048_norm_10layers(conf)
+    conf.net_latent_layers = 20
+    conf.net_latent_skip_layers = list(range(1, conf.net_latent_layers))
+    return conf
+
+
+def latent_256_batch_size(conf: TrainConfig):
+    conf.batch_size = 256
+    conf.eval_ema_every_samples = 100_000_000
+    conf.eval_every_samples = 100_000_000
+    conf.sample_every_samples = 1_000_000
+    conf.save_every_samples = 2_000_000
+    conf.total_samples = 301_000_000
+    return conf
+
+
+def latent_512_batch_size(conf: TrainConfig):
+    conf.batch_size = 512
+    conf.eval_ema_every_samples = 100_000_000
+    conf.eval_every_samples = 100_000_000
+    conf.sample_every_samples = 1_000_000
+    conf.save_every_samples = 5_000_000
+    conf.total_samples = 501_000_000
+    return conf
+
+
+def latent_2048_batch_size(conf: TrainConfig):
+    conf.batch_size = 2048
+    conf.eval_ema_every_samples = 200_000_000
+    conf.eval_every_samples = 200_000_000
+    conf.sample_every_samples = 4_000_000
+    conf.save_every_samples = 20_000_000
+    conf.total_samples = 1_501_000_000
+    return conf
+
+
+def adamw_weight_decay(conf: TrainConfig):
+    conf.optimizer = OptimizerType.adamw
+    conf.weight_decay = 0.01
+    return conf
+
+
+def ffhq128_autoenc_latent():
+    conf = pretrain_ffhq128_autoenc130M()
+    conf = latent_diffusion128_config(conf)
+    conf = latent_mlp_2048_norm_10layers(conf)
+    conf = latent_256_batch_size(conf)
+    conf = adamw_weight_decay(conf)
+    conf.total_samples = 101_000_000
+    conf.latent_loss_type = LossType.l1
+    conf.latent_beta_scheduler = 'const0.008'
+    conf.name = 'ffhq128_autoenc_latent'
+    return conf
+
+
+def ffhq256_autoenc_latent():
+    conf = pretrain_ffhq256_autoenc()
+    conf = latent_diffusion128_config(conf)
+    conf = latent_mlp_2048_norm_10layers(conf)
+    conf = latent_256_batch_size(conf)
+    conf = adamw_weight_decay(conf)
+    conf.total_samples = 101_000_000
+    conf.latent_loss_type = LossType.l1
+    conf.latent_beta_scheduler = 'const0.008'
+    conf.eval_ema_every_samples = 200_000_000
+    conf.eval_every_samples = 200_000_000
+    conf.sample_every_samples = 4_000_000
+    conf.name = 'ffhq256_autoenc_latent'
+    return conf
+
+
+def horse128_autoenc_latent():
+    conf = pretrain_horse128()
+    conf = latent_diffusion128_config(conf)
+    conf = latent_2048_batch_size(conf)
+    conf = latent_mlp_2048_norm_20layers(conf)
+    conf.total_samples = 2_001_000_000
+    conf.latent_beta_scheduler = 'const0.008'
+    conf.latent_loss_type = LossType.l1
+    conf.name = 'horse128_autoenc_latent'
+    return conf
+
+
+def bedroom128_autoenc_latent():
+    conf = pretrain_bedroom128()
+    conf = latent_diffusion128_config(conf)
+    conf = latent_2048_batch_size(conf)
+    conf = latent_mlp_2048_norm_20layers(conf)
+    conf.total_samples = 2_001_000_000
+    conf.latent_beta_scheduler = 'const0.008'
+    conf.latent_loss_type = LossType.l1
+    conf.name = 'bedroom128_autoenc_latent'
+    return conf
+
+
+def celeba64d2c_autoenc_latent():
+    conf = pretrain_celeba64d2c_72M()
+    conf = latent_diffusion_config(conf)
+    conf = latent_512_batch_size(conf)
+    conf = latent_mlp_2048_norm_10layers(conf)
+    conf = adamw_weight_decay(conf)
+    # just for the name
+    conf.continue_from = PretrainConfig('200M',
+                                        f'log-latent/{conf.name}/last.ckpt')
+    conf.postfix = '_300M'
+    conf.total_samples = 301_000_000
+    conf.latent_beta_scheduler = 'const0.008'
+    conf.latent_loss_type = LossType.l1
+    conf.name = 'celeba64d2c_autoenc_latent'
+    return conf

DiffAE_support_utils.py

@@ -0,0 +1,33 @@
+from statistics import median
+from skimage.metrics import structural_similarity
+
+def getSSIM(gt, out, gt_flag=None, data_range=1):
+    if gt_flag is None:  # all of the samples have GTs
+        gt_flag = [True]*gt.shape[0]
+
+    vals = []
+    for i in range(gt.shape[0]):
+        if not gt_flag[i]:
+            continue
+        vals.extend(
+            structural_similarity(
+                gt[i, j, ...], out[i, j, ...], data_range=data_range
+            )
+            for j in range(gt.shape[1])
+        )
+    return median(vals)
+
+def ema(source, target, decay):
+    source_dict = source.state_dict()
+    target_dict = target.state_dict()
+    for key in source_dict.keys():
+        target_dict[key].data.copy_(target_dict[key].data * decay +
+                                    source_dict[key].data * (1 - decay))
+
+
+class WarmupLR:
+    def __init__(self, warmup) -> None:
+        self.warmup = warmup
+
+    def __call__(self, step):
+        return min(step, self.warmup) / self.warmup

config.json

@@ -0,0 +1,34 @@
+{
+  "ampmode": "16-mixed",
+  "architectures": [
+    "DiffAE"
+  ],
+  "auto_map": {
+    "AutoConfig": "DiffAEConfig.DiffAEConfig",
+    "AutoModel": "DiffAE.DiffAE"
+  },
+  "batch_size": 9,
+  "data_name": "ukbb",
+  "diffusion_type": "beatgans",
+  "grey2RGB": -1,
+  "in_channels": 1,
+  "input_shape": [
+    50,
+    128,
+    128
+  ],
+  "is3D": true,
+  "latent_dim": 128,
+  "lr": 0.0001,
+  "model_type": "DiffAE",
+  "net_ch": 32,
+  "out_channels": 1,
+  "sample_every_batches": 1000,
+  "sample_size": 4,
+  "seed": 1701,
+  "test_ema": true,
+  "test_emb_only": true,
+  "test_with_TEval": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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