Update jobs/process/TrainVAEProcess.py

jobs/process/TrainVAEProcess.py

@@ -7,6 +7,7 @@ from collections import OrderedDict
 
 from PIL import Image
 from PIL.ImageOps import exif_transpose
+from einops import rearrange
 from safetensors.torch import save_file, load_file
 from torch.utils.data import DataLoader, ConcatDataset
 import torch
@@ -17,18 +18,22 @@ from jobs.process import BaseTrainProcess
 from toolkit.image_utils import show_tensors
 from toolkit.kohya_model_util import load_vae, convert_diffusers_back_to_ldm
 from toolkit.data_loader import ImageDataset
-from toolkit.losses import ComparativeTotalVariation, get_gradient_penalty, PatternLoss
+from toolkit.losses import ComparativeTotalVariation, get_gradient_penalty, PatternLoss, total_variation
 from toolkit.metadata import get_meta_for_safetensors
 from toolkit.optimizer import get_optimizer
 from toolkit.style import get_style_model_and_losses
 from toolkit.train_tools import get_torch_dtype
 from diffusers import AutoencoderKL
 from tqdm import tqdm
+import math
+import torchvision.utils
 import time
 import numpy as np
-from .models.vgg19_critic import Critic
+from .models.critic import Critic
 from torchvision.transforms import Resize
 import lpips
+import random
+import traceback
 
 IMAGE_TRANSFORMS = transforms.Compose(
     [
@@ -42,13 +47,21 @@ def unnormalize(tensor):
     return (tensor / 2 + 0.5).clamp(0, 1)
 
 
+def channel_dropout(x, p=0.5):
+    keep_prob = 1 - p
+    mask = torch.rand(x.size(0), x.size(1), 1, 1, device=x.device, dtype=x.dtype) < keep_prob
+    mask = mask / keep_prob  # scale
+    return x * mask
+
+
 class TrainVAEProcess(BaseTrainProcess):
     def __init__(self, process_id: int, job, config: OrderedDict):
         super().__init__(process_id, job, config)
         self.data_loader = None
         self.vae = None
         self.device = self.get_conf('device', self.job.device)
-        self.vae_path = self.get_conf('vae_path', required=True)
+        self.vae_path = self.get_conf('vae_path', None)
+        self.eq_vae = self.get_conf('eq_vae', False)
         self.datasets_objects = self.get_conf('datasets', required=True)
         self.batch_size = self.get_conf('batch_size', 1, as_type=int)
         self.resolution = self.get_conf('resolution', 256, as_type=int)
@@ -65,11 +78,25 @@ class TrainVAEProcess(BaseTrainProcess):
         self.content_weight = self.get_conf('content_weight', 0, as_type=float)
         self.kld_weight = self.get_conf('kld_weight', 0, as_type=float)
         self.mse_weight = self.get_conf('mse_weight', 1e0, as_type=float)
-        self.tv_weight = self.get_conf('tv_weight', 1e0, as_type=float)
+        self.mv_loss_weight = self.get_conf('mv_loss_weight', 0, as_type=float)
+        self.tv_weight = self.get_conf('tv_weight', 0, as_type=float)
+        self.ltv_weight = self.get_conf('ltv_weight', 0, as_type=float)
+        self.lpm_weight = self.get_conf('lpm_weight', 0, as_type=float) # latent pixel matching
         self.lpips_weight = self.get_conf('lpips_weight', 1e0, as_type=float)
         self.critic_weight = self.get_conf('critic_weight', 1, as_type=float)
-        self.pattern_weight = self.get_conf('pattern_weight', 1, as_type=float)
+        self.pattern_weight = self.get_conf('pattern_weight', 0, as_type=float)
         self.optimizer_params = self.get_conf('optimizer_params', {})
+        self.vae_config = self.get_conf('vae_config', None)
+        self.dropout = self.get_conf('dropout', 0.0, as_type=float)
+        self.train_encoder = self.get_conf('train_encoder', False, as_type=bool)
+        self.random_scaling = self.get_conf('random_scaling', False, as_type=bool)
+        
+        if not self.train_encoder:
+            # remove losses that only target encoder
+            self.kld_weight = 0
+            self.mv_loss_weight = 0
+            self.ltv_weight = 0
+            self.lpm_weight = 0
 
         self.blocks_to_train = self.get_conf('blocks_to_train', ['all'])
         self.torch_dtype = get_torch_dtype(self.dtype)
@@ -133,7 +160,11 @@ class TrainVAEProcess(BaseTrainProcess):
             for dataset in self.datasets_objects:
                 print(f" - Dataset: {dataset['path']}")
                 ds = copy.copy(dataset)
-                ds['resolution'] = self.resolution
+                dataset_res = self.resolution
+                if self.random_scaling:
+                    # scale 2x to allow for random scaling
+                    dataset_res = int(dataset_res * 2)
+                ds['resolution'] = dataset_res
                 image_dataset = ImageDataset(ds)
                 datasets.append(image_dataset)
 
@@ -142,7 +173,7 @@ class TrainVAEProcess(BaseTrainProcess):
                 concatenated_dataset,
                 batch_size=self.batch_size,
                 shuffle=True,
-                num_workers=6
+                num_workers=16
             )
 
     def remove_oldest_checkpoint(self):
@@ -153,6 +184,13 @@ class TrainVAEProcess(BaseTrainProcess):
             for folder in folders[:-max_to_keep]:
                 print(f"Removing {folder}")
                 shutil.rmtree(folder)
+        # also handle CRITIC_vae_42_000000500.safetensors format for critic
+        critic_files = glob.glob(os.path.join(self.save_root, f"CRITIC_{self.job.name}*.safetensors"))
+        if len(critic_files) > max_to_keep:
+            critic_files.sort(key=os.path.getmtime)
+            for file in critic_files[:-max_to_keep]:
+                print(f"Removing {file}")
+                os.remove(file)
 
     def setup_vgg19(self):
         if self.vgg_19 is None:
@@ -218,6 +256,62 @@ class TrainVAEProcess(BaseTrainProcess):
         else:
             return torch.tensor(0.0, device=self.device)
 
+    def get_mean_variance_loss(self, latents: torch.Tensor):
+        if self.mv_loss_weight > 0:
+            # collapse rows into channels
+            latents_col = rearrange(latents, 'b c h (gw w) -> b (c gw) h w', gw=latents.shape[-1])
+            mean_col = latents_col.mean(dim=(2, 3), keepdim=True)
+            std_col  = latents_col.std(dim=(2, 3), keepdim=True, unbiased=False)
+            mean_loss_col = (mean_col ** 2).mean()
+            std_loss_col  = ((std_col - 1) ** 2).mean()
+
+            # collapse columns into channels
+            latents_row = rearrange(latents, 'b c (gh h) w -> b (c gh) h w', gh=latents.shape[-2])
+            mean_row = latents_row.mean(dim=(2, 3), keepdim=True)
+            std_row  = latents_row.std(dim=(2, 3), keepdim=True, unbiased=False)
+            mean_loss_row = (mean_row ** 2).mean()
+            std_loss_row  = ((std_row - 1) ** 2).mean()
+
+            # do a global one
+            
+            mean = latents.mean(dim=(2, 3), keepdim=True)
+            std  = latents.std(dim=(2, 3), keepdim=True, unbiased=False)
+            mean_loss_global = (mean ** 2).mean()
+            std_loss_global  = ((std - 1) ** 2).mean()
+
+            return (mean_loss_col + std_loss_col + mean_loss_row + std_loss_row + mean_loss_global + std_loss_global) / 3
+        else:
+            return torch.tensor(0.0, device=self.device)
+        
+    def get_ltv_loss(self, latent):
+        # loss to reduce the latent space variance
+        if self.ltv_weight > 0:
+            return total_variation(latent).mean()
+        else:
+            return torch.tensor(0.0, device=self.device)
+        
+    def get_latent_pixel_matching_loss(self, latent, pixels):
+        if self.lpm_weight > 0:
+            with torch.no_grad():
+                pixels = pixels.to(latent.device, dtype=latent.dtype)
+                # resize down to latent size
+                pixels = torch.nn.functional.interpolate(pixels, size=(latent.shape[2], latent.shape[3]), mode='bilinear', align_corners=False)
+                
+                # mean the color channel and then expand to latent size
+                pixels = pixels.mean(dim=1, keepdim=True)
+                pixels = pixels.repeat(1, latent.shape[1], 1, 1)
+                # match the mean std of latent
+                latent_mean = latent.mean(dim=(2, 3), keepdim=True)
+                latent_std = latent.std(dim=(2, 3), keepdim=True)
+                pixels_mean = pixels.mean(dim=(2, 3), keepdim=True)
+                pixels_std = pixels.std(dim=(2, 3), keepdim=True)
+                pixels = (pixels - pixels_mean) / (pixels_std + 1e-6) * latent_std + latent_mean
+                
+            return torch.nn.functional.mse_loss(latent.float(), pixels.float())
+            
+        else:
+            return torch.tensor(0.0, device=self.device)
+
     def get_tv_loss(self, pred, target):
         if self.tv_weight > 0:
             get_tv_loss = ComparativeTotalVariation()
@@ -277,7 +371,39 @@ class TrainVAEProcess(BaseTrainProcess):
                 input_img = img
                 img = IMAGE_TRANSFORMS(img).unsqueeze(0).to(self.device, dtype=self.torch_dtype)
                 img = img
-                decoded = self.vae(img).sample
+                latent = self.vae.encode(img).latent_dist.sample()
+                
+                latent_img = latent.clone()
+                bs, ch, h, w = latent_img.shape
+                grid_size = math.ceil(math.sqrt(ch))
+                pad = grid_size * grid_size - ch
+
+                # take first item in batch
+                latent_img = latent_img[0]  # shape: (ch, h, w)
+
+                if pad > 0:
+                    padding = torch.zeros((pad, h, w), dtype=latent_img.dtype, device=latent_img.device)
+                    latent_img = torch.cat([latent_img, padding], dim=0)
+
+                # make grid
+                new_img = torch.zeros((1, grid_size * h, grid_size * w), dtype=latent_img.dtype, device=latent_img.device)
+                for x in range(grid_size):
+                    for y in range(grid_size):
+                        if x * grid_size + y < ch:
+                            new_img[0, x * h:(x + 1) * h, y * w:(y + 1) * w] = latent_img[x * grid_size + y]
+                latent_img = new_img
+                # make rgb
+                latent_img = latent_img.repeat(3, 1, 1).unsqueeze(0)
+                latent_img = (latent_img / 2 + 0.5).clamp(0, 1)
+                
+                # resize to 256x256
+                latent_img = torch.nn.functional.interpolate(latent_img, size=(self.resolution, self.resolution), mode='nearest')
+                latent_img = latent_img.squeeze(0).cpu().permute(1, 2, 0).float().numpy()
+                latent_img = (latent_img * 255).astype(np.uint8)
+                # convert to pillow image
+                latent_img = Image.fromarray(latent_img)
+                
+                decoded = self.vae.decode(latent).sample
                 decoded = (decoded / 2 + 0.5).clamp(0, 1)
                 # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
                 decoded = decoded.cpu().permute(0, 2, 3, 1).squeeze(0).float().numpy()
@@ -289,9 +415,10 @@ class TrainVAEProcess(BaseTrainProcess):
                 input_img = input_img.resize((self.resolution, self.resolution))
                 decoded = decoded.resize((self.resolution, self.resolution))
 
-                output_img = Image.new('RGB', (self.resolution * 2, self.resolution))
+                output_img = Image.new('RGB', (self.resolution * 3, self.resolution))
                 output_img.paste(input_img, (0, 0))
                 output_img.paste(decoded, (self.resolution, 0))
+                output_img.paste(latent_img, (self.resolution * 2, 0))
 
                 scale_up = 2
                 if output_img.height <= 300:
@@ -326,12 +453,20 @@ class TrainVAEProcess(BaseTrainProcess):
         self.print(f"Loading VAE")
         self.print(f" - Loading VAE: {path_to_load}")
         if self.vae is None:
-            self.vae = AutoencoderKL.from_pretrained(path_to_load)
+            if path_to_load is not None:
+                self.vae = AutoencoderKL.from_pretrained(path_to_load)
+            elif self.vae_config is not None:
+                self.vae = AutoencoderKL(**self.vae_config)
+            else:
+                raise ValueError('vae_path or ae_config must be specified')
 
         # set decoder to train
         self.vae.to(self.device, dtype=self.torch_dtype)
-        self.vae.requires_grad_(False)
-        self.vae.eval()
+        if self.eq_vae:
+            self.vae.encoder.train()
+        else:
+            self.vae.requires_grad_(False)
+            self.vae.eval()
         self.vae.decoder.train()
         self.vae_scale_factor = 2 ** (len(self.vae.config['block_out_channels']) - 1)
 
@@ -374,6 +509,10 @@ class TrainVAEProcess(BaseTrainProcess):
         if train_all:
             params = list(self.vae.decoder.parameters())
             self.vae.decoder.requires_grad_(True)
+            if self.train_encoder:
+                # encoder
+                params += list(self.vae.encoder.parameters())
+                self.vae.encoder.requires_grad_(True)
         else:
             # mid_block
             if train_all or 'mid_block' in self.blocks_to_train:
@@ -388,12 +527,13 @@ class TrainVAEProcess(BaseTrainProcess):
                 params += list(self.vae.decoder.conv_out.parameters())
                 self.vae.decoder.conv_out.requires_grad_(True)
 
-        if self.style_weight > 0 or self.content_weight > 0 or self.use_critic:
+        if self.style_weight > 0 or self.content_weight > 0:
             self.setup_vgg19()
-            self.vgg_19.requires_grad_(False)
+            # self.vgg_19.requires_grad_(False)
             self.vgg_19.eval()
-            if self.use_critic:
-                self.critic.setup()
+        
+        if self.use_critic:
+            self.critic.setup()
 
         if self.lpips_weight > 0 and self.lpips_loss is None:
             # self.lpips_loss = lpips.LPIPS(net='vgg')
@@ -426,6 +566,9 @@ class TrainVAEProcess(BaseTrainProcess):
             "style": [],
             "content": [],
             "mse": [],
+            "mvl": [],
+            "ltv": [],
+            "lpm": [],
             "kl": [],
             "tv": [],
             "ptn": [],
@@ -435,6 +578,9 @@ class TrainVAEProcess(BaseTrainProcess):
         epoch_losses = copy.deepcopy(blank_losses)
         log_losses = copy.deepcopy(blank_losses)
         # range start at self.epoch_num go to self.epochs
+        
+        latent_size = self.resolution // self.vae_scale_factor
+        
         for epoch in range(self.epoch_num, self.epochs, 1):
             if self.step_num >= self.max_steps:
                 break
@@ -442,8 +588,20 @@ class TrainVAEProcess(BaseTrainProcess):
                 if self.step_num >= self.max_steps:
                     break
                 with torch.no_grad():
-
                     batch = batch.to(self.device, dtype=self.torch_dtype)
+                    
+                    if self.random_scaling:
+                        # only random scale 0.5 of the time
+                        if random.random() < 0.5:
+                            # random scale the batch
+                            scale_factor = 0.25
+                        else:
+                            scale_factor = 0.5
+                        new_size = (int(batch.shape[2] * scale_factor), int(batch.shape[3] * scale_factor))
+                        # make sure it is vae divisible
+                        new_size = (new_size[0] // self.vae_scale_factor * self.vae_scale_factor,
+                                    new_size[1] // self.vae_scale_factor * self.vae_scale_factor)
+                            
 
                     # resize so it matches size of vae evenly
                     if batch.shape[2] % self.vae_scale_factor != 0 or batch.shape[3] % self.vae_scale_factor != 0:
@@ -451,27 +609,92 @@ class TrainVAEProcess(BaseTrainProcess):
                                                 batch.shape[3] // self.vae_scale_factor * self.vae_scale_factor))(batch)
 
                     # forward pass
+                # grad only if eq_vae
+                with torch.set_grad_enabled(self.train_encoder):
                     dgd = self.vae.encode(batch).latent_dist
                     mu, logvar = dgd.mean, dgd.logvar
                     latents = dgd.sample()
-                    latents.detach().requires_grad_(True)
-
-                pred = self.vae.decode(latents).sample
-
-                with torch.no_grad():
-                    show_tensors(
-                        pred.clamp(-1, 1).clone(),
-                        "combined tensor"
-                    )
+                    
+                    if self.eq_vae:
+                        # process flips, rotate, scale
+                        latent_chunks = list(latents.chunk(latents.shape[0], dim=0))
+                        batch_chunks = list(batch.chunk(batch.shape[0], dim=0))
+                        out_chunks = []
+                        for i in range(len(latent_chunks)):
+                            try:
+                                do_rotate = random.randint(0, 3)
+                                do_flip_x = random.randint(0, 1)
+                                do_flip_y = random.randint(0, 1)
+                                do_scale = random.randint(0, 1)
+                                if do_rotate > 0:
+                                    latent_chunks[i] = torch.rot90(latent_chunks[i], do_rotate, (2, 3))
+                                    batch_chunks[i] = torch.rot90(batch_chunks[i], do_rotate, (2, 3))
+                                if do_flip_x > 0:
+                                    latent_chunks[i] = torch.flip(latent_chunks[i], [2])
+                                    batch_chunks[i] = torch.flip(batch_chunks[i], [2])
+                                if do_flip_y > 0:
+                                    latent_chunks[i] = torch.flip(latent_chunks[i], [3])
+                                    batch_chunks[i] = torch.flip(batch_chunks[i], [3])
+                                
+                                # resize latent to fit
+                                if latent_chunks[i].shape[2] != latent_size or latent_chunks[i].shape[3] != latent_size:
+                                    latent_chunks[i] = torch.nn.functional.interpolate(latent_chunks[i], size=(latent_size, latent_size), mode='bilinear', align_corners=False)
+                                
+                                # if do_scale > 0:
+                                #     scale = 2
+                                #     start_latent_h = latent_chunks[i].shape[2]
+                                #     start_latent_w = latent_chunks[i].shape[3]
+                                #     start_batch_h = batch_chunks[i].shape[2]
+                                #     start_batch_w = batch_chunks[i].shape[3]
+                                #     latent_chunks[i] = torch.nn.functional.interpolate(latent_chunks[i], scale_factor=scale, mode='bilinear', align_corners=False)
+                                #     batch_chunks[i] = torch.nn.functional.interpolate(batch_chunks[i], scale_factor=scale, mode='bilinear', align_corners=False)
+                                #     # random crop. latent is smaller than match but crops need to match
+                                #     latent_x = random.randint(0, latent_chunks[i].shape[2] - start_latent_h)
+                                #     latent_y = random.randint(0, latent_chunks[i].shape[3] - start_latent_w)
+                                #     batch_x = latent_x * self.vae_scale_factor
+                                #     batch_y = latent_y * self.vae_scale_factor
+                                    
+                                #     # crop
+                                #     latent_chunks[i] = latent_chunks[i][:, :, latent_x:latent_x + start_latent_h, latent_y:latent_y + start_latent_w]
+                                #     batch_chunks[i] = batch_chunks[i][:, :, batch_x:batch_x + start_batch_h, batch_y:batch_y + start_batch_w]
+                            except Exception as e:
+                                print(f"Error processing image {i}: {e}")
+                                traceback.print_exc()
+                                raise e
+                            out_chunks.append(latent_chunks[i])
+                        latents = torch.cat(out_chunks, dim=0)
+                        # do dropout
+                        if self.dropout > 0:
+                            forward_latents = channel_dropout(latents, self.dropout)
+                        else:
+                            forward_latents = latents
+                            
+                        # resize batch to resolution if needed
+                        if batch_chunks[0].shape[2] != self.resolution or batch_chunks[0].shape[3] != self.resolution:
+                            batch_chunks = [torch.nn.functional.interpolate(b, size=(self.resolution, self.resolution), mode='bilinear', align_corners=False) for b in batch_chunks]
+                        batch = torch.cat(batch_chunks, dim=0)
+                                
+                    else:
+                        latents.detach().requires_grad_(True)
+                        forward_latents = latents
+                    
+                forward_latents = forward_latents.to(self.device, dtype=self.torch_dtype)
+                
+                if not self.train_encoder:
+                    # detach latents if not training encoder
+                    forward_latents = forward_latents.detach()
+
+                pred = self.vae.decode(forward_latents).sample
 
                 # Run through VGG19
-                if self.style_weight > 0 or self.content_weight > 0 or self.use_critic:
+                if self.style_weight > 0 or self.content_weight > 0:
                     stacked = torch.cat([pred, batch], dim=0)
                     stacked = (stacked / 2 + 0.5).clamp(0, 1)
                     self.vgg_19(stacked)
 
                 if self.use_critic:
-                    critic_d_loss = self.critic.step(self.vgg19_pool_4.tensor.detach())
+                    stacked = torch.cat([pred, batch], dim=0)
+                    critic_d_loss = self.critic.step(stacked.detach())
                 else:
                     critic_d_loss = 0.0
 
@@ -489,7 +712,8 @@ class TrainVAEProcess(BaseTrainProcess):
                 tv_loss = self.get_tv_loss(pred, batch) * self.tv_weight
                 pattern_loss = self.get_pattern_loss(pred, batch) * self.pattern_weight
                 if self.use_critic:
-                    critic_gen_loss = self.critic.get_critic_loss(self.vgg19_pool_4.tensor) * self.critic_weight
+                    stacked = torch.cat([pred, batch], dim=0)
+                    critic_gen_loss = self.critic.get_critic_loss(stacked) * self.critic_weight
 
                     # do not let abs critic gen loss be higher than abs lpips * 0.1 if using it
                     if self.lpips_weight > 0:
@@ -502,8 +726,42 @@ class TrainVAEProcess(BaseTrainProcess):
                         critic_gen_loss *= crit_g_scaler
                 else:
                     critic_gen_loss = torch.tensor(0.0, device=self.device, dtype=self.torch_dtype)
-
-                loss = style_loss + content_loss + kld_loss + mse_loss + tv_loss + critic_gen_loss + pattern_loss + lpips_loss
+                
+                if self.mv_loss_weight > 0:
+                    mv_loss = self.get_mean_variance_loss(latents) * self.mv_loss_weight
+                else:
+                    mv_loss = torch.tensor(0.0, device=self.device, dtype=self.torch_dtype)
+                
+                if self.ltv_weight > 0:
+                    ltv_loss = self.get_ltv_loss(latents) * self.ltv_weight
+                else:
+                    ltv_loss = torch.tensor(0.0, device=self.device, dtype=self.torch_dtype)
+                    
+                if self.lpm_weight > 0:
+                    lpm_loss = self.get_latent_pixel_matching_loss(latents, batch) * self.lpm_weight
+                else:
+                    lpm_loss = torch.tensor(0.0, device=self.device, dtype=self.torch_dtype)
+
+                loss = style_loss + content_loss + kld_loss + mse_loss + tv_loss + critic_gen_loss + pattern_loss + lpips_loss + mv_loss + ltv_loss
+                
+                # check if loss is NaN or Inf
+                if torch.isnan(loss) or torch.isinf(loss):
+                    self.print(f"Loss is NaN or Inf, stopping at step {self.step_num}")
+                    self.print(f" - Style loss: {style_loss.item()}")
+                    self.print(f" - Content loss: {content_loss.item()}")
+                    self.print(f" - KLD loss: {kld_loss.item()}")
+                    self.print(f" - MSE loss: {mse_loss.item()}")
+                    self.print(f" - LPIPS loss: {lpips_loss.item()}")
+                    self.print(f" - TV loss: {tv_loss.item()}")
+                    self.print(f" - Pattern loss: {pattern_loss.item()}")
+                    self.print(f" - Critic gen loss: {critic_gen_loss.item()}")
+                    self.print(f" - Critic D loss: {critic_d_loss}")
+                    self.print(f" - Mean variance loss: {mv_loss.item()}")
+                    self.print(f" - Latent TV loss: {ltv_loss.item()}")
+                    self.print(f" - Latent pixel matching loss: {lpm_loss.item()}")
+                    self.print(f" - Total loss: {loss.item()}")
+                    self.print(f" - Stopping training")
+                    exit(1)
 
                 # Backward pass and optimization
                 optimizer.zero_grad()
@@ -533,8 +791,17 @@ class TrainVAEProcess(BaseTrainProcess):
                     loss_string += f" crG: {critic_gen_loss.item():.2e}"
                 if self.use_critic:
                     loss_string += f" crD: {critic_d_loss:.2e}"
-
-                if self.optimizer_type.startswith('dadaptation') or \
+                if self.mv_loss_weight > 0:
+                    loss_string += f" mvl: {mv_loss:.2e}"
+                if self.ltv_weight > 0:
+                    loss_string += f" ltv: {ltv_loss:.2e}"
+                if self.lpm_weight > 0:
+                    loss_string += f" lpm: {lpm_loss:.2e}"
+                
+
+                if hasattr(optimizer, 'get_avg_learning_rate'):
+                    learning_rate = optimizer.get_avg_learning_rate()
+                elif self.optimizer_type.startswith('dadaptation') or \
                         self.optimizer_type.lower().startswith('prodigy'):
                     learning_rate = (
                             optimizer.param_groups[0]["d"] *
@@ -562,6 +829,9 @@ class TrainVAEProcess(BaseTrainProcess):
                 epoch_losses["ptn"].append(pattern_loss.item())
                 epoch_losses["crG"].append(critic_gen_loss.item())
                 epoch_losses["crD"].append(critic_d_loss)
+                epoch_losses["mvl"].append(mv_loss.item())
+                epoch_losses["ltv"].append(ltv_loss.item())
+                epoch_losses["lpm"].append(lpm_loss.item())
 
                 log_losses["total"].append(loss_value)
                 log_losses["lpips"].append(lpips_loss.item())
@@ -573,6 +843,9 @@ class TrainVAEProcess(BaseTrainProcess):
                 log_losses["ptn"].append(pattern_loss.item())
                 log_losses["crG"].append(critic_gen_loss.item())
                 log_losses["crD"].append(critic_d_loss)
+                log_losses["mvl"].append(mv_loss.item())
+                log_losses["ltv"].append(ltv_loss.item())
+                log_losses["lpm"].append(lpm_loss.item())
 
                 # don't do on first step
                 if self.step_num != start_step:
@@ -609,4 +882,4 @@ class TrainVAEProcess(BaseTrainProcess):
             # reset epoch losses
             epoch_losses = copy.deepcopy(blank_losses)
 
-        self.save()
+        self.save()