Work on vae trainer

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 +78,24 @@ 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)
+        
+        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)
@@ -142,7 +168,7 @@ class TrainVAEProcess(BaseTrainProcess):
                 concatenated_dataset,
                 batch_size=self.batch_size,
                 shuffle=True,
-                num_workers=6
+                num_workers=8
             )
 
     def remove_oldest_checkpoint(self):
@@ -153,6 +179,13 @@ 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 +251,62 @@ 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 +366,39 @@ 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 +410,10 @@ 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 +448,20 @@ 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 +504,10 @@ 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 +522,13 @@ 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 +561,9 @@ class TrainVAEProcess(BaseTrainProcess):
             "style": [],
             "content": [],
             "mse": [],
+            "mvl": [],
+            "ltv": [],
+            "lpm": [],
             "kl": [],
             "tv": [],
             "ptn": [],
@@ -451,27 +589,83 @@ 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)
+                    
+                    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])
+                                # 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
+                        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(latents).sample
-
-                with torch.no_grad():
-                    show_tensors(
-                        pred.clamp(-1, 1).clone(),
-                        "combined tensor"
-                    )
+                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 +683,8 @@ 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 +697,42 @@ class TrainVAEProcess(BaseTrainProcess):
                         critic_gen_loss *= crit_g_scaler
                 else:
                     critic_gen_loss = torch.tensor(0.0, device=self.device, dtype=self.torch_dtype)
+                
+                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
+                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 +762,17 @@ 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.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 self.optimizer_type.startswith('dadaptation') or \
+                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 +800,9 @@ 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 +814,9 @@ 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:

jobs/process/models/critic.py

@@ -0,0 +1,229 @@
+import glob
+import os
+from typing import TYPE_CHECKING, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+from safetensors.torch import load_file, save_file
+
+from toolkit.losses import get_gradient_penalty
+from toolkit.metadata import get_meta_for_safetensors
+from toolkit.optimizer import get_optimizer
+from toolkit.train_tools import get_torch_dtype
+
+
+class MeanReduce(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, inputs):
+        # global mean over spatial dims (keeps channel/batch)
+        return torch.mean(inputs, dim=(2, 3), keepdim=True)
+
+
+class SelfAttention2d(nn.Module):
+    """
+    Lightweight self-attention layer (SAGAN-style) that keeps spatial
+    resolution unchanged. Adds minimal params / compute but improves
+    long-range modelling – helpful for variable-sized inputs.
+    """
+    def __init__(self, in_channels: int):
+        super().__init__()
+        self.query = nn.Conv1d(in_channels, in_channels // 8, 1)
+        self.key   = nn.Conv1d(in_channels, in_channels // 8, 1)
+        self.value = nn.Conv1d(in_channels, in_channels,      1)
+        self.gamma = nn.Parameter(torch.zeros(1))
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        flat = x.view(B, C, H * W)                    # (B,C,N)
+        q = self.query(flat).permute(0, 2, 1)         # (B,N,C//8)
+        k = self.key(flat)                            # (B,C//8,N)
+        attn = torch.bmm(q, k)                        # (B,N,N)
+        attn = attn.softmax(dim=-1)                   # softmax along last dim
+        v = self.value(flat)                          # (B,C,N)
+        out = torch.bmm(v, attn.permute(0, 2, 1))     # (B,C,N)
+        out = out.view(B, C, H, W)                    # restore spatial dims
+        return self.gamma * out + x                   # residual
+
+
+class CriticModel(nn.Module):
+    def __init__(self, base_channels: int = 64):
+        super().__init__()
+
+        def sn_conv(in_c, out_c, k, s, p):
+            return nn.utils.spectral_norm(
+                nn.Conv2d(in_c, out_c, kernel_size=k, stride=s, padding=p)
+            )
+
+        layers = [
+            # initial down-sample
+            sn_conv(3, base_channels, 3, 2, 1),
+            nn.LeakyReLU(0.2, inplace=True),
+        ]
+
+        in_c = base_channels
+        # progressive downsamples ×3 (64→128→256→512)
+        for _ in range(3):
+            out_c = min(in_c * 2, 1024)
+            layers += [
+                sn_conv(in_c, out_c, 3, 2, 1),
+                nn.LeakyReLU(0.2, inplace=True),
+            ]
+            # single attention block after reaching 256 channels
+            if out_c == 256:
+                layers += [SelfAttention2d(out_c)]
+            in_c = out_c
+
+        # extra depth (keeps spatial size)
+        layers += [
+            sn_conv(in_c, 1024, 3, 1, 1),
+            nn.LeakyReLU(0.2, inplace=True),
+
+            # final 1-channel prediction map
+            sn_conv(1024, 1, 3, 1, 1),
+            MeanReduce(),        # → (B,1,1,1)
+            nn.Flatten(),        # → (B,1)
+        ]
+
+        self.main = nn.Sequential(*layers)
+
+    def forward(self, inputs):
+        # force full-precision inside AMP ctx for stability
+        with torch.cuda.amp.autocast(False):
+            return self.main(inputs.float())
+
+
+if TYPE_CHECKING:
+    from jobs.process.TrainVAEProcess import TrainVAEProcess
+    from jobs.process.TrainESRGANProcess import TrainESRGANProcess
+
+
+class Critic:
+    process: Union['TrainVAEProcess', 'TrainESRGANProcess']
+
+    def __init__(
+        self,
+        learning_rate=1e-5,
+        device='cpu',
+        optimizer='adam',
+        num_critic_per_gen=1,
+        dtype='float32',
+        lambda_gp=10,
+        start_step=0,
+        warmup_steps=1000,
+        process=None,
+        optimizer_params=None,
+    ):
+        self.learning_rate = learning_rate
+        self.device = device
+        self.optimizer_type = optimizer
+        self.num_critic_per_gen = num_critic_per_gen
+        self.dtype = dtype
+        self.torch_dtype = get_torch_dtype(self.dtype)
+        self.process = process
+        self.model = None
+        self.optimizer = None
+        self.scheduler = None
+        self.warmup_steps = warmup_steps
+        self.start_step = start_step
+        self.lambda_gp = lambda_gp
+
+        if optimizer_params is None:
+            optimizer_params = {}
+        self.optimizer_params = optimizer_params
+        self.print = self.process.print
+        print(f" Critic config: {self.__dict__}")
+
+    def setup(self):
+        self.model = CriticModel().to(self.device)
+        self.load_weights()
+        self.model.train()
+        self.model.requires_grad_(True)
+        params = self.model.parameters()
+        self.optimizer = get_optimizer(
+            params,
+            self.optimizer_type,
+            self.learning_rate,
+            optimizer_params=self.optimizer_params,
+        )
+        self.scheduler = torch.optim.lr_scheduler.ConstantLR(
+            self.optimizer,
+            total_iters=self.process.max_steps * self.num_critic_per_gen,
+            factor=1,
+            verbose=False,
+        )
+
+    def load_weights(self):
+        path_to_load = None
+        self.print(f"Critic: Looking for latest checkpoint in {self.process.save_root}")
+        files = glob.glob(os.path.join(self.process.save_root, f"CRITIC_{self.process.job.name}*.safetensors"))
+        if files:
+            latest_file = max(files, key=os.path.getmtime)
+            print(f" - Latest checkpoint is: {latest_file}")
+            path_to_load = latest_file
+        else:
+            self.print(" - No checkpoint found, starting from scratch")
+        if path_to_load:
+            self.model.load_state_dict(load_file(path_to_load))
+
+    def save(self, step=None):
+        self.process.update_training_metadata()
+        save_meta = get_meta_for_safetensors(self.process.meta, self.process.job.name)
+        step_num = f"_{str(step).zfill(9)}" if step is not None else ''
+        save_path = os.path.join(
+            self.process.save_root, f"CRITIC_{self.process.job.name}{step_num}.safetensors"
+        )
+        save_file(self.model.state_dict(), save_path, save_meta)
+        self.print(f"Saved critic to {save_path}")
+
+    def get_critic_loss(self, vgg_output):
+        # (caller still passes combined [pred|target] images)
+        if self.start_step > self.process.step_num:
+            return torch.tensor(0.0, dtype=self.torch_dtype, device=self.device)
+
+        warmup_scaler = 1.0
+        if self.process.step_num < self.start_step + self.warmup_steps:
+            warmup_scaler = (self.process.step_num - self.start_step) / self.warmup_steps
+
+        self.model.eval()
+        self.model.requires_grad_(False)
+
+        vgg_pred, _ = torch.chunk(vgg_output.float(), 2, dim=0)
+        stacked_output = self.model(vgg_pred)
+        return (-torch.mean(stacked_output)) * warmup_scaler
+
+    def step(self, vgg_output):
+        self.model.train()
+        self.model.requires_grad_(True)
+        self.optimizer.zero_grad()
+
+        critic_losses = []
+        inputs = vgg_output.detach().to(self.device, dtype=torch.float32)
+
+        vgg_pred, vgg_target = torch.chunk(inputs, 2, dim=0)
+        stacked_output = self.model(inputs).float()
+        out_pred, out_target = torch.chunk(stacked_output, 2, dim=0)
+
+        # hinge loss + gradient penalty
+        loss_real = torch.relu(1.0 - out_target).mean()
+        loss_fake = torch.relu(1.0 + out_pred).mean()
+        gradient_penalty = get_gradient_penalty(self.model, vgg_target, vgg_pred, self.device)
+        critic_loss = loss_real + loss_fake + self.lambda_gp * gradient_penalty
+
+        critic_loss.backward()
+        torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)
+        self.optimizer.step()
+        self.scheduler.step()
+        critic_losses.append(critic_loss.item())
+
+        return float(np.mean(critic_losses))
+
+    def get_lr(self):
+        if self.optimizer_type.startswith('dadaptation'):
+            return (
+                self.optimizer.param_groups[0]["d"]
+                * self.optimizer.param_groups[0]["lr"]
+            )
+        return self.optimizer.param_groups[0]["lr"]

jobs/process/models/vgg19_critic.py

@@ -33,11 +33,20 @@ class Vgg19Critic(nn.Module):
         super(Vgg19Critic, self).__init__()
         self.main = nn.Sequential(
             # input (bs, 512, 32, 32)
-            nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1),
+            # nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1),
+            nn.utils.spectral_norm(  # SN keeps D’s scale in check
+                nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1)
+            ),
             nn.LeakyReLU(0.2),  # (bs, 512, 16, 16)
-            nn.Conv2d(1024, 1024, kernel_size=3, stride=2, padding=1),
+            # nn.Conv2d(1024, 1024, kernel_size=3, stride=2, padding=1),
+            nn.utils.spectral_norm(
+                nn.Conv2d(1024, 1024, kernel_size=3, stride=2, padding=1)
+            ),
             nn.LeakyReLU(0.2),  # (bs, 512, 8, 8)
-            nn.Conv2d(1024, 1024, kernel_size=3, stride=2, padding=1),
+            # nn.Conv2d(1024, 1024, kernel_size=3, stride=2, padding=1),
+            nn.utils.spectral_norm(
+                nn.Conv2d(1024, 1024, kernel_size=3, stride=2, padding=1)
+            ),
             # (bs, 1, 4, 4)
             MeanReduce(),  # (bs, 1, 1, 1)
             nn.Flatten(),  # (bs, 1)
@@ -47,7 +56,9 @@ class Vgg19Critic(nn.Module):
         )
 
     def forward(self, inputs):
-        return self.main(inputs)
+        # return self.main(inputs)
+        with torch.cuda.amp.autocast(False):
+            return self.main(inputs.float())
 
 
 if TYPE_CHECKING:
@@ -92,7 +103,7 @@ class Critic:
         print(f" Critic config: {self.__dict__}")
 
     def setup(self):
-        self.model = Vgg19Critic().to(self.device, dtype=self.torch_dtype)
+        self.model = Vgg19Critic().to(self.device)
         self.load_weights()
         self.model.train()
         self.model.requires_grad_(True)
@@ -142,7 +153,8 @@ class Critic:
         # set model to not train for generator loss
         self.model.eval()
         self.model.requires_grad_(False)
-        vgg_pred, vgg_target = torch.chunk(vgg_output, 2, dim=0)
+        # vgg_pred, vgg_target = torch.chunk(vgg_output, 2, dim=0)
+        vgg_pred, vgg_target = torch.chunk(vgg_output.float(), 2, dim=0)
 
         # run model
         stacked_output = self.model(vgg_pred)
@@ -157,20 +169,34 @@ class Critic:
         self.optimizer.zero_grad()
 
         critic_losses = []
-        inputs = vgg_output.detach()
-        inputs = inputs.to(self.device, dtype=self.torch_dtype)
+        # inputs = vgg_output.detach()
+        # inputs = inputs.to(self.device, dtype=self.torch_dtype)
+        inputs = vgg_output.detach().to(self.device, dtype=torch.float32)
         self.optimizer.zero_grad()
 
         vgg_pred, vgg_target = torch.chunk(inputs, 2, dim=0)
 
+        # stacked_output = self.model(inputs).float()
+        # out_pred, out_target = torch.chunk(stacked_output, 2, dim=0)
+
+        # # Compute gradient penalty
+        # gradient_penalty = get_gradient_penalty(self.model, vgg_target, vgg_pred, self.device)
+
+        # # Compute WGAN-GP critic loss
+        # critic_loss = -(torch.mean(out_target) - torch.mean(out_pred)) + self.lambda_gp * gradient_penalty
+        
         stacked_output = self.model(inputs).float()
         out_pred, out_target = torch.chunk(stacked_output, 2, dim=0)
 
-        # Compute gradient penalty
+        # ── hinge loss ──
+        loss_real = torch.relu(1.0 - out_target).mean()
+        loss_fake = torch.relu(1.0 + out_pred).mean()
+
+        # gradient penalty (unchanged helper)
         gradient_penalty = get_gradient_penalty(self.model, vgg_target, vgg_pred, self.device)
 
-        # Compute WGAN-GP critic loss
-        critic_loss = -(torch.mean(out_target) - torch.mean(out_pred)) + self.lambda_gp * gradient_penalty
+        critic_loss = loss_real + loss_fake + self.lambda_gp * gradient_penalty
+        
         critic_loss.backward()
         torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)
         self.optimizer.step()