Small updates and bug fixes for various things

extensions_built_in/sd_trainer/SDTrainer.py

@@ -764,6 +764,7 @@ class SDTrainer(BaseSDTrainProcess):
         conditional_embeds: Union[PromptEmbeds, None] = None,
         unconditional_embeds: Union[PromptEmbeds, None] = None,
         batch: Optional['DataLoaderBatchDTO'] = None,
+        is_primary_pred: bool = False,
         **kwargs,
     ):
         dtype = get_torch_dtype(self.train_config.dtype)
@@ -1553,6 +1554,7 @@ class SDTrainer(BaseSDTrainProcess):
                             conditional_embeds=conditional_embeds.to(self.device_torch, dtype=dtype),
                             unconditional_embeds=unconditional_embeds,
                             batch=batch,
+                            is_primary_pred=True,
                             **pred_kwargs
                         )
                     self.after_unet_predict()

jobs/process/TrainVAEProcess.py

@@ -18,7 +18,7 @@ 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, total_variation
+from toolkit.losses import ComparativeTotalVariation, get_gradient_penalty, PatternLoss, total_variation, total_variation_deltas
 from toolkit.metadata import get_meta_for_safetensors
 from toolkit.optimizer import get_optimizer
 from toolkit.style import get_style_model_and_losses
@@ -283,10 +283,33 @@ class TrainVAEProcess(BaseTrainProcess):
         else:
             return torch.tensor(0.0, device=self.device)
         
-    def get_ltv_loss(self, latent):
+    def get_ltv_loss(self, latent, images):
         # loss to reduce the latent space variance
         if self.ltv_weight > 0:
-            return total_variation(latent).mean()
+            with torch.no_grad():
+                images = images.to(latent.device, dtype=latent.dtype)
+                # resize down to latent size
+                images = torch.nn.functional.interpolate(images, size=(latent.shape[2], latent.shape[3]), mode='bilinear', align_corners=False)
+                
+                # mean the color channel and then expand to latent size
+                images = images.mean(dim=1, keepdim=True)
+                images = images.repeat(1, latent.shape[1], 1, 1)
+                
+                # normalize to a mean of 0 and std of 1
+                images_mean = images.mean(dim=(2, 3), keepdim=True)
+                images_std = images.std(dim=(2, 3), keepdim=True)
+                images = (images - images_mean) / (images_std + 1e-6)
+                
+                # now we target the same std of the image for the latent space as to not reduce to 0
+            
+            latent_tv = torch.abs(total_variation_deltas(latent))
+            images_tv = torch.abs(total_variation_deltas(images))
+            loss = torch.abs(latent_tv - images_tv) # keep it spatially aware
+            loss = loss.mean(dim=2, keepdim=True)
+            loss = loss.mean(dim=3, keepdim=True)  # mean over height and width
+            loss = loss.mean(dim=1, keepdim=True)  # mean over channels
+            loss = loss.mean()
+            return loss
         else:
             return torch.tensor(0.0, device=self.device)
         
@@ -733,7 +756,7 @@ class TrainVAEProcess(BaseTrainProcess):
                     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
+                    ltv_loss = self.get_ltv_loss(latents, batch) * self.ltv_weight
                 else:
                     ltv_loss = torch.tensor(0.0, device=self.device, dtype=self.torch_dtype)
                     

toolkit/lora_special.py

@@ -7,7 +7,7 @@ import re
 import sys
 from typing import List, Optional, Dict, Type, Union
 import torch
-from diffusers import UNet2DConditionModel, PixArtTransformer2DModel, AuraFlowTransformer2DModel
+from diffusers import UNet2DConditionModel, PixArtTransformer2DModel, AuraFlowTransformer2DModel, WanTransformer3DModel
 from transformers import CLIPTextModel
 from toolkit.models.lokr import LokrModule
 
@@ -523,6 +523,14 @@ class LoRASpecialNetwork(ToolkitNetworkMixin, LoRANetwork):
                 transformer.pos_embed = self.transformer_pos_embed
                 transformer.proj_out = self.transformer_proj_out
             
+            elif base_model is not None and base_model.arch == "wan21":
+                transformer: WanTransformer3DModel = unet
+                self.transformer_pos_embed = copy.deepcopy(transformer.patch_embedding)
+                self.transformer_proj_out = copy.deepcopy(transformer.proj_out)
+
+                transformer.patch_embedding = self.transformer_pos_embed
+                transformer.proj_out = self.transformer_proj_out
+
             else:
                 unet: UNet2DConditionModel = unet
                 unet_conv_in: torch.nn.Conv2d = unet.conv_in
@@ -539,7 +547,8 @@ class LoRASpecialNetwork(ToolkitNetworkMixin, LoRANetwork):
         all_params = super().prepare_optimizer_params(text_encoder_lr, unet_lr, default_lr)
 
         if self.full_train_in_out:
-            if self.is_pixart or self.is_auraflow or self.is_flux:
+            base_model = self.base_model_ref() if self.base_model_ref is not None else None
+            if self.is_pixart or self.is_auraflow or self.is_flux or (base_model is not None and base_model.arch == "wan21"):
                 all_params.append({"lr": unet_lr, "params": list(self.transformer_pos_embed.parameters())})
                 all_params.append({"lr": unet_lr, "params": list(self.transformer_proj_out.parameters())})
             else:

toolkit/losses.py

@@ -14,6 +14,22 @@ def total_variation(image):
     return ((torch.sum(torch.abs(image[:, :, :, :-1] - image[:, :, :, 1:])) +
              torch.sum(torch.abs(image[:, :, :-1, :] - image[:, :, 1:, :]))) / n_elements)
     
+def total_variation_deltas(image):
+    """
+    Compute per-pixel total variation deltas.
+    Input:
+    - image: Tensor of shape (N, C, H, W)
+    Returns:
+    - Tensor with shape (N, C, H, W), padded to match input shape
+    """
+    dh = torch.zeros_like(image)
+    dv = torch.zeros_like(image)
+
+    dh[:, :, :, :-1] = torch.abs(image[:, :, :, 1:] - image[:, :, :, :-1])
+    dv[:, :, :-1, :] = torch.abs(image[:, :, 1:, :] - image[:, :, :-1, :])
+
+    return dh + dv
+
 
 class ComparativeTotalVariation(torch.nn.Module):
     """