Added code to handle diffusion feature extraction loss

extensions_built_in/sd_trainer/SDTrainer.py

@@ -32,6 +32,7 @@
 from diffusers import EMAModel
 import math
 from toolkit.train_tools import precondition_model_outputs_flow_match
+from toolkit.models.diffusion_feature_extraction import DiffusionFeatureExtractor, load_dfe
 
 
 def flush():
@@ -78,6 +79,8 @@ def _unscale_grads_replacer(optimizer, inv_scale, found_inf, allow_fp16):
 
         self.cached_blank_embeds: Optional[PromptEmbeds] = None
         self.cached_trigger_embeds: Optional[PromptEmbeds] = None
+
+        self.dfe: Optional[DiffusionFeatureExtractor] = None
 
 
     def before_model_load(self):
@@ -178,6 +181,11 @@ def hook_before_train_loop(self):
                 # move back to cpu
                 self.sd.text_encoder_to('cpu')
                 flush()
+
+        if self.train_config.diffusion_feature_extractor_path is not None:
+            self.dfe = load_dfe(self.train_config.diffusion_feature_extractor_path)
+            self.dfe.to(self.device_torch)
+            self.dfe.eval()
 
 
     def process_output_for_turbo(self, pred, noisy_latents, timesteps, noise, batch):
@@ -285,6 +293,7 @@ def calculate_loss(
     ):
         loss_target = self.train_config.loss_target
         is_reg = any(batch.get_is_reg_list())
+        additional_loss = 0.0
 
         prior_mask_multiplier = None
         target_mask_multiplier = None
@@ -367,7 +376,17 @@ def calculate_loss(
             target = (noise - batch.latents).detach()
         else:
             target = noise
-
+
+        if self.dfe is not None:
+            # do diffusion feature extraction on target
+            with torch.no_grad():
+                rectified_flow_target = noise.float() - batch.latents.float()
+                target_features = self.dfe(torch.cat([rectified_flow_target, noise.float()], dim=1))
+
+            # do diffusion feature extraction on prediction
+            pred_features = self.dfe(torch.cat([noise_pred.float(), noise.float()], dim=1))
+            additional_loss += torch.nn.functional.mse_loss(pred_features, target_features, reduction="mean")
+
         if target is None:
             target = noise
 
@@ -487,7 +506,7 @@ def calculate_loss(
             loss = loss + norm_std_loss
 
 
-        return loss
+        return loss + additional_loss
 
     def preprocess_batch(self, batch: 'DataLoaderBatchDTO'):
         return batch

toolkit/config_modules.py

@@ -400,6 +400,9 @@ def __init__(self, **kwargs):
         self.paramiter_swapping_factor = kwargs.get('paramiter_swapping_factor', 0.1)
         # bypass the guidance embedding for training. For open flux with guidance embedding
         self.bypass_guidance_embedding = kwargs.get('bypass_guidance_embedding', False)
+
+        # diffusion feature extractor
+        self.diffusion_feature_extractor_path = kwargs.get('diffusion_feature_extractor_path', None)
 
 
 class ModelConfig:

toolkit/models/diffusion_feature_extraction.py

@@ -0,0 +1,55 @@
+import torch
+import os
+from torch import nn
+from safetensors.torch import load_file
+
+
+class DFEBlock(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.conv1 = nn.Conv2d(channels, channels, 3, padding=1)
+        self.conv2 = nn.Conv2d(channels, channels, 3, padding=1)
+        self.act = nn.GELU()
+
+    def forward(self, x):
+        x_in = x
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.act(x)
+        x = x + x_in
+        return x
+
+
+class DiffusionFeatureExtractor(nn.Module):
+    def __init__(self, in_channels=32):
+        super().__init__()
+        num_blocks = 6
+        self.conv_in = nn.Conv2d(in_channels, 512, 1)
+        self.conv_pool = nn.Conv2d(512, 512, 3, stride=2, padding=1)
+        self.blocks = nn.ModuleList([DFEBlock(512) for _ in range(num_blocks)])
+        self.conv_out = nn.Conv2d(512, 512, 1)
+
+    def forward(self, x):
+        x = self.conv_in(x)
+        x = self.conv_pool(x)
+        for block in self.blocks:
+            x = block(x)
+        x = self.conv_out(x)
+        return x
+
+
+def load_dfe(model_path) -> DiffusionFeatureExtractor:
+    dfe = DiffusionFeatureExtractor()
+    if not os.path.exists(model_path):
+        raise FileNotFoundError(f"Model file not found: {model_path}")
+    # if it ende with safetensors
+    if model_path.endswith('.safetensors'):
+        state_dict = load_file(model_path)
+    else:
+        state_dict = torch.load(model_path, weights_only=True)
+        if 'model_state_dict' in state_dict:
+            state_dict = state_dict['model_state_dict']
+
+    dfe.load_state_dict(state_dict)
+    dfe.eval()
+    return dfe