diff --git a/flux_train_network.py b/flux_train_network.py
index def44155..c619afac 100644
--- a/flux_train_network.py
+++ b/flux_train_network.py
@@ -336,24 +336,24 @@ class FluxNetworkTrainer(train_network.NetworkTrainer):
 
     def get_noise_pred_and_target(
         self,
-        args,
-        accelerator,
+        args: argparse.Namespace,
+        accelerator: Accelerator,
         noise_scheduler,
-        latents,
-        batch,
+        latents: torch.FloatTensor,
+        batch: dict[str, torch.Tensor],
         text_encoder_conds,
-        unet: flux_models.Flux,
+        unet,
         network,
-        weight_dtype,
-        train_unet,
+        weight_dtype: torch.dtype,
+        train_unet: bool,
         is_train=True,
-    ):
+    ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.IntTensor, torch.Tensor | None]:
         # Sample noise that we'll add to the latents
         noise = torch.randn_like(latents)
         bsz = latents.shape[0]
 
         # get noisy model input and timesteps
-        noisy_model_input, timesteps, sigmas = flux_train_utils.get_noisy_model_input_and_timesteps(
+        noisy_model_input, timesteps, sigmas = flux_train_utils.get_noisy_model_input_and_timestep(
             args, noise_scheduler, latents, noise, accelerator.device, weight_dtype
         )
 
@@ -448,7 +448,7 @@ class FluxNetworkTrainer(train_network.NetworkTrainer):
                 )
                 target[diff_output_pr_indices] = model_pred_prior.to(target.dtype)
 
-        return model_pred, target, timesteps, weighting
+        return model_pred, noisy_model_input, target, timesteps, weighting
 
     def post_process_loss(self, loss, args, timesteps, noise_scheduler):
         return loss
diff --git a/library/custom_train_functions.py b/library/custom_train_functions.py
index ad3e69ff..18ad8234 100644
--- a/library/custom_train_functions.py
+++ b/library/custom_train_functions.py
@@ -4,7 +4,7 @@ import argparse
 import random
 import re
 from torch.types import Number
-from typing import List, Optional, Union
+from typing import List, Optional, Union, Callable
 from .utils import setup_logging
 
 setup_logging()
@@ -502,6 +502,106 @@ def apply_masked_loss(loss, batch) -> torch.FloatTensor:
     loss = loss * mask_image
     return loss
 
+def diffusion_dpo_loss(loss: torch.Tensor, call_unet: Callable[[],torch.Tensor], apply_loss: Callable[[torch.Tensor], torch.Tensor], beta_dpo: float):
+    """
+    DPO loss
+
+    Args:
+        loss: pairs of w, l losses B//2, C, H, W
+        call_unet: function to call unet
+        apply_loss: function to apply loss
+        beta_dpo: beta_dpo weight
+
+    Returns:
+        tuple: 
+            - loss: mean loss of C, H, W
+            - metrics:
+                - total_loss: mean loss of C, H, W
+                - raw_model_loss: mean loss of C, H, W
+                - ref_loss: mean loss of C, H, W
+                - implicit_acc: accumulated implicit of C, H, W
+
+    """
+
+    model_loss = loss.mean(dim=list(range(1, len(loss.shape))))
+    model_loss_w, model_loss_l = model_loss.chunk(2)
+    raw_model_loss = 0.5 * (model_loss_w.mean() + model_loss_l.mean())
+    model_diff = model_loss_w - model_loss_l
+
+    # ref loss
+    with torch.no_grad():
+        ref_noise_pred = call_unet()
+        ref_loss =  apply_loss(ref_noise_pred)
+        ref_loss = ref_loss.mean(dim=list(range(1, len(ref_loss.shape))))
+        ref_losses_w, ref_losses_l = ref_loss.chunk(2)
+        ref_diff = ref_losses_w - ref_losses_l
+        raw_ref_loss = ref_loss.mean()
+
+
+    scale_term = -0.5 * beta_dpo
+    inside_term = scale_term * (model_diff - ref_diff)
+    loss = -1 * torch.nn.functional.logsigmoid(inside_term)
+
+    implicit_acc = (inside_term > 0).sum().float() / inside_term.size(0)
+    implicit_acc += 0.5 * (inside_term == 0).sum().float() / inside_term.size(0)
+
+    metrics = {
+        "total_loss": model_loss.detach().mean().item(),
+        "raw_model_loss": raw_model_loss.detach().mean().item(),
+        "ref_loss": raw_ref_loss.detach().item(),
+        "implicit_acc": implicit_acc.detach().item(),
+    }
+
+    return loss, metrics
+
+def mapo_loss(loss: torch.Tensor, mapo_weight: float, num_train_timesteps=1000) -> tuple[torch.Tensor, dict[str, int | float]]:
+    """
+    MaPO loss 
+
+    Args:
+        loss: pairs of w, l losses B//2, C, H, W
+        mapo_loss: mapo weight
+        num_train_timesteps: number of timesteps
+
+    Returns:
+        tuple: 
+            - loss: mean loss of C, H, W
+            - metrics:
+                - total_loss: mean loss of C, H, W
+                - ratio_loss: mean ratio loss of C, H, W
+                - model_losses_w: mean loss of w losses of C, H, W
+                - model_losses_l: mean loss of l losses of C, H, W
+                - win_score : mean win score of C, H, W
+                - lose_score : mean lose score of C, H, W
+
+    """
+    model_loss = loss.mean(dim=list(range(1, len(loss.shape))))
+
+    snr = 0.5
+    model_losses_w, model_losses_l = model_loss.chunk(2)
+    log_odds = (snr * model_losses_w) / (torch.exp(snr * model_losses_w) - 1) - (
+        snr * model_losses_l
+    ) / (torch.exp(snr * model_losses_l) - 1)
+
+    # Ratio loss.
+    # By multiplying T to the inner term, we try to maximize the margin throughout the overall denoising process.
+    ratio = torch.nn.functional.logsigmoid(log_odds * num_train_timesteps)
+    ratio_losses = mapo_weight * ratio
+
+    # Full MaPO loss
+    loss = model_losses_w.mean(dim=list(range(1, len(model_losses_w.shape)))) - ratio_losses.mean(dim=list(range(1, len(ratio_losses.shape))))
+
+    metrics = {
+        "total_loss": loss.detach().mean().item(),
+        "ratio_loss": -ratio_losses.detach().mean().item(),
+        "model_losses_w": model_losses_w.detach().mean().item(),
+        "model_losses_l": model_losses_l.detach().mean().item(),
+        "win_score": ((snr * model_losses_w) / (torch.exp(snr * model_losses_w) - 1)).detach().mean().item(),
+        "lose_score": ((snr * model_losses_l) / (torch.exp(snr * model_losses_l) - 1)).detach().mean().item(),
+    }
+
+    return loss, metrics
+
 
 """
 ##########################################
diff --git a/library/flux_train_utils.py b/library/flux_train_utils.py
index 0e73a01d..b8cf40de 100644
--- a/library/flux_train_utils.py
+++ b/library/flux_train_utils.py
@@ -359,7 +359,7 @@ def denoise(
 
 
 # region train
-def get_sigmas(noise_scheduler, timesteps, device, n_dim=4, dtype=torch.float32):
+def get_sigmas(noise_scheduler, timesteps, device, n_dim=4, dtype=torch.float32) -> torch.FloatTensor:
     sigmas = noise_scheduler.sigmas.to(device=device, dtype=dtype)
     schedule_timesteps = noise_scheduler.timesteps.to(device)
     timesteps = timesteps.to(device)
@@ -390,7 +390,7 @@ def compute_density_for_timestep_sampling(
     return u
 
 
-def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None):
+def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas) -> torch.Tensor:
     """Computes loss weighting scheme for SD3 training.
 
     Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
@@ -407,35 +407,43 @@ def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None):
     return weighting
 
 
-def get_noisy_model_input_and_timesteps(
+def get_noisy_model_input_and_timestep(
     args, noise_scheduler, latents: torch.Tensor, noise: torch.Tensor, device, dtype
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
+    """
+    Returns:
+        tuple[
+            noisy_model_input: noisy at sigma applied to latent
+            timesteps: timesteps betweeen 1.0 and 1000.0
+            sigmas: sigmas between 0.0 and 1.0
+        ]
+    """
     bsz, _, h, w = latents.shape
     assert bsz > 0, "Batch size not large enough"
-    num_timesteps = noise_scheduler.config.num_train_timesteps
+    num_timesteps: int = noise_scheduler.config.num_train_timesteps
     if args.timestep_sampling == "uniform" or args.timestep_sampling == "sigmoid":
         # Simple random sigma-based noise sampling
         if args.timestep_sampling == "sigmoid":
             # https://github.com/XLabs-AI/x-flux/tree/main
-            sigmas = torch.sigmoid(args.sigmoid_scale * torch.randn((bsz,), device=device))
+            sigma = torch.sigmoid(args.sigmoid_scale * torch.randn((bsz,), device=device))
         else:
-            sigmas = torch.rand((bsz,), device=device)
+            sigma = torch.rand((bsz,), device=device)
 
-        timesteps = sigmas * num_timesteps
+        timestep = sigma * num_timesteps
     elif args.timestep_sampling == "shift":
         shift = args.discrete_flow_shift
-        sigmas = torch.randn(bsz, device=device)
-        sigmas = sigmas * args.sigmoid_scale  # larger scale for more uniform sampling
-        sigmas = sigmas.sigmoid()
-        sigmas = (sigmas * shift) / (1 + (shift - 1) * sigmas)
-        timesteps = sigmas * num_timesteps
+        sigma = torch.randn(bsz, device=device)
+        sigma = sigma * args.sigmoid_scale  # larger scale for more uniform sampling
+        sigma = sigma.sigmoid()
+        sigma = (sigma * shift) / (1 + (shift - 1) * sigma)
+        timestep = sigma * num_timesteps
     elif args.timestep_sampling == "flux_shift":
-        sigmas = torch.randn(bsz, device=device)
-        sigmas = sigmas * args.sigmoid_scale  # larger scale for more uniform sampling
-        sigmas = sigmas.sigmoid()
+        sigma = torch.randn(bsz, device=device)
+        sigma = sigma * args.sigmoid_scale  # larger scale for more uniform sampling
+        sigma = sigma.sigmoid()
         mu = get_lin_function(y1=0.5, y2=1.15)((h // 2) * (w // 2)) # we are pre-packed so must adjust for packed size 
-        sigmas = time_shift(mu, 1.0, sigmas)
-        timesteps = sigmas * num_timesteps
+        sigma = time_shift(mu, 1.0, sigma)
+        timestep = sigma * num_timesteps
     else:
         # Sample a random timestep for each image
         # for weighting schemes where we sample timesteps non-uniformly
@@ -447,28 +455,29 @@ def get_noisy_model_input_and_timesteps(
             mode_scale=args.mode_scale,
         )
         indices = (u * num_timesteps).long()
-        timesteps = noise_scheduler.timesteps[indices].to(device=device)
-        sigmas = get_sigmas(noise_scheduler, timesteps, device, n_dim=latents.ndim, dtype=dtype)
+        timestep: torch.Tensor = noise_scheduler.timesteps[indices].to(device=device)
+        sigma = get_sigmas(noise_scheduler, timestep, device, n_dim=latents.ndim, dtype=dtype)
 
     # Broadcast sigmas to latent shape
-    sigmas = sigmas.view(-1, 1, 1, 1)
+    sigma = sigma.view(-1, 1, 1, 1)
 
     # Add noise to the latents according to the noise magnitude at each timestep
     # (this is the forward diffusion process)
     if args.ip_noise_gamma:
+        assert isinstance(args.ip_noise_gamma, float)
         xi = torch.randn_like(latents, device=latents.device, dtype=dtype)
         if args.ip_noise_gamma_random_strength:
             ip_noise_gamma = (torch.rand(1, device=latents.device, dtype=dtype) * args.ip_noise_gamma)
         else:
             ip_noise_gamma = args.ip_noise_gamma
-        noisy_model_input = (1.0 - sigmas) * latents + sigmas * (noise + ip_noise_gamma * xi)
+        noisy_model_input = (1.0 - sigma) * latents + sigma * (noise + ip_noise_gamma * xi)
     else:
-        noisy_model_input = (1.0 - sigmas) * latents + sigmas * noise
+        noisy_model_input = (1.0 - sigma) * latents + sigma * noise
 
-    return noisy_model_input.to(dtype), timesteps.to(dtype), sigmas
+    return noisy_model_input.to(dtype), timestep.to(dtype), sigma
 
 
-def apply_model_prediction_type(args, model_pred, noisy_model_input, sigmas):
+def apply_model_prediction_type(args, model_pred: torch.FloatTensor, noisy_model_input, sigmas):
     weighting = None
     if args.model_prediction_type == "raw":
         pass
diff --git a/library/flux_utils.py b/library/flux_utils.py
index 8be1d63e..7e8f9f61 100644
--- a/library/flux_utils.py
+++ b/library/flux_utils.py
@@ -347,7 +347,7 @@ def prepare_img_ids(batch_size: int, packed_latent_height: int, packed_latent_wi
     return img_ids
 
 
-def unpack_latents(x: torch.Tensor, packed_latent_height: int, packed_latent_width: int) -> torch.Tensor:
+def unpack_latents(x: torch.FloatTensor, packed_latent_height: int, packed_latent_width: int) -> torch.FloatTensor:
     """
     x: [b (h w) (c ph pw)] -> [b c (h ph) (w pw)], ph=2, pw=2
     """
diff --git a/library/sd3_train_utils.py b/library/sd3_train_utils.py
index c4079884..fe03e8fc 100644
--- a/library/sd3_train_utils.py
+++ b/library/sd3_train_utils.py
@@ -895,7 +895,7 @@ def compute_density_for_timestep_sampling(
     return u
 
 
-def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None):
+def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas: torch.Tensor) -> torch.Tensor:
     """Computes loss weighting scheme for SD3 training.
 
     Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
diff --git a/library/train_util.py b/library/train_util.py
index 08bb7dfd..178b0948 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -1700,10 +1700,14 @@ class BaseDataset(torch.utils.data.Dataset):
                     latents = image_info.latents_flipped
                     alpha_mask = None if image_info.alpha_mask is None else torch.flip(image_info.alpha_mask, [1])
 
+                target_size = (latents.shape[2] * 8, latents.shape[1] * 8)
                 image = None
 
                 images.append(image)
                 latents_list.append(latents)
+                original_sizes_hw.append((int(original_size[1]), int(original_size[0])))
+                crop_top_lefts.append((int(crop_ltrb[1]), int(crop_ltrb[0])))
+                target_sizes_hw.append((int(target_size[1]), int(target_size[0])))
             elif image_info.latents_npz is not None:  # FineTuningDatasetまたはcache_latents_to_disk=Trueの場合
                 latents, original_size, crop_ltrb, flipped_latents, alpha_mask = (
                     self.latents_caching_strategy.load_latents_from_disk(image_info.latents_npz, image_info.bucket_reso)
@@ -1715,12 +1719,16 @@ class BaseDataset(torch.utils.data.Dataset):
                 latents = torch.FloatTensor(latents)
                 if alpha_mask is not None:
                     alpha_mask = torch.FloatTensor(alpha_mask)
+                target_size = (latents.shape[2] * 8, latents.shape[1] * 8)
 
                 image = None
 
                 images.append(image)
                 latents_list.append(latents)
                 alpha_mask_list.append(alpha_mask)
+                original_sizes_hw.append((int(original_size[1]), int(original_size[0])))
+                crop_top_lefts.append((int(crop_ltrb[1]), int(crop_ltrb[0])))
+                target_sizes_hw.append((int(target_size[1]), int(target_size[0])))
             else:
                 if isinstance(image_info, ImageSetInfo):
                     for absolute_path in image_info.absolute_paths:
@@ -2543,11 +2551,11 @@ class ControlNetDataset(BaseDataset):
                 subset.token_warmup_min,
                 subset.token_warmup_step,
                 resize_interpolation=subset.resize_interpolation,
-                subset.preference,
-                subset.preference_caption_prefix,
-                subset.preference_caption_suffix,
-                subset.non_preference_caption_prefix,
-                subset.non_preference_caption_suffix,
+                preference=subset.preference,
+                preference_caption_prefix=subset.preference_caption_prefix,
+                preference_caption_suffix=subset.preference_caption_suffix,
+                non_preference_caption_prefix=subset.non_preference_caption_prefix,
+                non_preference_caption_suffix=subset.non_preference_caption_suffix,
             )
             db_subsets.append(db_subset)
 
diff --git a/pytest.ini b/pytest.ini
index 484d3aef..34b7e9c1 100644
--- a/pytest.ini
+++ b/pytest.ini
@@ -6,3 +6,4 @@ filterwarnings =
     ignore::DeprecationWarning
     ignore::UserWarning
 		ignore::FutureWarning
+pythonpath = .
diff --git a/sd3_train_network.py b/sd3_train_network.py
index cdb7aa4e..fdb2b356 100644
--- a/sd3_train_network.py
+++ b/sd3_train_network.py
@@ -323,7 +323,7 @@ class Sd3NetworkTrainer(train_network.NetworkTrainer):
         weight_dtype,
         train_unet,
         is_train=True,
-    ):
+    ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.IntTensor, torch.Tensor | None]:
         # Sample noise that we'll add to the latents
         noise = torch.randn_like(latents)
 
@@ -389,7 +389,7 @@ class Sd3NetworkTrainer(train_network.NetworkTrainer):
 
                 target[diff_output_pr_indices] = model_pred_prior.to(target.dtype)
 
-        return model_pred, target, timesteps, weighting
+        return model_pred, noisy_model_input, target, timesteps, weighting
 
     def post_process_loss(self, loss, args, timesteps, noise_scheduler):
         return loss
diff --git a/tests/library/test_custom_train_functions_diffusion_dpo.py b/tests/library/test_custom_train_functions_diffusion_dpo.py
new file mode 100644
index 00000000..140ecf54
--- /dev/null
+++ b/tests/library/test_custom_train_functions_diffusion_dpo.py
@@ -0,0 +1,164 @@
+import torch
+from unittest.mock import Mock
+
+from library.custom_train_functions import diffusion_dpo_loss
+
+def test_diffusion_dpo_loss_basic():
+    batch_size = 4
+    channels = 4
+    height, width = 64, 64
+    
+    # Create dummy loss tensor
+    loss = torch.rand(batch_size, channels, height, width)
+    
+    # Mock the call_unet and apply_loss functions
+    mock_unet_output = torch.rand(batch_size, channels, height, width)
+    call_unet = Mock(return_value=mock_unet_output)
+    
+    mock_loss_output = torch.rand(batch_size, channels, height, width)
+    apply_loss = Mock(return_value=mock_loss_output)
+    
+    beta_dpo = 0.1
+    
+    result, metrics = diffusion_dpo_loss(loss, call_unet, apply_loss, beta_dpo)
+    
+    # Check return types
+    assert isinstance(result, torch.Tensor)
+    assert isinstance(metrics, dict)
+    
+    # Check expected metrics are present
+    expected_keys = ["total_loss", "raw_model_loss", "ref_loss", "implicit_acc"]
+    for key in expected_keys:
+        assert key in metrics
+        assert isinstance(metrics[key], float)
+    
+    # Verify mocks were called correctly
+    call_unet.assert_called_once()
+    apply_loss.assert_called_once_with(mock_unet_output)
+
+def test_diffusion_dpo_loss_shapes():
+    # Test with different tensor shapes
+    shapes = [
+        (2, 4, 32, 32),    # Small tensor
+        (4, 16, 64, 64),  # Medium tensor
+        (6, 32, 128, 128),  # Larger tensor
+    ]
+    
+    for shape in shapes:
+        loss = torch.rand(*shape)
+        
+        # Create mocks
+        mock_unet_output = torch.rand(*shape)
+        call_unet = Mock(return_value=mock_unet_output)
+        
+        mock_loss_output = torch.rand(*shape)
+        apply_loss = Mock(return_value=mock_loss_output)
+        
+        result, metrics = diffusion_dpo_loss(loss, call_unet, apply_loss, 0.1)
+        
+        # The result should be a scalar tensor
+        assert result.shape == torch.Size([shape[0] // 2])
+        
+        # All metrics should be scalars
+        for val in metrics.values():
+            assert isinstance(val, float)
+
+def test_diffusion_dpo_loss_beta_values():
+    batch_size = 2
+    channels = 4
+    height, width = 64, 64
+    
+    loss = torch.rand(batch_size, channels, height, width)
+    
+    # Create consistent mock returns
+    mock_unet_output = torch.rand(batch_size, channels, height, width)
+    mock_loss_output = torch.rand(batch_size, channels, height, width)
+    
+    # Test with different beta values
+    beta_values = [0.0, 0.1, 1.0, 10.0]
+    results = []
+    
+    for beta in beta_values:
+        call_unet = Mock(return_value=mock_unet_output)
+        apply_loss = Mock(return_value=mock_loss_output)
+        
+        result, metrics = diffusion_dpo_loss(loss, call_unet, apply_loss, beta)
+        results.append(result.item())
+    
+    # With increasing beta, results should be different
+    # This test checks that beta affects the output
+    assert len(set(results)) > 1, "Different beta values should produce different results"
+
+def test_diffusion_dpo_implicit_acc():
+    batch_size = 4
+    channels = 4
+    height, width = 64, 64
+    
+    # Create controlled test data where winners have lower loss
+    w_loss = torch.ones(batch_size//2, channels, height, width) * 0.2
+    l_loss = torch.ones(batch_size//2, channels, height, width) * 0.8
+    loss = torch.cat([w_loss, l_loss], dim=0)
+    
+    # Make the reference loss similar but with less difference
+    ref_w_loss = torch.ones(batch_size//2, channels, height, width) * 0.3
+    ref_l_loss = torch.ones(batch_size//2, channels, height, width) * 0.7
+    ref_loss = torch.cat([ref_w_loss, ref_l_loss], dim=0)
+    
+    call_unet = Mock(return_value=torch.zeros_like(loss))  # Dummy, won't be used
+    apply_loss = Mock(return_value=ref_loss)
+    
+    # With a positive beta, model_diff > ref_diff should lead to high implicit accuracy
+    result, metrics = diffusion_dpo_loss(loss, call_unet, apply_loss, 1.0)
+    
+    # Implicit accuracy should be high (model correctly identifies preferences)
+    assert metrics["implicit_acc"] > 0.5
+
+def test_diffusion_dpo_gradient_flow():
+    batch_size = 4
+    channels = 4
+    height, width = 64, 64
+    
+    # Create loss tensor that requires gradients
+    loss = torch.rand(batch_size, channels, height, width, requires_grad=True)
+    
+    # Create mock outputs
+    mock_unet_output = torch.rand(batch_size, channels, height, width)
+    call_unet = Mock(return_value=mock_unet_output)
+    
+    mock_loss_output = torch.rand(batch_size, channels, height, width)
+    apply_loss = Mock(return_value=mock_loss_output)
+    
+    # Compute loss
+    result, _ = diffusion_dpo_loss(loss, call_unet, apply_loss, 0.1)
+    
+    # Check that gradients flow
+    result.mean().backward()
+    
+    # Verify gradients flowed through
+    assert loss.grad is not None
+
+def test_diffusion_dpo_no_ref_grad():
+    batch_size = 4
+    channels = 4
+    height, width = 64, 64
+    
+    loss = torch.rand(batch_size, channels, height, width, requires_grad=True)
+    
+    # Set up mock that tracks if it was called with no_grad
+    mock_unet_output = torch.rand(batch_size, channels, height, width)
+    call_unet = Mock(return_value=mock_unet_output)
+    
+    mock_loss_output = torch.rand(batch_size, channels, height, width, requires_grad=True)
+    apply_loss = Mock(return_value=mock_loss_output)
+    
+    # Run function
+    result, _ = diffusion_dpo_loss(loss, call_unet, apply_loss, 0.1)
+    result.mean().backward()
+    
+    # Check that the reference loss has no gradients (was computed with torch.no_grad())
+    # This is a bit tricky to test directly, but we can verify call_unet was called
+    call_unet.assert_called_once()
+    apply_loss.assert_called_once()
+    
+    # The mock_loss_output should not receive gradients as it's used inside torch.no_grad()
+    assert mock_loss_output.grad is None
diff --git a/tests/library/test_custom_train_functions_mapo.py b/tests/library/test_custom_train_functions_mapo.py
new file mode 100644
index 00000000..88228f72
--- /dev/null
+++ b/tests/library/test_custom_train_functions_mapo.py
@@ -0,0 +1,116 @@
+import torch
+import numpy as np
+
+from library.custom_train_functions import mapo_loss
+
+
+def test_mapo_loss_basic():
+    batch_size = 4
+    channels = 4
+    height, width = 64, 64
+
+    # Create dummy loss tensor with shape [B, C, H, W]
+    loss = torch.rand(batch_size, channels, height, width)
+    mapo_weight = 0.5
+
+    result, metrics = mapo_loss(loss, mapo_weight)
+
+    # Check return types
+    assert isinstance(result, torch.Tensor)
+    assert isinstance(metrics, dict)
+
+    # Check required metrics are present
+    expected_keys = ["total_loss", "ratio_loss", "model_losses_w", "model_losses_l", "win_score", "lose_score"]
+    for key in expected_keys:
+        assert key in metrics
+        assert isinstance(metrics[key], float)
+
+
+def test_mapo_loss_different_shapes():
+    # Test with different tensor shapes
+    shapes = [
+        (2, 4, 32, 32),  # Small tensor
+        (4, 16, 64, 64),  # Medium tensor
+        (6, 32, 128, 128),  # Larger tensor
+    ]
+
+    for shape in shapes:
+        loss = torch.rand(*shape)
+        result, metrics = mapo_loss(loss, 0.5)
+
+        # The result should be a scalar tensor
+        assert result.shape == torch.Size([])
+
+        # All metrics should be scalars
+        for val in metrics.values():
+            assert np.isscalar(val)
+
+
+def test_mapo_loss_with_zero_weight():
+    loss = torch.rand(4, 3, 64, 64)
+    result, metrics = mapo_loss(loss, 0.0)
+
+    # With zero mapo_weight, ratio_loss should be zero
+    assert metrics["ratio_loss"] == 0.0
+
+    # result should be equal to mean of model_losses_w
+    assert torch.isclose(result, torch.tensor(metrics["model_losses_w"]))
+
+
+def test_mapo_loss_with_different_timesteps():
+    loss = torch.rand(4, 4, 32, 32)
+
+    # Test with different timestep values
+    timesteps = [1, 10, 100, 1000]
+
+    for ts in timesteps:
+        result, metrics = mapo_loss(loss, 0.5, ts)
+
+        # Check that the results are different for different timesteps
+        if ts > 1:
+            result_prev, metrics_prev = mapo_loss(loss, 0.5, ts // 10)
+            # Log odds should be affected by timesteps, so ratio_loss should change
+            assert metrics["ratio_loss"] != metrics_prev["ratio_loss"]
+
+
+def test_mapo_loss_win_loss_scores():
+    # Create a controlled input where win losses are lower than lose losses
+    batch_size = 4
+    channels = 4
+    height, width = 64, 64
+
+    # Create losses where winning examples have lower loss
+    w_loss = torch.ones(batch_size // 2, channels, height, width) * 0.1
+    l_loss = torch.ones(batch_size // 2, channels, height, width) * 0.9
+
+    # Concatenate to create the full loss tensor
+    loss = torch.cat([w_loss, l_loss], dim=0)
+
+    # Run the function
+    result, metrics = mapo_loss(loss, 0.5)
+
+    # Win score should be higher than lose score (better performance)
+    assert metrics["win_score"] > metrics["lose_score"]
+
+    # Model losses for winners should be lower
+    assert metrics["model_losses_w"] < metrics["model_losses_l"]
+
+
+def test_mapo_loss_gradient_flow():
+    # Test that gradients flow through the loss function
+    batch_size = 4
+    channels = 4
+    height, width = 64, 64
+
+    # Create a loss tensor that requires grad
+    loss = torch.rand(batch_size, channels, height, width, requires_grad=True)
+    mapo_weight = 0.5
+
+    # Compute loss
+    result, _ = mapo_loss(loss, mapo_weight)
+
+    # Check that gradients flow
+    result.backward()
+
+    # If gradients flow, loss.grad should not be None
+    assert loss.grad is not None
diff --git a/train_network.py b/train_network.py
index 040d5204..7711f4ca 100644
--- a/train_network.py
+++ b/train_network.py
@@ -43,6 +43,8 @@ from library.custom_train_functions import (
     add_v_prediction_like_loss,
     apply_debiased_estimation,
     apply_masked_loss,
+    diffusion_dpo_loss,
+    mapo_loss
 )
 from library.utils import setup_logging, add_logging_arguments
 
@@ -255,18 +257,18 @@ class NetworkTrainer:
 
     def get_noise_pred_and_target(
         self,
-        args,
-        accelerator,
+        args: argparse.Namespace,
+        accelerator: Accelerator,
         noise_scheduler,
-        latents,
-        batch,
+        latents: torch.FloatTensor,
+        batch: dict[str, torch.Tensor],
         text_encoder_conds,
         unet,
         network,
-        weight_dtype,
-        train_unet,
+        weight_dtype: torch.dtype,
+        train_unet: bool,
         is_train=True,
-    ):
+    ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.IntTensor, torch.Tensor | None]:
         # Sample noise, sample a random timestep for each image, and add noise to the latents,
         # with noise offset and/or multires noise if specified
         noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
@@ -321,9 +323,9 @@ class NetworkTrainer:
                 network.set_multiplier(1.0)  # may be overwritten by "network_multipliers" in the next step
                 target[diff_output_pr_indices] = noise_pred_prior.to(target.dtype)
 
-        return noise_pred, target, timesteps, None
+        return noise_pred, noisy_latents, target, timesteps, None
 
-    def post_process_loss(self, loss, args, timesteps: torch.IntTensor, noise_scheduler) -> torch.FloatTensor:
+    def post_process_loss(self, loss: torch.Tensor, args, timesteps: torch.IntTensor, noise_scheduler) -> torch.FloatTensor:
         if args.min_snr_gamma:
             loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
         if args.scale_v_pred_loss_like_noise_pred:
@@ -380,10 +382,12 @@ class NetworkTrainer:
         is_train=True,
         train_text_encoder=True,
         train_unet=True,
-    ) -> torch.Tensor:
+        multipliers=1.0,
+    ) -> tuple[torch.Tensor, dict[str, float | int]]:
         """
         Process a batch for the network
         """
+        metrics: dict[str, float | int] = {}
         with torch.no_grad():
             if "latents" in batch and batch["latents"] is not None:
                 latents = typing.cast(torch.FloatTensor, batch["latents"].to(accelerator.device))
@@ -446,7 +450,7 @@ class NetworkTrainer:
                         text_encoder_conds[i] = encoded_text_encoder_conds[i]
 
         # sample noise, call unet, get target
-        noise_pred, target, timesteps, weighting = self.get_noise_pred_and_target(
+        noise_pred, noisy_latents, target, timesteps, weighting = self.get_noise_pred_and_target(
             args,
             accelerator,
             noise_scheduler,
@@ -468,85 +472,34 @@ class NetworkTrainer:
             loss = apply_masked_loss(loss, batch)
 
         if args.beta_dpo is not None:
-            model_loss = loss.mean(dim=list(range(1, len(loss.shape))))
-            model_loss_w, model_loss_l = model_loss.chunk(2)
-            raw_model_loss = 0.5 * (model_loss_w.mean() + model_loss_l.mean())
-            model_diff = model_loss_w - model_loss_l
-
-            # ref loss
-            with torch.no_grad():
-                # disable network for reference
+            def call_unet():
                 accelerator.unwrap_model(network).set_multiplier(0.0)
+                ref_noise_pred = self.call_unet(
+                    args,
+                    accelerator,
+                    unet,
+                    noisy_latents.requires_grad_(train_unet),
+                    timesteps,
+                    text_encoder_conds,
+                    batch,
+                    weight_dtype,
+                )
 
-                with accelerator.autocast():
-                    ref_noise_pred = self.call_unet(
-                        args,
-                        accelerator,
-                        unet,
-                        noisy_latents.requires_grad_(train_unet),
-                        timesteps,
-                        text_encoder_conds,
-                        batch,
-                        weight_dtype,
-                    )
+                # reset network multipliers
+                accelerator.unwrap_model(network).set_multiplier(1.0)
+                return ref_noise_pred
+            def apply_loss(ref_noise_pred):
+                huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler)
                 ref_loss = train_util.conditional_loss(
                     ref_noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
                 )
                 if args.masked_loss or ("alpha_masks" in batch and batch["alpha_masks"] is not None):
                     ref_loss = apply_masked_loss(ref_loss, batch)
+                return ref_loss
 
-                ref_loss = ref_loss.mean(dim=list(range(1, len(ref_loss.shape))))
-                ref_losses_w, ref_losses_l = ref_loss.chunk(2)
-                ref_diff = ref_losses_w - ref_losses_l
-                raw_ref_loss = ref_loss.mean()
-
-                # reset network multipliers
-                accelerator.unwrap_model(network).set_multiplier(multipliers)
-
-            scale_term = -0.5 * args.beta_dpo
-            inside_term = scale_term * (model_diff - ref_diff)
-            loss = -1 * torch.nn.functional.logsigmoid(inside_term)
-
-            implicit_acc = (inside_term > 0).sum().float() / inside_term.size(0)
-            implicit_acc += 0.5 * (inside_term == 0).sum().float() / inside_term.size(0)
-
-            accelerator.log({
-                "total_loss": model_loss.detach().mean().item(),
-                "raw_model_loss": raw_model_loss.detach().mean().item(),
-                "ref_loss": raw_ref_loss.detach().item(),
-                "implicit_acc": implicit_acc.detach().item(),
-            }, step=global_step)
+            loss, metrics = diffusion_dpo_loss(loss, call_unet, apply_loss, args.beta_dpo)
         elif args.mapo_weight is not None:
-            model_loss = loss.mean(dim=list(range(1, len(loss.shape))))
-
-            snr = 0.5
-            model_losses_w, model_losses_l = model_loss.chunk(2)
-            log_odds = (snr * model_losses_w) / (torch.exp(snr * model_losses_w) - 1) - (
-                snr * model_losses_l
-            ) / (torch.exp(snr * model_losses_l) - 1)
-
-            # Ratio loss.
-            # By multiplying T to the inner term, we try to maximize the margin throughout the overall denoising process.
-            ratio = torch.nn.functional.logsigmoid(log_odds * noise_scheduler.config.num_train_timesteps)
-            ratio_losses = args.mapo_weight * ratio
-
-            # Full MaPO loss
-            loss = model_losses_w.mean(dim=list(range(1, len(model_losses_w.shape)))) - ratio_losses.mean(dim=list(range(1, len(ratio_losses.shape))))
-
-            accelerator.log({
-                "total_loss": loss.detach().mean().item(),
-                "ratio_loss": -ratio_losses.mean().detach().item(),
-                "model_losses_w": model_losses_w.mean().detach().item(),
-                "model_losses_l": model_losses_l.mean().detach().item(),
-                "win_score": ((snr * model_losses_w) / (torch.exp(snr * model_losses_w) - 1))
-                .mean()
-                .detach()
-                .item(),
-                "lose_score": ((snr * model_losses_l) / (torch.exp(snr * model_losses_l) - 1))
-                .mean()
-                .detach()
-                .item()
-            }, step=global_step)
+            loss, metrics = mapo_loss(loss, args.mapo_weight, noise_scheduler.config.num_train_timesteps)
         else:
             loss = loss.mean([1, 2, 3])
 
@@ -555,7 +508,7 @@ class NetworkTrainer:
 
         loss = self.post_process_loss(loss, args, timesteps, noise_scheduler)
 
-        return loss.mean()
+        return loss.mean(), metrics
 
     def train(self, args):
         session_id = random.randint(0, 2**32)
@@ -1482,7 +1435,7 @@ class NetworkTrainer:
                     # preprocess batch for each model
                     self.on_step_start(args, accelerator, network, text_encoders, unet, batch, weight_dtype, is_train=True)
 
-                    loss = self.process_batch(
+                    loss, batch_metrics = self.process_batch(
                         batch,
                         text_encoders,
                         unet,
@@ -1584,7 +1537,7 @@ class NetworkTrainer:
                         mean_grad_norm,
                         mean_combined_norm,
                     )
-                    self.step_logging(accelerator, logs, global_step, epoch + 1)
+                    self.step_logging(accelerator, {**logs, **batch_metrics}, global_step, epoch + 1)
 
                 # VALIDATION PER STEP: global_step is already incremented
                 # for example, if validate_every_n_steps=100, validate at step 100, 200, 300, ...