From 8fc30f820595f80ec3f09738cc4cf01f441c41b7 Mon Sep 17 00:00:00 2001
From: catboxanon <122327233+catboxanon@users.noreply.github.com>
Date: Mon, 21 Oct 2024 07:34:33 -0400
Subject: [PATCH 1/5] Fix training for V-pred and ztSNR

1) Updates debiased estimation loss function for V-pred.
2) Prevents now-deprecated scaling of loss if ztSNR is enabled.
---
 fine_tune.py                         | 4 ++--
 library/custom_train_functions.py    | 7 +++++--
 library/train_util.py                | 5 +++++
 sdxl_train.py                        | 4 ++--
 sdxl_train_control_net_lllite.py     | 4 ++--
 sdxl_train_control_net_lllite_old.py | 4 ++--
 train_db.py                          | 4 ++--
 train_network.py                     | 4 ++--
 train_textual_inversion.py           | 4 ++--
 train_textual_inversion_XTI.py       | 4 ++--
 10 files changed, 26 insertions(+), 18 deletions(-)

diff --git a/fine_tune.py b/fine_tune.py
index b556672d..19a35229 100644
--- a/fine_tune.py
+++ b/fine_tune.py
@@ -383,10 +383,10 @@ def train(args):
 
                     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:
+                    if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
                         loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                     if args.debiased_estimation_loss:
-                        loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
+                        loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
 
                     loss = loss.mean()  # mean over batch dimension
                 else:
diff --git a/library/custom_train_functions.py b/library/custom_train_functions.py
index 2a513dc5..faf44304 100644
--- a/library/custom_train_functions.py
+++ b/library/custom_train_functions.py
@@ -96,10 +96,13 @@ def add_v_prediction_like_loss(loss, timesteps, noise_scheduler, v_pred_like_los
     return loss
 
 
-def apply_debiased_estimation(loss, timesteps, noise_scheduler):
+def apply_debiased_estimation(loss, timesteps, noise_scheduler, v_prediction=False):
     snr_t = torch.stack([noise_scheduler.all_snr[t] for t in timesteps])  # batch_size
     snr_t = torch.minimum(snr_t, torch.ones_like(snr_t) * 1000)  # if timestep is 0, snr_t is inf, so limit it to 1000
-    weight = 1 / torch.sqrt(snr_t)
+    if v_prediction:
+        weight = 1 / (snr_t + 1)
+    else:
+        weight = 1 / torch.sqrt(snr_t)
     loss = weight * loss
     return loss
 
diff --git a/library/train_util.py b/library/train_util.py
index 27910dc9..adb983d2 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -3731,6 +3731,11 @@ def verify_training_args(args: argparse.Namespace):
         raise ValueError(
             "scale_v_pred_loss_like_noise_pred can be enabled only with v_parameterization / scale_v_pred_loss_like_noise_predはv_parameterizationが有効なときのみ有効にできます"
         )
+    
+    if args.scale_v_pred_loss_like_noise_pred and args.zero_terminal_snr:
+        raise ValueError(
+            "zero_terminal_snr enabled. scale_v_pred_loss_like_noise_pred will not be used / zero_terminal_snrが有効です。scale_v_pred_loss_like_noise_predは使用されません"
+        )
 
     if args.v_pred_like_loss and args.v_parameterization:
         raise ValueError(
diff --git a/sdxl_train.py b/sdxl_train.py
index e0a8f2b2..44ee9233 100644
--- a/sdxl_train.py
+++ b/sdxl_train.py
@@ -725,12 +725,12 @@ def train(args):
 
                     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:
+                    if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
                         loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                     if args.v_pred_like_loss:
                         loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
                     if args.debiased_estimation_loss:
-                        loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
+                        loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
 
                     loss = loss.mean()  # mean over batch dimension
                 else:
diff --git a/sdxl_train_control_net_lllite.py b/sdxl_train_control_net_lllite.py
index 5ff060a9..436f0e19 100644
--- a/sdxl_train_control_net_lllite.py
+++ b/sdxl_train_control_net_lllite.py
@@ -474,12 +474,12 @@ def train(args):
 
                 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:
+                if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
                     loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                 if args.v_pred_like_loss:
                     loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
                 if args.debiased_estimation_loss:
-                    loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
+                    loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
 
                 loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
 
diff --git a/sdxl_train_control_net_lllite_old.py b/sdxl_train_control_net_lllite_old.py
index 292a0463..8fba9eba 100644
--- a/sdxl_train_control_net_lllite_old.py
+++ b/sdxl_train_control_net_lllite_old.py
@@ -434,12 +434,12 @@ def train(args):
 
                 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:
+                if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
                     loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                 if args.v_pred_like_loss:
                     loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
                 if args.debiased_estimation_loss:
-                    loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
+                    loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
 
                 loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
 
diff --git a/train_db.py b/train_db.py
index 2c7f0258..d5a94a56 100644
--- a/train_db.py
+++ b/train_db.py
@@ -370,10 +370,10 @@ def train(args):
 
                 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:
+                if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
                     loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                 if args.debiased_estimation_loss:
-                    loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
+                    loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
 
                 loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
 
diff --git a/train_network.py b/train_network.py
index 044ec3aa..790fbfc9 100644
--- a/train_network.py
+++ b/train_network.py
@@ -993,12 +993,12 @@ class NetworkTrainer:
 
                     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:
+                    if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
                         loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                     if args.v_pred_like_loss:
                         loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
                     if args.debiased_estimation_loss:
-                        loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
+                        loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
 
                     loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
 
diff --git a/train_textual_inversion.py b/train_textual_inversion.py
index 96e7bd50..10b34db5 100644
--- a/train_textual_inversion.py
+++ b/train_textual_inversion.py
@@ -598,12 +598,12 @@ class TextualInversionTrainer:
 
                     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:
+                    if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
                         loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                     if args.v_pred_like_loss:
                         loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
                     if args.debiased_estimation_loss:
-                        loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
+                        loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
 
                     loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
 
diff --git a/train_textual_inversion_XTI.py b/train_textual_inversion_XTI.py
index efb59137..084b90c6 100644
--- a/train_textual_inversion_XTI.py
+++ b/train_textual_inversion_XTI.py
@@ -483,10 +483,10 @@ def train(args):
                 loss = loss * loss_weights
                 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:
+                if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
                     loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                 if args.debiased_estimation_loss:
-                    loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
+                    loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
 
                 loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
 

From e1b63c2249345e4f14c10cbb252da68157ac13b7 Mon Sep 17 00:00:00 2001
From: catboxanon <122327233+catboxanon@users.noreply.github.com>
Date: Mon, 21 Oct 2024 08:12:53 -0400
Subject: [PATCH 2/5] Only add warning for deprecated scaling vpred loss
 function

---
 fine_tune.py                         |  2 +-
 library/train_util.py                | 11 ++++++-----
 sdxl_train.py                        |  2 +-
 sdxl_train_control_net_lllite.py     |  2 +-
 sdxl_train_control_net_lllite_old.py |  2 +-
 train_db.py                          |  2 +-
 train_network.py                     |  2 +-
 train_textual_inversion.py           |  2 +-
 train_textual_inversion_XTI.py       |  2 +-
 9 files changed, 14 insertions(+), 13 deletions(-)

diff --git a/fine_tune.py b/fine_tune.py
index 19a35229..c79f97d2 100644
--- a/fine_tune.py
+++ b/fine_tune.py
@@ -383,7 +383,7 @@ def train(args):
 
                     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 and not args.zero_terminal_snr:
+                    if args.scale_v_pred_loss_like_noise_pred:
                         loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                     if args.debiased_estimation_loss:
                         loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
diff --git a/library/train_util.py b/library/train_util.py
index adb983d2..f479dcc6 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -3727,15 +3727,16 @@ def verify_training_args(args: argparse.Namespace):
     if args.adaptive_noise_scale is not None and args.noise_offset is None:
         raise ValueError("adaptive_noise_scale requires noise_offset / adaptive_noise_scaleを使用するにはnoise_offsetが必要です")
 
+    if args.scale_v_pred_loss_like_noise_pred:
+        logger.warning(
+            f"scale_v_pred_loss_like_noise_pred is deprecated. it is suggested to use min_snr_gamma or debiased_estimation_loss"
+            + " / scale_v_pred_loss_like_noise_pred は非推奨です。min_snr_gammaまたはdebiased_estimation_lossを使用することをお勧めします"
+        )
+
     if args.scale_v_pred_loss_like_noise_pred and not args.v_parameterization:
         raise ValueError(
             "scale_v_pred_loss_like_noise_pred can be enabled only with v_parameterization / scale_v_pred_loss_like_noise_predはv_parameterizationが有効なときのみ有効にできます"
         )
-    
-    if args.scale_v_pred_loss_like_noise_pred and args.zero_terminal_snr:
-        raise ValueError(
-            "zero_terminal_snr enabled. scale_v_pred_loss_like_noise_pred will not be used / zero_terminal_snrが有効です。scale_v_pred_loss_like_noise_predは使用されません"
-        )
 
     if args.v_pred_like_loss and args.v_parameterization:
         raise ValueError(
diff --git a/sdxl_train.py b/sdxl_train.py
index 44ee9233..b533b274 100644
--- a/sdxl_train.py
+++ b/sdxl_train.py
@@ -725,7 +725,7 @@ def train(args):
 
                     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 and not args.zero_terminal_snr:
+                    if args.scale_v_pred_loss_like_noise_pred:
                         loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                     if args.v_pred_like_loss:
                         loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
diff --git a/sdxl_train_control_net_lllite.py b/sdxl_train_control_net_lllite.py
index 436f0e19..0e67cde5 100644
--- a/sdxl_train_control_net_lllite.py
+++ b/sdxl_train_control_net_lllite.py
@@ -474,7 +474,7 @@ def train(args):
 
                 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 and not args.zero_terminal_snr:
+                if args.scale_v_pred_loss_like_noise_pred:
                     loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                 if args.v_pred_like_loss:
                     loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
diff --git a/sdxl_train_control_net_lllite_old.py b/sdxl_train_control_net_lllite_old.py
index 8fba9eba..4a01f9e2 100644
--- a/sdxl_train_control_net_lllite_old.py
+++ b/sdxl_train_control_net_lllite_old.py
@@ -434,7 +434,7 @@ def train(args):
 
                 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 and not args.zero_terminal_snr:
+                if args.scale_v_pred_loss_like_noise_pred:
                     loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                 if args.v_pred_like_loss:
                     loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
diff --git a/train_db.py b/train_db.py
index d5a94a56..e7cf3cde 100644
--- a/train_db.py
+++ b/train_db.py
@@ -370,7 +370,7 @@ def train(args):
 
                 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 and not args.zero_terminal_snr:
+                if args.scale_v_pred_loss_like_noise_pred:
                     loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                 if args.debiased_estimation_loss:
                     loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
diff --git a/train_network.py b/train_network.py
index 790fbfc9..7bf125dc 100644
--- a/train_network.py
+++ b/train_network.py
@@ -993,7 +993,7 @@ class NetworkTrainer:
 
                     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 and not args.zero_terminal_snr:
+                    if args.scale_v_pred_loss_like_noise_pred:
                         loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                     if args.v_pred_like_loss:
                         loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
diff --git a/train_textual_inversion.py b/train_textual_inversion.py
index 10b34db5..37349da7 100644
--- a/train_textual_inversion.py
+++ b/train_textual_inversion.py
@@ -598,7 +598,7 @@ class TextualInversionTrainer:
 
                     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 and not args.zero_terminal_snr:
+                    if args.scale_v_pred_loss_like_noise_pred:
                         loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                     if args.v_pred_like_loss:
                         loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
diff --git a/train_textual_inversion_XTI.py b/train_textual_inversion_XTI.py
index 084b90c6..fac0787b 100644
--- a/train_textual_inversion_XTI.py
+++ b/train_textual_inversion_XTI.py
@@ -483,7 +483,7 @@ def train(args):
                 loss = loss * loss_weights
                 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 and not args.zero_terminal_snr:
+                if args.scale_v_pred_loss_like_noise_pred:
                     loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                 if args.debiased_estimation_loss:
                     loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)

From 0e7c5929336173e30d7932c0706eaf61a7d396f4 Mon Sep 17 00:00:00 2001
From: catboxanon <122327233+catboxanon@users.noreply.github.com>
Date: Tue, 22 Oct 2024 11:19:34 -0400
Subject: [PATCH 3/5] Remove scale_v_pred_loss_like_noise_pred deprecation

https://github.com/kohya-ss/sd-scripts/pull/1715#issuecomment-2427876376
---
 library/train_util.py | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/library/train_util.py b/library/train_util.py
index f479dcc6..27910dc9 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -3727,12 +3727,6 @@ def verify_training_args(args: argparse.Namespace):
     if args.adaptive_noise_scale is not None and args.noise_offset is None:
         raise ValueError("adaptive_noise_scale requires noise_offset / adaptive_noise_scaleを使用するにはnoise_offsetが必要です")
 
-    if args.scale_v_pred_loss_like_noise_pred:
-        logger.warning(
-            f"scale_v_pred_loss_like_noise_pred is deprecated. it is suggested to use min_snr_gamma or debiased_estimation_loss"
-            + " / scale_v_pred_loss_like_noise_pred は非推奨です。min_snr_gammaまたはdebiased_estimation_lossを使用することをお勧めします"
-        )
-
     if args.scale_v_pred_loss_like_noise_pred and not args.v_parameterization:
         raise ValueError(
             "scale_v_pred_loss_like_noise_pred can be enabled only with v_parameterization / scale_v_pred_loss_like_noise_predはv_parameterizationが有効なときのみ有効にできます"

From be14c062674973d0e4fee1eb4527e04707bb72b8 Mon Sep 17 00:00:00 2001
From: catboxanon <122327233+catboxanon@users.noreply.github.com>
Date: Tue, 22 Oct 2024 12:13:51 -0400
Subject: [PATCH 4/5] Remove v-pred warnings

Different model architectures, such as SDXL, can take advantage of
v-pred. It doesn't make sense to include these warnings anymore.
---
 gen_img.py            | 2 --
 gen_img_diffusers.py  | 2 --
 library/train_util.py | 4 ----
 3 files changed, 8 deletions(-)

diff --git a/gen_img.py b/gen_img.py
index 59bcd5b0..9427a894 100644
--- a/gen_img.py
+++ b/gen_img.py
@@ -1495,8 +1495,6 @@ def main(args):
     highres_fix = args.highres_fix_scale is not None
     # assert not highres_fix or args.image_path is None, f"highres_fix doesn't work with img2img / highres_fixはimg2imgと同時に使えません"
 
-    if args.v_parameterization and not args.v2:
-        logger.warning("v_parameterization should be with v2 / v1でv_parameterizationを使用することは想定されていません")
     if args.v2 and args.clip_skip is not None:
         logger.warning("v2 with clip_skip will be unexpected / v2でclip_skipを使用することは想定されていません")
 
diff --git a/gen_img_diffusers.py b/gen_img_diffusers.py
index 2c40f1a0..04db4e9b 100644
--- a/gen_img_diffusers.py
+++ b/gen_img_diffusers.py
@@ -2216,8 +2216,6 @@ def main(args):
     highres_fix = args.highres_fix_scale is not None
     # assert not highres_fix or args.image_path is None, f"highres_fix doesn't work with img2img / highres_fixはimg2imgと同時に使えません"
 
-    if args.v_parameterization and not args.v2:
-        logger.warning("v_parameterization should be with v2 / v1でv_parameterizationを使用することは想定されていません")
     if args.v2 and args.clip_skip is not None:
         logger.warning("v2 with clip_skip will be unexpected / v2でclip_skipを使用することは想定されていません")
 
diff --git a/library/train_util.py b/library/train_util.py
index 27910dc9..100ef475 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -3698,10 +3698,6 @@ def verify_training_args(args: argparse.Namespace):
         global HIGH_VRAM
         HIGH_VRAM = True
 
-    if args.v_parameterization and not args.v2:
-        logger.warning(
-            "v_parameterization should be with v2 not v1 or sdxl / v1やsdxlでv_parameterizationを使用することは想定されていません"
-        )
     if args.v2 and args.clip_skip is not None:
         logger.warning("v2 with clip_skip will be unexpected / v2でclip_skipを使用することは想定されていません")
 

From b1e6504007aca20d15155d5c9fe880fb5e0002b8 Mon Sep 17 00:00:00 2001
From: Kohya S <ykumeykume@gmail.com>
Date: Fri, 25 Oct 2024 18:56:25 +0900
Subject: [PATCH 5/5] update README

---
 README.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/README.md b/README.md
index de5cddb9..ce28d004 100644
--- a/README.md
+++ b/README.md
@@ -143,6 +143,9 @@ The majority of scripts is licensed under ASL 2.0 (including codes from Diffuser
   - bitsandbytes, transformers, accelerate and huggingface_hub are updated. 
   - If you encounter any issues, please report them.
 
+- Fixed a bug where the loss weight was incorrect when `--debiased_estimation_loss` was specified with `--v_parameterization`. PR [#1715](https://github.com/kohya-ss/sd-scripts/pull/1715) Thanks to catboxanon! See [the PR](https://github.com/kohya-ss/sd-scripts/pull/1715) for details.
+  - Removed the warning when `--v_parameterization` is specified in SDXL and SD1.5. PR [#1717](https://github.com/kohya-ss/sd-scripts/pull/1717)
+
 - There was a bug where the min_bucket_reso/max_bucket_reso in the dataset configuration did not create the correct resolution bucket if it was not divisible by bucket_reso_steps. These values are now warned and automatically rounded to a divisible value. Thanks to Maru-mee for raising the issue. Related PR [#1632](https://github.com/kohya-ss/sd-scripts/pull/1632)
 
 - `bitsandbytes` is updated to 0.44.0. Now you can use `AdEMAMix8bit` and `PagedAdEMAMix8bit` in the training script. PR [#1640](https://github.com/kohya-ss/sd-scripts/pull/1640) Thanks to sdbds!