From 1e8108fec9962333e4cf2a8db1dcedf657049900 Mon Sep 17 00:00:00 2001
From: liesen <liesen.dev@gmail.com>
Date: Sat, 24 Aug 2024 01:38:17 +0300
Subject: [PATCH 1/2] Handle args.v_parameterization properly for MinSNR and
 changed prediction target

---
 sdxl_train.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/sdxl_train.py b/sdxl_train.py
index 46d7860b..14b25965 100644
--- a/sdxl_train.py
+++ b/sdxl_train.py
@@ -590,7 +590,11 @@ def train(args):
                 with accelerator.autocast():
                     noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding)
 
-                target = noise
+                if args.v_parameterization:
+                    # v-parameterization training
+                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
+                else:
+                    target = noise
 
                 if (
                     args.min_snr_gamma
@@ -606,7 +610,7 @@ def train(args):
                     loss = loss.mean([1, 2, 3])
 
                     if args.min_snr_gamma:
-                        loss = apply_snr_weight(loss, timesteps, noise_scheduler, 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:
                         loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                     if args.v_pred_like_loss:

From d5c076cf9007f86f6dd1b9ecdfc5531336774b2f Mon Sep 17 00:00:00 2001
From: Kohya S <ykumeykume@gmail.com>
Date: Sat, 24 Aug 2024 21:21:39 +0900
Subject: [PATCH 2/2] update readme

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

diff --git a/README.md b/README.md
index 946df58f..81a54937 100644
--- a/README.md
+++ b/README.md
@@ -139,6 +139,7 @@ The majority of scripts is licensed under ASL 2.0 (including codes from Diffuser
 
 ### Working in progress
 
+- `--v_parameterization` is available in `sdxl_train.py`. The results are unpredictable, so use with caution. PR [#1505](https://github.com/kohya-ss/sd-scripts/pull/1505) Thanks to liesened!
 - Fused optimizer is available for SDXL training. PR [#1259](https://github.com/kohya-ss/sd-scripts/pull/1259) Thanks to 2kpr!
   - The memory usage during training is significantly reduced by integrating the optimizer's backward pass with step. The training results are the same as before, but if you have plenty of memory, the speed will be slower.
   - Specify the `--fused_backward_pass` option in `sdxl_train.py`. At this time, only AdaFactor is supported. Gradient accumulation is not available.