From 58431f102cf39c3c8a569f32d71b2ea8caa461e1 Mon Sep 17 00:00:00 2001
From: Youlun Peng <116731168+YoulunPeng@users.noreply.github.com>
Date: Tue, 29 Apr 2025 14:47:02 +0200
Subject: [PATCH] Set LANCZOS as the default interpolation for image resizing
 in ControlNet training (#11449)

Set LANCZOS as the default interpolation for image resizing
---
 examples/controlnet/train_controlnet_flux.py | 17 +++++++++++++++--
 1 file changed, 15 insertions(+), 2 deletions(-)

diff --git a/examples/controlnet/train_controlnet_flux.py b/examples/controlnet/train_controlnet_flux.py
index edee3fbe55..232d3da8e8 100644
--- a/examples/controlnet/train_controlnet_flux.py
+++ b/examples/controlnet/train_controlnet_flux.py
@@ -639,6 +639,15 @@ def parse_args(input_args=None):
         action="store_true",
         help="Enable model cpu offload and save memory.",
     )
+    parser.add_argument(
+        "--image_interpolation_mode",
+        type=str,
+        default="lanczos",
+        choices=[
+            f.lower() for f in dir(transforms.InterpolationMode) if not f.startswith("__") and not f.endswith("__")
+        ],
+        help="The image interpolation method to use for resizing images.",
+    )
 
     if input_args is not None:
         args = parser.parse_args(input_args)
@@ -736,9 +745,13 @@ def get_train_dataset(args, accelerator):
 
 
 def prepare_train_dataset(dataset, accelerator):
+    interpolation = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper(), None)
+    if interpolation is None:
+        raise ValueError(f"Unsupported interpolation mode {interpolation=}.")
+
     image_transforms = transforms.Compose(
         [
-            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
+            transforms.Resize(args.resolution, interpolation=interpolation),
             transforms.CenterCrop(args.resolution),
             transforms.ToTensor(),
             transforms.Normalize([0.5], [0.5]),
@@ -747,7 +760,7 @@ def prepare_train_dataset(dataset, accelerator):
 
     conditioning_image_transforms = transforms.Compose(
         [
-            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
+            transforms.Resize(args.resolution, interpolation=interpolation),
             transforms.CenterCrop(args.resolution),
             transforms.ToTensor(),
             transforms.Normalize([0.5], [0.5]),