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[train_dreambooth_lora_flux_advanced] Add LANCZOS as the default interpolation mode for image resizing (#11472)

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* [train_controlnet_sdxl] Add LANCZOS as the default interpolation mode for image resizing

* [train_dreambooth_lora_flux_advanced] Add LANCZOS as the default interpolation mode for image resizing
This commit is contained in:
Yash
2025-05-03 03:44:41 +05:30
committed by GitHub
parent ed6cf52572
commit ec3d58286d
1 changed files with 14 additions and 2 deletions
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16
examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py
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@@ -770,6 +770,15 @@ def parse_args(input_args=None):
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
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)
@@ -1034,7 +1043,10 @@ class DreamBoothDataset(Dataset):
self.instance_images.extend(itertools.repeat(img, repeats))
self.pixel_values = []
train_resize = transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR)
interpolation = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper(), None)
if interpolation is None:
raise ValueError(f"Unsupported interpolation mode {interpolation=}.")
train_resize = transforms.Resize(size, interpolation=interpolation)
train_crop = transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size)
train_flip = transforms.RandomHorizontalFlip(p=1.0)
train_transforms = transforms.Compose(
@@ -1078,7 +1090,7 @@ class DreamBoothDataset(Dataset):
self.image_transforms = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.Resize(size, interpolation=interpolation),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),