dog_dreambooth_if.py

train_pipeline = [
    dict(type="torchvision/Resize", size=64, interpolation="bilinear"),
    dict(type="RandomCrop", size=64),
    dict(type="RandomHorizontalFlip", p=0.5),
    dict(type="torchvision/ToTensor"),
    dict(type="torchvision/Normalize", mean=[0.5], std=[0.5]),
    dict(type="PackInputs"),
]
train_dataloader = dict(
    batch_size=4,
    num_workers=4,
    dataset=dict(
        type="HFDreamBoothDataset",
        dataset="diffusers/dog-example",
        instance_prompt="a photo of sks dog",
        pipeline=train_pipeline),
    sampler=dict(type="InfiniteSampler", shuffle=True),
)

val_dataloader = None
val_evaluator = None
test_dataloader = val_dataloader
test_evaluator = val_evaluator

custom_hooks = [
    dict(
        type="VisualizationHook",
        prompt=["A photo of sks dog in a bucket"] * 4,
        by_epoch=False,
        interval=100),
    dict(type="LoRASaveHook"),
]