Source code for face_spiga_controlnet

import torchvision
from mmengine.dataset import DefaultSampler

from diffengine.datasets import HFControlNetDataset
from diffengine.datasets.transforms import (
    DumpImage,
    PackInputs,
    RandomCrop,
    RandomHorizontalFlip,
    TorchVisonTransformWrapper,
)
from diffengine.engine.hooks import ControlNetSaveHook, VisualizationHook


[docs]train_pipeline = [
    dict(
        type=TorchVisonTransformWrapper,
        transform=torchvision.transforms.Resize,
        size=512,
        interpolation="bilinear",
        keys=["img", "condition_img"]),
    dict(type=RandomCrop, size=512, keys=["img", "condition_img"]),
    dict(type=RandomHorizontalFlip, p=0.5, keys=["img", "condition_img"]),
    dict(type=TorchVisonTransformWrapper,
         transform=torchvision.transforms.ToTensor, keys=["img", "condition_img"]),
    dict(type=DumpImage, max_imgs=10, dump_dir="work_dirs/dump"),
    dict(type=TorchVisonTransformWrapper,
         transform=torchvision.transforms.Normalize, mean=[0.5], std=[0.5]),
    dict(type=PackInputs, input_keys=["img", "condition_img", "text"]),
]


[docs]train_dataloader = dict(
    batch_size=4,
    num_workers=4,
    dataset=dict(
        type=HFControlNetDataset,
        dataset="multimodalart/facesyntheticsspigacaptioned",
        condition_column="spiga_seg",
        caption_column="image_caption",
        pipeline=train_pipeline),
    sampler=dict(type=DefaultSampler, shuffle=True),
)



[docs]val_dataloader = None


[docs]val_evaluator = None


[docs]test_dataloader = val_dataloader


[docs]test_evaluator = val_evaluator



[docs]custom_hooks = [
    dict(
        type=VisualizationHook,
        prompt=["a close up of a man with a mohawkcut and a purple shirt"] * 4,
        condition_image=[
            'https://datasets-server.huggingface.co/assets/multimodalart/facesyntheticsspigacaptioned/--/multimodalart--facesyntheticsspigacaptioned/train/1/spiga_seg/image.jpg'  # noqa
        ] * 4),
    dict(type=ControlNetSaveHook),
]