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Browse files- make_a_scene/00000.tar +3 -0
- make_a_scene/checkpoint_63.0.pt +3 -0
- make_a_scene/img_config.yaml +75 -0
make_a_scene/00000.tar
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version https://git-lfs.github.com/spec/v1
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oid sha256:a3d625c328a138b60509ec6ef65a418db3cd157fb152b42d651a922676c3fd2c
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size 611409920
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make_a_scene/checkpoint_63.0.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:14994c5eb72bc0d79eb50b2f5641b5790bff1c12ae9adfac07693a8c38eb8cd4
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size 1176181403
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make_a_scene/img_config.yaml
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mode: pretrain_image
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devices:
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- 0
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- 1
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- 2
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- 3
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- 4
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- 5
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- 6
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#- 7
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total_steps: 4700000
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accumulate_grad: 5
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resume: True
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#checkpoint: /home/ubuntu/Make-A-Scene/outputs/pretrain_image/2022-06-10/06-36-07/checkpoint.pt
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checkpoint: /home/ubuntu/Make-A-Scene/outputs/pretrain_image/2022-06-13/22-05-22/checkpoint_21.0.pt
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log_period: 50
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save_period: 1000
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batch_size: 3 # 192 for 256 model and 128 for 512 model
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model:
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_target_: models.VQBASE
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embed_dim: 256
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n_embed: 8192
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init_steps: 3000
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reservoir_size: 12500 # 2e5 / 8
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ddconfig:
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z_channels: 256
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in_channels: 3
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out_channels: 3
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channels: [128, 128, 128, 256, 512, 512] # [1, 1, 2, 4, 4]
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num_res_blocks: 2
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resolution: 512
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attn_resolutions:
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- 32
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dropout: 0.0
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optimizer:
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vq:
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lr: 5e-6
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betas:
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- 0.5
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- 0.9
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disc:
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lr: 4.5e-6
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betas:
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- 0.5
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- 0.9
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dataset:
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_target_: Data.dataset_preprocessor_web.S3ProcessedDataset
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resampled: True
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names:
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- cc3m
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- cc12m
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# path: file:D:/PycharmProjects/Make-A-Scene/server/Make-A-Scene/dataset/coco/{00000..00004}.tar
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# path: file:D:/PycharmProjects/Make-A-Scene/server/Make-A-Scene/dataset/coco/great_dataset.tar
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loss:
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#_target_: losses.VQVAEWithBCELoss
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_target_: losses.loss_img.VQLPIPSWithDiscriminator
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disc_start: 250001
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disc_weight: 0.8
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codebook_weight: 1.0
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dataloader:
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batch_size: ${batch_size}
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num_workers: 8
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pin_memory: True
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hydra:
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job:
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chdir: True
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run:
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dir: ./outputs/${mode}/${now:%Y-%m-%d}/${now:%H-%M-%S}
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