mirror of
https://github.com/hpcaitech/Open-Sora.git
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febf3ad4b2
* upload v2.0 * update docs * [hotfix] fit latest fa3 (#802) * update readme * update readme * update readme * update train readme * update readme * update readme: motion score * cleaning video dc ae WIP * update config * add dependency functions * undo cleaning * use latest dcae * complete high compression training * update hcae config * cleaned up vae * update ae.md * further cleanup * update vae & ae paths * align naming of ae * [hotfix] fix ring attn bwd for fa3 (#803) * train ae default without wandb * update config * update evaluation results * added hcae report * update readme * update readme demo * update readme demo * update readme gif * display demo directly in readme * update paper * delete files --------- Co-authored-by: Hongxin Liu <lhx0217@gmail.com> Co-authored-by: Shen-Chenhui <shen_chenhui@u.nus.edu> Co-authored-by: wuxiwen <wuxiwen.simon@gmail.com>
72 lines
1.4 KiB
Python
72 lines
1.4 KiB
Python
_base_ = ["image.py"]
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bucket_config = {
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"_delete_": True,
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"768px": {
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1: (1.0, 20),
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16: (1.0, 8),
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20: (1.0, 8),
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24: (1.0, 8),
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28: (1.0, 8),
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32: (1.0, 8),
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36: (1.0, 4),
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40: (1.0, 4),
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44: (1.0, 4),
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48: (1.0, 4),
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52: (1.0, 4),
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56: (1.0, 4),
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60: (1.0, 4),
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64: (1.0, 4),
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68: (1.0, 3),
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72: (1.0, 3),
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76: (1.0, 3),
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80: (1.0, 3),
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84: (1.0, 3),
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88: (1.0, 3),
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92: (1.0, 3),
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96: (1.0, 3),
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100: (1.0, 2),
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104: (1.0, 2),
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108: (1.0, 2),
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112: (1.0, 2),
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116: (1.0, 2),
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120: (1.0, 2),
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124: (1.0, 2),
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128: (1.0, 2), # 30s
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},
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}
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condition_config = dict(
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t2v=1,
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i2v_head=7,
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)
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grad_ckpt_settings = (100, 100)
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patch_size = 1
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model = dict(
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from_pretrained=None,
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grad_ckpt_settings=grad_ckpt_settings,
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in_channels=128,
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cond_embed=True,
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patch_size=patch_size,
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)
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ae = dict(
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_delete_=True,
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type="dc_ae",
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model_name="dc-ae-f32t4c128",
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from_pretrained="./ckpts/F32T4C128_AE.safetensors",
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from_scratch=True,
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scaling_factor=0.493,
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use_spatial_tiling=True,
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use_temporal_tiling=True,
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spatial_tile_size=256,
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temporal_tile_size=32,
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tile_overlap_factor=0.25,
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)
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is_causal_vae = False
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ae_spatial_compression = 32
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ckpt_every = 250
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lr = 3e-5
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optim = dict(lr=lr)
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