Hugging Face's logo

BonanDing
/
worldmem-baseline-evals

evaluation
minecraft
realestate10k
world-model
Model card Files
xet
 Community
worldmem-baseline-evals
File size: 2,344 Bytes
59630ba
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
# Important: Be careful when modifying this file! The fields in file will be overridden by the dataset dependent config file in the `configurations/dataset_experiment` folder so consider making changes there instead! 


defaults:
  - base_pytorch_algo
  - backbone: dit3d

# dataset-dependent configurations
x_shape: ${dataset.observation_shape}
max_frames: ${dataset.max_frames}
n_frames: ${dataset.n_frames}
frame_skip: ${dataset.frame_skip}
context_frames: ${dataset.context_length}
latent: ${dataset.latent}
data_mean: ${dataset.data_mean}
data_std: ${dataset.data_std}
external_cond_dim: ${dataset.external_cond_dim}
external_cond_stack: ${dataset.external_cond_stack}
external_cond_processing: ${dataset.external_cond_processing}
# training hyperparameters
compile: False
weight_decay: 1e-3
optimizer_beta: [0.9, 0.99]
lr_scheduler:
  name: constant_with_warmup
  num_warmup_steps: 5000
noise_level: random_independent
uniform_future:
  enabled: false
fixed_context:
  enabled: false
  indices: null # defaults to context_frames
  dropout: 0
variable_context:
  enabled: false
  prob: 0
  dropout: 0
# sampling scheme
chunk_size: -1
scheduling_matrix: full_sequence
replacement: noisy_scale

diffusion:
  is_continuous: False
  timesteps: 1000
  beta_schedule: cosine
  schedule_fn_kwargs:
    shift: 1.0
  use_causal_mask: False
  clip_noise: 20.0
  # training
  objective: pred_v
  loss_weighting:
    strategy: fused_min_snr
    snr_clip: 5.0
    cum_snr_decay: 0.9
  # sampling
  sampling_timesteps: 50
  ddim_sampling_eta: 0.0
  # (For full sequence diffusion)
  reconstruction_guidance: 0.0

# vae
vae:
  pretrained_path: null
  pretrained_kwargs: {}
  use_fp16: True
  batch_size: 2

checkpoint:
  reset_optimizer: False
  strict: True

# video generation tasks
tasks:
  prediction:
    enabled: True
    history_guidance:
      name: conditional
    keyframe_density: null
    sliding_context_len: null
  interpolation:
    enabled: False
    history_guidance:
      name: conditional
    max_batch_size: null

# logging
logging:
  deterministic: 0
  loss_freq: 100
  grad_norm_freq: 100
  max_num_videos: 8
  n_metrics_frames: null
  metrics:
    - fvd
    - is
    - fid
    - lpips
    - mse
    - psnr
    - ssim
  metrics_batch_size: 16
  sanity_generation: False # generate video samples during sanity check
  raw_dir: null