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n_tokens: 3
pose_cond_dim: 5
use_plucker: true
focal_length: 0.35
customized_validation: true
condition_similar_length: 8
log_video: true
relative_embedding: true
cond_only_on_qk: true
add_pose_embed: false
use_domain_adapter: false
use_reference_attention: true
add_frame_timestep_embedder: true
is_interactive: true
diffusion:
sampling_timesteps: 20
beta_schedule: sigmoid
objective: pred_v
use_fused_snr: True
cum_snr_decay: 0.96
clip_noise: 20.
ddim_sampling_eta: 0.0
stabilization_level: 15
schedule_fn_kwargs: {}
use_snr: False
use_cum_snr: False
snr_clip: 5.0
timesteps: 1000
# architecture
architecture:
network_size: 64
attn_heads: 4
attn_dim_head: 64
dim_mults: [1, 2, 4, 8]
resolution: ${dataset.resolution}
attn_resolutions: [16, 32, 64, 128]
use_init_temporal_attn: True
use_linear_attn: True
time_emb_type: rotary
weight_decay: 2e-3
warmup_steps: 10000
optimizer_beta: [0.9, 0.99]
action_cond_dim: 25
n_frames: 8
frame_skip: 1
frame_stack: 1
uncertainty_scale: 1
guidance_scale: 0.0
chunk_size: 1 # -1 for full trajectory diffusion, number to specify diffusion chunk size
scheduling_matrix: autoregressive
noise_level: random_all
causal: True
x_shape: [3, 360, 640]
context_frames: 1
diffusion_path: yslan/worldmem_checkpoints/diffusion_only.ckpt
vae_path: yslan/worldmem_checkpoints/vae_only.ckpt
pose_predictor_path: yslan/worldmem_checkpoints/pose_prediction_model_only.ckpt
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