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_target_: salad.models.phase2.Phase2Model
network:
_target_: salad.model_components.network.CondDiffNetwork
input_dim: 512
residual: true
context_dim: 16 # gaussian condition dim.
context_embedding_dim: 512
embedding_dim: 512
encoder_use_time: false
encoder_type: transformer
decoder_type: transformer_encoder # we don't use cross attention.
enc_num_layers: 6
dec_num_layers: 6
use_timestep_embedder: true
timestep_embedder_dim: 128
variance_schedule:
_target_: salad.model_components.variance_schedule.VarianceSchedule
num_steps: &time_steps 1000
beta_1: 1e-4
beta_T: 0.05
mode: linear
# optimizer
lr: 1e-4
batch_size: 64
# dataset
dataset_kwargs:
data_path: spaghetti_chair_latents.hdf5
repeat: 3
data_keys: ["s_j_affine", "g_js_affine"]
global_normalization: &normalization null
global_normalization: *normalization # normalize pi, eigenvalues.
num_timesteps: *time_steps
faster: true
validation_step: 10
no_run_validation: false
spaghetti_tag: "chairs_large" # or airplanes, tables
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