run_name_:
ckpt_path: ${oc.env:ONESCIENCE_DATASETS_DIR}/la-proteina/checkpoints_laproteina
ckpt_name: 
autoencoder_ckpt_path: 

# Job management
ncpus_: 24
seed: 5
gen_njobs: 1  # Leave as 1
eval_njobs: 1  # Leave as 1

# All sampling arugments that we want to change frequently
generation:
  n_recycle: 0  # Leave as 0

  # General sampling params
  args:
    # nsteps: 400
    # self_cond: True
    nsteps: 20
    self_cond: False

    # Guidance
    guidance_w: 1.0   # guidance model weights, 1.0 for w/o CFG and autoguidance, 0.0 for excluding the main model
    ag_ratio: 0.0   # a value between 0 and 1, determining the proportion of autoguidance v.s. classifier-free guidance, 1.0 for all autoguidance, 0.0 for all CFG -- ag not supported for now
    ag_ckpt_path: null  # path to bad model checkpoint

    save_trajectory_every: 0 # at which step interval to save trajectory snapshots of generation, 0 for no saving

  # Model-specific sampling arguments
  model:
    bb_ca:
      schedule:
        mode: log
        p: 2.0
      gt:
        mode: "1/t"
        p: 1.0
        clamp_val: null
      simulation_step_params:
        sampling_mode: sc  # Options are: vf (plain fow matching) or sc (using score, where parameters below matter)
        sc_scale_noise: 0.1  # scale used to multiply noise if mode == sc
        sc_scale_score: 1.0  # scale used to multiply score if mode == sc
        t_lim_ode: 0.98  # At what point between 0 and 1 we switch to ODE with score scaling (for sc mode)
        t_lim_ode_below: 0.02  # Before what point between 0 and 1 we use sc mode (for vf_ss mode)
        center_every_step: True  # Whether to center the sample every step
    local_latents:
      schedule:
        mode: power
        p: 2.0
      gt:
        mode: "tan"
        p: 1.0
        clamp_val: null
      simulation_step_params:
        sampling_mode: sc
        sc_scale_noise: 0.1
        sc_scale_score: 1.0
        t_lim_ode: 0.98
        t_lim_ode_below: 0.02
        center_every_step: False