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Running on Zero
| exp_root_dir: "outputs" | |
| name: "diffusion_gs_obja_4views_relposes" | |
| tag: "${rmspace:${system.shape_model_type}+lr${system.optimizer.args.lr},_}" | |
| seed: 0 | |
| data_type: "Objaverse-datamodule" | |
| data: | |
| local_dir: "DATAPATH_TO_GOBJAVERSE_JSON" | |
| image_dir: "DATAPATH_TO_GOBJAVERSE" | |
| gen_idxs: [30,33,36,39] ## | |
| sel_views: 6 | |
| gen_views: 4 | |
| training_res: [256, 256] | |
| batch_size: 4 | |
| num_workers: 4 | |
| norm_camera: True | |
| norm_radius: 3. #1.8 | |
| gen_rel_idxs: True | |
| system_type: "diffusion-gs-system" | |
| system: | |
| num_inference_steps: 30 | |
| shape_model_type: "diffusion-gs-model" | |
| shape_model: | |
| width: 1024 | |
| in_channels: 9 #rgb+plucker | |
| patch_size: 8 | |
| n_gaussians: 2 | |
| dim_heads: 64 | |
| num_layers: 24 | |
| prior_distribution: 'gaussian' #sphere_gaussian | |
| use_flash: true | |
| use_checkpoint: true | |
| noise_scheduler_type: "diffusionGS.models.scheduler.ddim_scheduler.DDIMScheduler" | |
| noise_scheduler: | |
| num_train_timesteps: 1000 | |
| prediction_type: "sample" | |
| loggers: | |
| wandb: | |
| enable: false | |
| project: "diffusionGS" | |
| name: image-to-shape-diffusion+${name}+${tag} | |
| loss: | |
| loss_type: "mse" | |
| lambda_diffusion: [150, 0., 1., 151] | |
| lambda_lpips: [150, 0., 0.5, 151] | |
| lambda_ssim: 0.0 | |
| lambda_pointsdist: [150, 1., 0., 151] | |
| lambda_xyz: [150, 0., 0.025, 151] | |
| lambda_depth: 0. | |
| optimizer: | |
| name: AdamW | |
| args: | |
| lr: 1.e-5 | |
| betas: [0.9, 0.99] | |
| eps: 1.e-8 | |
| scheduler: | |
| name: CosineAnnealingLR | |
| args: | |
| T_max: 500000 | |
| eta_min: 1e-6 | |
| trainer: | |
| num_nodes: 1 | |
| max_epochs: 1000000 | |
| log_every_n_steps: 5 | |
| num_sanity_val_steps: 1 | |
| check_val_every_n_epoch: 2 | |
| accumulate_grad_batches: 1 | |
| gradient_clip_val: 0.5 | |
| enable_progress_bar: true | |
| precision: 16-mixed | |
| strategy: 'ddp_find_unused_parameters_true' | |
| checkpoint: | |
| save_last: true | |
| save_top_k: -1 | |
| every_n_train_steps: 1000 |