| wandb: | |
| entity: r-ap | |
| run_id: 082oe47w | |
| experiment: | |
| name: muse-multi | |
| project: muse-prod | |
| output_dir: output/ | |
| max_train_examples: 28500 | |
| max_eval_examples: 1000 | |
| save_every: 1000 | |
| eval_every: 700 | |
| generate_every: 200 | |
| log_every: 50 | |
| log_grad_norm_every: 100000000 | |
| resume_from_checkpoint: latest | |
| resume_lr_scheduler: true | |
| checkpoints_total_limit: 4 | |
| logging_dir: output/logs | |
| model: | |
| vq_model: | |
| type: vqgan | |
| text_encoder: | |
| type: clip | |
| pretrained: openMUSE/clip-vit-large-patch14-text-enc | |
| transformer: | |
| vocab_size: 8256 | |
| hidden_size: 1024 | |
| intermediate_size: 2816 | |
| num_hidden_layers: 22 | |
| num_attention_heads: 16 | |
| in_channels: 768 | |
| block_out_channels: | |
| - 768 | |
| block_has_attention: | |
| - true | |
| block_num_heads: 12 | |
| num_res_blocks: 3 | |
| res_ffn_factor: 4 | |
| patch_size: 1 | |
| encoder_hidden_size: 768 | |
| add_cross_attention: true | |
| project_encoder_hidden_states: true | |
| codebook_size: 8192 | |
| num_vq_tokens: 256 | |
| initializer_range: 0.02 | |
| norm_type: rmsnorm | |
| layer_norm_eps: 1.0e-06 | |
| ln_elementwise_affine: true | |
| use_encoder_layernorm: false | |
| use_bias: false | |
| hidden_dropout: 0.0 | |
| attention_dropout: 0.0 | |
| use_codebook_size_for_output: true | |
| use_empty_embeds_for_uncond: true | |
| add_cond_embeds: true | |
| cond_embed_dim: 768 | |
| add_micro_cond_embeds: true | |
| micro_cond_encode_dim: 256 | |
| micro_cond_embed_dim: 1280 | |
| force_down_up_sample: true | |
| architecture: uvit | |
| enable_xformers_memory_efficient_attention: true | |
| dataset: | |
| preprocessing: | |
| max_seq_length: 77 | |
| resolution: 256 | |
| optimizer: | |
| name: adamw | |
| params: | |
| learning_rate: 0.0001 | |
| scale_lr: false | |
| beta1: 0.9 | |
| beta2: 0.999 | |
| weight_decay: 0.01 | |
| epsilon: 1.0e-08 | |
| lr_scheduler: | |
| scheduler: constant_with_warmup | |
| params: | |
| learning_rate: ${optimizer.params.learning_rate} | |
| warmup_steps: 100 | |
| training: | |
| gradient_accumulation_steps: 1 | |
| batch_size: 20 | |
| mixed_precision: 'no' | |
| enable_tf32: true | |
| use_ema: true | |
| ema_decay: 0.9999 | |
| ema_update_after_step: 0 | |
| ema_update_every: 1 | |
| seed: 13399 | |
| max_train_steps: 20000 | |
| overfit_one_batch: false | |
| cond_dropout_prob: 0.1 | |
| min_masking_rate: 0.0 | |
| label_smoothing: 0.1 | |
| max_grad_norm: null | |
| guidance_scale: 8 | |
| generation_timesteps: 16 | |
| use_soft_code_target: false | |
| use_stochastic_code: false | |
| soft_code_temp: 1.0 | |
| mask_schedule: cosine | |
| mask_contiguous_region_prob: 0.15 | |
| config: configs/segmentation.yaml | |