Upload tim_xl_p1_t2i.yaml
Browse files- tim_xl_p1_t2i.yaml +81 -0
tim_xl_p1_t2i.yaml
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model:
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transport:
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target: tim.schedulers.transports.OT_FM
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params:
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P_mean: 0.0
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P_std: 1.6
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sigma_d: 1.0
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unified_dcm_loss:
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diffusion_ratio: 0.5
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consistency_ratio: 0.1
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derivative_type: dde
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differential_epsilon: 0.005
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weight_time_type: sqrt
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weight_time_tangent: True
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network:
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target: tim.models.t2i.tim_model.TiM
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params:
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input_size: 16
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patch_size: 1
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in_channels: 32
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depth: 28
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hidden_size: 1152
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cap_feat_dim: 1152
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num_heads: 16
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encoder_depth: 8
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qk_norm: True
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z_dim: 768
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new_condition: t-r
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use_new_embed: True
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distance_aware: True
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lora_hidden_size: 384
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# pretrained_vae:
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vae_dir: mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers
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# text encoder
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text_encoder_dir: google/gemma-3-1b-it
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proportion_empty_prompts: 0.1
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use_last_hidden_state: True
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max_seq_length: 256
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# repa encoder
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enc_dir: checkpoints/radio/radio-v2.5-b_half.pth.tar
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proj_coeff: 1.0
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# ema
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use_ema: True
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ema_decay: 0.9999
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data:
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data_type: image_ms
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dataset:
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root_dir: datasets/t2i_toy_dataset
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packed_json: datasets/t2i_toy_dataset/bucket_sampler.json
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jsonl_dir: datasets/t2i_toy_dataset/data_info.jsonl
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dataloader:
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num_workers: 4
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batch_size: 128 # Batch size (per device) for the training dataloader.
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training:
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tracker: null
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max_train_steps: 500000
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checkpointing_steps: 1000
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checkpoints_total_limit: 2
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resume_from_checkpoint: latest
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learning_rate: 1.0e-4
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learning_rate_base_batch_size: 512
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scale_lr: True
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lr_scheduler: constant # "linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"]
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lr_warmup_steps: 0
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gradient_accumulation_steps: 1
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optimizer:
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target: torch.optim.AdamW
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params:
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# betas: ${tuple:0.9, 0.999}
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betas: [0.9, 0.95]
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weight_decay: 1.0e-2
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eps: 1.0e-6
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max_grad_norm: 1.0
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proportion_empty_prompts: 0.0
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mixed_precision: bf16 # ["no", "fp16", "bf16"]
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allow_tf32: True
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validation_steps: 500
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checkpoint_list: [100000, 200000, 300000, 400000]
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