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Upload 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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+
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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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+
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+
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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]