Ethanfel
d642255e70
Integrates GIMM-VFI alongside existing BIM/EMA/SGM models. Key feature: generates all intermediate frames in one forward pass (no recursive 2x passes needed for 4x/8x). - Vendor gimm_vfi_arch/ from kijai/ComfyUI-GIMM-VFI with device fixes - Two variants: RAFT-based (~80MB) and FlowFormer-based (~123MB) - Auto-download checkpoints from HuggingFace (Kijai/GIMM-VFI_safetensors) - Three new nodes: Load GIMM-VFI Model, GIMM-VFI Interpolate, GIMM-VFI Segment Interpolate - single_pass toggle: True=arbitrary timestep (default), False=recursive like other models - ds_factor parameter for high-res input downscaling Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
58 lines
968 B
YAML
58 lines
968 B
YAML
trainer: stage_inr
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dataset:
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type: vimeo_arb
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path: ./data/vimeo90k/vimeo_septuplet
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aug: true
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arch:
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type: gimmvfi_r
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ema: true
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modulated_layer_idxs: [1]
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coord_range: [-1., 1.]
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hyponet:
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type: mlp
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n_layer: 5 # including the output layer
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hidden_dim: [128] # list, assert len(hidden_dim) in [1, n_layers-1]
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use_bias: true
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input_dim: 3
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output_dim: 2
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output_bias: 0.5
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activation:
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type: siren
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siren_w0: 1.0
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initialization:
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weight_init_type: siren
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bias_init_type: siren
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loss:
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subsample:
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type: random
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ratio: 0.1
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optimizer:
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type: adamw
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init_lr: 0.00008
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weight_decay: 0.00004
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betas: [0.9, 0.999]
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ft: true
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warmup:
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epoch: 1
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multiplier: 1
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buffer_epoch: 0
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min_lr: 0.000008
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mode: fix
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start_from_zero: True
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max_gn: null
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experiment:
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amp: True
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batch_size: 4
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total_batch_size: 32
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epochs: 60
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save_ckpt_freq: 10
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test_freq: 10
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test_imlog_freq: 10
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