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>
78 lines
2.1 KiB
Python
78 lines
2.1 KiB
Python
from yacs.config import CfgNode as CN
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_CN = CN()
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_CN.name = ""
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_CN.suffix = ""
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_CN.gamma = 0.8
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_CN.max_flow = 400
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_CN.batch_size = 6
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_CN.sum_freq = 100
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_CN.val_freq = 5000000
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_CN.image_size = [432, 960]
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_CN.add_noise = False
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_CN.critical_params = []
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_CN.transformer = "latentcostformer"
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_CN.model = "pretrained_ckpt/flowformer_sintel.pth"
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# latentcostformer
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_CN.latentcostformer = CN()
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_CN.latentcostformer.pe = "linear"
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_CN.latentcostformer.dropout = 0.0
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_CN.latentcostformer.encoder_latent_dim = 256 # in twins, this is 256
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_CN.latentcostformer.query_latent_dim = 64
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_CN.latentcostformer.cost_latent_input_dim = 64
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_CN.latentcostformer.cost_latent_token_num = 8
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_CN.latentcostformer.cost_latent_dim = 128
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_CN.latentcostformer.arc_type = "transformer"
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_CN.latentcostformer.cost_heads_num = 1
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# encoder
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_CN.latentcostformer.pretrain = True
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_CN.latentcostformer.context_concat = False
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_CN.latentcostformer.encoder_depth = 3
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_CN.latentcostformer.feat_cross_attn = False
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_CN.latentcostformer.patch_size = 8
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_CN.latentcostformer.patch_embed = "single"
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_CN.latentcostformer.no_pe = False
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_CN.latentcostformer.gma = "GMA"
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_CN.latentcostformer.kernel_size = 9
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_CN.latentcostformer.rm_res = True
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_CN.latentcostformer.vert_c_dim = 64
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_CN.latentcostformer.cost_encoder_res = True
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_CN.latentcostformer.cnet = "twins"
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_CN.latentcostformer.fnet = "twins"
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_CN.latentcostformer.no_sc = False
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_CN.latentcostformer.only_global = False
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_CN.latentcostformer.add_flow_token = True
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_CN.latentcostformer.use_mlp = False
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_CN.latentcostformer.vertical_conv = False
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# decoder
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_CN.latentcostformer.decoder_depth = 32
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_CN.latentcostformer.critical_params = [
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"cost_heads_num",
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"vert_c_dim",
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"cnet",
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"pretrain",
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"add_flow_token",
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"encoder_depth",
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"gma",
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"cost_encoder_res",
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]
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### TRAINER
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_CN.trainer = CN()
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_CN.trainer.scheduler = "OneCycleLR"
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_CN.trainer.optimizer = "adamw"
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_CN.trainer.canonical_lr = 12.5e-5
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_CN.trainer.adamw_decay = 1e-4
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_CN.trainer.clip = 1.0
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_CN.trainer.num_steps = 120000
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_CN.trainer.epsilon = 1e-8
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_CN.trainer.anneal_strategy = "linear"
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def get_cfg():
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return _CN.clone()
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