Add GIMM-VFI support (NeurIPS 2024) with single-pass arbitrary-timestep interpolation

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>

gimm_vfi_arch/configs/gimmvfi_r_arb.yaml

@@ -0,0 +1,57 @@
+trainer: stage_inr
+dataset:
+  type: vimeo_arb
+  path: ./data/vimeo90k/vimeo_septuplet
+  aug: true
+
+arch: 
+  type: gimmvfi_r
+  ema: true
+  modulated_layer_idxs: [1]
+
+  coord_range: [-1., 1.]
+
+  hyponet:
+    type: mlp
+    n_layer: 5 # including the output layer
+    hidden_dim: [128] # list, assert len(hidden_dim) in [1, n_layers-1]
+    use_bias: true
+    input_dim: 3
+    output_dim: 2
+    output_bias: 0.5
+    activation:
+      type: siren
+      siren_w0: 1.0
+    initialization:
+      weight_init_type: siren
+      bias_init_type: siren
+
+loss:
+  subsample:
+    type: random
+    ratio: 0.1
+
+optimizer:
+  type: adamw
+  init_lr: 0.00008
+  weight_decay: 0.00004
+  betas: [0.9, 0.999] 
+  ft: true
+  warmup:
+    epoch: 1
+    multiplier: 1
+    buffer_epoch: 0
+    min_lr: 0.000008
+    mode: fix
+    start_from_zero: True
+  max_gn: null
+
+experiment:
+  amp: True
+  batch_size: 4
+  total_batch_size: 32
+  epochs: 60
+  save_ckpt_freq: 10
+  test_freq: 10
+  test_imlog_freq: 10
+