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/generalizable_INR/configs.py

@@ -0,0 +1,57 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# ginr-ipc: https://github.com/kakaobrain/ginr-ipc
+# --------------------------------------------------------
+
+from typing import List, Optional
+from dataclasses import dataclass, field
+
+from omegaconf import OmegaConf, MISSING
+from .modules.module_config import HypoNetConfig
+
+
+@dataclass
+class GIMMConfig:
+    type: str = "gimm"
+    ema: Optional[bool] = None
+    ema_value: Optional[float] = None
+    fwarp_type: str = "linear"
+    hyponet: HypoNetConfig = field(default_factory=HypoNetConfig)
+    coord_range: List[float] = MISSING
+    modulated_layer_idxs: Optional[List[int]] = None
+
+    @classmethod
+    def create(cls, config):
+        # We need to specify the type of the default DataEncoderConfig.
+        # Otherwise, data_encoder will be initialized & structured as "unfold" type (which is default value)
+        # hence merging with the config with other type would cause config error.
+        defaults = OmegaConf.structured(cls(ema=False))
+        config = OmegaConf.merge(defaults, config)
+        return config
+
+
+@dataclass
+class GIMMVFIConfig:
+    type: str = "gimmvfi"
+    ema: Optional[bool] = None
+    ema_value: Optional[float] = None
+    fwarp_type: str = "linear"
+    rec_weight: float = 0.1
+    hyponet: HypoNetConfig = field(default_factory=HypoNetConfig)
+    raft_iter: int = 20
+    coord_range: List[float] = MISSING
+    modulated_layer_idxs: Optional[List[int]] = None
+
+    @classmethod
+    def create(cls, config):
+        # We need to specify the type of the default DataEncoderConfig.
+        # Otherwise, data_encoder will be initialized & structured as "unfold" type (which is default value)
+        # hence merging with the config with other type would cause config error.
+        defaults = OmegaConf.structured(cls(ema=False))
+        config = OmegaConf.merge(defaults, config)
+        return config