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