Ethanfel
1de086569c
Integrate EMA-VFI alongside existing BIM-VFI with three new ComfyUI nodes: Load EMA-VFI Model, EMA-VFI Interpolate, and EMA-VFI Segment Interpolate. Architecture files vendored from MCG-NJU/EMA-VFI with device-awareness fixes (removed hardcoded .cuda() calls), warp cache management, and relative imports. InputPadder extended to support EMA-VFI's replicate center-symmetric padding. Auto-installs timm dependency on first load. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
26 lines
1.1 KiB
Python
26 lines
1.1 KiB
Python
import torch
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backwarp_tenGrid = {}
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def clear_warp_cache():
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"""Free all cached grid tensors (call between frame pairs to reclaim VRAM)."""
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backwarp_tenGrid.clear()
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def warp(tenInput, tenFlow):
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k = (str(tenFlow.device), str(tenFlow.size()))
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if k not in backwarp_tenGrid:
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tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=tenFlow.device).view(
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1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1)
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tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=tenFlow.device).view(
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1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3])
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backwarp_tenGrid[k] = torch.cat(
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[tenHorizontal, tenVertical], 1).to(tenFlow.device)
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tenFlow = torch.cat([tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0),
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tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1)
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g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1)
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return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True)
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