- Add RIFE v4.25 architecture support in rife_arch.py (Head with
16/4 channels, 5 IFBlocks with c=192/128/96/64/32, scale_list
[16,8,4,2,1], no ensemble)
- Add rife425.pth to export_onnx.py with HF fallback URL
- Update load_rife_config.json: dropdown now only shows rife425 and
rife49 (removed rife47/48/417/426/sudo_rife4 that never worked)
- Set rife425 as default
- Make TORCH_DEVICE fallback to CPU for ONNX export on non-CUDA
- Pin polygraphy==0.49.26 for TensorRT 10.15.1 compatibility
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- Auto-install now only installs the matching TensorRT wheels instead of the
multi-GB cuda-toolkit package from PyPI.
- Use a single pip install -r call per requirements file for faster dependency
resolution and better cache reuse.
- Add install-success/failure markers to avoid repeating the install on every
ComfyUI startup.
- Fix pyproject.toml Repository and PublisherId to point to
huchukato/ComfyUI-RIFE-TensorRT-Auto.
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Requirements updates:
- Default to CUDA 13 with TensorRT 10.14.1.48 (requirements.txt)
- Add cuda-toolkit version constraints (>=13.0.0,<13.1.0)
- Rename requirements_cu13.txt -> requirements_cu12.txt for legacy support
- Add cuda-toolkit constraint for CUDA 12 (>=12.8.0,<13.0.0)
CUDA toolkit detection:
- Add _setup_cuda_dll_path() to auto-detect CUDA installation
- Check CUDA_PATH, CUDA_HOME, and default Windows install paths
- Handle CUDA 13+ bin/x64 subdirectory for cudart64.dll
- Display download link if CUDA not found
Engine building updates:
- Add resolution profiles (small: 480-896px, medium: 720-1280px)
- Add 'custom' profile with CustomResolutionConfig node
- Include profile name in engine filename for separate caching
- Reduce default VRAM usage from ~15GB to ~2-3GB
