feat: CUDA 13 default, auto-detection, and resolution profiles
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
This commit is contained in:
silveroxides
+118
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@@ -1,11 +1,56 @@
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import torch
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import os
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import sys
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from pathlib import Path
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from comfy.model_management import get_torch_device
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from .vfi_utilities import preprocess_frames, postprocess_frames, generate_frames_rife, logger
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from .trt_utilities import Engine
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from .utilities import download_file, ColoredLogger
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import folder_paths
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import time
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# Auto-detect CUDA toolkit and add DLL path before importing polygraphy
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def _setup_cuda_dll_path():
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"""Auto-detect CUDA toolkit and add cudart64 DLL path on Windows."""
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if not sys.platform.startswith("win"):
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return
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cuda_root = None
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# Check for CUDA_PATH or CUDA_HOME environment variables
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cuda_root = os.environ.get("CUDA_PATH") or os.environ.get("CUDA_HOME")
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if not cuda_root:
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# Try default Windows install location
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program_files = os.environ.get("PROGRAMFILES")
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if program_files:
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cuda_base = Path(program_files) / "NVIDIA GPU Computing Toolkit" / "CUDA"
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if cuda_base.exists():
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# Find highest version directory
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versions = sorted([d for d in cuda_base.iterdir() if d.is_dir()], reverse=True)
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if versions:
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cuda_root = str(versions[0])
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if cuda_root:
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cuda_path = Path(cuda_root)
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# CUDA 13.0+ puts cudart64 in bin/x64 subdirectory
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cuda_bin_x64 = cuda_path / "bin" / "x64"
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if cuda_bin_x64.exists() and any(cuda_bin_x64.glob("cudart64*.dll")):
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os.add_dll_directory(str(cuda_bin_x64))
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return
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# Fallback to regular bin directory for older CUDA versions
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cuda_bin = cuda_path / "bin"
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if cuda_bin.exists() and any(cuda_bin.glob("cudart64*.dll")):
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os.add_dll_directory(str(cuda_bin))
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return
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# CUDA toolkit not found - print warning with download link
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print("[ComfyUI-Rife-TensorRT] WARNING: CUDA toolkit not found.")
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print(" Set CUDA_PATH environment variable or install CUDA toolkit.")
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print(" Download: https://developer.nvidia.com/cuda-13-0-2-download-archive")
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_setup_cuda_dll_path()
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from polygraphy import cuda
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import comfy.model_management as mm
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import tensorrt
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@@ -13,10 +58,11 @@ import json
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ENGINE_DIR = os.path.join(folder_paths.models_dir, "tensorrt", "rife")
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# Image dimensions for TensorRT engine building
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IMAGE_DIM_MIN = 256
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IMAGE_DIM_OPT = 512
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IMAGE_DIM_MAX = 3840
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# Default resolution profiles (fallback if config is missing)
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DEFAULT_RESOLUTION_PROFILES = {
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"small": {"min": 480, "opt": 512, "max": 896},
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"medium": {"min": 720, "opt": 1024, "max": 1280}
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}
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# Logger for this module
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rife_logger = ColoredLogger("ComfyUI-Rife-Tensorrt")
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@@ -58,6 +104,34 @@ def load_node_config(config_filename="load_rife_config.json"):
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# Load the configuration once when the module is imported
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LOAD_RIFE_NODE_CONFIG = load_node_config()
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class CustomResolutionConfig:
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"""Node to configure custom resolution dimensions for TensorRT engine building."""
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"min_dim": ("INT", {"default": 480, "min": 64, "max": 4096, "step": 8, "tooltip": "Minimum resolution dimension"}),
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"opt_dim": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 8, "tooltip": "Optimal resolution dimension (most common)"}),
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"max_dim": ("INT", {"default": 896, "min": 64, "max": 4096, "step": 8, "tooltip": "Maximum resolution dimension"}),
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}
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}
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RETURN_TYPES = ("RIFE_RESOLUTION_CONFIG",)
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RETURN_NAMES = ("resolution_config",)
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FUNCTION = "configure"
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CATEGORY = "tensorrt"
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DESCRIPTION = "Configure custom resolution dimensions for RIFE TensorRT engine."
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def configure(self, min_dim, opt_dim, max_dim):
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config = {
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"min": min_dim,
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"opt": opt_dim,
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"max": max_dim,
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}
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return (config,)
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class AutoLoadRifeTensorrtModel:
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@classmethod
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def INPUT_TYPES(cls):
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@@ -74,10 +148,23 @@ class AutoLoadRifeTensorrtModel:
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precision_default = precision_config.get("default", "fp16")
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precision_tooltip = precision_config.get("tooltip", "Select precision.")
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# Resolution profile configuration
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profile_config = LOAD_RIFE_NODE_CONFIG.get("resolution_profile", {})
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profile_options = profile_config.get("options", ["small", "medium"])
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# Ensure 'custom' is always available
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if "custom" not in profile_options:
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profile_options = profile_options + ["custom"]
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profile_default = profile_config.get("default", "small")
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profile_tooltip = profile_config.get("tooltip", "Resolution range for TensorRT engine. Use 'custom' with the INT inputs below.")
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return {
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"required": {
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"model": (model_options, {"default": model_default, "tooltip": model_tooltip}),
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"precision": (precision_options, {"default": precision_default, "tooltip": precision_tooltip}),
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"resolution_profile": (profile_options, {"default": profile_default, "tooltip": profile_tooltip}),
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},
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"optional": {
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"custom_config": ("RIFE_RESOLUTION_CONFIG", {"tooltip": "Custom resolution config (used when profile='custom')"}),
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}
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}
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@@ -87,7 +174,7 @@ class AutoLoadRifeTensorrtModel:
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DESCRIPTION = "Load RIFE tensorrt models, they will be built automatically if not found."
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FUNCTION = "load_rife_tensorrt_model"
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def load_rife_tensorrt_model(self, model, precision):
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def load_rife_tensorrt_model(self, model, precision, resolution_profile, custom_config=None):
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tensorrt_models_dir = os.path.join(folder_paths.models_dir, "tensorrt", "rife")
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onnx_models_dir = os.path.join(folder_paths.models_dir, "onnx")
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@@ -96,12 +183,32 @@ class AutoLoadRifeTensorrtModel:
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onnx_model_path = os.path.join(onnx_models_dir, f"{model}.onnx")
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# Build tensorrt model path with detailed naming
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# Get resolution dimensions based on profile
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if resolution_profile == "custom":
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if custom_config is None:
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rife_logger.warning("Custom profile selected but no custom_config provided. Using defaults.")
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dim_min, dim_opt, dim_max = 480, 512, 896
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else:
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dim_min = custom_config.get("min", 480)
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dim_opt = custom_config.get("opt", 512)
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dim_max = custom_config.get("max", 896)
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# Use dimensions in profile name for custom engines
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profile_name = f"custom_{dim_min}_{dim_opt}_{dim_max}"
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else:
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profiles = LOAD_RIFE_NODE_CONFIG.get("resolution_profiles", DEFAULT_RESOLUTION_PROFILES)
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profile = profiles.get(resolution_profile, DEFAULT_RESOLUTION_PROFILES["small"])
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dim_min = profile.get("min", 480)
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dim_opt = profile.get("opt", 512)
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dim_max = profile.get("max", 896)
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profile_name = resolution_profile
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rife_logger.info(f"Using resolution profile '{profile_name}': min={dim_min}, opt={dim_opt}, max={dim_max}")
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# Build tensorrt model path with detailed naming (includes profile)
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engine_channel = 3
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engine_min_batch, engine_opt_batch, engine_max_batch = 1, 1, 1
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engine_min_h, engine_opt_h, engine_max_h = IMAGE_DIM_MIN, IMAGE_DIM_OPT, IMAGE_DIM_MAX
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engine_min_w, engine_opt_w, engine_max_w = IMAGE_DIM_MIN, IMAGE_DIM_OPT, IMAGE_DIM_MAX
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tensorrt_model_path = os.path.join(tensorrt_models_dir, f"{model}_{precision}_{engine_min_batch}x{engine_channel}x{engine_min_h}x{engine_min_w}_{engine_opt_batch}x{engine_channel}x{engine_opt_h}x{engine_opt_w}_{engine_max_batch}x{engine_channel}x{engine_max_h}x{engine_max_w}_{tensorrt.__version__}.trt")
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engine_min_h, engine_opt_h, engine_max_h = dim_min, dim_opt, dim_max
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engine_min_w, engine_opt_w, engine_max_w = dim_min, dim_opt, dim_max
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tensorrt_model_path = os.path.join(tensorrt_models_dir, f"{model}_{precision}_{profile_name}_{engine_min_batch}x{engine_channel}x{engine_min_h}x{engine_min_w}_{engine_opt_batch}x{engine_channel}x{engine_opt_h}x{engine_opt_w}_{engine_max_batch}x{engine_channel}x{engine_max_h}x{engine_max_w}_{tensorrt.__version__}.trt")
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if not os.path.exists(tensorrt_model_path):
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if not os.path.exists(onnx_model_path):
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@@ -204,11 +311,13 @@ class AutoRifeTensorrt:
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NODE_CLASS_MAPPINGS = {
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"AutoRifeTensorrt": AutoRifeTensorrt,
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"AutoLoadRifeTensorrtModel": AutoLoadRifeTensorrtModel,
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"CustomResolutionConfig": CustomResolutionConfig,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"AutoRifeTensorrt": "Auto RIFE TensorRT",
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"AutoLoadRifeTensorrtModel": "(Down)load RIFE TensorRT Model",
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"CustomResolutionConfig": "RIFE Custom Resolution Config",
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}
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__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
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@@ -12,5 +12,14 @@
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"options": ["fp16", "fp32"],
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"default": "fp16",
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"tooltip": "Precision to build the tensorrt engines. Loaded from config."
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},
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"resolution_profile": {
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"options": ["small", "medium"],
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"default": "small",
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"tooltip": "Resolution range for TensorRT engine. Small: 480-896px, Medium: 720-1280px. Smaller ranges use less VRAM."
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},
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"resolution_profiles": {
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"small": { "min": 480, "opt": 512, "max": 896 },
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"medium": { "min": 720, "opt": 1024, "max": 1280 }
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}
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}
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@@ -2,9 +2,9 @@
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# ComfyUI Rife TensorRT ⚡
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[](https://www.python.org/downloads/release/python-31211/)
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[](https://developer.nvidia.com/cuda-downloads)
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[](https://developer.nvidia.com/tensorrt)
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[](https://www.python.org/downloads/)
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[](https://developer.nvidia.com/cuda-13-0-2-download-archive)
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[](https://developer.nvidia.com/tensorrt)
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[](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en)
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@@ -40,21 +40,32 @@ cd ./ComfyUI-Rife-Tensorrt
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pip install -r requirements.txt
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```
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### ⚠️ Installation Issues (CUDA Versions)
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This node defaults to **CUDA 12** libraries (`requirements.txt`).
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If you are using **CUDA 13**, or if you experience dependency errors, please install the specific requirements file for your system:
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### ⚠️ CUDA Version Selection
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**For CUDA 13:**
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This node defaults to **CUDA 13** (RTX 50 series, driver 580+).
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**For CUDA 12 (RTX 30/40 series):**
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```bash
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pip install -r requirements_cu13.txt
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pip install -r requirements_cu12.txt
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```
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**For CUDA 12 (Default):**
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**For CUDA 13 (Default):**
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```bash
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pip install -r requirements.txt
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```
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This ensures the correct `tensorrt-libs` and `tensorrt-bindings` are installed to match your system.
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### 📦 CUDA Toolkit Required
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The node automatically detects your CUDA installation via `CUDA_PATH` or `CUDA_HOME` environment variables.
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If CUDA is not detected, download from: https://developer.nvidia.com/cuda-13-0-2-download-archive
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### 🎯 Resolution Profiles
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The node supports resolution profiles to optimize VRAM usage:
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- **small**: 480-896px (recommended for most video generation)
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- **medium**: 720-1280px (for higher resolution videos)
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- **custom**: Connect a "RIFE Custom Resolution Config" node for manual control
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The following RIFE models are supported and will be automatically downloaded and built:
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- **rife49_ensemble_True_scale_1_sim** (default) - Latest and most accurate
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@@ -68,21 +79,26 @@ Models are automatically downloaded from [HuggingFace](https://huggingface.co/yu
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1. **Load Model**: Insert `Right Click -> Add Node -> tensorrt -> Load Rife Tensorrt Model`
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- Choose your preferred RIFE model (rife47, rife48, or rife49)
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- Select precision (fp16 recommended for speed, fp32 for maximum accuracy)
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- Select resolution profile (small, medium, or custom)
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- The model will be automatically downloaded and TensorRT engine built on first use
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2. **Process Frames**: Insert `Right Click -> Add Node -> tensorrt -> Rife Tensorrt`
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- Connect the loaded model from step 1
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- Input your video frames
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- Configure interpolation settings (multiplier, CUDA graph, etc.)
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- Image resolutions between `256x256` and `3840x3840` are supported
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## 🤖 Environment tested
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- WSL Ubuntu 24.04.03 LTS, Cuda 12.9, Tensorrt 10.13.3.9, Python 3.12.11, RTX 5080 GPU
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- Windows (Not tested, but should work)
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- Windows 11, CUDA 13.0, TensorRT 10.14.1.48, Python 3.12, RTX 5070 Ti
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- WSL Ubuntu 24.04.03 LTS, CUDA 12.9, TensorRT 10.13.3.9, Python 3.12.11, RTX 5080
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## 🚨 Updates
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### January 2026
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- **CUDA 13 Default**: Updated to CUDA 13.0 and TensorRT 10.14.1.48
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- **Auto CUDA Detection**: Automatically finds CUDA toolkit and DLL paths
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- **Resolution Profiles**: Added small/medium/custom profiles to reduce VRAM usage
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### December 2025
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- **Automatic Model Management**: No more manual downloads! Models are automatically downloaded from HuggingFace and TensorRT engines are built on demand
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- **Improved Workflow**: New two-node system with `Load Rife Tensorrt Model` + `Rife Tensorrt` for better organization
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+6
-4
@@ -1,7 +1,9 @@
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# Default to CUDA 12 as it is the standard for TRT 10.13
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tensorrt-cu12==10.13.3.9
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tensorrt-cu12-libs==10.13.3.9
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tensorrt-cu12-bindings==10.13.3.9
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# Default: CUDA 13 (RTX 50 series, CUDA 13.0+)
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# For CUDA 12, use requirements_cu12.txt instead
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tensorrt_cu13==10.14.1.48
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tensorrt_cu13_bindings==10.14.1.48
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tensorrt_cu13_libs==10.14.1.48
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cuda-toolkit>=13.0.0,<13.1.0
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einops
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colored
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polygraphy
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@@ -0,0 +1,12 @@
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# Legacy: CUDA 12 (RTX 30/40 series, CUDA 12.x)
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# For CUDA 13, use requirements.txt instead
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tensorrt-cu12==10.13.3.9
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tensorrt-cu12-libs==10.13.3.9
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tensorrt-cu12-bindings==10.13.3.9
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cuda-toolkit>=12.8.0,<13.0.0
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einops
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colored
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polygraphy
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cuda-python
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requests
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tqdm
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@@ -1,9 +0,0 @@
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tensorrt-cu13==10.13.3.9
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tensorrt-cu13-libs==10.13.3.9
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tensorrt-cu13-bindings==10.13.3.9
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einops
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colored
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polygraphy
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cuda-python
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requests
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tqdm
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