silveroxides
5cff7f7dc7
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
325 lines
14 KiB
Python
325 lines
14 KiB
Python
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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import json
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ENGINE_DIR = os.path.join(folder_paths.models_dir, "tensorrt", "rife")
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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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# Function to load configuration
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def load_node_config(config_filename="load_rife_config.json"):
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"""Loads node configuration from a JSON file."""
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current_dir = os.path.dirname(__file__)
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config_path = os.path.join(current_dir, config_filename)
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default_config = {
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"model": {
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"options": ["rife49_ensemble_True_scale_1_sim"],
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"default": "rife49_ensemble_True_scale_1_sim",
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"tooltip": "Default model (fallback from code)"
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},
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"precision": {
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"options": ["fp16", "fp32"],
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"default": "fp16",
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"tooltip": "Default precision (fallback from code)"
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}
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}
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try:
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with open(config_path, 'r') as f:
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config = json.load(f)
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rife_logger.info(f"Successfully loaded configuration from {config_filename}")
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return config
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except FileNotFoundError:
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rife_logger.warning(f"Configuration file '{config_path}' not found. Using default fallback configuration.")
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return default_config
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except json.JSONDecodeError:
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rife_logger.error(f"Error decoding JSON from '{config_path}'. Using default fallback configuration.")
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return default_config
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except Exception as e:
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rife_logger.error(f"An unexpected error occurred while loading '{config_path}': {e}. Using default fallback.")
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return default_config
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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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# Use the pre-loaded configuration
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model_config = LOAD_RIFE_NODE_CONFIG.get("model", {})
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precision_config = LOAD_RIFE_NODE_CONFIG.get("precision", {})
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# Provide sensible defaults if keys are missing in the config
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model_options = model_config.get("options", ["rife49_ensemble_True_scale_1_sim"])
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model_default = model_config.get("default", "rife49_ensemble_True_scale_1_sim")
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model_tooltip = model_config.get("tooltip", "Select a RIFE model.")
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precision_options = precision_config.get("options", ["fp16", "fp32"])
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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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RETURN_NAMES = ("rife_trt_model",)
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RETURN_TYPES = ("RIFE_TRT_MODEL",)
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CATEGORY = "tensorrt"
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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, 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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os.makedirs(tensorrt_models_dir, exist_ok=True)
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os.makedirs(onnx_models_dir, exist_ok=True)
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onnx_model_path = os.path.join(onnx_models_dir, f"{model}.onnx")
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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 = 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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onnx_model_download_url = f"https://huggingface.co/yuvraj108c/rife-onnx/resolve/main/{model}.onnx"
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rife_logger.info(f"Downloading {onnx_model_download_url}")
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download_file(url=onnx_model_download_url, save_path=onnx_model_path)
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else:
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rife_logger.info(f"ONNX model found at: {onnx_model_path}")
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rife_logger.info(f"Building TensorRT engine for {onnx_model_path}: {tensorrt_model_path}")
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mm.soft_empty_cache()
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s = time.time()
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engine = Engine(tensorrt_model_path)
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engine.build(
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onnx_path=onnx_model_path,
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fp16=True if precision == "fp16" else False,
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input_profile=[
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{
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"img0": [(engine_min_batch, engine_channel, engine_min_h, engine_min_w), (engine_opt_batch, engine_channel, engine_opt_h, engine_opt_w), (engine_max_batch, engine_channel, engine_max_h, engine_max_w)],
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"img1": [(engine_min_batch, engine_channel, engine_min_h, engine_min_w), (engine_opt_batch, engine_channel, engine_opt_h, engine_opt_w), (engine_max_batch, engine_channel, engine_max_h, engine_max_w)],
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}
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],
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)
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e = time.time()
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rife_logger.info(f"Time taken to build: {(e-s)} seconds")
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rife_logger.info(f"Loading TensorRT engine: {tensorrt_model_path}")
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mm.soft_empty_cache()
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engine = Engine(tensorrt_model_path)
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engine.load()
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return (engine,)
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class AutoRifeTensorrt:
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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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"frames": ("IMAGE", {"tooltip": "Input frames for video frame interpolation"}),
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"rife_trt_model": ("RIFE_TRT_MODEL", {"tooltip": "Tensorrt model built and loaded"}),
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"clear_cache_after_n_frames": ("INT", {"default": 100, "min": 1, "max": 1000, "tooltip": "Clear CUDA cache after processing this many frames"}),
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"multiplier": ("INT", {"default": 2, "min": 1, "tooltip": "Frame interpolation multiplier"}),
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"use_cuda_graph": ("BOOLEAN", {"default": True, "tooltip": "Use CUDA graph for better performance"}),
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"keep_model_loaded": ("BOOLEAN", {"default": False, "tooltip": "Keep model loaded in memory after processing"}),
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},
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}
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RETURN_TYPES = ("IMAGE", )
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FUNCTION = "vfi"
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CATEGORY = "tensorrt"
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OUTPUT_NODE=True
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def vfi(
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self,
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frames,
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rife_trt_model,
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clear_cache_after_n_frames=100,
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multiplier=2,
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use_cuda_graph=True,
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keep_model_loaded=False,
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):
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B, H, W, C = frames.shape
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shape_dict = {
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"img0": {"shape": (1, 3, H, W)},
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"img1": {"shape": (1, 3, H, W)},
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"output": {"shape": (1, 3, H, W)},
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}
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cudaStream = cuda.Stream()
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# Use the provided model directly
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engine = rife_trt_model
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logger(f"Using loaded TensorRT engine")
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# Activate and allocate buffers for the engine
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engine.activate()
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engine.allocate_buffers(shape_dict=shape_dict)
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frames = preprocess_frames(frames)
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def return_middle_frame(frame_0, frame_1, timestep):
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timestep_t = torch.tensor([timestep], dtype=torch.float32).to(get_torch_device())
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# s = time.time()
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output = engine.infer({"img0": frame_0, "img1": frame_1, "timestep": timestep_t}, cudaStream, use_cuda_graph)
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# e = time.time()
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# print(f"Time taken to infer: {(e-s)*1000} ms")
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result = output['output']
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return result
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result = generate_frames_rife(frames, clear_cache_after_n_frames, multiplier, return_middle_frame)
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out = postprocess_frames(result)
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if not keep_model_loaded:
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engine.reset()
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return (out,)
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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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