update readme + logging
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yuvraj108c
@@ -46,7 +46,7 @@ class RifeTensorrt:
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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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output = engine.infer({"img0": frame_0, "img1": frame_1, "timestep": timestep_t}, cudaStream, use_cuda_graph)
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output = engine.infer({"img0": frame_0, "img1": frame_1, "timestep": timestep_t}, cudaStream)
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result = output['output']
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return result
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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/release/python-3123//)
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[](https://developer.nvidia.com/cuda-downloads)
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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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@@ -12,11 +12,22 @@
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</div>
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This project provides a [TensorRT](https://github.com/NVIDIA/TensorRT) implementation of [RIFE](https://github.com/hzwer/ECCV2022-RIFE) for ultra fast frame interpolation inside ComfyUI
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## ⭐ Support
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If you like my projects and wish to see updates and new features, please consider supporting me. It helps a lot!
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This project is licensed under [CC BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/), everyone is FREE to access, use, modify and redistribute with the same license.
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[](https://github.com/yuvraj108c/ComfyUI-Depth-Anything-Tensorrt)
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[](https://github.com/yuvraj108c/ComfyUI-Upscaler-Tensorrt)
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[](https://github.com/yuvraj108c/ComfyUI-Dwpose-Tensorrt)
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[](https://github.com/yuvraj108c/ComfyUI-Rife-Tensorrt)
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If you like the project, please give me a star! ⭐
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[](https://github.com/yuvraj108c/ComfyUI-Whisper)
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[](https://github.com/yuvraj108c/ComfyUI_InvSR)
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[](https://github.com/yuvraj108c/ComfyUI-Thera)
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[](https://github.com/yuvraj108c/ComfyUI-Video-Depth-Anything)
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[](https://github.com/yuvraj108c/ComfyUI-PiperTTS)
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[](https://www.buymeacoffee.com/yuvraj108cZ)
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[](https://paypal.me/yuvraj108c)
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---
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@@ -59,7 +70,7 @@ Models are automatically downloaded from [HuggingFace](https://huggingface.co/yu
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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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- Configure interpolation settings (multiplier, etc.)
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- Image resolutions between `256x256` and `3840x3840` are supported
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## 🤖 Environment tested
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@@ -69,10 +80,10 @@ Models are automatically downloaded from [HuggingFace](https://huggingface.co/yu
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## 🚨 Updates
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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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- **Updated Dependencies**: TensorRT updated to 10.13.3.9 for better performance and compatibility
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### 08 June 2026
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- **Automatic Model Management**: No more manual downloads! Models are automatically downloaded from HuggingFace and TensorRT engines are built on demand. [PR#14](https://github.com/yuvraj108c/ComfyUI-Rife-Tensorrt/pull/14) by [@reaperhammer](https://github.com/reaperhammer)
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- **Improved Workflow + Codebase**: New two-node system with `Load Rife Tensorrt Model` + `Rife Tensorrt` for better organization
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- **Remove cuda-python**: No more cuda installation issues on windows
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## 👏 Credits
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+19
-9
@@ -7,7 +7,9 @@ import einops
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from comfy.model_management import soft_empty_cache, get_torch_device
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import numpy as np
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from comfy.utils import ProgressBar
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from colored import Fore, Back, Style
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from colored import Fore, Back, Style
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from .utilities import rife_logger
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from tqdm import tqdm
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DEVICE = get_torch_device()
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@@ -25,9 +27,6 @@ def load_file_from_github_release(model_type, ckpt_name):
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error_str = '\n\n'.join(error_strs)
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raise Exception(f"Tried all GitHub base urls to download {ckpt_name} but no suceess. Below is the error log:\n\n{error_str}")
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def logger(msg):
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print(f'{Style.reset}{Fore.cyan}⚡ [Rife Tensorrt] - {msg}{Style.reset}')
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def preprocess_frames(frames):
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return einops.rearrange(frames[..., :3], "n h w c -> n c h w")
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@@ -45,7 +44,15 @@ def generate_frames_rife(
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out_len = 0
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number_of_frames_processed_since_last_cleared_cuda_cache = 0
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pbar = ProgressBar(len(frames))
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pbar = ProgressBar(len(frames)-1)
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bar_format = "[\033[94mComfyUI-Rife-Tensorrt\033[0m|\033[92mINFO\033[0m] - \033[92m{desc}: {percentage:3.0f}%|{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}]"
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progress_bar = tqdm(
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total=len(frames)-1,
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desc="Interpolating",
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bar_format=bar_format,
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disable=((len(frames)-1) == 1)
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)
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for frame_itr in range(len(frames) - 1): # Skip the final frame since there are no frames after it
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@@ -67,22 +74,25 @@ def generate_frames_rife(
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if number_of_frames_processed_since_last_cleared_cuda_cache >= clear_cache_after_n_frames:
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soft_empty_cache()
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number_of_frames_processed_since_last_cleared_cuda_cache = 0
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logger("Clearing cache...")
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rife_logger.info("Clearing cache...")
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pbar.update(1)
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progress_bar.update(1)
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progress_bar.refresh()
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progress_bar.close()
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# Append final frame
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output_frames[out_len] = frames[-1:]
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# Get actual frame shape from first interpolated frame (CHW format)
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actual_frame = output_frames[0]
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h, w = actual_frame.shape[1], actual_frame.shape[2]
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logger(f"done! - {out_len} total frames output at resolution: {h}x{w}")
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rife_logger.info(f"done! - {out_len} total frames output at resolution: {h}x{w}")
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out_len += 1
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# clear cache for courtesy
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soft_empty_cache()
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logger("Final clearing cache done ...")
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rife_logger.info("Final clearing cache done ...")
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#
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res = output_frames[:out_len]
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return res
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