### Install Project Dependencies Source: https://github.com/tntwise/real-video-enhancer/blob/2.3.7/backend/src/pytorch/DRBA/README.md Installs the necessary Python packages for the project using pip. It clones the repository and then installs requirements from the 'requirements.txt' file. The 'cupy' package is optional and used for performance acceleration. ```bash git clone https://github.com/routineLife1/DRBA.git cd DRBA pip3 install -r requirements.txt ``` -------------------------------- ### Clone Rife Repository and Install Dependencies Source: https://github.com/tntwise/real-video-enhancer/wiki/Convert-Rife-Models-to-NCNN-format This snippet demonstrates cloning the Practical-RIFE GitHub repository and installing necessary Python dependencies from a requirements file. Ensure you have Git and pip installed. ```git git clone https://github.com/hzwer/Practical-RIFE.git && cd Practical-RIFE ``` ```bash pip install -r requirements.txt ``` -------------------------------- ### Trace PyTorch Model for PNNX Export Source: https://github.com/tntwise/real-video-enhancer/wiki/Convert-Rife-Models-to-NCNN-format This code snippet traces a PyTorch model (self.flownet) using torch.jit.trace and saves it as a '.pt' file for use with PNNX. It defines a test input shape and a timestep value. Ensure PyTorch is installed. ```python test_input = torch.rand(1, 3, 256, 256) timestep = torch.Tensor([1]) mod = torch.jit.trace(self.flownet,(test_input,test_input,torch.Tensor([1]))) mod.save('rife.pt') ``` -------------------------------- ### Modify IFnet Forward Function for ONNX Export Source: https://github.com/tntwise/real-video-enhancer/wiki/Convert-Rife-Models-to-NCNN-format These code snippets modify the `forward` function within the `IFBlock` class for ONNX export. They involve redefining input variables, adjusting timestep calculations, and altering how warped images are computed. Ensure PyTorch is installed. ```python if training == False: channel = x.shape[1] // 2 img0 = x[:, :channel] img1 = x[:, channel:] ``` ```python x = torch.cat((img0,img1),1) timestep = (x[:, :1] * 0 + 1) * timestep ``` ```python warped_img0 = img0**flow[:,:3] warped_img1 = img1**flow[:,1:4] ``` ```python wf0 = f0**flow[:, 1:2] wf1 = f1**flow[:, 2:3] ``` ```python return merged[3] ``` ```python class IFBlock(nn.Module): def forward(self, x, flow=None, scale=1): try: scale = scale.item() except Exception as e: pass ``` -------------------------------- ### Perform Video Interpolation (Speed vs. Quality) Source: https://github.com/tntwise/real-video-enhancer/blob/2.3.7/backend/src/pytorch/DRBA/README.md Executes video frame interpolation using the 'infer.py' script. Two examples are provided: one prioritizing speed with the 'rife' model and another prioritizing quality with the 'gmfss_union' model. Both commands specify input/output files, target frame rate, and scene change detection threshold. ```bash # For speed preference python infer.py -m rife -i input.mp4 -o output.mp4 -fps 60 -scale 1.0 -s -st 0.3 # For quality preference python infer.py -m gmfss_union -i input.mp4 -o output.mp4 -fps 60 -scale 1.0 -s -st 0.3 ``` -------------------------------- ### Cloning the Real Video Enhancer Repository (Git) Source: https://github.com/tntwise/real-video-enhancer/blob/2.3.7/README.md Instructions for cloning the Real Video Enhancer project repository using Git. It provides commands for both the latest nightly build and a specific stable version. ```git # Nightly git clone --recurse-submodules https://github.com/TNTwise/REAL-Video-Enhancer # Stable git clone --recurse-submodules https://github.com/TNTwise/REAL-Video-Enhancer --branch 2.3.6 ``` -------------------------------- ### Download Test Images Source: https://github.com/tntwise/real-video-enhancer/wiki/Convert-Rife-Models-to-NCNN-format This command downloads two sample image files ('0.png' and '2.png') from a GitHub repository using wget. These images are used as input for the inference script. ```bash wget https://raw.githubusercontent.com/TNTwise/Rife-NCNN-Model-Comparisons/c053eaf9b51fa07467954d4d8ed1cf752b1fd68b/0.png && wget https://raw.githubusercontent.com/TNTwise/Rife-NCNN-Model-Comparisons/c053eaf9b51fa07467954d4d8ed1cf752b1fd68b/2.png ``` -------------------------------- ### Building the Real Video Enhancer Application (Python) Source: https://github.com/tntwise/real-video-enhancer/blob/2.3.7/README.md Command to build the Real Video Enhancer application using the provided build script. It requires specifying a build option and indicates whether to copy backends. This command is intended for Python environments. ```python python3 build.py --build BUILD_OPTION --copy_backend ``` -------------------------------- ### Clone and Run Comparison Script (Python) Source: https://github.com/tntwise/real-video-enhancer/wiki/What-RIFE-Model-to-use? This script allows users to clone the comparison repository and run a test on custom images to evaluate different RIFE models. It requires Python 3 and Git. ```bash git clone https://github.com/TNTwise/Rife-NCNN-Model-Comparisons.git && cd Rife-NCNN-Model-Comparisons python3 render_all.py ``` -------------------------------- ### Run Inference Script Source: https://github.com/tntwise/real-video-enhancer/wiki/Convert-Rife-Models-to-NCNN-format This command executes the inference script 'inference_img.py' with specified input images ('0.png', '2.png') and an experiment number (1). Ensure the script and image files are in the correct directory. ```python python3 inference_img.py --img 0.png 2.png --exp 1 ``` -------------------------------- ### Video Interpolation Command-Line Options Source: https://github.com/tntwise/real-video-enhancer/blob/2.3.7/backend/src/pytorch/DRBA/README.md Details the available command-line arguments for the 'infer.py' script used for video interpolation. It covers model selection, input/output paths, frame rate, interpolation times, scene change detection, hardware acceleration, and scaling factor. ```bash Usage: python infer.py -m model -i in_video -o out_video [options]... -h show this help -m model model name (rife, gmfss, gmfss_union) (default=rife) -i input input video path (absolute path of input video) -o output output video path (absolute path of output video) -fps dst_fps target frame rate (default=60) -t times interpolation times (default=-1, if specified, the times mode will be used as priority) -s enable_scdet enable scene change detection (default False) -st scdet_threshold ssim scene detection threshold (default=0.3) -hw hwaccel enable hardware acceleration encode (default Disable) (require nvidia graph card) -scale scale flow scale factor (default=1.0), generally use 1.0 with 1080P and 0.5 with 4K resolution ``` -------------------------------- ### PNNX Model Conversion: BinaryOp to rife.Warp and Slice Adjustments Source: https://github.com/tntwise/real-video-enhancer/wiki/Convert-Rife-Models-to-NCNN-format This transformation applies to PNNX models, converting 'BinaryOp' (pow) to 'rife.Warp' and adjusting 'Crop' (slice) parameters. This ensures compatibility with newer versions of the rife-ncnn-vulkan model. ```text BinaryOp pow_43 2 1 4 201 202 0=6 Crop slice_120 1 1 199 203 -23310=1,4 -23311=1,0 -23309=1,1 BinaryOp pow_44 2 1 12 203 204 0=6 Crop slice_121 1 1 198 205 -23310=1,2 -23311=1,0 -23309=1,1 BinaryOp pow_45 2 1 40 205 206 0=6 Crop slice_122 1 1 197 207 -23310=1,3 -23311=1,0 -23309=1,2 BinaryOp pow_46 2 1 32 207 208 0=6 to Crop slice_119 1 1 200 201 -23310=1,2 -23311=1,0 -23309=1,0 rife.Warp warp_43 2 1 4 201 202 0=6 Crop slice_120 1 1 199 203 -23310=1,4 -23311=1,0 -23309=1,2 rife.Warp warp_44 2 1 12 203 204 0=6 Crop slice_121 1 1 198 205 -23310=1,2 -23311=1,0 -23309=1,2 rife.Warp warp_45 2 1 40 205 206 0=6 Crop slice_122 1 1 197 207 -23310=1,4 -23311=1,0 -23309=1,2 rife.Warp warp_46 2 1 32 207 208 0=6 ``` -------------------------------- ### Adjusting Crop Slices and Operators for Video Enhancement Source: https://github.com/tntwise/real-video-enhancer/wiki/Convert-Rife-Models-to-NCNN-format This snippet demonstrates how to adjust cropping parameters and operator types within a param file. The transformation modifies slice definitions and replaces specific operators to optimize video enhancement processes. ```text #1,0 / 1,3 = [:, :3] Crop Slice_188 1 1 325_splitncnn_7 335 -23309=1,0 -23310=1,3 -23311=1,0 rife.Warp warp_189 2 1 in0_splitncnn_3 335 336 0=6 #1,1 / 1,1 = [:, 1:4] Crop Slice_194 1 1 325_splitncnn_6 341 -23309=1,1 -23310=1,4 -23311=1,0 rife.Warp warp_195 2 1 in1_splitncnn_3 341 342 0=6 To This: #1,0 / 1,2 = [:, :2] Crop Slice_188 1 1 325_splitncnn_7 335 -23309=1,0 -23310=1,2 -23311=1,0 rife.Warp warp_189 2 1 in0_splitncnn_3 335 336 0=6 # 1,2 / 1,4 = [:, 2:4] Crop Slice_194 1 1 325_splitncnn_6 341 -23309=1,2 -23310=1,4 -23311=1,0 rife.Warp warp_195 2 1 in1_splitncnn_3 341 342 0=6 ``` -------------------------------- ### Convert ONNX Model to NCNN Format Source: https://github.com/tntwise/real-video-enhancer/wiki/Convert-Rife-Models-to-NCNN-format This command converts a PyTorch model saved in '.pt' format to the NCNN format using the PNNX tool. It specifies input shapes, whether to use FP16 precision, and the optimization level. Ensure PNNX is compiled and in your PATH. ```bash ./pnnx rife.pt inputshape=[1,3,256,256],[1,3,256,256],[1] fp16=1 optlevel=2 ``` -------------------------------- ### Replace Pow Operator with rife.Warp in Param Files Source: https://github.com/tntwise/real-video-enhancer/wiki/Convert-Rife-Models-to-NCNN-format This transformation involves replacing 'BinaryOp' with 'rife.Warp' and adjusting the associated parameters. This is a common step when modifying video processing pipelines to ensure compatibility with new operators or optimizations. ```text BinaryOp Pow_189 2 1 in0_splitncnn_3 335 336 0=6 to rife.Warp warp_189 2 1 in0_splitncnn_3 335 336 0=6 ``` -------------------------------- ### Export PyTorch Model to ONNX Format Source: https://github.com/tntwise/real-video-enhancer/wiki/Convert-Rife-Models-to-NCNN-format This code snippet exports a PyTorch model (self.flownet) to the ONNX format. It requires the PyTorch library and specifies input names, output names, and ONNX opset version. The input tensor shapes are defined for demonstration purposes. ```python test_input = torch.rand(1, 3, 256, 256) timestep = torch.Tensor([0.5]) torch.onnx.export( self.flownet, (test_input,test_input, torch.Tensor([0.5])), "rife.onnx", verbose=False, opset_version=11, input_names=["in0", "in1", "in2"], output_names=["out0"], ) ``` -------------------------------- ### Transforming Crop and Warp Operators for Larger Conversions Source: https://github.com/tntwise/real-video-enhancer/wiki/Convert-Rife-Models-to-NCNN-format This code snippet illustrates a specific transformation for crop and warp functions when dealing with conversions larger than 4.6. It modifies slice definitions and operator indices to ensure correct processing. ```text #1,1 / 1,2 = [:, 1:2] Crop Slice_200 1 1 325_splitncnn_5 347 -23309=1,1 -23310=1,2 -23311=1,0 rife.Warp warp_201 2 1 154_splitncnn_2 347 348 0=6 #1,1 / 1,2 = [:, 2:3] Crop Slice_206 1 1 325_splitncnn_4 353 -23309=1,2 -23310=1,3 -23311=1,0 rife.Warp warp_207 2 1 166_splitncnn_2 353 354 0=6 To This: ``` #1,0 / 1,2 = [:, :2] Crop Slice_200 1 1 325_splitncnn_5 347 -23309=1,0 -23310=1,2 -23311=1,0 rife.Warp warp_201 2 1 154_splitncnn_2 347 348 0=6 #1,2 / 1,4 = [:, 2:4] Crop Slice_206 1 1 325_splitncnn_4 353 -23309=1,2 -23310=1,4 -23311=1,0 rife.Warp warp_207 2 1 166_splitncnn_2 353 354 0=6 ``` === COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.