From 3dbc2c0942c98dfabe7f651dd80c8c56573f468f Mon Sep 17 00:00:00 2001 From: zpeng11 Date: Mon, 2 Feb 2026 00:02:58 -0500 Subject: [PATCH] update:update documents --- PerformanceTest.md | 123 +++++++ README.md | 243 ++++++-------- assets/complete_building_log.txt | 548 ------------------------------- 3 files changed, 223 insertions(+), 691 deletions(-) create mode 100644 PerformanceTest.md delete mode 100644 assets/complete_building_log.txt diff --git a/PerformanceTest.md b/PerformanceTest.md new file mode 100644 index 0000000..e2eb39d --- /dev/null +++ b/PerformanceTest.md @@ -0,0 +1,123 @@ +# 测试环境 +### 基于 +* Release版本v0.3 +* 测试平台为windows10 22H2 +* CPU Intel 10400f +* GPU Nvidia 3060 12G, Nvidia 1660 Super 6G, ARC A770 16G，RX580 8G. + +### 测试定量 +* Jpeg compression 和 Input Simplify 设为 medium +* 使用debug input 和 debug output +* 不使用eyebrow参数 +* 帧数限制60fps +* 英伟达显卡使用TensorRT测试 + +观察开始后约5-10秒的数值记录, 即不考虑长期缓存效果(没时间测) + +# 测试结果 +请注意测试结果仅限参考显卡大概性能，具体性能取决于用户周边配置和显卡驱动。 + +GPU | model | VRAM Cache | RAM Cache | Interpolation | SR | Frame Rate | GPU Usage +:---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: +A770 | sepe_half | N/A | No | No | No | 33fps | 97% +A770 | sepe_full | N/A | No | No | No | 48fps | 97% +A770 | stan_half | N/A | No | No | No | 27fps | 97% +A770 | stan_full | N/A | No | No | No | 29fps | 97% +A770 | sepe_half | N/A | 2GB | No | No | 30fps | 82% +A770 | sepe_full | N/A | 2GB | No | No | 43fps | 78% +A770 | stan_half | N/A | 2GB | No | No | 30fps | 82% +A770 | stan_full | N/A | 2GB | No | No | 23fps | 85% +A770 | sepe_full | N/A | No | No | x2_half | 17fps | 97% +A770 | sepe_full | N/A | No | No | x4_half | 29fps | 97% +A770 | sepe_full | N/A | No | No | x4_full | 25fps | 93% +A770 | sepe_full | N/A | No | x2_half | No | 60fps | 93% +A770 | sepe_full | N/A | No | x3_half | No | 60fps | 85% +A770 | sepe_full | N/A | No | x4_half | No | 60fps | 81% +-- | -- | --| --|--|--|--|-- +RX580 | sepe_full | N/A | No | No | No | 12fps | 98% +RX580 | stan_full | N/A | No | No | No | 15fps | 98% +RX580 | sepe_full | N/A | 2GB | No | No | 11fps | 93% +RX580 | stan_full | N/A | 2GB | No | No | 13fps | 93% +RX580 | stan_full | N/A | No | No | x2_half | 10fps | 97% +RX580 | stan_full | N/A | No | No | x2_full | 10fps | 97% +RX580 | stan_full | N/A | No | No | x4_half | 11fps | 98% +RX580 | stan_full | N/A | No | No | x4_full | 11fps | 98% +RX580 | stan_full | N/A | No | x2_half | no | 22fps | 98% +RX580 | stan_full | N/A | No | x2_full | no | 22fps | 98% +RX580 | stan_full | N/A | No | x3_half | no | 28fps | 98% +RX580 | stan_full | N/A | No | x3_full | no | 28fps | 98% +RX580 | stan_full | N/A | No | x4_half | no | 33fps | 98% +RX580 | stan_full | N/A | No | x4_full | no | 33fps | 98% +-- | -- | --| --|--|--|--|-- +1660s | sepe_half | No | No | No | No | 38fps | 95% +1660s | sepe_full | No | No | No | No | 28fps | 95% +1660s | stan_half | No | No | No | No | 21fps | 96% +1660s | stan_full | No | No | No | No | 12fps | 98% +1660s | sepe_half | 2GB | No | No | No | 39fps | 95% +1660s | sepe_full | 2GB | No | No | No | 28fps | 95% +1660s | stan_half | 2GB | No | No | No | 29fps | 96% +1660s | stan_full | 2GB | No | No | No | 12fps | 96% +1660s | sepe_half | 2GB | 2GB | No | No | 40fps | 75% +1660s | sepe_full | 2GB | 2GB | No | No | 24fps | 80% +1660s | stan_half | 2GB | 2GB | No | No | 26fps | 83% +1660s | stan_full | 2GB | 2GB | No | No | 12fps | 92% +1660s | sepe_half | No | No | No | x2_half | 23fps | 96% +1660s | sepe_half | No | No | No | x2_full | 18fps | 96% +1660s | sepe_half | No | No | No | x4_half | 23fps | 96% +1660s | sepe_half | No | No | No | x4_full | 26fps | 97% +1660s | sepe_half | No | No | x2_half | No | 63fps | 75% +1660s | sepe_half | No | No | x2_full | No | 63fps | 80% +1660s | sepe_half | No | No | x3_half | No | 63fps | 65% +1660s | sepe_half | No | No | x3_full | No | 64fps | 66% +1660s | sepe_half | No | No | x4_half | No | 64fps | 53% +1660s | sepe_half | No | No | x4_full | No | 64fps | 60% +-- | -- | --| --|--|--|--|-- +3060 | sepe_half | No | No | No | No | 63fps | 62% +3060 | sepe_full | No | No | No | No | 59fps | 93% +3060 | stan_half | No | No | No | No | 63fps | 80% +3060 | stan_full | No | No | No | No | 31fps | 96% +3060 | sepe_half | 2GB | No | No | No | 63fps | 60% +3060 | sepe_full | 2GB | No | No | No | 60fps | 93% +3060 | stan_half | 2GB | No | No | No | 63fps | 76% +3060 | stan_full | 2GB | No | No | No | 33fps | 96% +3060 | sepe_half | 2GB | 2GB | No | No | 63fps | 58% +3060 | sepe_full | 2GB | 2GB | No | No | 46fps | 70% +3060 | stan_half | 2GB | 2GB | No | No | 43fps | 62% +3060 | stan_full | 2GB | 2GB | No | No | 27fps | 80% +3060 | sepe_half | No | No | No | x2_half | 53fps | 85% +3060 | sepe_half | No | No | No | x2_full | 30fps | 94% +3060 | sepe_half | No | No | No | x4_half | 49fps | 90% +3060 | sepe_half | No | No | No | x4_full | 45fps | 90% +3060 | sepe_half | No | No | x2_half | No | 64fps | 45% +3060 | sepe_half | No | No | x2_full | No | 63fps | 50% +3060 | sepe_half | No | No | x3_half | No | 64fps | 35% +3060 | sepe_half | No | No | x3_full | No | 64fps | 42% +3060 | sepe_half | No | No | x4_half | No | 64fps | 30% +3060 | sepe_half | No | No | x4_full | No | 64fps | 40% + +# 已知问题: +* A770: rife插帧全精度会出现奇怪结果, waifu2x 超分的全精度会输出全绿色 +* RX580 tha3 模型半精度会输出奇怪结果 +* 3060 有时出现奇怪运行时占用上升，猜测为插拔显卡导致的驱动问题。 + +# DirectML 思考 +DirectML 允许所有 Windows 显卡设备以及 AIPC 的 NPU 运行此项目，但由于不同设备的 Kernel 在不同精度下实现区别和驱动兼容问题， +导致 DirectML 的运行结果在某个模型的某些精度出现不可用的扭曲结果，且运行性能与对模型的常识理解不相符。 + +例如适用于本项目插帧的RIFE模型的调用封装经历过一轮迭代, +由原先的n次输入(N=1,C,H,W)独立图像调用RIFE模型生成n个独立结果， +改为输入(N=n,C,H,W)的叠加图像一次性调用RIFE模型得到叠加结果， +此改动下模型获得了稍小的显存占用且在TensorRT实现中获得一定性能提升（虽然改动本意并不在此）。 +接着在笔者环境中的因特尔 A770 显卡 DirectML 在RIFE模型迭代后全精度推理结果由原先的符合预期变为扭曲图像， +半精度迭代后模型不仅保持推理结果符合预期，且相较模型迭代前得到了一定性能提升。 + +再例如笔者测试某 AMD 核显运行时发现其运行THA3的半精度模型时会因为此设备无法运行其中的某个kernel而将kernel运算单独卸载到cpu进行， +但保留了其他大部分能在核显上运行的kernel，由此推理模型需要结果在两个设备间反复，带来了极大的性能倒退。 + +目前阶段，受限于Python OnnxRuntime api 的支持匮乏（相较C++），使用 python 运行 Directml 无法避免上述问题， +在代码层面无法在运行前做出是否适配的条件判断，也无法在出现kernel卸载到cpu性能剧降时raise错误将运行打断。 + +所以，DirectML运行本项目的任意模型出现奇怪结果，或不符合预期的性能倒退，都应该属于意料之中 +换个模型精度，观察调试输出是否有DirectML黄字警告 通常可以解决大部分问题。 +具体某个硬件在DirectML的运行异常修复，已远超本项目的目标范围，请自行验证失效kernel，向硬件厂商和微软求助并期待驱动更新带来解决 +（建议买张N卡吧别折腾自己了）。 \ No newline at end of file diff --git a/README.md b/README.md index 35d5107..1d4138e 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,7 @@ Updates: * 使用 https://github.com/zpeng11/ezvtuber-rt/tree/main 项目转换的ONNX模型，实现TensorRT加速以及非N卡支持，在半精度和全精度下均有加速（具体取决于显卡） * 为A卡和I卡提供DirectML支持，人人都能玩。 * 引入[RIFE](https://github.com/hzwer/ECCV2022-RIFE)模型进行插帧，极限帧数提升达到50%-100%，配合锁帧可以有效降低使用占用。 -* 使用 [TurboJPEG](https://github.com/Dobatymo/turbojpeg-python) 获得成倍缓存命中率提升，在长时间使用上显卡减负效果相当出色。 +* 使用 `brotli` 压缩器获得成倍缓存命中率提升，在长时间使用上显卡减负效果相当出色。 * 使用 [waifu2x](https://github.com/nagadomi/waifu2x) 和 [real-esrgan](https://github.com/xinntao/Real-ESRGAN) 项目带来的输出超分辨率，对比anime4k效果提升显著（也使用更多gpu占用） * 更漂亮的 WxPython 新启动器界面，带中文介绍更加简单易用 * 添加Spout2 支持输出原生透明通道给OBS @@ -24,12 +24,12 @@ Updates: ### 硬件 -- 支持FaceID的iPhone（使用ifacialmocap软件，需购买，需要稳定的WIFI连接）或网络摄像头（使用OpenCV） +- 支持无摄像头鼠标捕获/USB网络摄像头/iPhone结构光面捕（ifacialmocap软件，需购买） - 任意5年内的游戏级显卡，N卡/A卡/I卡均可使用，某些性能强的核显也可尝试，请参考[性能测试结果](PerformanceTest.md) ### 软件 -- 本方案在Windows 10上测试可用 -- OBS或Unity Capture（虚拟摄像头方案） +- 本方案在Windows 10/11上测试可用 +- OBS 使用经验 - Photoshop或其他图片处理软件 - 科学上网方案，看懂英文网站和报错的能力 @@ -40,100 +40,13 @@ Updates: * [谷歌网盘](https://drive.google.com/drive/folders/1cYj18EfVQ2Cl348_rkCu_fgaasHTI_io?usp=drive_link) ### 安装Spout2 OBS插件(可选) -安装整合包附带的 `OBS_Spout2_Plugin_Install_v1.8.exe`， 安装包来自 https://github.com/Off-World-Live/obs-spout2-plugin 项目，可以提供透明通道给OBS。 - -### 构建TensorRT模型（N卡专用，可选） -双击`02.构建TensorRT模型（N卡使用）.bat`，若是英伟达用户将会开始TensorRT构建, 每台配置仅需构建一次，更新显卡驱动可能需要重新构建 -额外需要>20min时间(取决于显卡)，结束后可以大幅提升运行速度（可简单理解为玩游戏之前编译着色器，和每个人显卡有关） -构建的开头和结束输出如下，请耐心等进度条百分百或完成后自动退出。 -若不慎关掉窗口也可以双击自动从断点重来，可以先放后台运行构建并尝试下一步运行暂时不勾选TensorRT： -``` -Start testing if TensorRT works on this machine - 0%| | 0/38 [00:00conda --version -conda 24.11.3 -``` -以上方式可以保证conda环境被你选定的Ananconda统一管理。若没有此需求可以忽略直接运行下一步，`01X.构建运行环境（XX源）.bat`脚本会自动安装miniconda。 - -### 克隆项目和子项目 -``` -git clone https://github.com/zpeng11/EasyVtuber.git -cd EasyVtuber -git submodule init -git submodule update --recursive --remote -``` - -### 安装环境 -根据网络环境双击运行`01A.构建运行环境（默认源）.bat`或者`01B.构建运行环境（国内源）.bat` -这两个脚本会构建名为`ezvtb_rt_venv`的conda环境 -或可以用如下命令手动创建： -``` -conda create -y -n ezvtb_rt_venv python=3.10 -conda activate ezvtb_rt_venv -conda install -y nvidia/label/cuda-12.6.3::cuda-nvcc-dev_win-64 -conda install -y conda-forge::pycuda -python -m pip install --upgrade pip wheel -python -m pip install nvidia-cudnn-cu12 -pip install tensorrt_cu12_libs==10.6.0 tensorrt_cu12_bindings==10.6.0 tensorrt==10.6.0 --extra-index-url https://pypi.nvidia.com -python -m pip install -r requirements.txt --no-warn-script-location -``` - -### 下载模型 -前往模型 release 地址 https://github.com/zpeng11/ezvtuber-rt/releases/tag/0.0.1 -下载 https://github.com/zpeng11/ezvtuber-rt/releases/download/0.0.1/20241220.zip 并解压到`data/models`文件夹下 - -### Pycharm 配置 -安装完成后，在Pycharm内打开本项目，右下角解释器菜单点开，`Add Interpreter...`->`Conda Environment`->`Existing environment` -选好自己电脑上的`conda.exe`和刚才创建好的`ezvtb_rt_venv`环境内的`python.exe` -点击OK，依赖全亮即可 - -### 检查环境模型，并构建TensorRT -在Conda环境中执行以下命令 -`python ezvtb_rt_interface.py` - -### 运行启动器 -在Conda环境中执行以下命令 -`python launcher2.py` - -### 环境错误排查 -当运行TensorRT构建或启动器出现错误时，请参考如下可能性： -1. 环境安装有错误（一般运行时缺少库99%的原因都是这个，pip下载并不稳定，各种网络问题都可能导致安装失败），检查源，手动删除`envs`文件夹再试, 对照 [此log](assets/complete_building_log.txt) 为成功案例排查原因。 实在不懂排查请使用整合包。 -2. 英伟达显卡但计算架构低于7.5（10系以及之前）不支持TensorRT - ## 输入输出设备 #### SPOUT2 OBS插件输出 @@ -143,7 +56,7 @@ python -m pip install -r requirements.txt --no-warn-script-location #### OBS Virtual Camera 无透明通道 -如果你选择自己进行抠像你可以直接仅输出RGB到OBS，使用补帧时可能也需要此方法 +次选方案，如果你选择自己进行抠像你可以直接仅输出RGB到OBS，使用补帧时可能也需要此方法 使用`视频采集设备`源，设置`滤镜`, 在`效果滤镜`添加`色值`并自定义颜色为纯黑色，多尝试调整颜色参数直到抠图合理 @@ -158,29 +71,21 @@ python -m pip install -r requirements.txt --no-warn-script-location 之后对OBS中的视频采集设备添加滤镜-着色器-选择你下载的`merge alpha2.hlsl`-关闭 这样透明通道就应用回左边的图像了 你可能需要手动调整一下裁剪把右侧的无用画面切掉 -(看不到着色器滤镜的话就是StreamFX没装好或者OBS不是最新版) +(看不到着色器滤镜的话就是StreamFX没装好或者OBS不是最新版)d -#### UnityCapture（Outdated，性能问题） - -存在未查明的性能瓶颈 -如果需要使用透明通道输出，参考 https://github.com/schellingb/UnityCapture#installation 安装好UnityCapture -只需要正常走完Install.bat，在OBS里能看到对应的设备（Unity Video Capture）就行 - -在OBS添加完摄像头以后，还需要手动配置一次摄像头属性才能支持ARGB -右键属性-取消激活-分辨率类型自定义-分辨率512x512(与`--output_size`参数一致)-视频格式ARGB-激活 - -#### iFacialMocap +#### iFacialMocap 面捕 目前最推荐的方案，使用苹果结构光硬件，捕捉效果追踪准确度都最好，demo中使用了此输入。 需要输入`ip:port`,其中`ip`会显示在手机上，`port`使用默认`49983` https://www.ifacialmocap.com/download/ 你大概率需要购买正式版（非广告，只是试用版不太够时长） -购买之前确认好自己的设备支持 -**不需要下载PC软件**，装好iOS端的软件即可，连接信息通过参数传入Python -游戏加速器，梯子等网络工具可能会导致ip失效，请自行解决。 +购买之前确认好自己的设备支持，装好iOS端的软件即可，连接信息通过参数传入Python +一般需要iphone与window在同一局域网下 +且游戏加速器，梯子等网络工具可能会导致ip失效，请自行解决。 -#### OpenSeeFace +#### OpenSeeFace 高精度摄像头面捕 +在没有苹果硬件时最推荐使用的捕捉方案，需要更多配置 https://github.com/emilianavt/OpenSeeFace/releases 直接下载最新版本的Release包并解压，或使用整合包附带的压缩包 之后进入解压目录的Binary文件夹 @@ -189,28 +94,34 @@ https://github.com/emilianavt/OpenSeeFace/releases 之后保存并双击`run.bat`运行，按照提示选择摄像头、分辨率、帧率，捕获正常的话可以看到输出画面 最后在启动器中选择OpenSeeFace输入，或添加启动参数`--osf 127.0.0.1:11573`即可接入OpenSeeFace +#### OpenCV 低精度摄像头面捕 + +此输入不需要配置，会尝试开启默认的USB或笔记本电脑摄像头开始面捕，稳定性和精确度略欠佳。 + +#### 鼠标音频输入 + +隐私考虑不想使用任何摄像头可使用这个输入，定时循环眨眼和呼吸，配合音频音量控制口型，可以在无摄像头时得到不错的动态。 + ## 性能配置(调参必读） ### 概念须知 #### 模型&精度 本项目完全基于AI算法生成，由3类AI模型提供服务（缝合怪）： -* THA模型：指本项目的基础模型，输入静态图像和姿态信息计算出新图像 +* THA模型：指本项目的基础模型，输入静态图像和姿态信息计算出新图像，有v3_seperable, v3_standard, v4, v4_student 四种版本。 * 插帧/补帧模型：指RIFE插帧模型，输入第1帧和第2帧，输出第1.5帧，类似DLSS3或小黄鸭，此模型是整体性能提升的关键 -* 超分模型：指waifu2x或realesrgan模型，输入一帧图像，解计算出放大且更清晰的同一帧，显著提升画面效果 +* 超分模型：指Anime4k, waifu2x或realesrgan模型，输入一帧图像，解算出放大且更清晰的同一帧，显著提升画面效果 -每一种模型提供了半精度(标记为half)和全精度(标记为full)选项，半精度通常运行速度比全精度快，但生图效果稍微逊色，需要根据性能进行取舍。 -THA模型有另外的`seperable`和`standard`两个版本，可以简单理解为`seperable`是性能更强但生图效果稍差的版本，所以加上两种精度THA模型一共有4种组合。 +每一种模型提供了半精度(标记为half)和全精度(标记为full)选项，半精度通常运行速度比全精度快，但生图效果稍微逊色，且兼容性可能不好。 #### 缓存 当我们当前的姿态与一秒前的姿态完全相同时，输入给模型的参数也完全相同，没有必要再做一次完全相同的运算得出完全相同的结果 -缓存的原理是将过去一段时间内产生过的结果储存起来。 -倘若发现姿态相同，就使用储存的结果输出而绕过模型运行（又叫缓存命中），从而减轻显卡负担。 -本项目同时使用显存VRAM和内存RAM用作缓存目的。在内存中还使用了JPEG格式对缓存的图片进行压缩。 +倘若发现姿态相同，就使用曾经的结果输出而绕过模型运行（又叫缓存命中），从而减轻显卡负担。 +本项目同时使用显存VRAM和内存RAM用作缓存目的。在内存中还对缓存的图片进行压缩。 缓存大小（单位GB）决定了缓存最多能使用的空间大小。更大的缓存能让缓存命中率更高，长时间使用时显卡占用更小。但也会挤占其他应用或游戏的空间。 需要自行理解并取舍。 -#### 输入简化 +#### 输入简化（量化） 输入简化与上文的缓存相辅相成，我们将姿态参数分入一些区间，这样相近的参数就可以命中同一个缓存。 举个非常简化的例子：缓存中的一个结果对应姿态参数`0.5`, 在不进行任何输入简化时，需要新输入参数完全相同于`0.5`即 `x = 0.5`才能判定命中并绕过GPU运算。 而开启了输入简化后，只需要新输入参数 `x` $\approx$ `0.5` 就可以命中。如`x = 0.49`或 `x = 0.51`. @@ -226,9 +137,9 @@ THA模型有另外的`seperable`和`standard`两个版本，可以简单理解 #### 超分 ![image](https://github.com/user-attachments/assets/afa78440-5122-4b57-b586-4487e125eb2f) -超分由waifu2x和realesrgan模型的两种精度一共4种组合，外加一种Anime4K纯cpu实现（效果比较差）。 +超分由waifu2x和realesrgan模型的两种精度一共4种组合，外加一种Anime4K实现。 通常选用半精度，根据使用半身或大头选择`waifu2x_x2_half`和`realesrgan_x4_half` -超分启动时会显著增加显卡占用，通常建议在低负载游戏或者杂谈状态下使用。 +超分启动时会显著增加显卡占用。 #### 插帧/补帧 ![image](https://github.com/user-attachments/assets/de392b93-139b-455b-b561-7713d26fca20) @@ -240,17 +151,70 @@ RIFE插帧的计算消耗比起一次THA计算要少约50%且画面观感差异 ## 脱离启动器运行本体 -`main.py` 的完全体运行命令请参考 `02B.启动器（调试输出）.bat` 的输出以及 `args.py`。 以下为部分基础参数 +`main.py` 的完全体运行命令请参考 `02B.启动器（调试输出）.bat` 的输出以及 `args.py`。 -参数名 | 值类型 | 说明 -:---: | :---: | :---: ---character|字符串|`character`目录下的输入图像文件名，不需要带扩展名 ---debug|无|打开OpenCV预览窗口输出渲染结果，如果没有任何输出配置，该参数默认生效 ---input|字符串|不使用iOS面捕时，传入要使用的摄像头设备名称，默认为设备0，有ifm参数时无效 ---ifm|字符串|使用iOS面捕时，传入设备的`IP:端口号`，如`192.168.31.182:49983` ---output_webcam|字符串|可用值为`obs` `unitycapture`，选择对应的输出种类，不传不输出到摄像头 ---extend_movement|浮点数|使用iOS面捕返回的头部位置，对模型输出图像进一步进行移动和旋转使得上半身可动
传入的数值表示移动倍率（建议值为1） ---output_size|字符串|格式为`256x256`，必须是4的倍数。
增大它并不会让图像更清晰，但配合extend_movement会增大可动范围 +## Installation(使用整合包请忽略) +可使用此安装方法对本项目二次开发 + +### 安装Anaconda（可选） +前往https://www.anaconda.com 安装Anaconda 并保证加入环境变量命令行可以找到，如： +``` +C:\Users\Eleven>conda --version +conda 24.11.3 +``` +以上方式可以保证conda环境被你选定的Ananconda统一管理。若没有此需求可以忽略直接运行下一步。 + +### 下载 TensorRT-RTX （Nvidia开发必选） +``` +curl -L -o trt_rtx.zip https://developer.nvidia.com/downloads/trt/rtx_sdk/secure/1.3/TensorRT-RTX-1.3.0.35-win10-amd64-cuda-12.9-Release-external.zip +tar -xf trt_rtx.zip "TensorRT-RTX-1.3.0.35" && ren "TensorRT-RTX-1.3.0.35" "TensorRT-RTX-1.3.0.35_cu129" && del trt_rtx.zip +# Adding the bin folder to PATH +cd TensorRT-RTX-1.3.0.35_cu129\bin && set PATH=%CD%;%PATH% +``` +也可以在Window 环境变量管理器 GUI中将`TensorRT-RTX-1.3.0.35_cu129\bin`在`PATH`环境变量中添加来持久化 + +### 准备Conda 环境 +``` +conda create -y -n easyvtuber python=3.10 +conda activate easyvtuber +conda install conda-forge::pycuda +conda install -c nvidia/label/cuda-12.9.1 cuda-nvcc-dev_win-64 cudnn cuda-runtime +``` + +### 安装 TensorRT-RTX 的 Python Binding +``` +pip install TensorRT-RTX-1.3.0.35_cu129\python\tensorrt_rtx-1.3.0.35-cp310-none-win_amd64.whl +``` + +### 克隆项目和子项目 +``` +git clone https://github.com/yuyuyzl/EasyVtuber.git +cd EasyVtuber +git submodule init +git submodule update --recursive --remote +``` + +### 安装环境 +``` +pip install -r requirements.txt --no-warn-script-location +``` + +### 下载模型 +前往模型 release 地址 https://drive.google.com/file/d/1pWKIpjWeqfpa3Rub185FVvxDr5H09pOi/view?usp=drive_link 并解压到`data/models`文件夹下 + +### Pycharm 配置（可选） +安装完成后，在Pycharm内打开本项目，右下角解释器菜单点开，`Add Interpreter...`->`Conda Environment`->`Existing environment` +选好自己电脑上的`conda.exe`和刚才创建好的`easyvtuber`环境内的`python.exe` +点击OK，依赖全亮即可 + +### 运行启动器 +在Conda环境中执行以下命令 +`python launcher2.py` + +### 环境错误排查 +当运行TensorRT构建或启动器出现错误时，请参考如下可能性： +1. 环境安装有错误（一般运行时缺少库99%的原因都是这个，pip下载并不稳定，各种网络问题都可能导致安装失败），请检查源。 +2. 英伟达显卡但计算架构低于7.5（10系以及之前）不支持TensorRT ## FAQ @@ -258,9 +222,12 @@ RIFE插帧的计算消耗比起一次THA计算要少约50%且画面观感差异 A卡I卡使用 DirectML，这个框架与显卡厂商的显卡驱动及 Direct12 实现相关，很多算子实现存在数值错误或未实现并行优化被迫降级到cpu运行。方案：1. 将显卡驱动更新至最新，2. 打开调试启动器运行并检查模型或算子是否被降级到cpu实现，3.阅读上文的模型调参环节，切换不同模型和精度多尝试。 如有兴趣了解更多请参考[性能测试结果](PerformanceTest.md)末尾对DirectML的思考。 -> Q2: 我开启超分辨率后，为什么补帧没效果了？ +> Q2: 为什么无法找到我自己准备的Vtuber图像/使用自己的图像会报错？ -v0.5的实现中，补帧和超分只能二选一，开启超分后补帧会自动失效。欢迎重新在上文中更新下载v0.7之后的版本。 +1. 确认图像改为512x512的png，带Alpha透明通道即32比特位宽。 +2. 确认放置位置为`data/images`，在选项中应当能看到文件名。 +3. 仍然无法正确启动请尝试删除`launch.json`文件并重新启动软件。 +4. 99%的错误情况都来自于准备的图像不满足模型需求 > Q3: 我开启补帧后为什么在 Spout2 输出中边缘会有些抖动？ @@ -270,16 +237,6 @@ v0.5的实现中，补帧和超分只能二选一，开启超分后补帧会自 请在GPU页面中点击`Video Decode`或`Video Processing`按钮可以看到下拉，请对这里的选项挨个尝试找到正确的程序负载，一般在 `cuda` 或 `3D` 或 `Compute_X` 或 `Graphics_X`。 -> Q5: 我的配置是 CPU带核显加独立显卡，这个核显性能挺不错，打游戏时还用不上，能否把项目跑到这上面？ +> Q6: 本机有两张显卡，如何使用第二张副卡使用这个项目？ -完全可行，其实界面中留显卡选项有部分原因就是这方面需求。具体操作如下：去除TensorRT勾选，开启设备管理器观察核显占用，从`1`开始挨个尝试`GPU Device`选项并启动，直到观察到核显开始占用上升即找到了核显对应的编号。（os：DirectML的API就是这么难用没办法）接下来有可能出现Q1中提及的问题，请参照上文解决。 -因为性能问题很有可能需要使用插帧来提高输出帧数。目前核显相关测试样本较少，欢迎分享运行效果。 - -> Q6: 我主机上插着两张英伟达显卡，如何使用第二张副卡使用这个项目？ -1. 对第二张卡构建TenosrRT：更改`02.构建TensorRT模型（N卡开启TensorRT使用）.bat` 中的 `python ezvtb_rt_interface.py` 一行，加上空格接参数cuda设备号，例如我的副卡设备号是`1`所以改成 `python ezvtb_rt_interface.py 1` 然后双击运行。 -2. 在启动器界面中选择相同的设备号。 - -> Q7: 为什么无法使用我自己准备的Vtuber图像/使用自己的图像会报错？ -1. 确认图像改为512x512的png，带Alpha透明通道即32比特位宽。 -2. 确认放置位置为`data/images`，在选项中应当能看到文件名。 -3. 仍然无法正确启动请尝试删除`launch.json`文件并重新启动软件。 +在运行前配置环境变量`EZVTB_DEVICE_ID`为你想要运行的GPU ID,此变量缺省为`0` diff --git a/assets/complete_building_log.txt b/assets/complete_building_log.txt deleted file mode 100644 index dcc6465..0000000 --- a/assets/complete_building_log.txt +++ /dev/null @@ -1,548 +0,0 @@ -C:\Users\Eleven\Desktop\EasyVtuber-main>cd /D "C:\Users\Eleven\Desktop\EasyVtuber-main\" - -C:\Users\Eleven\Desktop\EasyVtuber-main>WHERE conda -INFO: Could not find files for the given pattern(s). - -C:\Users\Eleven\Desktop\EasyVtuber-main>IF 1 NEQ 0 ( -if not exist "envs" mkdir envs - if not exist "envs\miniconda3" mkdir envs\miniconda3 - IF not EXIST C:\Users\Eleven\Desktop\EasyVtuber-main\envs\miniconda3\Scripts ( - - mkdir C:\Users\Eleven\Desktop\EasyVtuber-main\envs\miniconda3 - echo "Downloading miniconda..." - powershell -Command "(New-Object Net.WebClient).DownloadFile('https://repo.anaconda.com/miniconda/Miniconda3-py310_24.9.2-0-Windows-x86_64.exe', '.\envs\miniconda3.exe')" - echo "Installling minconda..." - start /wait "" C:\Users\Eleven\Desktop\EasyVtuber-main\envs\miniconda3.exe /S /AddToPath=0 /RegisterPython=0 /InstallationType=JustMe /D=C:\Users\Eleven\Desktop\EasyVtuber-main\envs\miniconda3 - echo "Successfully install minconda" - del envs\miniconda3.exe -) -) -"Downloading miniconda..." -"Installling minconda..." -"Successfully install minconda" - -C:\Users\Eleven\Desktop\EasyVtuber-main>IF EXIST C:\Users\Eleven\Desktop\EasyVtuber-main\envs\miniconda3\Scripts SET PATH=C:\Users\Eleven\Desktop\EasyVtuber-main\envs\miniconda3\Scripts;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.6\bin;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.6\libnvvp;;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0\;C:\Windows\System32\OpenSSH\;C:\Program Files (x86)\NVIDIA Corporation\PhysX\Common;C:\Program Files\Git\cmd;C:\Program Files\Tailscale\;C:\Program Files\NVIDIA Corporation\Nsight Compute 2024.3.2\;C:\Program Files\NVIDIA Corporation\NVIDIA NvDLISR;C:\Program Files\dotnet\;C:\Users\Eleven\AppData\Local\Programs\Python\Python312\Scripts\;C:\Users\Eleven\AppData\Local\Programs\Python\Python312\;C:\Users\Eleven\.cargo\bin;C:\Users\Eleven\AppData\Local\Microsoft\WindowsApps;C:\Users\Eleven\AppData\Local\Programs\Microsoft VS Code\bin;C:\Program Files\Git\bin;D:\ffmpeg\bin; - -C:\Users\Eleven\Desktop\EasyVtuber-main>call activate -# conda environments: -# - C:\EasyVtuber\envs\miniconda3 - C:\EasyVtuber\envs\miniconda3\envs\ezvtb_rt_venv -base * C:\Users\Eleven\Desktop\EasyVtuber-main\envs\miniconda3 - -Retrieving notices: ...working... done -Channels: - - defaults -Platform: win-64 -Collecting package metadata (repodata.json): done -Solving environment: done - -## Package Plan ## - - environment location: C:\Users\Eleven\Desktop\EasyVtuber-main\envs\miniconda3 - - -The following packages will be downloaded: - - package | build - ---------------------------|----------------- - anaconda-anon-usage-0.5.0 |py310hfc23b7f_100 28 KB - brotli-python-1.0.9 | py310h5da7b33_9 346 KB - ca-certificates-2024.11.26 | haa95532_0 132 KB - certifi-2024.12.14 | py310haa95532_0 162 KB - cffi-1.17.1 | py310h827c3e9_1 254 KB - conda-24.11.2 | py310haa95532_0 948 KB - conda-libmamba-solver-24.11.1| pyhd3eb1b0_0 39 KB - conda-package-handling-2.4.0| py310haa95532_0 297 KB - conda-package-streaming-0.11.0| py310haa95532_0 31 KB - cpp-expected-1.1.0 | h214f63a_0 131 KB - cryptography-43.0.3 | py310hbd6ee87_1 1.3 MB - libarchive-3.7.7 | h9243413_0 1.8 MB - libcurl-8.11.1 | haff574d_0 382 KB - libmamba-2.0.5 | hcd6fe79_1 4.7 MB - libmambapy-2.0.5 | py310h214f63a_1 459 KB - libsolv-0.7.30 | hf2fb9eb_1 473 KB - libssh2-1.11.1 | h2addb87_0 312 KB - libxml2-2.13.5 | h24da03e_0 2.9 MB - menuinst-2.2.0 | py310h5da7b33_0 200 KB - nlohmann_json-3.11.2 | h6c2663c_0 126 KB - packaging-24.2 | py310haa95532_0 175 KB - pluggy-1.5.0 | py310haa95532_0 39 KB - requests-2.32.3 | py310haa95532_1 101 KB - simdjson-3.10.1 | h214f63a_0 275 KB - spdlog-1.11.0 | h59b6b97_0 195 KB - vc-14.40 | haa95532_2 10 KB - vs2015_runtime-14.42.34433 | h9531ae6_2 1.2 MB - zstandard-0.23.0 | py310h4fc1ca9_1 342 KB - ------------------------------------------------------------ - Total: 17.3 MB - -The following NEW packages will be INSTALLED: - - cpp-expected pkgs/main/win-64::cpp-expected-1.1.0-h214f63a_0 - nlohmann_json pkgs/main/win-64::nlohmann_json-3.11.2-h6c2663c_0 - simdjson pkgs/main/win-64::simdjson-3.10.1-h214f63a_0 - spdlog pkgs/main/win-64::spdlog-1.11.0-h59b6b97_0 - -The following packages will be UPDATED: - - anaconda-anon-usa~ 0.4.4-py310hfc23b7f_100 --> 0.5.0-py310hfc23b7f_100 - brotli-python 1.0.9-py310hd77b12b_8 --> 1.0.9-py310h5da7b33_9 - ca-certificates 2024.9.24-haa95532_0 --> 2024.11.26-haa95532_0 - certifi 2024.8.30-py310haa95532_0 --> 2024.12.14-py310haa95532_0 - cffi 1.17.1-py310h827c3e9_0 --> 1.17.1-py310h827c3e9_1 - conda 24.9.2-py310haa95532_0 --> 24.11.2-py310haa95532_0 - conda-libmamba-so~ 24.9.0-pyhd3eb1b0_0 --> 24.11.1-pyhd3eb1b0_0 - conda-package-han~ 2.3.0-py310haa95532_0 --> 2.4.0-py310haa95532_0 - conda-package-str~ 0.10.0-py310haa95532_0 --> 0.11.0-py310haa95532_0 - cryptography 43.0.0-py310h89fc84f_0 --> 43.0.3-py310hbd6ee87_1 - libarchive 3.7.4-h9243413_0 --> 3.7.7-h9243413_0 - libcurl 8.9.1-h0416ee5_0 --> 8.11.1-haff574d_0 - libmamba 1.5.8-h99b1521_3 --> 2.0.5-hcd6fe79_1 - libmambapy 1.5.8-py310h77c03ed_3 --> 2.0.5-py310h214f63a_1 - libsolv 0.7.24-h23ce68f_1 --> 0.7.30-hf2fb9eb_1 - libssh2 1.11.0-h291bd65_0 --> 1.11.1-h2addb87_0 - libxml2 2.13.1-h24da03e_2 --> 2.13.5-h24da03e_0 - menuinst 2.1.2-py310h5da7b33_0 --> 2.2.0-py310h5da7b33_0 - packaging 24.1-py310haa95532_0 --> 24.2-py310haa95532_0 - pluggy 1.0.0-py310haa95532_1 --> 1.5.0-py310haa95532_0 - requests 2.32.3-py310haa95532_0 --> 2.32.3-py310haa95532_1 - vc 14.40-h2eaa2aa_1 --> 14.40-haa95532_2 - vs2015_runtime 14.40.33807-h98bb1dd_1 --> 14.42.34433-h9531ae6_2 - zstandard 0.23.0-py310h4fc1ca9_0 --> 0.23.0-py310h4fc1ca9_1 - - - -Downloading and Extracting Packages: - -Preparing transaction: done -Verifying transaction: done -Executing transaction: done -Channels: - - defaults -Platform: win-64 -Collecting package metadata (repodata.json): done -Solving environment: done - -## Package Plan ## - - environment location: C:\Users\Eleven\Desktop\EasyVtuber-main\envs\miniconda3\envs\ezvtb_rt_venv - - added / updated specs: - - python=3.10 - - -The following packages will be downloaded: - - package | build - ---------------------------|----------------- - python-3.10.16 | h4607a30_1 16.3 MB - ------------------------------------------------------------ - Total: 16.3 MB - -The following NEW packages will be INSTALLED: - - bzip2 pkgs/main/win-64::bzip2-1.0.8-h2bbff1b_6 - ca-certificates pkgs/main/win-64::ca-certificates-2024.11.26-haa95532_0 - libffi pkgs/main/win-64::libffi-3.4.4-hd77b12b_1 - openssl pkgs/main/win-64::openssl-3.0.15-h827c3e9_0 - pip pkgs/main/win-64::pip-24.2-py310haa95532_0 - python pkgs/main/win-64::python-3.10.16-h4607a30_1 - setuptools pkgs/main/win-64::setuptools-75.1.0-py310haa95532_0 - sqlite pkgs/main/win-64::sqlite-3.45.3-h2bbff1b_0 - tk pkgs/main/win-64::tk-8.6.14-h0416ee5_0 - tzdata pkgs/main/noarch::tzdata-2024b-h04d1e81_0 - vc pkgs/main/win-64::vc-14.40-haa95532_2 - vs2015_runtime pkgs/main/win-64::vs2015_runtime-14.42.34433-h9531ae6_2 - wheel pkgs/main/win-64::wheel-0.44.0-py310haa95532_0 - xz pkgs/main/win-64::xz-5.4.6-h8cc25b3_1 - zlib pkgs/main/win-64::zlib-1.2.13-h8cc25b3_1 - - - -Downloading and Extracting Packages: - -Preparing transaction: done -Verifying transaction: done -Executing transaction: done -# -# To activate this environment, use -# -# $ conda activate ezvtb_rt_venv -# -# To deactivate an active environment, use -# -# $ conda deactivate - - -# conda environments: -# - C:\EasyVtuber\envs\miniconda3 - C:\EasyVtuber\envs\miniconda3\envs\ezvtb_rt_venv -base C:\Users\Eleven\Desktop\EasyVtuber-main\envs\miniconda3 -ezvtb_rt_venv * C:\Users\Eleven\Desktop\EasyVtuber-main\envs\miniconda3\envs\ezvtb_rt_venv - -Channels: - - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - - defaults -Platform: win-64 -Collecting package metadata (repodata.json): done -Solving environment: done - -## Package Plan ## - - environment location: C:\Users\Eleven\Desktop\EasyVtuber-main\envs\miniconda3\envs\ezvtb_rt_venv - - added / updated specs: - - pycuda - - -The following packages will be downloaded: - - package | build - ---------------------------|----------------- - appdirs-1.4.4 | pyhd8ed1ab_1 14 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - boost-1.78.0 | py310h220cb41_4 555 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - boost-cpp-1.78.0 | h5b4e17d_0 17.0 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - ca-certificates-2024.12.14 | h56e8100_0 154 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - cudatoolkit-11.8.0 | h09e9e62_11 638.9 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - importlib-metadata-8.5.0 | pyha770c72_1 28 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - intel-openmp-2024.2.1 | h57928b3_1083 1.8 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - libblas-3.9.0 | 26_win64_mkl 3.6 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - libcblas-3.9.0 | 26_win64_mkl 3.6 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - libhwloc-2.9.1 | h51c2c0f_0 2.4 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - libiconv-1.17 | h8ffe710_0 698 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - liblapack-3.9.0 | 26_win64_mkl 3.6 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - lz4-c-1.9.4 | hcfcfb64_0 131 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - mako-1.3.8 | pyhd8ed1ab_0 66 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - markupsafe-2.1.2 | py310h8d17308_0 25 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - mkl-2024.2.2 | h66d3029_15 98.3 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - numpy-1.24.3 | py310hd02465a_0 5.7 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - openssl-3.1.0 | hcfcfb64_3 7.1 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - platformdirs-4.3.6 | pyhd8ed1ab_1 20 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - pthreads-win32-2.9.1 | hfa6e2cd_3 141 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - pycuda-2022.2.2 | py310ha2c4f5d_0 378 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - python_abi-3.10 | 2_cp310 4 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - pytools-2024.1.6 | pyhd8ed1ab_0 72 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - tbb-2021.9.0 | h91493d7_0 151 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - typing_extensions-4.12.2 | pyha770c72_1 39 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - ucrt-10.0.22621.0 | h57928b3_1 547 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - zipp-3.21.0 | pyhd8ed1ab_1 21 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - ------------------------------------------------------------ - Total: 784.9 MB - -The following NEW packages will be INSTALLED: - - appdirs anaconda/cloud/conda-forge/noarch::appdirs-1.4.4-pyhd8ed1ab_1 - boost anaconda/cloud/conda-forge/win-64::boost-1.78.0-py310h220cb41_4 - boost-cpp anaconda/cloud/conda-forge/win-64::boost-cpp-1.78.0-h5b4e17d_0 - cudatoolkit anaconda/cloud/conda-forge/win-64::cudatoolkit-11.8.0-h09e9e62_11 - importlib-metadata anaconda/cloud/conda-forge/noarch::importlib-metadata-8.5.0-pyha770c72_1 - intel-openmp anaconda/cloud/conda-forge/win-64::intel-openmp-2024.2.1-h57928b3_1083 - libblas anaconda/cloud/conda-forge/win-64::libblas-3.9.0-26_win64_mkl - libcblas anaconda/cloud/conda-forge/win-64::libcblas-3.9.0-26_win64_mkl - libhwloc anaconda/cloud/conda-forge/win-64::libhwloc-2.9.1-h51c2c0f_0 - libiconv anaconda/cloud/conda-forge/win-64::libiconv-1.17-h8ffe710_0 - liblapack anaconda/cloud/conda-forge/win-64::liblapack-3.9.0-26_win64_mkl - libxml2 pkgs/main/win-64::libxml2-2.13.5-h24da03e_0 - lz4-c anaconda/cloud/conda-forge/win-64::lz4-c-1.9.4-hcfcfb64_0 - mako anaconda/cloud/conda-forge/noarch::mako-1.3.8-pyhd8ed1ab_0 - markupsafe anaconda/cloud/conda-forge/win-64::markupsafe-2.1.2-py310h8d17308_0 - mkl anaconda/cloud/conda-forge/win-64::mkl-2024.2.2-h66d3029_15 - numpy anaconda/cloud/conda-forge/win-64::numpy-1.24.3-py310hd02465a_0 - platformdirs anaconda/cloud/conda-forge/noarch::platformdirs-4.3.6-pyhd8ed1ab_1 - pthreads-win32 anaconda/cloud/conda-forge/win-64::pthreads-win32-2.9.1-hfa6e2cd_3 - pycuda anaconda/cloud/conda-forge/win-64::pycuda-2022.2.2-py310ha2c4f5d_0 - python_abi anaconda/cloud/conda-forge/win-64::python_abi-3.10-2_cp310 - pytools anaconda/cloud/conda-forge/noarch::pytools-2024.1.6-pyhd8ed1ab_0 - tbb anaconda/cloud/conda-forge/win-64::tbb-2021.9.0-h91493d7_0 - typing_extensions anaconda/cloud/conda-forge/noarch::typing_extensions-4.12.2-pyha770c72_1 - ucrt anaconda/cloud/conda-forge/win-64::ucrt-10.0.22621.0-h57928b3_1 - zipp anaconda/cloud/conda-forge/noarch::zipp-3.21.0-pyhd8ed1ab_1 - zstd pkgs/main/win-64::zstd-1.5.6-h8880b57_0 - -The following packages will be UPDATED: - - ca-certificates pkgs/main::ca-certificates-2024.11.26~ --> anaconda/cloud/conda-forge::ca-certificates-2024.12.14-h56e8100_0 - openssl pkgs/main::openssl-3.0.15-h827c3e9_0 --> anaconda/cloud/conda-forge::openssl-3.1.0-hcfcfb64_3 - - - -Downloading and Extracting Packages: - -Preparing transaction: done -Verifying transaction: done -Executing transaction: | "By downloading and using the CUDA Toolkit conda packages, you accept the terms and conditions of the CUDA End User License Agreement (EULA): https://docs.nvidia.com/cuda/eula/index.html" - -done -Channels: - - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - - defaults -Platform: win-64 -Collecting package metadata (repodata.json): done -Solving environment: done - -## Package Plan ## - - environment location: C:\Users\Eleven\Desktop\EasyVtuber-main\envs\miniconda3\envs\ezvtb_rt_venv - - added / updated specs: - - pytorch-cpu - - -The following packages will be downloaded: - - package | build - ---------------------------|----------------- - blas-1.0 | mkl 6 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - filelock-3.13.1 | py310haa95532_0 22 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - fsspec-2024.6.1 | py310haa95532_0 286 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - gmpy2-2.1.2 | py310h7f96b67_0 160 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - intel-openmp-2023.1.0 | h59b6b97_46320 2.7 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - jinja2-3.1.4 | py310haa95532_1 281 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - libuv-1.48.0 | h827c3e9_0 322 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - mkl-2023.1.0 | h6b88ed4_46358 155.9 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - mkl-service-2.4.0 | py310h2bbff1b_1 44 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - mkl_fft-1.3.11 | py310h827c3e9_0 168 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - mkl_random-1.2.8 | py310hc64d2fc_0 257 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - mpc-1.1.0 | h7edee0f_1 260 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - mpfr-4.0.2 | h62dcd97_1 1.5 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - mpir-3.0.0 | hec2e145_1 1.3 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - mpmath-1.3.0 | py310haa95532_0 834 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - networkx-3.3 | py310haa95532_0 2.5 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - numpy-1.26.4 | py310h055cbcc_0 11 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - numpy-base-1.26.4 | py310h65a83cf_0 8.6 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - pytorch-2.3.1 |cpu_py310h0ce1571_0 111.2 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - pytorch-cpu-2.3.1 | haa95532_0 21 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - sympy-1.13.3 | py310haa95532_0 11.3 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - ------------------------------------------------------------ - Total: 297.6 MB - -The following NEW packages will be INSTALLED: - - blas anaconda/pkgs/main/win-64::blas-1.0-mkl - filelock anaconda/pkgs/main/win-64::filelock-3.13.1-py310haa95532_0 - fsspec anaconda/pkgs/main/win-64::fsspec-2024.6.1-py310haa95532_0 - gmpy2 anaconda/pkgs/main/win-64::gmpy2-2.1.2-py310h7f96b67_0 - jinja2 anaconda/pkgs/main/win-64::jinja2-3.1.4-py310haa95532_1 - libuv anaconda/pkgs/main/win-64::libuv-1.48.0-h827c3e9_0 - mkl-service anaconda/pkgs/main/win-64::mkl-service-2.4.0-py310h2bbff1b_1 - mkl_fft anaconda/pkgs/main/win-64::mkl_fft-1.3.11-py310h827c3e9_0 - mkl_random anaconda/pkgs/main/win-64::mkl_random-1.2.8-py310hc64d2fc_0 - mpc anaconda/pkgs/main/win-64::mpc-1.1.0-h7edee0f_1 - mpfr anaconda/pkgs/main/win-64::mpfr-4.0.2-h62dcd97_1 - mpir anaconda/pkgs/main/win-64::mpir-3.0.0-hec2e145_1 - mpmath anaconda/pkgs/main/win-64::mpmath-1.3.0-py310haa95532_0 - networkx anaconda/pkgs/main/win-64::networkx-3.3-py310haa95532_0 - numpy-base anaconda/pkgs/main/win-64::numpy-base-1.26.4-py310h65a83cf_0 - pytorch anaconda/pkgs/main/win-64::pytorch-2.3.1-cpu_py310h0ce1571_0 - pytorch-cpu anaconda/pkgs/main/win-64::pytorch-cpu-2.3.1-haa95532_0 - sympy anaconda/pkgs/main/win-64::sympy-1.13.3-py310haa95532_0 - -The following packages will be REMOVED: - - libblas-3.9.0-26_win64_mkl - libcblas-3.9.0-26_win64_mkl - liblapack-3.9.0-26_win64_mkl - -The following packages will be UPDATED: - - numpy anaconda/cloud/conda-forge::numpy-1.2~ --> anaconda/pkgs/main::numpy-1.26.4-py310h055cbcc_0 - -The following packages will be SUPERSEDED by a higher-priority channel: - - intel-openmp anaconda/cloud/conda-forge::intel-ope~ --> anaconda/pkgs/main::intel-openmp-2023.1.0-h59b6b97_46320 - mkl anaconda/cloud/conda-forge::mkl-2024.~ --> anaconda/pkgs/main::mkl-2023.1.0-h6b88ed4_46358 - - - -Downloading and Extracting Packages: - -Preparing transaction: done -Verifying transaction: done -Executing transaction: done -Looking in indexes: https://mirrors.aliyun.com/pypi/simple/ -Requirement already satisfied: pip in c:\users\eleven\desktop\easyvtuber-main\envs\miniconda3\envs\ezvtb_rt_venv\lib\site-packages (24.2) -Collecting pip - Downloading https://mirrors.aliyun.com/pypi/packages/ef/7d/500c9ad20238fcfcb4cb9243eede163594d7020ce87bd9610c9e02771876/pip-24.3.1-py3-none-any.whl (1.8 MB) - ---------------------------------------- 1.8/1.8 MB 1.4 MB/s eta 0:00:00 -Requirement already satisfied: wheel in c:\users\eleven\desktop\easyvtuber-main\envs\miniconda3\envs\ezvtb_rt_venv\lib\site-packages (0.44.0) -Collecting wheel - Downloading https://mirrors.aliyun.com/pypi/packages/0b/2c/87f3254fd8ffd29e4c02732eee68a83a1d3c346ae39bc6822dcbcb697f2b/wheel-0.45.1-py3-none-any.whl (72 kB) -Installing collected packages: wheel, pip - Attempting uninstall: wheel - Found existing installation: wheel 0.44.0 - Uninstalling wheel-0.44.0: - Successfully uninstalled wheel-0.44.0 - Attempting uninstall: pip - Found existing installation: pip 24.2 - Uninstalling pip-24.2: - Successfully uninstalled pip-24.2 -Successfully installed pip-24.3.1 wheel-0.45.1 -Looking in indexes: https://mirrors.aliyun.com/pypi/simple/ -Collecting nvidia-cudnn-cu12 - Downloading https://mirrors.aliyun.com/pypi/packages/07/1d/3c7055e1d189f55ed25183f50e1d82f477024289f925df22ac89c0f4d387/nvidia_cudnn_cu12-9.6.0.74-py3-none-win_amd64.whl (502.8 MB) - ---------------------------------------- 502.8/502.8 MB 6.3 MB/s eta 0:00:00 -Collecting nvidia-cublas-cu12 (from nvidia-cudnn-cu12) - Downloading https://mirrors.aliyun.com/pypi/packages/84/f7/985e9bdbe3e0ac9298fcc8cfa51a392862a46a0ffaccbbd56939b62a9c83/nvidia_cublas_cu12-12.6.4.1-py3-none-win_amd64.whl (434.5 MB) - 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Downloading https://pypi.nvidia.com/nvidia-cuda-runtime-cu12/nvidia_cuda_runtime_cu12-12.6.77-py3-none-win_amd64.whl (891 kB) - ---------------------------------------- 891.8/891.8 kB 10.2 MB/s eta 0:00:00 -Collecting tensorrt-cu12==10.6.0 (from tensorrt==10.6.0) - Downloading https://pypi.nvidia.com/tensorrt-cu12/tensorrt-cu12-10.6.0.tar.gz (18 kB) - Preparing metadata (setup.py) ... done -Building wheels for collected packages: tensorrt, tensorrt-cu12 - Building wheel for tensorrt (setup.py) ... done - Created wheel for tensorrt: filename=tensorrt-10.6.0-py2.py3-none-any.whl size=16345 sha256=3fc94cd935389ea9535e279dc81bdf07b97da751150f64a1f45c39ba8b5c2663 - Stored in directory: c:\users\eleven\appdata\local\pip\cache\wheels\42\bd\86\598d26504705081ad82ae23a08a869322ee5a8bdb410d5d298 - Building wheel for tensorrt-cu12 (setup.py) ... done - Created wheel for tensorrt-cu12: filename=tensorrt_cu12-10.6.0-py2.py3-none-any.whl size=17568 sha256=1856795aec69a0f6767ab62c0962d586ea16c3301c0f7040cde242c5f92f9abc - Stored in directory: c:\users\eleven\appdata\local\pip\cache\wheels\13\99\06\326119ed088736cbd6ded866ada42bb29bbb67bed34903e57b -Successfully built tensorrt tensorrt-cu12 -Installing collected packages: tensorrt_cu12_bindings, tensorrt-cu12, nvidia-cuda-runtime-cu12, tensorrt_cu12_libs, tensorrt -Successfully installed nvidia-cuda-runtime-cu12-12.6.77 tensorrt-10.6.0 tensorrt-cu12-10.6.0 tensorrt_cu12_bindings-10.6.0 tensorrt_cu12_libs-10.6.0 -Looking in indexes: https://mirrors.aliyun.com/pypi/simple/ -Requirement already satisfied: numpy==1.26.4 in c:\users\eleven\desktop\easyvtuber-main\envs\miniconda3\envs\ezvtb_rt_venv\lib\site-packages (from -r requirements.txt (line 1)) (1.26.4) -Collecting scipy (from -r requirements.txt (line 2)) - Downloading https://mirrors.aliyun.com/pypi/packages/51/8c/c2d371111961f737ae08881f654cf54eca796c42ec0429add2a06df97049/scipy-1.15.0-cp310-cp310-win_amd64.whl (43.9 MB) - ---------------------------------------- 43.9/43.9 MB 6.5 MB/s eta 0:00:00 -Collecting matplotlib (from -r requirements.txt (line 3)) - 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This could take a while. - Downloading https://mirrors.aliyun.com/pypi/packages/97/e1/bdb42dde046507250083862520428901af3f5182faac50d3df5f13a4ff3d/onnx-1.13.0-cp310-cp310-win_amd64.whl (12.2 MB) - ---------------------------------------- 12.2/12.2 MB 6.5 MB/s eta 0:00:00 - Downloading https://mirrors.aliyun.com/pypi/packages/b0/f0/5e383f64293857e86c300cb70010cce1ca6f038f1563b85501d30404ad2a/onnx-1.12.0-cp310-cp310-win_amd64.whl (11.5 MB) - ---------------------------------------- 11.5/11.5 MB 6.2 MB/s eta 0:00:00 -Requirement already satisfied: typing-extensions>=3.6.2.1 in c:\users\eleven\desktop\easyvtuber-main\envs\miniconda3\envs\ezvtb_rt_venv\lib\site-packages (from onnx->-r requirements.txt (line 12)) (4.12.2) -Collecting coloredlogs (from onnxruntime-directml->-r requirements.txt (line 13)) - Downloading https://mirrors.aliyun.com/pypi/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB) -Requirement already satisfied: sympy in c:\users\eleven\desktop\easyvtuber-main\envs\miniconda3\envs\ezvtb_rt_venv\lib\site-packages (from onnxruntime-directml->-r requirements.txt (line 13)) (1.13.3) -Collecting colorama (from tqdm->-r requirements.txt (line 15)) - Downloading https://mirrors.aliyun.com/pypi/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl (25 kB) -Collecting future (from ffmpeg-python>=0.2.0->pyanime4k==2.5.2->-r requirements.txt (line 9)) - Downloading https://mirrors.aliyun.com/pypi/packages/da/71/ae30dadffc90b9006d77af76b393cb9dfbfc9629f339fc1574a1c52e6806/future-1.0.0-py3-none-any.whl (491 kB) -Collecting CFFI>=1.0 (from sounddevice>=0.4.4->mediapipe->-r requirements.txt (line 6)) - Downloading https://mirrors.aliyun.com/pypi/packages/d1/b6/0b0f5ab93b0df4acc49cae758c81fe4e5ef26c3ae2e10cc69249dfd8b3ab/cffi-1.17.1-cp310-cp310-win_amd64.whl (181 kB) -Collecting humanfriendly>=9.1 (from coloredlogs->onnxruntime-directml->-r requirements.txt (line 13)) - Downloading https://mirrors.aliyun.com/pypi/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl (86 kB) -Collecting ml_dtypes>=0.4.0 (from jax->mediapipe->-r requirements.txt (line 6)) - Downloading https://mirrors.aliyun.com/pypi/packages/6b/7e/bc54ae885e4d702e60a4bf50aa9066ff35e9c66b5213d11091f6bffb3036/ml_dtypes-0.5.1-cp310-cp310-win_amd64.whl (209 kB) -Collecting opt_einsum (from jax->mediapipe->-r requirements.txt (line 6)) - Downloading https://mirrors.aliyun.com/pypi/packages/23/cd/066e86230ae37ed0be70aae89aabf03ca8d9f39c8aea0dec8029455b5540/opt_einsum-3.4.0-py3-none-any.whl (71 kB) -Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\users\eleven\desktop\easyvtuber-main\envs\miniconda3\envs\ezvtb_rt_venv\lib\site-packages (from sympy->onnxruntime-directml->-r requirements.txt (line 13)) (1.3.0) -Collecting pycparser (from CFFI>=1.0->sounddevice>=0.4.4->mediapipe->-r requirements.txt (line 6)) - Downloading https://mirrors.aliyun.com/pypi/packages/13/a3/a812df4e2dd5696d1f351d58b8fe16a405b234ad2886a0dab9183fb78109/pycparser-2.22-py3-none-any.whl (117 kB) -Collecting pyreadline3 (from humanfriendly>=9.1->coloredlogs->onnxruntime-directml->-r requirements.txt (line 13)) - Downloading https://mirrors.aliyun.com/pypi/packages/5a/dc/491b7661614ab97483abf2056be1deee4dc2490ecbf7bff9ab5cdbac86e1/pyreadline3-3.5.4-py3-none-any.whl (83 kB) -Installing collected packages: pyopengl, flatbuffers, turbojpeg, six, scipy, pyvirtualcam, pyreadline3, pyparsing, pycparser, protobuf, Pillow, packaging, opt_einsum, opencv_python, opencv-contrib-python, ml_dtypes, kiwisolver, future, fonttools, cycler, contourpy, colorama, attrs, absl-py, tqdm, python-dateutil, pynput, onnx, jaxlib, humanfriendly, ffmpeg-python, CFFI, sounddevice, pyanime4k, matplotlib, jax, coloredlogs, onnxruntime-directml, mediapipe -Successfully installed CFFI-1.17.1 Pillow-9.1.0 absl-py-2.1.0 attrs-24.3.0 colorama-0.4.6 coloredlogs-15.0.1 contourpy-1.3.1 cycler-0.12.1 ffmpeg-python-0.2.0 flatbuffers-24.12.23 fonttools-4.55.3 future-1.0.0 humanfriendly-10.0 jax-0.4.38 jaxlib-0.4.38 kiwisolver-1.4.8 matplotlib-3.10.0 mediapipe-0.10.11 ml_dtypes-0.5.1 onnx-1.12.0 onnxruntime-directml-1.20.1 opencv-contrib-python-4.10.0.84 opencv_python-4.5.5.64 opt_einsum-3.4.0 packaging-24.2 protobuf-3.20.1 pyanime4k-2.5.2 pycparser-2.22 pynput-1.7.6 pyopengl-3.1.7 pyparsing-3.2.1 pyreadline3-3.5.4 python-dateutil-2.9.0.post0 pyvirtualcam-0.9.1 scipy-1.15.0 six-1.17.0 sounddevice-0.5.1 tqdm-4.67.1 turbojpeg-0.0.2 -Press any key to continue . . . \ No newline at end of file