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PerformanceTest.md
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# 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）。
README.md
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@@ -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 [00:00<?, ?it/s][01/09/2025-22:36:52] [TRT] [I] [MemUsageChange] Init CUDA: CPU +89, GPU +0, now: CPU 6758, GPU 1009 (MiB)
[01/09/2025-22:36:57] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +1358, GPU +194, now: CPU 8423, GPU 1203 (MiB)
[01/09/2025-22:36:57] [TRT] [I] Loading ONNX file from path ./data/models\rife_512\x2\fp32.onnx...
[01/09/2025-22:36:57] [TRT] [I] Beginning ONNX file parsing
[01/09/2025-22:36:57] [TRT] [I] Completed parsing of ONNX file
[01/09/2025-22:36:57] [TRT] [I] Input number: 2
[01/09/2025-22:36:57] [TRT] [I] Output number: 2
[01/09/2025-22:36:57] [TRT] [I] Building an engine from file ./data/models\rife_512\x2\fp32.onnx; this may take a while...
....
....
....
[01/10/2025-21:16:53] [TRT] [I] [BlockAssignment] Started assigning block shifts. This will take 66 steps to complete.
[01/10/2025-21:16:53] [TRT] [I] [BlockAssignment] Algorithm ShiftNTopDown took 3.4012ms to assign 7 blocks to 66 nodes requiring 218136064 bytes.
[01/10/2025-21:16:53] [TRT] [I] Total Activation Memory: 218135552 bytes
[01/10/2025-21:16:53] [TRT] [I] Total Weights Memory: 2724242 bytes
[01/10/2025-21:16:53] [TRT] [I] Compiler backend is used during engine execution.
[01/10/2025-21:16:53] [TRT] [I] Engine generation completed in 160.706 seconds.
[01/10/2025-21:16:53] [TRT] [I] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 89 MiB, GPU 317 MiB
[01/10/2025-21:16:53] [TRT] [I] Completed creating Engine
[01/10/2025-21:16:53] [TRT] [I] Saving engine to file ./data/models\waifu2x_upconv\fp16\upconv_7\art\scale2x.trt
[01/10/2025-21:16:53] [TRT] [I] Completed saving engine
100%|██████████████████████████████████████████████████████████████████████████████████| 38/38 [46:54<00:00, 74.07s/it]
```
使用Spout2插件可以提供透明通道给OBS，访问 https://github.com/Off-World-Live/obs-spout2-plugin/releases 项目，请选择与你的OBS版本兼容的插件安装exe。
### 启动项目
双击`02A.启动器.bat` 或 `02B.启动器（调试输出）.bat` 启动器，如下图启动。如果上一步构建未完成或者使用A/I卡，需要**手动**将TensorRT去除勾选：
双击`02A.启动器.bat` 或 `02B.启动器（调试输出）.bat` 启动器，如下图启动。
![](assets/02success.png)
之后请移步后文的输入输出&性能配置板块自行调节。如有问题请先参考本页末的FAQ再提出。
## Installation(科学上网且使用Git)
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，`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<br>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。<br>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`
assets/complete_building_log.txt
-548
View File
@@ -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)
---------------------------------------- 434.5/434.5 MB 6.2 MB/s eta 0:00:00
Installing collected packages: nvidia-cublas-cu12, nvidia-cudnn-cu12
Successfully installed nvidia-cublas-cu12-12.6.4.1 nvidia-cudnn-cu12-9.6.0.74
Looking in indexes: https://pypi.org/simple, https://pypi.nvidia.com
Collecting tensorrt_cu12_libs==10.6.0
Downloading https://pypi.nvidia.com/tensorrt-cu12-libs/tensorrt_cu12_libs-10.6.0-py2.py3-none-win_amd64.whl (1075.0 MB)
---------------------------------------- 1.1/1.1 GB 8.2 MB/s eta 0:00:00
Collecting tensorrt_cu12_bindings==10.6.0
Downloading https://pypi.nvidia.com/tensorrt-cu12-bindings/tensorrt_cu12_bindings-10.6.0-cp310-none-win_amd64.whl (814 kB)
---------------------------------------- 814.1/814.1 kB 12.0 MB/s eta 0:00:00
Collecting tensorrt==10.6.0
Downloading https://pypi.nvidia.com/tensorrt/tensorrt-10.6.0.tar.gz (16 kB)
Preparing metadata (setup.py) ... done
Collecting nvidia-cuda-runtime-cu12 (from tensorrt_cu12_libs==10.6.0)
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))
Downloading https://mirrors.aliyun.com/pypi/packages/5a/85/b9a54d64585a6b8737a78a61897450403c30f39e0bd3214270bb0b96f002/matplotlib-3.10.0-cp310-cp310-win_amd64.whl (8.0 MB)
---------------------------------------- 8.0/8.0 MB 6.7 MB/s eta 0:00:00
Collecting protobuf==3.20.1 (from -r requirements.txt (line 4))
Downloading https://mirrors.aliyun.com/pypi/packages/c0/9c/bb88091287418ae1cf8af2bb9ed9710748a562b9abc227e4884d687a8650/protobuf-3.20.1-cp310-cp310-win_amd64.whl (903 kB)
---------------------------------------- 903.8/903.8 kB 6.8 MB/s eta 0:00:00
Collecting pynput==1.7.6 (from -r requirements.txt (line 5))
Downloading https://mirrors.aliyun.com/pypi/packages/02/27/4de87850ff87c8dcecaaf8d27f28cec89ef17eeb6938f250449cb2635e06/pynput-1.7.6-py2.py3-none-any.whl (89 kB)
Collecting mediapipe (from -r requirements.txt (line 6))
Downloading https://mirrors.aliyun.com/pypi/packages/f6/7a/448885fc960a972d8bf1939602919fdaa8de17f505b6c50782a074e7b70e/mediapipe-0.10.20-cp310-cp310-win_amd64.whl (51.0 MB)
---------------------------------------- 51.0/51.0 MB 6.2 MB/s eta 0:00:00
Collecting opencv_python==4.5.5.64 (from -r requirements.txt (line 7))
Downloading https://mirrors.aliyun.com/pypi/packages/48/c3/798bd7b8f78430f82ec0660b753106717e4e4bb8032ce56f77d8577cb24b/opencv_python-4.5.5.64-cp36-abi3-win_amd64.whl (35.4 MB)
---------------------------------------- 35.4/35.4 MB 6.3 MB/s eta 0:00:00
Collecting Pillow==9.1.0 (from -r requirements.txt (line 8))
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Collecting pyanime4k==2.5.2 (from -r requirements.txt (line 9))
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Collecting pyvirtualcam==0.9.1 (from -r requirements.txt (line 10))
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Collecting onnxruntime-directml (from -r requirements.txt (line 13))
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Collecting turbojpeg (from -r requirements.txt (line 14))
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Collecting fonttools>=4.22.0 (from matplotlib->-r requirements.txt (line 3))
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Collecting kiwisolver>=1.3.1 (from matplotlib->-r requirements.txt (line 3))
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Collecting packaging>=20.0 (from matplotlib->-r requirements.txt (line 3))
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Collecting pyparsing>=2.3.1 (from matplotlib->-r requirements.txt (line 3))
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Collecting flatbuffers>=2.0 (from mediapipe->-r requirements.txt (line 6))
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WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='mirrors.aliyun.com', port=443): Read timed out. (read timeout=15)")': /pypi/simple/opencv-contrib-python/
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INFO: pip is looking at multiple versions of mediapipe to determine which version is compatible with other requirements. This could take a while.
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Collecting sounddevice>=0.4.4 (from mediapipe->-r requirements.txt (line 6))
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INFO: pip is looking at multiple versions of onnx to determine which version is compatible with other requirements. This could take a while.
Collecting onnx (from -r requirements.txt (line 12))
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INFO: pip is still looking at multiple versions of onnx to determine which version is compatible with other requirements. This could take a while.
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Downloading https://mirrors.aliyun.com/pypi/packages/b0/f0/5e383f64293857e86c300cb70010cce1ca6f038f1563b85501d30404ad2a/onnx-1.12.0-cp310-cp310-win_amd64.whl (11.5 MB)
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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))
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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))
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Collecting future (from ffmpeg-python>=0.2.0->pyanime4k==2.5.2->-r requirements.txt (line 9))
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Collecting CFFI>=1.0 (from sounddevice>=0.4.4->mediapipe->-r requirements.txt (line 6))
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Collecting humanfriendly>=9.1 (from coloredlogs->onnxruntime-directml->-r requirements.txt (line 13))
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Collecting ml_dtypes>=0.4.0 (from jax->mediapipe->-r requirements.txt (line 6))
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Collecting opt_einsum (from jax->mediapipe->-r requirements.txt (line 6))
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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))
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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
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