feat: Add PyTorch 2.6+ compatibility and RTX 5070 support

analyze-gemini.md

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+# MuseTalk 环境兼容性分析报告 (Gemini CLI) - V10 (终极方案: 原生适配 Blackwell)
+
+## 1. 基础环境现状
+- **操作系统**: Ubuntu 24.04.3 LTS (noble)
+- **Python**: 3.10 (Conda 虚拟环境: musetalk)
+- **CUDA**: 12.8 (Driver 570.211.01)
+- **GPU**: NVIDIA GeForce RTX 5070 (12GB 显存)
+- **FFmpeg**: 6.1.1 (系统已安装，状态良好)
+- **系统库**: `libsndfile1` 已预装
+
+## 2. 核心部署结论
+
+- **硬件支持**: **完全支持**。12GB 显存满足 MuseTalk 推理需求，RTX 50 系列架构性能卓越。
+- **软件策略**: **执行终极原生方案 (方案 B)**。
+    - 采用原生支持 RTX 50 系列 Blackwell 架构 (`sm_120`) 的 `PyTorch 2.7.0 (cu128)`。由于硬件架构太新，旧版 PyTorch 2.4 的 PTX 无法向前兼容。
+    - 为了适配 PyTorch 2.7.0，项目源码中的 8 处兼容性报错（`torch.load` 等）已被自动修改完毕。
+    - 采用**源码硬核编译安装 `mmcv`** 路线，突破无预编译包的限制。
+- **风险点控制**:
+    - **NumPy 必须 < 2.0.0**：NumPy 2.x 会导致 OpenCV/Librosa 崩溃。
+    - **Huggingface_hub == 0.36.2**：必须满足 transformers 4.39.2 (<1.0) 约束。
+    - **Gradio == 5.24.0**：确保 UI 稳定性并兼容旧版 Pillow。
+    - **Pillow 必须 < 10.0.0**：Pillow 10+ 移除了 `Image.ANTIALIAS`，会导致 `moviepy 1.0.3` 运行时直接崩溃。
+
+## 3. 标准部署步骤 (用户态执行)
+
+### A. 环境准备 (建议彻底重置)
+为避免之前的失败安装残留幽灵依赖，强烈建议**先删后建**：
+```bash
+conda deactivate
+conda remove -n musetalk --all -y
+conda create -n musetalk python=3.10 -y
+conda activate musetalk
+pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
+```
+
+### B. 安装原生架构 PyTorch
+```bash
+# 安装原生满血支持 Blackwell (sm_120) 的最新引擎
+pip install torch==2.7.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128 --force-reinstall
+```
+*(注意：MMCV 的安装已移至后续源码编译环节)*
+
+### C. 预装关键冲突依赖 (重要顺序)
+```bash
+# 1. 预装构建工具并锁死 NumPy (Python 3.12 必需)
+pip install --upgrade pip setuptools wheel "numpy<2.0.0" 
+
+# 2. 解决 chumpy 编译问题 (避免 PEP 517 构建隔离报错)
+pip install chumpy --no-build-isolation
+
+# 3. 强行锁定三方黄金版本 (解决 transformers/gradio 冲突)
+pip uninstall hf-gradio -y
+pip install "huggingface_hub==0.36.2" "gradio==5.24.0" "gradio-client==1.8.0" --force-reinstall
+
+# 4. 再次检查并加固 NumPy (防止被上面的安装带跑)
+pip install "numpy<2.0.0"
+```
+
+### D. 安装 MMLab 核心组件 (使用原生 pip 源码硬核编译)
+由于我们使用了 PyTorch 2.7.0 + CUDA 12.8，官方**绝对没有**可用的预编译包，必须进行硬核的源码编译：
+```bash
+pip install -U openmim ninja Cython
+# 关键步骤：Ubuntu 24.04 默认 GCC 13 编译 CUDA 极易失败，必须使用 -allow-unsupported-compiler 放宽限制。
+# 同时必须禁用编译隔离（--no-build-isolation），否则找不到构建依赖 pkg_resources！
+NVCC_APPEND_FLAGS="-allow-unsupported-compiler" pip install "mmcv>=2.1.0" --no-build-isolation --no-cache-dir
+mim install "mmdet>=3.1.0"
+mim install "mmpose>=1.3.0"
+```
+
+### E. 安装项目剩余依赖
+```bash
+pip install -r requirements-rtx5070.txt
+```
+
+## 4. 故障排查与修复记录 (Troubleshooting)
+
+| 问题 | 原因 | 修复方式 |
+|------|------|----------|
+| `ModuleNotFoundError: No module named 'mmdet'` | `mmpose` 的核心依赖缺失 | 执行 `mim install "mmdet>=3.1.0"` |
+| `ImportError: cannot import name 'FaceAlignment'` | 全局包冲突 | `pip uninstall face_detection -y`；代码已改为相对导入 |
+| `ImportError: huggingface-hub...` | transformers 版本冲突 | 强行执行 `pip install "huggingface_hub==0.36.2" --force-reinstall` |
+| `AttributeError: module 'numpy' has no attribute...` | 使用了 NumPy 2.x | 强行降级 `pip install "numpy<2.0.0"` |
+| `chumpy` 安装失败 | 隔离构建模式问题 (setup.py import pip) | 使用 `--no-build-isolation` 且需提前装好 setuptools |
+| `AttributeError: module 'PIL.Image' has no attribute 'ANTIALIAS'` | Pillow 10+ 移除了该属性，与 moviepy 1.0.3 冲突 | 强制锁定 `pip install "pillow<10.0.0"` |
+
+## 5. 验证与运行
+1. **核心验证**: 运行 `python3 -c "import mmcv; from mmcv.ops import nms; print('验证成功: OK')"`
+2. **下载权重**: `bash download_weights.sh`
+3. **推理测试**: `bash inference.sh v1.5 normal`
+
+---
+*更新日期: 2026年5月8日 (V10 - 回到方案B，原生支持 RTX 5070 并完成源码改造)*

app.py

@@ -168,6 +168,17 @@ def download_model():
 from musetalk.utils.blending import get_image
 from musetalk.utils.face_parsing import FaceParsing
 from musetalk.utils.audio_processor import AudioProcessor
+import torch
+
+# --- PyTorch 2.6+ Compatibility Monkey Patch ---
+_original_load = torch.load
+def _patched_load(*args, **kwargs):
+    if 'weights_only' not in kwargs:
+        kwargs['weights_only'] = False
+    return _original_load(*args, **kwargs)
+torch.load = _patched_load
+# ---------------------------------------------
+
 from musetalk.utils.utils import get_file_type, get_video_fps, datagen, load_all_model
 from musetalk.utils.preprocessing import get_landmark_and_bbox, read_imgs, coord_placeholder, get_bbox_range
 

chumpy-0.70/Makefile

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+all:
+
+upload:
+	rm -r dist
+	python setup.py sdist
+	twine upload dist/*
+
+test:
+	# For some reason the import changes for Python 3 caused the Python 2 test
+	# loader to give up without loading any tests. So we discover them ourselves.
+	# python -m unittest
+	find chumpy -name 'test_*.py' | sed -e 's/\.py$$//' -e 's/\//./' | xargs python -m unittest
+
+coverage: clean qcov
+qcov: all
+	env LD_PRELOAD=$(PRELOADED) coverage run --source=. -m unittest discover -s .
+	coverage html
+	coverage report -m

chumpy-0.70/PKG-INFO

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+Metadata-Version: 1.1
+Name: chumpy
+Version: 0.70
+Summary: chumpy
+Home-page: https://github.com/mattloper/chumpy
+Author: Matthew Loper
+Author-email: matt.loper@gmail.com
+License: MIT
+Description: UNKNOWN
+Platform: UNKNOWN
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Science/Research
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 2
+Classifier: Programming Language :: Python :: 2.7
+Classifier: Programming Language :: Python :: 3
+Classifier: Operating System :: MacOS :: MacOS X
+Classifier: Operating System :: POSIX :: Linux

chumpy-0.70/chumpy/__init__.py

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+from .ch import *
+from .logic import *
+
+from .optimization import minimize
+from . import extras
+from . import testing
+from .version import version as __version__
+
+from .version import version as __version__
+
+from numpy import bool, int, float, complex, object, unicode, str, nan, inf
+
+def test():
+    from os.path import split
+    import unittest
+    test_loader= unittest.TestLoader()
+    test_loader = test_loader.discover(split(__file__)[0])
+    test_runner = unittest.TextTestRunner()
+    test_runner.run( test_loader )
+
+
+demos = {}
+
+demos['scalar'] = """
+import chumpy as ch
+
+[x1, x2, x3] = ch.array(10), ch.array(20), ch.array(30)
+result = x1+x2+x3
+print result # prints [ 60.]
+print result.dr_wrt(x1) # prints 1
+"""
+
+demos['show_tree'] = """
+import chumpy as ch
+
+[x1, x2, x3] = ch.array(10), ch.array(20), ch.array(30)
+for i in range(3): x2 = x1 + x2 + x3
+
+x2.dr_wrt(x1) # pull cache 
+x2.dr_wrt(x3) # pull cache
+x1.label='x1' # for clarity in show_tree()
+x2.label='x2' # for clarity in show_tree()
+x3.label='x3' # for clarity in show_tree()
+x2.show_tree(cachelim=1e-4) # in MB
+"""
+
+demos['matrix'] = """
+import chumpy as ch
+
+x1, x2, x3, x4 = ch.eye(10), ch.array(1), ch.array(5), ch.array(10)
+y = x1*(x2-x3)+x4
+print y
+print y.dr_wrt(x2)
+"""
+
+demos['linalg'] = """
+import chumpy as ch
+
+m = [ch.random.randn(100).reshape((10,10)) for i in range(3)]
+y = m[0].dot(m[1]).dot(ch.linalg.inv(m[2])) * ch.linalg.det(m[0])
+print y.shape
+print y.dr_wrt(m[0]).shape
+"""
+
+demos['inheritance'] = """
+import chumpy as ch
+import numpy as np
+
+class Sin(ch.Ch):
+
+    dterms = ('x',)
+
+    def compute_r(self):
+        return np.sin(self.x.r)
+
+    def compute_dr_wrt(self, wrt):
+        import scipy.sparse
+        if wrt is self.x:
+            result = np.cos(self.x.r)
+            return scipy.sparse.diags([result.ravel()], [0]) if len(result)>1 else np.atleast_2d(result)
+
+x1 = Ch([10,20,30])
+result = Sin(x1) # or "result = Sin(x=x1)"
+print result.r
+print result.dr_wrt(x1)
+"""
+
+demos['optimization'] = """
+import chumpy as ch
+
+x = ch.zeros(10)
+y = ch.zeros(10)
+
+# Beale's function
+e1 = 1.5 - x + x*y
+e2 = 2.25 - x  + x*(y**2)
+e3 = 2.625 - x + x*(y**3)
+
+objective = {'e1': e1, 'e2': e2, 'e3': e3}
+ch.minimize(objective, x0=[x,y], method='dogleg')
+print x # should be all 3.0
+print y # should be all 0.5
+"""
+
+
+
+
+def demo(which=None):
+    if which not in demos:
+        print('Please indicate which demo you want, as follows:')
+        for key in demos:
+            print("\tdemo('%s')" % (key,))
+        return
+
+    print('- - - - - - - - - - - <CODE> - - - - - - - - - - - -')
+    print(demos[which])
+    print('- - - - - - - - - - - </CODE> - - - - - - - - - - - -\n')
+    exec('global np\n' + demos[which], globals(), locals())