Comments (8)
Sorry for that, can you download the file from the follow link ?
https://www.johnvansickle.com/ffmpeg/old-releases/ffmpeg-4.4-amd64-static.tar.xz
from echomimic.
I already have the file and im having difficulty how to properly install. Im not a programmer :(
What is meant by: export FFMPEG_PATH=/path/to/ffmpeg-4.4-amd64-static
from echomimic.
OK, is your operating system windows or linux?
Firstliy, copy the full path of ffmpeg-4.4-amd64-static, like /home/XXX/ffmpeg-4.4-amd64-static(linux) or D:/xxxx/ffmpeg-4.4-amd64-static(windows)
Then, here is the easiest way to solve your problem, open the 'infer_audio2vid.py', change the line 32 to your own path:
current:
after change:
from echomimic.
Thanks, i think the above suggestion helped but i have a new error:
(echomimic) D:\AI\EchoMimic>python -u infer_audio2vid.py
C:\Users\Renel\anaconda3\envs\echomimic\lib\site-packages\diffusers\utils\outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
torch.utils._pytree._register_pytree_node(
C:\Users\Renel\anaconda3\envs\echomimic\lib\site-packages\diffusers\utils\outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
torch.utils._pytree._register_pytree_node(
please download ffmpeg-static and export to FFMPEG_PATH.
For example: export FFMPEG_PATH=/musetalk/ffmpeg-4.4-amd64-static
Cannot initialize model with low cpu memory usage because accelerate
was not found in the environment. Defaulting to low_cpu_mem_usage=False
. It is strongly recommended to install accelerate
for faster and less memory-intense model loading. You can do so with:
pip install accelerate
.
Traceback (most recent call last):
File "infer_audio2vid.py", line 243, in
main()
File "infer_audio2vid.py", line 97, in main
vae = AutoencoderKL.from_pretrained(
File "C:\Users\Renel\anaconda3\envs\echomimic\lib\site-packages\torch\nn\modules\module.py", line 1173, in to
return self._apply(convert)
File "C:\Users\Renel\anaconda3\envs\echomimic\lib\site-packages\torch\nn\modules\module.py", line 779, in _apply
module._apply(fn)
File "C:\Users\Renel\anaconda3\envs\echomimic\lib\site-packages\torch\nn\modules\module.py", line 779, in _apply
module._apply(fn)
File "C:\Users\Renel\anaconda3\envs\echomimic\lib\site-packages\torch\nn\modules\module.py", line 804, in apply
param_applied = fn(param)
File "C:\Users\Renel\anaconda3\envs\echomimic\lib\site-packages\torch\nn\modules\module.py", line 1159, in convert
return t.to(
File "C:\Users\Renel\anaconda3\envs\echomimic\lib\site-packages\torch\cuda_init.py", line 284, in _lazy_init
raise AssertionError("Torch not compiled with CUDA enabled")
AssertionError: Torch not compiled with CUDA enabled
from echomimic.
from echomimic.
reinstall torch and torchvision may solve the problem.
pip install torch==2.0.1
pip install torchvision==0.15.2
but we are sorry that we only ensure the codes can be run on a graphic card with at least 16G memory. OOM may occurs on 3080(10G) :(
from echomimic.
我是windows11 python3.10环境下,按照要求安装了对应版本的torch,但我的cuda是12.5的。依然提示这个,安装的时候我用的清华源,是清华源的问题?
File "C:\Users\Administrator.conda\envs\echo\lib\site-packages\torch\nn\modules\module.py", line 1143, in convert
return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
File "C:\Users\Administrator.conda\envs\echo\lib\site-packages\torch\cuda_init_.py", line 239, in _lazy_init
raise AssertionError("Torch not compiled with CUDA enabled")
AssertionError: Torch not compiled with CUDA enabled
PS H:\AIaudio_Live\EchoMimic> pip list
Package Version
accelerate 0.32.1
antlr4-python3-runtime 4.9.3
av 11.0.0
certifi 2024.7.4
charset-normalizer 3.3.2
colorama 0.4.6
decorator 4.4.2
diffusers 0.24.0
einops 0.4.1
facenet-pytorch 2.5.0
ffmpeg-python 0.2.0
filelock 3.15.4
fsspec 2024.6.1
future 1.0.0
huggingface-hub 0.23.4
idna 3.7
imageio 2.34.2
imageio-ffmpeg 0.5.1
importlib_metadata 8.0.0
intel-openmp 2021.4.0
Jinja2 3.1.4
lightning-utilities 0.11.3.post0
MarkupSafe 2.1.5
mkl 2021.4.0
moviepy 1.0.3
mpmath 1.3.0
networkx 3.3
numpy 1.26.4
omegaconf 2.3.0
opencv-python 4.10.0.84
packaging 24.1
pillow 10.4.0
pip 24.0
proglog 0.1.10
psutil 6.0.0
PyYAML 6.0.1
regex 2024.5.15
requests 2.32.3
safetensors 0.4.3
setuptools 69.5.1
sympy 1.13.0
tbb 2021.13.0
tokenizers 0.19.1
torch 2.0.1
torchaudio 2.0.2
torchmetrics 1.4.0.post0
torchtyping 0.1.4
torchvision 0.15.2
tqdm 4.66.4
transformers 4.42.3
typeguard 4.3.0
typing_extensions 4.12.2
urllib3 2.2.2
wheel 0.43.0
zipp 3.19.2
from echomimic.
我是windows11 python3.10环境下,按照要求安装了对应版本的torch,但我的cuda是12.5的。依然提示这个,安装的时候我用的清华源,是清华源的问题?
File "C:\Users\Administrator.conda\envs\echo\lib\site-packages\torch\nn\modules\module.py", line 1143, in convert return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking) File "C:\Users\Administrator.conda\envs\echo\lib\site-packages\torch\cuda__init__.py", line 239, in _lazy_init raise AssertionError("Torch not compiled with CUDA enabled") AssertionError: Torch not compiled with CUDA enabled PS H:\AIaudio_Live\EchoMimic> pip list Package Version
accelerate 0.32.1 antlr4-python3-runtime 4.9.3 av 11.0.0 certifi 2024.7.4 charset-normalizer 3.3.2 colorama 0.4.6 decorator 4.4.2 diffusers 0.24.0 einops 0.4.1 facenet-pytorch 2.5.0 ffmpeg-python 0.2.0 filelock 3.15.4 fsspec 2024.6.1 future 1.0.0 huggingface-hub 0.23.4 idna 3.7 imageio 2.34.2 imageio-ffmpeg 0.5.1 importlib_metadata 8.0.0 intel-openmp 2021.4.0 Jinja2 3.1.4 lightning-utilities 0.11.3.post0 MarkupSafe 2.1.5 mkl 2021.4.0 moviepy 1.0.3 mpmath 1.3.0 networkx 3.3 numpy 1.26.4 omegaconf 2.3.0 opencv-python 4.10.0.84 packaging 24.1 pillow 10.4.0 pip 24.0 proglog 0.1.10 psutil 6.0.0 PyYAML 6.0.1 regex 2024.5.15 requests 2.32.3 safetensors 0.4.3 setuptools 69.5.1 sympy 1.13.0 tbb 2021.13.0 tokenizers 0.19.1 torch 2.0.1 torchaudio 2.0.2 torchmetrics 1.4.0.post0 torchtyping 0.1.4 torchvision 0.15.2 tqdm 4.66.4 transformers 4.42.3 typeguard 4.3.0 typing_extensions 4.12.2 urllib3 2.2.2 wheel 0.43.0 zipp 3.19.2
解决了,不要用清华源,去pytorch官网下载2.4G版本那个torch
https://download.pytorch.org/whl/torch_stable.html
但会出现新的问题
File "C:\Users\Administrator.conda\envs\echo\lib\site-packages\torch\jit_trace.py", line 1220, in wrapper
return fn(*args, **kwargs)
File "C:\Users\Administrator.conda\envs\echo\lib\site-packages\torchvision\ops\boxes.py", line 94, in _batched_nms_coordinate_trick
keep = nms(boxes_for_nms, scores, iou_threshold)
File "C:\Users\Administrator.conda\envs\echo\lib\site-packages\torchvision\ops\boxes.py", line 41, in nms
return torch.ops.torchvision.nms(boxes, scores, iou_threshold)
File "C:\Users\Administrator.conda\envs\echo\lib\site-packages\torch_ops.py", line 502, in call
return self._op(*args, **kwargs or {})
NotImplementedError: Could not run 'torchvision::nms' with arguments from the 'CUDA' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'torchvision::nms' is only available for these backends: [CPU, QuantizedCPU, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMeta, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PythonDispatcher].
确实是facenet_pytorch版本的问题,facenet_pytorch我重新升级安装后版本为0.26 需要torch2.2.0以上,重新安装后问题解决,执行程序后也生成了output文件,但出现了新的问题:
PS H:\AIaudio_Live\EchoMimic> python .\infer_audio2vid.py
C:\Users\Administrator.conda\envs\echo\lib\site-packages\diffusers\utils\outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
torch.utils._pytree._register_pytree_node(
C:\Users\Administrator.conda\envs\echo\lib\site-packages\diffusers\utils\outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
torch.utils._pytree._register_pytree_node(
please download ffmpeg-static and export to FFMPEG_PATH.
For example: export FFMPEG_PATH=/musetalk/ffmpeg-4.4-amd64-static
Some weights of the model checkpoint were not used when initializing UNet2DConditionModel:
['down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_q.weight, down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_k.weight, down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_v.weight, down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_out.0.weight, down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_out.0.bias, down_blocks.0.attentions.0.transformer_blocks.0.norm2.weight, down_blocks.0.attentions.0.transformer_blocks.0.norm2.bias, down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_q.weight, down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_k.weight, down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_v.weight, down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_out.0.weight, down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_out.0.bias, down_blocks.0.attentions.1.transformer_blocks.0.norm2.weight, down_blocks.0.attentions.1.transformer_blocks.0.norm2.bias, down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_q.weight, down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.weight, down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v.weight, down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0.weight, down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0.bias, down_blocks.1.attentions.0.transformer_blocks.0.norm2.weight, down_blocks.1.attentions.0.transformer_blocks.0.norm2.bias, down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_q.weight, down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.weight, down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v.weight, down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0.weight, down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0.bias, down_blocks.1.attentions.1.transformer_blocks.0.norm2.weight, down_blocks.1.attentions.1.transformer_blocks.0.norm2.bias, down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_q.weight, down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_k.weight, down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_v.weight, down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_out.0.weight, down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_out.0.bias, down_blocks.2.attentions.0.transformer_blocks.0.norm2.weight, down_blocks.2.attentions.0.transformer_blocks.0.norm2.bias, down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_q.weight, down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_k.weight, down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_v.weight, down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_out.0.weight, down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_out.0.bias, down_blocks.2.attentions.1.transformer_blocks.0.norm2.weight, down_blocks.2.attentions.1.transformer_blocks.0.norm2.bias, up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_q.weight, up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.weight, up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v.weight, up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0.weight, up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0.bias, up_blocks.1.attentions.0.transformer_blocks.0.norm2.weight, up_blocks.1.attentions.0.transformer_blocks.0.norm2.bias, up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_q.weight, up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.weight, up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v.weight, up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0.weight, up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0.bias, up_blocks.1.attentions.1.transformer_blocks.0.norm2.weight, up_blocks.1.attentions.1.transformer_blocks.0.norm2.bias, up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_q.weight, up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_k.weight, up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_v.weight, up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_out.0.weight, up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_out.0.bias, up_blocks.1.attentions.2.transformer_blocks.0.norm2.weight, up_blocks.1.attentions.2.transformer_blocks.0.norm2.bias, up_blocks.2.attentions.0.transformer_blocks.0.attn2.to_q.weight, up_blocks.2.attentions.0.transformer_blocks.0.attn2.to_k.weight, up_blocks.2.attentions.0.transformer_blocks.0.attn2.to_v.weight, up_blocks.2.attentions.0.transformer_blocks.0.attn2.to_out.0.weight, up_blocks.2.attentions.0.transformer_blocks.0.attn2.to_out.0.bias, up_blocks.2.attentions.0.transformer_blocks.0.norm2.weight, up_blocks.2.attentions.0.transformer_blocks.0.norm2.bias, up_blocks.2.attentions.1.transformer_blocks.0.attn2.to_q.weight, up_blocks.2.attentions.1.transformer_blocks.0.attn2.to_k.weight, up_blocks.2.attentions.1.transformer_blocks.0.attn2.to_v.weight, up_blocks.2.attentions.1.transformer_blocks.0.attn2.to_out.0.weight, up_blocks.2.attentions.1.transformer_blocks.0.attn2.to_out.0.bias, up_blocks.2.attentions.1.transformer_blocks.0.norm2.weight, up_blocks.2.attentions.1.transformer_blocks.0.norm2.bias, up_blocks.2.attentions.2.transformer_blocks.0.attn2.to_q.weight, up_blocks.2.attentions.2.transformer_blocks.0.attn2.to_k.weight, up_blocks.2.attentions.2.transformer_blocks.0.attn2.to_v.weight, up_blocks.2.attentions.2.transformer_blocks.0.attn2.to_out.0.weight, up_blocks.2.attentions.2.transformer_blocks.0.attn2.to_out.0.bias, up_blocks.2.attentions.2.transformer_blocks.0.norm2.weight, up_blocks.2.attentions.2.transformer_blocks.0.norm2.bias, up_blocks.3.attentions.0.transformer_blocks.0.attn2.to_q.weight, up_blocks.3.attentions.0.transformer_blocks.0.attn2.to_k.weight, up_blocks.3.attentions.0.transformer_blocks.0.attn2.to_v.weight, up_blocks.3.attentions.0.transformer_blocks.0.attn2.to_out.0.weight, up_blocks.3.attentions.0.transformer_blocks.0.attn2.to_out.0.bias, up_blocks.3.attentions.0.transformer_blocks.0.norm2.weight, up_blocks.3.attentions.0.transformer_blocks.0.norm2.bias, up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_q.weight, up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_k.weight, up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_v.weight, up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_out.0.weight, up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_out.0.bias, up_blocks.3.attentions.1.transformer_blocks.0.norm2.weight, up_blocks.3.attentions.1.transformer_blocks.0.norm2.bias, up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_q.weight, up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_k.weight, up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_v.weight, up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_out.0.weight, up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_out.0.bias, up_blocks.3.attentions.2.transformer_blocks.0.norm2.weight, up_blocks.3.attentions.2.transformer_blocks.0.norm2.bias, mid_block.attentions.0.transformer_blocks.0.attn2.to_q.weight, mid_block.attentions.0.transformer_blocks.0.attn2.to_k.weight, mid_block.attentions.0.transformer_blocks.0.attn2.to_v.weight, mid_block.attentions.0.transformer_blocks.0.attn2.to_out.0.weight, mid_block.attentions.0.transformer_blocks.0.attn2.to_out.0.bias, mid_block.attentions.0.transformer_blocks.0.norm2.weight, mid_block.attentions.0.transformer_blocks.0.norm2.bias, conv_norm_out.weight, conv_norm_out.bias, conv_out.weight, conv_out.bias']
AttributeError: 'float' object has no attribute 'rint'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\Users\Administrator.conda\envs\echo\lib\site-packages\numpy\core\fromnumeric.py", line 59, in _wrapfunc
return bound(*args, **kwds)
TypeError: loop of ufunc does not support argument 0 of type float which has no callable rint method
During handling of the above exception, another exception occurred:
AttributeError: 'float' object has no attribute 'rint'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "H:\AIaudio_Live\EchoMimic\infer_audio2vid.py", line 243, in
main()
File "H:\AIaudio_Live\EchoMimic\infer_audio2vid.py", line 192, in main
xyxy = np.round(xyxy).astype('int')
File "C:\Users\Administrator.conda\envs\echo\lib\site-packages\numpy\core\fromnumeric.py", line 3360, in round
return _wrapfunc(a, 'round', decimals=decimals, out=out)
File "C:\Users\Administrator.conda\envs\echo\lib\site-packages\numpy\core\fromnumeric.py", line 68, in _wrapfunc
return _wrapit(obj, method, *args, **kwds)
File "C:\Users\Administrator.conda\envs\echo\lib\site-packages\numpy\core\fromnumeric.py", line 45, in _wrapit
result = getattr(asarray(obj), method)(*args, **kwds)
TypeError: loop of ufunc does not support argument 0 of type float which has no callable rint method
from echomimic.
Related Issues (20)
- 请问怎么在webUI中使用infra_ audio2vid acc. py?
- Release Dataset? HOT 1
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- no './assets/mask_image_512.png'
- 请问一下没有N卡的机器可以启动服务么?
- 如果video_length超过face_mask_tensor的长度会崩溃 HOT 2
- motion_sync skipped. Please replace the pose dir with the driven video to enable it. HOT 3
- FileNotFoundError: [Errno 2] No such file or directory: './pretrained_weights/denoising_unet_acc.pth' HOT 2
- Support for Selected Landmark
- Add a blinking option
- 流式生成带alpha通道的视频,让视频背景可以透明,仅保留人物,这将是未来的必备的一个功能趋势!!! HOT 2
- Please delete
- 为什么face_locator_tensor 这么慢呀。。。69s一步
- Help guys “”face_img = cv2.resize(face_img, (width, height))“” webui issue. HOT 2
- audio&pose-drived generate multi-hand result
- Getting below issue, please help git lfs pull is in just downloading, how many gb?
- How to get facial landmarks?
- Difference between reference_unet and reference_unet_pose
- How do you accelerate the inference?
- 执行python -u infer_audio2vid.py 报错:No module named 'cv2'
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