Launching Web UI with arguments:
/root/miniconda3/lib/python3.11/site-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be removed in 0.17. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional.
warnings.warn(
No module 'xformers'. Proceeding without it.
Loading weights [6ce0161689] from /root/automatic1111-stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.safetensors
Creating model from config: /root/automatic1111-stable-diffusion-webui/configs/v1-inference.yaml
LatentDiffusion: Running in eps-prediction mode
DiffusionWrapper has 859.52 M params.
Applying cross attention optimization (Doggettx).
Textual inversion embeddings loaded(0):
Model loaded in 5.2s (load weights from disk: 0.4s, create model: 0.4s, apply weights to model: 3.2s, apply half(): 0.2s, move model to device: 0.5s, load textual inversion embeddings: 0.4s).
Running on local URL: http://127.0.0.1:7860
To create a public link, set share=True in launch().
then i run it,
To create a public link, set share=True in launch().
Using cuda for inference.
Reading video frames...
Number of frames available for inference: 936
(80, 1321)
Length of mel chunks: 491
0%| | 0/4 [00:00<?, ?it/s] 0%| | 0/31 [00:00<?, ?it/s]
I have the same thing. if you reduce the resolution by 2 times then it starts to work, but I care about the result without upscaling then using other results. I have seen a similar problem with many users, but never found an answer with a solution. I don't think the problem is related to a bad pc, because I have rtx 3060 (not top, but it should be enough) and + saw a person with 40 series video card with similar problem. When rendering in task manager it says that the graphics card is loaded at 19-24 percent, so the problem is that for some reason it doesn't want to use the whole graphics card
(base) root@zhixingren:~# cd automatic1111-stable-diffusion-webui (base) root@zhixingren:~/automatic1111-stable-diffusion-webui# python launch.py Python 3.11.5 (main, Sep 11 2023, 13:54:46) [GCC 11.2.0] Commit hash: 0cc0ee1bcb4c24a8c9715f66cede06601bfc00c8 Installing requirements for Web UI Installing wav2lip_uhq requirement: dlib-bin Installing wav2lip_uhq requirement: opencv-python Installing wav2lip_uhq requirement: pillow Installing wav2lip_uhq requirement: librosa==0.10.0.post2 Installing wav2lip_uhq requirement: opencv-contrib-python Installing wav2lip_uhq requirement: git+https://github.com/suno-ai/bark.git Installing wav2lip_uhq requirement: insightface==0.7.3 Installing wav2lip_uhq requirement: onnx==1.14.0 Installing wav2lip_uhq requirement: onnxruntime==1.15.0 Installing wav2lip_uhq requirement: onnxruntime-gpu==1.15.0 Installing wav2lip_uhq requirement: opencv-python>=4.8.0
Launching Web UI with arguments: /root/miniconda3/lib/python3.11/site-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be removed in 0.17. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional. warnings.warn( No module 'xformers'. Proceeding without it. Loading weights [6ce0161689] from /root/automatic1111-stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.safetensors Creating model from config: /root/automatic1111-stable-diffusion-webui/configs/v1-inference.yaml LatentDiffusion: Running in eps-prediction mode DiffusionWrapper has 859.52 M params. Applying cross attention optimization (Doggettx). Textual inversion embeddings loaded(0): Model loaded in 5.2s (load weights from disk: 0.4s, create model: 0.4s, apply weights to model: 3.2s, apply half(): 0.2s, move model to device: 0.5s, load textual inversion embeddings: 0.4s). Running on local URL: http://127.0.0.1:7860
To create a public link, set
share=True
inlaunch()
.then i run it,
To create a public link, set
share=True
inlaunch()
. Using cuda for inference. Reading video frames... Number of frames available for inference: 936 (80, 1321) Length of mel chunks: 491 0%| | 0/4 [00:00<?, ?it/s] 0%| | 0/31 [00:00<?, ?it/s]no run down ,help