updated tutorial: [ Ссылка ] - Our Discord : [ Ссылка ]. This video I am showing how to downgrade CUDA and xformers version for proper training and I am showing how to do LoRA training with 8GB GPU. If I have been of assistance to you and you would like to show your support for my work, please consider becoming a patron on 🥰 [ Ссылка ]
Playlist of Stable Diffusion Tutorials, #Automatic1111 and Google Colab Guides, DreamBooth, Textual Inversion / Embedding, #LoRA, AI Upscaling, Pix2Pix, Img2Img:
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This CUDA downgrade will not be necessary probably after the extensions get updated. However it is not certain when will they get updated. Meanwhile you can downgrade and use CUDA 11.6.
Stable Diffusion Playlist : [ Ссылка ]
The commands you need to execute with order to downgrade CUDA
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Run CMD as administrator if you get error
1:
activate
2:
pip uninstall torch torchvision
3:
pip uninstall torchaudio
4:
pip uninstall xformers
5:
pip install torch torchvision --extra-index-url [ Ссылка ]
6:
pip install -U -I --no-deps [ Ссылка ]
These below are specific hashes used in video but not necessary to use. You can install newest version of both DreamBooth and Automatic1111 and just downgrade CUDA with the above commands.
Automatic 1111 commit : dc8d1f4f8beb546089abd107db3432e03339c9c0
Dreambooth commit : c544ee11aee0085a7fbb7fdda65898dea2145f0c
Watch this video for learning how to use FileWords:
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#xformers
OUTLINE
0:00 Introduction to How to downgrade CUDA version
1:46 Automatic1111 will ask you to upgrade CUDA. Don't yet.
2:03 How to downgrade your CUDA version in your Automatic1111 installation folder
4:30 How to install DreamBooth extension
5:07 How to install and use dev branch of DreamBooth extension
6:42 How to stash local changes to checkout different git branch
7:13 How to start LoRA training for 8 GB VRAM GPUs
8:22 Settings and setup for LoRA training
13:36 How to generate ckpt file from LoRA training checkpoint
Sure, here are some additional details on how transformers can be used with CUDA-enabled NVIDIA hardware:
Transfer learning: Transfer learning is a technique that can be used to leverage pre-trained transformer models, such as BERT or GPT-2, to improve the performance of NLP tasks with limited training data. NVIDIA's hardware and software can be used to fine-tune these pre-trained models on specific NLP tasks, allowing for faster convergence and higher accuracy.
Customization and optimization: The flexibility of transformers allows for a wide range of customization options and optimization techniques. NVIDIA's software libraries can be used to implement custom activation functions, weight initialization schemes, and other architectural modifications to improve model performance. In addition, CUDA enables developers to optimize the transformer models for specific hardware configurations, such as different numbers of GPUs, to achieve the best performance.
Real-time applications: Transformers can be used for real-time NLP applications, such as chatbots and speech recognition, which require low latency and high throughput. NVIDIA's hardware and software can be used to optimize transformer models for real-time applications by reducing inference time and increasing throughput.
Natural language generation: Transformers can be used for natural language generation (NLG) tasks, such as text summarization and language translation. NVIDIA's hardware and software can be used to optimize transformer models for NLG tasks, by improving the generation speed and quality of the output.
Deployment: NVIDIA's software libraries, such as TensorRT, can be used to optimize and deploy transformer models to various production environments, such as cloud-based services and edge devices. This allows for the efficient deployment of transformer models in a variety of real-world applications.
Overall, transformers and CUDA-enabled NVIDIA hardware provide a powerful combination for accelerating NLP tasks, including training and inference of transformer models, transfer learning, customization and optimization, real-time applications, natural language generation, and deployment to production environments.
