stable_diffusion_arc VS llama2.openvino

Compare stable_diffusion_arc vs llama2.openvino and see what are their differences.

llama2.openvino

This sample shows how to implement a llama-based model with OpenVINO runtime (by OpenVINO-dev-contest)
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stable_diffusion_arc llama2.openvino
4 3
63 43
- -
7.8 7.9
about 2 months ago 2 months ago
Jupyter Notebook Python
BSD 2-clause "Simplified" License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

stable_diffusion_arc

Posts with mentions or reviews of stable_diffusion_arc. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-05-26.

llama2.openvino

Posts with mentions or reviews of llama2.openvino. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-05.

What are some alternatives?

When comparing stable_diffusion_arc and llama2.openvino you can also consider the following projects:

RealScaler - RealScaler - image/video AI upscaler app (Real-ESRGAN)

openvino - OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference

stable-diffusion-webui - Stable Diffusion web UI

llama.onnx - LLaMa/RWKV onnx models, quantization and testcase

pyprobml - Python code for "Probabilistic Machine learning" book by Kevin Murphy

openvino_notebooks - 📚 Jupyter notebook tutorials for OpenVINO™

tiny_llm_finetuner - LLM finetuning on Intel XPUs - LoRA on intel discrete GPUs

nlp-tutorial - Natural Language Processing Tutorial for Deep Learning Researchers

DeepLearningExamples - State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.