Open Source AI Image to Video Software List

 Lots of Image to Video software mobile and desktop apps exist with fees/charges/subscriptions required. I did a search for github/open-source based versions and came up with below, which l will individually verify as time allows.

Several open-source AI projects on GitHub enable image-to-motion generation or animation. These range from research-oriented frameworks to user-friendly node-based engines. 

Here are some notable open-source projects for image-to-motion AI software available on GitHub:

• MotionPro: This project provides an image-to-video generation model with precise object and camera motion control. It features a user-friendly Gradio demo interface that uses simple brushing and dragging for control, making it a good option for those seeking a balance of power and usability.
  • GitHub Repository: HiDream-ai/MotionPro
• Open-Sora: An initiative to efficiently produce high-quality AI videos, aiming to make advanced video generation accessible to all. It provides the model, tools, and details for research and creativity within the field of content creation.
  • GitHub Repository: hpcaitech/Open-Sora
• OpenMoCap: This project is an open-source application for optical motion capture based on digital image analysis techniques. While its main focus is on human motion capture from video, the principles are applicable to motion generation, and it provides a full pipeline from tracking to reconstruction.
  • GitHub Repository: davidflam/OpenMoCap
• aiNodes Engine: This is a Python-based, open-source desktop node engine for AI image and motion picture generation/editing. It's highly flexible, supports various models like Stable Diffusion and ControlNet, and uses a visual node-based workflow similar to Unreal Engine's execution chain.
  • GitHub Repository: XmYx/ainodes-engine
• Animate Anything: This fine-grained open-domain image animation project focuses on animating arbitrary objects with motion guidance. It provides code and examples to adapt a single reference image to various motion patterns.
  • GitHub Repository: alibaba/animate-anything 

When choosing a project, consider the required hardware (some models need significant VRAM), the desired level of control (e.g., precise motion control vs. general animation), and your comfort level with installation and coding. 

Running AI Benchmark

 Here is how l got this useful AI benchmark python program to successfully run on my several machines.

 Note it seems to only work with Python 3.7

"AI Benchmark is an open source python library for evaluating AI performance of various hardware platforms, including CPUs, GPUs and TPUs."

AI-Benchmark website source link - click here .

Designed and Developed by Andrey Ignatov - click here for the creators project website. 

Link on installing Tensorflow and nvidia cudatoolkit [for GPU] , click here.

Using Anaconda conda

1)

Create an environment with the specific python version needed using:

conda create --name myenv37 python=3.7

2)

Activate it

conda activate myenv37

3)

Install tensorflow

pip install tensorflow

then run:

pip install ai-benchmark

Screenshots:

4)

Inside your conda environment session console:   run python

Enter the code below:

from ai_benchmark import AIBenchmark
benchmark = AIBenchmark()
results = benchmark.run()

Some system results below:

Laptop - Lenovo Legion 5 Intel i7 -Windows 11 - 32Gb RAM with RTX 4060 6Gb VRAM  [ benchmark result with NO GPU support]

For GPU, l ran below - see previous TensorFlow Link:

conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
# Anything above 2.10 is not supported on the GPU on Windows Native
python -m pip install "tensorflow<2.11"
# Verify the installation:
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

I got:

Results [w/GPU on my Lenovo laptop]

So with CPU alone l got an AI benchmark of 1912, with my GPU l got a result of 19797, thats a difference of a factor by 10.35!

Results [w/CPU alone on my Workstation PC - Windows 11 - AMD w/64Gb RAM and the Nvidia RTX3090 GPU]

Results [w/GPU alone on my Workstation PC - RTX 3090 24Gb VRAM ]

So with CPU alone on my powerful desktop main ML workstation l got an AI benchmark of 1205, with my GPU l got a result of 34248, that's a difference of a factor by 28.4!

A difference also of almost 3X between my laptop and my core PC.

When l get hold/access to a PC with the top RTX 5090/5090ti l will try to run this benchmark again.

The RTX 3090 has significantly more CUDA cores than the RTX 4060. The RTX 3090 has 10,496 CUDA cores, while the RTX 4060 has 3,072 CUDA cores. 

This means the RTX 3090 has approximately 3.4 times the number of CUDA cores found in the RTX 4060.

The NVIDIA RTX 5090 has 21,760 CUDA cores, more than double the 10,496 CUDA cores of the RTX 3090, representing a massive generational leap in parallel processing power with its Blackwell architecture, alongside faster memory and enhanced RT/Tensor cores, leading to significantly higher overall performance. 

Key Takeaways:

• Massive Core Count: The RTX 5090's core count is significantly higher, enabling much faster parallel processing for tasks like rendering, AI, and simulations.
• New Architecture: The shift from Ampere (3090) to Blackwell (5090) means each core is also more powerful and efficient.
• Performance Boost: This huge core increase, combined with faster memory (GDDR7 vs GDDR6X) and better Tensor Cores, leads to substantial performance gains, not just an incremental update.
•