Working with Tigris in Pixeltable
This tutorial demonstrates how to configure Pixeltable to use Tigris for storage. This lets you store unlimited amounts of images in Tigris' global data plane, allowing your images to load fast everywhere.
Pixeltable's RunwayML integration lets you generate and transform images and videos using RunwayML's Gen-4.5, Veo, and other models, directly from your Pixeltable tables.
Providers · Notebook · Updated
Colab runs it in your browser. If it calls a model provider, it asks for that provider's API key in a cell.
pip install pixeltable runwayml
RUNWAYML_API_SECRETrunwayml.text_to_video()runwayml.image_to_video()runwayml.text_to_image()runwayml.video_to_video()pxt.create_table()add_computed_column()insert()t2v = pxt.create_table('runwayml_demo.text_to_video', {'prompt': pxt.String})t2v.add_computed_column(response=runwayml.text_to_video(t2v.prompt, model='gen4.5', ratio='1280:720', duration=5))# Extract the video URL from the responset2v.add_computed_column(video=t2v.response['output'][0].astype(pxt.Video))
One cell of the notebook, copied as it is. Open it in Colab for the rest.
This tutorial demonstrates how to configure Pixeltable to use Tigris for storage. This lets you store unlimited amounts of images in Tigris' global data plane, allowing your images to load fast everywhere.
Use Together AI from Pixeltable for chat completions and image generation.
Twelve Labs provides multimodal embeddings that project text, images, audio, and video into the same semantic space. This enables true cross-modal search - the most powerful feature of this integration.
This tutorial demonstrates how to use Pixeltable's built-in vLLM integration to run local LLMs with high-throughput inference.