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2. Comparison of YouTube downloaders - Wikipedia

   en.wikipedia.org/wiki/Comparison_of_YouTube_down...

   Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Help; Learn to edit; Community portal; Recent changes; Upload file

3. youtube-dl - Wikipedia

   en.wikipedia.org/wiki/Youtube-dl

   youtube-dl <url> The path of the output can be specified as: (file name to be included in the path) youtube-dl -o <path> <url> To see the list of all of the available file formats and sizes: youtube-dl -F <url> The video can be downloaded by selecting the format code from the list or typing the format manually: youtube-dl -f <format/code> <url>

4. Flux (text-to-image model) - Wikipedia

   en.wikipedia.org/wiki/Flux_(text-to-image_model)

   Flux (also known as FLUX.1) is a text-to-image model developed by Black Forest Labs, based in Freiburg im Breisgau, Germany. Black Forest Labs were founded by former employees of Stability AI . As with other text-to-image models, Flux generates images from natural language descriptions, called prompts .

5. Ideogram (text-to-image model) - Wikipedia

   en.wikipedia.org/wiki/Ideogram_(text-to-image_model)

   Ideogram was founded in 2022 by Mohammad Norouzi, William Chan, Chitwan Saharia, and Jonathan Ho to develop a better text-to-image model. [3]It was first released with its 0.1 model on August 22, 2023, [4] after receiving $16.5 million in seed funding, which itself was led by Andreessen Horowitz and Index Ventures.

6. Sora (text-to-video model) - Wikipedia

   en.wikipedia.org/wiki/Sora_(text-to-video_model)

   Re-captioning is used to augment training data, by using a video-to-text model to create detailed captions on videos. [ 7 ] OpenAI trained the model using publicly available videos as well as copyrighted videos licensed for the purpose, but did not reveal the number or the exact source of the videos. [ 5 ]

7. Text-to-video model - Wikipedia

   en.wikipedia.org/wiki/Text-to-video_model

   A text-to-video model is a machine learning model that uses a natural language description as input to produce a video relevant to the input text. [1] Advancements during the 2020s in the generation of high-quality, text-conditioned videos have largely been driven by the development of video diffusion models .