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2. Dream Machine (text-to-video model) - Wikipedia

   en.wikipedia.org/wiki/Dream_Machine_(text-to...

   Dream Machine is a text-to-video model created by the San Francisco-based generative artificial intelligence company Luma Labs, which had previously created Genie, a 3D model generator. It was released to the public on June 12, 2024, which was announced by the company in a post on X alongside examples of videos it created. [1]

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

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

   Sora is a text-to-video model developed by OpenAI. The model generates short video clips based on user prompts, and can also extend existing short videos. Sora was released publicly for ChatGPT Plus and ChatGPT Pro users in December 2024. [1] [2]

4. 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 .

5. Google DeepMind’s new Veo 2 AI video generator trounces ...

   www.aol.com/finance/google-deepmind-veo-2-ai...

   Just seven months after it unveiled its Veo AI video generator, Alphabet division Google DeepMind has announced Veo 2.. The new tool can generate videos of up to 4K resolution, whereas the first ...

6. 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.

7. DALL-E - Wikipedia

   en.wikipedia.org/wiki/DALL-E

   CLIP is a separate model based on contrastive learning that was trained on 400 million pairs of images with text captions scraped from the Internet. Its role is to "understand and rank" DALL-E's output by predicting which caption from a list of 32,768 captions randomly selected from the dataset (of which one was the correct answer) is most ...