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  2. Wombo - Wikipedia

    en.wikipedia.org/wiki/Wombo

    Dream is an image and video generation app powered by Stable Diffusion. It can be used to create images from text using a variety of style presets. It can also generate a deepfake using 5-10 images of source material. The app includes a premium tier, which gives users priority processing time and no in-app ads. [1] Wombo processes images in the ...

  3. Vyond - Wikipedia

    en.wikipedia.org/wiki/Vyond

    On May 3, 2023, Vyond announced Vyond Go, an artificial intelligence-based feature in the video maker that generates videos based on user-provided prompts, similar to ChatGPT. [26] The first public beta was released on June 28, 2023, and is accessible to all users. However, it can only be used 3 times every 24 hours. [27]

  4. DALL-E - Wikipedia

    en.wikipedia.org/wiki/DALL-E

    DALL-E was revealed by OpenAI in a blog post on 5 January 2021, and uses a version of GPT-3 [5] modified to generate images.. On 6 April 2022, OpenAI announced DALL-E 2, a successor designed to generate more realistic images at higher resolutions that "can combine concepts, attributes, and styles". [6]

  5. OpenAI releases AI video generator Sora to all customers - AOL

    www.aol.com/openai-releases-ai-video-generator...

    OpenAI said that all videos created by Sora will have C2PA⁠ metadata and watermarking as the default setting to allow users to identify video created by the program.

  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. Text-to-video model - Wikipedia

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

    By utilizing a pre-trained image diffusion model as a base generator, the model efficiently generated high-quality and coherent videos. Fine-tuning the pre-trained model on video data addressed the domain gap between image and video data, enhancing the model's ability to produce realistic and consistent video sequences. [14]

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