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By adjusting the "image weight" parameter, users can prioritize either the content of the prompt or the characteristics of the image. For instance, setting a higher weight will ensure that the generated result closely follows the image's structure and details, while a lower weight allows the text prompt to have more influence over the final output.
Operating systems that use SLP 1.0 check for a particular text-string in a computer's BIOS upon booting. If the text string does not match the information stored in the particular installation's OEM BIOS files, the system prompts the user to activate their copy as normal. SLP 2.0 to SLP 2.5 work in a similar manner.
Example of prompt engineering for text-to-image generation, with Fooocus In 2022, text-to-image models like DALL-E 2 , Stable Diffusion , and Midjourney were released to the public. [ 67 ] These models take text prompts as input and use them to generate AI art images.
DALL-E, DALL-E 2, and DALL-E 3 (stylised DALL·E, and pronounced DOLL-E) are text-to-image models developed by OpenAI using deep learning methodologies to generate digital images from natural language descriptions known as prompts. The first version of DALL-E was announced in January 2021. In the following year, its successor DALL-E 2 was released.
Draw: add shapes or text to an image. Decorate: add a border or frame to an image. Special effects: blur, sharpen, threshold, or tint an image. Animation: assemble a GIF animation file from a sequence of images. Text and comments: insert descriptive or artistic text in an image. Image identification: describe the format and attributes of an image.
An image conditioned on the prompt an astronaut riding a horse, by Hiroshige, generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.
Diagram of the latent diffusion architecture used by Stable Diffusion The denoising process used by Stable Diffusion. The model generates images by iteratively denoising random noise until a configured number of steps have been reached, guided by the CLIP text encoder pretrained on concepts along with the attention mechanism, resulting in the desired image depicting a representation of the ...
Further, one can take a list of caption-image pairs, convert the images into strings of symbols, and train a standard GPT-style transformer. Then at test time, one can just give an image caption, and have it autoregressively generate the image. This is the structure of Google Parti. [34]