Parti: Revolutionizing Text-to-Image Generation
Parti, the Pathways Autoregressive Text-to-Image model, is a game-changer in the field of image generation. It treats text-to-image generation as a sequence-to-sequence modeling problem, similar to machine translation, and benefits from advancements in large language models.
The model uses the powerful image tokenizer, ViT-VQGAN, to encode images as sequences of discrete tokens. This allows it to reconstruct high-quality, visually diverse images. By scaling its encoder-decoder up to 20 billion parameters, Parti achieves consistent quality improvements.
Parti demonstrates state-of-the-art performance with a zero-shot FID score of 7.23 and a fine-tuned FID score of 3.22 on MS-COCO. It also shows effectiveness across a wide variety of categories and difficulty aspects in analyses on Localized Narratives and PartiPrompts.
When comparing different scales of Parti models, human evaluators preferred the 20B model for its image realism and image-text match. This model excels at prompts that are abstract, require world knowledge, specific perspectives, or writing and symbol rendering.
Parti can handle long, complex prompts that require accurate reflection of world knowledge, composition of multiple participants and objects with fine-grained details and interactions, and adherence to specific image formats and styles.
PartiPrompts (P2) is a rich set of over 1600 prompts in English that can be used to measure the model's capabilities across various categories and challenge aspects.
However, like any model, Parti has its limitations. While it produces high-quality outputs for a broad range of prompts, it may encounter challenges with improper handling of negation or indication of absence.
Despite the many opportunities text-to-image models like Parti bring, they also introduce risks. There are potential impacts on bias and safety, visual communication, disinformation, and creativity and art. Current models like Parti are trained on large, often noisy, image-text datasets that may contain biases.
To address these concerns, the team behind Parti has decided not to release the models, code, or data for public use without further safeguards. They are focusing on model bias measurement and mitigation strategies.
In conclusion, Parti is a significant advancement in text-to-image generation, but it also highlights the need for careful consideration of the ethical and practical implications of such technologies.
