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Scaling up GANs for Text-to-Image Synthesis present our 1B-parameter GigaGAN, achieving lower FID than Stable Diffusion v1.5, DALL·E 2, and Parti-750M. It generates 512px outputs at 0.13s, orders of magnitude faster than diffusion and autoregressive models, and inherits the disentangled, continuous, and controllable latent space of GANs abs: project page:

AK

499,728 subscribers

278,115 Aufrufe • vor 3 Jahren •via X (Twitter)

Gaming Bildung Wissenschaft & Technologie

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10 Kommentare

Profilbild von Daniel Losey 🔀

Daniel Losey 🔀vor 3 Jahren

amazing

Profilbild von David Marx (@digthatdata.bsky.social)

David Marx (@digthatdata.bsky.social)vor 3 Jahren

GANs are back baybee

Profilbild von Nicolay Mausz

Nicolay Mauszvor 3 Jahren

Adobe research - I guess this will be part of CC

Profilbild von Draz ⚛️

Draz ⚛️vor 3 Jahren

The upscaling is quite insane on how it accurately fills in details

Profilbild von Nerdy Rodent 🐀🤓💻

Nerdy Rodent 🐀🤓💻vor 3 Jahren

It’s been hours now, why isn’t it showing up? 😉

Profilbild von Asriel H

Asriel Hvor 3 Jahren

It has the same schema of injecting latent vector into every scaling layer as StyleGAN has

Profilbild von okaris

okarisvor 3 Jahren

The examples provided don’t look as good as diffusion models. Some details obscured or looking weird.

Profilbild von Adhik Joshi

Adhik Joshivor 3 Jahren

Weights aren't open-source

Profilbild von Julien Genoud

Julien Genoudvor 3 Jahren

The 4k upsampler 🤯

Profilbild von Clarence Hu

Clarence Huvor 3 Jahren

paging @gwern

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StyleDrop: Text-to-Image Generation in Any Style introduce StyleDrop, a method that enables the synthesis of images that faithfully follow a specific style using a text-to-image model. The proposed method is extremely versatile and captures nuances and details of a user-provided style, such as color schemes, shading, design patterns, and local and global effects. It efficiently learns a new style by fine-tuning very few trainable parameters (less than 1% of total model parameters) and improving the quality via iterative training with either human or automated feedback. Better yet, StyleDrop is able to deliver impressive results even when the user supplies only a single image that specifies the desired style. An extensive study shows that, for the task of style tuning text-to-image models, StyleDrop implemented on Muse convincingly outperforms other methods, including DreamBooth and textual inversion on Imagen or Stable Diffusion. paper page:

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Collaborative Score Distillation for Consistent Visual Synthesis paper page: Generative priors of large-scale text-to-image diffusion models enable a wide range of new generation and editing applications on diverse visual modalities. However, when adapting these priors to complex visual modalities, often represented as multiple images (e.g., video), achieving consistency across a set of images is challenging. In this paper, we address this challenge with a novel method, Collaborative Score Distillation (CSD). CSD is based on the Stein Variational Gradient Descent (SVGD). Specifically, we propose to consider multiple samples as "particles" in the SVGD update and combine their score functions to distill generative priors over a set of images synchronously. Thus, CSD facilitates seamless integration of information across 2D images, leading to a consistent visual synthesis across multiple samples. We show the effectiveness of CSD in a variety of tasks, encompassing the visual editing of panorama images, videos, and 3D scenes. Our results underline the competency of CSD as a versatile method for enhancing inter-sample consistency, thereby broadening the applicability of text-to-image diffusion models.

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Pear AI

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Stable Virtual Camera: Generative View Synthesis with Diffusion Models Hint: Check the project website. It is awesome! Contributions: 1. A training strategy for jointly modeling large viewpoint changes and temporal smoothness. 2. A two-pass procedural sampling method for smooth video generation along arbitrarily long camera trajectories. 3. A comprehensive benchmark that evaluates NVS methods across different datasets and settings. 4. An open-source release of model weights to support future research.

MrNeRF

38,746 Aufrufe • vor 1 Jahr