Instant Text Pro 7
Yvette Burkhammer
Write like native speakers. Our language tool provides you the opportunity to overcome language barriers. Everyone of us should be able to improve our texts to a native speaker level. In the matter of seconds. InstaText works as a rewriter that can rephrase, paraphrase or correct my sentence, paragraph or even entire article.
Offers much more and deeper ideas than the usual grammar, spelling, and punctuation correction that grammar checkers do. Sentences are completely rewritten when necessary, and improvements are suggested based on the broader context of the topic.
Take your scientific writing to a new level. Several case studies have shown that InstaText helps to significantly increase acceptance rates and shorten the time to publication.
Ok, I know the difference between a date picker set with text, and a date picker set via an instance. Depending on the format, they will both show. If I set the date with a text string, for example, 'Mon, May 22, '23", it looks good, but the instance will revert to either today, or Jan 1, 1900 in some odd circumstances.
What I need is to set the date picker to a date programatically instead of user input directly, as in, I have picked a date from a list view, and need the date picker to set to that date.
With using this:
image871209 33 KB
I can see :
as plain as day, but the instance of said date is
Not what I need.
So, after trying to set the date with an instant
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the results are the same, it still reports back the incorrect date.
The only way I have been able to report back the correct date is to physically go in and set the date as one normally would. Is there a way to set the instant of a date picker without direct user input??
I am going through the tutorial, but am am thinking something is broken....
I have uploaded a video to demonstrate ...
this is me trying to attach two blocks to try samples from the aforementioned tutorial. Is it me? am I missing something?
Instantly get the live text from an input field without waiting for that slight delay from bubble in recognizing the entered value. Can be used for application such as displaying number of characters entered in real-time.
Instant Text Alerts are SMS text alerts that go out within 5 minutes of any highly questionable (? red alert) activity. These alerts allow partners to rapidly reach out and address a concern with the device user, offering more immediate help in the middle of temptation.
Simple enough and it works fairly well but you need to push space / comma / period / etc before it is replaced. Is there a way instantly replace it without any further interaction - it's replace as soon as the criteria is matched?
If the player talks to the Bike Shop owner in Cerulean City while pressing B through the dialogue, it will temporarily enable "instant text", a condition in which the frame delay between printing each character is 0.
This trick generated debate by Pokmon speedrunners as to whether it would be considered a glitch, for the sake of setting down rules for Red's Any% glitchless category. The glitch was allowed in the category at one point, for the same reasons as the Marowak ghost skip but was later prohibited from it. "Instant text" runs need to take into account various possible routines that may be called throughout the remainder of the game that would set the text speed back to what it was previously, significantly changing the route, which was one of the reasons cited by opponents to its allowance in Any% glitchless.
Text-to-3D generation, which aims to synthesize vivid 3D objects from text prompts, has attracted much attention from the computer vision community. While several existing works have achieved impressive results for this task, they mainly rely on a time-consuming optimization paradigm. Specifically, these methods optimize a neural field from scratch for each text prompt, taking approximately one hour or more to generate one object. This heavy and repetitive training cost impedes their practical deployment. In this paper, we propose a novel framework for fast text-to-3D generation, dubbed Instant3D. Once trained, Instant3D is able to create a 3D object for an unseen text prompt in less than one second with a single run of a feedforward network. We achieve this remarkable speed by devising a new network that directly constructs a 3D triplane from a text prompt. The core innovation of our Instant3D lies in our exploration of strategies to effectively inject text conditions into the network. In particular, we propose to combine three key mechanisms: cross-attention, style injection, and token-to-plane transformation, which collectively ensure precise alignment of the output with the input text. Furthermore, we propose a simple yet effective activation function, the scaled-sigmoid, to replace the original sigmoid function, which speeds up the training convergence by more than ten times. Finally, to address the Janus (multi-head) problem in 3D generation, we propose an adaptive Perp-Neg algorithm that can dynamically adjust its concept negation scales according to the severity of the Janus problem during training, effectively reducing the multi-head effect. Extensive experiments on a wide variety of benchmark datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods both qualitatively and quantitatively, while achieving significantly better efficiency.
Comparison of existing SOTA methods with our proposal. Existing methods optimize a randomly initialized NeRF from scratch for each text prompt, which usually requires more than 10,000 iterations, taking hours to converge. In contrast, our approach is designed to learn a text-conditioned NeRF, which takes much less training computational cost and has strong generalization ability on new prompts. It is able to generate a conditional 3D representation (triplane) of a 3D object for an unseen text prompt in one single pass of a feedforward network, taking about 25ms.
Overview of the proposed Instant3D, which applies a conditional decoder network to map a text prompt to a corresponding triplane. Three condition mechanisms, i.e., cross-attention, style injection, and token-to-plane transformation, are seamlessly combined to bridge text and 3D, tackling the issue of weak supervision from SDS. Given a random camera pose, a 2D image is rendered from the conditioned triplane through coordinate-based feature sampling, point density and albedo prediction, and differentiable volume rendering. For albedo activation, we propose a scaled-sigmoid function, effectively accelerating the training convergence. During training, the view image is first diffused by adding random noise and then fed into a pretrained UNet conditioned on the text prompt for denoising, which provides the gradient of the SDS loss. We also present an adaptive Perp-Neg algorithm to better solve the Janus problem in our framework. During inference, our Instant3D can infer a faithful 3D object from an unseen text prompt in less than one second.
Text-to-3D with diffusion models has achieved remarkable progress in recent years. However, existing methods either rely on score distillation-based optimization which suffer from slow inference, low diversity and Janus problems, or are feed-forward methods that generate low quality results due to the scarcity of 3D training data. In this paper, we propose Instant3D, a novel method that generates high-quality and diverse 3D assets from text prompts in a feed-forward manner. We adopt a two-stage paradigm, which first generates a sparse set of four structured and consistent views from text in one shot with a fine-tuned 2D text-to-image diffusion model, and then directly regresses the NeRF from the generated images with a novel transformer-based sparse-view reconstructor. Through extensive experiments, we demonstrate that our method can generate high-quality, diverse and Janus-free 3D assets within 20 seconds, which is two order of magnitude faster than previous optimization-based methods that can take 1 to 10 hours.
Figure 1. The overall pipeline of Instant3D. We finetune Stable Diffusion with a relatively small amount of 3D data (10K shapes) to generate 2 by 2 grid images, where each quadrant contains a view of the same object at a fixed camera position. A transformer-based reconstructor takes in these 4 view images and outputs a triplane representation of NeRF.
Instant messaging is an internet-based text communication where two or more people can exchange information in real-time using the same application. Modern instant messaging allows users to send almost unlimited amounts of text and media in various formats, free of charge.
All instant messaging apps require an internet connection for the messages to get through and can therefore be installed and accessed from any device that has the capability to connect to a network. Message history is also synchronized across all devices the application is installed on.
Instant messaging apps allow you to send unlimited amounts of texts to recipients all over the world, completely free of charge. You can also attach images, videos, PDFs, or any kind of file. This makes instant messaging an appropriate channel for live customer support for small businesses.
Select theReact to this messagebutton next to an instant message you've received. A selection of available reactions will display. New reactions will be added frequently and may differ by region. Click the arrows (or swipe, on mobile) to choose from additional reactions available. Select theCustomize your reactionsbutton toadd or remove reactions in your message reaction picker. Learn more aboutadding custom reactions using chat commands.
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