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span-arch · 1 year
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Just some Rococo remodeling suggestions.... #neural_architecture #aiarchitecture #midjourneyart #midjourney #diffusionmodels #newparadigm #machinelearning #diffusion_model #disco_difussion #stablediffusion #aiarchitects #posthuman #postdigital #architecture #architecturedesign #ontology #epistemology #archinect #archilover #archdaily #midjourneyarchitecture #arch2o (at Ann Arbor, Michigan) https://www.instagram.com/p/CkySyeduvR4/?igshid=NGJjMDIxMWI=
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neyropencil · 1 year
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Thank you for visiting my gallery. I would be immensely grateful for your support. You can find many of my other creations here:
patreon.com/LewdNet799
t.me/+eOhie7JzohZmNDYy
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funcis · 5 months
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Nice things with my friend AI
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learnopencv1 · 1 year
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limbicnation · 2 years
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The dark Tower
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govindhtech · 16 days
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MaxDiffusion: Efficient inference from diffusion models
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AI Inference with Google Cloud GPUs and TPUs
There is a growing need for high-performance, low-cost AI inference (serving) in the quickly changing field of artificial intelligence. JetStream and MaxDiffusion are two new open source software products that we introduced this week.
Starting with Cloud TPUs, JetStream is a new inference engine for XLA devices. With up to three times more inferences per dollar for large language models (LLMs) than earlier Cloud TPU inference engines, JetStream is particularly designed for LLMs and marks a major advancement in both performance and cost effectiveness. JetStream provides support for JAX models via MaxText, Google’s highly scalable, high-performance reference implementation for LLMs that users may fork to expedite their development, and PyTorch models via PyTorch/XLA.
The equivalent of MaxText for latent diffusion models, MaxDiffusion simplifies the process of training and serving diffusion models that are optimized for optimal performance on XLA devices, beginning with Cloud TPUs.
Furthermore, Google is pleased to provide the most recent MLPerf Inference v4.0 performance results, which highlight the strength and adaptability of Google Cloud’s A3 virtual machines (VMs) driven by NVIDIA H100 GPUs.
JetStream: Cost-effective, high-performance LLM inference
Image credit to Google cloud
With their ability to power a broad variety of applications including natural language comprehension, text production, and language translation, LLMs are at the vanguard of the AI revolution. Google developed JetStream, an inference engine that offers up to three times more inferences per dollar than earlier Cloud TPU inference engines, to lower the LLM inference costs for their clients.
Advanced speed optimizations are included in JetStream, including sliding window attention, continuous batching, and int8 quantization for weights, activations, and key-value (KV) caching. And JetStream supports your favourite framework, whether you’re using PyTorch or JAX. Google provide optimized MaxText and PyTorch/XLA versions of popular open models, such Gemma and Llama, for maximum cost-efficiency and speed, to further expedite your LLM inference procedures.
JetStream provides up to 4783 tokens/second for open models, such as Gemma in MaxText and Llama 2 in PyTorch/XLA, using Cloud TPU v5e-8:Image credit to Google cloud
Because of JetStream’s excellent speed and efficiency, Google Cloud users pay less for inference, increasing the accessibility and affordability of LLM inference:Image credit to Google cloud
JetStream is used by clients like Osmos to reduce the time it takes for LLM inference tasks:
“At Osmos, Google is created a data transformation engine driven by AI to assist businesses in growing their commercial partnerships by automating data processing. In order to map, evaluate, and convert the often disorganized and non-standard data that is received from clients and business partners into excellent, useable data, intelligence must be applied to each row of data. High-performance, scalable, and reasonably priced AI infrastructure for inference, training, and fine-tuning is required to do this.
Google Cloud TPU v5e
For their end-to-end AI processes, Google is decided on Cloud TPU v5e with MaxText, JAX, and JetStream for this reason. They used Google Cloud to rapidly and easily use JetStream to install Google’s most recent Gemma open model for inference on Cloud TPU v5e, and MaxText to fine-tune the model on billions of tokens. All are able to see results in a matter of hours rather than days thanks to Google’s AI-optimized hardware and software stack.”
Google are driving the next wave of AI applications by giving academics and developers an open-source, robust, and affordable framework for LLM inference. JetStream can help you explore new avenues in natural language processing and expedite your journey, regardless of your experience level with LLMs or AI.
With JetStream, experience LLM inference as it will be in the future. To get started on your next LLM project and learn more about JetStream, visit Google’s GitHub site. Long-term development and maintenance of JetStream on GitHub and via Google Cloud Customer Care are Google commitments. To further enhance the state of the art, they are extending an invitation to the community to collaborate on projects and make changes.
MaxDiffusion
Diffusion models are revolutionizing computer vision, much as LLMs revolutionized natural language processing. Google developed MaxDiffusion, a set of open-source diffusion-model reference implementations, to lower the expenses associated with installing these models for our clients. These JAX-written solutions are very efficient, scalable, and adaptable; for computer vision, imagine MaxText.
MaxDiffusion offers high-performance implementations of diffusion model building blocks, including high-throughput picture data loading, convolutions, and cross attention. MaxDiffusion is designed to be very flexible and customizable. Whether you’re a developer looking to include state-of-the-art gen AI capabilities into your products or a researcher pushing the limits of picture production, MaxDiffusion offers the framework you need to be successful.
Utilizing the full potential of Cloud TPUs’ high speed and scalability, the MaxDiffusion implementation of the new SDXL-Lightning model delivers 6 images/s on Cloud TPU v5e-4 and throughput increases linearly to 12 images/s on Cloud TPU v5e-8.Image credit to Google cloud
Additionally, MaxDiffusion is economical, much like MaxText and JetStream; producing 1000 photos on Cloud TPU v5e-4 or Cloud TPU v5e-8 only costs $0.10.Image credit to Google cloud
Google Cloud is being used by clients like Codeway to increase cost-effectiveness for diffusion model inference at scale:
“At Codeway, Google develop popular applications and games that are used by over 115 million users in 160 countries worldwide. An AI-powered programme called “Wonder,” for instance, transforms words into digital artworks, while “Facedance” causes faces to dance with a variety of entertaining animations. Millions of people need access to AI, which means a very scalable and economical inference infrastructure is needed. Compared to competing inference systems, Google were able to serve diffusion models 45% quicker and handle 3.6 times more queries per hour using Cloud TPU v5e. This result in considerable infrastructure cost reductions at our size and enables us to economically reach even more consumers with AI-powered products.
A scalable, adaptable, and high-performance basis for picture production is offered by MaxDiffusion. MaxDiffusion can help you along the way, regardless of your level of experience with computer vision or whether you’re just getting started with picture production.
To find out more about MaxDiffusion and to get started on your next creative project, go over to Google’s  GitHub repository.
Good outcomes in MLPerf 4.0 Inference for A3 Virtual Machines
Google made the broad availability of A3 virtual machines known in August 2023. The A3s are designed to train and handle difficult tasks such as LLMs, and they are powered by eight NVIDIA H100 Tensor Core GPUs in a single virtual machine. NVIDIA H100 GPU-powered A3 Mega, which doubles A3’s GPU-to-GPU networking capacity, will be on sale next month.
Google provided 20 results utilising A3 VMs for the MLPerf Inference v4.0 benchmark testing, spanning seven models, including the new Stable Diffusion XL and Llama 2 (70B) benchmarks:
RetinaNet (Offline and on Server)
3D U-Net: accuracy of 99.9% and 99% (Offline)
BERT: (Server and Offline) accuracy of 99 and 99%
99.9% accuracy for DLRM v2 (Server and Offline)
GPT-J: accuracy rates of 99% and 99% (Server and Offline)
Stable Diffusion XL (Offline and on the server)
Llama 2: Accuracy (Server and Offline): 99% and 99%
Every result fell between 0 and 5% of the maximum performance shown in NVIDIA’s submissions. These outcomes demonstrate how closely Google Cloud and NVIDIA have collaborated to provide workload-optimized end-to-end solutions for gen AI and LLMs.
Using NVIDIA GPUs with Google Cloud TPUs to power AI in the future
With the help of software breakthroughs like JetStream, MaxText, and MaxDiffusion, as well as hardware improvements in Google Cloud TPUs and NVIDIA GPUs, Google’s AI inference innovation enables their clients to develop and expand AI applications. Developers may reach new levels of LLM inference performance and cost-efficiency with JetStream, opening up new possibilities for applications using natural language processing. With the help of MaxDiffusion, developers and researchers may investigate the full potential of diffusion models to generate images more quickly. Google’s strong MLPerf4.0 inference results on NVIDIA H100 Tensor Core GPU-powered A3 virtual machines (VMs) demonstrate the capability and adaptability of Cloud GPUs.
Read more on Govindhtech.com
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cesar-pena · 1 year
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aBiogenesis from Markos Kay on Vimeo.
The origins of life. May this bring a moment of peace and joy into your day. Thank you @agentsdelfuturo for this magical soundscape.
Also! Free wallpapers on my Behance where you can also read a little about the science of aBiogenesis. Happy Holidays!
#generativeart #abstractanimation #biology #scienceillustration #sciart #neuralnetworks #diffusionmodels #markoskay #mrkism
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dliilb · 11 months
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aBiogenesis from Markos Kay on Vimeo.
The origins of life. May this bring a moment of peace and joy into your day. Thank you @agentsdelfuturo for this magical soundscape.
Also! Free wallpapers on my Behance where you can also read a little about the science of aBiogenesis. Happy Holidays!
#generativeart #abstractanimation #biology #scienceillustration #sciart #neuralnetworks #diffusionmodels #markoskay #mrkism
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cyberlabe · 1 year
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Models by developer.
In terms of major companies participating in startups,note that Microsoft invested 1 billion dollars in OpenAI and helps them with the de-velopment of models. As well, note that Google acquired Deepmind in 2014. In termsof universities, note that VisualGPT was developed by KAUST, Carnegie Mellon Uni-versity and Nanyang Technological University and that the Human Motion DiffusionModel was developed by Tel Aviv University, Israel. As well, other projects are de-veloped by a company in collaboration with a university. Concretely, this is the casefor Stable Diffsion (Runway, Stability AI and LMU MUNICH), Soundify (Runway andCarnegie Mellon University) and DreamFusion (Google and UC Berkeley)
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petermorse · 1 year
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aBiogenesis from Markos Kay on Vimeo.
The origins of life. May this bring a moment of peace and joy into your day. Thank you @agentsdelfuturo for this magical soundscape.
Also! Free wallpapers on my Behance where you can also read a little about the science of aBiogenesis. Happy Holidays!
#generativeart #abstractanimation #biology #scienceillustration #sciart #neuralnetworks #diffusionmodels #markoskay #mrkism
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span-arch · 1 year
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It was interesting to rerun some of my older prompts about Gothic architecture on the --v 4 of Midjourney. Not better, but different. Results are more crisp, leaning towards more realism, reducing the space of interpretation. #neural_architecture #aiarchitecture #midjourneyart #midjourney #diffusionmodels #newparadigm #machinelearning #diffusion_model #disco_difussion #stablediffusion #aiarchitects #posthuman #aiarchitecture #archinect #archilover #ARCHDAILY (at Ann Arbor, Michigan) https://www.instagram.com/p/CoaPxwouhVW/?igshid=NGJjMDIxMWI=
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neyropencil · 1 year
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Thank you for visiting my gallery. I would be immensely grateful for your support. You can find many of my other creations here:
patreon.com/LewdNet799
t.me/+eOhie7JzohZmNDYy
deviantart.com/afrogusar
https://www.instagram.com/neyropencil/
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span-arch · 1 year
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In 1816 Georg Friedrich Schinkel designed the stage setting for a production of Mozarts "Die Zauberflöte". The published print of the design for the hall of stars of the Queen of the Night's palace graced the cover of a recording of the opera by Herbert von Karajan. My father gifted me this recording when I was a kid. I could dive into this image for hours, mesmerized by the music and the powerful image on the cover. Here are some images of alternative "Zauberflöte" stage designs. The last image is Schinkel's rendering of the Hall of Stars.... Still more powerful than anything Midjourney, and my poor imagination, could come up with. #neural_architecture #aiarchitecture #midjourneyart #midjourney #diffusionmodels #newparadigm #machinelearning #diffusion_model #disco_difussion #stablediffusion #aiarchitects #posthuman #postdigital #architecture #architecturedesign #ontology #epistemology #gpt3 #open_ai #archdaily #archinect #archilovers #architecturedesign #estrangement #defamiliarization #ostranenie (at Vienna, Austria) https://www.instagram.com/p/Ck8Wa8suDeQ/?igshid=NGJjMDIxMWI=
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span-arch · 1 year
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Memories of a Rococo palazetto encapsulated in a platonic body. #neural_architecture #aiarchitecture #midjourneyart #midjourney #diffusionmodels #newparadigm #machinelearning #diffusion_model #disco_difussion #stablediffusion #aiarchitects #posthuman #postdigital #architecture #architecturedesign #ontology #epistemology (at Piacenza) https://www.instagram.com/p/ClAVSPvuy2M/?igshid=NGJjMDIxMWI=
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span-arch · 1 year
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AI & Architecture One of my favorite things about doing lectures is the Q&A afterwards, as it reveals some of the more interesting effects of working with AI in Architecture. One of the questions that occasionally arise involve the presence of cultural artefacts in the dataset. Midjourney currently uses the LIAON dataset that contains 5.3 billion annotated image sentence pairs. Especially interesting is the LIAON Aesthetics section with over 240 million annotated images. It seems evident that just statistically speaking there must be massive amounts of images related to all cultures in the dataset. Right? Well....Hold on. This can only be true for cultures that do have an image based tradition. It is also only true for cultures that are abundently documented in photographs, scans etc. This means that as much as we want to collect every image in the internet to create a dataset -assuming that this will reduce bias- will always result in bias. Image based traditions will always be preferred in a concept like this? What about traditions that do not rely on images, or text? The images below had the prompt "an ephemeral villa in the Alps". A couple of months ago this would have resulted in something very different, far more akin of blending architecture with the landscape. Now, with V4 -the one that uses the LIAON Aesthetics dataset- it includes immediately a gabled roof. I guess it is a salient feature of Alpine architecture. What do you think? #neural_architecture #aiarchitecture #midjourneyart #midjourney #diffusionmodels #newparadigm #machinelearning #diffusion_model #disco_difussion #stablediffusion #aiarchitects #posthuman #postdigital #architecture #architecturedesign #ontology #epistemology #alpine_architecture #alps (at Innsbruck, Astria) https://www.instagram.com/p/CloQoe4OvjU/?igshid=NGJjMDIxMWI=
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neyropencil · 11 months
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Thank you for visiting my gallery. I would be immensely grateful for your support. You can find many of my other creations here:
patreon.com/LewdNet799
t.me/+eOhie7JzohZmNDYy
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