Share and discover more about AI with social posts from the community.huggingface/OpenAi
microsoft/Phi-3.5-mini-instruct Model Summary
Phi-3.5-mini is a lightweight, state-of-the-art open model built upon datasets used for Phi-3 - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data. The model belongs to the Phi-3 model family and supports 128K token context length. The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning, proximal policy optimization, and direct preference optimization to ensure precise instruction adherence and robust safety measures.
🏡 Phi-3 Portal 📰 Phi-3 Microsoft Blog 📖 Phi-3 Technical Report 👩🍳 Phi-3 Cookbook 🖥️ Try It
Phi-3.5-mini is a lightweight, state-of-the-art open model built upon datasets used for Phi-3 - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data. The model belongs to the Phi-3 model family and supports 128K token context length. The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning, proximal policy optimization, and direct preference optimization to ensure precise instruction adherence and robust safety measures.
🏡 Phi-3 Portal 📰 Phi-3 Microsoft Blog 📖 Phi-3 Technical Report 👩🍳 Phi-3 Cookbook 🖥️ Try It
lllyasviel/flux1-dev-bnb-nf4 from hugging face
Main page: https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/981
Update:
Always use V2 by default.
V2 is quantized in a better way to turn off the second stage of double quant.
V2 is 0.5 GB larger than the previous version, since the chunk 64 norm is now stored in full precision float32, making it much more precise than the previous version. Also, since V2 does not have second compression stage, it now has less computation overhead for on-the-fly decompression, making the inference a bit faster.
The only drawback of V2 is being 0.5 GB larger.
Main model in bnb-nf4 (v1 with chunk 64 norm in nf4, v2 with chunk 64 norm in float32)
T5xxl in fp8e4m3fn
CLIP-L in fp16
VAE in bf16![[Major Update] BitsandBytes Guidelines and Flux · lllyasviel/stable-diffusion-webui-forge · Discussion #981](/static/https://cdn4.telesco.pe/file/vr_asDqRXIRCx2Q-_Q5UFe517LMU0_yqcHLbegjsyY4AK6-UkgKDywsrHHmpPX4Uqie6GVDJfy5z17cwXnA1voX8hCKnSSOEvcCU10l14VhYUzcMVyhZ8CJbtu1xfcYC-m1USvgfBMMpQ1kazAZQXRDug5tq06s3REeRok-LLq8_3g78rtYL0Z6k3HySfuZRFtBeN0J-USy0iYQ982PyGc346-WDnZUUwByO_31ekELv2M-I_oW7CdPoduUcFobyEH0fYm1DuB6Ri7zXtTXamrWZydpXwrMjljcdX1iv-CgluKw8UgXl2wuUSpAW7VQmGzpPWDP8TjpAQTWbo-eZbQ.jpg)
Main page: https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/981
Update:
Always use V2 by default.
V2 is quantized in a better way to turn off the second stage of double quant.
V2 is 0.5 GB larger than the previous version, since the chunk 64 norm is now stored in full precision float32, making it much more precise than the previous version. Also, since V2 does not have second compression stage, it now has less computation overhead for on-the-fly decompression, making the inference a bit faster.
The only drawback of V2 is being 0.5 GB larger.
Main model in bnb-nf4 (v1 with chunk 64 norm in nf4, v2 with chunk 64 norm in float32)
T5xxl in fp8e4m3fn
CLIP-L in fp16
VAE in bf16
GitHub
[Major Update] BitsandBytes Guidelines and Flux · lllyasviel/stable-diffusion-webui-forge · Discussion #981
(Before we start, Forge now supports UI presets like this. Click this GIF to zoom in.) (Again, before we start, to the best of my knowledge, I am the first one who made the BitsandBytes low bit acc...
TurboEdit: Instant text-based image editing
We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models. We introduce an encoder based iterative inversion technique. The inversion network is conditioned on the input image and the reconstructed image from the previous step, allowing for correction of the next reconstruction towards the input image. We demonstrate that disentangled controls can be easily achieved in the few-step diffusion model by conditioning on an (automatically generated) detailed text prompt. To manipulate the inverted image, we freeze the noise maps and modify one attribute in the text prompt (either manually or via instruction based editing driven by an LLM), resulting in the generation of a new image similar to the input image with only one attribute changed. It can further control the editing strength and accept instructive text prompt. Our approach facilitates realistic text-guided image edits in real-time, requiring only 8 number of functional evaluations (NFEs) in inversion (one-time cost) and 4 NFEs per edit. Our method is not only fast, but also significantly outperforms state-of-the-art multi-step diffusion editing techniques.
Related Links
Few step diffusion model SDXL-Turbo.
StyleGAN based iterative image inversion method ReStyle.
Concurrent few step diffusion image editing works Renoise and another method also calls TurboEdit.
This website is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Website source code based on the Nerfies project page. If you want to reuse their source code, please credit them appropriately.
Project Page: https://betterze.github.io/TurboEdit/
We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models. We introduce an encoder based iterative inversion technique. The inversion network is conditioned on the input image and the reconstructed image from the previous step, allowing for correction of the next reconstruction towards the input image. We demonstrate that disentangled controls can be easily achieved in the few-step diffusion model by conditioning on an (automatically generated) detailed text prompt. To manipulate the inverted image, we freeze the noise maps and modify one attribute in the text prompt (either manually or via instruction based editing driven by an LLM), resulting in the generation of a new image similar to the input image with only one attribute changed. It can further control the editing strength and accept instructive text prompt. Our approach facilitates realistic text-guided image edits in real-time, requiring only 8 number of functional evaluations (NFEs) in inversion (one-time cost) and 4 NFEs per edit. Our method is not only fast, but also significantly outperforms state-of-the-art multi-step diffusion editing techniques.
Related Links
Few step diffusion model SDXL-Turbo.
StyleGAN based iterative image inversion method ReStyle.
Concurrent few step diffusion image editing works Renoise and another method also calls TurboEdit.
This website is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Website source code based on the Nerfies project page. If you want to reuse their source code, please credit them appropriately.
Project Page: https://betterze.github.io/TurboEdit/
alvdansen/plushy-world-flux
Model description
This style features charming, whimsical characters with exaggerated proportions and soft, rounded forms. The 3D renders have a high level of detail, with textures that appear tactile and inviting, from fluffy fur to smooth, glossy surfaces. The overall aesthetic combines cute, cartoon-like designs with realistic lighting and materials, creating a magical and endearing world that feels both fantastical and tangible.
Sponsored by Glif!
https://huggingface.co/alvdansen/plushy-world-flux
Model description
This style features charming, whimsical characters with exaggerated proportions and soft, rounded forms. The 3D renders have a high level of detail, with textures that appear tactile and inviting, from fluffy fur to smooth, glossy surfaces. The overall aesthetic combines cute, cartoon-like designs with realistic lighting and materials, creating a magical and endearing world that feels both fantastical and tangible.
Sponsored by Glif!
https://huggingface.co/alvdansen/plushy-world-flux
huggingface.co
alvdansen/plushy-world-flux · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
fofr/flux-80s-cyberpunk
https://replicate.com/fofr/flux-80s-cyberpunk Run time and cost
This model costs approximately $0.059 to run on Replicate, or 16 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.
To see how much you've spent, go to your dashboard.
This model runs on Nvidia H100 GPU hardware. Predictions typically complete within 39 seconds. The predict time for this model varies significantly based on the inputs.
https://replicate.com/fofr/flux-80s-cyberpunk Run time and cost
This model costs approximately $0.059 to run on Replicate, or 16 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.
To see how much you've spent, go to your dashboard.
This model runs on Nvidia H100 GPU hardware. Predictions typically complete within 39 seconds. The predict time for this model varies significantly based on the inputs.
Replicate
fofr/flux-80s-cyberpunk – Replicate
A flux lora trained on a 1980s cyberpunk aesthetic
Subtle Highlights, Wild Fractals, and Tricky Tic-Tac-Toe
This week's CodePen community highlights include a CSS trick for creating marker-like text highlights from Cassidy Williams, a collection of truly Wild Fractals from week two of the August #CodePenChallenge, and a game of tic-tac-toe with a twist from Stefan Matei.
Plus, Amit Sheen takes us to the park with a robot pal, and Vincent Van Gogh drops in on a Pen from Josetxu.
This week's CodePen community highlights include a CSS trick for creating marker-like text highlights from Cassidy Williams, a collection of truly Wild Fractals from week two of the August #CodePenChallenge, and a game of tic-tac-toe with a twist from Stefan Matei.
Plus, Amit Sheen takes us to the park with a robot pal, and Vincent Van Gogh drops in on a Pen from Josetxu.
HuggingFaceM4 /Idefics3-8B-Llama3
Transformers version: until the next Transformers pypi release, please install Transformers from source and use this PR to be able to use Idefics3. TODO: change when new version.
Idefics3
Idefics3 is an open multimodal model that accepts arbitrary sequences of image and text inputs and produces text outputs. The model can answer questions about images, describe visual content, create stories grounded on multiple images, or simply behave as a pure language model without visual inputs. It improves upon Idefics1 and Idefics2, significantly enhancing capabilities around OCR, document understanding and visual reasoning.
We release the checkpoints under the Apache 2.0.
Model Summary
Developed by: Hugging Face
Model type: Multi-modal model (image+text)
Language(s) (NLP): en
License: Apache 2.0
Parent Models: google/siglip-so400m-patch14-384 and meta-llama/Meta-Llama-3.1-8B-Instruct
Resources for more information:
Idefics1 paper: OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents
Idefics2 paper: What matters when building vision-language models?
Idefics3 paper: Coming soon (TODO)https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3
Transformers version: until the next Transformers pypi release, please install Transformers from source and use this PR to be able to use Idefics3. TODO: change when new version.
Idefics3
Idefics3 is an open multimodal model that accepts arbitrary sequences of image and text inputs and produces text outputs. The model can answer questions about images, describe visual content, create stories grounded on multiple images, or simply behave as a pure language model without visual inputs. It improves upon Idefics1 and Idefics2, significantly enhancing capabilities around OCR, document understanding and visual reasoning.
We release the checkpoints under the Apache 2.0.
Model Summary
Developed by: Hugging Face
Model type: Multi-modal model (image+text)
Language(s) (NLP): en
License: Apache 2.0
Parent Models: google/siglip-so400m-patch14-384 and meta-llama/Meta-Llama-3.1-8B-Instruct
Resources for more information:
Idefics1 paper: OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents
Idefics2 paper: What matters when building vision-language models?
Idefics3 paper: Coming soon (TODO)https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3
huggingface.co
HuggingFaceM4/Idefics3-8B-Llama3 · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
AI Tools of the week
1.🎥 Guidde - Magically create video documentation that explains the most complex things through simple step-by-step guides.
2.💡 MuckBrass - Uncover market-validated startup ideas with AI-powered analysis of search trends and competition.
🤖 Splutter AI - Launch custom AI chatbots for websites, supercharging support, marketing, and sales with 24/7 automation.
📞 LangCall - Skip long waits with AI agents that navigate phone menus, handle conversations, and connect you only when needed.
🔍 MiniPerplx - Streamline web search with advanced functions like weather updates, event tracking, and literary analysis.
✅ Sourcer AI - Combat online misinformation with real-time AI-powered fact-checking for instant credibility assessment.
📊 PPT GPTSci - Convert images into fully editable PowerPoint slides through a user-friendly interface with high-quality output.
📖 Narrative Nooks - Unlock personalized learning with interactive stories, 24/7 tutoring support, and engaging lessons.
💬 AI Chat Bot - Skyrocket sales with multilingual AI chatbots, offering easy setup, customizable features, and seamless integration.
🎶 Songifier - Identify songs instantly using lyric matching, bridging the gap between fragmentary lyric recall and full song access.
📊 Shortimize - Analyze and optimize shorts with cross-platform tracking, AI-powered viral video search, and in-depth analytics.
📚 GetQuiz - Turn reading into lasting knowledge with AI companion that generates quizzes directly in Telegram.
🎵 AudioStack - Revolutionize audio production with AI-powered tools that create professional-quality content 10,000x faster.
🐦 BioIt - Craft your perfect Twitter bio by answering a few simple questions, generating unique, catchy intros.
🎭 Immersim AI - Create and explore infinite interactive universes with seamless storytelling and dynamic character interactions.
1.🎥 Guidde - Magically create video documentation that explains the most complex things through simple step-by-step guides.
2.💡 MuckBrass - Uncover market-validated startup ideas with AI-powered analysis of search trends and competition.
🤖 Splutter AI - Launch custom AI chatbots for websites, supercharging support, marketing, and sales with 24/7 automation.
📞 LangCall - Skip long waits with AI agents that navigate phone menus, handle conversations, and connect you only when needed.
🔍 MiniPerplx - Streamline web search with advanced functions like weather updates, event tracking, and literary analysis.
✅ Sourcer AI - Combat online misinformation with real-time AI-powered fact-checking for instant credibility assessment.
📊 PPT GPTSci - Convert images into fully editable PowerPoint slides through a user-friendly interface with high-quality output.
📖 Narrative Nooks - Unlock personalized learning with interactive stories, 24/7 tutoring support, and engaging lessons.
💬 AI Chat Bot - Skyrocket sales with multilingual AI chatbots, offering easy setup, customizable features, and seamless integration.
🎶 Songifier - Identify songs instantly using lyric matching, bridging the gap between fragmentary lyric recall and full song access.
📊 Shortimize - Analyze and optimize shorts with cross-platform tracking, AI-powered viral video search, and in-depth analytics.
📚 GetQuiz - Turn reading into lasting knowledge with AI companion that generates quizzes directly in Telegram.
🎵 AudioStack - Revolutionize audio production with AI-powered tools that create professional-quality content 10,000x faster.
🐦 BioIt - Craft your perfect Twitter bio by answering a few simple questions, generating unique, catchy intros.
🎭 Immersim AI - Create and explore infinite interactive universes with seamless storytelling and dynamic character interactions.
ResShift 1-Click Windows, RunPod, Massed Compute, Kaggle Installers with Amazing Gradio APP and Batch Image Processing. ResShift is Efficient Diffusion Model for Image Super-resolution by Residual Shifting (NeurIPS 2023, Spotlight).
Official Repo : https://github.com/zsyOAOA/ResShift
I have developed a very advanced Gradio APP.
Developed APP Scripts and Installers : https://www.patreon.com/posts/110331752
Features
It supports following tasks:
Real-world image super-resolution
Bicubic (resize by Matlab) image super-resolution
Blind Face Restoration
Automatically saving all generated image with same name + numbering if necessary
Randomize seed feature for each generation
Batch image processing - give input and output folder paths and it batch process all images and saves
1-Click to install on Windows, RunPod, Massed Compute and Kaggle (free account)
Windows Requirements
Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git
If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial
https://youtu.be/-NjNy7afOQ0
How to Install on Windows
Make sure that you have the above requirements
Extract files into a folder like c:/reshift_v1
Double click Windows_Install.bat and it will automatically install everything for you with an isolated virtual environment folder (VENV)
After that double click Windows_Start_app.bat and start the app
When you first time use a task it will download necessary models (all under 500 MB) into accurate folders
If during download it fails, file gets corrupted sadly it doesn't verify that so delete files inside weights and restart
How to Install on RunPod, Massed Compute, Kaggle
Follow the Massed_Compute_Instructions_READ.txt and Runpod_Instructions_READ.txt
For Kaggle follow the notebook written steps
An example video of how to use my RunPod, Massed Compute scripts and Kaggle notebook can be seen
https://youtu.be/wG7oPp01COg
Official Repo : https://github.com/zsyOAOA/ResShift
I have developed a very advanced Gradio APP.
Developed APP Scripts and Installers : https://www.patreon.com/posts/110331752
Features
It supports following tasks:
Real-world image super-resolution
Bicubic (resize by Matlab) image super-resolution
Blind Face Restoration
Automatically saving all generated image with same name + numbering if necessary
Randomize seed feature for each generation
Batch image processing - give input and output folder paths and it batch process all images and saves
1-Click to install on Windows, RunPod, Massed Compute and Kaggle (free account)
Windows Requirements
Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git
If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial
https://youtu.be/-NjNy7afOQ0
How to Install on Windows
Make sure that you have the above requirements
Extract files into a folder like c:/reshift_v1
Double click Windows_Install.bat and it will automatically install everything for you with an isolated virtual environment folder (VENV)
After that double click Windows_Start_app.bat and start the app
When you first time use a task it will download necessary models (all under 500 MB) into accurate folders
If during download it fails, file gets corrupted sadly it doesn't verify that so delete files inside weights and restart
How to Install on RunPod, Massed Compute, Kaggle
Follow the Massed_Compute_Instructions_READ.txt and Runpod_Instructions_READ.txt
For Kaggle follow the notebook written steps
An example video of how to use my RunPod, Massed Compute scripts and Kaggle notebook can be seen
https://youtu.be/wG7oPp01COg
GitHub
GitHub - zsyOAOA/ResShift: ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting (NeurIPS@2023 Spotlight…
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting (NeurIPS@2023 Spotlight, TPAMI@2024) - zsyOAOA/ResShift
Introducing Hugging Face Similar: a Chrome extension to find relevant datasets!
✨ Adds a "Similar Datasets" section to Hugging Face dataset pages
🔍 Recommendations based on dataset READMEs
🏗 Powered by https://huggingface.co/chromadb and https://huggingface.co/Snowflake embeddings.
You can try it here: https://chromewebstore.google.com/detail/hugging-face-similar/aijelnjllajooinkcpkpbhckbghghpnl?authuser=0&hl=en.
I am very happy to get feedback on whether this could be useful or not 🤗
✨ Adds a "Similar Datasets" section to Hugging Face dataset pages
🔍 Recommendations based on dataset READMEs
🏗 Powered by https://huggingface.co/chromadb and https://huggingface.co/Snowflake embeddings.
You can try it here: https://chromewebstore.google.com/detail/hugging-face-similar/aijelnjllajooinkcpkpbhckbghghpnl?authuser=0&hl=en.
I am very happy to get feedback on whether this could be useful or not 🤗
huggingface.co
chromadb (chroma)
Org profile for chroma on Hugging Face, the AI community building the future.
Serving Meta Llama 3.1 405B on Google Cloud is now possible via the Hugging Face Deep Learning Containers (DLCs) for Text Generation Inference (TGI)
In this post, we showcase how to deploy
meta-llama/Meta-Llama-3.1-405B-Instruct-FP8
on an A3 instance with 8 x H100 GPUs on Vertex AI
Thanks to the Hugging Face DLCs for TGI and Google Cloud Vertex AI, deploying a high-performance text generation container for serving Large Language Models (LLMs) has never been easier. And we’re not going to stop here – stay tuned as we enable more experiences to build AI with open models on Google Cloud!
Read the full post at https://huggingface.co/blog/llama31-on-vertex-ai
In this post, we showcase how to deploy
meta-llama/Meta-Llama-3.1-405B-Instruct-FP8
on an A3 instance with 8 x H100 GPUs on Vertex AI
Thanks to the Hugging Face DLCs for TGI and Google Cloud Vertex AI, deploying a high-performance text generation container for serving Large Language Models (LLMs) has never been easier. And we’re not going to stop here – stay tuned as we enable more experiences to build AI with open models on Google Cloud!
Read the full post at https://huggingface.co/blog/llama31-on-vertex-ai
huggingface.co
Deploy Meta Llama 3.1 405B on Google Cloud Vertex AI
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
How The Washington Post Uses AI to Empower Journalists 🔍📰
An exciting new example in the world of AI-assisted journalism! The Post has developed an internal tool called "Hayatacker" that's enhancing in-depth reporting. Here's why it matters:
🎥 What it does:
• Extracts stills from video files
• Processes on-screen text
• Labels objects in images
🗳 First big project:
Analyzed 745 Republican campaign ads on immigration (Jan-Jun 2024)
🤝 Human-AI collaboration:
• AI extracts and organizes data
• Reporters verify and analyze findings
🔎 Thorough approach:
• Manual review of all 745 ads
• Reverse image searches when context is lacking
• Cross-referencing with AdImpact transcripts
💡 Key insight from WaPo's Senior Editor for AI strategy Phoebe Connelly:
"The more exciting choice is putting AI in the hands of reporters early on in the process."
This tool showcases how AI can augment journalistic capabilities without replacing human insight and verification. It's a powerful example of technology enhancing, not replacing, traditional reporting skills.
👉 Read the full article and the methodology: https://www.washingtonpost.com/elections/interactive/2024/republican-campaign-ads-immigration-border-security/
An exciting new example in the world of AI-assisted journalism! The Post has developed an internal tool called "Hayatacker" that's enhancing in-depth reporting. Here's why it matters:
🎥 What it does:
• Extracts stills from video files
• Processes on-screen text
• Labels objects in images
🗳 First big project:
Analyzed 745 Republican campaign ads on immigration (Jan-Jun 2024)
🤝 Human-AI collaboration:
• AI extracts and organizes data
• Reporters verify and analyze findings
🔎 Thorough approach:
• Manual review of all 745 ads
• Reverse image searches when context is lacking
• Cross-referencing with AdImpact transcripts
💡 Key insight from WaPo's Senior Editor for AI strategy Phoebe Connelly:
"The more exciting choice is putting AI in the hands of reporters early on in the process."
This tool showcases how AI can augment journalistic capabilities without replacing human insight and verification. It's a powerful example of technology enhancing, not replacing, traditional reporting skills.
👉 Read the full article and the methodology: https://www.washingtonpost.com/elections/interactive/2024/republican-campaign-ads-immigration-border-security/
Washington Post
Republicans flood TV with misleading ads about immigration, border
The Washington Post analyzed more than 700 campaign ads that mention immigration to see how the issue is being presented to voters.
How to Use SwarmUI & Stable Diffusion 3 on Cloud Services Kaggle (free),
In this video, I demonstrate how to install and use #SwarmUI on cloud services. If you lack a powerful GPU or wish to harness more GPU power, this video is essential. You’ll learn how to install and utilize SwarmUI, one of the most powerful Generative AI interfaces, on Massed Compute, RunPod, and Kaggle (which offers free dual T4 GPU access for 30 hours weekly). This tutorial will enable you to use SwarmUI on cloud GPU providers as easily and efficiently as on your local PC. Moreover, I will show how to use Stable Diffusion 3 (#SD3) on cloud. SwarmUI uses #ComfyUI backend.
🔗 The Public Post (no login or account required) Shown In The Video With The Links ➡️ https://www.patreon.com/posts/stableswarmui-3-106135985
🔗 Windows Tutorial for Learn How to Use SwarmUI ➡️ https://youtu.be/HKX8_F1Er_w
🔗 How to download models very fast to Massed Compute, RunPod and Kaggle and how to upload models or files to Hugging Face very fast tutorial ➡️ https://youtu.be/X5WVZ0NMaTg
🔗 SECourses Discord ➡️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388
🔗 Stable Diffusion GitHub Repo (Please Star, Fork and Watch) ➡️ https://github.com/FurkanGozukara/Stable-Diffusion
In this video, I demonstrate how to install and use #SwarmUI on cloud services. If you lack a powerful GPU or wish to harness more GPU power, this video is essential. You’ll learn how to install and utilize SwarmUI, one of the most powerful Generative AI interfaces, on Massed Compute, RunPod, and Kaggle (which offers free dual T4 GPU access for 30 hours weekly). This tutorial will enable you to use SwarmUI on cloud GPU providers as easily and efficiently as on your local PC. Moreover, I will show how to use Stable Diffusion 3 (#SD3) on cloud. SwarmUI uses #ComfyUI backend.
🔗 The Public Post (no login or account required) Shown In The Video With The Links ➡️ https://www.patreon.com/posts/stableswarmui-3-106135985
🔗 Windows Tutorial for Learn How to Use SwarmUI ➡️ https://youtu.be/HKX8_F1Er_w
🔗 How to download models very fast to Massed Compute, RunPod and Kaggle and how to upload models or files to Hugging Face very fast tutorial ➡️ https://youtu.be/X5WVZ0NMaTg
🔗 SECourses Discord ➡️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388
🔗 Stable Diffusion GitHub Repo (Please Star, Fork and Watch) ➡️ https://github.com/FurkanGozukara/Stable-Diffusion
Patreon
SwarmUI Master Tutorial - Use Stable Diffusion 3 (SD3) and FLUX model with Amazing Performance | SECourses: Tutorials, Guides,…
Get more from SECourses: Tutorials, Guides, Resources, Training, FLUX, MidJourney, Voice Clone, TTS, ChatGPT, GPT, LLM, Scripts by Furkan Gözü on Patreon
Basic Usage of StableSwarmUI
So you want to know how to get started with StableSwarmUI, huh? It's easy!
For the most part, just download the installer and follow the instructions on screen. Everything explains itself, even the settings and parameters all have ? clickables that explain what they do!
Nonetheless, here's a step-by-step you can follow:
Installing
Step one: Install StableSwarmUI.
Once you've ran the basic program-installation, if all went well, it will open a web interface to select basic install settings.
Agree to the SD license
Pick a theme (I think default is best, but you got options)
Pick who the UI is for (usually just Yourself or Yourself on LAN)
Pick what backend(s) to install. If you already have ComfyUI or another backend you can skip this - if not, pick one. I recommend ComfyUI for local usage.
Pick any model(s) you want to download. If you already have some you can skip this, if not, I recommend SDXL 1.0.
Confirm you want the settings you selected, and install.
Once this is done, it should automatically redirect you to the main interface.
(You can close the server at any time by just closing that console window it pulls up, and you can start it again via the desktop icon, or the launch script in the folder).
https://github.com/Stability-AI/StableSwarmUI/blob/master/docs/Basic%20Usage.md
So you want to know how to get started with StableSwarmUI, huh? It's easy!
For the most part, just download the installer and follow the instructions on screen. Everything explains itself, even the settings and parameters all have ? clickables that explain what they do!
Nonetheless, here's a step-by-step you can follow:
Installing
Step one: Install StableSwarmUI.
Once you've ran the basic program-installation, if all went well, it will open a web interface to select basic install settings.
Agree to the SD license
Pick a theme (I think default is best, but you got options)
Pick who the UI is for (usually just Yourself or Yourself on LAN)
Pick what backend(s) to install. If you already have ComfyUI or another backend you can skip this - if not, pick one. I recommend ComfyUI for local usage.
Pick any model(s) you want to download. If you already have some you can skip this, if not, I recommend SDXL 1.0.
Confirm you want the settings you selected, and install.
Once this is done, it should automatically redirect you to the main interface.
(You can close the server at any time by just closing that console window it pulls up, and you can start it again via the desktop icon, or the launch script in the folder).
https://github.com/Stability-AI/StableSwarmUI/blob/master/docs/Basic%20Usage.md
GitHub
StableSwarmUI/docs/Basic Usage.md at master · Stability-AI/StableSwarmUI
StableSwarmUI, A Modular Stable Diffusion Web-User-Interface, with an emphasis on making powertools easily accessible, high performance, and extensibility. - Stability-AI/StableSwarmUI
More on NVIDIA NIM at SIGGRAPH
At SIGGRAPH, NVIDIA also introduced generative AI models and NIM microservices for the OpenUSD framework to accelerate developers’ abilities to build highly accurate virtual worlds for the next evolution of AI.
To experience more than 100 NVIDIA NIM microservices with applications across industries, visit ai.nvidia.com.
Categories: Cloud | Deep Learning | Generative AI
Tags: #Artificial Intelligence | #DGX Cloud | #NVIDIA NIM | #SIGGRAPH 2024
At SIGGRAPH, NVIDIA also introduced generative AI models and NIM microservices for the OpenUSD framework to accelerate developers’ abilities to build highly accurate virtual worlds for the next evolution of AI.
To experience more than 100 NVIDIA NIM microservices with applications across industries, visit ai.nvidia.com.
Categories: Cloud | Deep Learning | Generative AI
Tags: #Artificial Intelligence | #DGX Cloud | #NVIDIA NIM | #SIGGRAPH 2024
Near-Instant Access to DGX Cloud Provides Accessible AI Acceleration
The NVIDIA DGX Cloud platform is purpose-built for generative AI, offering developers easy access to reliable accelerated computing infrastructure that can help them bring production-ready applications to market faster.
The platform provides scalable GPU resources that support every step of AI development, from prototype to production, without requiring developers to make long-term AI infrastructure commitments.
Hugging Face inference-as-a-service on NVIDIA DGX Cloud powered by NIM microservices offers easy access to compute resources that are optimized for AI deployment, enabling users to experiment with the latest AI models in an enterprise-grade environment.
The NVIDIA DGX Cloud platform is purpose-built for generative AI, offering developers easy access to reliable accelerated computing infrastructure that can help them bring production-ready applications to market faster.
The platform provides scalable GPU resources that support every step of AI development, from prototype to production, without requiring developers to make long-term AI infrastructure commitments.
Hugging Face inference-as-a-service on NVIDIA DGX Cloud powered by NIM microservices offers easy access to compute resources that are optimized for AI deployment, enabling users to experiment with the latest AI models in an enterprise-grade environment.
NVIDIA partnered with
@huggingface
to launch inference-as-a-service, which helps devs quickly prototype with open-source #AI models hosted on the Hugging Face Hub and deploy them in production.
https://blogs.nvidia.com/blog/hugging-face-inference-nim-microservices-dgx-cloud/?ncid=so-nvsh-956728&source=everyonesocial&network=twitter&userID=8dd032b4-66d4-4548-974c-7d67cfb161c8&es_id=3d879cd612
@huggingface
to launch inference-as-a-service, which helps devs quickly prototype with open-source #AI models hosted on the Hugging Face Hub and deploy them in production.
https://blogs.nvidia.com/blog/hugging-face-inference-nim-microservices-dgx-cloud/?ncid=so-nvsh-956728&source=everyonesocial&network=twitter&userID=8dd032b4-66d4-4548-974c-7d67cfb161c8&es_id=3d879cd612
NVIDIA Blog
Hugging Face Offers Developers Inference-as-a-Service Powered by NVIDIA NIM
New service brings up to 5x better token efficiency with popular AI models to millions of developers and enables immediate access to NIM microservices running on NVIDIA DGX Cloud.
Comprehensive quantization algorithms (weight-only & activation)
🤗 Seamless integration with Hugging Face models
📁 Safetensors-based format for vLLM compatibility
🦙 Large model support via HF accelerate, including Llama 405B
https://github.com/vllm-project/llm-compressor
🤗 Seamless integration with Hugging Face models
📁 Safetensors-based format for vLLM compatibility
🦙 Large model support via HF accelerate, including Llama 405B
https://github.com/vllm-project/llm-compressor
GitHub
GitHub - vllm-project/llm-compressor: Transformers-compatible library for applying various compression algorithms to LLMs for optimized…
Transformers-compatible library for applying various compression algorithms to LLMs for optimized deployment with vLLM - vllm-project/llm-compressor
What’s New in July 2024
We added 20+ models to the Hugging Face collection in the Azure AI model catalog in July. These included multilingual models (focus on Chinese, Dutch, Arabic, South-East Asian), embedding models, text generation (SLM and LLM) and models with a domain-specific focus (e.g., biomedical). The table below summarizes additions by task and notable features. Click model name to view related model cards on Azure AI for more details. In the next section, we’ll put the spotlight on a couple of models or model families that may be of particular interest to developers exploring SLMs or multilingual applications.
https://techcommunity.microsoft.com/t5/ai-ai-platform-blog/new-hugging-face-models-on-azure-ai-multilingual-slm-and-biomed/ba-p/4211881?wt.mc_id=twitter_4211881_organicsocial_reactor
We added 20+ models to the Hugging Face collection in the Azure AI model catalog in July. These included multilingual models (focus on Chinese, Dutch, Arabic, South-East Asian), embedding models, text generation (SLM and LLM) and models with a domain-specific focus (e.g., biomedical). The table below summarizes additions by task and notable features. Click model name to view related model cards on Azure AI for more details. In the next section, we’ll put the spotlight on a couple of models or model families that may be of particular interest to developers exploring SLMs or multilingual applications.
https://techcommunity.microsoft.com/t5/ai-ai-platform-blog/new-hugging-face-models-on-azure-ai-multilingual-slm-and-biomed/ba-p/4211881?wt.mc_id=twitter_4211881_organicsocial_reactor
TECHCOMMUNITY.MICROSOFT.COM
New Hugging Face Models on Azure AI: Multilingual, SLM and BioMed- July 2024 Update
In May, we announced a deepened partnership with Hugging Face and we continue to add more leading-edge Hugging Face models to the Azure AI model catalog on a..