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SLAM XCAM 8K VR180 3D Camera
8K 30FPS VR180 3D Video | Dual 1/1.5″ CMOS Sensors | 10-bit Color | Snapdragon8 GN2 | Android13 | 6.67″AMOLED|5000mAh |100Mbps Data
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Invoke.com – The Gen AI Platform for Pro Studios
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How does Stable Diffusion work?
https://stable-diffusion-art.com/how-stable-diffusion-work/
Stable Diffusion is a latent diffusion model that generates AI images from text. Instead of operating in the high-dimensional image space, it first compresses the image into the latent space.
Stable Diffusion belongs to a class of deep learning models called diffusion models. They are generative models, meaning they are designed to generate new data similar to what they have seen in training. In the case of Stable Diffusion, the data are images.
Why is it called the diffusion model? Because its math looks very much like diffusion in physics. Let’s go through the idea.
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Hunyuan video-to-video re-styling
The open-source community has figured out how to run Hunyuan V2V using LoRAs.
You’ll need to install Kijai’s ComfyUI-HunyuanLoom and LoRAs, which you can either train yourself or find on Civitai.
1) you’ll need HunyuanLoom, after install, workflow found in the repo.
https://github.com/logtd/ComfyUI-HunyuanLoom
2) John Wick lora found here.
https://civitai.com/models/1131159/john-wick-hunyuan-video-lora -
Seaweed APT – Diffusion Adversarial Post-Training for One-Step Video Generation
https://cdn.seaweed-apt.com/assets/showreel/seaweed-apt.mp4
This demonstrate large-scale text-to-video generation with a single neural function evaluation (1NFE) by using our proposed adversarial post-training technique. Our model generates 2 seconds of 1280×720 24fps videos in real-time
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Pyper – a flexible framework for concurrent and parallel data-processing in Python
Pyper is a flexible framework for concurrent and parallel data-processing, based on functional programming patterns.
https://github.com/pyper-dev/pyper
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Jacob Bartlett – Apple is Killing Swift
https://blog.jacobstechtavern.com/p/apple-is-killing-swift
Jacob Bartlett argues that Swift, once envisioned as a simple and composable programming language by its creator Chris Lattner, has become overly complex due to Apple’s governance. Bartlett highlights that Swift now contains 217 reserved keywords, deviating from its original goal of simplicity. He contrasts Swift’s governance model, where Apple serves as the project lead and arbiter, with other languages like Python and Rust, which have more community-driven or balanced governance structures. Bartlett suggests that Apple’s control has led to Swift’s current state, moving away from Lattner’s initial vision.
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