Advancements in quantum computing pose a potential threat to Bitcoin’s security. Google’s recent progress with its Willow quantum-computing chip has highlighted the possibility that future quantum computers could break the encryption protecting Bitcoin, enabling hackers to access secure digital wallets and potentially causing significant devaluation.
Researchers estimate that a quantum computer capable of such decryption is likely more than a decade away. Nonetheless, the Bitcoin developer community faces the complex task of upgrading the system to incorporate quantum-resistant encryption methods. Achieving consensus within the decentralized community may be a slow process, and users would eventually need to transfer their holdings to quantum-resistant addresses to safeguard their assets.
A quantum-powered attack on Bitcoin could also negatively impact traditional financial markets, possibly leading to substantial losses and a deep recession. To mitigate such threats, President-elect Donald Trump has proposed creating a strategic reserve for the government’s Bitcoin holdings.
Nodes: Install missing nodes in the workflow through the manager.
Models: Make sure not to mix SD1.5 and SDLX models. Follow the details under the pdf below.
General suggesions: – Comfy Org / Flux.1 [dev] Checkpoint model (fp8) The manager will put it under checkpoints, which will not work. Make sure to put it under the models/unet folder for the Load Diffusion Model node to work.
– same for realvisxlV50_v50LightningBakedvae.safetensors it should go under models/vae
Tencent just made Hunyuan3D 2.1 open-source. This is the first fully open-source, production-ready PBR 3D generative model with cinema-grade quality. https://github.com/Tencent-Hunyuan/Hunyuan3D-2.1
What makes it special? • Advanced PBR material synthesis brings realistic materials like leather, bronze, and more to life with stunning light interactions. • Complete access to model weights, training/inference code, data pipelines. • Optimized to run on accessible hardware. • Built for real-world applications with professional-grade output quality.
They’re making it accessible to everyone: • Complete open-source ecosystem with full documentation. • Ready-to-use model weights and training infrastructure. • Live demo available for instant testing. • Comprehensive GitHub repository with implementation details.
About 576 megapixels for the entire field of view.
Consider a view in front of you that is 90 degrees by 90 degrees, like looking through an open window at a scene. The number of pixels would be:
90 degrees * 60 arc-minutes/degree * 1/0.3 * 90 * 60 * 1/0.3 = 324,000,000 pixels (324 megapixels).
At any one moment, you actually do not perceive that many pixels, but your eye moves around the scene to see all the detail you want. But the human eye really sees a larger field of view, close to 180 degrees. Let’s be conservative and use 120 degrees for the field of view. Then we would see:
RASTERIZATION Rasterisation (or rasterization) is the task of taking the information described in a vector graphics format OR the vertices of triangles making 3D shapes and converting them into a raster image (a series of pixels, dots or lines, which, when displayed together, create the image which was represented via shapes), or in other words “rasterizing” vectors or 3D models onto a 2D plane for display on a computer screen.
For each triangle of a 3D shape, you project the corners of the triangle on the virtual screen with some math (projective geometry). Then you have the position of the 3 corners of the triangle on the pixel screen. Those 3 points have texture coordinates, so you know where in the texture are the 3 corners. The cost is proportional to the number of triangles, and is only a little bit affected by the screen resolution.
In computer graphics, a raster graphics orbitmap image is a dot matrix data structure that represents a generally rectangular grid of pixels (points of color), viewable via a monitor, paper, or other display medium.
With rasterization, objects on the screen are created from a mesh of virtual triangles, or polygons, that create 3D models of objects. A lot of information is associated with each vertex, including its position in space, as well as information about color, texture and its “normal,” which is used to determine the way the surface of an object is facing.
Computers then convert the triangles of the 3D models into pixels, or dots, on a 2D screen. Each pixel can be assigned an initial color value from the data stored in the triangle vertices.
Further pixel processing or “shading,” including changing pixel color based on how lights in the scene hit the pixel, and applying one or more textures to the pixel, combine to generate the final color applied to a pixel.
The main advantage of rasterization is its speed. However, rasterization is simply the process of computing the mapping from scene geometry to pixels and does not prescribe a particular way to compute the color of those pixels. So it cannot take shading, especially the physical light, into account and it cannot promise to get a photorealistic output. That’s a big limitation of rasterization.
There are also multiple problems:
If you have two triangles one is behind the other, you will draw twice all the pixels. you only keep the pixel from the triangle that is closer to you (Z-buffer), but you still do the work twice.
The borders of your triangles are jagged as it is hard to know if a pixel is in the triangle or out. You can do some smoothing on those, that is anti-aliasing.
You have to handle every triangles (including the ones behind you) and then see that they do not touch the screen at all. (we have techniques to mitigate this where we only look at triangles that are in the field of view)
Transparency is hard to handle (you can’t just do an average of the color of overlapping transparent triangles, you have to do it in the right order)
