With mamba, it’s easy to set up software environments. A software environment is simply a set of different libraries, applications and their dependencies. The power of environments is that they can co-exist: you can easily have an environment called py27 for Python 2.7 and one called py310 for Python 3.10, so that multiple of your projects with different requirements have their dedicated environments. This is similar to “containers” and images. However, mamba makes it easy to add, update or remove software from the environments.
Playbook3d.com is a diffusion-based render engine that reduces the time to final image with AI. It is accessible via web editor and API with support for scene segmentation and re-lighting, integration with production pipelines and frame-to-frame consistency for image, video, and real-time 3D formats.
7:59-9:50 Justine Bateman: “I mean first I want to give people, help people have a little bit of a definition of what generative AI is— think of it as like a blender and if you have a blender at home and you turn it on, what does it do? It depends on what I put into it, so it cannot function unless it’s fed things.
Then you turn on the blender and you give it a prompt, which is your little spoon, and you get a little spoonful—little Frankenstein spoonful—out of what you asked for. So what is going into the blender? Every but a hundred years of film and television or many, many years of, you know, doctor’s reports or students’ essays or whatever it is.
In the film business, in particular, that’s what we call theft; it’s the biggest violation. And the term that continues to be used is “all we did.” I think the CTO of OpenAI—believe that’s her position; I forget her name—when she was asked in an interview recently what she had to say about the fact that they didn’t ask permission to take it in, she said, “Well, it was all publicly available.”
And I will say this: if you own a car—I know we’re in New York City, so it’s not going to be as applicable—but if I see a car in the street, it’s publicly available, but somehow it’s illegal for me to take it. That’s what we have the copyright office for, and I don’t know how well staffed they are to handle something like this, but this is the biggest copyright violation in the history of that office and the US government”
On March 28, ComfyUI-Manager will be moving to the Comfy-Org GitHub organization as Comfy-Org/ComfyUI-Manager. This represents a natural evolution as they continue working to improve the custom node experience for all ComfyUI users.
What This Means For You
This change is primarily about improving support and development velocity. There are a few practical considerations:
Automatic GitHub redirects will ensure all existing links, git commands, and references to the repository will continue to work seamlessly without any action needed
For developers: Any existing PRs and issues will be transferred to the new repository location
For users: ComfyUI-Manager will continue to function exactly as before—no action needed
For workflow authors: Resources that reference ComfyUI-Manager will continue to work without interruption
AccVideo is a novel efficient distillation method to accelerate video diffusion models with synthetic datset. This method is 8.5x faster than HunyuanVideo.
“There are many good reasons to be concerned about the rise of generative AI(…). Unfortunately, there are also many good reasons to be concerned about copyright’s growing prevalence in the policy discourse around AI’s regulation. Insisting that copyright protects an exclusive right to use materials for text and data mining practices (whether for informational analysis or machine learning to train generative AI models) is likely to do more harm than good. As many others have explained, imposing copyright constraints will certainly limit competition in the AI industry, creating cost-prohibitive barriers to quality data and ensuring that only the most powerful players have the means to build the best AI tools (provoking all of the usual monopoly concerns that accompany this kind of market reality but arguably on a greater scale than ever before). It will not, however, prevent the continued development and widespread use of generative AI.”
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“(…) As Michal Shur-Ofry has explained, the technical traits of generative AI already mean that its outputs will tend towards the dominant, likely reflecting ‘a relatively narrow, mainstream view, prioritizing the popular and conventional over diverse contents and narratives.’ Perhaps, then, if the political goal is to push for equality, participation, and representation in the AI age, critics’ demands should focus not on exclusivity but inclusivity. If we want to encourage the development of ethical and responsible AI, maybe we should be asking what kind of material and training data must be included in the inputs and outputs of AI to advance that goal. Certainly, relying on copyright and the market to dictate what is in and what is out is unlikely to advance a public interest or equality-oriented agenda.”
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“If copyright is not the solution, however, it might reasonably be asked: what is? The first step to answering that question—to producing a purposively sound prescription and evidence-based prognosis, is to correctly diagnose the problem. If, as I have argued, the problem is not that AI models are being trained on copyright works without their owners’ consent, then requiring copyright owners’ consent and/or compensation for the use of their work in AI-training datasets is not the appropriate solution. (…)If the only real copyright problem is that the outputs of generative AI may be substantially similar to specific human-authored and copyright-protected works, then copyright law as we know it already provides the solution.”
Meshtron provides a simple and scalable, data-driven solution for generating intricate, artist-like meshes of up to 64K faces at 1024-level coordinate resolution. This is over an order of magnitude higher face count and 8x higher coordinate resolution compared to existing methods.
Spectral sensitivity of eye is influenced by light intensity. And the light intensity determines the level of activity of cones cell and rod cell. This is the main characteristic of human vision. Sensitivity to individual colors, in other words, wavelengths of the light spectrum, is explained by the RGB (red-green-blue) theory. This theory assumed that there are three kinds of cones. It’s selectively sensitive to red (700-630 nm), green (560-500 nm), and blue (490-450 nm) light. And their mutual interaction allow to perceive all colors of the spectrum.
