Shock and awe — Creator of free Adobe clones unveils open-source Microsoft Office Excel, PowerPoint, and Word replications — Autodesk tools and other subscription-based services may come next
tomshardware.comu/Jenna_AI — 3 days ago
IA News & Research
Suivi en temps réel de la révolution IA : modèles, outils et recherche.
How many in this situation, forbidden to lie
u/Jenna_AI — 3 days ago
Top executives at Anthropic, OpenAI and other AI companies are privately gaming out scenarios for a public and political revolt after a catastrophic AI event.
axios.comu/NAStrahl — 4 days ago
This happened for real, this is why GPU price are climbing
u/Inner-Schedule8998 — 3 days ago
▲ 540 r/StableDiffusion
Update: Kroma, A Krea-2 Fine Tune
[ I'm posting this on behalf of u/LodestoneRock since for some reason his post on this sub keep getting auto removed by Reddit. ]
Hey everyone, First of all, thank you to everyone for supporting this project, and to the Krea team who made the best uncompromised base model. Unlike the previous run, where I had to un-distill the model, the Krea team kindly provided us with their real base model. The training is much cheaper than the previous Chroma run. Though cheap is relative, it’s still costing me about ~10K RTX pro 6000 hours (3+ months on 4xGPU machine) running locally, this includes a bunch of failed experiments and bugs during training. Also thank you to ComfyOrg for the 2 RTX pro 6000 donations ♥️
The Base Krea is also not collapsed which allows me to do OPD (RL) directly ontop of it. As a result, you will get a good generation out of the box. Here are some results:
You can download them here: https://huggingface.co/lodestones/Kroma
(Civitai link soon)
So what’s next?
There are several stale projects like my pixel space run chroma radiance and zeta chroma, but with the limited compute, I have decided to not continue them for now since compute is at a premium atm. Right now the focus is to create a better dataset (where about 90% of the quality comes from) while continuing to add more knowledge to kroma in the background. I do want to tackle video generation models like h3 in the near future once the dataset curation is done, but fine tuning the video model has astronomical compute cost so I'm looking for funding for more hardware to train a model. I firmly believe that hardware funding donations help a ton because we can indefinitely do more experiments in a sustainable way without having to worry about rental cost and psychological burden of “burned money” on failed experiments. I have a target of getting 4 more RTX pro 6000 but that’s like 60 grand in total which is an insane amount of money! So I don’t expect to reach this goal immediately, it's more about chipping in one card at a time at the pace of the accumulated donations. Every contribution helps, no matter the amount, this project simply isn’t possible without your support!
My discord: discord.gg/SQVcWVbqKx
[ I'll let Lodestone post his support and donation links in a comment. ]
u/KadahCoba — 4 days ago
▲ 204 r/StableDiffusion
Some realistic / cinematic Kroma gens
Euler / Simple / 8 steps / CFG = 1.0
u/AgeNo5351 — 3 days ago
▲ 40 r/StableDiffusion
Overwhelmed with Minimax 3 combinations
I've been testing MiniMax H3 in ComfyUI for a while now, and I'm definitely not new to ComfyUI, but I'm getting frustrated with the sheer number of configurations people recommend.
Everywhere I look, someone is saying, "Use this model with this LoRA, this sampler, Euler, these steps, this node combination, and it works perfectly." Then someone else recommends a completely different setup that supposedly works even better.
At this point, there are so many combinations of models, LoRAs, samplers, schedulers, nodes, and workflows that testing every variation becomes a project in itself.
Has anyone actually put together a centralized comparison or benchmark of what works best for specific use cases?
For example:
Image-to-Video: Which model, workflow, and settings consistently produce the best results?
Text-to-Video: What's the most reliable configuration?
Performance vs. Quality: Which settings offer the best balance without wasting GPU resources? I'm using a 5090 with 64gb memory. Some Kijai nodes have dramatically sped up my workflow, so has another I found recently a spectrum node.
LoRAs and Samplers: Which combinations have actually been tested side by side rather than just recommended based on personal preference?
Character video swap: does Refmod work for this?
I understand that results are subjective and different use cases require different settings. I'm not expecting one configuration to be perfect for everything.
I had to vibecode a presets node where I save all the tests as a present.
Does something like this already exist, or are we all just independently reinventing the wheel?
u/Lightningstormz — 3 days ago
Mathematicians Will Have To Learn To Philosophically Adapt Like the Go Players after AlphaGo
10 years after AlphaGo beat Lee Sedol the top Go players still barely understand the “why” of moves played by top AI Go models. A study in 2022, showed that the best go player Shin Jin-seo only plays the best AI moves 37.5% of the time. Before 2016 humans were the fundamental discoverers and understanders of making and playing better and better Go.
Now, AI is so far ahead of humans it’s near unthinkable for our top experts to ever catch up. So what does that mean for Go? Is it dead? Is it no longer worth pursuing because AI is better than humans at it? Each Go player had to answer that for themselves. Just like every Chess player had to ask themselves that when Deep Blue beat Kasparov in 1997. Lee Sedol famously retired shortly after his matches with AlphaGo and has remarked, “Before AI, we sought something greater. I learned Go as an art” and “My reason for playing Go has vanished”.
However, people still play Go and Chess for their own sake! The arguably best chess player of all time Magnus Carlson came after AI and people’s skill in both games/sports have only gotten better because of AI and not worse.
So what is there to learn? I see a lot of mathematicians lamenting the bewilderment and incomprehensibility of the recent wave of AI proofs. The 1st is that AI does not explain. While at first AlphaGo played similarly to humans as it was trained on human data. Famously, a later model AlphaGo Zero was trained on the rules of Go alone. The result? A better model with incomprehensible to human moves. These models have no language component, only the result of the best learned move. Michael Redmond, the only Westerner to ever reach 9-dan, has a series breaking down AI Go games and back then he could only express his great confusion as to the “why” of AI’s moves. A decade later and much of what AI does in Go is not understood, but humans have understood more about the game of Go with it. A prime example of that is the early 3-3 invasion. Humans historically understood this move as a mistake, but have since learned it is actually a very good move with AI. What does that mean? Yes, the math proofs are confusing and largely human illegible at first glance. However, there is still much to be gained from mining from the fruits of this tree. The masters of Chess and Go have all transitioned to studying AI to improve the collective human knowledge, and so too will math. The world and time still moves forward. Some people may quit, but by studying results of AI there is both lots to learned from it for its own sake.
(Not everyone is content with this of course. Fischer Random/Chess 960 is a direct result of people finding normal chess getting too stale, too much memorization of the best moves. It’s important to mentally prepare yourself for this in maths as well)
2nd. Question why you do math. You must adapt to a future where a large part of math is studying the results of AI, this is inevitable. For now, only the largest AI companies worth billions of dollars have access to these model’s internally. However, that is not what it will be in the future. We can see this with history of Go models. At first the model was private. Go players could only study the results of games played between the AI and pros and games released by DeepMind themselves. However now every player has access to a model better than the best player in the world, KataGo, on their own computers! While people may think that OpenAI dropping 700 papers at once is unhelpful. What will we do when every single person on this planet can each individually access these models? Within Go the results are clear. Both a democratization of Go knowledge and an increase in the collective human understanding of the game itself. Math is already on this trajectory. Just like the most optimal moves in Go, the math is out there to be discovered in the universe that of things that are true but unknown. AI is really good at learning things with verifiable rules. It will be up to us to learn and decode its results into human understanding.
u/Carbinkisgod — 4 days ago
▲ 100 r/StableDiffusion
AuroraIMG-6M Released!
It's a tiny 6M parameter text to image model, and it can make recognizable images at 64x64.
Check out the model card for samples, a detailed explanation about it's architecture and training, and a quick start!
u/Tall_Abrocoma_3533 — 3 days ago
▲ 13 r/StableDiffusion
A/B Blind Taste Test - Krea2-Turbo vs. Qwen-Image 2.1 Turbo
Since Qwen 2.1 Turbo dropped suddenly right after my last taste test, figured might as well do one for it as well.
Instructions:
Each image is either Krea2-Turbo or Qwen 2.1 Turbo, using the exact same prompt.
You do not know which one is which, it could be either A or B on each individual image.
Pick your favorite based on preference and list them.
You can also guess which one of each is Krea2 or Qwen 2.1 Turbo.
The answer key will be posted 24 hours after posting.
Technical Setup
This time all images were generated at 2MP, using 8 steps. Krea2-Turbo was the fp8 model and text encoder, but Qwen was BF16 for both model and text encoder.
Krea2 used Euler-Simple, where as the still less understood Qwen Turbo I went with Linear Quadratic (I know there is an official sigma curve for it, but its close enough) and since Euler seems also not be a good sampler, I went with the tried and trusted Heun, which gave comparable inference times to Krea2 (Qwen 2.1 Turbo is about twice as fast as Krea2-Turbo on Euler).
I did test (and have in the past) different samplers and schedulers in Krea2 but overall they generally seem to all be equal, so I did not differ from the standard Euler-Simple.
Prompts:
All prompts are the same for both models. They were generated in ChatGPT, expanding off basic ideas. Some of the expanded prompts were not great and were tweaked manually when both models demonstrated consistent inconsistencies with the prompt idea, and reran on both models. Each prompt was ran up to 10 times to find a suitable generation.
The prompts for each image are here: https://paste.sh/yDAHasri#TveZa0FgYZ-UJ89Exlvpbelm
u/Murky-Relation481 — 3 days ago
I am building a web application to transform images into Gaussian Splats
I’ve been playing with orbit-style videos from video models (lately Minimax H3 in ComfyUI) and kept thinking they’d make great input for Gaussian Splatting. The catch was that the usual pipeline means installing COLMAP, a trainer, CUDA and so on. That’s a lot of friction just to try one idea.
So I built Gausify Orbit: https://orbit.gausify.com/
You drop in your video and the whole thing runs locally in your browser on WebGPU:
- Pulls frames from the video
- Optional background removal with RMBG-1.4 (BRIA), running through ONNX Runtime Web
- Camera poses from a small structure-from-motion step
- Trains the splat with Brush
- Preview the result and export it as PLY or SOG
No Python, no CUDA, no account. Your video never leaves your machine. The models download once, and after that the app works offline too. If you want to show someone a result, you can share a temporary private link that opens in a viewer (it even supports WebXR).
Since it all runs on your GPU, how fast it goes depends on your hardware. You also need a browser with WebGPU (recent Chrome or Edge work best). AI videos aren’t perfectly 3D-consistent either, so some generations reconstruct much better than others. Slow, steady orbits around one centered subject give the best results.
Tip for better results: I’ve been using this LoRA by pablodawson to get cleaner orbits out of Minimax H3: https://huggingface.co/pablodawson/MiniMax-H3-360-Orbit-LoRA
It makes the camera move in a much smoother, more consistent 360° orbit around the subject. That helps a lot here, because the reconstruction step relies on the camera following a predictable path. In my tests, videos made with it got better camera poses and noticeably sharper splats than plain prompting. Highly recommend it if you’re generating orbit videos for this.
ComfyUI Workflow: https://github.com/obraia/ComfyUI/blob/main/workflows/minimax-h3-gausify-orbit.json
u/obraiadev — 4 days ago
▲ 122 r/StableDiffusion
I built a free anime character & prompt library for AI art creator
Hey everyone!
I'm an AI art creator, and for a while now I've been wanting a better way to organize character prompts, find inspiration, and experiment with different scenes without having to dig through a bunch of different sources.
So I decided to build my own little website called Senpai's Waifu Library.
It started as something I was making just for myself, but I figured if I was finding it useful, other people might too. So I decided to make it public!
The website includes:
- A growing library of anime characters and their prompts
- Reusable scene prompts you can mix with different characters
- Search and filtering to make finding things easier
- Easy-to-copy prompts for your own generations
It's completely free, and I'm still working on adding more characters, tools, and features as I go.
I'm not a professional web developer, so this has also been a pretty fun learning experience for me.
I'd genuinely love to hear what you think! Especially if there's something you'd like to see added or improved.
u/SenpaizCreations — 3 days ago
Accidentally chose 2 megapixels instead of .2 megapixels on render before bed last night
Interesting thing is the render still worked. I saw this other thread and wondered - can we go beyond 1?
https://www.reddit.com/r/comfyui/comments/1w11oer/tip_for_minimax_h3_max_quality/
u/wildmonkeywrangler — 3 days ago
The man is smooth. We’re not ready for fake videos
u/Adopilabira — 4 days ago
I trained a small transformer to fly a boids flock just by watching, then checked where it keeps the three rules
i wrote a small boid simulator (12 birds), recorded it flying then saved it, and trained a transformer to predict each bird's next move without it knowing about any boid rules.
(built in Python and PyTorch: a transformer written from scratch, one token per bird, two attention layers, about 414k weights, trained on a laptop CPU)
what I found:
it flies the flock. R² 0.990 to 0.994 on clips it never trained on, across 4 full runs. It also flies 50 birds (it has only trained on 12).
the three rules were easy to read out of its hidden states with a linear probe, and some could even be read before any training.
the rule: alignment (line up with your neighbours) was readable after the first attention layer, but if I make the second layer hear only itself, it is gone across all runs.
what i wasn't expecting: my first ever model was not using the flock at all. With 8 ticks of history, the previous answer was already in the input, and a small network that saw only one bird beat the whole transformer.
what i am still not sure about: making a layer attend only itself is something the model never went through during training. is that a fair test, or is some of the decay due to strange input fed to the model?
u/rage_81 — 4 days ago
Training an AI to Drive with Natural Selection
I love making hard things intuitive. I hope you enjoy this one!
Let me know if you have any questions.
This technique is called neuroevolution: training a neural network through evolutionary methods like selection and mutation, without gradient descent.
u/egehancry — 5 days ago