Benchmarks don’t capture real-world complexity like latency, domain-specific tasks, or edge cases. Enterprises often need more than raw performance, also needing reliability, ease of integration, and robust vendor support. Enterprise money will support the industries providing these services.
… it is also reasonable to assume that anything you put into the app or their website will be going to the Chinese government as well, so factor that in as well.
Tneration models can create high-quality images from input prompts. However, they struggle to support the consistent generation of identity-preserving requirements for storytelling.
Our approach 1Prompt1Story concatenates all prompts into a single input for T2I diffusion models, initially preserving character identities.
The Chinese AI lab DeepSeek recently released their new reasoning model R1, which is supposedly (a) better than the current best reasoning models (OpenAI’s o1- series), and (b) was trained on a GPU cluster a fraction the size of any of the big western AI labs.
DeepSeek uses a reinforcement learning approach, not a fine-tuning approach. There’s no need to generate a huge body of chain-of-thought data ahead of time, and there’s no need to run an expensive answer-checking model. Instead, the model generates its own chains-of-thought as it goes.
The secret behind their success? A bold move to train their models using FP8 (8-bit floating-point precision) instead of the standard FP32 (32-bit floating-point precision). … By using a clever system that applies high precision only when absolutely necessary, they achieved incredible efficiency without losing accuracy. … The impressive part? These multi-token predictions are about 85–90% accurate, meaning DeepSeek R1 can deliver high-quality answers at double the speed of its competitors.
a novel method for generating hyper-quality 4K textured mesh under only 30 seconds, providing 3D assets ready for commercial applications such as games, movies, and VR/AR.
To measure the contrast ratio you will need a light meter. The process starts with you measuring the main source of light, or the key light.
Get a reading from the brightest area on the face of your subject. Then, measure the area lit by the secondary light, or fill light. To make sense of what you have just measured you have to understand that the information you have just gathered is in F-stops, a measure of light. With each additional F-stop, for example going one stop from f/1.4 to f/2.0, you create a doubling of light. The reverse is also true; moving one stop from f/8.0 to f/5.6 results in a halving of the light.
There are three models, two are available now, and a third open-weight version is coming soon:
FLUX.1 Kontext [pro]: State-of-the-art performance for image editing. High-quality outputs, great prompt following, and consistent results.
FLUX.1 Kontext [max]: A premium model that brings maximum performance, improved prompt adherence, and high-quality typography generation without compromise on speed.
Coming soon: FLUX.1 Kontext [dev]: An open-weight, guidance-distilled version of Kontext.
We’re so excited with what Kontext can do, we’ve created a collection of models on Replicate to give you ideas:
