Workflow is just regular Wan2.2 fp8 6 steps (2 steps high noise, 4 steps low), lighting lora on the high noise expert then interpolated with this wf (I believe it came from the wan2.2 Kijai folder). Initial images all coming from nanobanana. I want to like Qwen but there’s something about the finish that feels …
Read more “I made a full music video with Wan2.2 featuring my AI artist”
As a machine learning engineer, you probably enjoy working on interesting tasks like experimenting with model architectures, fine-tuning hyperparameters, and analyzing results.
For those building with AI, most are in it to change the world — not twiddle their thumbs. So when inspiration strikes, the last thing anyone wants is to spend hours waiting for the latest AI models to download to their development environment. That’s why today we’re announcing a deeper partnership between Hugging Face and …
Read more “Expanding support for AI developers on Hugging Face”
Mere hours after OpenAI updated its flagship foundation model GPT-5 to GPT-5.1, promising reduced token usage overall and a more pleasant personality with more preset options, Chinese search giant Baidu unveiled its next-generation foundation model, ERNIE 5.0, alongside a suite of AI product upgrades and strategic international expansions. The goal: to position as a global …
When it comes to navigating their surroundings, machines have a natural disadvantage compared to humans. To help hone the visual perception abilities they need to understand the world, researchers have developed a novel training dataset for improving spatial awareness in robots.
submitted by /u/Artefact_Design [link] [comments]
A good language model should learn correct language usage, free of biases and errors.
TL;DR Using Google’s new Veo 3.1 video model, we created a breathtaking 1 minute 40 second FPV drone flight through mountain valleys, and it took just 15 minutes to generate. Imagine soaring through alpine valleys, gliding between snowy peaks, and diving toward rivers that twist like silver ribbons below, all without leaving your desk. That’s …
Conditional generative modeling aims to learn a conditional data distribution from samples containing data-condition pairs. For this, diffusion and flow-based methods have attained compelling results. These methods use a learned (flow) model to transport an initial standard Gaussian noise that ignores the condition to the conditional data distribution. The model is hence required to learn …