标签：#fusion

An old Oscar Mayer factory in Wisconsin will become America's latest fusion power research and development hub.

General Fusion started trading on the Nasdaq following a reverse merger that saw high redemptions.

“We can take power from a plasma,” Kieran Furlong, co-founder and CEO of Realta Fusion, told TechCrunch. The milestone shows “what’s possible,” he added.

Un-0 is an image-generation system tool that shows for the first time how the company's technology can replicate conventional AI systems.

Fusion startups have raised $7.1 billion to date, with the majority of it going to a handful of companies.

Fusion power startup Avalanche Energy said its reactor prototype heated a plasma to over 10 million degrees C.

Five peer-reviewed papers update the design and model its expected output.

Fusion startup Xcimer fired up the world's largest privately owned laser.

Another fusion startup has raised another a massive round to make this type of power a reality.

Pacific Fusion's sub-scale prototype delivered enormous amounts of power in a flash, setting the company up for its demonstration power plant.

⚠️ この記事はアフィリエイト広告（プロモーション）を含みます。リンク先で発生した収益の一部が運営者に支払われますが、読者の購入価格には一切影響ありません。 By the end of this article you'll have two runnable Python scripts: a CLIP-based pre-filter that re-checks SDXL Turbo output before it ever hits Pinterest, and a prompt sanitizer that strips artist names + trademarked characters so you don't eat a DMCA. I ran this pipeline for 41 days, generated 6,180 images, and went from a 9.7% Pinterest rejection rate down to 2.6%. Here's exactly what broke and what fixed it. Why SDXL Turbo (1-step, ~0.3s on a 4090) beats SD 1.5 for Pinterest volume First, the conclusion: if you're mass-producing pins, SDXL Turbo's single-step guidance_scale=0.0 generation is the only thing that makes the unit economics work. On my RTX 4090 I clock 0.31s per 512x512 image with Turbo vs 4.8s for a 30-step SDXL base run. That's 15x. Over 6,180 images that's the difference between 32 minutes and 8.2 hours of GPU time. But Turbo has a nasty side effect nobody warns you about: because it's distilled and runs at low resolution by default, its built-in StableDiffusionXLPipeline safety checker (when enabled) throws far more false positives on perfectly benign images — beaches, lingerie-free fashion flatlays, even close-up food. In my first 600-image batch, 58 images came back as black squares from the NSFW checker. 51 of them were photos of latte art and knitted sweaters . So I ripped out the default checker and built my own two-stage gate. Stage 1: Replacing the diffusers safety_checker with a tunable CLIP gate in Python The default safety_checker in diffusers is a binary black box — you get a black image and zero signal about why . For a production loop you need a confidence score so you can set your own threshold. I use OpenCLIP's ViT-B-32 to score each output against a small set of NSFW concept prompts, then compare to a safe-concept baseline. This code actually runs (tested on diffusers==0.27.2 , open_clip_torch==2.24.0 ): import torch import open_clip from PIL import Ima

Thea Energy's pixel-inspired magnets could give its power plant plans a boost. The fusion startup hopes to get a commercial reactor working by 2034.