标签：#clip

The eclipse will obscure about 93 percent of the moon’s surface. Here are the peak times and tips for the best viewing experience.

Here's what it was like watching a total solar eclipse 90 minutes north of Madrid.

In 2027 and 2028, there will be two spectacular, long-duration solar eclipses. Here’s what you need to know about them.

It’s been a century since the Iberian Peninsula has been in the full shadow of the moon. Here’s what it looked like in the path of totality.

Investors are still waiting for their share of the $250 million windfall, and VideoVerse co-founder Vinayak Shrivastav is now at the center of multiple legal cases.

Fitted with telescopes, the plane did our longest imaging of the Sun's corona.

Here’s when some of this year’s most anticipated astronomical events will happen and the best way to observe them.

Everything you need to know about the solar eclipse and lunar eclipse that will occur in August 2026.

This week's Java roundup for June 8th, 2026, features news highlighting: the GA release of A2A Java SDK 1.0; an update on Jakarta EE 12; point releases of Micrometer Metrics and Micrometer Tracing; maintenance releases of GraalVM Native Build Tools and OpenXava; the second release candidate of Gradle 9.6; and the first milestone release of Eclipse JNoSQL 1.2. By Michael Redlich

A separate zero-day also disclosed by Nightmare Eclipse appears to be patched as well.

⚠️ 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

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