Image 1 — SenseNova-U1.5 now runs in ComfyUI (custom node v0.2.0) : 8B unified model, T2I + editing, ~17GB peak VRAM
Image 2 — SenseNova-U1.5 now runs in ComfyUI (custom node v0.2.0) : 8B unified model, T2I + editing, ~17GB peak VRAM
Image 3 — SenseNova-U1.5 now runs in ComfyUI (custom node v0.2.0) : 8B unified model, T2I + editing, ~17GB peak VRAM
▲ 21 r/comfyui

SenseNova-U1.5 now runs in ComfyUI (custom node v0.2.0) : 8B unified model, T2I + editing, ~17GB peak VRAM

Custom node v0.2.0 is out and U1.5 works in ComfyUI now.

What it does: text-to-image, image editing (single and multi-image reference), and region-controlled edits via masks / bboxes / visual markers. Native 4K. It's a unified model, so understanding and generation share one backbone — no separate VAE, no CLIP/T5 text encoder in the graph.

VRAM: peak allocated is 17.34 GiB for T2I and 20.00 GiB for editing, using the offload modes. So a 24GB card handles both. There are full / fast / balanced / low modes to trade speed for footprint — balanced and low got 36–50% faster in the last release, and outputs are bit-identical across modes.

Repo: https://github.com/OpenSenseNova/SenseNova-U1

ComfyUI node: https://github.com/OpenSenseNova/SenseNova-U1/pull/244

u/qqzjy — 14 hours ago
▲ 7 r/sleep

How do you actually tell if you have insomnia or if you're just a bad sleeper?

I've been lying awake until 2 or 3am almost every night for the past two months and I'm losing my mind. Some nights I fall asleep fine but jolt awake at 4am and just stare at the ceiling until my alarm goes off. I keep googling how to tell if you have insomnia and every article just lists the same vague checklist that could describe literally anyone who's had a stressful week. I'm exhausted at work, I'm snapping at my family over nothing, and I still have no idea if this is actual insomnia or if I'm just being dramatic. Has anyone else gone through this and figured out where the line actually is?

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u/qqzjy — 20 days ago
▲ 201 r/ChatGPT

threw 800k tokens of legal docs into MiniMax Code and it found a clause buried in page 312 that i missed twice manually

been doing m&a due diligence work for about three years. the document review part is brutal. typical deal room has ndas, financial statements, board minutes, email threads, ip schedules, all in separate pdfs. for a mid size deal thats easily 400 to 800 pages.

tried MiniMax Code this week. loaded an actual deal package into it. 14 documents, roughly 800k tokens total. ndas, two years of quarterly financials, board minutes from 6 meetings, and about 200 pages of email correspondence between counsel.

the test that impressed me: asked it to find every instance where anyone referenced changing revenue recognition methodology. it pulled three hits. one from the q3 board minutes on page 89, one from a cfo email on page 312, and one from a footnote in the year end financials. the page 312 email is one i personally missed on two separate manual reviews.

asked a follow up about whether the board formally approved the methodology change or just discussed it. it pointed out that the minutes say discussed but the cfo email says implementing which is exactly the kind of gap that matters in due diligence.

for context ive been doing this with chatgpt by splitting docs into chunks and running multiple passes. works but you lose cross document connections constantly. this was the first time i loaded everything in one shot and asked questions across the full set.

not saying its perfect. it occasionally paraphrased where i wanted exact quotes and one of the 14 docs seemed to get less attention than the others. but for a first pass review this cut my time roughly in half.

anyone else testing long context on actual professional workloads or is everyone still running benchmarks on synthetic needle tests

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u/qqzjy — 2 months ago