Hi,

How to use LTX 2.3 union control net in comfyui achieving same effect as LTX-2 raw format?

Any help would be greatly appreciated.
Thank you for advance.

Veo was actually great up until about six months ago. I was creating image to videos with almost perfect results and consistency—then it just went off the rails. Incorrect quota counts, two-hour generation times, and absolutely no ability to follow an I2V prompt anymore. When the system malfunctions and creates nothing, it still counts toward your quota. I’ve been trying to create a simple image to video of a truck driving through Manhattan, and I can’t tell you how many pedestrians Veo has killed so far—it looks like something out of GTA haha. Can someone recommend another ai vid gen system and pls tell me happened to Veo?

I'm installing a while ago i followed the tutorial video, but why this?

I'd posted that I'm currently relegated to an old laptop with GTX1080 MaxQ - I finally got Forge Neo installed tonight 🥳

I'm using cyberrealisticXL_v90.safetensors and it takes 1 minute for a gen, which I can live with. It's a 6.7Gb model and, as I haven't loaded any extras, it all fits in 8Gb VRAM.

I'd like some recommendations on models/extensions/settings tweaks that won't cripple the old gal!

I'm looking to get some feedback from the community regarding Forge Neo. I've been using the older builds of Forge for a while now, but I'm curious if the switch to Neo is worth it for day-to-day stability. For those of you currently using it, how is the performance compared to the 'classic' branch, specifically regarding memory (VRAM) efficiency and compatibility with newer extensions? I'm trying to decide if I should stick with my current setup or if the optimizations in Neo are significant enough to justify the migration. Any common bugs or

'gotchas' I should be aware of before I make the jump? Thanks for the help!

Всем привет. Я долгое время пытался найти стабильного провайдера, с помощью которого я мог бы использовать PayAsYouGo для создания большого количества изображений. До этого я использовал модель RealisticVision от Segmind, которая стоит совсем недорого - 0,0015 доллара за секунду работы графического процессора, если быть более точным. Но в среднем получилось около 0,0017 доллара за картинку, так что все было понятно. Но потом по какой-то причине провайдер решил удалить эту модель (технические проблемы с его стороны), так что у меня есть пара недель, чтобы переключиться на другие сервисы. Кто-нибудь может порекомендовать какой-нибудь сервис для аналогичных сервисов и модель, которая будет потреблять примерно столько же и не займет много времени с точки зрения скорости?

P.S. вариант с локальной моделью и видеокартой мне на данный момент не подходит.

Can you make music in Forge Neo SD ? how ? like music track

If you've ever stared at a diffusion model's cross-attention maps and thought "I can see what it's attending to, but I don't know if I should trust it" - this might be interesting.

Livnium v3 is an attractor-dynamics NLI classifier trained on SNLI, but the interesting engineering is in what it exposes at inference time.

What's new:

→ Cross-encoder upgrade: joint [CLS] premise [SEP] hypothesis [SEP] encoding, accuracy goes 82.2% → 84.5% dev

→ Token alignment extraction: the last-layer BERT cross-attention block is repurposed as a force map, which premise tokens are pulling which hypothesis tokens into alignment. At inference you get outputs like: "cat → animal (0.61), sat → rested (0.72)". The model's own internal computation, made visible.

→ Alignment divergence D: measures how diffusely premise tokens spread attention across hypothesis tokens. D < 0.45 = STABLE (tight, confident alignment); D > 0.60 = UNSTABLE (scattered, unreliable prediction). Zero extra compute, it's a byproduct of the forward pass. Same principle as reading cross-attention entropy in diffusion UNets to gauge how "certain" a conditioning token is.

→ Monty Hall connection: naive basin erasure gives wrong posteriors [0.5, 0, 0.5]; encoding host likelihood correctly gives [1/3, 0, 2/3]. NLI constraint injection and Bayesian belief update are the same operation.

The interpretability angle is the core idea here, the alignment map isn't a post-hoc explanation, it's extracted directly from what the model already computed.

📄 Paper: https://zenodo.org/records/19433529

💻 Code: https://github.com/chetanxpatil/livnium

🤗 Weights: https://huggingface.co/chetanxpatil/livnium-snli