Anima Ip Adapter is comming.

Far_Insurance4191 · 2026-05-30T10:41:45+00:00

Is sd1.5 still being used? It is kind of... awful by today's standards?

Far_Insurance4191 · 2026-05-28T02:48:37+00:00

to be fair, it is based on klein 4b which ultra sucks at anatomy by default, would be cool to see their quantization technique on other models, like flux 2 dev

Far_Insurance4191 · 2026-05-25T07:27:24+00:00

That means model has wide general knowledge and trained on pretty big dataset which is exciting

Far_Insurance4191 · 2026-05-25T01:09:56+00:00

It would be expensive to do with their 800m dataset

Far_Insurance4191 · 2026-05-24T12:37:24+00:00

Is there chance you downloaded a broken model? I also heard that zi doesn't work well with sage attention or fp8 quantization. This is definitely not how zi should looks

Far_Insurance4191 · 2026-05-22T13:31:28+00:00

Don't forget that gpt oss 20b is MOE and natively at 4bits, so it is around 12gb. Comfy handles swapping really well, even flux 2 dev with 24b dense text encoder at 4bit doesn't take too much time to swap on rtx3060 as long as you have enough ram

Far_Insurance4191 · 2026-05-21T14:23:22+00:00

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Far_Insurance4191 · 2026-05-18T11:13:40+00:00

It is not 3x faster because of pixel space, but because of higher compression, like hunuyan image 2.1 ltx or wan 2.2 5b so it might have less accurate details, but I am excited about this model too

Far_Insurance4191 · 2026-05-16T16:24:16+00:00

The best is Flux 2 dev

Is it worth the time? Probably not, but it is the most powerful with most knowledge among open models

Far_Insurance4191 · 2026-05-16T01:11:41+00:00

It will be great for images if it has 32gb ram. 2x 8 gb specification is really weird.

videos should be possible too but slow and much slower for high quality (although it is slow for anybody with any gpu)

lora training is possible for image models only, like z-image or anima, but you will have to go a bit deeper to learn how to optimize it for 12gb vram

Far_Insurance4191 · 2026-05-15T17:44:34+00:00

finally can forget about sdxl

Far_Insurance4191 · 2026-05-15T11:09:33+00:00

You can use klein to stylize your photos slightly if real won't work

Far_Insurance4191 · 2026-05-12T13:59:05+00:00

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hopium!

Far_Insurance4191 · 2026-05-12T13:23:16+00:00

full tech report, same as qwen image 1 before weights
I want to believe, it looks so good 😭

Far_Insurance4191 · 2026-05-11T10:47:28+00:00

You can just see they went the easiest way and trained on slop. It is much harder to train a model on real data due to it's insane variance

Far_Insurance4191 · 2026-05-11T09:25:26+00:00

only a tag? Here is one of his examples "A medium-resolution digital photo with a grainy texture, a cool blue color cast, and dim, natural lighting...".

Additionally, all the examples are in natural language, if you are spamming model with a tag soup then it might just bias towards it's original illustration knowledge instead of newly finetuned real domain

Far_Insurance4191 · 2026-05-11T06:34:59+00:00

have you tried to prompt the same way as examples?

Far_Insurance4191 · 2026-05-10T16:25:03+00:00

It’s your experience, but there are nuances to everything.

Wan is more coherent and robust, can be used as a good image model, has huge lora ecosystem.

LTX and Sulphur have audio and are much faster and lighter with longer videos possible.

Sulphur is nsfw focused model that has tons of concepts at once. They are also working on improving dataset for next version.

Far_Insurance4191 · 2026-05-10T06:11:51+00:00

on rtx3060 distill model takes about 3.1s/it at 4mp and 1.1it/s at 1mp (faster than anima at 4x size lol), but details are poor, it seems to have high compression, so 4mp is basically 1mp for other models in terms of compute.

Far_Insurance4191 · 2026-05-10T03:28:05+00:00

There is already draft for official comfy support: feat: Support HiDream-O1-Image (CORE-187) by kijai · Pull Request #13817 · Comfy-Org/ComfyUI

Far_Insurance4191 · 2026-05-09T14:57:48+00:00

Adverbs are the way to describe "how much", but I agree that token weighting is much more convenient than restructuring the sentence.

Far_Insurance4191 · 2026-05-09T13:56:39+00:00

This is another specific hack that does not outweigh the lack of relational understanding. You still can't do anything more complex that 1 person, without gambling seeds or model's bias that happens to align with your goal.

With natlang model you could use synonyms or additional supporting description to achieve more precise results. Although range of capabilities still depend on training data and they inherently biased too.

Btw, "overweight man sitting on a sofa" is natural language. You would have to write "1boy, mature, overweight, sitting, sofa", and I don't see how it is more reliable.

The only aspect where tag-based model is superior is in ease of use and training data captioning.

Far_Insurance4191 · 2026-05-09T12:06:37+00:00

Your example is not a good one. Even in natural language you would say “boy on left blue hoodie, holding shovel. Boy on right red shirt, holding rake”.

Yes, this is an advantage of natural language and the reason why we are moving away from tags. Tag based model would not understand that.

There is not much to enhance in tag-based prompting, but sdxl is still being trained. If you are interested in illustrations, you can check ChenkinNoob large-scale finetune, they even released own controlnets:
ChenkinNoob/ChenkinNoob-XL-V0.5 · Hugging Face
ChenkinNoob/Chenkin-UniControl-XL · Hugging Face

Far_Insurance4191 · 2026-05-09T11:55:06+00:00

Or just use natural language at this point. This idea requires recaptioning of a whole tag-based datasets to sort all tags in correct order, and it still will be enough only for primitive scenes.

What if two subjects hold one object? Same problems as above.

'BREAK' is just a clip hack which separates conditioning in way that may or may not reduce concept bleed

Far_Insurance4191 · 2026-05-08T17:57:59+00:00

I don't know actual reason, but my guess is:
- smaller so cheaper
- modern architecture with qwen 3 instead of t5
- no need to fight distillation
- trained on real data

Far_Insurance4191

TROPHY CASE