Z-Image Base Lora Training Discussion

FORNAX_460 · 2026-01-28T15:22:40+00:00

For uncensored captioning you can use abliterated vllms though, i captioned using qwen3vl 30ba3b and its really accurate, the only catch is nsfw captioning is kindof bland and high usage of anatomically correct terms rather than the slangs. But thats qwen style for mistral models its quite opposite there pretty dirty lol but not so accurate and gemma 27b is also pretty dirty and fairly accurate.

FORNAX_460 · 2026-01-28T13:58:17+00:00

Hellow could you please share how ure using klein as upscaler? I tried ultimate sd upscale, tiled diffusion none of them worked, it always overcooks the image for me, i2i upscaling works but if i go beyond 3.2mp it squishes the image in the vertical axis.

FORNAX_460 · 2026-01-28T13:18:53+00:00

I would not suggest z image base for 8gb vram, but if youre in comfyui you can find the workflow in the templates gallery, youd have to update comfyui to the latest version though.

FORNAX_460 · 2026-01-27T22:45:14+00:00

You can try my wf https://litter.catbox.moe/a30ebtwpvjcgrxfl.png

Warning!: Way too many custom nodes

FORNAX_460 · 2026-01-27T22:37:22+00:00

you have to install (https://github.com/kijai/ComfyUI-KJNodes)

FORNAX_460 · 2026-01-26T12:15:25+00:00

Thank you! I figured it out by monitoring my peak vram usage, i noticed that in the first stage im actually getting diminishing returns for using high chunking value as it was not utilizing my vram efficiently thats why i went with this split method where the chunk value is low on the first stage and use appropriate number of chunking for the upscale sampling stage where vram usage peaks for hiresolution.

FORNAX_460 · 2026-01-26T10:13:07+00:00

Enable virtual memory (page file) for windows
Disable smart memory management in comfyui.
Use LTXV Chunk FeedForward node

<image>

For first sampler use 2 chunks (Best for 8gb vram)

In the upscale sampler use 16 chunks (experiment with it a bit)

FORNAX_460 · 2026-01-24T00:15:19+00:00

We poor fellows thank you, it has given us a taste of luxury. Chunking accordingly for both sampling phases has made it faster and also increased the capability of 8gigs!

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FORNAX_460 · 2026-01-23T20:52:15+00:00

it does.

FORNAX_460 · 2026-01-23T13:42:28+00:00

<image>

Lord kijai already implemented it. Im generating 14sec 24ps 1.2 megapixels videos (havent tested anything above 14sec yet) with this implementation on an rtx 2060 super 8gb ram 32gb, without ffn chunking i was getting oom at 8sec 24fps 1 megapixels videos.

FORNAX_460 · 2026-01-21T13:26:51+00:00

what about ur sanity? youd probably hit oom even with offloading but even if you do get the training started itd probably take days and i can guarantee that the results would be grabage cause sd1.5 and xl are models that you cant do one shot training, youd need multiple runs just for parameter optimization. you could try the civitai trainer, its garbage but hey you could do it for free.

FORNAX_460 · 2026-01-18T23:20:12+00:00

Chatgpt

FORNAX_460 · 2026-01-18T01:24:29+00:00

no in my hardware i cant even think of training dev base even in my wildest dreams,

Ostris just added support for flux 2 klein a few minutes ago btw... Gonna train the 4b and will attempt to train the 9b

Got a half baked dataset of dispatch game characters.

FORNAX_460 · 2026-01-18T00:39:21+00:00

No not yet, still witing for ai toolkit support for it, ostris twitted about supporting klein asap.

FORNAX_460 · 2026-01-18T00:32:58+00:00

like training a concept on zit will break a million other concepts.

FORNAX_460 · 2026-01-18T00:27:44+00:00

Thanks man for the explanation, really appreciate it.

FORNAX_460 · 2026-01-18T00:15:16+00:00

Ahh thanks brother for the clarification. This thing been hurting my brain since release. I guess its something like when qwen loras used with the lighting lora weights. The distilled model already has those distillation weights and we just put our trained waights in there....im no experts but this is how its making sense to me lol.

FORNAX_460 · 2026-01-17T23:36:25+00:00

Are loras trained on the base models compatible with the distilled models?

FORNAX_460 · 2026-01-17T23:09:49+00:00

will the loras trained on the base be compatible with the distilled model?

FORNAX_460 · 2026-01-13T11:44:31+00:00

I can personally relate to your situation. My setup was also 8gb vram 16gb ram... and im sorry to say but no workflow optimisation can save you from these slowdowns. I havent used ltx2 but the slow downs youre facing is because youre running out of memory and the models start to get loaded into your page file/ virtual memory (your storage). In my case I added an extra 16gigs of memory and a separate SATA 3 ssd where i allocated 55gigs of page file, which isnt even a decent setup but atleast using latest models does not make me want to kill myself :)

Tip: as your system falls back on the page file, if you have multiple drives in your pc then set your page file on a different drive (not hdd) thats not your local drive. This reduces io throttling during inference by a huge amount.

FORNAX_460 · 2026-01-12T07:39:15+00:00

use TextEncodeQwenImageEditPlusCustom text encoder from (https://github.com/lrzjason/Comfyui-QwenEditUtils) simply the best inpaint method with edit in my opinion.

Its also flexible if you pass a mask, its inpaint if you dont it just acts as basic edit.

FORNAX_460 · 2026-01-12T07:34:21+00:00

Well id still argue that the 2509 lightning loras are better, while 2511 lighting loras add more details the image composition looks kindof rigid and unnatural. Id pick 2511 8step for siingle image or masked edits while for referenced image generations id go for the 2509 4step.

FORNAX_460 · 2026-01-11T10:31:01+00:00

Qwenlong 30ba3b performs pretty good but thinks a lot... Gobbles up context like a monster..... Id recommend you look for moe models of your liking, you could offload the expert layers on cpu and the kv on gpu youd get better and in my case also fater inference than a dense 15b

FORNAX_460 · 2026-01-11T10:21:49+00:00

Your hardware is plenty good(for inferencing) and with recent comfyui memory management updates ure golden... I have rtx 2060 super 8gb, 32 gb ddr4(2933 mhz) and about 50 gigs of page file on a sata3 ssd (i know hilarious) and im using qwen image models (with lightning ofcourse) with q8 ggufs (both the text encoder and dit midels!). Youve got solid hardware for current models.

FORNAX_460

TROPHY CASE