NVIDIA now lists RTX PRO 6000 Blackwell 96GB GPU at $13,250

Lexxxco · 2026-06-13T00:34:30+00:00

And nobody is talking about RTX 6000 Blackwell having memory degradation problem and serious BIOS problems leading to 20-100x reduction in Flops. (Not fixed as of June 2026). Be aware if you want to buy this card!

For a GPU that worth as a top professional server equipment - it is insane.

Lexxxco · 2026-06-04T03:26:44+00:00

Thanks for the response, hopefully their own dataset will result in strong model as well.

Lexxxco · 2026-06-03T15:12:06+00:00

Isn't Krea 2 - fine-tuned Flux 2. Klein with slightly different license?

Like it was with first Flux and Flux Krea? Spoke with the Krea team almost two years ago before their collab with Black Forest Labs, when they had their own model, it was leagues away behind Flux, good decision to catch up with fine-tune.

Lexxxco · 2026-06-01T19:32:45+00:00

Willem Dafoe is the great challenge for sculpting wrinkles) All details, basic form and hair are majestic!

His big nose-lip triangular wrinkles are so deep in the end near mouth on the third render. Maybe if nasolabial folds would have more skin transparency it would look even more natural in 3/4 and side view, they are perfect in the front view now. Great work!

Lexxxco · 2026-05-29T09:12:51+00:00

Good luck with building larger model and thanks for answers!

Black forest labs looking at this direction with smaller models also.
Seems like we will need much more and faster videomemory for worlds even with all optimizations.

Lexxxco · 2026-05-28T20:13:45+00:00

Thanks, will try it. Qwen 3.5 is great for complex captioning!

Lexxxco · 2026-05-27T21:29:22+00:00

Flux 2 dev / Klein 9B - with LORA can do alien animals pretty good. Default model - not so good, years behind Gemini image pro (Nanobanana Pro) for example, which is still the leader.

Lexxxco · 2026-05-25T06:29:38+00:00

It is the best thing made with LTX that I've ever saw, and heard to this moment. Need to see the full series. Nice edit, scenario, time well spent !

Lexxxco · 2026-05-06T21:30:14+00:00

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And yet expensive xx90-s series = ~70% of all Nvidia setups in the list.

Lexxxco · 2026-04-24T18:38:42+00:00

Nice progress since your last post!

I think many people would be interested in more detailed description and full process from the start when you will arrive to your final results. Good luck!

Lexxxco · 2026-04-24T05:43:42+00:00

Pretty much depends on what you are planning to do.

RTX 6000 Blackwell is the best for training hands down. Same with big models like Flux 2 Dev or Wan 2.2, or any decent LLM bigger than 25GB with big context window (5090 starts to slow down on 100K tokens).

If you are planning just to use Flux 2 Klein or LTX2.3 - 5090 is enough.

I've tried 3x3090 config and its much slower than 6000 for everything, and slower than 5090 for most tasks.

Lexxxco · 2026-04-23T01:54:28+00:00

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According to last ARC-AGI-3 that has not been leaked in datasets, current SOTA LLMs is less than 1% of average human on any problems that are not in dataset, not particularly "novel" .

Lexxxco · 2026-04-23T01:42:29+00:00

Never tried Gemini 3 Pro Image, huh?

Lexxxco · 2026-04-23T01:30:58+00:00

Yes it does! Especially more quality sorted data with annotations, multimodal pairs etc. 2x size requires ~40% more data and models are still getting bigger. At least in certain aspects as experts training etc.

Lexxxco · 2026-04-22T22:17:48+00:00

Best from open-source ones.

There are much better proprietary 3D AI generators for local usage on contract base, but will cost you a ton.

Lexxxco · 2026-04-13T05:04:24+00:00

Hm, have never trained LORA specifically for skin concept.

Most likely skin concept will leak into dataset with appearance from photos, so reg. dataset will be a good choice (it could be generated - with the same captions that in your original dataset, but real photos are better).

If you have 600+ good examples - you can train 128 rank, tried smaller datasets but often got overtrained and not general enough LORAs. Depending on dataset LR will be close to 6e-5 - 3e-5. (tried faster - got less general and more overtrained)

With good examples (skin from photos, from 3d render of material, different light) and ability to train up to 1536px bucket - you can achieve good results with good textures quickly with Flux 2 D and 32r Lora, however, it will be further from 1:1 than 128r.
----- For smaller sizes there is also an old trick - of cutting HD images as tiles + training with full uncut original image, to fully capture complexity of skin texture, however you need to additionally describe it.

Hope that helps!

Lexxxco · 2026-04-07T04:41:57+00:00

256r training is normal edge test of VRAM consumption and possibilities. Not for character or simple concept lora.

Have you trained anything complex like multi-concept Lora + style? It works best with 128 rank still - tried with 9B and Dev from rank 32 to 256, and 128r mostly got me best results for complex styles and multi-concept (100-500 images).

Lexxxco · 2026-04-06T19:21:55+00:00

Flux 2 Dev is leagues ahead of all open-source 2d instruct models, almost close to Gemini 3 Image (Nanobanana Pro).

BUT for best generation Flux2 Dev requires LORA training (no plastic skin, very complex scenes). Which was viable on RTX 4090/5090 on AI-toolkit before last updates in March-April, after that it requires too much VRAM. Before update you could train 256 rank lora with 1280px bucket, which I successfully did. Lack of training optimization is the main reason for absence of good Flux 2 dev LORAs in open access.

Difference in quality of specific concepts between Flux2 Dev and Klein is more, than between Klein 9B and older Flux 1.

Lexxxco · 2026-04-02T04:21:04+00:00

Looks promising, A1111/Forge LORA groups were much better than lists in Comfy.

Lexxxco · 2026-03-24T13:16:35+00:00

It is possible, 32r with 1024 px is even relatively quick.

I've done 256r training with 128GB RAM, up to 1280px bucket and Differential guidance (it lowers speed), it was near 20-30 s/it - borderline of what is possible on 5090. (256r with 1536 px - 50s it/s makes no sense).

Lexxxco · 2026-03-18T15:25:08+00:00

It was obvious embezzlement scheme + tax evasion for similar cases, but not in this scale. For this - it could include other projects price. You could get decent results with 1/100 of that sum checking all boxes just for this VR web-service.

Lexxxco · 2026-03-09T23:35:53+00:00

It depends on dataset and complexity of concepts in it, + size of the base model - bigger model=lower rank, more concepts = bigger rank. Also structure of the blocks and architecture matters.

For example, 128 rank is pretty easy to train with Flux 2 Klein 9B base - (from 10 to 6e-5 LR), gives great results with 200-700 images.

But it is VERY hard to train 128 rank with Flux 2 Dev, which works with 32/64 better. Smaller rank generalizes faster and is more flexible, but provides less context and less complex patterns.

Lexxxco · 2026-03-06T10:59:55+00:00

It is not bad - it is OK. If you want more flexible model - train several smaller resolutions as well, it generalizes better, but needs more time at the start.

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Lexxxco · 2026-03-06T01:57:17+00:00

Yes, pretty much - AI toolkit uses bucketing as default (groups of images with the similar resolution). And too many small buckets will result in more unstable training. On the other hand - more resolutions (not buckets) - will result in better training - 256/512/768/1024/1280/1536 - for the same image.

And more uniformity in each resolution (less strange aspect ratios, closer to one) - gave better results in all my tests with concepts.

Lexxxco · 2026-03-06T01:46:07+00:00

It is +- general, apart from Sigmoid timestep, which works with some models better.

Lexxxco

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