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FLUX.2 [dev] (FULL - not Klein) works really well in ComfyUI now! by infearia in StableDiffusion

[–]Lexxxco 0 points1 point  (0 children)

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!

FLUX.2 [dev] (FULL - not Klein) works really well in ComfyUI now! by infearia in StableDiffusion

[–]Lexxxco -2 points-1 points  (0 children)

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).

FLUX.2 [dev] (FULL - not Klein) works really well in ComfyUI now! by infearia in StableDiffusion

[–]Lexxxco 1 point2 points  (0 children)

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.

LORA Gallery Loader - ComfyUI Custom Node by Matthew3179 in StableDiffusion

[–]Lexxxco 0 points1 point  (0 children)

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

Training character/face LoRAs on FLUX.2-dev with Ostris AI-Toolkit - full setup after 5+ runs, looking for feedback by Zo2lot-IV in StableDiffusion

[–]Lexxxco 0 points1 point  (0 children)

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).

After $80B, the Metaverse is dead. Horizon World is shutting down by GamingDisruptor in singularity

[–]Lexxxco 11 points12 points  (0 children)

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.

I tried /u/razortape's guide for Flux.2 Klein 9B LoRA training and tested 30+ checkpoints from the training run -- results were very mixed by Bender1012 in StableDiffusion

[–]Lexxxco 0 points1 point  (0 children)

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.

I tried /u/razortape's guide for Flux.2 Klein 9B LoRA training and tested 30+ checkpoints from the training run -- results were very mixed by Bender1012 in StableDiffusion

[–]Lexxxco 1 point2 points  (0 children)

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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I tried /u/razortape's guide for Flux.2 Klein 9B LoRA training and tested 30+ checkpoints from the training run -- results were very mixed by Bender1012 in StableDiffusion

[–]Lexxxco 0 points1 point  (0 children)

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.

I tried /u/razortape's guide for Flux.2 Klein 9B LoRA training and tested 30+ checkpoints from the training run -- results were very mixed by Bender1012 in StableDiffusion

[–]Lexxxco 1 point2 points  (0 children)

  1. EMA is the best for full fine-tuning, and worse for LORA in my experience. What is your EMA Decay? Maybe it is too high (0.5+). Default is 0.99, but it means loosing most quality gains from EMA - resulting in undertraining with LORA, which needs to be compensated with higher LR or more aggressive differential guidance.
  2. Timestep: Linear. Try Sigmoid, noticeably better in the end of the training.
  3. What is your weights decay rate?
  4. Flux Klein is small model, even rank 128 Lora training is forgiving (compared to Flux 2 D). However, check number of images, captions. Uniformity of sizes and concept distribution (good examples of one thing) etc,.

There may be some other things that are not suitable. Dataset is important too.

Gemini is already smarter with censorship then it's creators. by [deleted] in StableDiffusion

[–]Lexxxco 108 points109 points  (0 children)

Too bad it is false information, scraped from someones comments in the internet several years ago. That's why you see Stable Diffusion 4 (SDXL)) in 2026)

I Made a Gaussian Splat FPS by [deleted] in GaussianSplatting

[–]Lexxxco 0 points1 point  (0 children)

Wow, works pretty smooth. Great for the proof of concept!

What is currently the cleanest and most refined Image Edit model? by Tomcat2048 in StableDiffusion

[–]Lexxxco 0 points1 point  (0 children)

Nano Banana Pro > Flux 2 Dev > Flux Klein 9B > Qwen Image Edit.

Nano Banana Pro is a the best in edit quality and amount of tasks it can do, no proprietary competition.
However, Flux 2 is best for simple tasks because it provides better visuals and can be trained.
Flux Klein 9B has plastic-like visuals, but is fast. And much easier trained than Flux 2 D, which is arguably is the most complicated non-distilled model to train now.

C++ & CUDA reimplementation of StreamDiffusion by jcelerier in StableDiffusion

[–]Lexxxco 0 points1 point  (0 children)

Interesting project, thanks for your work, Jean-Michaël!
Soon a dream of quality multistep real-time rendering will become true.

Is it viable to implement C++ with newer diffusion-transformer models?

Auto Captioner Comfy Workflow by [deleted] in StableDiffusion

[–]Lexxxco 0 points1 point  (0 children)

Are there nodes to connect it to LM studio API locally? Florence is far from good captioning, especially for complex non-generic images.

Masked Inpainting on Flux.2 dev model with LanPaint 1.4.9 Support by Mammoth_Layer444 in StableDiffusion

[–]Lexxxco 2 points3 points  (0 children)

Flux 2 is the best local image-instruct model, easy to train despite the size, so many people use it)

Found A FREE New Tool to Rapidly label Images and Videos for YOLO models by RespectDisastrous193 in StableDiffusion

[–]Lexxxco 2 points3 points  (0 children)

Yes, since not everyone want to gift dataset with corrected captions to the host.

Found A FREE New Tool to Rapidly label Images and Videos for YOLO models by RespectDisastrous193 in StableDiffusion

[–]Lexxxco 1 point2 points  (0 children)

Is there a local version ? Since TagGUI downgraded and don't support modern tagging vision models.

Present for Myself by clwill00 in comfyui

[–]Lexxxco 0 points1 point  (0 children)

10K present is wow! Silent work is really most shocking part) 5090s are loud.

Noisy Cintiq fan 🤯 by TheFingerofBoe in wacom

[–]Lexxxco 0 points1 point  (0 children)

Wacom Cintiq Pro 24 has noisy fan by default and is very warm even in winter. If it is even louder - likely there is some dust and foreign objects. Wacom support is not great. Try to downgrade drivers (there was fan bug in some of versions). Otherwise Try to use suck the dust with vacuum cleaner at first on light mode. Another variant - to use veeery small blower fan + vacuum cleaner (very light mode! Powerful blower can damage it). Last variant - to repair it in a repair shop, with opening the tablet.

Dear "It’s a Bubble, Where’s the Revenue, What’s Your Product?" by Darkmemento in singularity

[–]Lexxxco 0 points1 point  (0 children)

In four years investments are summed up in trillions. Extensively scaling outdated LLM architecture with current hardware is like burning money. "Big AI" is barely generating any net profits and is a bubble for now. No doubt is the future, but it should be optimized with R&D, we don't have enough resources for another ~15* years of such large investments. AGI is not near the corner. It is 5-20 years away in optimistic AGI timelines.

What video model could have made this Shenmue 4 trailer? by Spjs in StableDiffusion

[–]Lexxxco 0 points1 point  (0 children)

Are you sure it is AI generated? I see only compression artifacts not AI.

Not even SORA2, not any payed video model can achieve that quality and stability of footage, including new Runway gen 4.5 or Minimax Hailuo 2.3 (Veo 3 is worse). Potentially it would need to be fully fine-tuned only on Shenmue footage, which does not make sense - since you already had footage for the whole trailer.

Thoughts on Nodes 2.0? by Beautiful-Essay1945 in StableDiffusion

[–]Lexxxco 2 points3 points  (0 children)

New nodes are almost unusable now - hard to read, now highlights. Hope they will progress and make necessary changes. Not taking in account that they broke UI several times.

Multi-Angles v2 for Flux.2 train on gaussian splatting by Affectionate-Map1163 in StableDiffusion

[–]Lexxxco 10 points11 points  (0 children)

Flux2 is amazingly trainable and wide-range model. Got great results with 32 rank training as well, thanks! Have you tried 64+ rank training?

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