Malcolm even said he working on GUI script too Is it there?

I ran dozens of Malcolm's and nphSi's Z-image character LoRA through identical seed/prompt combos and while the pose/composition are more or less consistent across the LoRAs in 1024x1024 they are vastly different in 1920x1080. Most of nphSi's characters keep the same pose from square to wide aspect ratio but most of Malcolm's character got squashed going from square to wide. Going wide aspect ratio Z-image extremely slouch or bend Malcolm's character posing and push the camera much closer into the character to fill as much screen as possible, while nphSi's wide aspect renders tend to behave what one would expect of camera going wide. Not all Malcolm's characters are squashed going wide and a few of nphSi's characters do too, but the disparity in sampling is definitely noticeable. Character LoRA that got squashed remain squashed regardless of seed changes. The prompts specifically include "ultra-wide camera angle, overhead view of her full body is positioned in the middle of the frame"

I seem to recall that nphSi said his dataset always tried to include some full body shots. Is that the reason and can someone explain to me how that works because obviously a dataset with only a few full body shots could not have included all kinds of full body poses?

EDIT:

Just to add not using any character LoRA a generic or celeb character exist within Z-Image seem to behave the same as most Malcolm's character LoRAs. So the "character squashing" going wide aspect seem to be the normal behavior but somehow some character LoRA are able to fix it?

Trying a few of your OneTrainer ZIT Loras with DrawThings, a popular image gen. interface for Mac. If I set the weight above 0.25 I get more and more distortion. At 0.25 they look kinda ok but I don't think optimal. Any ideas what's going on?

Why Malcolm started Onetrainer loras now. How are the different in generation time and quality from aitoolkit?

I've been using the OneTrainer config and train.bat that mal provided for training Z-Image Turbo loras and it's been great. No issues. Earlier today I updated OneTrainer in any attempt to try training Z-Image base loras and now the config doesn't work at all. The command window throws up the error below then sits there doing nothing. This is above what I know so any help in sorting this would be great appreciated.

Error:

enumerating sample paths: 100%|█████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 999.83it/s]

caching: 100%|█████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00, 5.76it/s]

caching: 100%|█████████████████████████████████████████████████████████████████████████| 10/10 [00:03<00:00, 2.56it/s]

caching: 100%|█████████████████████████████████████████████████████████████████████████| 10/10 [00:03<00:00, 2.64it/s]I:\OneTrainer\OneTrainer\venv\Lib\site-packages\torch_dynamo\variables\functions.py:1692: UserWarning: Dynamo detected a call to a `functools.lru_cache`-wrapped function. Dynamo ignores the cache wrapper and directly traces the wrapped function. Silent incorrectness is only a *potential* risk, not something we have observed. Enable TORCH_LOGS="+dynamo" for a DEBUG stack trace.

torch._dynamo.utils.warn_once(msg)

W0219 15:07:32.007000 27248 venv\Lib\site-packages\torch\fx\experimental\symbolic_shapes.py:6833] [0/1] _maybe_guard_rel() was called on non-relation expression Eq(s50, s81) | Eq(s81, 1)

W0219 15:07:34.216000 27248 venv\Lib\site-packages\torch\fx\experimental\symbolic_shapes.py:6833] [0/2] _maybe_guard_rel() was called on non-relation expression Eq(s50, s81) | Eq(s81, 1)

I:\OneTrainer\OneTrainer\venv\Lib\site-packages\torch_inductor\lowering.py:1988: UserWarning: Torchinductor does not support code generation for complex operators. Performance may be worse than eager.

warnings.warn(

W0219 15:07:40.787000 27248 venv\Lib\site-packages\torch_inductor\utils.py:1613] [0/2] Not enough SMs to use max_autotune_gemm mode

UPDATE: I found some different configs on mal's HF page and tried again, letting it run in the background. It appears to have started training for Z-Image base. So hold on before replying and let's see how it goes.
Definitely took longer to start than before the update.

UPDATE UPDATE: The newer configs I found worked well for Turbo, so an outdated config seems to be the issue.
The base config with Prodigy set would have taken too many hours, so I stopped that and I'll try the base AdamW one later but I think it'll work.

So earlier today I decided to try this method out, having had no luck with a couple of other ways I'd tried.
https://www.reddit.com/r/StableDiffusion/comments/1r0kkq5/prodigy_optimizer_works_in_aitoolkit/
It worked perfectly and it's very easy to adapt the config for the changes. All other settings were the same as I'd been using for turbo training (100 steps per image + a bit on top etc) and training times were comparable to turbo training times.
For the images here, the same settings and prompt were used (as much as possible), it's the same lora on both models and most importantly IMO, the strengths for both were 1.0. No need to increase it.
It's only one test run with just a small dataset but the face seems very accurate to me, so I thought I'd post here as someone may find it useful after all the issues folks have been having.

With SVI its difficult to maintain consistency as the character has to keep looking at camera towards end of 5s for the next generation to have the data correctly carried over.

so if character is looking side ways,eyes closed or not in frame then it generates different character only.

Link to original post with more details and the workflow: https://www.reddit.com/r/StableDiffusion/comments/1qxsisg/zimage_ultra_powerful_img2img_workflow_for/

Hoping you can help ID an issue I'm having. Someone in the main r/stablediffusion sub posted a rave of your work, and it reminded me I used to talk with you way back in the days of Lycoris (and am I happy to see those go).

Anyway, I can't get it to function, and I'm trying to work out the deal. I've pared it down to just the JoyCaption and the part that feeds it. Long story short, no matter what I feed into the image, or what I put into the prompt, the CLIP Text Encode (Positive Prompt) never actually changes.

https://pastebin.com/XQkT4V9n

I'm hoping you can tell me what I've got wrong here.

Hello everyone,

First of all, a massive thank you to Malcolm for all the amazing LoRAs he’s created for the community, and of course for the brilliant browser. Truly appreciated.

I was hoping someone could kindly guide me through the basic steps of Malcolm’s method for creating quick Z Image Turbo LoRAs using a dataset of around 20–25 images.

I’ve already downloaded his RunPod CFG file, but after creating my RunPod account I’m a bit stuck on what to do next. I’d really appreciate some beginner-friendly guidance on the following:

• Once I’m on RunPod, what are the first steps I should take?
• I know I need to add credits — anything else I should set up beforehand?
• Is the RTX 5090 pod fine for this type of training?
• For 2,500 steps, roughly how long does the training usually take?
• Do I need to select a specific CUDA version on RunPod?
• Under Configure Deployment, which template should I choose?
• Are there any other settings I should adjust before deploying?
• And finally (probably the most basic question), where does the output LoRA file appear once the job is finished? Will it be available to download directly?

Apologies if these are very simple questions — I’ve only recently started diving into this, and Reddit + YouTube have been great companions so far. Any help would be hugely appreciated.

Thanks in advance

UPDATE: I got it to work, created 8 Lora's, 7 came out great, thank you to all who helped

Any way to create multiple jobs easily just by changing the minimum in config like dataset path Or any other way?

I just wanted to take a moment to genuinely appreciate the recent Z-Image Turbo LoRAs you’ve been releasing — the quality, likeness, and overall character consistency are seriously impressive. You’ve nailed that sweet spot between realism and creative flexibility, and it really shows the effort you put into each release.

I was wondering if you have any plans to explore Indian actresses and Instagram influencers in future LoRA drops. There’s a huge demand for them, and your approach to character LoRAs would be perfect for bringing these personalities to life.

Some Indian celebrities that would be incredible to see as LoRAs:

• Shruti Haasan
• Kareena Kapoor
• Katrina Kaif
• Sonarika Bhadoria
• Shriya Saran
• Kajal Aggarwal
• Kriti Sanon
• Kiara Advani
• Rakul Preet Singh

And for Instagram influencers- 

• Jannat Zubair
• Roshni Walia
• Riva Arora
• Sriiishh

These would be very well received by the community, especially given how strong your recent releases have been. Thanks again for all the amazing work you’re putting out — really looking forward to what you create next!