you have over 24GB of VRAM you should be good to go with *„NONE“*. *„float8“* would be slower. 

Now head over to the *„Training“* shard. Change the *„Timestep Type“* to *„Sigmoid“* and activate *„Cache Text Embeddings“*. Don’t change anything else here, specially not the *„Learning Rate“*, increasing it could cause it to crash. After you trained your first model you can try to increase the steps, if you think you need more, but 3000 should be enough.

After this, go the *„Dataset“* shard. Here you just select your dataset. 

The last step are the sample prompts. If you’re using a trigger word, make sure you include it in your sample prompt, otherwise the samples wouldn’t apply the LoRA properly. 

Now you can start your training job.



The training should take about 2 hours on a RTX 5090. After the training is done, go through your samples and decide which model is the best. After all my tries, I found the 3000 steps one the most useful.

And that’s it, now you got a Character LoRA for Z-Image Turbo.



**A few words in the end**

I hope this guide helped you guys to just easily train a Character LoRA for ZIT without your brain melting. I know that I didn’t explain much about what each setting does, but this wasn’t my intent with this guide. It just should walk you easily through the steps which are needed to train a LoRA. 

I’m open for hints or improvements of the guide and I would love to exchange information and research about this topic.

https://redd.it/1pqjav6
@rStableDiffusion

This is your ai girlfriend
https://redd.it/1pqk9jq
@rStableDiffusion

This is how i am able to use Wan2.2 fp8 scaled models successfully on a 12GB 3060 with 16 GB RAM.

A little info before i start. When i try generating the normal way with the default workflow, the high noise part always succeeds, but it OOMs or outright crashes when switching to the low noise node. So now i know atleast the high noise works.

I also saw someone use the low noise model as a T2I generator. So i tried that and it worked without issues. So both of the models work individually but not continously on this card.

So what if there was a way to save the generated high noise data, and then feed that into the low noise node after clearing tha RAM and VRAM.


Here is the method i tried that worked.



https://pastebin.com/4v1tq2ML



step 1 - Disable the low noise group so only the high noise group is active. Click run. it will

save the data with the 'Save Latent' node.

After its done, it should save a .latent file in outputs/latents.


step 2 - Important. Unload models and execution cache.

you can use this

https://preview.redd.it/t4bm4rcd558g1.png?width=559&format=png&auto=webp&s=7a2127e439dd5ec2d19db57e3bded7fd4db2d459





or if you have installed christools, use these two





https://preview.redd.it/gew5jybh558g1.png?width=1155&format=png&auto=webp&s=dc2bb969a65254e8326c0533b78f010e1a8dd71d





sometimes you have to click this twice to work. make sure vram is cleared or it will definately throw out an OOM




step 3 - Disable the high noise group and enable the low noise group.


step 4 - Open the output/latents folder and drag the .latent file on this node. or just upload it

the normal way.

https://preview.redd.it/8nsmoehi658g1.png?width=203&format=png&auto=webp&s=7e0f5aeee21fe23cb947f6cadd4ccc6aa732448c


Click run.

https://reddit.com/link/1pqip5g/video/mlokkyta758g1/player

this is generated using fp8 scaled model on 3060 and 16 GB ram.




https://reddit.com/link/1pqip5g/video/hb3gncql758g1/player

here is the the same video with upscaled and with frame interpolation, The output set to 32fps.





the original video is 640x640, 97 frames, took 160 seconds on high and 120 seconds on low. thats around 5 minutes. the frame interpolated took a minute longer.


if you are using an older GPU and you are stuck with weaker quant ggufs like Q4, try this method with Q5 or Q6.


I am sure there is a better way to do all this. like adding the Clean vram node between the switch. It always runs out of memory for me. This is the way that has worked for me.


You can also generate multiple high noise latents at once. And then feed that data to the low noise node one by one. That way you can generate multiple videos with just loading both the models once.

https://redd.it/1pqip5g
@rStableDiffusion

Qwen-Image-Layered Released on Huggingface
https://huggingface.co/Qwen/Qwen-Image-Layered

https://redd.it/1pqnghp
@rStableDiffusion

Advice for beginners just starting out in generative AI

Run away fast, don't look back.... forget you ever learned of this AI... save yourself before it's too late... because once you start, it won't end.... you'll be on your PC all day, your drive will fill up with Loras that you will probably never use. Your GPU will probably need to be upgraded, as well as your system ram. Your girlfriend or wife will probably need to be upgraded also, as no way will they be able to compete with the virtual women you create.


too late for me....



https://redd.it/1pqqfqv
@rStableDiffusion

TurboDiffusion: Accelerating Wan by 100-200 times . Models available on huggingface

https://redd.it/1pqxxoh
@rStableDiffusion

NoobAI Flux2VAE Prototype

https://redd.it/1pqy27v
@rStableDiffusion

New Desktop UI for Z-Image made by the creator of Stable-Fast!
https://redd.it/1pr5a03
@rStableDiffusion

GOONING ADVICE: Train a WAN2.2 T2V LoRA or a Z-Image LoRA and then Animate with WAN?

What’s the best method of making my waifu turn tricks?

https://redd.it/1pqxw2j
@rStableDiffusion

I added a lot more resources in photographic tools for SDXL.

https://redd.it/1praani
@rStableDiffusion