Qwen Image LoRA trainings Stage 1 results and pre-made configs published - As low as training with 6 GB GPUs - Stage 2 research will hopefully improve quality even more - Images generated with 8-steps lightning LoRA + SECourses Musubi Tuner trained LoRA in 8 steps + 2x Latent Upscale
Added 2025-09-04 14:49:25 +0000 UTCQwen Image LoRA trainings Stage 1 results and pre-made configs published - As low as training with 6 GB GPUs - Stage 2 research will hopefully improve quality even more - Images generated with 8-steps lightning LoRA + SECourses Musubi Tuner trained LoRA in 8 steps + 2x Latent Upscale
1-click to install SECourses Musubi Tuner app and pre-made training configs shared here : https://www.patreon.com/posts/137551634
Hopefully a full video tutorial will be made after Stage 2 R&D trainings completed
Example training made on the hardest training which is training a person and it works really good. Therefore, it shall work even much better on style training, item training, product training, character training and such
Stage 1 took more than 35 unique R&D Qwen LoRA training
1-Click installer currently fully supporting Windows (local), RunPod (Linux & Cloud) and Massed Compute (Linux & recommend Cloud) training for literally every GPU like RTX 3000, 4000, 5000 series or H100, B200, L40, etc
28 images weak dataset is used for this training
More angles having dataset would perform definitely better
Moreover, i will make a research for a better activation token as well rather than ohwx
After Stage 2, I am expecting hopefully much better results
As a caption, i recommend to use only ohwx nothing else, not even class token
Image prompts randomly generated with Gemini 2.5 in Google AI Studio for free
Download app and configs : https://www.patreon.com/posts/137551634
How to Generate Images
In the zip file of this post : https://www.patreon.com/posts/114517862
We have Amazing_SwarmUI_Presets_v21.json made for SwarmUI
Import it and i am using Qwen Image 8 Steps Ultra Fast to generate images and then apply Upscale Images 2X to make them 4x resolution (1328x1328 to 2656x2656)
Of course in addition to preset don't forget to select your trained LoRA - I used LoRA strength / scale = 1
This tutorial shows it : https://youtu.be/3BFDcO2Ysu4
Comments
yes it doesnt keep step count you have to recalculate. ofc there will be configs for every gpu same as lora hopefully
Furkan Gözükara
2025-10-10 23:38:55 +0000 UTCFor the dreambooth option that's being worked on in the merged main branch, I think it was said works as low as 5 GB VRAM. But doesn't that slow it down a lot? Will there be the option to have it run at the higher VRAM but without going over the amount in our GPUs (eg. say an option for about 20 or 22 GB VRAM use for 24 GB GPUS, whatever would be best). Would it also be possible to have the app so that if you stop a training (either Lora or Dreambooth) that the step and epoch counts will continue from where they left off when resuming from a state file? eg. currently what happens is, if you stop a lora training after eg. 1 epoch or 800 steps, that if you tell it to resume from the last saved state (say from saved epoch 1's state), then it starts the step counts again at 1, and it thinks it still needs to go to from step 1 to the same total steps as before (which it doesn't) and gives a low % complete after restarting. If I stopped it after step 800's state was saved and then resumed it with that state, it would be better I think if the first step it shows after resuming is step 801, and so the % complete it shows would be about the same as it was on the state you're resuming from.
cool1
2025-10-08 22:29:17 +0000 UTClora done i will update the post. i am working on dreambooth / fine tuning.
Furkan Gözükara
2025-09-24 11:32:17 +0000 UTCAre the results of the newer Lora training tests coming soon? Is that still being tested or the results checked? Is dreambooth training possible with it? Someone said on on github that he'd run Qwen fine tuning with Musubi trainer (though he originally got out of memory errors). So maybe it could be added to a GUI. Though I don't know if it needs to be done at lower res for <=24 GB VRAM PCs. Though there's also the Hunyuan 2.1 that might be good/better for Loras in future if it's higher res and if it can run and train with the quantization.
cool1
2025-09-24 11:25:15 +0000 UTCover 128 should work but i didnt test. the reason is that when you go over 128 the benefit is so little. i think above 128 doing fine tuning / dreambooth more logical
Furkan Gözükara
2025-09-13 23:05:32 +0000 UTCIn your testing have you tried any tests with net rank >128 if it supports that? kohya ss GUI supported over 128 net rank. Though your previous GUI used to say under the net rank "[RECOMMENDED] LoRa rank/dimension. 16 for Qwen Image. Higher = more capacity bug larger files. Range: 8-128". It no longer shows a range under it now. I assume the text was removed because qwen image should likely have net rank >16. But if it supports>128 net rank I assume that could give better quality for the Lora, even though i twould probably increase VRAM/RAM and training time to increase it a bit above 128. I assume the "recommended" in it meant those were only the recommended settings, and if so maybe it's worth trying over 128.
cool1
2025-09-12 14:30:15 +0000 UTCplease look screenshots. give parent folder path and click generate dataset button
Furkan Gözükara
2025-09-10 21:39:25 +0000 UTCI'm getting RuntimeError: Latent caching failed with return code 1 -- and I created cache_dir to match the toml
doug m
2025-09-10 18:33:53 +0000 UTC
