ohh so thats why.. damn...wish i knew this before paying for runpod
ohh so thats why.. damn...wish i knew this before paying for runpod
Fyep that is unfortunate they dont have browser gui
Ftheir gui also sucks so bad
CSince I've been deep in training with the command line, I had a few thoughts on what might be needed to get it fully working for models like Qwen .
The most important thing would be a dropdown at the top to choose the trainer script, like for qwen_image_train_network.py or flux_kontext_train_network.py, since they use diferent scripts.
In the Advanced tab, I definately needed options for the attention mechanism (--sdpa / --xformers) and the fp8_base / fp8_scaled toggles.
Those were super important to get my training running on a 24GB card without errors.
Also, the Network Module dropdown needs to have the specific options for each model, like networks.lora_qwen_image. My training failed until I found the right one for Qwen.
One small thing, maybe the precision setting (bf16/fp16) could be a dropdown insted of checkboxes?
Might be less confusing.
Just some ideas from my recent training hell lol.
The layout is great though, really clean. Keep up the awesome work!
The most important thing would be a dropdown at the top to choose the trainer script, like for qwen_image_train_network.py or flux_kontext_train_network.py, since they use diferent scripts.
In the Advanced tab, I definately needed options for the attention mechanism (--sdpa / --xformers) and the fp8_base / fp8_scaled toggles.
Those were super important to get my training running on a 24GB card without errors.
Also, the Network Module dropdown needs to have the specific options for each model, like networks.lora_qwen_image. My training failed until I found the right one for Qwen.
One small thing, maybe the precision setting (bf16/fp16) could be a dropdown insted of checkboxes?
Might be less confusing.
Just some ideas from my recent training hell lol.
The layout is great though, really clean. Keep up the awesome work!
Fi am simplifying
Fqwen tab only qwen 
COk. But always open gate to add more functionality later xd
F
F
F
CHere are the most critical things:
FP8 Options: We definately need two checkboxes in the "Advanced" tab: one for --fp8_base and one for --fp8_scaled. These were the biggest VRAM savers and the training wouldn't even start without them.
Blocks to Swap: Also in "Advanced", we need an input box for --blocks_to_swap. For a 1024x1024 training on a 24GB card, I had to use a value of 20 to prevent crashes.
Attention Mode: A dropdown to select the attention mechanism is also essential.
My training only worked after I set it to --sdpa.
Network Module: This one is critical! In the "LoRA Parameters" tab, the dropdown for Network Module absolutely needs to have networks.lora_qwen_image as an option. The generic networks.lora module made my training crash with an error.
One other small idea, maybe the Precision setting in the "Source Model" tab could be a dropdown menu (bf16, fp16) insted of separate checkboxes?
Might prevent users from accidentally clicking more than one.
For bonus points, adding fields for advanced stuff like LoRA+ (the network_args field you have is perfect for this!) and PyTorch Dynamo (--dynamo_backend) would make this the ultimate tool.
Hope this feedback from my command-line struggles is helpful! The GUI is already looking amazing. Can't wait to try it when it's ready. Keep up the awesome work!
FP8 Options: We definately need two checkboxes in the "Advanced" tab: one for --fp8_base and one for --fp8_scaled. These were the biggest VRAM savers and the training wouldn't even start without them.
Blocks to Swap: Also in "Advanced", we need an input box for --blocks_to_swap. For a 1024x1024 training on a 24GB card, I had to use a value of 20 to prevent crashes.
Attention Mode: A dropdown to select the attention mechanism is also essential.
My training only worked after I set it to --sdpa.
Network Module: This one is critical! In the "LoRA Parameters" tab, the dropdown for Network Module absolutely needs to have networks.lora_qwen_image as an option. The generic networks.lora module made my training crash with an error.
One other small idea, maybe the Precision setting in the "Source Model" tab could be a dropdown menu (bf16, fp16) insted of separate checkboxes?
Might prevent users from accidentally clicking more than one.
For bonus points, adding fields for advanced stuff like LoRA+ (the network_args field you have is perfect for this!) and PyTorch Dynamo (--dynamo_backend) would make this the ultimate tool.
Hope this feedback from my command-line struggles is helpful! The GUI is already looking amazing. Can't wait to try it when it's ready. Keep up the awesome work!
F
CYou think about everything
CBoss
CI learned a lot from you btw.
Jnetwork dim , network alpha, vae_cache cpu?
Jnext thing you know , you rewrote koyha_ss gui 
Athis is just amazing
Athe doctor
Amaster of neural thinking
ADIM NETWORKS
Ajusst amazing
Jneural thinking > neurotic thinking
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F
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Fi will also add a feature to auto remember and reload last used config
CCaching will be automatically before trening process or we will need do do separate?
JAwesome
JI am impressed 
JWill you make it generic or easily adaptable so you can reuse it for Wan 2.2 training?
Fgood question i need to ensure it happens auto
Fwhen selected
Fyes i plan to add wan 2.2 as well
Fafter qwen done
CHow is going ?? WHen you plan publish ?? I asking as i am about start new big dataset trainig and not sure if wait for GUI or ... ???
S@cyberbol what GPU did you use for Wan2.2 training?
C4090 (24 GB VRAM) – it was fine, but I’m now collecting a better dataset for my final LoRA for WAN 2.2.
For example, right now when I train WAN 2.2 with high noise and low noise… the low noise results are actually better when I use my old LoRA from WAN 2.1.
So currently I’m using WAN 2.2 high noise for the high noise pass, and my old WAN 2.1 LoRA for the low noise pass.
I’m not sure why… Most of the issues seem to be with human eyes.
I think what I need to do is:
for low noise → collect a dataset with high resolution, close-ups on skin details, eyes, etc.
for high noise → use more general shots of the body, face, etc.
For example, right now when I train WAN 2.2 with high noise and low noise… the low noise results are actually better when I use my old LoRA from WAN 2.1.
So currently I’m using WAN 2.2 high noise for the high noise pass, and my old WAN 2.1 LoRA for the low noise pass.
I’m not sure why… Most of the issues seem to be with human eyes.
I think what I need to do is:
for low noise → collect a dataset with high resolution, close-ups on skin details, eyes, etc.
for high noise → use more general shots of the body, face, etc.
CKey Training Parameters for WAN2.2 (for RTX 4090)
For the High-Noise model:
Optimizer: adamw8bit (a key optimization for 24GB VRAM)
Precision: fp16 and fp8_base
Learning Rate: 3e-4
LR Scheduler: polynomial with power=8
Network Dim / Alpha (LoRA Size): 16 / 16
Gradient Checkpointing: Enabled
Timestep Range:
min_timestep: 875
max_timestep: 1000
For the Low-Noise model:
All parameters are identical to the High-Noise model, except for the timestep range.
Optimizer: adamw8bit
Precision: fp16 and fp8_base
Learning Rate: 3e-4 or other 2e-4
LR Scheduler: polynomial with power=8
Network Dim / Alpha (LoRA Size): 16 / 16
Gradient Checkpointing: Enabled
Timestep Range:
min_timestep: 0
max_timestep: 875
For the High-Noise model:
Optimizer: adamw8bit (a key optimization for 24GB VRAM)
Precision: fp16 and fp8_base
Learning Rate: 3e-4
LR Scheduler: polynomial with power=8
Network Dim / Alpha (LoRA Size): 16 / 16
Gradient Checkpointing: Enabled
Timestep Range:
min_timestep: 875
max_timestep: 1000
For the Low-Noise model:
All parameters are identical to the High-Noise model, except for the timestep range.
Optimizer: adamw8bit
Precision: fp16 and fp8_base
Learning Rate: 3e-4 or other 2e-4
LR Scheduler: polynomial with power=8
Network Dim / Alpha (LoRA Size): 16 / 16
Gradient Checkpointing: Enabled
Timestep Range:
min_timestep: 0
max_timestep: 875
Fmy research would take at best several days
Cuntil publish gui ?? ohhhh..
Fyep
Fi need to train and test
Cok, so i will use just command by now
CI'm looking for the latest info and best practices for training WAN 2.2 LoRAs, especially for character likeness on a 24GB card.
From what I've gathered so far, it seems the best dataset approach is to train the high-noise model on low-resolution videos (to learn motion) and the low-noise model on high-resolution images (to learn the fine details).
I also saw in the new Musubi-Tuner docs that you can apparently train both high and low noise LoRAs at the same time now, using the --dit (for low) and --dit_high_noise (for high) parameters in a single command. For 24GB VRAM, the key seems to be adding the --lazy_loading option to only keep the active model on the GPU.
Has anyone tried this simultaneous training method yet?
From what I've gathered so far, it seems the best dataset approach is to train the high-noise model on low-resolution videos (to learn motion) and the low-noise model on high-resolution images (to learn the fine details).
I also saw in the new Musubi-Tuner docs that you can apparently train both high and low noise LoRAs at the same time now, using the --dit (for low) and --dit_high_noise (for high) parameters in a single command. For 24GB VRAM, the key seems to be adding the --lazy_loading option to only keep the active model on the GPU.
Has anyone tried this simultaneous training method yet?
JNo need for the lazy-loading as you can just train the two lora's after each other (in command line it auto continues to the 2nd command)
CI know. But y can also Train both same time
JIt probably wont be faster, and you could decide to just turn off your pc after the 1st lora training if you decide that
CProbably you right. I just experiment..we will know much better in few weeks probably..
J+ I may want to use less epochs or learning rate for the other model
Ji noticed the high noise model gets overtrained quicker
