4. Have you tried the Catvton model ? It takes model and clothing images as input and generates mode
- Have you tried the Catvton model ? It takes model and clothing images as input and generates model wearing the input clothing
UNo, I haven't tried. Thanks for sharing. I will try it now. How was your experience with it? Does it work well with different lightning conditions?
SI've tried Simple Catvton workflow. It may not produce exact matching clothing sometimes(color/pattern mismatch). I've seen videos using some advanced workflow to get an exact clothing match. I haven't tried it yet.
SPlease let me know about your experience if you implement it for your usecase
USure, I will update you after trying it. Thank you.
Fthey bleed
FMedium
Thoroughly experimented with Fine-Tuning / DreamBooth training of Flux-dev-de-distill, PixelWave v03, Verus Vision and base FLUX Dev model…
Fof course you can use
Fespecially a model that already can generate similar quality / resembling images of what you want
J@Furkan Gözükara SECourses I am using a 48GB to create some finetuning. I used the 48 config and the learning rate is set at: 0.000004. That seems really low. It's taking about 7 hours on 4500 steps.
WI mean I train using SDXL not Flux i already saw some lora contain many characters
4I saw the tutorial, it is really impossible to train a model with my 4060 8gb and 32gb of ram ?
4And on runpod there is an new option to automatcly fine tune a model what do you think ?
Fyes it is normal
Fsdxl is different
Fyes you need 64 gb
Fprobably lower quality
Fcompared to what you can get
4fuck..
Fyou can try this. use fp8 lora
Ffp8 base
Fit may fit
WSo in theory I can use your config of SDXL train multi characters using different caption for each character to get the model understand multi concept right ?
4I can use the fp8 flux model i install with your swarmui installer ?
Fyes it has fp8 flux
4I know the difference quality between fp8 and fp16 is not huge when using flux but for a dreambooth traning is it the same ?
Fbut dont use scaled i am not sure if tht works
Fby the way
Flatest fine tuning downloader
Fdownloads fp8 model already
F
Fwe cant train fp8 with dreambooth
Fbut lora should work fairly fine
Fselect base fp8 on interface
4Ok i will try thanks 
JI am following your extracted LoRA tutorial. However getting an error. I have tried all save percisions and network rank sizes. I have checked V2 and unchecked it. Can seem to get past this error. Happening to 3 different finetunes. So I know it's got to be something I am not seeing.
new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
KeyError: 'time_embed.0.weight'
new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
KeyError: 'time_embed.0.weight'
J
Xhow to make a lokr like this? with that net dim, usually kohya only allow to put 100000, https://civitai.com/models/292553?modelVersionId=1568463 (this is the lokr and the creator).
Eyou can add it in as an extra argument
Eor modify the gui
Xguidance please
Ehere i updated the file for you, replace it before starting the gui
you should be able to change the values of lokr now
the file to replace is in kohya_gui
you should be able to change the values of lokr now
the file to replace is in kohya_gui
lora_gui.py93.35KB
XI did replace, tho it's the same
Edid you restart your gui? are you sure you replaced the right file?
Esee in that file there is this
)
with gr.Row() as network_row:
network_dim = gr.Slider(
minimum=1,
maximum=9999999999,
label="Network Rank (Dimension)",
value=8,
step=1,
interactive=True,
)
network_alpha = gr.Slider(
minimum=0.00001,
maximum=9999999999,
label="Network Alpha",
value=1,
step=0.00001,
interactive=True,
info="alpha for LoRA weight scaling",
)
with gr.Row(visible=False) as convolution_row:
# locon= gr.Checkbox(label='Train a LoCon instead of a general LoRA (does not support v2 base models) (may not be able to some utilities now)', value=False)
conv_dim = gr.Slider(
minimum=0,
maximum=9999999999,
value=1,
step=1,
label="Convolution Rank (Dimension)",
)
conv_alpha = gr.Slider(
minimum=0,
maximum=9999999999,
value=1,
step=1,
label="Convolution Alpha",
)
)
with gr.Row() as network_row:
network_dim = gr.Slider(
minimum=1,
maximum=9999999999,
label="Network Rank (Dimension)",
value=8,
step=1,
interactive=True,
)
network_alpha = gr.Slider(
minimum=0.00001,
maximum=9999999999,
label="Network Alpha",
value=1,
step=0.00001,
interactive=True,
info="alpha for LoRA weight scaling",
)
with gr.Row(visible=False) as convolution_row:
# locon= gr.Checkbox(label='Train a LoCon instead of a general LoRA (does not support v2 base models) (may not be able to some utilities now)', value=False)
conv_dim = gr.Slider(
minimum=0,
maximum=9999999999,
value=1,
step=1,
label="Convolution Rank (Dimension)",
)
conv_alpha = gr.Slider(
minimum=0,
maximum=9999999999,
value=1,
step=1,
label="Convolution Alpha",
)
Xprobably, "kohya_ss\kohya_gui\lora_gui.py" this one right?
X
Fi trained LyCORIS. it is lower quality but has better multi concept training
Fyou can change our lora config LyCORIS however it may require some additional parameters which i dont remember
