Also why you using 30 steps, not 20 as usual in Flux ?
Also why you using 30 steps, not 20 as usual in Flux ?
CI trying use TWO GPU A6000 at time to generate GRID of all my checkpoints, but Idk if this is possible ? Its any adventage use rented 2xA6000 to generate picures ?
N@Furkan Gözükara SECourses I'm trying to start dreamboot training with config 10GB_GPU_8400MB_18.2_second_it_Tier_1.json but getting out of memory error. GPU - 4060ti 16GB. Tried with lora, lora starts with no problems, so no errors in installer
Nsame with 8gb config
Nram is 32GB
CYou need minimum 48gb of RAM
Ftrue i use supir to upscale later
Fmore steps slightly yields better
Fyes totally
Fshown this in videos numeraous times
Fyou add backend
FRAM error
Fyou need at least 48 GB
Fpeople upgraded and issue fixed
CYes i added, Hope add this ok
C
Nis there a way use ssd space to increase ram. I guess you did something like that in lora video. Or I'm wrong?
Fyep looks great
Fvirtual RAM
Fbut doesnt work in this case
Fyou need physical ram
Fvirtual RAM useful for model loading saving etc
Nah( thanks for explanation
CI did so far Realism GRID photo but I cant achive Blue beard lol
Con any cheackpoint. Maybe I ised to low STEPS. 20 only., Later when i manualy do 40 steps i beard was blue on one pic
CIt sing two GPU I think
CNow I generating GRID of Stylised photos. Its over 3oo 0f them
Fye it is hard one :d
CTomorrow I will ask you guys to help me choose best checkpoint as , I cant decide lol.
Then I will try reduce size from 23 to 11.
Then I will try reduce size from 23 to 11.
Fye it is hard 
Fwith flux all images are good
Fsize reduce works 0 quality loss
CThats way I want reduce. I managed already send all checkpoints to HugginsFace. 10x23gb, so when I decide which one I like... will download to local PC and change size. Not watchen yet video about reducing size , so I hope its possible on local PC, or must be done at Massed Comp
Ftotally possible
Cok, I think I need cancel genrate GRID of Stylized as its still almost 2H and Im very sleepy. lol. If I copy from Massed Comp folder GRID, can I continue tomorrow generate this GRID ?
Fi think should work
Fmake sure to backup everything on massed compute gets deleted
Fonce terminate
Ci know, checked 10x times lol If definitelly all my 23gb files are safe on HuginsFace haha
CGoodnight,
Dobranoc,
İyi geceler
Dobranoc,
İyi geceler
ZDobranoc
Mguys do we have some prompts that works really good with generating images using stable diffusion models ?
Fi sometimes check civitai
Fto see prompts
Falso after writing some good working prompts
Fusing chatgpt to get new ones works
Mthanks
Msorry guys i am asking alot, but i need to.
Manyone have got some good flux-dev-de-destill OneTrainer finetuning configurations ?
T@Furkan Gözükara SECourses Could you check v9 configs for 8GB again.. with Torch 2.5, i get out of memory errors, With 2.4 - it works but very slow. It was working on an earlier version of your 8GB configs. File "E:\StabilityMatrix-RAID\Kohya_Flux\kohya_ss\sd-scripts\library\flux_models.py", line 830, in _forward
attn = attention(q, k, v, pe=pe, attn_mask=attn_mask)
File "E:\StabilityMatrix-RAID\Kohya_Flux\kohya_ss\sd-scripts\library\flux_models.py", line 449, in attention
x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.90 GiB. GPU 0 has a total capacity of 8.00 GiB of which 0 bytes is free. Of the allocated memory 8.05 GiB is allocated by PyTorch, and 2.07 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
steps: 0%|
attn = attention(q, k, v, pe=pe, attn_mask=attn_mask)
File "E:\StabilityMatrix-RAID\Kohya_Flux\kohya_ss\sd-scripts\library\flux_models.py", line 449, in attention
x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.90 GiB. GPU 0 has a total capacity of 8.00 GiB of which 0 bytes is free. Of the allocated memory 8.05 GiB is allocated by PyTorch, and 2.07 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
steps: 0%|
Fcan you try with SDPA?
