RealVisXL5 was released
RealVisXL5 was released
Fwow i should test
Ftesting flux clip large trainings
Dseems to be a big training effort, not just remerge: "Hello everyone! An early version of RealVisXL V5.0 (560k steps) is now available on Hugging Face. The model is still in training, and the final version will be released on Hugging Face and CivitAI at the end of August. You can leave your feedback on this version in the comments below."
G@Dr. Furkan Gözükara Bro, I am going to sleep now its 6am haha, training loras all night, I fixed all my machines now. Anyway, I want to Train 1000 images (a style of art) I want to train that but doing it on 1 A6000 it would take like a week right? What system you recommend and how much would it cost me on Mass compute? 4-8 GPU A6000? If its $3-5 an HR I am down to pay it. Please do recommend the correct VM to use on there and do you have a video setting one up as I only use local, never rented hardware before. Let me know thanks and gn brother! Totally worth the Patreon fee! 
Fget 4x A6000 on massed compute
Fit is 1.25$ per hour with our coupon
Fi made the multi gpu training tutorial so it will be published today or tomorrow hopefully
V@Dr. Furkan Gözükara two models have been changed by the author, %cd /kaggle/temp/VAE
!wget "https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/ae.safetensors" -O "ae.safetensors" !wget "https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/flux1-schnell.safetensors" -O "flux1-schnell.safetensors" am I right? another question it seems to take a few mins to get 1 image from flux model on kaggle, right?
!wget "https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/ae.safetensors" -O "ae.safetensors" !wget "https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/flux1-schnell.safetensors" -O "flux1-schnell.safetensors" am I right? another question it seems to take a few mins to get 1 image from flux model on kaggle, right?
V@Dr. Furkan Gözükara another question, if I want to use custom nodes in swarm UI, I did not see the folder, do I have to create one? Autocompletions Users-log.ldb clip_vision tensorrt
Embeddings Users.ldb comfy-auto-model.yaml unet
Lora VAE controlnet upscale_models
Stable-Diffusion clip diffusion_models, or where are the custom node folder?
Embeddings Users.ldb comfy-auto-model.yaml unet
Lora VAE controlnet upscale_models
Stable-Diffusion clip diffusion_models, or where are the custom node folder?
M@Dr. Furkan Gözükara I've test on windows on my PC with A4000 (8go) and it worked, however as I suspected it use shared memory during the step "move text encoder to gpu" as there is not enough memory on the GPU, that's why it OOM on linux, because it can't use shared memory, then it's passed back to the cpu freeing memory and the rest is loaded that require less than 8go so during training it's stay under 8go. Is there a way to not move the text encoder to gpu, like having it calculated on cpu directly, having it use shared memory or making it use less VRAM, so it will work also on linux ?
Fyes links changed
Fi see what you mean now
Fso you get error during cache
Fyou need to ask this to kohya
Fcant linux has shared vram?
MOk I could maybe create a ticket on it's git
MI search but didn't find a way to do that it seem not implemented in linux nvidia driver
Fplease make a detailed reply here and tag @kohya-ss : https://github.com/kohya-ss/sd-scripts/pull/1374
GitHub
Replace SD3Tokenizer with the original CLIP-L/G/T5 tokenizers.
Extend the max token length to 256 for T5XXL.
Refactor caching for latents.
Refactor caching for Text Encoder outputs
Extract arch...
Extend the max token length to 256 for T5XXL.
Refactor caching for latents.
Refactor caching for Text Encoder outputs
Extract arch...
Fthere are also other expers some of the may chime in
Mok thank you I will do that
Vdoes it take a few mins in smarmui on kaggle for flux model?
Vto create 1 image, I mean? seeming very slow
Fye it is slow. kaggle doesnt give good gpus :/
Fi wish they started giving bf16
Fwe could trian flux there 
Fi trained 24 images on 4x gpu and even 240 epoch is not overtrained. this is impact of effective batch size increase
JHi there, may I ask you something, for kohya_ss, for generate the meta_lat.json for the full finetuning of flux, do you know which path of the model should I set as argument, the path to flux1-dev.safetensors seems to not working
Fi didnt research it yet. hopefully next week
Vyes I am able to do use swarmUI now, thanks a lot
JWhats the best tutorial for Flux image to image generation for product images.?
Fi never done that but i have shown image to image
Fbut not for flux
D@Dr. Furkan Gözükara new test with high TE learning rate, now with unique tokens no class and person class regularization, I see that using person class avoid class mixing
Dthese are my regularizations
Dworks much better if I use man class it overtrains the class man, en mix them all
Dis a couple of thousands of regularization pictures
Fdo you see improved results?
Fi am about to compare 800 images generation
Fwaiting grid to finish
F780 - each one 48 steps FLUX image
Dyes but is a preliminary test, I stopped and restarted the training with different settings a couple of times
Dnow I started the real test
DI will report in a few hours
Dremember my goal is to train multiple people no concept or class bleading
Dlest see if I succeed
Fgreat
DIn my last test I wasn't using unique tokens and I changed tokens and removed class in the middle of a training resuming..
DIt was a mess
Dother note, all my regularization are captioned