Hello Dr, do you think we will be able to do something similar to dreambooth style full finetune of
Hello Dr, do you think we will be able to do something similar to dreambooth style full finetune of qwen? Is this possible?
筋I would like to ask everyone, I used 38 high-definition half-body portrait materials to fine-tune the DreamBooth training, with a configuration of 48G and 200 training rounds. Currently, the results from round 120 to round 200 are indeed becoming more similar to the characters in the material set, but I encountered two problems: 1. The test results for cartoon images are all realistic people, 2. If the character is relatively small in the image, there is only a very small probability that the face resembles the original.
JDid you caption the cartoon images with a cartoon keyword, and photorealism images with "photo" or a similar keyword?
JMaybe also introduce images where the face is more distant in your training ? Maybe you could pad the training images twice, but the second time with a big black border, so the face comes out relatively small?
Fdreambooth later i think
Ffirst lora
Fhow do you test?=
F2 : yes zoom in character
筋comfyui，Grid test
筋I relabeled the data again, added a realistic style, and plan to retrain for DB testing.
Fyou are training a game character or something?
筋I'm using the krea flux base model. I don't know if this is the reason.
筋I'm training a LoRA of a character similar to you using my own photos. However, currently, when I write the cartoon prompt, it turns into a real - life person.
筋According to your suggestion, I train in the form of DB to achieve the effect of LoRA.
Foh you train real person and want cartoon output?
筋I saw that there are cartoon - style characters in your videos.
Fyes they are hard
Fif you want cartoon flux kontext much better
FYouTubeSECourses
Black Forest Labs (BFL) publisher of FLUX models kept their promise and published the FLUX.1 Kontex DEV model today. So I didn't sleep and after doing huge research and test non-stop, I have prepared this excellent step by step tutorial that will show you how to use this amazing model. With FLUX Kontext you can edit any part of the image in any ...
Fand flux krea will make it even harder
Ffor cartoon
筋I can use kontext, and I have successfully trained my own control lora. At present, I just want to replicate the process in your tutorial, use a dataset of real - person photos to train a DreamBooth (DB) to achieve a personal model with generalization ability.
筋Thank you for your reply.
Fuse flux dev not krea for this task
Fflux krea is optimized for realism
筋Thank you for your suggestion. I'm re - modifying the main model for multi - GPU retraining.
Ffor multi gpu loras works great with 48 gb gpus
Fdreambooth need 80 gb gpus
筋4X 4090 48G
Fawesome
Fi wish they were sold worldwide
Fdefinitely better than 32 gb 5090
TTried training character checkpoint with 50 image set 7000 steps from flux krea and result is much worse than flux dev counterpart so far. Much less likeness, seems more weird results
Fi feel krea need more epochs
KI found using a character Lora that I trained with flux on flux krea is giving me the textures of krea with the character. Vs the finetune I was getting fuzzy images
Tis a 80gb gpu needed for flux krea Dreambooth/Finetune?
As far as I remember for flux dev DB/Finetune a 48gb gpu was enough
As far as I remember for flux dev DB/Finetune a 48gb gpu was enough
F6 gb sufficient
Fif you want multiple gpu yes
ZHi,
I was trying to generate using my trained dreambooth model on Runpod. It shown this error in SwarmUI. When I run it in massedcompute, it's working fine. Do you what is the cause of this error?
Thank you
I was trying to generate using my trained dreambooth model on Runpod. It shown this error in SwarmUI. When I run it in massedcompute, it's working fine. Do you what is the cause of this error?
Thank you
Fmodel download failed
Frm -r modelpath
Fredownload
S@Dr. Furkan Gözükara Hey Dr, is there a theoratical limit with checkpoint training regarding dataset ? I have a good 350 images, is that too much or I would just need to train it longer ?
Uuisato_
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EHi Furkan, I want to confirm something about your captioning theory.
For a small and very consistent dataset (around 50 solid product photos on white background), is it still best practice to only use the token + class as caption (like CCCSNOOO bag), and let the images carry all visual attributes?
Or in such small homogeneous datasets, could adding more detailed captions (e.g. color, material, shape) actually help stabilize training with T5?
For a small and very consistent dataset (around 50 solid product photos on white background), is it still best practice to only use the token + class as caption (like CCCSNOOO bag), and let the images carry all visual attributes?
Or in such small homogeneous datasets, could adding more detailed captions (e.g. color, material, shape) actually help stabilize training with T5?
JGood question! AFAIk depends on what you want. iI you dont caption a certain thing, it will always generate it in that thing, unless you really force it in a prompt.
If you caption something (like color, material) you can more easily change the color , material later.
I think Furkan will confirm this
If you caption something (like color, material) you can more easily change the color , material later.
I think Furkan will confirm this
EThanks for the clarification! If T5 is active with such a small and homogeneous dataset, could relying only on token + class captions cause it to fall back to it's pretraining bias (e.g. anime/fantasy)? In that case, would the safer option be to disable T5?
Also, around what epoch should the results typically start to become visible? I’ve tested the token + class approach, but even after 190 epochs I still mostly get strange anime-like outputs. Does that indicate that the LoRA simply needs more training, or that the captioning strategy itself is unsuitable for this setup?
Also, around what epoch should the results typically start to become visible? I’ve tested the token + class approach, but even after 190 epochs I still mostly get strange anime-like outputs. Does that indicate that the LoRA simply needs more training, or that the captioning strategy itself is unsuitable for this setup?
JI have no idea about T5, is that Clip?
What model are you training on? SDXL, Pony , Flux, Qwen, Wan 2.1, Wan 2.2?
What model are you training on? SDXL, Pony , Flux, Qwen, Wan 2.1, Wan 2.2?
JWhat's your learning rate
EFlux. It's this config setup.