is fast but not as fast as shcnell right?
is fast but not as fast as shcnell right?
Fyep
Fschnell fast comes from lesser step
Fbut step speed should be same
F
Fis this flux?
Fso slow
Fuse swarmui
Fit is 2.2 it / second
Fdont forget to add --fast to server backend
Fit is FLux , in Comfy maybe is the workflow im using it goes faster on Forge, I'm too used to it by now
FI also do a bunch ois stuff with animation mostly
Fso, comfy all the way
PIs it possible to train against the Mochi preview models in Kohya? https://huggingface.co/Comfy-Org/mochi_preview_repackaged/tree/main/split_files/diffusion_models
Fi didnt see any work for it
Da part of this was merged into the Prodigy package and anyone can use it in other trainers
ZHi, could you tell me if I can train another character in DreamBooth using the same flux.dev model that was additionally trained on a different character? For example, I trained it on Arnold Schwarzenegger’s appearance, and now I want to create a dataset for Sylvester Stallone, or, for instance, train it in a specific style like Dark Fantasy.
Fflux gets bleed
Fnot working as expected
Fbut stable diffusion 3.5 large my next research hopefully
A@Furkan Gözükara SECourses sorry, I'm a bit new, and I have a question about the finetunig, in XL model, could you help me?
Fbut i will be going sleep soon
AUmm, okay, I'll be quick. I'm trying to fine-tune an XL model, but the results from the epochs are coming out blank. My guess is that it's due to the low number of steps, which were set to 700, using a dataset of 1.5k images. I’d like to know what configuration you recommend for finetuning. If it’s too complex to address now, we can discuss it in more detail tomorrow.
Fblank sounds error
FPatreon
Get more from SECourses: FLUX, Tutorials, Guides, Resources, Training, Scripts on Patreon
FPatreon
Get more from SECourses: FLUX, Tutorials, Guides, Resources, Training, Scripts on Patreon
Ablack, error, this
Aooh perfect, ty
Fyour checkpoints looks like broken
Awhy?
Fyou got black output
F
Fwhich config did you use?
Ause, LR 6e-3, Text encoder 3e-6, 32 Batch Size, 10 epoch, and Gradient accumulate steps 2
Afinetunig, illustr XL Model, in format safetensorts
Feach config unique . if you use my config
Fand if you want 32 batch size
Fmultiply my learning rate size with 5.7
Athe configuration of this video https://www.youtube.com/watch?v=EEV8RPohsbw, I was guided by the last tutorial I published about flux.
YouTubeSECourses
Patreon post for config files 
https://www.patreon.com/posts/89213064
SECourses Discord To Get Full Support
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My LinkedIn
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Aok
Fthen checkpoints making black is the issue
Fdid you try model without training?
Fthat base model you are using?
Ait is an XL model, I talked to the creator, and he recommended me not to use clip skip, and to add extra parameters to Adafactor.
Jthe Mochi1 videos are really fun to create, are there any future plans for img2video?
Fcomfyui just told me they planning
Fgreat news
LHi, I want to train a rock band, it's a trio: vocals and guitar (leader), bass, drums. I plan to distribute the photos in folders: folder with photos of the singer, folder with photos of the bassist, folder with photos of the drummer, and folder with photos of the band complete. Has anyone trained Lora for more than one person? What photo distribution do you recommend?
Ldataset/
├── Band_complete/ # Group photos of the entire band
│ ├── Band_complete_001.jpg
│ ├── Band_complete_002.jpg
│ └── ... (X photos)
├── Leader/ # Individual photos of the leader
│ ├── Leader_001.jpg
│ ├── Leader_002.jpg
│ └── ... (Y photos)
├── Bassist/ # Individual photos of the bassist
│ ├── Bassist_001.jpg
│ ├── Bassist_002.jpg
│ └── ... (Z photos)
└── Drummer/ # Individual photos of the drummer
├── Drummer_001.jpg
├── Drummer_002.jpg
└── ... (W photos)
├── Band_complete/ # Group photos of the entire band
│ ├── Band_complete_001.jpg
│ ├── Band_complete_002.jpg
│ └── ... (X photos)
├── Leader/ # Individual photos of the leader
│ ├── Leader_001.jpg
│ ├── Leader_002.jpg
│ └── ... (Y photos)
├── Bassist/ # Individual photos of the bassist
│ ├── Bassist_001.jpg
│ ├── Bassist_002.jpg
│ └── ... (Z photos)
└── Drummer/ # Individual photos of the drummer
├── Drummer_001.jpg
├── Drummer_002.jpg
└── ... (W photos)
LFolder Structure and Photo Distribution
dataset/
├── Band_complete/ # Group photos of the entire band
│ ├── Band_complete_001.jpg
│ ├── Band_complete_002.jpg
│ └── ... (X photos)
├── Leader/ # Individual photos of the leader
│ ├── Leader_001.jpg
│ ├── Leader_002.jpg
│ └── ... (Y photos)
├── Bassist/ # Individual photos of the bassist
│ ├── Bassist_001.jpg
│ ├── Bassist_002.jpg
│ └── ... (Z photos)
└── Drummer/ # Individual photos of the drummer
├── Drummer_001.jpg
├── Drummer_002.jpg
└── ... (W photos) Distribution of Photos
Band_complete: Approximately 30% of total photos.
Leader: 40% of total photos (assuming the leader is more prominent).
Bassist: 15% of total photos.
Drummer: 15% of total photos.
dataset/
├── Band_complete/ # Group photos of the entire band
│ ├── Band_complete_001.jpg
│ ├── Band_complete_002.jpg
│ └── ... (X photos)
├── Leader/ # Individual photos of the leader
│ ├── Leader_001.jpg
│ ├── Leader_002.jpg
│ └── ... (Y photos)
├── Bassist/ # Individual photos of the bassist
│ ├── Bassist_001.jpg
│ ├── Bassist_002.jpg
│ └── ... (Z photos)
└── Drummer/ # Individual photos of the drummer
├── Drummer_001.jpg
├── Drummer_002.jpg
└── ... (W photos) Distribution of Photos
Band_complete: Approximately 30% of total photos.
Leader: 40% of total photos (assuming the leader is more prominent).
Bassist: 15% of total photos.
Drummer: 15% of total photos.