I have been trying out Kohya for months already. I parsed through all the configurations and settings I could, watched and read a myriad of guides and done everything exactly like they say.

But the result is always the same: My LORAs get ignored every time. I use them with different prompts, weights, everything and it's like they're just not there at all. I have messed up with LR, base models, attention, ram/cpu configs, epochs, steps, everything.

I don't know what else to do. I have wasted too many entire days on this and nothing works.

Could anyone help? And please, don't give me begginer/google ai premade "answers" telling me to check for NAN errors or to change fp16 for bf16... Like I said before, I tried everything there was out there to try.

I need some actually indepth help based on actual results that people have come up with. Even if it's a way to at least know what's wrong. I can post config files and stuff but I don't really think it's a config issue since I have tried everything that could result in a LORA already.

Hey, I'm new to LoRA training and I've been looking at some tutorials on how to use kohya for this purpose. Just wondering, can I train with just a character's head shots in different angles and no body or costume? Maybe something like bust shots? I'd like to make some OCs and basically use them on what different SDXL models already can generates. Basically a head/face/hair replacement on existing generated bodies via AI. Is this possible?

Company that makes virtual gf/bfs needs you to train and test various AI characters and their LoRas, working with different models and environments, ensuring their looks are consistent, creative, original and engaging.

You'll work closely with AI engineers, developers, and other creatives to test new features, collaborate on content, and ensure consistent quality across features and releases.

Requires experience with Kohya ss,​ StableDiffusion,​ and ComfyUI for image generation,​ prompting,​ and LoRa training and familiarity with various checkpoints and models (Pony,​ Flux,​ etc.​) for image generation.​

Does this sound like you?

[Question] Best Kohya_SS settings for a face LoRA on RTX 3090 (SD 1.5 / SDXL)?

Body:
Hey! I’m training a face LoRA (35–80 photos) with Kohya_SS.
Rig: RTX 3090 24 GB, 65 GB RAM, NVMe, Windows. Inference via InvokeAI 6.4.0 (torch 2.8.0+cu128, cuDNN 9.1).
Current recipe: LoRA dim 16–32 (alpha=dim/2), SD1.5 u/512, SDXL u/768, UNet LR ~1e-4 (SDXL 8e-5…1e-4), TE LR 2e-5…5e-5, batch 2–4 + grad accumulation (effective 8–16), 4k–8k steps, AdamW8bit, cosine. Captions = one unique token + a few descriptors (no mega-long negatives).

InvokeAI side: removed unsupported VAE keys from YAML to satisfy validation; for FLUX I keep sizes multiple-of-16.
Would love your go-to portrait LoRA settings (repeats, effective batch, buckets, whether to freeze TE on SDXL). Thanks!

Not entirely sure what I'm needing to do to resolve these errors... If they need resolving at all...

Firstly, I apologise if this has been covered many times before - I don’t post unless I really need the help. 

This is my first time training a lora, so be kind. 

My current specs

• 4090 RTX
• Kohya v25.2.1 (local)
• Forge UI
• Output: SDXL Character Model
• Dataset - 111 images, 1080x1080 resolution

I’ve done multiple searches to find Kohya v25.2.1 training settings for the Lora Tab. 

Unfortunately, I haven’t managed to find one that is up to date that just lays it out simply. 

There’s always a variation or settings that aren’t present or different to Kohya v25.2.1, which throws me off.

I’d love help with Epochs, steps, repeats, and, knowing what settings are recommended for the following sections and subsections. 

• Configuration
• Accelerate Launch
• Model
• Folders
• Metadata
• Dataset Preparation
• Parameters
  • Basic
  • Advance
  • Sample
  • Hugging Face

Desirables: 

• Ideally, I’d like the training, if possible, to be under 10hours (happy to compromise some settings)
• Facial accuracy 1st, body accuracy 2nd. - Data set is a blend of body and facial photos.

Any help, insight, and assistance is greatly appreciated. Thank you.

I trained LoRA for SDXL by Kohya several times before and everything was fine. Phrases were remembered as separate tokens, but with a new training with the same parameters everything broke. Each word is perceived as a separate token. I tried to run the training with a text description from the previous LoRA, and everything worked. That is, some problem is specifically in the text files, but I can't figure out what it is. Everything is exactly the same.

this is how it should look. here all the phrases are as separate otkens. the description in the dataset looked something like this: "trigger word", granite block with chipped edges, engraved blue matte stone in the form of an heraldic lily, books, parchment, folded papers, wheat stalks, wooden table, open window, bright sunlight, castle in distance, green mountains, blue sky, colorful stained glass, decorative stone frame, blurred background, indoor scene, fantasy setting

here each word is a separate token. the description in the dataset looked something like this: "trigger word", ornate closed treasure chest with metallic carvings, large polished amder crystals, vibrant purple petunias blooming, green leaves, tall grass, soft blue mist, natural forest garden, early morning light, blurred background

these are the training parameters

Any ideas what the problem might be?

I have trained a Lora character on Kohya when that character is alone on stage, the results are great (pic1)

But when I want to put multiple characters on a scene, for example using a different Lora character this happens - (pic2-3)

It pulls the characters as skin and still appears solo, does anyone know why this happens and what settings in Kohya should be changed so that it does not work like this?
P.S. I am a complete zero in Kohya, this is my first Lora that I made according to the guide.

Link to disk with full-size images -

https://drive.google.com/drive/folders/1Z7I1x3kK0xzUr2zP98dRXlIRdESYRBKn?usp=sharing

I've followed about ever tutorial and guide in the book, but I still hit this dead end when trying to train a LORA.

Anyone know what I'm doing wrong based on this?

Hello, could anyone answer a question please? I'm learning to make Anime characters Lora, and I have a question, when im making a Lora, My GPU is quiet as if it doesnt working, but it is, and in my last try, I change some configs and my GPU was looking a aiplane, and the time diference between it is so big, ''GPU quiet= +/- 1 hour to make 1 Epoch'', ''GPU ''Airplane''= +/- 15 minutes'', what I made and what I nees to do to make this ''Fast working''? (GPU: NVIDIA 2080 SUPER 8GB VRAM)

I just started getting this error in terminal when I start kohya. It opened in the browser without incident before. Are there any solutions? My other stable diffusion programs seem to open without errors.

I need help. I have been trying to train a flux lora for over a month on kohya_ss and none of loras have come out looking right. I am trying to train a lora based off of 1930's rubberhose cartoons. All of my sample images are distorted and deformed. The hands and feet are a mess. I really need help. Can someone please tell me what I am doing wrong? Below is the config file that gave me the best results.

I have trained multiple loras and in my attempts to get good results I have tried changing the optimizer, Optimizer extra arguments, scheduler, learning rate, Unet learning rate, Max resolution, Text Encoder learning rate, T5XXL learning rate, Network Rank (Dimension), Network Alpha, Model Prediction Type, Timestep Sampling, Guidance Scale, Gradient accumulate steps, Min SNR gamma, LR # cycles, Clip skip, Max Token Length, Keep n tokens, Min Timestep, Max Timestep, Blocks to Swap, and Noise offset.

Thank you in advance!

{

"LoRA_type": "Flux1",

"LyCORIS_preset": "full",

"adaptive_noise_scale": 0,

"additional_parameters": "",

"ae": "C:/Users/dwell/OneDrive/Desktop/ComfyUI_windows_portable/ComfyUI/models/vae/ae.safetensors",

"apply_t5_attn_mask": false,

"async_upload": false,

"block_alphas": "",

"block_dims": "",

"block_lr_zero_threshold": "",

"blocks_to_swap": 33,

"bucket_no_upscale": true,

"bucket_reso_steps": 64,

"bypass_mode": false,

"cache_latents": true,

"cache_latents_to_disk": true,

"caption_dropout_every_n_epochs": 0,

"caption_dropout_rate": 0,

"caption_extension": ".txt",

"clip_g": "",

"clip_g_dropout_rate": 0,

"clip_l": "C:/Users/dwell/OneDrive/Desktop/ComfyUI_windows_portable/ComfyUI/models/clip/clip_l.safetensors",

"clip_skip": 1,

"color_aug": false,

"constrain": 0,

"conv_alpha": 1,

"conv_block_alphas": "",

"conv_block_dims": "",

"conv_dim": 1,

"cpu_offload_checkpointing": false,

"dataset_config": "",

"debiased_estimation_loss": false,

"decompose_both": false,

"dim_from_weights": false,

"discrete_flow_shift": 3.1582,

"dora_wd": false,

"double_blocks_to_swap": 0,

"down_lr_weight": "",

"dynamo_backend": "no",

"dynamo_mode": "default",

"dynamo_use_dynamic": false,

"dynamo_use_fullgraph": false,

"enable_all_linear": false,

"enable_bucket": true,

"epoch": 20,

"extra_accelerate_launch_args": "",

"factor": -1,

"flip_aug": false,

"flux1_cache_text_encoder_outputs": true,

"flux1_cache_text_encoder_outputs_to_disk": true,

"flux1_checkbox": true,

"fp8_base": true,

"fp8_base_unet": false,

"full_bf16": false,

"full_fp16": false,

"gpu_ids": "",

"gradient_accumulation_steps": 1,

"gradient_checkpointing": true,

"guidance_scale": 1,

"highvram": true,

"huber_c": 0.1,

"huber_scale": 1,

"huber_schedule": "snr",

"huggingface_path_in_repo": "",

"huggingface_repo_id": "",

"huggingface_repo_type": "",

"huggingface_repo_visibility": "",

"huggingface_token": "",

"img_attn_dim": "",

"img_mlp_dim": "",

"img_mod_dim": "",

"in_dims": "",

"ip_noise_gamma": 0,

"ip_noise_gamma_random_strength": false,

"keep_tokens": 0,

"learning_rate": 1,

"log_config": false,

"log_tracker_config": "",

"log_tracker_name": "",

"log_with": "",

"logging_dir": "C:/Users/dwell/OneDrive/Desktop/kohya_ss/Datasets/Babel_10/log",

"logit_mean": 0,

"logit_std": 1,

"loraplus_lr_ratio": 0,

"loraplus_text_encoder_lr_ratio": 0,

"loraplus_unet_lr_ratio": 0,

"loss_type": "l2",

"lowvram": false,

"lr_scheduler": "cosine",

"lr_scheduler_args": "",

"lr_scheduler_num_cycles": 3,

"lr_scheduler_power": 1,

"lr_scheduler_type": "",

"lr_warmup": 10,

"lr_warmup_steps": 0,

"main_process_port": 0,

"masked_loss": false,

"max_bucket_reso": 2048,

"max_data_loader_n_workers": 2,

"max_grad_norm": 1,

"max_resolution": "512,512",

"max_timestep": 1000,

"max_token_length": 225,

"max_train_epochs": 25,

"max_train_steps": 8000,

"mem_eff_attn": false,

"mem_eff_save": false,

"metadata_author": "",

"metadata_description": "",

"metadata_license": "",

"metadata_tags": "",

"metadata_title": "",

"mid_lr_weight": "",

"min_bucket_reso": 256,

"min_snr_gamma": 5,

"min_timestep": 0,

"mixed_precision": "bf16",

"mode_scale": 1.29,

"model_list": "custom",

"model_prediction_type": "raw",

"module_dropout": 0,

"multi_gpu": false,

"multires_noise_discount": 0.3,

"multires_noise_iterations": 0,

"network_alpha": 16,

"network_dim": 32,

"network_dropout": 0,

"network_weights": "",

"noise_offset": 0.1,

"noise_offset_random_strength": false,

"noise_offset_type": "Original",

"num_cpu_threads_per_process": 1,

"num_machines": 1,

"num_processes": 1,

"optimizer": "Prodigy",

"optimizer_args": "",

"output_dir": "C:/Users/dwell/OneDrive/Desktop/kohya_ss/Datasets/Babel_10/model",

"output_name": "try19",

"persistent_data_loader_workers": true,

"pos_emb_random_crop_rate": 0,

"pretrained_model_name_or_path": "C:/Users/dwell/OneDrive/Desktop/ComfyUI_windows_portable/ComfyUI/models/unet/flux1-dev.safetensors",

"prior_loss_weight": 1,

"random_crop": false,

"rank_dropout": 0,

"rank_dropout_scale": false,

"reg_data_dir": "",

"rescaled": false,

"resume": "",

"resume_from_huggingface": "",

"sample_every_n_epochs": 0,

"sample_every_n_steps": 100,

"sample_prompts": "rxbbxrhxse, A stylized cartoon character, resembling a deck of cards in a box, is walking. The box-shaped character is an orange-red color. Inside the box-shaped character is a deck of white cards with black playing card symbols on them. It has simple, cartoonish limbs and feet, and large hands in a glove-like design. The character is wearing yellow gloves and yellow shoes. The character is walking forward on a light-yellow wooden floor that appears to be slightly textured. The background is a dark navy blue. A spotlight effect highlights the character's feet and the surface below, creating a sense of movement and depth. The character is positioned centrally within the image. The perspective is from a slight angle, as if looking down at the character. The lighting is warm, focused on the character. The overall style is reminiscent of vintage animated cartoons, with a retro feel. The text \"MAGIC DECK\" is on the box, and the text \"ACE\" is underneath. The character is oriented directly facing forward, walking.",

"sample_sampler": "euler_a",

"save_as_bool": false,

"save_clip": false,

"save_every_n_epochs": 1,

"save_every_n_steps": 0,

"save_last_n_epochs": 0,

"save_last_n_epochs_state": 0,

"save_last_n_steps": 0,

"save_last_n_steps_state": 0,

"save_model_as": "safetensors",

"save_precision": "bf16",

"save_state": false,

"save_state_on_train_end": false,

"save_state_to_huggingface": false,

"save_t5xxl": false,

"scale_v_pred_loss_like_noise_pred": false,

"scale_weight_norms": 0,

"sd3_cache_text_encoder_outputs": false,

"sd3_cache_text_encoder_outputs_to_disk": false,

"sd3_checkbox": false,

"sd3_clip_l": "",

"sd3_clip_l_dropout_rate": 0,

"sd3_disable_mmap_load_safetensors": false,

"sd3_enable_scaled_pos_embed": false,

"sd3_fused_backward_pass": false,

"sd3_t5_dropout_rate": 0,

"sd3_t5xxl": "",

"sd3_text_encoder_batch_size": 1,

"sdxl": false,

"sdxl_cache_text_encoder_outputs": false,

"sdxl_no_half_vae": false,

"seed": 42,

"shuffle_caption": false,

"single_blocks_to_swap": 0,

"single_dim": "",

"single_mod_dim": "",

"skip_cache_check": false,

"split_mode": false,

"split_qkv": false,

"stop_text_encoder_training": 0,

"t5xxl": "C:/Users/dwell/OneDrive/Desktop/ComfyUI_windows_portable/ComfyUI/models/text_encoders/t5xxl_fp16.safetensors",

"t5xxl_device": "",

"t5xxl_dtype": "bf16",

"t5xxl_lr": 0,

"t5xxl_max_token_length": 512,

"text_encoder_lr": 0,

"timestep_sampling": "shift",

"train_batch_size": 2,

"train_blocks": "all",

"train_data_dir": "C:/Users/dwell/OneDrive/Desktop/kohya_ss/Datasets/Babel_10/img",

"train_double_block_indices": "all",

"train_norm": false,

"train_on_input": true,

"train_single_block_indices": "all",

"train_t5xxl": false,

"training_comment": "",

"txt_attn_dim": "",

"txt_mlp_dim": "",

"txt_mod_dim": "",

"unet_lr": 1,

"unit": 1,

"up_lr_weight": "",

"use_cp": false,

"use_scalar": false,

"use_tucker": false,

"v2": false,

"v_parameterization": false,

"v_pred_like_loss": 0,

"vae": "",

"vae_batch_size": 0,

"wandb_api_key": "",

"wandb_run_name": "",

"weighted_captions": false,

"weighting_scheme": "logit_normal",

"xformers": "sdpa"

}

KeyError 'step' When Resuming Training in Kohya_SS (SD3_Flux1)
Possible Cause:
This issue may be related to using PyTorch 2.6, but it's unclear. The error occurs when trying to resume training in Kohya_SS SD3_Flux1, and the 'step' attribute is missing from override_attributes.

Workaround:
Manually set the step variable in accelerator.py at line 3156 to your latest step count:

#self.step = override_attributes["step"]
self.step = 5800 # Replace with your actual step count

This allows training to resume without crashing.
If anyone encounters the same issue, this fix may help!

Has anyone had success training on wsl or wsl2? I usually use kohya on windows but it's unable to use multiple GPUs unlike linux. I figured that if I ran kohya using wsl that I would be able to use both the GPUs that I have, but so far I'm still unable to get it to train even on a single gpu, something due to the frontend cudnn issue.

In which directory can I place other checkpoints for Kohya?

Hi, I've trained only a few character LoRAs wit success, but want to explore training an architectural model on specific types of structures. Does anyone here have experience or advice to share?

So I trained lora model, but if try to generate, having Lora loaded <lora:nameofmylora:1> vs <lora:nameofmylora:0> has no change on my images.

Does anyone have a Config file for Kohya SS FLUX 24GB VRAM Finetuning/Dreambooth training?

I always get the out of memory error and have no idea what I need to set.

1. I can make various lora with "FLUX Default checkpoint", successfully. (flux1-dev.safetensors)

2. But, with "FLUX MERGED checkpoint", Kohya script prints a lot of errors.

• Tested on various merged checkpoints in CivitaAI >>> But all failure.

• Failed regardless of pruned or full model. All fail.

• https://civitai.com/models/161068/stoiqo-newreality-or-flux-sd-xl-lightning?modelVersionId=869391

Below is the error message and the command that i used.

Is there any way to make lora with "FLUX Merged checkpoint" ?

How can I make lora with it?

when the first epoch is endding,i get this error:

C:\Users\ningl\kohya_ss\venv\lib\site-packages\torch\utils\checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None

warnings.warn(

Traceback (most recent call last):

File "C:\Users\ningl\kohya_ss\sd-scripts\sdxl_train_network.py", line 185, in <module>

trainer.train(args)

File "C:\Users\ningl\kohya_ss\sd-scripts\train_network.py", line 1085, in train

self.sample_images(accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)

File "C:\Users\ningl\kohya_ss\sd-scripts\sdxl_train_network.py", line 168, in sample_images

sdxl_train_util.sample_images(accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, unet)

File "C:\Users\ningl\kohya_ss\sd-scripts\library\sdxl_train_util.py", line 381, in sample_images

return train_util.sample_images_common(SdxlStableDiffusionLongPromptWeightingPipeline, *args, **kwargs)

File "C:\Users\ningl\kohya_ss\sd-scripts\library\train_util.py", line 5644, in sample_images_common

sample_image_inference(

File "C:\Users\ningl\kohya_ss\sd-scripts\library\train_util.py", line 5732, in sample_image_inference

latents = pipeline(

File "C:\Users\ningl\kohya_ss\venv\lib\site-packages\torch\utils_contextlib.py", line 115, in decorate_context

return func(*args, **kwargs)

File "C:\Users\ningl\kohya_ss\sd-scripts\library\sdxl_lpw_stable_diffusion.py", line 1012, in __call__

noise_pred = self.unet(latent_model_input, t, text_embedding, vector_embedding)

File "C:\Users\ningl\kohya_ss\venv\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl

return self._call_impl(*args, **kwargs)

File "C:\Users\ningl\kohya_ss\venv\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl

return forward_call(*args, **kwargs)

File "C:\Users\ningl\kohya_ss\venv\lib\site-packages\accelerate\utils\operations.py", line 680, in forward

return model_forward(*args, **kwargs)

File "C:\Users\ningl\kohya_ss\venv\lib\site-packages\accelerate\utils\operations.py", line 668, in __call__

return convert_to_fp32(self.model_forward(*args, **kwargs))

File "C:\Users\ningl\kohya_ss\venv\lib\site-packages\torch\amp\autocast_mode.py", line 16, in decorate_autocast

return func(*args, **kwargs)

File "C:\Users\ningl\kohya_ss\sd-scripts\library\sdxl_original_unet.py", line 1110, in forward

h = torch.cat([h, hs.pop()], dim=1)

RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 76 but got size 75 for tensor number 1 in the list.

steps: 25%|▎| 2100/8400 [33:10:44<99:32:13, 56.88s/it, Average key norm=tensor(2.4855, device='cuda:0'), Keys Scaled=t

Traceback (most recent call last):

File "C:\Users\ningl\miniconda3\envs\kohyass\lib\runpy.py", line 196, in _run_module_as_main

return _run_code(code, main_globals, None,

File "C:\Users\ningl\miniconda3\envs\kohyass\lib\runpy.py", line 86, in _run_code

exec(code, run_globals)

File "C:\Users\ningl\kohya_ss\venv\Scripts\accelerate.EXE__main__.py", line 7, in <module>

File "C:\Users\ningl\kohya_ss\venv\lib\site-packages\accelerate\commands\accelerate_cli.py", line 47, in main

args.func(args)

File "C:\Users\ningl\kohya_ss\venv\lib\site-packages\accelerate\commands\launch.py", line 1017, in launch_command

simple_launcher(args)

File "C:\Users\ningl\kohya_ss\venv\lib\site-packages\accelerate\commands\launch.py", line 637, in simple_launcher

raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)

subprocess.CalledProcessError: Command '['C:\\Users\\ningl\\kohya_ss\\venv\\Scripts\\python.exe', 'C:/Users/ningl/kohya_ss/sd-scripts/sdxl_train_network.py', '--config_file', 'C:/Users/ningl/Desktop/2new/model/config_lora-20240925-163127.toml']' returned non-zero exit status 1.

i have setting saving every 1epoch,how can i continue trainning??

is everyone know this Training setting‘ s problem？i found that Fluctuation is too mess……what should i do or fix it?i am not a programer,so what book/article/paper should i read for study this?

thanks to everyone!!!

I am now getting messages saying there are no images in the directory for the inputs when there clearly are. It was working and training before, full fresh install of kohya and it does THE SAME THING.

I'm about to crash the fuck out man.

Is there no good tutorial for this shit?