Flux Schnell lora size is too small (1.42 MB) and Does not generate images of the character that it was trained on.

I trained a custom character model using flux schnell, that training was success however the resulting lora is too small (1.42mb). And When I use that lora on the minimal inference I get totally different character that is not even close. My training dataset are well prepared and captioned well. Here are my configurations:

Config.toml:
```
pretrained_model_name_or_path = "/teamspace/studios/this_studio/kohya_ss/base/flux1-schnell.safetensors"
clip_l = "/teamspace/studios/this_studio/kohya_ss/base/clip_l.safetensors"
t5xxl = "/teamspace/studios/this_studio/kohya_ss/base/t5xxl_fp16.safetensors"
ae = "/teamspace/studios/this_studio/kohya_ss/base/ae.safetensors"
caption_extension = ".txt"
resolution = "1024,1024"
cache_latents = true
enable_bucket = true
bucket_no_upscale = true
output_dir = "/teamspace/studios/this_studio/kohya_ss/sd-scripts/trainings/model"
output_name = "w3r4jman"
save_precision = "bf16"
save_every_n_epochs = 1
train_batch_size = 2
max_token_length = 225
xformers = true
max_train_epochs = 10
persistent_data_loader_workers = true
gradient_checkpointing = true
mixed_precision = "bf16"
logging_dir = "/teamspace/studios/this_studio/kohya_ss/sd-scripts/trainings/log"
sample_every_n_epochs = 1
sample_prompts = "/teamspace/studios/this_studio/kohya_ss/sd-scripts/trainings/prompt.txt"
sample_sampler = "euler_a"
optimizer_type = "AdamW8bit"
learning_rate = 0.0001
lr_scheduler = "cosine_with_restarts"
lr_warmup_steps = 500
lr_scheduler_num_cycles = 3
dataset_config = "/teamspace/studios/this_studio/kohya_ss/sd-scripts/trainings/dataset_config.toml"
unet_lr = 0.0001
text_encoder_lr = 5e-5
network_module = "networks.lora"
network_dim = 8
network_alpha = 1
```

dataset_config.toml:
```
[[datasets]]
[[datasets.subsets]]
image_dir = '/teamspace/studios/this_studio/kohya_ss/sd-scripts/trainings/img'
caption_extension = '.txt'
class_tokens = 'w3r4jman'
num_repeats = 30
```

When I trained on sdxl base model on the same dataset and config, I got a lora size 57mb and that works good. So where is the problem in this case, can someone help me.

Start by reproducing the Flux Schnell training with Config.toml and dataset_config.toml, then compare the generated LoRA with the working SDXL run using the same dataset. Check the training and minimal-inference entry points to determine whether the 1.42 MB output and character mismatch are expected or indicate a configuration problem. Done means identifying the cause and documenting a verified correction or limitation.

Written by the indexing model from the issue text.