Low loss but bad results, what am I doing wrong?

After training TI for 1500 steps, I can get down to a loss of about 0.05, much better than the previous 0.1. However, the results are still bad. Very bad. Here is what the training input params looks like:

```
/home/ubuntu/anaconda3/envs/pt13/bin/accelerate launch --num_cpu_threads_per_process 2 train_textual_inversion.py \
--pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5" \
--train_data_dir="/home/ubuntu/sd-scripts/training_images/train_person/" \
--output_dir="./models" \
--resolution=512 \
--train_batch_size=1 \
--learning_rate=1e-4 \
--max_train_steps=1500 \
--save_every_n_epochs=1 \
--save_model_as="safetensors" \
--clip_skip=2 \
--seed=42 \
--color_aug \
--use_8bit_adam \
--lr_scheduler="cosine" \
--use_object_template \
--token_string="qwerty" \
--init_word="*" \
--num_vectors_per_token=8
```

Moreover, here is the last recorded training log:

`steps: 100%|███████████████████████████████████████████████████████████████| 1500/1500 [09:24<00:00, 2.66it/s, loss=0.0542]`

Start with train_textual_inversion.py and the supplied training command, then reproduce the 1500-step run using the stated parameters and inspect the resulting output alongside the 0.0542 loss log. The issue is resolved only when the cause of the poor results is identified and a verified training configuration or code fix is established.

Written by the indexing model from the issue text.