SDPrompt-RetNet-300M
This model is a RetNet model trained from scratch using https://github.com/syncdoth/RetNet. It achieves the following results on the evaluation set:
- Loss: 0.3616
Usage
pip install transformers safetensors timm
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
MODEL_NAME = "isek-ai/SDPrompt-RetNet-300M"
DEVICE = "cuda"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
trust_remote_code=True,
).to(DEVICE)
streamer = TextStreamer(tokenizer)
prompt = "<s>1girl"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
_ = model.generate(
inputs["input_ids"],
max_new_tokens=256,
do_sample=True,
top_p=0.9,
top_k=20,
temperature=0.9,
streamer=streamer,
)
# <s> 1girl, absurdres, animal ear fluff, animal ears, bangs, bare shoulders, black hair, blue archive, blunt bangs, blush, closed mouth, collarbone, commentary request, eyes visible through hair, green eyes, hair between eyes, halo, hand on own face, hand up, highres, jacket, kisaki blue archive, long hair, long sleeves, looking at viewer, open clothes, open jacket, shinonome asu, simple background, solo, track jacket, upper body, white background, white jacket</s>
Model description
This model is trained with Stable Diffusion prompts and Danbooru tags to generate prompts for image generation models.
Training data
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0006
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
2.6714 | 0.03 | 1000 | 2.5787 |
2.1551 | 0.07 | 2000 | 2.3981 |
2.1439 | 0.1 | 3000 | 2.1160 |
1.8406 | 0.14 | 4000 | 1.9138 |
1.7485 | 0.17 | 5000 | 1.7847 |
1.6417 | 0.21 | 6000 | 1.7120 |
1.6084 | 0.24 | 7000 | 1.6055 |
1.4805 | 0.28 | 8000 | 1.5946 |
1.5524 | 0.31 | 9000 | 1.5027 |
1.4425 | 0.35 | 10000 | 1.4876 |
1.4007 | 0.38 | 11000 | 1.4364 |
1.4637 | 0.42 | 12000 | 1.3896 |
1.3211 | 0.45 | 13000 | 1.3968 |
1.3246 | 0.49 | 14000 | 1.3403 |
1.3461 | 0.52 | 15000 | 1.3156 |
1.2897 | 0.56 | 16000 | 1.2977 |
1.2748 | 0.59 | 17000 | 1.2823 |
1.2424 | 0.62 | 18000 | 1.2649 |
1.348 | 0.66 | 19000 | 1.2134 |
1.1797 | 0.69 | 20000 | 1.2030 |
1.2116 | 0.73 | 21000 | 1.2033 |
1.1702 | 0.76 | 22000 | 1.1453 |
1.1027 | 0.8 | 23000 | 1.1597 |
1.1932 | 0.83 | 24000 | 1.1506 |
1.3669 | 0.87 | 25000 | 1.1428 |
1.0705 | 0.9 | 26000 | 1.1239 |
1.1474 | 0.94 | 27000 | 1.1239 |
1.0879 | 0.97 | 28000 | 1.1168 |
0.9879 | 1.01 | 29000 | 1.0848 |
0.9928 | 1.04 | 30000 | 1.0953 |
0.9095 | 1.08 | 31000 | 1.1043 |
1.0423 | 1.11 | 32000 | 1.0823 |
0.9478 | 1.15 | 33000 | 1.0840 |
0.9979 | 1.18 | 34000 | 1.0387 |
1.0316 | 1.22 | 35000 | 1.0282 |
1.0531 | 1.25 | 36000 | 1.0369 |
0.919 | 1.28 | 37000 | 1.0398 |
1.0596 | 1.32 | 38000 | 1.0410 |
0.9076 | 1.35 | 39000 | 0.9889 |
0.9698 | 1.39 | 40000 | 1.0004 |
0.9633 | 1.42 | 41000 | 1.0038 |
0.9622 | 1.46 | 42000 | 0.9933 |
0.9809 | 1.49 | 43000 | 0.9805 |
0.9496 | 1.53 | 44000 | 0.9755 |
0.9435 | 1.56 | 45000 | 0.9759 |
0.9337 | 1.6 | 46000 | 0.9615 |
0.8844 | 1.63 | 47000 | 0.9524 |
0.9039 | 1.67 | 48000 | 0.9567 |
0.905 | 1.7 | 49000 | 0.9430 |
0.9491 | 1.74 | 50000 | 0.9205 |
0.8464 | 1.77 | 51000 | 0.9109 |
0.9384 | 1.81 | 52000 | 0.9056 |
0.8121 | 1.84 | 53000 | 0.8969 |
0.8381 | 1.88 | 54000 | 0.8869 |
0.8171 | 1.91 | 55000 | 0.8946 |
0.9024 | 1.94 | 56000 | 0.8993 |
0.84 | 1.98 | 57000 | 0.9011 |
0.6702 | 2.01 | 58000 | 0.8876 |
0.6278 | 2.05 | 59000 | 0.8716 |
0.6876 | 2.08 | 60000 | 0.8546 |
0.6754 | 2.12 | 61000 | 0.8639 |
0.6479 | 2.15 | 62000 | 0.8425 |
0.698 | 2.19 | 63000 | 0.8533 |
0.708 | 2.22 | 64000 | 0.8407 |
0.7021 | 2.26 | 65000 | 0.8160 |
0.5881 | 2.29 | 66000 | 0.8251 |
0.6181 | 2.33 | 67000 | 0.8205 |
0.6789 | 2.36 | 68000 | 0.8066 |
0.6452 | 2.4 | 69000 | 0.8037 |
0.6483 | 2.43 | 70000 | 0.7915 |
0.5868 | 2.47 | 71000 | 0.7864 |
0.6257 | 2.5 | 72000 | 0.7895 |
0.6593 | 2.53 | 73000 | 0.7718 |
0.5957 | 2.57 | 74000 | 0.7490 |
0.6351 | 2.6 | 75000 | 0.7481 |
0.699 | 2.64 | 76000 | 0.7628 |
0.566 | 2.67 | 77000 | 0.7590 |
0.5892 | 2.71 | 78000 | 0.7628 |
0.6052 | 2.74 | 79000 | 0.7633 |
0.6494 | 2.78 | 80000 | 0.7588 |
0.5917 | 2.81 | 81000 | 0.7118 |
0.508 | 2.85 | 82000 | 0.6857 |
0.523 | 2.88 | 83000 | 0.6738 |
0.4894 | 2.92 | 84000 | 0.6713 |
0.5096 | 2.95 | 85000 | 0.6625 |
0.352 | 2.99 | 86000 | 0.6802 |
0.3927 | 3.02 | 87000 | 0.6606 |
0.3468 | 3.06 | 88000 | 0.6546 |
0.3368 | 3.09 | 89000 | 0.6520 |
0.352 | 3.12 | 90000 | 0.6495 |
0.3613 | 3.16 | 91000 | 0.6324 |
0.3501 | 3.19 | 92000 | 0.6227 |
0.3269 | 3.23 | 93000 | 0.6091 |
0.3583 | 3.26 | 94000 | 0.6153 |
0.3278 | 3.3 | 95000 | 0.6178 |
0.3216 | 3.33 | 96000 | 0.6208 |
0.3383 | 3.37 | 97000 | 0.6195 |
0.3326 | 3.4 | 98000 | 0.6088 |
0.3081 | 3.44 | 99000 | 0.5956 |
0.3459 | 3.47 | 100000 | 0.5840 |
0.3139 | 3.51 | 101000 | 0.5712 |
0.3087 | 3.54 | 102000 | 0.5677 |
0.2798 | 3.58 | 103000 | 0.5566 |
0.3166 | 3.61 | 104000 | 0.5332 |
0.2981 | 3.65 | 105000 | 0.5333 |
0.3027 | 3.68 | 106000 | 0.5276 |
0.2815 | 3.72 | 107000 | 0.5024 |
0.2294 | 3.75 | 108000 | 0.5081 |
0.2452 | 3.78 | 109000 | 0.4824 |
0.2733 | 3.82 | 110000 | 0.4695 |
0.3001 | 3.85 | 111000 | 0.4627 |
0.2322 | 3.89 | 112000 | 0.4580 |
0.2362 | 3.92 | 113000 | 0.4402 |
0.2488 | 3.96 | 114000 | 0.4263 |
0.2449 | 3.99 | 115000 | 0.3999 |
0.1798 | 4.03 | 116000 | 0.4038 |
0.1956 | 4.06 | 117000 | 0.4037 |
0.1831 | 4.1 | 118000 | 0.4040 |
0.1802 | 4.13 | 119000 | 0.4039 |
0.1641 | 4.17 | 120000 | 0.4029 |
0.1769 | 4.2 | 121000 | 0.4016 |
0.1564 | 4.24 | 122000 | 0.4026 |
0.1552 | 4.27 | 123000 | 0.3988 |
0.1806 | 4.31 | 124000 | 0.3995 |
0.1783 | 4.34 | 125000 | 0.3995 |
0.1736 | 4.38 | 126000 | 0.3940 |
0.1657 | 4.41 | 127000 | 0.3913 |
0.1598 | 4.44 | 128000 | 0.3871 |
0.1599 | 4.48 | 129000 | 0.3831 |
0.1606 | 4.51 | 130000 | 0.3776 |
0.1639 | 4.55 | 131000 | 0.3754 |
0.1736 | 4.58 | 132000 | 0.3742 |
0.1653 | 4.62 | 133000 | 0.3703 |
0.1708 | 4.65 | 134000 | 0.3681 |
0.1729 | 4.69 | 135000 | 0.3674 |
0.1564 | 4.72 | 136000 | 0.3660 |
0.1734 | 4.76 | 137000 | 0.3641 |
0.163 | 4.79 | 138000 | 0.3632 |
0.1585 | 4.83 | 139000 | 0.3626 |
0.1603 | 4.86 | 140000 | 0.3619 |
0.1751 | 4.9 | 141000 | 0.3617 |
0.1622 | 4.93 | 142000 | 0.3617 |
0.161 | 4.97 | 143000 | 0.3617 |
0.1541 | 5.0 | 144000 | 0.3616 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.0.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
- Downloads last month
- 27
Inference API (serverless) does not yet support model repos that contain custom code.