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---
tags:
- generated_from_trainer
- retnet
model-index:
- name: sdprompt-retnet-300m
results: []
license: mit
datasets:
- Gustavosta/Stable-Diffusion-Prompts
- FredZhang7/anime-prompts-180K
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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
```
```py
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
- [Gustavosta/Stable-Diffusion-Prompts](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts)
- [FredZhang7/anime-prompts-180K](https://huggingface.co/datasets/FredZhang7/anime-prompts-180K)
## 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 |