File size: 15,924 Bytes
2d91860 6776258 2d91860 6776258 bdf051b c079400 bdf051b c079400 bdf051b 98322b2 d4c96b2 c079400 98322b2 bdf051b 46f064e bdf051b a9247d4 bdf051b c079400 bdf051b c079400 bdf051b c079400 bdf051b c079400 bdf051b c079400 bdf051b 6776258 f349f5c 6776258 039e1c6 6776258 2369a0b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 |
---
license: other
license_name: fair-ai-public-license-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
datasets:
- KBlueLeaf/danbooru2023-webp-4Mpixel
- KBlueLeaf/danbooru2023-sqlite
language:
- en
library_name: diffusers
---
# Kohaku XL Δelta
***The best "SDXL anime base model that has been trained by an 'individual'."***
join us: https://discord.gg/tPBsKDyRR5
<style>
.custom-table {
width: 100%;
height: 100%;
border-collapse: collapse;
margin-top: 0em;
border: none;
}
.custom-table img{
margin-top: 0px;
}
.custom-table tr{
border: none !important;
}
.custom-table td {
vertical-align: top;
padding: 0;
box-shadow: 0px 0px 0px 0px rgba(0, 0, 0, 0.15);
}
.custom-image-container {
position: relative;
width: 100%;
margin-bottom: 0em;
overflow: hidden;
border-radius: 10px;
transition: transform .7s;
/* Smooth transition for the container */
}
.custom-image-container:hover {
transform: scale(1.05);
/* Scale the container on hover */
}
.custom-image-container:hover .custom-image {
opacity: 0
}
.custom-image-container:hover
.hover-image {
opacity: 1;
}
.custom-image {
width: 100%;
height: auto;
object-fit: cover;
border-radius: 10px;
transition: transform .7s, opacity .5s;
margin-bottom: 0em;
}
.hover-image {
position: absolute;
top: 0;
left: 0;
opacity: 0;
transition: opacity .5s;
}
.nsfw-filter {
filter: blur(8px); /* Apply a blur effect */
transition: filter 0.3s ease; /* Smooth transition for the blur effect */
}
.custom-image-container:hover .nsfw-filter {
filter: none; /* Remove the blur effect on hover */
}
.overlay {
position: absolute;
bottom: 0;
left: 0;
right: 0;
color: white;
width: 100%;
height: 100%;
display: flex;
flex-direction: column;
justify-content: center;
align-items: center;
font-size: 1vw;
font-style: bold;
text-align: center;
opacity: 0;
/* Keep the text fully opaque */
background: linear-gradient(0deg, rgba(0, 0, 0, 0.6) 60%, rgba(0, 0, 0, 0) 100%);
transition: opacity .5s;
}
.custom-image-container:hover .overlay {
opacity: 1;
/* Make the overlay always visible */
}
.overlay-text {
background: linear-gradient(45deg, #130c28, #ca76e2);
-webkit-background-clip: text;
color: transparent;
/* Fallback for browsers that do not support this effect */
text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.7);
/* Enhanced text shadow for better legibility */
.overlay-subtext {
font-size: 0.75em;
margin-top: 0.5em;
font-style: italic;
}
.overlay,
.overlay-subtext {
text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.5);
}
</style>
<table class="custom-table">
<tr>
<td style="width: 36.8275862068966%;">
<div class="custom-image-container">
<div class="image-wrapper">
<img class="custom-image" src="sample/table/th-1.png" alt="sample1">
<img class="hover-image" src="sample/table/1.png" alt="sample1">
<div class="overlay" style="font-size: 1vw; font-style: bold;">
Stream in the forest
<div class="overlay-subtext" style="font-size: 0.75em; font-style: italic;">"by KBlueLeaf"</div>
</div>
</div>
</div>
</td>
<td style="width: 1.37931034482759%;"></td>
<td style="width: 61.7931034482759%;">
<table class="custom-table" style="margin: 0 !important; padding: 0 !important;">
<tr style="height: 30.8858996897622%;">
<td>
<div class="custom-image-container">
<div class="image-wrapper">
<img class="custom-image" src="sample/table/th-2-1.png" alt="sample2">
<img class="hover-image" src="sample/table/2-1.png" alt="sample2">
<div class="overlay" style="font-size: 1vw; font-style: bold;">
The rise
<div class="overlay-subtext" style="font-size: 0.75em; font-style: italic;">"by KBlueLeaf"</div>
</div>
</div>
</div>
</td>
</tr>
<tr style="height: 3.1023784901758%;"></tr>
<tr style="height: 66.0117218200621%;">
<td>
<table class="custom-table" style="margin: 0 !important; padding: 0 !important;">
<tr>
<td style="width: 47.47023828125%">
<div class="custom-image-container">
<div class="image-wrapper">
<img class="custom-image" src="sample/table/th-2-2-1.png" alt="sample3">
<img class="hover-image" src="sample/table/2-2-1.png" alt="sample3">
<div class="overlay" style="font-size: 1vw; font-style: bold;">
Looking back
<div class="overlay-subtext" style="font-size: 0.75em; font-style: italic;">"by KBlueLeaf"</div>
</div>
</div>
</div>
</td>
<td style="width: 2.23214285714286%;"></td>
<td style="width: 50.2976188616071%">
<table class="custom-table" style="margin: 0 !important; padding: 0 !important;">
<tr style="height: 24.6997434804245%;">
<td>
<div class="custom-image-container">
<div class="image-wrapper">
<img class="custom-image" src="sample/table/th-2-2-2-1.png" alt="sample4">
<img class="hover-image" src="sample/table/2-2-2-1.png" alt="sample4">
<div class="overlay" style="font-size: 1vw; font-style: bold;">
Flower
<div class="overlay-subtext" style="font-size: 0.75em; font-style: italic;">"by KBlueLeaf"</div>
</div>
</div>
</div>
</td>
</tr>
<tr style="height: 4.69973878068567%;"></tr>
<tr style="height: 70.6005177388899%;">
<td>
<div class="custom-image-container">
<div class="image-wrapper">
<img class="custom-image" src="sample/table/th2-2-2-2-2.png" alt="sample5">
<img class="hover-image" src="sample/table/2-2-2-2.png" alt="sample5">
<div class="overlay" style="font-size: 1vw; font-style: bold;">
"The Cat"
<div class="overlay-subtext" style="font-size: 0.75em; font-style: italic;">"by KBlueLeaf"</div>
</div>
</div>
</div>
</td>
</tr>
</table>
</td>
</tr>
</table>
</td>
</tr>
</table>
</td>
</tr>
</table>
## Introduction
Kohaku XL Delta, the fourth major iteration in the Kohaku XL series, features a 3.6 million images dataset, LyCORIS fine-tuning[1], trained on comsumer-level hardware, and is fully open-sourced.
## Usage
**the "base" version is "before train" one!!!!**
Here's a simple format to make using this model a breeze:
```
<1girl/1boy/1other/...>, <character>, <series>, <artists>, <special tags>, <general tags>
```
Special tags(quality, rating, and date) actually fall under general tags. But it's a good idea to group all these tags before the general tags.
**While Kohaku XL Delta has mastered few artists' styles with high fidelity. However, users are strongly encouraged to blend multiple artist tags to explore new styles, rather than attempting to replicate the style of any specific artist.**
#### Tags
All the danbooru tags with at least 1000 popularity should work.
All the danbooru tags with at least 100 popularity can possibly work with high emphasis.
Remember to remove all the underscore in tags. (Underscores in short tags are not be removed, which are very likely part of emoji tags.)
### Special Tags
- **Quality tag**s: masterpiece, best quality, great quality, good quality, normal quality, low quality, worst quality
- **Rating tags**: safe, sensitive, nsfw, explicit
- **Date tags**: newest, recent, mid, early, old
**Quality Tags**
Quality tags are assigned based on the percentile rankings of the favorite count (fav_count) within each rating category to avoid bias on nsfw content (Animagine XL v3 have met this problem), organized from high to low as follows: 95th, 85th, 75th, 50th, 25th, and 10th percentiles. This creates seven distinct quality levels separated by six thresholds.
**Rating tags**
* **General**: safe
* **Sensitive**: sensitive
* **Questionable**: nsfw
* **Explicit**: nsfw, explicit
Note: During training, content tagged as "explicit" is also considered under "nsfw" to ensure a comprehensive understanding.
**Date tags**
Date tags are based on the upload dates of the images, as the metadata does not include the actual creation dates.
The periods are categorized as follows:
* 2005~2010: old
* 2011~2014: early
* 2015~2017: mid
* 2018~2020: recent
* 2021~2024: newest
### Emphasis
Given the short training period, some tags might not have been learned well. Through experimentation, increasing the "emphasis weight" to between 1.5 and 2.5 can still yield descent results, especially for character or artist tags.
For sd-webui users, please use version>=1.8.0 and switch the emphasis mode to "No norm" to prevent potential NaN issues.
### Resolution
This model is trained for resolutions from ARB 1024x1024 with minimum resolution 256 and maximum resolution 4096. This means you can use the standard SDXL resolution. However, opting for a slightly higher resolution than 1024x1024 is recommended. Applying a hires-fix is also suggested for better results.
For more information, please check out the metadata of the sample images provided.
## How This Model Came to Be
### Dataset
The dataset for training this model was sourced from [HakuBooru](https://github.com/KohakuBlueleaf/HakuBooru), comprising 3.6 million images selected from the [danbooru2023](https://huggingface.co/datasets/KBlueLeaf/danbooru2023-webp-4Mpixel) dataset.[2][3]
A selection process was employed to choose 1 million posts from IDs 0 to 2,999,999, another million from IDs 3,000,000 to 4,999,999, and all posts after ID 5,000,000, totaling 4.1 million posts. After filtering out deleted posts, gold account posts and those without images (which could be GIFs or MP4s), the final dataset comprised 3.6 million images.
The selection was essentially random, but a fixed seed was utilized to ensure reproducibility.
**Further Process**
* **Shuffle tags**: The order of general tags was shuffled in each step.
* **Tag dropout**: Randomly, 10% of general tags were dropped in each step.
### Training
The training of Kohaku XL Delta was facilitated by the [LyCORIS](https://github.com/KohakuBlueleaf/LyCORIS) project and the trainer from [kohya-ss/sd-scripts](https://github.com/kohya-ss/sd-scripts). [1][4]
**Base Model Refinement**
Our investigation indicated that training the "token_embedding" and "position_embedding" within CLIP, or the "positional_embedding" in openCLIP, may not be beneficial for fine-tuning on a small to medium scale, particularly with smaller batch sizes.[5][6]
Consequently, we reverted to the original token and position embeddings from TE1 and TE2 models. Following this, we combined the restored gamma rev2 and beta7 models through a weighted sum (weight=0.5), forming the foundational model for Kohaku XL Delta.
This foundational model, referred to as "delta-pre2" or "delta base," serves as a preliminary version without further training, positioning its capabilities between Kohaku XL gamma rev2 and Kohaku XL beta7.
**Algorithm: LoKr**[7]
The model was trained using the LoKr algorithm with full matrix triggered and a factor of 2~8 for different modules. The aim was to demonstrate the applicability of LoRA/LyCORIS in training base models.
The original LoKr file size is under 800MB, and the TE was not frozen. The original LoKr file also be provided as "delta-lokr" version.
For detailed settings, refer to the LyCORIS config file.
**Other Training Details**
- **Hardware**: Dual RTX 3090s
- **Num Train Images**: 3,665,398
- **Batch Size**: 4
- **Grad Accumulation Step**: 16
- **Equivalent Batch Size**: 128
- **Total Epoch**: 1
- **Total Steps**: 28638
- **Optimizer**: Lion8bit
- **Learning Rate**: 4e-5 for UNet / 1e-5 for TE
- **LR Scheduler**: Constant
- **Warmup Steps**: 100
- **Weight Decay**: 0.1
- **Betas**: 0.9, 0.95
- **Min SNR Gamma**: 5
- **Resolution**: 1024x1024
- **Min Bucket Resolution**: 256
- **Max Bucket Resolution**: 4096
- **Mixed Precision**: FP16
**Warning**: Versions 0.36.0~0.41.0 of bitsandbytes have significant [bugs](https://github.com/TimDettmers/bitsandbytes/issues/659) in the 8bit optimizer that could compromise training, so updating is essential.[8]
**Training Cost**
Utilizing DDP with two RTX 3090s, completing 1 epoch across the 3.6 million image dataset took approximately 17 to 18 days. Each step for an equivalent batch size of 128 took about 51 to 51.5 seconds to complete.
### What's Next
Delta is likely the last big update for Kohaku XL, but that doesn't mean I'm done tinkering with it. And I won't ensure this is actually the last one.
I'm thinking about running it through a few more epochs or maybe beefing up the dataset to 5 million images soon. Plus, I'm considering trying out DoKr with a bit of a bigger setup for some experimental tweaks.
(Funny thing, Delta started off as an experiment too, but turned out so well it became a main release!)
## Special Thanks
AngelBottomless & Nyanko7: danbooru2023 dataset[3]
Kohya-ss: Trainer[4]
ChatGPT/GPT4: Refine this model card
---
***AI art should be looked like AI, not like humans.***
---
## Reference & Resource
### Reference
[1] **Shih-Ying Yeh**, Yu-Guan Hsieh, Zhidong Gao, Bernard B W Yang, Giyeong Oh, & Yanmin Gong (2024). Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluation. In The Twelfth International Conference on Learning Representations.
https://arxiv.org/abs/2309.14859
[2] HakuBooru - text-image dataset maker for booru style image platform. https://github.com/KohakuBlueleaf/HakuBooru
[3] Danbooru2023: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset.
https://huggingface.co/datasets/nyanko7/danbooru2023
[4] kohya-ss/sd-scripts.
https://github.com/kohya-ss/sd-scripts
[5] Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
https://github.com/huggingface/transformers/blob/b647acdb53d251cec126b79e505bac11821d7c93/src/transformers/models/clip/modeling_clip.py#L204-L205
[6] OpenCLIP - An open source implementation of CLIP.
https://github.com/mlfoundations/open_clip/blob/73fa7f03a33da53653f61841eb6d69aef161e521/src/open_clip/transformer.py#L598-L604
[7] LyCORIS - Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion.
https://github.com/KohakuBlueleaf/LyCORIS/blob/main/docs/Algo-Details.md#lokr
[8] TimDettmers/bitsandbytes - issue 659/152/227/262 - Wrong indented lines cause bugs for a long time.
https://github.com/TimDettmers/bitsandbytes/issues/659
### Resource
* Kohaku XL beta. https://civitai.com/models/162577/kohaku-xl-beta
* Kohaku XL gamma. https://civitai.com/models/270291/kohaku-xl-gamma
## Appendix
Sample images will be put in here later.
You can check the sample folder at first. |