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--- |
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license: other |
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license_name: fair-ai-public-license-1.0-sd |
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license_link: https://freedevproject.org/faipl-1.0-sd/ |
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datasets: |
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- KBlueLeaf/danbooru2023-webp-4Mpixel |
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- KBlueLeaf/danbooru2023-sqlite |
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language: |
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- en |
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library_name: diffusers |
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pipeline_tag: text-to-image |
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--- |
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# Kohaku XL Zeta |
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join us: https://discord.gg/tPBsKDyRR5 |
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<style> |
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.image-viewer {position: relative;width: 100%;margin: 0 auto;display: flex;flex-flow: wrap;align-items: center;justify-content: center;} |
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.image-viewer input[type="radio"] {display: none;} |
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.image-viewer label {border-radius: 10%;padding: 20px;background-color: #B398F5;background-size: cover;background-position: center;cursor: pointer;color: black;margin: 8px;} |
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.image-viewer label:hover {background-color: #4C88F5;padding: 24px;margin: 4px;} |
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.image-viewer input[type="radio"]:checked + label {background-color: #6296F5;padding: 28px;margin: 0px;} |
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.image-container {position: relative;width: 100%;height: 50vh;margin: 1rem 1rem 0 0;} |
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.inner-container {position: absolute;width:100%;height: 100%;display: flex;align-items: center;justify-content: center;} |
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.inner-container img {border-radius: 10px;max-height: 100%;max-width: 100%;height: 0;width: 0;opacity: 0;transition: opacity 0.5s ease, height 0.25s ease, width 0.25s ease;} |
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#image1:checked ~ .image-container img:nth-child(1),#image2:checked ~ .image-container img:nth-child(2),#image3:checked ~ .image-container img:nth-child(3),#image4:checked ~ .image-container img:nth-child(4),#image5:checked ~ .image-container img:nth-child(5),#image6:checked ~ .image-container img:nth-child(6),#image7:checked ~ .image-container img:nth-child(7),#image8:checked ~ .image-container img:nth-child(8),#image9:checked ~ .image-container img:nth-child(9),#image10:checked ~ .image-container img:nth-child(10),#image11:checked ~ .image-container img:nth-child(11),#image12:checked ~ .image-container img:nth-child(12) {height: auto; width:auto; opacity: 1;} |
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#image1l{background-image: url("/KBlueLeaf/Kohaku-XL-Zeta/resolve/main/sample-images/00015-3807569455.jpg");} |
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#image2l{background-image: url("/KBlueLeaf/Kohaku-XL-Zeta/resolve/main/sample-images/00096-1093286410.jpg");} |
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#image3l{background-image: url("/KBlueLeaf/Kohaku-XL-Zeta/resolve/main/sample-images/00117-2417076749.jpg");} |
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#image4l{background-image: url("/KBlueLeaf/Kohaku-XL-Zeta/resolve/main/sample-images/00118-2417076750.jpg");} |
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#image5l{background-image: url("/KBlueLeaf/Kohaku-XL-Zeta/resolve/main/sample-images/00123-2659559372.jpg");} |
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#image6l{background-image: url("/KBlueLeaf/Kohaku-XL-Zeta/resolve/main/sample-images/01_2085.jpg");} |
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#image7l{background-image: url("/KBlueLeaf/Kohaku-XL-Zeta/resolve/main/sample-images/02082.jpg");} |
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#image8l{background-image: url("/KBlueLeaf/Kohaku-XL-Zeta/resolve/main/sample-images/02088.jpg");} |
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#image9l{background-image: url("/KBlueLeaf/Kohaku-XL-Zeta/resolve/main/sample-images/02091.jpg");} |
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#image10l{background-image: url("/KBlueLeaf/Kohaku-XL-Zeta/resolve/main/sample-images/02_02084.jpg");} |
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#image11l{background-image: url("/KBlueLeaf/Kohaku-XL-Zeta/resolve/main/sample-images/03_02086.jpg");} |
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#image12l{background-image: url("/KBlueLeaf/Kohaku-XL-Zeta/resolve/main/sample-images/04_02081.jpg");} |
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</style> |
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<div class="image-viewer"> |
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<input type="radio" id="image1" name="image-switcher" checked> |
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<label for="image1" id="image1l"></label> |
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<input type="radio" id="image2" name="image-switcher"> |
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<label for="image2" id="image2l"></label> |
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<input type="radio" id="image3" name="image-switcher"> |
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<label for="image3" id="image3l"></label> |
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<input type="radio" id="image4" name="image-switcher"> |
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<label for="image4" id="image4l"></label> |
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<input type="radio" id="image5" name="image-switcher"> |
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<label for="image5" id="image5l"></label> |
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<input type="radio" id="image6" name="image-switcher"> |
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<label for="image6" id="image6l"></label> |
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<input type="radio" id="image7" name="image-switcher"> |
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<label for="image7" id="image7l"></label> |
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<input type="radio" id="image8" name="image-switcher"> |
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<label for="image8" id="image8l"></label> |
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<input type="radio" id="image9" name="image-switcher"> |
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<label for="image9" id="image9l"></label> |
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<input type="radio" id="image10" name="image-switcher"> |
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<label for="image10" id="image10l"></label> |
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<input type="radio" id="image11" name="image-switcher"> |
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<label for="image11" id="image11l"></label> |
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<input type="radio" id="image12" name="image-switcher"> |
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<label for="image12" id="image12l"></label> |
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<div class="image-container"> |
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<div class="inner-container"> |
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<img src="sample-images/00015-3807569455.jpg" alt="image1" /> |
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<img src="sample-images/00096-1093286410.jpg" alt="image2" /> |
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<img src="sample-images/00117-2417076749.jpg" alt="image3" /> |
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<img src="sample-images/00118-2417076750.jpg" alt="image4" /> |
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<img src="sample-images/00123-2659559372.jpg" alt="image5" /> |
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<img src="sample-images/01_2085.jpg" alt="image6" /> |
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<img src="sample-images/02082.jpg" alt="image7" /> |
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<img src="sample-images/02088.jpg" alt="image8" /> |
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<img src="sample-images/02091.jpg" alt="image9" /> |
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<img src="sample-images/02_02084.jpg" alt="image10" /> |
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<img src="sample-images/03_02086.jpg" alt="image11" /> |
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<img src="sample-images/04_02081.jpg" alt="image12" /> |
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</div> |
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</div> |
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</div> |
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--- |
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## Highlights |
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- Resume from Kohaku-XL-Epsilon rev2 |
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- More stable, long/detailed prompt is not a requirement now. |
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- Better fidelity on style and character, support more style. |
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- CCIP metric surpass Sanae XL anime. have over 2200 character with CCIP score > 0.9 in 3700 character set. |
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- Trained on both danbooru tags and natural language, better ability on nl caption. |
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- Trained on combined dataset, not only danbooru |
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- danbooru (7.6M images, last id 7832883, 2024/07/10) |
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- pixiv (filtered from 2.6M special set, will release the url set) |
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- pvc figure (around 30k images, internal source) |
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- realbooru (around 90k images, for regularization) |
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- 8.46M images in total |
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- Since the model is trained on both kind of caption, the ctx length limit is extended to 300. |
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## Usage (PLEASE READ THIS SECTION) |
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### Recommended Generation Settings |
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- resolution: 1024x1024 or similar pixel count |
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- cfg scale: 3.5~6.5 |
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- sampler/scheduler: |
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- Euler (A) / any scheduler |
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- DPM++ series / exponential scheduler |
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- for other sampler, I personally recommend exponential scheduler. |
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- step: 12~50 |
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### Prompt Gen |
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DTG series prompt gen can still be used on KXL zeta. |
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A brand new prompt gen for cooperating both tag and nl caption is under developing. |
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### Prompt Format |
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As same as Kohaku XL Epsilon or Delta, but you can replace "general tags" with "natural language caption". |
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You can also put both together. |
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### Special Tags |
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- Quality tags: masterpiece, best quality, great quality, good quality, normal quality, low quality, worst quality |
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- Rating tags: safe, sensitive, nsfw, explicit |
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- Date tags: newest, recent, mid, early, old |
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#### Rating tags |
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General: safe |
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Sensitive: sensitive |
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Questionable: nsfw |
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Explicit: nsfw, explicit |
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## Dataset |
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For better ability on some certain concepts, I use full danbooru dataset instead of filterd one. |
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Than use crawled Pixiv dataset (from 3~5 tag with popularity sort) as addon dataset. |
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Since Pixiv's search system only allow 5000 page per tag so there is not much meaningful image, and some of them are duplicated with danbooru set(but since I want to reinforce these concept I directly ignore the duplication) |
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As same as kxl eps rev2, I add realbooru and pvc figure images for more flexibility on concept/style. |
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## Training |
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- Hardware: Quad RTX 3090s |
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- Dataset |
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- Num Images: 8,468,798 |
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- Resolution: 1024x1024 |
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- Min Bucket Resolution: 256 |
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- Max Bucket Resolution: 4096 |
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- Caption Tag Dropout: 0.2 |
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- Caption Group Dropout: 0.2 (for dropping tag/nl caption entirely) |
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- Training |
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- Batch Size: 4 |
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- Grad Accumulation Step: 32 |
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- Equivalent Batch Size: 512 |
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- Total Epoch: 1 |
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- Total Steps: 16548 |
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- Training Time: 430 hours (wall time) |
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- Mixed Precision: FP16 |
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- Optimizer |
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- Optimizer: Lion8bit |
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- Learning Rate: 1e-5 for UNet / TE training disabled |
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- LR Scheduler: Constant (with warmup) |
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- Warmup Steps: 100 |
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- Weight Decay: 0.1 |
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- Betas: 0.9, 0.95 |
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- Diffusion |
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- Min SNR Gamma: 5 |
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- Debiased Estimation Loss: Enabled |
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- IP Noise Gamma: 0.05 |
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## Why do you still use SDXL but not any Brand New DiT-Based Models? |
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Unless any one give me reasonable compute resources or any team release efficient enough DiT or I will not train any DiT-based anime base model. <br> |
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But if you give me 8xH100 for an year, I can even train lot of DiT from scratch (If you want) |
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## License: |
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Fair-AI-public-1.0-sd |
