NOTE: ALL CHARACTERS IN THE IMAGES ARE ADULTS.
<h2>How to Use This Model</h2>
USE THEM SIMULTANEOUSLY. In this case, you need to download both <code>akagi_azurlane.pt</code> and 
<code>akagi_azurlane.safetensors</code>, then use <code>akagi_azurlane.pt</code> as texture inversion embedding, and use
<code>akagi_azurlane.safetensors</code> as LoRA at the same time.
それらを同時に使用してください。この場合、<code>akagi_azurlane.pt</code>と<code>akagi_azurlane.safetensors</code>の両方をダウンロード
する必要があります。<code>akagi_azurlane.pt</code>をテクスチャ反転埋め込みとして使用し、同時に<code>akagi_azurlane.safetensors</code>をLoRAとして使用してください。
同时使用它们。在这种情况下，您需要下载<code>akagi_azurlane.pt</code>和<code>akagi_azurlane.safetensors</code>这两个文件，然后将<code>akagi_azurlane.pt</code>用作纹理反转嵌入，
同时使用<code>akagi_azurlane.safetensors</code>作为LoRA。
(Translated with ChatGPT)
The trigger word is <code>akagi_azurlane</code>, and the recommended tags are <code>masterpiece, best quality, highres, solo, {akagi_azurlane:1.10}, animal_ears, fox_ears, long_hair, red_eyes, breasts, brown_hair, tail, large_breasts, fox_tail, multiple_tails, bangs, cleavage, smile, fox_girl, animal_ear_fluff, black_hair</code>.
<h2>How This Model Is Trained</h2>
This model is trained with <a href="https://github.com/7eu7d7/HCP-Diffusion" rel="ugc">HCP-Diffusion</a>. 
And the auto-training framework is maintained by <a href="https://huggingface.co/deepghs" rel="ugc">DeepGHS Team</a>.
<h2>Why Some Preview Images Not Look Like Akagi Azurlane</h2>
All the prompt texts used on the preview images (which can be viewed by clicking on the images) 
are automatically generated using clustering algorithms based on feature information extracted from the 
training dataset. The seed used during image generation is also randomly generated, and the images have 
not undergone any selection or modification. As a result, there is a possibility of the mentioned 
issues occurring.
In practice, based on our internal testing, most models that experience such issues perform better in 
actual usage than what is seen in the preview images. The only thing you may need to do is fine-tune 
the tags you use.
<h2>I Felt This Model May Be Overfitting or Underfitting, What Shall I Do</h2>
Our model has been published on <a href="https://huggingface.co/CyberHarem/akagi_azurlane" rel="ugc">huggingface repository - CyberHarem/akagi_azurlane</a>, where
models of all the steps are saved. Also, we published the training dataset on 
<a href="https://huggingface.co/datasets/CyberHarem/akagi_azurlane" rel="ugc">huggingface dataset - CyberHarem/akagi_azurlane</a>, which may be helpful to you.
<h2>Why Not Just Using The Better-Selected Images</h2>
Our model's entire process, from data crawling, training, to generating preview images and publishing, 
is 100% automated without any human intervention. It's an interesting experiment conducted by our team, 
and for this purpose, we have developed a complete set of software infrastructure, including data filtering, 
automatic training, and automated publishing. Therefore, if possible, we would appreciate more feedback or 
suggestions as they are highly valuable to us.

akagi/赤城/赤城 (Azur Lane)

akagi_azurlane.pt

akagi_azurlane.safetensors

v1.4

v1.0

Mod from <a target="_blank" rel="noopener noreferrer nofollow" href="https://civitai.com/models/125757/akagi-azur-lane">akagi/赤城/赤城 (Azur Lane) - v1.4 | Stable Diffusion LoRA | Civitai</a>Trigger WordsAKAGI_AZURLANEANIMAL_EARS, LONG_HAIR, FOX_EARS, RED_EYES, BROWN_HAIR, BREASTS, BANGS, LARGE_BREASTS, TAIL, FOX_TAIL, MULTIPLE_TAILS, SMILE, CLEAVAGE, FOX_GIRL, ANIMAL_EAR_FLUFFNOTE: ALL CHARACTERS IN THE IMAGES ARE ADULTS.How to Use This ModelUSE THEM SIMULTANEOUSLY. In this case, you need to download both akagi_azurlane.pt and akagi_azurlane.safetensors, then use akagi_azurlane.pt as texture inversion embedding, and use akagi_azurlane.safetensors as LoRA at the same time.それらを同時に使用してください。この場合、akagi_azurlane.ptとakagi_azurlane.safetensorsの両方をダウンロード する必要があります。akagi_azurlane.ptをテクスチャ反転埋め込みとして使用し、同時にakagi_azurlane.safetensorsをLoRAとして使用してください。同时使用它们。在这种情况下，您需要下载akagi_azurlane.pt和akagi_azurlane.safetensors这两个文件，然后将akagi_azurlane.pt用作纹理反转嵌入， 同时使用akagi_azurlane.safetensors作为LoRA。(Translated with ChatGPT)The trigger word is akagi_azurlane, and the recommended tags are masterpiece, best quality, highres, solo, {akagi_azurlane:1.10}, animal_ears, fox_ears, long_hair, red_eyes, breasts, brown_hair, tail, large_breasts, fox_tail, multiple_tails, bangs, cleavage, smile, fox_girl, animal_ear_fluff, black_hair.How This Model Is TrainedThis model is trained with <a target="_blank" rel="ugc" href="https://github.com/7eu7d7/HCP-Diffusion">HCP-Diffusion</a>. And the auto-training framework is maintained by <a target="_blank" rel="ugc" href="https://huggingface.co/deepghs">DeepGHS Team</a>.Why Some Preview Images Not Look Like Akagi AzurlaneAll the prompt texts used on the preview images (which can be viewed by clicking on the images) are automatically generated using clustering algorithms based on feature information extracted from the training dataset. The seed used during image generation is also randomly generated, and the images have not undergone any selection or modification. As a result, there is a possibility of the mentioned issues occurring.In practice, based on our internal testing, most models that experience such issues perform better in actual usage than what is seen in the preview images. The only thing you may need to do is fine-tune the tags you use.I Felt This Model May Be Overfitting or Underfitting, What Shall I DoOur model has been published on <a target="_blank" rel="ugc" href="https://huggingface.co/CyberHarem/akagi_azurlane">huggingface repository - CyberHarem/akagi_azurlane</a>, where models of all the steps are saved. Also, we published the training dataset on <a target="_blank" rel="ugc" href="https://huggingface.co/datasets/CyberHarem/akagi_azurlane">huggingface dataset - CyberHarem/akagi_azurlane</a>, which may be helpful to you.Why Not Just Using The Better-Selected ImagesOur model's entire process, from data crawling, training, to generating preview images and publishing, is 100% automated without any human intervention. It's an interesting experiment conducted by our team, and for this purpose, we have developed a complete set of software infrastructure, including data filtering, automatic training, and automated publishing. Therefore, if possible, we would appreciate more feedback or suggestions as they are highly valuable to us.

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