<span style="background-color:rgb(21, 21, 23);color:rgb(249, 250, 251);font-family:quote-cjk-patch, Inter, system-ui, -apple-system, BlinkMacSystemFont, &quot;Segoe UI&quot;, Roboto, Oxygen, Ubuntu, Cantarell, &quot;Open Sans&quot;, &quot;Helvetica Neue&quot;, sans-serif;font-size:16px">This LoRA is designed to emphasize the buttocks of female subjects (18+) in kneeling or squatting positions<span style="background-color:rgb(21, 21, 23);color:rgb(249, 250, 251);font-family:quote-cjk-patch, Inter, system-ui, -apple-system, BlinkMacSystemFont, &quot;Segoe UI&quot;, Roboto, Oxygen, Ubuntu, Cantarell, &quot;Open Sans&quot;, &quot;Helvetica Neue&quot;, sans-serif;font-size:16px">, focusing specifically on the exposed glutes. The model covers a wide range of variations—from a slight kneel and partial crouch to a deep full squat - while consistently maintaining focus on the rear anatomy. The aesthetic remains suggestive and erotic, with exposure depending on the chosen angle and strength.<hr />Subject &amp; Pose DescriptionThe subject is depicted in kneeling or squatting postures, with the buttocks prominently framed and accentuated. Variations include:<ul><li>Upright kneeling</li><li>Squat-kneel hybrid pose</li><li>Forward-leaning kneel</li><li>Partial squat (crouch)</li><li>Full deep squat</li></ul>In all cases, the glutes remain the primary focal point. Depending on the camera angle and strength settings, genitalia may become partially or fully visible.<hr />Strength0.8 - 1.2: Subtle presentation of the pose 1.3 - 2.0: Intense and reliable pose adherence 2.0 - 2.5: Aggressive posing with highly pronounced anatomical emphasis. *At this level, it is essential to combine this LoRA with other LoRA models to recover fine details and maintain anatomical coherence. In many cases, this range results in full exposure of genitalia.<hr />Image Format RecommendationThe vast majority of training images are in vertical orientation. Therefore, it is highly recommended to generate images in portrait mode (9:16 aspect ratio) for optimal composition and framing.<hr />Training &amp; Captioning Methodology<ul><li>Each training image underwent detailed analysis and captioning, taking approximately 4 minutes per image across 8 distinct sections.</li><li>Captions frequently exceed 300 words to capture every nuance of the composition, including object and body descriptions, environment, and camera framing.</li></ul><hr />Trigger Words &amp; ConceptsWhile the LoRA responds strongly to natural language prompts describing the pose and focus, it works best with concepts such as:<ul><li><code>kneeling</code></li><li><code>squatting</code></li><li><code>full squat</code></li><li><code>deep crouch</code></li><li><code>exposed buttocks from behind</code></li><li><code>rear view</code></li></ul>Best PracticeThis model was trained exclusively on natural language captions generated by a vision model (Qwen). No Danbooru-style tags were used in the training data.For best results, use descriptive full sentences in your prompts rather than comma-separated tag lists.<hr />Combining with Other LoRAsTo achieve a wider variety of poses and stylistic variations, it is strongly recommended to mix this LoRA with other LoRA models. This can introduce different body angles, clothing styles, environments, or artistic interpretations while retaining the core focus on the prone and recumbent rear‑glute emphasis.<hr />License &amp; Usage<ul><li>You are free to use this model in your projects, including merging or mixing it with other models.</li><li>If you include this LoRA in any merge or mix, please give proper credit.</li></ul><hr />Support the CreatorIf you enjoy my work and want to help cover the electricity costs required to train and refine these models, please consider supporting me:Address: <a target="_blank" rel="ugc" href="https://ko-fi.com/weirddreamer">https://ko-fi.com/weirddreamer</a> (Donations are always appreciated!)

Ass squatting/kneeling from behind (Krea2)

Dataset Size:128 high-quality, manually curated imagesData Processing:Images were meticulously filtered using advanced duplicate removal tools to ensure maximum variety and uniqueness. Additionally, the dataset underwent an experimental texture enhancement process designed to bring out finer surface details, skin textures, and overall tactile quality in the final outputs.Captions:Tagging was performed using Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-Q4_K_M

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