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--- |
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license: apache-2.0 |
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base_model: beanimaXL_v10 |
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model_type: lora |
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tags: |
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- stable-diffusion |
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- sdxl |
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- lora |
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- character |
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- anime |
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--- |
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# Our Azath — Sakuta & Yui Character LoRA |
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**Repository:** `toonsquare/Our_Azath` |
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**Characters:** Sakuta, Yui |
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**Base Model:** `beanimaXL_v10 (SDXL)` |
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**Model Type:** Character LoRA (SDXL) |
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**License:** Apache-2.0 |
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--- |
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## Overview |
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This repository contains a **character-focused LoRA model** trained for the characters |
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**Sakuta** and **Yui** from the IP **_“Our Azath”_**. |
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The primary objective of this model is to preserve **clear character identity** while supporting stable generation across a wide range of compositions and visual variations. |
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Special attention was given to **Yui’s hairstyle**, which is a key visual identifier of the character. |
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--- |
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## Characters & Trigger Words |
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### Sakuta |
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- **Trigger word:** `az_bunta` |
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--- |
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### Yui |
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Yui’s hairstyle is a defining feature of her character design. |
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To improve generation accuracy and control, **Yui was trained with separate hairstyle-specific trigger words**. |
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- **Base trigger:** `az_yui` |
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- **Ponytail hairstyle:** `az_yui, ponytail hair` |
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- **Hair bun hairstyle:** `az_yui, bun hair` |
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Using the appropriate trigger word helps maintain consistency between hairstyle and character identity. |
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--- |
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## Dataset Construction & Training Pipeline |
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This model follows a structured, repeatable pipeline designed to maximize character consistency and visual quality. |
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--- |
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### 1. Original Image Acquisition (from IP Team) |
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High-quality reference images were provided directly by the IP team for both characters. |
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For each character, images were collected across multiple compositions and viewpoints to ensure consistent identity representation. |
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--- |
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### 2. Data Augmentation Using Flux |
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All reference images were processed using a **Flux-based generation pipeline** to produce large-scale augmented datasets. |
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- Generated images per character: ~**1,000** |
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- Purpose: increase pose, framing, and contextual diversity |
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### 3. Manual Curation |
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All generated images were manually reviewed. |
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The following were removed during the curation process: |
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- Duplicate or near-duplicate images |
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- Incorrect generations (identity drift, malformed anatomy, etc.) |
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- Low-quality outputs (noise, poor composition, low detail) |
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After curation, approximately **500–600 images** per dataset were selected for training. |
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--- |
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### 4. Yui Hairstyle-Specific Dataset Separation |
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Unlike Sakuta, **Yui required additional dataset separation due to hairstyle variation**. |
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To achieve this: |
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- **Ponytail Yui** |
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- Separate reference images were provided by the IP team |
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- Images were augmented via Flux |
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- Manual curation applied |
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- **Hair bun Yui** |
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- Separate reference images were provided by the IP team |
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- Images were augmented via Flux |
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- Manual curation applied |
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Each hairstyle-specific dataset was associated with its own trigger word (`yui_ponytail`, `yui_bun`) to allow precise control during generation. |
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--- |
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### 5. LoRA Training |
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The curated datasets were used to train a **Standard LoRA model on top of an SDXL base model**. |
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- **Base model:** `beanimaXL_v10` |
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- **Architecture:** Stable Diffusion XL (SDXL) |
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The training process focused on: |
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- Preserving character identity |
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- Maintaining hairstyle consistency for Yui |
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- Allowing reasonable variation in pose, framing, and context |
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--- |
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## LoRA Training Configuration (Reference) |
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> ⚠️ The following configuration reflects the settings used during training as closely as possible. |
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> Values are provided **for reference only**. |
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<details> |
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<summary>Click to expand training configuration</summary> |
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```json |
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{ |
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"LoRA_type": "Standard", |
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"epoch": 50, |
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"train_batch_size": 3, |
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"learning_rate": 3e-05, |
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"unet_lr": 3e-05, |
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"text_encoder_lr": 3e-05, |
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"optimizer": "AdamW", |
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"mixed_precision": "fp16", |
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"network_dim": 32, |
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"network_alpha": 32, |
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"clip_skip": 1, |
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"max_resolution": "1024,1024", |
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"min_snr_gamma": 5, |
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"gradient_checkpointing": true, |
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"cache_latents": true, |
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"enable_bucket": true, |
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"save_every_n_epochs": 1, |
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"sdxl": true, |
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"save_precision": "fp16" |
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} |
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``` |
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</details> |
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--- |
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## Usage Guide |
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### Basic Prompt |
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```text |
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az_bunta, [description] |
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``` |
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```text |
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az_yui, [description] |
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``` |
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### Hairstyle-Specific Prompts (Yui) |
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```text |
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az_yui, ponytail hair, [description] |
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``` |
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```text |
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az_yui, bun hair, [description] |
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``` |
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### Tips |
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* Place the character trigger early in the prompt for stronger identity locking. |
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* Always use the hairstyle-specific trigger for Yui to avoid hairstyle mixing. |
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* Explicitly specifying composition (e.g., close-up, bust shot, full body) improves control. |
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* Extreme stylistic deviations may weaken character consistency. |
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--- |
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## Intended Use & Limitations |
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* This model is designed for **character-consistent image generation** of Sakuta and Yui. |
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* Hairstyle accuracy for Yui depends on using the correct trigger word. |
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* Mixing conflicting hairstyle prompts may reduce consistency. |
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* Radical style changes or photorealistic prompts may degrade character identity. |
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--- |
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## License |
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This model is released under the **Apache License 2.0**. |
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You are free to: |
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* Use the model commercially |
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* Modify and redistribute it |
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Please ensure compliance with the Apache-2.0 license terms and any applicable IP usage policies. |
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--- |
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## Credits |
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* Characters: **Sakuta, Yui** (*Our Azath*) |
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* Training & curation: **Toonsquare** |
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* Data source: IP Team |
