Anime-faithful character LoRA for Senko-san from The Helpful Fox Senko-san, for use with Illustrious XL v0.1 (I recommend the Nova Furry XL checkpoint).

Built from an entirely new dataset of 1,000 native 1024 × 1024 NovelAI 5.0 Curated generations, this ORIGINAL-ANIME version captures the look and feeling of the original TV anime. Each training image was generated to resemble a frame from an episode, emphasizing softer linework, familiar colors, and faithful character design.

ORIGINAL-ANIME?

Where the HD version emphasizes ultra-crisp detail and high-fidelity rendering, ORIGINAL-ANIME prioritizes Senko’s on-screen likeness and the softer, screencap-like finish of the series. It is designed for txt2img and img2img character generation with strong identity consistency and flexible natural-language prompting.

This LoRA is an ULTRA variant built using the same prompt set and dataset structure as the previously released ULTRA variants, with all images newly generated in NovelAI 5.0 Curated to match the original TV anime’s look. Its 598-image core character set establishes Senko’s identity across varied angles, expressions, poses, and framing, while its 402 activity scenes expand that foundation with the camera angles, shot types, and poses found across a wide range of activities.

The Trigger Word

senko_san

Quick Generation Settings

Setting Recommended Value Base Model Illustrious XL v0.1 (or compatible checkpoints) Trigger Tag senko_san Helpful Tags 1girl, solo, hair ornament LoRA Weight 0.85 – 1.0 (0.85 for outfit flexibility, 1.0 for full likeness) Sampler Euler a Steps 28 CFG Scale 7 Resolution 1024 × 1024 Clip Skip 2

LoRA Weight

• 0.85 — good character likeness with more room for outfit and scene control.

• 0.90 — balanced likeness and prompt flexibility.

• 1.0 — strongest Senko likeness.

• 0.70 — still usable, with a softer and less forceful character likeness.

Negative Prompt

Heavy negative prompting is not required. A simple negative prompt is sufficient:

worst quality, low quality, blurry

Optional extended negative if you encounter artifacts:

lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, artifacts, signature, watermark, username, blurry

Example Prompts

1. Traditional Shrine Maiden / Kimono (txt2img)

Prompt:
masterpiece, best quality, senko_san, 1girl, fox ears, kitsune, kimono, smiling, tea cup, traditional japanese room
Weight: 1.0 | Steps: 28 | Sampler: Euler a | CFG: 7

2. Modern Streetwear (txt2img)

Prompt:
masterpiece, best quality, senko_san, 1girl, fox ears, kitsune, black leather motorcycle jacket, blue jeans, city street, night, neon lights
Weight: 0.85 | Steps: 28 | Sampler: Euler a | CFG: 7

3. Casual Morning (txt2img)

Prompt:
masterpiece, best quality, senko_san, 1girl, fox ears, oversized white hoodie, holding coffee mug, modern kitchen, morning light
Weight: 0.90 | Steps: 28 | Sampler: Euler a | CFG: 7

4. Img2img / Inpainting Replacement

Prompt:
senko_san, 1girl, looking at viewer, smile, hair ornament
Weight: 0.85–1.0 | Denoise: 0.55–0.70 | Resolution: 1024 × 1024

Dataset & Tagging Procedure

The dataset contains 1,000 high-resolution 1024×1024 images.

Tagging followed a multi-stage workflow combining two automated taggers, human review, custom tooling, and AI-assisted policy iteration:

1. Two-Stage Auto-Tagging:

   • Step 1: An initial auto-tagging pass was performed using Civitai's built-in auto-tagger.

   • Step 2: A subsequent pass was run using the new PixAI auto-tagger (available on Hugging Face).

2. Manual Review & Baseline Policy:

   • A representative set of 102 images was manually reviewed by hand to establish the initial tagging dictionary and baseline rules.

   • An initial draft policy was created interactively with an AI agent based on these reviewed images.

3. Custom Tooling:

   • Tag curation was managed using a customized version of toshiaki1729's dataset editor, modified to allow AI agents to assist with dataset review and tag management.

   • The tool takes the raw auto-tagger output, applies the active policy rules, and outputs the curated tag set.

4. AI-Assisted Policy Iteration:

   • The auto-tagger outputs were loaded into the custom editor.

   • An AI agent analyzed the output to surface edge cases, normalize inconsistent terminology, and categorize tags under explicit policy rules: Keep, Reject, or Unknown.

   • As new cases were surfaced, the policy was refined interactively and reapplied across the dataset to strip noise, prevent concept bleed, and produce the final curated tag set.

Training Specifications

| Parameter | Setting |

|---|---|

| Training Engine | ostris/ai-toolkit |

| Base Model | Illustrious XL v0.1 |

| Architecture | SDXL |

| Precision | BF16 |

| Training Steps | 2,000 |

| Batch Size | 2 |

| Gradient Accumulation | 1 |

| Network Rank / Alpha | Dim 32 / Alpha 32 |

| Optimizer | Automagic |

| UNet Learning Rate | 1e-4 |

| Text Encoder Learning Rate | 1e-5 |

| LR Scheduler | Cosine |

| Tag Shuffling | Enabled |

| Preserve Tag | 1 | (Preserve Tag 1: senko_san)

| Tag Dropout | 0.05 |

| Clip Skip | 2 |