README.md

---
license: other
license_name: circlestone-labs-non-commercial-license
license_link: https://huggingface.co/circlestone-labs/Anima/blob/main/LICENSE.md
base_model: circlestone-labs/Anima
pipeline_tag: text-to-image
tags:
  - anime
  - lokr
  - finetune
  - Anima
---

# Anima-Telescopa
ALL PREVIEW IMAGES HAVE COMFYUI METADATA.
<p align="center">
  <img src="https://huggingface.co/RicemanT/Anima-Telescopa/resolve/main/images/hero.png" width="80%" />
</p>

<table>
<tr>
<td width="25%"><img src="https://huggingface.co/RicemanT/Anima-Telescopa/resolve/main/images/sample1.png" width="100%"/></td>
<td width="25%"><img src="https://huggingface.co/RicemanT/Anima-Telescopa/resolve/main/images/sample2.png" width="100%"/></td>
<td width="25%"><img src="https://huggingface.co/RicemanT/Anima-Telescopa/resolve/main/images/sample3.png" width="100%"/></td>
<td width="25%"><img src="https://huggingface.co/RicemanT/Anima-Telescopa/resolve/main/images/sample4.png" width="100%"/></td>
</tr>
<tr>
<td width="25%"><img src="https://huggingface.co/RicemanT/Anima-Telescopa/resolve/main/images/sample5.png" width="100%"/></td>
<td width="25%"><img src="https://huggingface.co/RicemanT/Anima-Telescopa/resolve/main/images/sample6.png" width="100%"/></td>
<td width="25%"><img src="https://huggingface.co/RicemanT/Anima-Telescopa/resolve/main/images/sample7.png" width="100%"/></td>
<td width="25%"><img src="https://huggingface.co/RicemanT/Anima-Telescopa/resolve/main/images/sample8.png" width="100%"/></td>
</tr>
</table>

---

## I. Introduction

A **full matrix LoKr** of [Anima Base v1.0](https://huggingface.co/circlestone-labs/Anima/blob/main/split_files/diffusion_models/anima-base-v1.0.safetensors) by [CircleStone Labs](https://huggingface.co/circlestone-labs), trained using the [LyCoRIS](https://github.com/kohakublueleaf/lycoris) full matrix LoKr method ([paper](https://arxiv.org/abs/2309.14859)).

This was my first attempt at fine-tuning an image model on a dataset above 10k samples. Alongside general practice, the run was also used to:

- Test whether a carefully hand-curated aesthetic dataset could improve Anima's background generation quality
- Compare full matrix LoKr training against a true full finetune on the same settings

Compared to other Anima fine-tunes such as Tdrussel's Aes B, Motimalu's KirazuriV4, duongve's AnimaYume, and the Silvermoon mixes, Telescopa is perhaps a lil worse on fine detail, stability, and knowledge/style retention — but it shows slightly stronger background composition and detail than the base model and most contemporaries. For a first larger-scale training run, I'm happy with the result.

This model is produced independently, as a hobbyist project, with no external funding.
This repo contain 3 model variant, the ideal epoch 10 LoKR, a earlier and more unstable epoch 3 that also doesnt have as much style bias or potential knowledge forgetting, and a failed epoch 10 full finetune.

---

## II. Model Details

|                                          |                                                                                                                    |
| ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------ |
| **Base model**                           | [circlestone-labs/Anima](https://huggingface.co/circlestone-labs/Anima) (v1.0)                                     |
| **Method**                               | Full Matrix LoKr ([LyCoRIS](https://github.com/kohakublueleaf/lycoris))                                            |
| **Trainer**                              | [Bluvoll diffusion-pipe fork](https://github.com/bluvoll/diffusion-pipe) (originally by Anima's creator, Tdrussel) |
| **Hardware**                             | 2x NVIDIA RTX A4000 16GB, courtesy of [Astromahdi's gpu.garden](https://gpu.garden/)                               |
| **Total training time**                  | ~3 days (~68 hours)                                                                                              |
| **Total samples seen (unbatched steps)** | ~100,000                                                                                                           |
| **Training resolutions**                 | 1024², 1280²                                                                                                       |

### Training run (initial 3-epoch run → extended 7-epoch run)

Full config: [TelescopaLOKR.toml](https://github.com/RicemanT/model-training-configs/blob/main/diffusion-pipe/Anima/configs/TelescopaLOKR.toml)

- **Samples seen:** ~30,000 + ~70,000
- **Learning rate:** 3e-6
- **LR scheduler:** Constant with warmup
- **LLM adaptor LR:** Disabled
- **Precision:** Full BF16
- **Optimizer:** AdamW8bit with Kahan summation (to offset BF16 precision roundoff)
- **Weight decay:** 0.01
- **Timestep sampling:** Logit-Normal, shift 4, sigmoid scale 1.3

### LoKr configuration

- Dim / rank: 16
- Alpha: 16
- Dtype: bfloat16
- Dropout: 0.0
- Factor: 2 (~800MB file size)

### Additional training features

- Tag dropout: 10%
- Caption dropout: 5%
- Mixed captions at a 25/25/25/25 ratio
- Tag shuffle

---

## III. Recommended Settings

These are the settings used for the sample images above (ComfyUI, `ModelSamplingAuraFlow` node):

- **Shift:** 5.0
- **Steps:** 28
- **CFG:** 4.0
- **Sampler:** `er_sde`
- **Scheduler:** simple

These are just my usual settings — feel free to experiment and really go buck wild with it, 'euler a' at same steps and `sa_solver_pece` at a lower 15–20 steps also recommended.

---

## IV. Dataset

[Full dataset](https://huggingface.co/datasets/RicemanT/Anime-Background-Finetuning-V1.1)

Sourced from Danbooru (curated by artist background quality) and [Akanyan's personal anime screencap collection scattered throughout his reddit account](https://www.reddit.com/r/anime/comments/4lhd5l/complete_monogatari_series_background_art/). Dataset tooling lives in the [`utils` folder](https://github.com/RicemanT/model-training-configs/tree/main/diffusion-pipe/Anima/utils) of the training repo.

**Curation:** ~43k Danbooru images plus ~7-8k screencaps (50k+ raw) were hand-reviewed for background quality — about two weeks of manual review, landing on a final **10,143-image** dataset.

**Processing:** PNGs converted to lossless WebP, resolutions capped at 2000px (cv2 `INTER_AREA`), deduplicated, and filtered for unusual aspect ratios. A YOLO → IOPaint watermark/logo removal pass was planned but skipped due to time constraints and unreliable open-source detection models.

**Tagging:** [convnextv2_huge.dbv4-full](https://huggingface.co/animetimm/convnextv2_huge.dbv4-full) by animetimm/DeepGHS, run on a free Colab T4.

**Captioning:** [Qwen 3.6 27B FP8](https://huggingface.co/Qwen/Qwen3.6-27B-FP8) via VLLM on an H100 (~3h for ~9k images, non-thinking mode), with the remaining ~1k finished on Qwen 3.6 27B Q6_K via llama.cpp on 2x A4000. Captioning [system prompt](https://github.com/RicemanT/model-training-configs/blob/main/diffusion-pipe/Anima/utils/Anima-Telescopa-captioning-sysprompt.md).

---

## V. Failed Full-Finetune Comparison

A full finetune run with matching settings was also attempted on 4x A4000, finishing in 1.5 days. Training was stable, but the resulting model lost a significant amount of character and artist-style knowledge, making it uncompetitive as an Anima tune — likely because a learning rate of 3e-6 was still too high for full finetuning at the batch size available. That config is included in the training repo for reference. Model file is included here.

---

## VI. Notes from the Training Diary

- **Always back up your dataset before deleting anything locally.** During transfer to the HF dataset repo, an upload silently failed to commit, and ~4k already-"transferred" screencap images (much of it curated Monogatari content) were lost after being deleted locally. The dataset was rebuilt with a more diverse series selection — which ended up improving overall balance.
- WebP isn't always smaller than JPEG — worth checking before batch-converting a dataset.

Full training diary (unedited, written without LLM assistance) (yes I used Claude to help partially write this model page because lazy, sorry lol): [TelescopaLOKR-diary.md](https://github.com/RicemanT/model-training-configs/blob/main/diffusion-pipe/Anima/notes/TelescopaLOKR-diary.md)

---

## VII. License

This model is a Derivative of Anima and is distributed under the same **[CircleStone Labs Non-Commercial License v1.1](https://huggingface.co/circlestone-labs/Anima/blob/main/LICENSE.md)** as the base model, with no additional restrictions. Non-commercial use only — see the license for full terms.

---

## VIII. Acknowledgments

Beeg thanks to:

- [CircleStone Labs](https://huggingface.co/circlestone-labs) — for Anima and the original diffusion-pipe trainer
- [Motimalu](https://civitai.red/user/motimalu) — Inspiration behind finetuning practices and configs, his diary was such a huge help for a completely beginner to get an idea behind how finetuning a model looks like
- [Bluvoll](https://github.com/bluvoll/diffusion-pipe) — diffusion-pipe fork used for training, and general training advice
- [Anzhc](https://huggingface.co/Anzhc) — general training advice
- [Akanyan](https://www.reddit.com/user/akanyan/) — for the goated collection of anime screencap from 9 years ago
- [Astromahdi](https://gpu.garden/) — compute via gpu.garden
- [KohakuBlueleaf](https://github.com/kohakublueleaf/lycoris) — LyCoRIS / LoKr method
- [animetimm/DeepGHS](https://huggingface.co/animetimm/convnextv2_huge.dbv4-full) — tagging model
- Format inspired by [Motimalu's Kirazuri diary](https://github.com/motimalu/diffusion-training-configs/blob/main/diffusion-pipe/anima/notes/kirazuri4.0-notes%20.md)