pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- sdxl
- lora
widget:
- output:
url: assets/image01.png
text: >-
<lora:BACKGWA:1>, backgwa, solo, upper body, masterpiece, high score,
great score, absurdres
parameters:
negative_prompt: >-
lowres, bad anatomy, bad hands, text, error, missing finger, extra
digits, fewer digits, cropped, worst quality, low quality, low score,
bad score, average score, signature, watermark, username, blurry
- output:
url: assets/image02.png
text: >-
<lora:BACKGWA:1>, backgwa, solo, open_mouth, masterpiece, high score,
great score, absurdres
parameters:
negative_prompt: >-
lowres, bad anatomy, bad hands, text, error, missing finger, extra
digits, fewer digits, cropped, worst quality, low quality, low score,
bad score, average score, signature, watermark, username, blurry
- output:
url: assets/image03.png
text: >-
<lora:BACKGWA:1>, backgwa, solo, full_body, squatting, looking at viewer,
masterpiece, high score, great score, absurdres
parameters:
negative_prompt: >-
lowres, bad anatomy, bad hands, text, error, missing finger, extra
digits, fewer digits, cropped, worst quality, low quality, low score,
bad score, average score, signature, watermark, username, blurry
base_model: BackGwa/LUMIERE-Q
instance_prompt: >-
backgwa, solo, pale blue hair, cat ears, golden eyes, navy beret, white shirt,
red ribbon, oversized sleeves, blue jacket
license:
- creativeml-openrail-m
- mit
Overview
Character-LoRA is an SDXL-based character LoRA created to reproduce the BACKGWA character using a copyright-conscious synthetic image dataset.
This LoRA was developed as part of research on whether a character LoRA can be trained without directly using original character images or artist-created works as training data.
Instead of collecting or reusing human-made artwork, the training dataset was constructed from AI-generated synthetic images based on structured character descriptions.
The full research document, methodology, instruction template, and JSON schema are available in the GitHub repository: BackGwa/Character-LoRA
Through this approach, the research examines a workflow for constructing character LoRA models while reducing dependence on original images and considering copyright, training rights, artistic style, and intellectual property.
Disclaimer
- This LoRA may be used freely for the user's own creative purposes. However, all copyright issues, legal responsibilities, and social or ethical consequences arising from any images, videos, or other outputs generated using this LoRA remain solely with the user.
- Users must ensure that generated outputs do not infringe upon the rights, reputation, or creative works of the original artist, character rights holder, or any third party.
- The creation and use of SFW and NSFW outputs are entirely at the user's own discretion and responsibility. The creator, distributor, and maintainer of this LoRA bear no responsibility or obligation regarding such outputs.
- By using this LoRA, the user assumes full and exclusive responsibility for any legal disputes, social issues, infringement of third-party rights, defamation, or unlawful acts that may arise.
- Users must comply with all applicable laws, platform rules, and ethical standards in their country or region when using this LoRA. Any consequences resulting from violations of such laws, rules, or standards shall be borne solely by the user.
- Under no circumstances shall the creator, distributor, or maintainer of this LoRA be liable for any direct, indirect, incidental, special, or consequential damages arising from the use or inability to use this LoRA.
- Redistribution of this LoRA by third parties is prohibited in principle. However, it may be permitted if the original creator is clearly credited and proper attribution is provided.
Trigger Word
Use the trigger word below to activate the BACKGWA character.
You can add extra prompts for outfit, expression, pose, background, or composition as needed.
backgwa
Usage
This LoRA was designed for use with SDXL-based model environments.
For the most stable and consistent results, it is recommended to use it with the LUMIERE-Q model, though it may also be used with other SDXL-compatible checkpoints.
Preview
- Prompt
- <lora:BACKGWA:1>, backgwa, solo, upper body, masterpiece, high score, great score, absurdres
- Negative Prompt
- lowres, bad anatomy, bad hands, text, error, missing finger, extra digits, fewer digits, cropped, worst quality, low quality, low score, bad score, average score, signature, watermark, username, blurry
- Prompt
- <lora:BACKGWA:1>, backgwa, solo, open_mouth, masterpiece, high score, great score, absurdres
- Negative Prompt
- lowres, bad anatomy, bad hands, text, error, missing finger, extra digits, fewer digits, cropped, worst quality, low quality, low score, bad score, average score, signature, watermark, username, blurry
- Prompt
- <lora:BACKGWA:1>, backgwa, solo, full_body, squatting, looking at viewer, masterpiece, high score, great score, absurdres
- Negative Prompt
- lowres, bad anatomy, bad hands, text, error, missing finger, extra digits, fewer digits, cropped, worst quality, low quality, low score, bad score, average score, signature, watermark, username, blurry
Training
This LoRA was trained without using artist-created images or human-made artwork as training data.
The dataset was generated with GPT Image 2.
A total of 80 synthetic images were generated as training candidates, and 48 images were selected for the final dataset based on consistency and quality.
To prepare the character description used for image generation, the original character image and supplementary information were provided to the locally executed gemma-4-E4B-it model.
The analysis used predefined instructions and a structured JSON schema to describe the character in a form suitable for subsequent synthetic image generation.
The selected synthetic images were then labeled using GPT-5.5.
The labels describe visible attributes such as expression, pose, composition, and other image-specific details used during training.
The LoRA was trained using sd-scripts on the SDXL-based LUMIERE-Q model.
| Parameter | Setting |
|---|---|
| Base Model | LUMIERE-Q |
| Dataset Size | 48 images |
| Epochs | 10 |
| Repeats | 10 |
| Resolution | 1024x1024 |
Research Repository
The research document and related materials are available at: BackGwa/Character-LoRA
License
The LoRA model is released under the CreativeML Open RAIL-M license.
The research document and repository materials are released under the MIT License, unless otherwise specified.