cute_cat_small_step
Model Introduction
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Model Parameters
Base Model | Tuner Type | Training Parameters | |||
---|---|---|---|---|---|
Batch Size | Epochs | Learning Rate | Resolution | ||
SD_XL1.0 | TEXT_SCE | 2 | 20 | 0.0001 | [1024, 1024] |
Data Type | Data Space | Data Name | Data Subset |
---|---|---|---|
Dataset zip | /mnt/workspace/jingyuan/scepter/cache/scepter_ui/datasets/cartton_cat-version-20240323_15_15_11 | default |
Model Performance
Given the input "cute cat, bright cartoon style," the following image may be generated:
Model Usage
Command Line Execution
- Run using Scepter's SDK, taking care to use different configuration files in accordance with the different base models, as per the corresponding relationships shown below
Base Model | LORA | SCE | TEXT_LORA | TEXT_SCE |
---|---|---|---|---|
SD1.5 | lora_cfg | sce_cfg | text_lora_cfg | text_sce_cfg |
SD2.1 | lora_cfg | sce_cfg | text_lora_cfg | text_sce_cfg |
SDXL | lora_cfg | sce_cfg | text_lora_cfg | text_sce_cfg |
- Running from Source Code
git clone https://github.com/modelscope/scepter.git
cd scepter
pip install -r requirements/recommended.txt
PYTHONPATH=. python scepter/tools/run_inference.py
--pretrained_model {this model folder}
--cfg {lora_cfg} or {sce_cfg} or {text_lora_cfg} or {text_sce_cfg}
--prompt 'cute cat, bright cartoon style'
--save_folder 'inference'
- Running after Installing Scepter (Recommended)
pip install scepter
python -m scepter/tools/run_inference.py
--pretrained_model {this model folder}
--cfg {lora_cfg} or {sce_cfg} or {text_lora_cfg} or {text_sce_cfg}
--prompt 'cute cat, bright cartoon style'
--save_folder 'inference'
Running with Scepter Studio
pip install scepter
# Launch Scepter Studio
python -m scepter.tools.webui
- Refer to the following guides for model usage.
(video url)
Model Reference
If you wish to use this model for your own purposes, please cite it as follows.
@misc{cute_cat_small_step,
title = {cute_cat_small_step, https://www.modelscope.cn/models/zhjf97/cute_cat_small_step},
author = {zhjf97},
year = {2024}
}
This model was trained using Scepter Studio; Scepter is an algorithm framework and toolbox developed by the Alibaba Tongyi Wanxiang Team. It provides a suite of tools and models for image generation, editing, fine-tuning, data processing, and more. If you find our work beneficial for your research, please cite as follows.
@misc{scepter,
title = {SCEPTER, https://github.com/modelscope/scepter},
author = {SCEPTER},
year = {2023}
}
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