overview
# Feedback Image Trainer
A Python package to fine-tune Stable Diffusion models using feedback-driven hyperparameter search with Optuna.
## Installation
```bash
pip install feedback_image_trainer
Usage
from feedback_image_trainer import run_study
run_study(
feedback_file="image_feedback.json",
model_path="runwayml/stable-diffusion-v1-5",
output_dir="fine_tuned_model",
trials=5
)
Input Data Format
The image_feedback.json file should contain a list of dictionaries with the following structure:
[
{
"image_path": "path/to/image.png",
"prompt": "A description of the image",
"feedback": 1
},
...
]
Requirements
- Python 3.8+
- torch>=2.0.0
- diffusers>=0.20.0
- transformers>=4.30.0
- accelerate>=0.20.0
- optuna>=2.0.0
- torchvision>=0.15.0
Metadata
Release files for feedback-image-trainer 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| feedback_image_trainer-0.1.0.tar.gz | 4.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| feedback_image_trainer-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 9.3 kB
Release files / feedback_image_trainer-0.1.0.tar.gz
| Download URL | feedback_image_trainer-0.1.0.tar.gz |
|---|---|
| Size | 4.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/6.1.0 CPython/3.12.6
|
Release files / feedback_image_trainer-0.1.0-py3-none-any.whl
| Download URL | feedback_image_trainer-0.1.0-py3-none-any.whl |
|---|---|
| Size | 5.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.12.6
|