Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/stupidcucumber/stablediffusion
A repository for fine-tuning StableDiffusion2.
https://github.com/stupidcucumber/stablediffusion
deep-neural-networks diffusion-models generation python stable-diffusion text-to-image torch training
Last synced: about 2 months ago
JSON representation
A repository for fine-tuning StableDiffusion2.
- Host: GitHub
- URL: https://github.com/stupidcucumber/stablediffusion
- Owner: stupidcucumber
- License: mit
- Created: 2024-03-05T08:13:00.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-03-15T09:55:16.000Z (10 months ago)
- Last Synced: 2024-03-15T20:00:52.658Z (10 months ago)
- Topics: deep-neural-networks, diffusion-models, generation, python, stable-diffusion, text-to-image, torch, training
- Language: Python
- Homepage:
- Size: 101 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## Prerequisites
This program depends on Python3.10.
# Implementation
TBD
# Usage
While designing this repository I kept in mind that end user will be using the following workflow:
My implementation uses DreamBooth technique to avoid forgetting. So to fine-tune any Text-to-Image StableDiffusion Pipeline you need to provide it with three things:
1. *Instance images.* Commonly 9-12 images of a particular instance you want Pipeline to reproduce.
2. *Instance prompt.* Following the DreamBooth recommendations you must prompt something like "A `instance-name` `class`". So if I want to reproduce the photos of Peter Pomalini I must write something like "A ppomalini person".
3. *Class prompt.* This prompt will be used to generate images of the same class as our instance, so I would recommend prompting "A person."
Then use generated data in training.
## Generate CLI
```
-h, --help show this help message and exit
--input-dir INPUT_DIR
Path to the directory containing instance images.
-c CONFIG, --config CONFIG
Config file of the model pipeline.
-ip INSTANCE_PROMPT, --instance-prompt INSTANCE_PROMPT
Prompt to the images of your instance.
-cp CLASS_PROMPT, --class-prompt CLASS_PROMPT
Prompt from which to generate images of the class instances.
--ratio RATIO Ratio between class images and instance images. As a default it will, generate 10 class images per one instance image.
--output-dir OUTPUT_DIR
Path to the folder where dataset will be stored. Will be created if not exist.
-d DEVICE, --device DEVICE
Device on which inputs and model itself will be stored.
```
## Train CLI
```
-h, --help show this help message and exit
--data DATA Path to the dataframe containing data for the training. Data must contain columns: ["image_path", "prompt"]
-c CONFIG, --config CONFIG
Configuration file of the model.
-e EPOCHS, --epochs EPOCHS
-d DEVICE, --device DEVICE
Device on which model and dataset will be placed on.
--batch-size BATCH_SIZE
Batchsize for the StableDiffusion
--output OUTPUT Path to the output folder, where all the weights will be contained.
```
## Evaluate CLI
```
-h, --help show this help message and exit
-p PROMPT, --prompt PROMPT
Text prompt to the diffusion model.
-n NUMBER, --number NUMBER
Number of examples to generate.
--config CONFIG Path to the config of the generating model.
-d DEVICE, --device DEVICE
Device on which model and inputs will be located.
-o OUTPUT_DIR, --output-dir OUTPUT_DIR
Output of the model.
```
# Example
TBD
# :warning: TODO:
Task name | Progress |
----------|----------|
Write script for evaluation|:white_check_mark:|
Write script for training|:white_check_mark:|
Implement logging|:white_square_button:|
Rethink the way models are being loaded|:white_square_button:|