stable-diffusion-cli 0.1.9

Stable Diffusion CLI
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[![](https://dcbadge.vercel.app/api/server/rzaesS82MT)](https://discord.gg/rzaesS82MT)


# Stable Diffusion CLI


Install the CLI tool:
```bash
cargo install stable-diffusion-cli
```

Get help to use the cli:
```bash
stable-diffusion --help
```

## Training requirements


- **kohya_ss**: Follow the installation guidelines here [https://github.com/bmaltais/kohya_ss](https://github.com/bmaltais/kohya_ss).


Setup the training environment:
```bash
stable-diffusion train setup
```

## Examples


#### Generation example


To generate an image, simply run:
```bash
stable-diffusion generate --prompt "A green apple"
```

To check all the generation parameters:
```bash
stable-diffusion generate --help
```

#### Training example


We have a [dataset with photos of Bacana](examples/training/lora/bacana/images), a Coton de Tuléar, conceptualized as `bacana white dog` to not mix with the existing `Coton de Tuléar` concept in the `Stable Diffusion XL` model.

Some of the training images in [examples/training/lora/bacana/images](examples/training/lora/bacana/images):
<p>
<img src="https://raw.githubusercontent.com/sensorial-systems/stable-diffusion/main/examples/training/lora/bacana/images/IMG_5175.PNG" width="128">
<img src="https://raw.githubusercontent.com/sensorial-systems/stable-diffusion/main/examples/training/lora/bacana/images/IMG_5176.PNG" width="128">
<img src="https://raw.githubusercontent.com/sensorial-systems/stable-diffusion/main/examples/training/lora/bacana/images/IMG_5180.PNG" width="128">
</p>

The training parameters looks like this:

```json
{
    "prompt": {
        "instance": "bacana",
        "class": "white dog"
    },
    "dataset": {
        "training": "images"
    },
    "network": {
        "dimension": 8,
        "alpha": 1.0
    },
    "output": {
        "name": "{prompt.instance}({prompt.class})d{network.dimension}a{network.alpha}",
        "directory": "./output"
    },
    "training": {
        "optimizer": "Adafactor",
        "learning_rate": {
            "scheduler": "Constant"
        }    
    }
}
```

Note that the `output.name` is a format string that captures the parameters values. This is useful for experimenting with different parameters and keeping track of them in the model file name.

Train the example with:
```bash
stable-diffusion train --config examples/training/lora/bacana/parameters.json
```

The LoRA safetensor file will be generated as
```bash
examples/training/lora/bacana/output/bacana(white dog)d8a1-000001.safetensors
examples/training/lora/bacana/output/bacana(white dog)d8a1.safetensors
```

Where, in this case, `bacana(white dog)d8a1-000001.safetensors` is the first epoch and `bacana(white dog)d8a1.safetensors` is the final epoch.

You can then
```bash
cd examples/training/lora/bacana/generation
```
and run
```bash
python generate.py
```
to test image generation with the LoRA model. The generated images will be present in [examples/training/lora/bacana/generation](examples/training/lora/bacana/generation).

Some of the generated images:
<p>
<img src="https://raw.githubusercontent.com/sensorial-systems/stable-diffusion/main/examples/training/lora/bacana/generation/bacana as a fireman.png" width="128" />
<img src="https://raw.githubusercontent.com/sensorial-systems/stable-diffusion/main/examples/training/lora/bacana/generation/bacana as a scientist.png" width="128" />
<img src="https://raw.githubusercontent.com/sensorial-systems/stable-diffusion/main/examples/training/lora/bacana/generation/bacana as an astronaut.png" width="128" />
</p>

## Development tips


### Debugging


To check the training folder structure required by `kohya_ss` set the `TRAINING_DIR` to, for example, `./training` like:

`TRAINING_DIR=./training stable-diffusion train ...`