SdPaint

A Python script that lets you paint on a canvas and sends that image every stroke to the automatic1111 API and updates the canvas when the image is generated.

Controls

Key / Mouse button	Control
Left button	Draw with the current brush size
Middle button	Draw with a white color brush
`e` + Left button	Eraser brush (bigger)
Scroll up / down	Increase / decrease brush size
`1` to `9`	Set brush size
`backspace`	Erase the entire sketch
`shift` + Left button	Draw a line between two clicks
`RETURN` or `ENTER`	Request image rendering
`ctrl` + `i`	Interrupt image rendering
`c`	Display current configuration while pressed
`p`	Edit prompt
`alt` + `p`	Edit negative prompt
`a`	Toggle autosave
`shift` + `t`	Cycle render wait time (+0.5s, or off)
`ctrl` + `p`	Pause dynamic rendering
`q`	Toggle quick rendering : low steps & HR fix off
`n`	Random seed value
`ctrl` + `n`	Edit seed value
`UP` / `DOWN`	Increase / decrease seed by 1
`ctrl`+ `s`	Save the current generated image
`ctrl`+ `o`	Open an image file as sketch
`ctrl`+ `d`	Call ControlNet detector (replace sketch)
`shift` + `ctrl`+ `d`	Cycle ControlNet detectors
`h`	Toggle HR fix
`shift` + `h`	Cycle HR fix scale
`shift` + `u`	Cycle HR upscalers
`shift` + `d`	Cycle denoising strengths
`shift` + `s`	Cycle samplers
`b`	Toggle batch rendering
`shift` + `b`	Cycle batch sizes
`shift` + `c`	Cycle CLIP skip settings
`shift` + `m`	Cycle ControlNel models
`shift` + `w`	Cycle ControlNel weights
`shift` + `g`	Cycle ControlNel guidance ends
`shift` + `ctrl` + `g`	Toggle ControlNel pixel perfect mode
`keypad 0`	Restore starting settings
`keypad 1-9`	Load custom rendering preset
`ctrl` + `keypad 1-9`	Save custom rendering preset
`alt` + `keypad 1-9`	Load custom ControlNet preset
`ctrl` + `alt` + `keypad 1-9`	Save custom ControlNet preset
`x` or `ESC`	Quit

Note : "Cycle" shortcuts type will wait for the shift key to be released before launching the rendering.

Installation

Windows: Link to step-by-step Windows installation instructions with screenshots

macOS: Link to step-by-step MacOS installation instructions with screenshots

Linux: To Do

TLDR; Web UI with the ControlNet extension, API mode enabled in settings, and the AI Models

Configuration

On first launch of the Start file, the script will create the config.json, controlnet.json and img2img.json configuration files as needed. The ControlNet available models for scribble and lineart will be automatically fetched from your API and set in configuration.

The config.json file handles global interface and script configuration. For these settings, the value used on application start is the first value of each of those list:

controlnet_models
detectors
samplers
hr_scales
hr_upscalers
denoising_strengths

The controlnet.json or img2img.json files can be used to configure the prompt, negative prompt, seed, controlnet model, etc. When you save the json file the program will use it after the next brush stroke or when you press enter.

A controlnet-high.json-dist example configuration file is available for better image quality, at the cost of longer rendering time. Use only with a powerful graphics card.

If you want to add additional models into your controlnet extension you can do so by adding the model folder into the models folder of the controlnet extension.

    ".\stable-diffusion-webui\extensions\sd-webui-controlnet\models"

Autosave

The images can be auto-saved after each rendering into outputs and outputs/autosave directories. The maximum number of images saved is set by autosave_images_max in config.json.

This feature can be disabled on start by setting autosave_images to "false".

Multiple ControlNet models

You can update the config.json "controlnet_models" list to have multiple ControlNet models available. You can then cycle between the models using the l and m keys.

The models set by default correspond to the ones available on https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors :

Scribble
Lineart
Lineart anime

Note: "pixel perfect" preprocessor mode is disabled by default, because it can be detrimental with scribble and lineart models when used with a resolution > 512. Try out and compare the results.

ControlNet detectors

You can also update the config.json "detectors" list to configure the line detection on the generated image. Cycle between detections with the d key.

The default detectors are:

Lineart
Lineart coarse
Sketch (pidinet)
Scribble (pidinet)

You can find the full list of supported modules with this URL http://127.0.0.1:7860/controlnet/module_list .

Custom presets

You can save the current rendering settings by using ctrl + keypad 1-9, and the current ControlNet settings by using ctrl + alt + keypad 1-9. Those presets persist in a local presets.json file and are available even after the application is restarted.

You can load rendering settings with keypad 1-9, and ControlNet settings with alt + keypad 1-9. Not all the saved fields are applied by default : the settings that are applied are determined respectively by the preset_fields and cn_preset_fields entries of the config.json file. For example, if you want to also apply the sampler value of the preset on recall, add 'sampler' to your preset_fields list.

Web interface

Alternatively, you can use web interface. Just run SdPaint\StartWeb.bat. After creating virtual environment and installing packages it will open interface in your default browser. More in this instruction

Img2img Experimental mode

Launch the program with --img2img --source <image_file_path> to watch an image file for changes, and use it as img2img source. If the script is launched without --source argument, a loading file dialog will be displayed. The img2img.json file is used in this mode.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT

mjmirza/SdPaint