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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)
z + Left button Draw zone to be erased
s + Left button Draw zone to be kept
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, with LCM LoRA if available
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 configs\config.json, configs\controlnet.json and configs\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 configs\controlnet.json or configs\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"

Quick mode / LCM LoRA

The quick mode is able to use a very low number of steps with Latent Consistency Models LoRAs . To be able to use it fully, please download the LCM LoRAs for SD 1.5 and/or SDXL on huggingface :

Save the safetensors files as LCM_LoRA_Weights.safetensors in the ./models/LoRA/ directory of your Stable Diffusion WebUI installation. The name of the LoRA is configurable in the JSON configuration files controlnet.json and img2img.json with the quick.lora entry. You can also configure the steps, cfg scale, sampler, and LoRA weight used. The default quick setting gives a good quality/performance ratio with DPM++ SDE Karras and 6 steps. HRFix is disabled when first entering quick mode, but you can activate it if wanted by pressing the h key.

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 configs\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 configs\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 configs\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