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Usage

Models

  • stablediffusion-inpainting: runwayml/stable-diffusion-inpainting, does not support img2img mode
  • stablediffusion-inpainting+img2img-v1.5: runwayml/stable-diffusion-inpainting + runwayml/stable-diffusion-v1-5, supports img2img mode, requires larger vRAM
  • stablediffusion-v1.5: runwayml/stable-diffusion-v1-5, inpainting with diffusers's legacy pipeline, low quality for outpainting, supports img2img mode
  • stablediffusion-v1.4: CompVis/stable-diffusion-v1-4, inpainting with diffusers's legacy pipeline, low quality for outpainting, supports img2img mode

Loading local model

Note that when loading a local checkpoint, you have to specify the correct model choice before setup.

python app.py --local_model path_to_local_model
# e.g. 
# diffusers model weights
python app.py --local_model ./models/runwayml/stable-diffusion-inpainting
python app.py --local_model models/CompVis/stable-diffusion-v1-4/model_index.json
# original model checkpoint
python app.py --local_model /home/user/checkpoint/model.ckpt

Loading remote model

Note that when loading a remote model, you have to specify the correct model choice before setup.

python app.py --remote_model model_name
# e.g. 
python app.py --remote_model hakurei/waifu-diffusion-v1-3

Using textual inversion embeddings

Put *.bin inside embeddings directory.

Using a dreambooth finetuned model

python app.py --remote_model model_name
# e.g.
python app.py --remote_model sd-dreambooth-library/pikachu
# or download the weight/checkpoint and load with
python app.py --local_model path_to_model

Model Path for Docker users

Docker users can specify a local model path or remote mode name within the web app.

Using fp32 mode or low vRAM mode (some GPUs might not work well fp16)

python app.py --fp32 --lowvram

HTTPS

python app.py --encrypt --ssl_keyfile path_to_ssl_keyfile --ssl_certfile path_to_ssl_certfile

Keyboard shortcut

The shortcut can be configured via config.yaml. Currently only support [key] or [Ctrl] + [key]

Default shortcuts are:

shortcut:
  clear: Escape
  load: Ctrl+o
  save: Ctrl+s
  export: Ctrl+e
  upload: Ctrl+u
  selection: 1
  canvas: 2
  eraser: 3
  outpaint: d
  accept: a
  cancel: c
  retry: r
  prev: q
  next: e
  zoom_in: z
  zoom_out: x
  random_seed: s

Glossary

(From diffusers' document https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py)

  • prompt: The prompt to guide the image generation.
  • step: The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
  • guidance_scale: Guidance scale as defined in Classifier-Free Diffusion Guidance. guidance_scale is defined as w of equation 2. of Imagen Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt,usually at the expense of lower image quality.
  • negative_prompt: The prompt or prompts not to guide the image generation.
  • Sample number: The number of images to generate per prompt
  • scheduler: A scheduler is used in combination with unet to denoise the encoded image latens.
  • eta: Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to DDIMScheduler, will be ignored for others.
  • strength: for img2img only, Conceptually, indicates how much to transform the reference image.