Stable diffusion command-line interface
Powered by Diffusers
Version 0.7.8 alpha
Python 3.11 / 3.12
Torch cu118 / cu121
Features
Installation
Help
Known Issues
FAQ
- Command-line interface
- Websockets server
- Curses terminal menu
- SD 1.5, SDXL, SDXL-Turbo
- VAE, TAESD, LoRA
- Text-to-Image, Image-to-Image, Inpaint
- Latent preview for SD/SDXL (bmp/webp)
- Upscaler Real-ESRGAN x2/x4/x4anime
- Read and write PNG with metadata
- Optimized for low vram
- Support CPU and GPU
- Support offline mode and http-proxy
- Fast startup
- JSON config file
- Extras: ComfyUI Bridge for VS Code
- Install Python 3.11 / 3.12
- Install Python packages in a venv (see install.sh or requirements.txt)
- The curses terminal menu is only available on Linux
Requirements: venv, nano, node, xdg-open
git clone https://github.com/nimadez/mental-diffusion.git
python3 -m venv ~/.venv/mdx # create a new venv
source ~/.venv/mdx/bin/activate # activate the venv
sh install.sh
deactivate
nano src/config.json
SD15: mdx.py -p "prompt" -c /sd15.safetensors -st 20 -g 7.5 -f img_{seed}
SDXL: mdx.py -p "prompt" -mode xl -c /sdxl.safetensors -w 1024 -h 1024 -st 30 -g 8.0 -f img_{seed}
Img2Img: mdx.py -p "prompt" -pipe img2img -i image.png -sr 0.5
Inpaint: mdx.py -p "prompt" -pipe inpaint -i image.png -m mask.png
Upscale: mdx.py -upx4 image.png
~/.venv/python3 src/mdx.py
~/.venv/python3 src/mdx.py -serv
open http://localhost:port
nano tests/ws-client.js && node tests/ws-client.js
openai/clip-vit-large-patch14 (hf-cache)
laion/CLIP-ViT-bigG-14-laion2B-39B-b160k (hf-cache)
madebyollin/taesd (hf-cache)
madebyollin/taesdxl (hf-cache)
RealESRGAN_x2plus.pth (optional, src/models/realesrgan)
RealESRGAN_x4plus.pth (optional, src/models/realesrgan)
RealESRGAN_x4plus_anime_6B.pth (optional, src/models/realesrgan)
[ config.json ]
use_cpu: use cpu instead of gpu
low_vram: if possible, it avoids cuda out of memory error
offline: you can enable it after downloading huggingface cache models
http_proxy: bypass or protect privacy (e.g. http://localhost:8118)
host: localhost, 192.168.1.10, 0.0.0.0
port: a valid port number
output: image output directory (e.g. ~/img_output)
onefile: when auto-upscale is enabled, the original image is not saved
interrupt_save: save image after interrupt (the preview gives a more suitable image)
[ command-line arguments ]
--help show this help message and exit
--server -serv start websockets server (port: config.json)
--upscaler -upx4 str /path-to-image.png, upscale image x4
--metadata -meta str /path-to-image.png, extract metadata from PNG
--model -mode str sd/xl, set checkpoint model type (def: config.json)
--pipeline -pipe str txt2img/img2img/inpaint, define pipeline (def: txt2img)
--checkpoint -c str checkpoint .safetensors path (def: config.json)
--vae -v str optional vae .safetensors path (def: null)
--lora -l str optional lora .safetensors path (def: null)
--lorascale -ls float 0.0-1.0, lora scale (def: 1.0)
--scheduler -sc str ddim, ddpm, lcm, pndm, euler_anc, euler, lms (def: config.json)
--prompt -p str positive prompt text input (def: sample)
--negative -n str negative prompt text input (def: empty)
--width -w int width value must be divisible by 8 (def: config.json)
--height -h int height value must be divisible by 8 (def: config.json)
--seed -s int seed number, -1 to randomize (def: -1)
--steps -st int steps from 1 to 100+ (def: 20)
--guidance -g float 0.0-20.0+, how closely linked to the prompt (def: 8.0)
--strength -sr float 0.0-1.0, how much respect the image should pay to the original (def: 1.0)
--image -i str PNG file path or base64 PNG (def: null)
--mask -m str PNG file path or base64 PNG (def: null)
--base64 -64 do not save the image to a file, get base64 only
--filename -f str filename prefix (no png extension)
--batch -b int enter number of repeats to run in batch (def: 1)
--preview -pv stepping is slower with preview enabled
--upscale -up str auto-upscale x2, x4, x4anime (def: null)
[ http server requests ]
curl http://localhost:port/preview --output preview.bmp
curl http://localhost:port/replay --output preview.webp
curl http://localhost:port/progress (return { step: int, timestep: int })
curl http://localhost:port/interrupt
curl http://localhost:port/config (return config.json)
* --server or --batch is recommended because there is no need to reload the checkpoint
* add "{seed}" to --filename, which will be replaced by seed later
* ~/ and $USER accepted for file and directory paths
* Juggernaut Aftermath, TRCVAE, World of Origami
* OpenDalleV1.1
* A cinematic shot of a baby racoon wearing an intricate italian priest robe.
:: Stepping is slower with preview enabled
We used the BMP format which has no compression.
Reminder: one solution is to set "pipe._guidance_scale" to 0.0 after 40%
:: Interrupt operation does not work
You have to wait for the current step to finish,
the interrupt operation is applied at the end of each step.
:: The server does not start from the terminal menu
If you are running MD for the first time and the
huggingface cache is missing, start the server using:
~/.venv/python3 src/mdx.py -serv
:: How to load SDXL with 3.5 GB VRAM?
To load SDXL, you need at least 16 GB RAM and a virtual-memory paging.
:: How to download HuggingFace models in a specific path?
ln -s /media/user/storage/path-to-huggingface-cache ~/.cache/huggingface
↑ Migrating to linux environment
↑ Back to the roots (diffusers)
↑ Ported to VS Code
↑ Switch from Diffusers to ComfyUI
↑ Upgrade from sdkit to Diffusers
↑ Undiff renamed to Mental Diffusion
↑ Undiff started with "sdkit"
↑ Created for my personal use
"AI will take us back to the age of terminals."
Code released under the MIT license.