diffused

🤗 Generate images with diffusion models:

diffused <model> <prompt>

Quick Start

Text-to-image:

uv run --with diffused diffused segmind/tiny-sd "red apple"

Or with pipx:

pipx run diffused segmind/tiny-sd "red apple"

Image-to-image:

uv run --with diffused diffused OFA-Sys/small-stable-diffusion-v0 "cat wizard" --image=https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png

Inpainting:

uv run --with diffused diffused kandinsky-community/kandinsky-2-2-decoder-inpaint "black cat" --image=https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png --mask-image=https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png

Prerequisites

• Python
• uv or pipx

CLI

Install the CLI with uv:

uv tool install diffused

Or with pipx:

pipx install diffused

model

Required (str): The diffusion model.

diffused segmind/SSD-1B "An astronaut riding a green horse"

See segmind/SSD-1B.

prompt

Required (str): The text prompt.

diffused dreamlike-art/dreamlike-photoreal-2.0 "cinematic photo of Godzilla eating sushi with a cat in a izakaya, 35mm photograph, film, professional, 4k, highly detailed"

--negative-prompt

Optional (str): What to exclude from the output image.

diffused stabilityai/stable-diffusion-2 "photo of an apple" --negative-prompt="blurry, bright photo, red"

With the short option:

diffused stabilityai/stable-diffusion-2 "photo of an apple" -np="blurry, bright photo, red"

--image

Optional (str): The input image path or URL. The initial image is used as a starting point for an image-to-image diffusion process.

diffused stabilityai/stable-diffusion-xl-refiner-1.0 "astronaut in a desert" --image=https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png

With the short option:

diffused stabilityai/stable-diffusion-xl-refiner-1.0 "astronaut in a desert" -i=./local/image.png

--mask-image

Optional (str): The mask image path or URL. Inpainting replaces or edits specific areas of an image. Create a mask image to inpaint images.

diffused kandinsky-community/kandinsky-2-2-decoder-inpaint "black cat" --image=https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png --mask-image=https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png

With the short option:

diffused kandinsky-community/kandinsky-2-2-decoder-inpaint "black cat" -i=inpaint.png -mi=inpaint_mask.png

--output

Optional (str): The output image filename.

diffused dreamlike-art/dreamlike-photoreal-2.0 "cat eating sushi" --output=cat.jpg

With the short option:

diffused dreamlike-art/dreamlike-photoreal-2.0 "cat eating sushi" -o=cat.jpg

--width

Optional (int): The output image width in pixels.

diffused stabilityai/stable-diffusion-xl-base-1.0 "dog in space" --width=1024

With the short option:

diffused stabilityai/stable-diffusion-xl-base-1.0 "dog in space" -W=1024

--height

Optional (int): The output image height in pixels.

diffused stabilityai/stable-diffusion-xl-base-1.0 "dog in space" --height=1024

With the short option:

diffused stabilityai/stable-diffusion-xl-base-1.0 "dog in space" -H=1024

--number

Optional (int): The number of output images. Defaults to 1.

diffused segmind/tiny-sd apple --number=2

With the short option:

diffused segmind/tiny-sd apple -n=2

--guidance-scale

Optional (int): How much the prompt influences the output image. A lower value leads to more deviation and creativity, whereas a higher value follows the prompt to a tee.

diffused stable-diffusion-v1-5/stable-diffusion-v1-5 "astronaut in a jungle" --guidance-scale=7.5

With the short option:

diffused stable-diffusion-v1-5/stable-diffusion-v1-5 "astronaut in a jungle" -gs=7.5

--inference-steps

Optional (int): The number of diffusion steps used during image generation. The more steps you use, the higher the quality, but the generation time will increase.

diffused CompVis/stable-diffusion-v1-4 "astronaut rides horse" --inference-steps=50

With the short option:

diffused CompVis/stable-diffusion-v1-4 "astronaut rides horse" -is=50

--strength

Optional (float): The noise added to the input image, which determines how much the output image deviates from the original image. Strength is used for image-to-image and inpainting tasks and is a multiplier to the number of denoising steps (--inference-steps).

diffused stabilityai/stable-diffusion-xl-refiner-1.0 "astronaut in swamp" --image=https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-sdxl-init.png --strength=0.5

With the short option:

diffused stabilityai/stable-diffusion-xl-refiner-1.0 "astronaut in swamp" -i=image.png -s=0.5

--seed

Optional (int): The seed for generating random numbers, ensuring reproducibility in image generation pipelines.

diffused stable-diffusion-v1-5/stable-diffusion-v1-5 "Labrador in the style of Vermeer" --seed=0

With the short option:

diffused stable-diffusion-v1-5/stable-diffusion-v1-5 "Labrador in the style of Vermeer" -S=1337

--device

Optional (str): The device to accelerate the computation (cpu, cuda, mps, xpu, xla, or meta).

diffused stable-diffusion-v1-5/stable-diffusion-v1-5 "astronaut on earth, 8k" --device=cuda

With the short option:

diffused stable-diffusion-v1-5/stable-diffusion-v1-5 "astronaut on earth, 8k" -d=cuda

--no-safetensors

Optional (bool): Whether to disable safetensors.

diffused runwayml/stable-diffusion-v1-5 "astronaut on mars" --no-safetensors

--version

Show the program's version number and exit:

diffused --version # diffused -v

--help

Show the help message and exit:

diffused --help # diffused -h

Script

Create a virtual environment and install the package with uv:

uv add diffused

Generate an image with a model and a prompt:

# script.py
from diffused import generate

images = generate(model="segmind/tiny-sd", prompt="apple")
images[0].save("apple.png")

Run the script:

uv run python script.py

Open the image:

open apple.png

See the API documentation.

License

MIT