Demo of Stable Diffusion usage, storing the prompt in metadata https://twitter.com/permutans/status/1562471438501548034

hf_stablediffusion_demo.py

import piexif
from torch import autocast

from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4",
    use_auth_token=True,
).to("cuda")

prompt = "a photo of a lilac and tan French Bulldog puppy frolicking in a park filled with tennis balls and butterflies, DSLR camera, high quality, sunny day, joyful feeling"
prompt_short = "fb-park"


def make_prompt_exif(prompt):
    """https://stackoverflow.com/a/63649983/"""
    exif_ifd = {piexif.ExifIFD.UserComment: f"Prompt: {prompt}".encode()}
    exif_dict = {"0th": {}, "Exif": exif_ifd, "1st": {}, "thumbnail": None, "GPS": {}}
    return piexif.dump(exif_dict)


for i in range(1):
    with autocast("cuda"):
        size = int(512 * 1.5)
        image = pipe(
            prompt,
            height=size,
            width=size,
            num_inference_steps=100,
            guidance_scale=11.0,
        )["sample"][0]

    image.save(f"{prompt_short}_{i:03d}.png", exif=make_prompt_exif(prompt))

hf_stablediffusion_img2img.py

from pathlib import Path

import piexif
import torch
from PIL import Image
from torch import autocast

from image_to_image import StableDiffusionImg2ImgPipeline, preprocess

pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4",
    revision="fp16",
    torch_dtype=torch.float16,
    use_auth_token=True,
).to("cuda")

prompt = "drawing of a dog, pencil sketch"
prompt_short = "draw-the-dog"

size = 512  # int(512 * 1.5)


def make_prompt_exif(prompt):
    """https://stackoverflow.com/a/63649983/"""
    exif_ifd = {piexif.ExifIFD.UserComment: f"Prompt: {prompt}".encode()}
    exif_dict = {"0th": {}, "Exif": exif_ifd, "1st": {}, "thumbnail": None, "GPS": {}}
    return piexif.dump(exif_dict)


def load_and_preprocess_init_image(image_path):
    init_image = Image.open(image_path).convert("RGB").resize((size, size))
    return preprocess(init_image)


image_path = Path("sketches") / "dog-ipad-sketch.jpg"
init_image = load_and_preprocess_init_image(image_path)

for i in range(100):
    with autocast("cuda"):
        image = pipe(
            prompt,
            init_image=init_image,
            num_inference_steps=100,
            guidance_scale=11.0,
        )["sample"][0]

    image.save(f"{prompt_short}_{i:03d}.png", exif=make_prompt_exif(prompt))

REPRO_CONDA_SETUP.md

conda create -n diffusers
conda activate diffusers
conda install -y pytorch torchvision torchaudio cudatoolkit=11.6 -c pytorch -c conda-forge
pip install diffusers transformers piexif

For hf_stablediffusion_img2img.py download https://github.com/huggingface/diffusers/blob/main/examples/inference/image_to_image.py