Parti Prompts

prompts = FileAttachment("PartiPrompts.tsv").tsv({array: true, typed: true})

viewof model = Inputs.radio(["sd15", "sd21", "sdxl", "sdxl-lcm", "sdxl-lightning-unet-1", "sdxl-lightning-unet-2", "sdxl-lightning-unet-4", "sdxl-lightning-unet-8", "sdxl-turbo", "stable-cascade"], {label: "Model", value: "stable-cascade"})
viewof prompt = Inputs.range([0, 1631], {step: 1, value: 321, label: "Prompt"})
viewof seed = Inputs.range([0, 3], {step: 1, value: 0, label: "Seed"})
viewof thumbnails = Inputs.toggle({label: "Use thumbs", values: ['_t', ''], value: '_t'})

img = html`<img width=256 height=256>`;

loadImage = (src) => new Promise((resolve, reject) => {
  const tempImg = new Image();
  tempImg.onload = () => resolve(src);
  tempImg.onerror = reject;
  tempImg.src = src;
});

updateImageSrc = async (model, prompt, seed) => {
  const src = `https://r2.anotherjesse.com/parti/${model}/${seed}/${prompt}${thumbnails}.jpg`;
  const loadedSrc = await loadImage(src);
  img.src = loadedSrc;
  if (thumbnails == '') {
    img.width = 512;
    img.height = 512;
  } else {
    img.width = 256;
    img.height = 256;
  }
  // return html`<em>${get_prompt(prompt)}</em> ${steps_c} + ${steps_b} steps`;
  return prompts[prompt+1]
};

updateImageSrc(model, prompt, seed);

Stable Cascade

SDXL

SDXL images were generated with 25 steps using the FP16 variant

from diffusers import StableDiffusionXLPipeline
import torch

pipeline = StableDiffusionXLPipeline.from_pretrained(
  "stabilityai/stable-diffusion-xl-base-1.0",
  torch_dtype=torch.float16,
  variant="fp16",
  use_safetensors=True,
  add_watermarker=False,
).to(DEVICE)

image = pipeline(prompt, 
    generator=torch.Generator("cuda").manual_seed(seed),
    num_inference_steps=25).images[0]

image.save("output.jpg")

SDXL Turbo

SDXL Turbo were generated with the diffuser defaults / guidance_scale of 0 / num_inference_steps = 1

from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image
import torch


pipeline = AutoPipelineForText2Image.from_pretrained(
  "stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16"
).to(DEVICE)

torch.manual_seed(seed)

with torch.no_grad():
    image = pipeline(
        prompt=prompt,
        guidance_scale=0.0,
        num_inference_steps=1
    ).images[0]

    image.save("output.jpg")