titles = { "Sampling steps": "How many times to imptove the generated image itratively; higher values take longer; very low values can produce bad results", "Sampling method": "Which algorithm to use to produce the image", "GFPGAN": "Restore low quality faces using GFPGAN neural network", "Euler a": "Euler Ancestral - very creative, each can get acompletely different pictures depending on step count, setting seps tohigher than 30-40 does not help", "DDIM": "Denoising Diffusion Implicit Models - best at inpainting", "Batch count": "How many batches of images to create", "Batch size": "How many image to create in a single batch", "CFG Scale": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results", "Seed": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result", "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt", "Loopback": "Process an image, use it as an input, repeat. Batch count determings number of iterations.", "SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back", "Just resize": "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.", "Crop and resize": "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.", "Resize and fill": "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.", "Mask blur": "How much to blur the mask before processing, in pixels.", "Masked content": "What to put inside the masked area before processing it with Stable Diffusion.", "fill": "fill it with colors of the image", "original": "keep whatever was there originally", "latent noise": "fill it with latent space noise", "latent nothing": "fill it with latent space zeroes", "Inpaint at full resolution": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image", "Denoising Strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image.", "Interrupt": "Stop processing images and return any results accumulated so far.", "Save": "Write image to a directory (default - log/images) and generation parameters into csv file.", "X values": "Separate values for X axis using commas.", "Y values": "Separate values for Y axis using commas.", "None": "Do not do anything special", "Prompt matrix": "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)", "X/Y plot": "Create a grid where images will have different parameters. Use inputs below to specify which parameterswill be shared by columns and rows", "Custom code": "Run python code. Advanced user only. Must run program with --allow-code for this to work", "Prompt S/R": "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others", } function gradioApp(){ return document.getElementsByTagName('gradio-app')[0].shadowRoot; } function addTitles(root){ root.querySelectorAll('span, button, select').forEach(function(span){ tooltip = titles[span.textContent]; if(!tooltip){ tooltip = titles[span.value]; } if(tooltip){ span.title = tooltip; } }) root.querySelectorAll('select').forEach(function(select){ if (select.onchange != null) return; select.onchange = function(){ select.title = titles[select.value] || ""; } }) } document.addEventListener("DOMContentLoaded", function() { var mutationObserver = new MutationObserver(function(m){ addTitles(gradioApp()); }); mutationObserver.observe( gradioApp(), { childList:true, subtree:true }) }); function selected_gallery_index(){ var gr = gradioApp() var buttons = gradioApp().querySelectorAll(".gallery-item") var button = gr.querySelector(".gallery-item.\\!ring-2") var result = -1 buttons.forEach(function(v, i){ if(v==button) { result = i } }) return result } function extract_image_from_gallery(gallery){ if(gallery.length == 1){ return gallery[0] } index = selected_gallery_index() if (index < 0 || index >= gallery.length){ return [] } return gallery[index]; }