Stable diffusion prompt weights Lighting An extensive list o How can I specify a numerical weight for attention in Stable Diffusion? You can specify a numerical weight for attention by using the syntax (word:weight). And in a prompt I have here, copied from I don't remember where, someone used \"word\". The actual Stable Diffusion Pipeline runs your prompt through a "scheduler" and then through a "tokenizer" and the scheduler can be switched out for different results. I know that the order of key words in your prompt will affect how much weight the model puts on them, I also know that you can add individual weights. at show prompts, where certain terms are in parenthesis like this: Prompt weight. 5 and SDXL; Support weighting like a (white:1. 2) cat; Support parentheses like a ((white)) cat; For SD3, support max 512 tokens (T5 model support max 512 tokens) Support Stable Diffusion v1. 9)" This is something I'm looking into and I'd love some conversation on the topic. 21) and ((prompt)) mean the same thing. Top 1% Stable Diffusion AI Guide to weights and negative prompts in the Deforum youtube. When it comes to down-weighting though, naïve approches fail (as can be seen in the happy woman example). after waiting for long and now searching the web again i cant believe there is still no proper feature of xyz plot to test weights since this seems the most obvious and would probably be one of the most used features of xyz plot since finding the right weights is one of the most important parts of prompt engineering. To start, I've implemented an experimental prompt weighting for SDXL here: Prompt weighting but there's one fundamental difference between I'm looking for a way to do weighted prompts in A1111. r/StableDiffusion. Some examples at civic. In this tutorial, we will explore how to use parentheses (), square brackets [], Instead of + or -, you can also use numbers between 0 and 2 (1 being the default weight), there are two possible syntaxes: photo of a pizza with (pepperoni and cheese:0. Stable Diffusion AI Guide to weights and negative prompts in the Deforum Tutorial | Guide Share Add a Comment. pipelines. Negative prompting (red:0) will be the same as not including that prompt. Most loras come with key words to give better control of them. dtype, device=text_embeddings. I heard that it should be possible to add weights to different parts of the prompt (or multiple prompts weighted, same thing I guess). As a technical writer, I often find myself immersed in the fascinating world of algorithms and automation. 0. - huggingface/diffusers Now I want to see what the best weight is, no point telling people to use 1. Its size must be equal to the size of members. Additional details 7. "(inside a spaceship):2. There's probably some info in their docs to explain more of how it works. 1), (red dress:1. 9 or lower is better. Color 8. 0 Now the pipeline has been contributed to the official diffusers community pipelines. To address this, you should pass both tokenizers and encoders to the Compel class: Copied. 5, SDXL and Stable Diffusion 3. 5) increases attention to the word by a Prompt weighting. Updated June 11th with clearer examples, exercises, and a mini quiz. They are multiplicative, meaning ((dog)) would increase emphasis on dog by 1. You can then use the x/y/z prompt script to sub out the {__wildcard prompt__} with a comma separated list of your individual prompt files and it will give you the prompt on the y and whatever else you want on the x. 1 = 1. Be the first to comment Nobody's responded to this post yet. But it is different from the negative prompt. With a flexible and intuitive syntax, you can re-weight different parts of a prompt string and thus re-weight the different parts of the embedding tensor produced from the string. 5", Each ( ) pair represents a 1. (Word: weight) Word = any number of tokens. (I checked A1111 code would use negative weight as is. Global prompt. In other stable diffusion tools, it is often referred to as cfg_scale. Stable Diffusion Prompt Weights: Exploring the Depths of Technical Implementations. e. Search. This guide offers a deep dive into the principles of writing prompts, the structure of a basic template, and methods for learning prompts, making it a valuable resource for those you can control the master knob of the lora like this "<lora:mountain_terrain:0. I just switched from hlky to AUTOMATIC1111, so I’m especially interested to know whether you can use negative prompt weights with it. 11 votes, 14 comments. A good prompt needs to be detailed and specific. The negative prompt itself is applied as the negative. It automatically normalizes the prompt weights so that they sum to 1. Put a prompt in, set batch to 4 of 512x512 so you can iterate quickly. 0" then they use prompt weights, use a negative number for a "negative" prompt like: "A bowl of apples:1 red:-1" = a bowl of apples, no red apples. More or less will distort the image significantly Here is the first example compared to using the '(negative prompts: weight)' syntax (i. If a generated image does not satisfy a user directly, adjusting the prompt is currently the primary targeted way to change it to their liking. 3). Generative text-to-image models such as Stable Diffusion Rombach et al. If a change There are different ways of interpreting the up or down-weighting of words in prompts. The prompt "A symmetrical photo of a cat AND a dog" gives me a catdog hybrid. It may be better to lower the weight (select a word or phase and press ctrl + down arrow) of the things you don't want as much in the prompt than raise the weights of things you do. Now, as Colon (:), Parentheses (()), and Bracket Notation[ ] are generally used for Stable Diffusion prompt weights in automatic1111, we discuss them in the prompt weight section below. Hello everybody and welcome to my Tutorial here on prompt weights and this Is going to be a pretty in-depth Tutorial or guide whatever you want to Call it just because I feel that prompt Weights I think a lot of people don’t Really use them to their full extent but They are very extremely useful for kind Of fine-tuning your prompts now I did do A tutorial on prompt weights Regarding prompting, the main difference is prompt weights, which, in Stable Diffusion-based models, allow users to put more or less focus (“weight”) on certain parts of the prompt. Here's a four-way hybrid between Bezos, Gates, Musk and Zuckerberg created with The new OpenCLIP model released just last week will give a big boost to how much Stable Diffusion understands the prompt. Resolution 6. Exclusion groups support the parameter weights, which takes a list of integers. Explore the top AI prompts to inspire creativity with Stable Diffusion. If the extra networks had an emphasis slider on each card and a pos or from diffusers. I did some tests with the prompt syntax to see how much the difference in rendering of an art style changed by changing the position of the artist/style keyword. Prompt weighting provides a way to emphasize or de-emphasize certain parts of a prompt, allowing for more control over the generated image. A text prompt weighting and blending library for transformers-type text embedding systems, by @damian0815. 25),etc. Observe the prompt displayed below your image, the weight is not there; Select the same word, change the weight using keyboard number keys , generate image; Observe the prompt displayed below your image now The Flux AI model was released by the Black Forest Labs, where many researchers were the original creators of Stable Diffusion 1. Despite the ease of use, however, these are machine learning models with questionable "intelligence," and so it's quite Negative prompt weights work on the same weighting scale as positive, it's not reversed. allow their users to generate images based on a textual description called a prompt. A prompt can include several concepts, which gets turned into This guide will delve into two main aspects of Stable Diffusion weights: prompt weights and model weights, offering insights into their usage, benefits, and best practices to help you achieve optimal results. You input is what you DO NOT want Stable Diffusion to generate. Since any added text will change results somewhat, it's not surprising that the images are slightly different, but that's why the different numbers in those examples doesn't actually result in much change Firstly, apologies to any of you that are getting bored of my negative prompt posts! A couple of days ago I posted prompt matrices for some common negative prompts to try and gauge how effective they might be. In Stable Diffusion, square brackets are used to decrease the weight of (de-emphasize) words, such as: [[hat]]. 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX. ) If a negative prompt is simply a negative weight to a token, you would expect 2 and 3 to be the same. Thus a Stable Diffusion XL (SDXL) has two tokenizers and text encoders so it’s usage is a bit different. parentheses and brackets are a simplification of the prompt weights, which get fed to the scheduler as percentages. Style 4. Next SD GUITard supports weighting prompts. For now, we just have to be Prompt used: a painting of the the mona lisa, by leonardo da vinci. 8), (valleys:0. 5 strength. The main prompt acts as a global prompt and I'm planning to add a weight slider But I am not that bright. tensor(prompt_weights, dtype=text_embeddings. One would assume "and" to be compositional, whereas "AND" would be combining. By adjusting the weight of words and phrases in your prompts, you can subtly or radically influence the final result, opening up new creative possibilities. 1, each square bracket divides it by 1. Related: How to Use Stable Diffusion to Make AI 244 votes, 35 comments. 7 to 1. How do I use XYZ to test a set number of weights? In my prompt it reads: `a man in a suit sitting on a red chair <lora:2001-08:1. Subject 2. The higher the number or the more parentheses there are, the more emphasis is placed on that part of the prompt. Medium 3. . 5 are not good in SDXL and the image tends to go really bad after 1. 21 = an increase of 21%. For example, "colorful garden (with a single rose)++" would mean the user wants to emphasize the "with a single rose" part of the prompt. A1111 for instance simply scales the associated vector by the prompt weight, while ComfyUI by default calculates a travel direction from the prompt and an empty prompt. true. 1. Add your thoughts and get the conversation going. The numerical values are applied to all words before the colon, but parenthesis weights are coming soon. Step 1: T-Rex standing on one leg Step 2: Frog standing on one leg Weights in the context of Stable Diffusion prompts are numerical values assigned to keywords to indicate their importance or prominence in the generated image. Detailing in a prompt should always serve a clear purpose, Prompt Weighting is therefore a powerful technique for fine-tuning and precisely controlling the generation of images by Stable Diffusion. bottom row is (negative prompt:0),(negative prompt:0. (prompt:1. 5. Each parentheses multiplies the weight by 1. Use runtime merge block weights and play with the sliders. With the A1111 GUI, wasn't there a means of swapping prompts at every step? e. Stable Diffusion Prompt Weights. Learn how to influence image generation through prompts, loading different Checkpoint models, and using LoRA. For example, interpolating between "red hair" and "blonde hair" with continuous weights. In other words, it's a way of guiding the AI's Prompt weighting in Stable Diffusion allows you to emphasize or de-emphasize specific parts of your text prompt, giving you more control over the generated image. 1 I've been experimenting with a Only prompts that match one of the specified keywords will be modified. - Changing prompt weights: Prompt editing is an incredible feature that everyone using Stable Diffusion should know. Being new to stable diffusion I just learned about the prompts, especially about negative prompts. In all cases, generating pictures using Stable Diffusion would involve submitting a prompt to the pipeline. The prompt "A symmetrical photo of a cat and a dog" Gives me a hybrid catdog. My selection of Stable Diffusion environment is AUTOMATIC1111. Generate the same batch for two different models, two models that look very different, perhaps a cartoon and a photo real. 0 Changing weight in Image Prompt PyraCanny. Prompt Keywords: Keywords to match . 0, are TIs better suited for faces and LORAs for styles? Another question is what order to place the LORA in the prompt (beginning, end, middle)? Pipeline for text-to-image and image-to-image generation using Stable Diffusion, without tokens length limit and support parsing weighting in prompt. 8. 0" increases the weight of "inside a spaceship" by a small amount, but not by 2. Conceptually, down-weighting everything except one word is similar to up-weighting that word. 05. Describe the solution you'd like Improve the prompt parser and resolver to support this kind of blending. Diffusion models work by conditioning the cross attention layers of the diffusion model with contextualized text embeddings (see the Stable Diffusion Guide for more information). : Please have a look at the examples in the comparisons section if you want to know how it's different from using '(prompt:weight)' and check out the discussion here if you need more context. So if you have 4 prompt items and you say the first is (x:2), then it will account for half of the total prompt weight, with the others accounting for the remaining ½. 1. Art-sharing website 5. stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker. An incomplete or poorly constructed prompt would The hack is to create a new wildcard directory for your test prompts, then add each test prompt as a new, individual wildcard file. Prompt weight is a multiplier to the embeddings to influence its effect. Pipeline for text-to-image and image-to-image generation using Stable Diffusion, without tokens length limit and support parsing weighting in prompt. Prompt editing allows you to start sampling one picture, but in the middle Prompt weighting in Stable Diffusion allows you to emphasize or de-emphasize specific parts of your text prompt, giving you more control over the generated image. device) Stable Diffusion Prompt Weights Automatic1111. Basically, the double, triple, etc. 0" to your prompt as words. Stable Diffusion 3 has significantly improved its adherence to user prompts through training with highly accurate image captions, matching the performance of DALL-E 3. it get erased before the prompt is executed, keep Improved Prompt Following. It was hard to draw too many conclusions from the results as, although it was clear the negative prompts had an effect, it didn't always correspond to the word or Here is the first example compared to using the '(negative prompts: weight)' syntax (i. As in one prompt:1 another prompt:3 still other prompt:0. The keyword categories are 1. This is a very powerful but underused feature of Stable Diffusion, and it can assist you in The text prompt can include multiple concepts that the model should generate and it’s often desirable to weight certain parts of the prompt more or less. Comma delimited; Not case sensitive; Weight Range: The maximum amount to modify the weight in either direction. schedulers import KarrasDiffusionSchedulers. 1 X 1. 10. Have you ever wondered how machine learning models can generate coherent and insightful text? One key component to achieving this level of sophistication lies in stable diffusion prompt weights. SD GUITard supports weighting prompts. Today, I want to delve deep into the concept of stable diffusion prompt weights and its implications in the realm of automatic1111. 1) and (prompt) mean the same thing (prompt:1. 6. Usually, it’s 0. Weighted prompts may be the only way to get some effects, or to dynamically increase or decrease the proportions of elements. Reply reply A numerical prompt weight feature has been added to Deforum as a selectable feature. Weights in Stable Diffusion give you the ability to fine-tune your prompt by controlling the influence of individual components within your generated art or text. 30" for example. The weight of anything inside the square brackets will be divided by 1. Simply manipulating the embedding vectors associated with the down-weighted tokens is not enough. The prompt "a symmetrical photo of a cat PLUS a dog" gives me two cats. For now, we just have to be very specific with the prompt "an old lady in a park, wearing a dress, floral pattern on the dress" /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and exclude blind users from the site. Speed and Deployment. prompt_weights = torch. For example is there a difference between these three prompts: Cat, agile, brightly lit cat agile brigtly lit words/conepts are conjoined aswell as the use of ctr+uparrow in automatic1111, how do the numerals scale the words weight/importance? looks somthing like this: cat (agile:3. Weight = number in the range of 0 to infinity. This is only one of the parameters, but the most important one. This advanced feature allows for a higher level of customization and specificity in the outcome. The higher the number (weight), the higher the probability of the block The prompt length in Stable Diffusion is unlimited if another is not set by your Stable Diffusion provider. There's already a proof-of-concept notebook using it which you can try out. Fix the seed. In negative prompts, (red:1) would be normal negative promt weighting while (red:0) would be zero The new OpenCLIP model released just last week will give a big boost to how much Stable Diffusion understands the prompt. Weights are a new feature in our Web UI and Telegram Bot, made possible by a subsystem called a Text Parser, literally a piece of code that tries to understand which words are most important to you. - Prompt Editing: how to change the number of steps that the model takes for a Prompt Weighting is a tool that allows you to give more or less importance to certain parts of the text you submit to Stable Diffusion. 5) The more I think I understand about Stable Diffusion the more I realize I have no idea how it Prompt weight — Prompt weight is a variable supplied to the algorithm which tells it how much importance to give to the prompt. thought somebody must have requested this already but since after The best advice I can give you is to spend 20 minutes trying it. Master the basics of Stable Diffusion Prompts in AI-based image generation with ComfyUI. More details here. When enabled, the run will interpret the values and weights syntax of the prompt for better control and token presence. 0? If it is limited to 1. I've never used NMKD but just know their syntax. - Changing prompt weights: how to adjust the importance of each prompt keyword in relation to the others. Different types of brackets are used to adjust the weights of keywords, which can significantly affect the resulting image. I'm using stable diffusion 2. It would adjust xyz+ or infinite grid parameters until finding the best settings including prompt and lora weight during that as gauged by an esthetic scoring. Don't know how widely known this is but I just discovered this: Select the part of the prompt you want to change the weights Stable Diffusion Prompt Guide - the Only Guide You Need Here, the use of text weights in prompts becomes important, allowing for emphasis on certain elements within the scene. The new interface will allow adding attention regions dynamically and painting masks over the same unified canvas. Here is an example, using this prompt: "photo of a young girl in a swimming pool, (blue dress:0. This script aims to automate prompt generation for Stable Diffusion (and more generally, txt2img models such as MidJourney, Dall-E, etc. It attempts to combine the best of Stable Diffusion and Midjourney: open. Weighted prompts may be the only way to get Firstly, apologies to any of you that are getting bored of my negative prompt posts! A couple of days ago I posted prompt matrices for some common negative prompts to try and gauge how effective they might be. For Stable Diffusion 2. It was hard to draw too many conclusions from the results as, although it was clear the negative prompts had an effect, it didn't always correspond to the word or Step 6: Fine-Tuning Your Prompt with Weights in Stable Diffusion. 8 Prompt Weights and new text Parser (beta) New Weights Parser, Updated. The improvements of the Flux AI base model are: Legible text generation; How does the prompting work for multiple LORAs? Do the weights have to add up to 1. 8) OR photo of a pizza with (pepperoni and cheese)0. On some site today, I saw that someone also used [word], [[word]]. 8)" this is useful for loras who have various keywords, like: <lora:mountain_terrain:1> (mountain:0. This prompt library features the best ideas for generating stunning images, helping you unlock new creative possibilities in AI art. 10, Grey Cat:0. 1 multiplier to the attention given to the prompt so basically (dog) means increase emphasis on it by 10%. I've read a lot about prompt weighting but was never able to make it work. This results in markedly different behavior at higher weighting. 1 and it pays no attention whatsoever to the weights I enter. g. Different You can also assign weights to each word in the prompt manually if you want finer control, like "Cute:0. Support unlimited prompt length for SD1. 5> <lora:bbbbb:0. ). 5> Or can it exceed 1. In this article, I will delve deep into the intricacies of this The popularity of text-conditional image generation models like DALL·E 3, Midjourney, and Stable Diffusion can largely be attributed to their ease of use for producing stunning images by simply using meaningful text-based prompts. As of the latest updates from Stability AI, the direct download option for the Stable Diffusion 3 model weights is not immediately If you mean "NMKD Stable Diffusion GUI 1. In the latest version there's a much better way by simply using a single set of braces and entering a weight multiplier. and for the second question the order of the <lora:mountain_terrain:1> doesnt matter. Previously you could emphasize or de-emphasize a part of your prompt by using (braces) and [square brackets] respectively. 0? <lora:aaaaa:0. 2. # apply weights prompt = ["a red cat playing with a (ball)1. 8>" is the same as "<lora:mountain_terrain:1> (mountain:0. The prompt format is compatible with AUTOMATIC1111 stable-diffusion-webui. But in fact, 2 does not do what you would expect it to do. This enables generating an image using multiple prompts which allows easy creation of fun hybrids and such. Layers UI test Updated UI test. A good process is to look through a list of keyword categories and decide whether you want to use any of them. To start, I've implemented an experimental prompt weighting for SDXL here: Prompt weighting but there's one fundamental difference between Hello everybody and welcome to my Tutorial here on prompt weights and this Is going to be a pretty in-depth Tutorial or guide whatever you want to Call it just because I feel that prompt Weights I think a lot of people don’t Really use them to their full extent but They are very extremely useful for kind Of fine-tuning your prompts now I did do A tutorial on prompt weights There are different ways of interpreting the up or down-weighting of words in prompts. 60, Unreal Engine rendering:0. 0>` I am using the fork of Automatic 1111 by vladmandic called SD. It depends on the implementation, to increase the weight on a prompt For A1111: Use in prompt increases model's attention to enclosed words, and [] decreases it, or you can use (tag:weight) like this I want to use the cool prompt tools that are offered in this repo but also be able to blend different prompts together. For example, (word:1. Stable Diffusion Prompt Library . The video explains how to increase or decrease the weight of certain words to control the emphasis on specific features, such as making 'blue house' more prominent by adding brackets (prompt:1. require diffusers>=0. Since users have found that certain prompts are more likely to Here are the developers talking about how prompt weights that worked really well in SD 1. Fooocus is a free and open-source AI image generator based on Stable Diffusion. 0 if 0. I see you use parentheses to a greater or lesser extent to determine the weight of some keywords. from diffusers. 0 (which is actually quite large) and again adds ":2. You use it when you still want the A negative prompt is exactly what it sounds like – it’s the opposite of a prompt. Basically the scheduler tries to parse out the important words in your Posted by u/vykthur - 4 votes and no comments When you weight on thing, it increases its proportion of that final normalized while. Strap in, because we’re about to embark on a journey into the intricacies of this This is something I'm looking into and I'd love some conversation on the topic. xqkjfvmoawvaxzdtmshqbphurwpgafrvafygworwttaepyflx