Stable diffusion prompt weight syntax python. Mixing words with the operator “OR” This will make Stable Diffusion select randomly between words included in construction at each step with equal chance. if ':' has no value defined, defaults to 1. It depends on the implementation, to increase the weight on a prompt. However, the prompt is also selected using the same seed (if the random prompt generator is used). py script: def split_weighted_subprompts(text): """. ”. Delve into creating effective prompts and mastering Stable Diffusion image generation. vae. Here is an example, using this prompt: "photo of a young girl in a swimming pool, (blue dress:0. 1 X 1. 5 base model. It provides a simple interface to quickly get insights into the underlying prompts of the image generation process. Sep 22, 2022 · delete the venv directory (wherever you cloned the stable-diffusion-webui, e. If the results start to look shitty (that sharp overly contrasted look), turn up the number of steps to compensate. This guide will show you how to weigh your prompts. The base syntax for this is: [from:to:when] This enables you to start by creating a picture for a I see you use parentheses to a greater or lesser extent to determine the weight of some keywords. This wins every time. Overview Unconditional image generation Text-to-image Image-to-image Inpainting Depth-to-image. The next step is to install the tools required to run stable diffusion; this step can take approximately 10 minutes. 9): 0. In this post, I will go through the workflow step-by-step. Append a word or phrase with -or +, or a weight between 0 and 2 (1=default), to decrease or increase "attention" (= a mix of per-token CFG weighting multiplier and, for -, a weighted blend with the prompt without the term). Syntax highlighting for prompts; Neural network, keyword, quality adjustment syntax, and weight adjustment symbol highlighting; Color prompt (optional) Weight highlighting; Show invisible characters; Support for txt2img and img2img; Can switch highlighting modes; Find and replace prompts; Support for regex searches; If lora-prompt-tool is Tasks. 0" to your prompt as words. I've seen the matrix script, but it seems a bit inefficient. This is not the same as a negative prompt. It's what I use. . I don't mean which words do what, but how order is weighted, what a () means (just weighting), if there's any way to group prompts, what a !, !!, or !!! means - which shows up in some prompts I've seen. However, the difference is that a random prompt is also generated using the chosen seed (if the prompt generator is used). bottom row is (negative prompt:0),(negative prompt:0. 9), decreasing it. Stable Diffusion Prompt Syntax: Adjust Strength with and [] Technique: An alternative to direct weighting is using and [] to adjust keyword strength. Understanding prompts – Word as vectors, CLIP. The steps in this workflow are: Build a base prompt. I've never used NMKD but just know their syntax. But the same can’t be said for negative prompts Would this be able to create separate iterations where "|" is treated as an "OR" in each of those lines and generate something that will always have the "Masterpiece, best quality", prompts, and then it will iterate between. co Jan 4, 2024 · Keyword weight (This syntax applies to AUTOMATIC1111 GUI. Don’t forget to click the refresh button next to the dropdown menu to see new models you’ve added. 18215 with torch. Command line option: --lowvram to make it work on GPUs with less than 3GB vram (enabled automatically on GPUs with low vram) Stable Diffusion Prompt Extractor - README. You use it when you still want the concept in the brackets, you just want to diminish it relative to the other concepts. 1 so 1. and [] Syntax. New stable diffusion finetune ( Stable unCLIP 2. Keyword weight syntax You can adjust the weight or importance of a keyword by using the syntax, (keyword: factor) where a factor less than 1 makes the keyword less important, and greater than 1 makes it more important. g. 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. The InvokeAI prompting language has the following features: Attention weighting#. Feb 17, 2024 · If you’re new, start with the v1. The concept can be: a pose, an artistic style, a texture, etc. Your second best way Positive and Negative prompts, which generally speaking can go anywhere, because they arm your words with weight, or take weight away from visual elements in Quicktip Changing prompt weights in Automatic1111. 1), increasing the keyword's strength, while [keyword] is like (keyword: 0. There’s a text box labeled Prompt. It can be different from the filename. If you mean " NMKD Stable Diffusion GUI 1. Update. Fooocus is an image generating software (based on Gradio ). Nov 15, 2023 · You can verify its uselessness by putting it in the negative prompt. indoors, cats, sleeping, open mouth, outdoors, dogs, laying down, tongue out, cafe, pandas, sleeping, open mouth, forest Aug 15, 2021 · Plus, unlike Colab, it works just as well from your phone. Stable Diffusion is cool! Build Stable Diffusion “from Scratch”. py or img2img. To add a LoRA with weight in AUTOMATIC1111 Stable Diffusion WebUI, use the following syntax in the prompt or the negative prompt: <lora: name: weight>. This tool is designed to extract metadata from images generated by Stable Diffusion. The percentage numbers represent their respective weight. repeats until no text remaining. You don't even need an account. "(inside a spaceship):2. factor is a value such that less than 1 means less important and larger than 1 means more important. 5. Stable Diffusion separates the imaging process into a diffusion process at runtime. support for stable-diffusion-2-1-unclip checkpoints that are used for generating image variations. In order to increase emphasis on a word or phrase, add a + or number between 1. Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are presentin its training data. Much like a writer staring at a blank page or a sculptor facing a block of marble, the initial step can often be the most daunting. It there an equivalent for AUTOMATIC1111? I would love to be able to combine it with the other prompt features in AUTOMATIC1111. To really drive home the difference in male versus female generations I went with the following prompt: photo, {4::man|woman}, athletic clothes Oct 8, 2023 · Go to Advanced--> Advanced--> Developer Debug Mode--> Mixing Image Prompt and Inpaint. Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding model card. To my surprise, I noticed that the comma in the prompt cuts the weight of individual keywords by moving them from left Here is the first example compared to using the '(negative prompts: weight)' syntax (i. weight is the emphasis applied to the LoRA model. Click on the “X type” drop-down menu and select “CFG I've tried square brackets [word], and the word:0 syntax but it doesn't work as expected. 05. 1 so ( ()) is 1. Choose a model. But even if I put red dress weight to 1 million and May 20, 2023 · Textual inversion: Teach the base model new vocabulary about a particular concept with a couple of images reflecting that concept. grabs all text up to the first occurrence of ':'. Techniques. 5x is strenghtened by the (positive) syntax ((dog:2. Read automaticsdocs every () is just the tag 1. The StableDiffusionImg2ImgPipeline uses the diffusion-denoising mechanism proposed in SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations by Chenlin The official Python community for Reddit! Stay up to date with the latest news, packages, and meta information relating to the Python programming language. 25),etc. 1 = 1. x, SD2. The more weight you add, the greater the risk of lower quality there is. Here is the first example compared to using the '(negative prompts: weight)' syntax (i. PR, (. Each set of parentheses increases the weight of the enclosed tokens. In this prompt, “cat” is assigned a weight of 2. Let words modulate diffusion – Conditional Diffusion, Cross Attention. 1 and 2 at the end. They are multiplicative, meaning ((dog)) would increase emphasis on dog by 1. Usually, higher is better but to a certain degree. : 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. You can weight prompts in Stable Diffusion using parentheses. Guide to Stable Diffusion Prompt Weights. Since it's going to produce a geometrically increasing pile of results based on Aug 22, 2022 · Go back to the create → Stable page again if you’re not still there, and right at the top of the page, activate the “Show advanced options” switch. The output: Important: be sure to uncheck default styles "Fooocus V2" Is there a way to do prompt weights in the AUTOMATIC1111 version? Question. Adding noise in a specific order governed by Gaussian distribution concepts is Stable UnCLIP 2. Prompt: `cat:2. Fix defects with inpainting. no_grad(): imgs = self. Upscale the image. If the seed is set to a number greater than -1: The process is similar to the second point in the previous section. The default weight = 1. Some of the documentation talks about adding plus signs or minus signs (like "test+ prompt-"), while other documentation talks about putting numbers after words or parenthesized groups (like "test1. The next one of the Stable Diffusion prompt examples is to modify keyword strength using and []. For NMKD: Use + after a word/phrase to make it more impactful, or Prompting-Features# Prompt Syntax Features#. The default we use is 25 steps which should be enough for generating any kind of image. With a clear understanding of Stable Diffusion Prompt Weights, you'll be able to craft your desired images like a pro. 0 it decreases the weight. Refinement prompt and generate image with good composition. 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 on, CTRL arrow up or down to change the weights. I've already researched, asked chatGPT, and I still haven't found the modifiers, nor the Feb 20, 2024 · A factor < 1 makes it less important, while a factor > 1 makes it more important in the Stable Diffusion prompt. This parameter controls the number of these denoising steps. Stable Diffusion is a latent diffusion model conditioned on the (non-pooled) text embeddings of a CLIP ViT-L/14 text encoder. If you’re faced with a choice Add this code somewhere in your txt2img. This is reflected in various randomizations. Diffusion models are taught by introducing additional pixels called noise into the image data. The concept doesn't have to actually exist in the real world. This blog series provides practical examples and straightforward explanations, helping you to understand and have full control over your Nov 25, 2022 · Follow these steps to experiment with your prompt using the CFG scale feature: Go to the “Script” and then choose “X/Y Plot. I'm specifically trying to fix human figures with a negative prompt words like: bad anatomy:-1 extra legs:-1 extra arms:-1 extra fingers:-1 poorly drawn hands:-1 poorly drawn feet:-1 disfigured:-1 out of frame:-1 tiling:-1 bad art:-1 deformed:-1 mutated:-1. C:\Users\you\stable-diffusion-webui\venv) check the environment variables (click the Start button, then type “environment properties” into the search bar and hit Enter. Now you’ll see a page that looks like Feb 13, 2023 · Steps. Input Image--> Image Prompt, upload the clothes image as prompt. Wait a few moments, and you'll have four AI-generated options to choose from. 0 depth model, in that you run it from the img2img tab, it extracts information from the input image (in this case, CLIP or OpenCLIP embeddings), and feeds those into the model in addition to the text prompt. Simply choose the category you want, copy the prompt and update as needed. "inside a spaceship:2. Text-to-Image with Stable Diffusion. For A1111: Use () in prompt increases model's attention to enclosed words, and [] decreases it, or you can use (tag:weight) like this (water:1. ; Open CMD in Python Environment: Opens a CMD window with the built-in python environment activated. true. Discover how to troubleshoot unclear images, remove unwanted artifacts, add/remove elements, and structure prompts for consistent, high-quality results. Enter a prompt, and click generate. To get more models, put them in the folder named stable-diffusion-webui > models > Stable-diffusion. Fooocus is a rethinking of Stable Diffusion and Midjourney’s designs: Learned from Stable Diffusion, the software is offline, open source, and free. " Be cautious not to overemphasize, as it can overpower the image. 5)) – dog is 0. Most loras come with key words to give better control of them. For example, we can adjust the weight of the keyword dog in the following prompt Apr 27, 2023 · 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). Open your command prompt and navigate to the stable-diffusion-webui folder using the following command: cd path / to / stable - diffusion - webui. 0, playful, eyes:1. 8, curious`. 1 and it pays no attention whatsoever to the weights I enter. 1. 0. Either works. For example, ((red hair)) has a higher weight than just "red hair. Head to Clipdrop, and select Stable Diffusion XL (or just click here ). OpenAI. If you have something to teach others post here. Now we need a method to decode the image from the latent space into the pixel space and transform this into a suitable PIL image format: def decode_img_latents(self, img_latents): img_latents = img_latents / 0. The following syntax is Sep 7, 2022 · In addition to the optimized version by basujindal, the additional tags following the prompt allows the model to run properly on a machine with NVIDIA or AMD 8+GB GPU. 5] Since, I am using 20 sampling steps, what this means is using the as the negative prompt in steps 1 – 10, and (ear:1. 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. 5) increases attention to the word by a factor of 1. Text-guided diffusion models generate images based on a given text prompt. In order to reduce emphasis on a word or phrase, add a -or number between 0 and 0. The Stable Diffusion model can also be applied to image-to-image generation by passing a text prompt and an initial image to condition the generation of new images. No more fumbling with ( ( ()))) Hope this helps. In the System Properties window, click “Environment Variables. How to use prompt weights in getimg. For the second you can highlight the word (s) and hold ctrl and press the up arrow to add weight. 21 = an increase of 21%. ; Merge Models: Allows you to merge/blend two models. Jan 4, 2024 · This syntax will make Stable Diffusion diffuse an entity described as 2 words simultaneously. 0 (which is actually quite large) and again adds ":2. Input Image--> Inpaint or Outpaint, mask the image area you want to replace with other cloths. Prompt weighting basics. 1-768. A '2' would result in twice as many selections of your variable, a '3' triple the amount, and so on. Each ( ) pair represents a 1. This cannot be overstated. 25). 242. Since a lot of people who are new to stable diffusion or other related projects struggle with finding the right prompts to get good results, I started a small cheat sheet with my personal templates to start. ago. Diffusion in latent space – AutoEncoderKL. hkly) you can do this: "something:1 something else:4". Stable Diffusion creates an image by starting with a canvas full of noise and denoise it gradually to reach the final output. 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. I have 2 questions, the first is: How much does adding a weight to a key word affect its prevalence vs putting it closer to the front of the prompt? 2: Does the order that the lora file is called in affect the result? For instance: (cinematic), (masterpiece), (landscape), (mountains The subreddit for all things related to Modded Minecraft for Minecraft Java Edition --- This subreddit was originally created for discussion around the FTB launcher and its modpacks but has since grown to encompass all aspects of modding the Java edition of Minecraft. 1, Hugging Face) at 768x768 resolution, based on SD2. Prompt weighting provides a way to emphasize or de-emphasize certain parts of a prompt, allowing for more control over the generated image. . You will get the same image as if you didn’t put anything. For instance, (keyword) is equivalent to (keyword: 1. If you have questions or are new to Python use r/learnpython Tips - Prompt syntax weighting and artistic styles differences. 9)" If prompt weighting worked, it would be much more likely to always get a red dress. Let's get deep into the hidden tricks of prompt engineering using Automatic1111's Web UI. The official Python community for Reddit! Stay up to date with the latest news, packages, and meta information relating to the Python programming language. RobMilliken. Dec 24, 2023 · In Stable Diffusion, square brackets are used to decrease the weight of (de-emphasize) words, such as: [[hat]]. Oct 12, 2023 · Conclusion: Diving into the realm of Stable Diffusion XL (SDXL 1. 1 multiplier to the attention given to the prompt so basically (dog) means increase emphasis on it by 10%. x, SDXL, Stable Video Diffusion and Stable Cascade; Asynchronous Queue system; Many optimizations: Only re-executes the parts of the workflow that changes between executions. Example: (Sun AND Moon AND Stars AND Satellites ) 3. Can Stable Diffusion save your prompts? Stable Diffusion does not have a built-in Jan 12, 2023 · Stable Diffusion is a text-to-image model that uses a frozen CLIP ViT-L/14 text encoder. Weighting prompts. On some site today, I saw that someone also used [word], [ [word]]. Address common prompt engineering issues, learn from the best practices, real user queries, and apply actionable tips. name is the name of the LoRA model. To start, head to VQGAN+CLIP on NightCafe Creator and click on the main “Start Creating” button. To weight your prompts you will add a weight number and two colons before your first variable term. Now use this as a negative prompt: [the: (ear:1. 0, emphasizing its importance. Aug 11, 2023 · Best of all, it's incredibly simple to use, so it's a great way to test out a generative AI model. Jun 11, 2023 · Mixing positive syntax with positive weights works (but isn’t advised) ((dog:0. ai. Mar 17, 2023 · Prompt editing allows you to start sampling one picture, but in the middle swap to something else. 9 at the end. Principle of Diffusion models (sampling, learning) Diffusion for Images – UNet architecture. 3 (prompt)0. 0), one quickly realizes that the key to unlocking its vast potential lies in the art of crafting the perfect prompt. Fooocus. #Introduction Welcome to the Stable Diffusion Prompt Extractor. Specific pipeline examples. Nov 30, 2023 · Stable Diffusion Prompt Weights. ) You can adjust the weight of a keyword by the syntax (keyword: factor). See full list on huggingface. For example, you might have seen many generated images whose negative prompt (np Step 5: Setup the Web-UI. Prompt templates for stable diffusion. And in a prompt I have here, copied from I don't remember where, someone used \"word\". • 1 yr. It works in the same way as the current support for the SD2. 0" increases the weight of "inside a spaceship" by a small amount, but not by 2. The weight of anything inside the square brackets will be divided by 1. Learned from Midjourney, the manual tweaking is not needed, and users only need to focus on the prompts and images. 1), (red dress:1. 25) decreases attention by a factor of 4 (1 / 0. For example, (word:1. Nov 22, 2023 · Step 2: Use the LoRA in the prompt. 6) if its less than 1. Pretty handy. Crafting the Perfect Prompt. Colon (:): The colon is used to assign a weight or importance to a specific word or concept in the prompt. 1. decode(img_latents) # load image in the CPU. 2. 9) in steps 11-20. The StableDiffusionImg2ImgPipeline uses the diffusion-denoising mechanism proposed in SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations by Chenlin Open Stable Diffusion CLI: Use Stable Diffusion in command-line interface. There's probably some info in their docs to explain more of how it works. 3"), while many samples talk about bulking up on parenthesis (like "(((test))) prompt"), but I can't seem to get clear what some of Sep 27, 2023 · The workflow is a multiple-step process. 5, while (word:0. Textual inversion Distributed inference with multiple GPUs Improve image quality with deterministic generation Control image brightness Prompt weighting Improve generation quality with FreeU. I'm using stable diffusion 2. This will also take up more RAM. Nov 20, 2023 · Enhance your prompt creation with our list of 35 Stable Diffusion prompt generators and 200 ideal keywords. 21 on the backend it’s getting transformed to the number use the number it’s nicer and easier to track. If you have questions or are new to Python use r/learnpython In my experience so far it helps to try turning the CFG Scale up, this function produces results more accurate to your prompt the higher it goes. This technique works for topic keywords and every category, like lighting and style. Final adjustment with photo-editing software. 2) or (water:0. e. This model allows for image variations and mixing operations as described in Hierarchical Text-Conditional Image Generation with CLIP Latents, and, thanks to its modularity, can be combined with other models such as KARLO. We provide a reference script for sampling, but there also exists a diffusers integration, which we expect to see more active community development. Diffusion models work by conditioning the cross attention layers of the diffusion model with Fully supports SD1. 0" and similar will not change weight at all, it's just adding ":2. 0)) – dog is 2x is strenghtened by the (positive) syntax and will most likely break your render as it makes this single prompt too strong. 1 1. uses the grabbed text as a sub-prompt, and takes the value following ':' as weight. in the sd-webui (prev. The most effective way to make the AI do what you want is to put the important words at the beginning of the prompt. ic my fn yk ys zh nd zj cj tq