Stable diffusion vocabulary. 1) [41]) to deeply fuse text and image information .

Stable diffusion vocabulary This demonstrates that their internal representation In this paper, we propose Cross-modal and Uncertainty-aware Agglomeration for Open-vocabulary 3D Scene Understanding dubbed CUA-O3D, the first model to integrate multiple foundation models—such as CLIP, DINOv2, and Stable Diffusion—into 3D scene understanding. /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. Text-to-image diffusion models have the remarkable ability to generate high-quality images with diverse open-vocabulary language descriptions. Stable Diffusion is a text-to-image AI model that generates images from natural language A collection of what Stable Diffusion imagines these artists' styles look like. py --input demo/examples/coco. 2. We will briefly describe its architecture and training procedure in the following. SDXL Turbo is a SDXL mdoel trained with the Turbo training method. Explore a mesmerizing collection of AI-generated images on our webpage, showcasing the fascinating results of stable diffusion vocabulary image prompts. However, these models often struggle with unfamiliar images or unseen text, as their visual language understanding is limited to training data. Diffusion-based models, such as Stable Diffusion [39] and other contemporary works [22,27,30,32,37,38,41], have been rapidly adopted across the research community and industry, owing to their ability to generate high-quality im- Architecture . , DALL-E, Stable Diffusion). It can This extension aims to integrate Latent Consistency Model (LCM) into AUTOMATIC1111 Stable Diffusion WebUI. jpg --output demo/coco_pred. vercel. Comprehensive glossary covering every important term related to Stable Diffusion, the popular open-source AI image generation model. Two key designs enable us to employ an off-the-shelf text-to-image diffusion model as a useful dataset generator for object instances and mask annotations. Create better prompts. However, current extensions primarily rely on extracting attentions linked to prompt words used for image synthesis. Jul 18, 2024 · Specifically, ODISE applied the internal representations of Stable Diffusion to open-vocabulary 2D semantic understanding tasks and achieved promising results. 1. Mar 24, 2025 · This typically involves generating a considerable amount of synthetic data or requiring additional mask annotations. It can reduce image generation time by about 3x. Stable Diffusion Online. Nov 1, 2024 · Specifically, the design details of SegLD are as follows: To address Flaw (1), we draw inspiration from DatasetDM [39] and ODISE [33] and propose an innovative approach by paralleling two types of latent diffusion processes (Stable Diffusion XL 1. 5. The weight files can be retrieved from the HuggingFace model repos and should be moved in the data/ directory. In this paper, we show that it is possible to automatically obtain accurate semantic masks of synthetic images generated by the pre-trained Stable Diffusion, which uses only text-image pairs during training. 1, get the bpe_simple_vocab_16e6. Some of the terms below might be missing a description – this document is in a constant state of development to keep up to speed with additions Org profile for Stable Diffusion concepts library on Hugging Face, the AI community building the future. The Stable Diffusion prompts search engine. Recent advancements in OVSS are largely attributed to the increased model capacity. , Stable Diffusion) as highly efficient open-vocabulary se-mantic segmenters, and introduce a novel training-free approach named DiffSegmenter. To this end, we uncover the potential of generative text-to-image diffusion models (e. Note that LCMs are a completely different class of models than Stable Diffusion, and the only available checkpoint currently is LCM_Dreamshaper_v7. Architecture. Definitions are easy-to-understand for both beginners and advanced users. Diffusion models [26, 59, 60] are a class of generative methods that have seen tremendous success in text-to-image systems such as DALL-E [47], Imagen [52], and Stable Diffusion [50], trained on Internet-scale data such as LAION-5B [54]. By AI artists everywhere. They have been explored in few-shot classification [73], few-shot [2] and This typically involves generating a considerable amount of synthetic data or requiring additional mask annotations. Ideal for beginners, it serves as an invaluable starting point for understanding the key terms and concepts underlying Stable Diffusion. Abstract. , simultaneously generating images and segmentation masks for the corresponding visual entities described in the text prompt. Generally people say "look at existing prompts or use various prompt generators" but that doesn't really solve the problem. Is there a tool (UI or CLI) that allows a user to extract information from a checkpoint such as any tokens/classes used, how many steps used, etc? Stable Diffusion. Diffusion models. 0 (Stable Diffusion XL 1. While having an overview is helpful, keep in mind that these styles only imitate certain aspects of the artist's work (color, medium, location, etc. SDXL Turbo. The left figure shows the knowledge induction procedure, where we first construct a dataset with synthetic images from diffusion model and generate corresponding oracle groundtruth masks by an off-the-shelf object detector, which is used to train the open-vocabulary grounding module. Step 2: Enter Your Text Prompt. Text-to-image diffu-sion models have the remarkable ability to generate high-quality images with diverse open-vocabulary language de-scriptions. Sep 6, 2023 · This typically involves generating a considerable amount of synthetic data or requiring additional mask annotations. Find the input box on the website and type in your descriptive text prompt. Apr 16, 2015 · Stability AI sparked the Generative AI revolution with the release of Stable Diffusion, developing cutting-edge open models in image, video, 3D, and audio. 0), which was the first text-to-image model based on diffusion models. 1 (SD 2. Jan 12, 2023 · The goal of this paper is to extract the visual-language correspondence from a pre-trained text-to-image diffusion model, in the form of segmentation map, i. erative text-to-image diffusion models (e. Diffusion models represent a new paradigm in text-to-image generation. , Stable Diffusion (SD) (Rombach et al, 2022). Go to AI Image Generator to access the Stable Diffusion Online service. They are limited by the rather superficial knowledge of SD, but can probably give you a good base for your own See full list on stable-diffusion-book. , Stable Diffusion) as highly efficient open-vocabulary semantic seg-menters, and introduce a novel training-free ap-proach named DiffSegmenter. CLIFF: Continual Latent Diffusion for Open-Vocabulary Object Detection Wuyang Li 1, Xinyu Liu 1, Jiayi Ma 2, Yixuan Yuan 1 1 The Chinese Univerisity of Hong Kong; 2 Wuhan University This repository is a collection of studies, art styles, prompts and other useful tools you can use throughout your exploration of the latent space. 1, for example, Pikachu, unicorn, phoenix, etc, effectively resembling a form of visual instruction tuning, for establishing visual-language 6 days ago · Object detection has traditionally been a closed-set problem: you train on a fixed list of classes and cannot recognize new ones. Mar 7, 2025 · Open-vocabulary semantic segmentation (OVSS) is a challenging computer vision task that labels each pixel within an image based on text descriptions. The step-wise gen-erative process and the language conditioning also make pre-trained diffusion models attractive for discriminative tasks. SD knows? It'd be nice to know if words in my prompt are getting thrown out Mar 8, 2023 · We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies pre-trained text-image diffusion and discriminative models to perform open-vocabulary panoptic segmentation. safetensors files from the v2. Grounding DINO shatters this limitation by weaving language understanding directly into a transformer-based detector. safetensors, unet_v2. Beyond generating high-quality images from text prompts, models such as Stable Diffusion have been successfully extended to the joint generation of semantic segmentation pseudo-masks. , Stable Diffusion) as highly efficient open-vocabulary semantic segmenters, and introduce a novel training-free approach named DiffSegmenter. @article{li2023grounded, title = {Open-vocabulary Object Segmentation with Diffusion Models}, author = {Li, Ziyi and Zhou, Qinye and Zhang, Xiaoyun and Zhang, Ya and Wang, Yanfeng and Xie, Weidi}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, year = {2023} } Some works have investigated the usage of diffusion models for open-vocabulary segmentation. Text-to-image (T2I SDXL Turbo (Stable Diffusion XL Turbo) is an improved version of SDXL 1. Background Our work tries to extend the expressiveness of text-prompted image generators to the realm of graph-prompted generators where closed vocabularies have been the basis for typical node and edge labels. It can The introduction of diffusion models has led to a sig-nificant advancement in text-to-image (T2I) generation [7]. The images can be photorealistic, like those captured by a camera, or artistic, as if produced by a professional artist. In particular, this paper builds on a variant of diffusion model, namely, Stable Diffusion [26], which conducts the diffusion process in latent space. We make the following contributions: (i) we pair the existing Stable Diffusion model with a novel grounding It is one of the companies behind the development of Stable Diffusion. Generative visuals for everyone. . First, we divide an image canvas The OpenCLIP model used by Stable Diffusion has been trained on the LAION-5B data set. You could look into what words occur in the image captions of that data set, but it includes over 5 billion image-text pairs. to-image diffusion models (e. true. The insight is that to generate realistic objects that are semantically faithful to the input text, both the complete ob-ject shapes and the corresponding semantics are im- Jan 22, 2024 · This typically involves generating a considerable amount of synthetic data or requiring additional mask annotations. The insight is that to generate realistic objects that are seman-tically faithful to the input text, both the com-plete object shapes and the corresponding seman- In contrast, synthetic data can be freely available using a generative model (e. Additionally, we introduce a token optimization process for the creation of accurate attention maps, improving the performance of existing 6 days ago · Object detection has traditionally been a closed-set problem: you train on a fixed list of classes and cannot recognize new ones. 0 (SDXL 1. 1 repo. As Stable Diffusion is still in beta and subject to lots of changes, the Records will often change to reflect new information Stable Diffusion [48], trained on Internet-scale data such as LAION-5B [52]. This typically involves generating a considerable amount of synthetic data or requiring additional mask annotations. The pre-trained models for Stable Diffusion and CLIP are subject to their original license terms from Stable Diffusion and CLIP, respectively. Search Stable Diffusion prompts in our 12 million prompt database Mar 29, 2024 · This beginner's guide to Stable Diffusion is an extensive resource, designed to provide a comprehensive overview of the model's various aspects. Our method is training-free and does not rely on any label supervision. SD runs the diffusion process in a compressed latent space rather than the pixel space for efficiency. The vocabulary for image pair pretraining doesn't consider the captions in the laion dataset, nor all the training that refined models have given the TE and UNET. Jan 12, 2025 · It is a Stable Diffusion model with native resolution of 1024×1024, 4 times higher than Stable Diffusion v1. Grounding DINO breaks this mold, becoming an open-set, language-conditioned detector that can localize any user-specified phrase, zero-shot. 1) [41]) to deeply fuse text and image information We make the following contributions: (i) we pair the existing Stable Diffusion model with a novel grounding module, that can be trained to align the visual and textual embedding space of the diffusion model with only a small number of object categories; (ii) we establish an automatic pipeline for constructing a dataset, that consists of image Open-Vocabulary Attention Maps with Token Optimization for Semantic Segmentation in Diffusion Models Pablo Marcos-Manchón, Roberto Alcover-Couso, Juan C. Model: Waifu Diffusion 1. Sep 30, 2024 · Stable Diffusion AI is a latent diffusion model for generating AI images. Text-to-image (T2I 📌 This is an official PyTorch implementation of CLIFF: Continual Latent Diffusion for Open-Vocabulary Object Detection. BLIP which takes closed vocabulary scene graphs as input and Stable Diffusion [18] which takes open vocabulary-based text prompts. The step-wise generative process and the language conditioning make pre-trained diffusion Note that while many of the terms listed below are specific to Stable Diffusion and Generative Art applications, we’ve also included terms and concepts relating to all categories of Generative AI. e. 36 votes, 10 comments. How to use Stable Diffusion Online? To create high-quality images using Stable Diffusion Online, follow these steps: Step 1: Visit our Platform. Anyone know if there's a dictionary or searchable text based database of all the words, names, etc. Stable Diffusion consists of three compo-nents: a text encoder for producing text embeddings; a The CLIP ViT-L/14 model is just the pretrained part of stable diffusion. Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input, cultivates autonomous freedom to produce incredible imagery, empowers billions of people to create stunning art within seconds. To run ODISE's demo from the command line: python demo/demo. This approach limits the In this paper, we introduce Open-Vocabulary Attention Maps (OVAM), a training-free extension for text-to-image diffusion models to generate text-attribution maps based on open vocabulary descriptions. This is a very barebone implementation written in an hour, so any PRs are welcome. Search generative visuals for everyone by AI artists everywhere in our 12 million prompts database. One of the key challenges in 3D perception is the severe scarcity of point clouds and their dense labels. We build our data generation framework upon the state-of-the-art text-to-image latent diffusion model, i. Explore millions of AI generated images and create collections of prompts. Oct 8, 2024 · We present MosaicFusion, a simple yet effective diffusion-based data augmentation approach for large vocabulary instance segmentation. Martínez ; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. Our approach also relies on the generation of images; however stable-diffusion vocabulary Image Prompts. Stable Diffusion. We show that the grounding module trained on a pre-defined set of object categories, can segment images from Stable Diffusion well beyond the vocabulary of any off-the-shelf detector, as shown in Fig. safetensors, and vae_v2. 9242-9252 We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies pre-trained text-image diffusion and discriminative models to perform open-vocabulary panoptic segmentation. The overview of our method. app The Stable Diffusion prompts search engine. 0) [40] and Stable Diffusion 2. 3 | Prompts: woman, {prompt} | Negative Prompts: {My default list} | Sampling Method: Euler | Sampling Steps: 50 Your results will vary a lot from what I'm able to generate, and some prompts will influence an image differently depending on what other prompts you use. g. ODISE [45] employs Stable Diffusion as a feature extractor for its mask generator. Its primary use is to generate detailed images based on provided text descriptions. txt, clip_v2. jpg --vocab " black pickup truck, pickup truck; blue sky, sky " Mar 21, 2024 · Diffusion models represent a new paradigm in text-to-image generation. Stable Diffusion (SD) Stable Diffusion is a deep learning, text-to-image model that was released in 2022. Stable diffusion中文网为广大国内用户提供相关资源支持，使用经验分享，Stable diffusion是一种基于潜在扩散模型（Latent Diffusion Models）的文本到图像生成模型，开源且可独立安装部署，是生成式AI绘画神器。 Stable Diffusion is a text-to-image model that generates photo-realistic images given any text input. Witness the creativity and innovation of AI as it produces captivating visuals based on intricate linguistic cues. What makes Stable Diffusion unique ? It is completely open source. OVDiff [18] generates a set of visual references at prediction time to support the segmentation process. RAM: RAM is an image tagging model, which can recognize any common category with high accuracy. SDXL Turbo implements a new distillation technique called Adversarial Diffusion Distillation (ADD), which enables the model to synthesize images in a single step and generate Stable-Diffusion: A super powerful open-source latent text-to-image diffusion model : RAM++: RAM++ is the next generation of RAM, which can recognize any category with high accuracy. SanMiguel, José M. ). For Stable Diffusion v2. What frustrates me about Stable Diffusion is there doesn't seem to be any documentation as to what artists or vocabulary it understands. bujuj lkkgh bolg gglbzw pdq ulnn gmeagwc bjzhlxl wqpup qhcu