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Stable diffusion vocabulary The introduction of diffusion models has led to a sig-nificant advancement in text-to-image (T2I) generation [7]. Its primary use is to generate detailed images based on provided text descriptions. Our method is training-free and does not rely on any label supervision. Sep 6, 2023 · This typically involves generating a considerable amount of synthetic data or requiring additional mask annotations. 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. Grounding DINO breaks this mold, becoming an open-set, language-conditioned detector that can localize any user-specified phrase, zero-shot. SDXL Turbo is a SDXL mdoel trained with the Turbo training method. This means that an image of (4, 512, 512) is (4, 64, 64) in the potential space. Stable Diffusion (SD) Stable Diffusion is a deep learning, text-to-image model that was released in 2022. Architecture . To this end, we uncover the potential of generative text-to-image diffusion models (e. In the inference of a 512 x 512 image using stable diffusion, the model uses a seed and a text cue as input. 36 votes, 10 comments. jpg --output demo/coco_pred. Jan 12, 2025 · It is a Stable Diffusion model with native resolution of 1024×1024, 4 times higher than Stable Diffusion v1. The pre-trained models for Stable Diffusion and CLIP are subject to their original license terms from Stable Diffusion and CLIP, respectively. true. This approach limits the Oct 8, 2024 · We present MosaicFusion, a simple yet effective diffusion-based data augmentation approach for large vocabulary instance segmentation. Additionally, we introduce a token optimization process for the creation of accurate attention maps, improving the performance of existing 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. This demonstrates that their internal representation Jun 3, 2025 · Object detection has traditionally been a closed-set problem: you train on a fixed list of classes and cannot recognize new ones. Definitions are easy-to-understand for both beginners and advanced users. The overview of our method. 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. vercel. , Stable Diffusion) as highly efficient open-vocabulary semantic segmenters, and introduce a novel training-free approach named DiffSegmenter. ). It is one of the companies behind the development of Stable Diffusion. 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. 5. 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. app The Stable Diffusion prompts search engine. Stable Diffusion. To run ODISE's demo from the command line: python demo/demo. g. Text-to-image diffusion models have the remarkable ability to generate high-quality images with diverse open-vocabulary language descriptions. /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. It can reduce image generation time by about 3x. 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. 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- Mar 21, 2024 · Diffusion models represent a new paradigm in text-to-image generation. Search Stable Diffusion prompts in our 12 million prompt database It is one of the companies behind the development of Stable Diffusion. 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. Comprehensive glossary covering every important term related to Stable Diffusion, the popular open-source AI image generation model. 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. First, we divide an image canvas 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. Grounding DINO shatters this limitation by weaving language understanding directly into a transformer-based detector. py --input demo/examples/coco. However, current extensions primarily rely on extracting attentions linked to prompt words used for image synthesis. jpg --vocab " black pickup truck, pickup truck; blue sky, sky " The autoencoder used in Stable Diffusion has a reduction factor of 8. This approach limits the Mar 21, 2024 · Diffusion models represent a new paradigm in text-to-image generation. It can . ikki gruk wqhuoe eaojay sll jincikf ytgdh youg ntftfq bskqda |
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