Adirik

Models by this creator

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styletts2

adirik

Total Score

4.2K

styletts2 is a text-to-speech (TTS) model developed by Yinghao Aaron Li, Cong Han, Vinay S. Raghavan, Gavin Mischler, and Nima Mesgarani. It leverages style diffusion and adversarial training with large speech language models (SLMs) to achieve human-level TTS synthesis. Unlike its predecessor, styletts2 models styles as a latent random variable through diffusion models, allowing it to generate the most suitable style for the text without requiring reference speech. It also employs large pre-trained SLMs, such as WavLM, as discriminators with a novel differentiable duration modeling for end-to-end training, resulting in improved speech naturalness. Model inputs and outputs styletts2 takes in text and generates high-quality speech audio. The model inputs and outputs are as follows: Inputs Text**: The text to be converted to speech. Beta**: A parameter that determines the prosody of the generated speech, with lower values sampling style based on previous or reference speech and higher values sampling more from the text. Alpha**: A parameter that determines the timbre of the generated speech, with lower values sampling style based on previous or reference speech and higher values sampling more from the text. Reference**: An optional reference speech audio to copy the style from. Diffusion Steps**: The number of diffusion steps to use in the generation process, with higher values resulting in better quality but longer generation time. Embedding Scale**: A scaling factor for the text embedding, which can be used to produce more pronounced emotion in the generated speech. Outputs Audio**: The generated speech audio in the form of a URI. Capabilities styletts2 is capable of generating human-level TTS synthesis on both single-speaker and multi-speaker datasets. It surpasses human recordings on the LJSpeech dataset and matches human performance on the VCTK dataset. When trained on the LibriTTS dataset, styletts2 also outperforms previous publicly available models for zero-shot speaker adaptation. What can I use it for? styletts2 can be used for a variety of applications that require high-quality text-to-speech generation, such as audiobook production, voice assistants, language learning tools, and more. The ability to control the prosody and timbre of the generated speech, as well as the option to use reference audio, makes styletts2 a versatile tool for creating personalized and expressive speech output. Things to try One interesting aspect of styletts2 is its ability to perform zero-shot speaker adaptation on the LibriTTS dataset. This means that the model can generate speech in the style of speakers it has not been explicitly trained on, by leveraging the diverse speech synthesis offered by the diffusion model. Developers could explore the limits of this zero-shot adaptation and experiment with fine-tuning the model on new speakers to further improve the quality and diversity of the generated speech.

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Updated 5/19/2024

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masactrl-sdxl

adirik

Total Score

643

masactrl-sdxl is an AI model developed by adirik that enables editing real or generated images in a consistent manner. It builds upon the Stable Diffusion XL (SDXL) model, expanding its capabilities for non-rigid image synthesis and editing. The model can perform prompt-based image synthesis and editing while maintaining the content of the source image. It integrates well with other controllable diffusion models like T2I-Adapter, allowing for stable and consistent results. masactrl-sdxl also generalizes to other Stable Diffusion-based models, such as Anything-V4. Model inputs and outputs The masactrl-sdxl model takes in a variety of inputs to generate or edit images, including text prompts, seed values, guidance scales, and other control parameters. The outputs are the generated or edited images, which are returned as image URIs. Inputs prompt1, prompt2, prompt3, prompt4**: Text prompts that describe the desired image or edit. seed**: A random seed value to control the stochastic generation process. guidance_scale**: The scale for classifier-free guidance, which controls the balance between the text prompt and the model's learned prior. masactrl_start_step**: The step at which to start the mutual self-attention control process. num_inference_steps**: The number of denoising steps to perform during the generation process. masactrl_start_layer**: The layer at which to start the mutual self-attention control process. Outputs An array of image URIs representing the generated or edited images. Capabilities masactrl-sdxl enables consistent image synthesis and editing by combining the content from a source image with the layout synthesized from the text prompt and additional controls. This allows for non-rigid changes to the image while maintaining the original content. The model can also be integrated with other controllable diffusion pipelines, such as T2I-Adapter, to obtain stable and consistent results. What can I use it for? With masactrl-sdxl, you can perform a variety of image synthesis and editing tasks, such as: Generating images based on text prompts while maintaining the content of a source image Editing real images by changing the layout while preserving the original content Integrating masactrl-sdxl with other controllable diffusion models like T2I-Adapter for more stable and consistent results Experimenting with the model's capabilities on other Stable Diffusion-based models, such as Anything-V4 Things to try One interesting aspect of masactrl-sdxl is its ability to enable video synthesis with dense consistent guidance, such as keypose and canny edge maps. By leveraging the model's consistent image editing capabilities, you could explore generating dynamic, coherent video sequences from a series of text prompts and additional control inputs.

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Updated 5/19/2024

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grounding-dino

adirik

Total Score

115

grounding-dino is an AI model that can detect arbitrary objects in images using human text inputs such as category names or referring expressions. It combines a Transformer-based detector called DINO with grounded pre-training to achieve open-vocabulary and text-guided object detection. The model was developed by IDEA Research and is available as a Cog model on Replicate. Similar models include GroundingDINO, which also uses the Grounding DINO approach, as well as other object detection models like stable-diffusion and text-extract-ocr. Model inputs and outputs grounding-dino takes an image and a comma-separated list of text queries describing the objects you want to detect. It then outputs the detected objects with bounding boxes and predicted labels. The model also allows you to adjust the confidence thresholds for the box and text predictions. Inputs image**: The input image to query query**: Comma-separated text queries describing the objects to detect box_threshold**: Confidence level threshold for object detection text_threshold**: Confidence level threshold for predicted labels show_visualisation**: Option to draw and visualize the bounding boxes on the image Outputs Detected objects with bounding boxes and predicted labels Capabilities grounding-dino can detect a wide variety of objects in images using just natural language descriptions. This makes it a powerful tool for tasks like content moderation, image retrieval, and visual analysis. The model is particularly adept at handling open-vocabulary detection, allowing you to query for any object, not just a predefined set. What can I use it for? You can use grounding-dino for a variety of applications that require object detection, such as: Visual search**: Quickly find specific objects in large image databases using text queries. Automated content moderation**: Detect inappropriate or harmful objects in user-generated content. Augmented reality**: Overlay relevant information on objects in the real world using text-guided object detection. Robotic perception**: Enable robots to understand and interact with their environment using language-guided object detection. Things to try Try experimenting with different types of text queries to see how the model handles various object descriptions. You can also play with the confidence thresholds to balance the precision and recall of the object detections. Additionally, consider integrating grounding-dino into your own applications to add powerful object detection capabilities.

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Updated 5/19/2024

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realvisxl-v3.0-turbo

adirik

Total Score

59

realvisxl-v3.0-turbo is a photorealistic image generation model based on the SDXL (Stable Diffusion XL) architecture, developed by Replicate user adirik. This model is part of the RealVisXL model collection and is available on Civitai. It aims to produce highly realistic and detailed images from text prompts. The model can be compared to similar photorealistic models like realvisxl4 and instant-id-photorealistic. Model Inputs and Outputs realvisxl-v3.0-turbo takes a variety of input parameters to control the image generation process. These include the prompt, negative prompt, input image, mask, dimensions, number of outputs, and various settings for the generation process. The model outputs one or more generated images as URIs. Inputs Prompt**: The text description that guides the image generation process. Negative Prompt**: Terms or descriptions to avoid in the generated image. Image**: An input image for use in img2img or inpaint modes. Mask**: A mask defining areas in the input image to preserve or alter. Width and Height**: The desired dimensions of the output image. Number of Outputs**: The number of images to generate. Scheduler**: The algorithm used for image generation. Number of Inference Steps**: The number of denoising steps in the generation process. Guidance Scale**: The influence of the classifier-free guidance. Prompt Strength**: The influence of the input prompt in img2img or inpaint modes. Seed**: A random seed for reproducible image generation. Refine**: The style of refinement to apply to the generated image. High Noise Frac**: The fraction of noise to use for the expert_ensemble_refiner. Refine Steps**: The number of steps for the base_image_refiner. Apply Watermark**: Whether to apply a watermark to the generated images. Disable Safety Checker**: Disable the safety checker for generated images. Outputs One or more generated images as URIs. Capabilities realvisxl-v3.0-turbo is capable of generating highly photorealistic images from text prompts. The model leverages the power of SDXL to produce detailed, lifelike results that can be used in a variety of applications, such as visual design, product visualization, and creative projects. What Can I Use It For? realvisxl-v3.0-turbo can be used for a wide range of applications that require photorealistic image generation. This includes creating product visualizations, designing book covers or album art, generating concept art for games or films, and more. The model can also be used to create unique and compelling digital art assets. By leveraging the capabilities of this model, users can streamline their creative workflows and explore new artistic possibilities. Things to Try One interesting aspect of realvisxl-v3.0-turbo is its ability to generate images with a high level of photorealism. Try experimenting with detailed prompts that describe complex scenes or objects, and see how the model handles the challenge. Additionally, try using the img2img and inpaint modes to refine or modify existing images, and explore the different refinement options to achieve the desired aesthetic.

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Updated 5/19/2024

AI model preview image

interior-design

adirik

Total Score

46

The interior-design model is a custom interior design pipeline API developed by adirik that combines several powerful AI technologies to generate realistic interior design concepts based on text and image inputs. It builds upon the Realistic Vision V3.0 inpainting pipeline, integrating it with segmentation and MLSD ControlNets to produce highly detailed and coherent interior design visualizations. This model is similar to other text-guided image generation and editing tools like stylemc and realvisxl-v3.0-turbo created by the same maintainer. Model inputs and outputs The interior-design model takes several input parameters to guide the image generation process. These include an input image, a detailed text prompt describing the desired interior design, a negative prompt to avoid certain elements, and various settings to control the generation process. The model then outputs a new image that reflects the provided prompt and design guidelines. Inputs image**: The provided image serves as a base or reference for the generation process. prompt**: The input prompt is a text description that guides the image generation process. It should be a detailed and specific description of the desired output image. negative_prompt**: This parameter allows specifying negative prompts. Negative prompts are terms or descriptions that should be avoided in the generated image, helping to steer the output away from unwanted elements. num_inference_steps**: This parameter defines the number of denoising steps in the image generation process. guidance_scale**: The guidance scale parameter adjusts the influence of the classifier-free guidance in the generation process. Higher values will make the model focus more on the prompt. prompt_strength**: In inpainting mode, this parameter controls the influence of the input prompt on the final image. A value of 1.0 indicates complete transformation according to the prompt. seed**: The seed parameter sets a random seed for image generation. A specific seed can be used to reproduce results, or left blank for random generation. Outputs The model outputs a new image that reflects the provided prompt and design guidelines. Capabilities The interior-design model can generate highly detailed and realistic interior design concepts based on text prompts and reference images. It can handle a wide range of design styles, from modern minimalist to ornate and eclectic. The model is particularly adept at generating photorealistic renderings of rooms, furniture, and decor elements that seamlessly blend together to create cohesive and visually appealing interior design scenes. What can I use it for? The interior-design model can be a powerful tool for interior designers, architects, and homeowners looking to explore and visualize new design ideas. It can be used to quickly generate realistic 3D renderings of proposed designs, allowing stakeholders to better understand and evaluate concepts before committing to physical construction or renovation. The model could also be integrated into online interior design platforms or real estate listing services to provide potential buyers with a more immersive and personalized experience of a property's interior spaces. Things to try One interesting aspect of the interior-design model is its ability to seamlessly blend different design elements and styles within a single interior scene. Try experimenting with prompts that combine contrasting materials, textures, and color palettes to see how the model can create visually striking and harmonious interior designs. You could also explore the model's capabilities in generating specific types of rooms, such as bedrooms, living rooms, or home offices, and see how the output varies based on the provided prompt and reference image.

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Updated 5/19/2024

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realvisxl-v4.0

adirik

Total Score

15

The realvisxl-v4.0 model is a powerful AI system for generating photorealistic images. It is an evolution of the realvisxl-v3.0-turbo model, which was based on the Stable Diffusion XL (SDXL) architecture. The realvisxl-v4.0 model aims to further improve the realism and quality of generated images, making it a valuable tool for a variety of applications. Model inputs and outputs The realvisxl-v4.0 model takes a text prompt as the primary input, which guides the image generation process. Users can also provide additional parameters such as a negative prompt, input image, mask, and various settings to control the output. The model generates one or more high-quality, photorealistic images as the output. Inputs Prompt**: A text description that specifies the desired output image Negative Prompt**: Terms or descriptions to avoid in the generated image Image**: An input image for use in img2img or inpaint modes Mask**: A mask defining areas to preserve or alter in the input image Width/Height**: The desired dimensions of the output image Num Outputs**: The number of images to generate Scheduler**: The algorithm used for the image generation process Num Inference Steps**: The number of denoising steps in the generation Guidance Scale**: The influence of the classifier-free guidance Prompt Strength**: The influence of the input prompt on the final image Seed**: A random seed for the image generation Refine**: The refining style to apply to the generated image High Noise Frac**: The fraction of noise to use for the expert_ensemble_refiner Refine Steps**: The number of steps for the base_image_refiner Apply Watermark**: Whether to apply a watermark to the generated images Disable Safety Checker**: Whether to disable the safety checker for the generated images Outputs One or more high-quality, photorealistic images based on the input parameters Capabilities The realvisxl-v4.0 model excels at generating photorealistic images across a wide range of subjects and styles. It can produce highly detailed and accurate representations of objects, scenes, and even fantastical elements like the "astronaut riding a rainbow unicorn" example. The model's ability to maintain a strong sense of realism while incorporating imaginative elements makes it a valuable tool for creative applications. What can I use it for? The realvisxl-v4.0 model can be used for a variety of applications, including: Visual Content Creation**: Generating photorealistic images for use in marketing, design, and entertainment Conceptual Prototyping**: Quickly visualizing ideas and concepts for products, environments, or experiences Artistic Exploration**: Combining realistic and fantastical elements to create unique and imaginative artworks Photographic Enhancement**: Improving the quality and realism of existing images through techniques like inpainting and refinement Things to try One interesting aspect of the realvisxl-v4.0 model is its ability to maintain a high level of realism while incorporating fantastical or surreal elements. Users can experiment with prompts that blend realistic and imaginative components, such as "a futuristic city skyline with floating holographic trees" or "a portrait of a wise, elderly wizard in a mystic forest". By exploring the boundaries between realism and imagination, users can unlock the model's creative potential and discover unique and captivating visual outcomes.

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Updated 5/19/2024

AI model preview image

marigold

adirik

Total Score

14

marigold is a diffusion model developed by adirik for monocular depth estimation. It uses a unique fine-tuning protocol to perform high-quality depth prediction from a single image. Compared to similar models like stylemc, gfpgan, bunny-phi-2-siglip, real-esrgan, and realvisxl-v4.0, marigold focuses specifically on the task of monocular depth estimation. Model inputs and outputs marigold takes an RGB or grayscale image as input and produces two depth map outputs - one grayscale and one spectral. The depth maps represent the estimated distance of each pixel from the camera, which can be useful for a variety of computer vision and 3D applications. Inputs image:** RGB or grayscale input image for the model, use an RGB image for best results. resize_input:** whether to resize the input image to max resolution of 768 x 768 pixels, default to True. num_infer:** number of inferences to be performed. if >1, multiple depth predictions are ensembled. A higher number yields better results but runs slower. denoise_steps:** number of inference denoising steps, more steps results in higher accuracy but slower inference speed. regularizer_strength:** ensembling parameter, weight of optimization regularizer. reduction_method:** ensembling parameter, method to merge aligned depth maps. Choose between ["mean", "median"]. max_iter:** ensembling parameter, max number of optimization iterations. seed:** (optional) seed for reproducibility, set to random if left as None. Outputs Two depth map images** - one grayscale and one spectral, representing the estimated distance of each pixel from the camera. Capabilities marigold is capable of producing high-quality depth maps from a single input image. This can be useful for a variety of computer vision tasks such as 3D reconstruction, object detection and segmentation, and augmented reality applications. What can I use it for? The depth maps generated by marigold can be used in a wide range of applications, such as: 3D reconstruction:** Combine multiple depth maps to create 3D models of scenes or objects. Object detection and segmentation:** Use the depth information to better identify and localize objects in an image. Augmented reality:** Integrate the depth maps into AR applications to create more realistic and immersive experiences. Robotics and autonomous vehicles:** Use the depth information for tasks like obstacle avoidance, navigation, and scene understanding. Things to try One interesting thing to try with marigold is to experiment with the different ensembling parameters, such as num_infer, denoise_steps, regularizer_strength, and reduction_method. By adjusting these settings, you can find the optimal balance between inference speed and depth map quality for your specific use case. Another idea is to combine the depth maps generated by marigold with other computer vision models, such as those for object detection or semantic segmentation. This can provide a richer understanding of the 3D structure of a scene and enable more advanced applications.

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Updated 5/19/2024

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realvisxl-v4.0-lightning

adirik

Total Score

12

realvisxl-v4.0-lightning is a powerful AI model for generating photorealistic images. It is an evolution of the RealVisXL V3.0 Turbo model, which was based on the SDXL architecture. The realvisxl-v4.0-lightning model builds on this foundation to deliver even more realistic and detailed images. Compared to similar models like realvisxl-v4.0, realvisxl4, and realvisxl-v3, the realvisxl-v4.0-lightning model is known for its ability to generate highly photorealistic images with exceptional detail and clarity. It excels at creating visuals that are difficult to distinguish from real-world photographs. Model inputs and outputs The realvisxl-v4.0-lightning model accepts a wide range of input parameters, allowing for fine-tuned control over the image generation process. These include the input prompt, negative prompt, image, mask, and various settings related to the image size, number of outputs, scheduler, and refinement. Inputs prompt**: The text description that guides the image generation process. This should be a detailed and specific description of the desired output. negative_prompt**: Terms or descriptions to be avoided in the generated image. image**: An input image for use in img2img or inpaint modes. mask**: Defines areas in the input image that should be preserved or altered during the inpainting process. width**: Sets the width of the output image. height**: Sets the height of the output image. num_outputs**: Specifies the number of images to be generated for a given prompt. Outputs Output images**: The generated photorealistic images based on the input parameters. Capabilities The realvisxl-v4.0-lightning model excels at generating highly detailed and realistic images across a wide range of subjects and scenes. It can seamlessly blend elements like people, animals, environments, and objects into cohesive, believable visuals. The model's ability to capture intricate details and textures is particularly impressive, making it a powerful tool for tasks such as product visualization, architectural rendering, and digital art. What can I use it for? The realvisxl-v4.0-lightning model can be leveraged for a variety of applications that require photorealistic imagery. Some potential use cases include: Product visualization**: Generate realistic product images for e-commerce, marketing, and design purposes. Architectural visualization**: Create immersive, high-fidelity renderings of buildings, interiors, and landscapes. Digital art and content creation**: Produce captivating, photographic-quality artwork and visual assets for various creative projects. Advertising and marketing**: Develop eye-catching, photorealistic visuals for advertising campaigns, social media content, and other marketing materials. Things to try Experiment with different prompts and input parameters to see the model's versatility in generating a wide range of photorealistic images. Try combining the realvisxl-v4.0-lightning model with other techniques, such as image inpainting or text-guided image editing, to unlock even more creative possibilities.

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Updated 5/19/2024

AI model preview image

dreamgaussian

adirik

Total Score

9

DreamGaussian is a generative AI model that uses Gaussian Splatting to efficiently create 3D content. Developed by the Replicate creator adirik, it builds on similar text-to-image and image-to-image models like StyleMC, GFPGAN, and Real-ESRGAN. Unlike those models focused on 2D image generation and enhancement, DreamGaussian aims to efficiently create 3D content from text prompts or input images. Model inputs and outputs DreamGaussian takes in either a text prompt or an input image, along with some additional parameters, and generates a 3D output. The input can be an image, a text description, or both. The model then samples points and renders them using Gaussian splatting to efficiently create a 3D object. Inputs Text**: A text prompt to describe the 3D object to generate Image**: An input image to convert to 3D Elevation**: The elevation angle of the input image Num Steps**: The number of iterations to run the generation process Image Size**: The target size for the preprocessed input image Num Point Samples**: The number of points to sample for the Gaussian Splatting Num Refinement Steps**: The number of refinement iterations to perform Outputs 3D Output**: A 3D object generated from the input text, image, and parameters Capabilities DreamGaussian can efficiently generate 3D content from text prompts or input images using the Gaussian Splatting technique. This allows for faster 3D content creation compared to traditional methods. The model can be used to generate a wide variety of 3D objects, from simple geometric shapes to complex organic forms. What can I use it for? DreamGaussian can be used for a variety of 3D content creation tasks, such as generating 3D assets for games, virtual environments, or product design. The efficient nature of the Gaussian Splatting approach makes it well-suited for rapid prototyping and iteration. Additionally, the model could be used to convert 2D images into 3D scenes, enabling new possibilities for 3D visualization and modeling. Things to try Experiment with different text prompts and input images to see the range of 3D objects DreamGaussian can generate. Try varying the input parameters, such as the number of steps, point samples, and refinement iterations, to find the optimal settings for your use case. Additionally, consider combining DreamGaussian with other AI models, such as LLAVA-13B or AbsoluteReality-v1.8.1, to explore more advanced 3D content creation workflows.

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Updated 5/19/2024

AI model preview image

hierspeechpp

adirik

Total Score

4

hierspeechpp is a zero-shot speech synthesizer developed by Replicate user adirik. It is a text-to-speech model that can generate speech from text and a target voice, enabling zero-shot speech synthesis. This model is similar to other text-to-speech models like styletts2, voicecraft, and whisperspeech-small, which also focus on generating speech from text or audio. Model inputs and outputs hierspeechpp takes in text or audio as input and generates an audio file as output. The model allows you to provide a target voice clip, which it will use to synthesize the output speech. This enables zero-shot speech synthesis, where the model can generate speech in the voice of the target speaker without requiring any additional training data. Inputs input_text**: (optional) Text input to the model. If provided, it will be used for the speech content of the output. input_sound**: (optional) Sound input to the model in .wav format. If provided, it will be used for the speech content of the output. target_voice**: A voice clip in .wav format containing the speaker to synthesize. denoise_ratio**: Noise control. 0 means no noise reduction, 1 means maximum noise reduction. text_to_vector_temperature**: Temperature for text-to-vector model. Larger value corresponds to slightly more random output. output_sample_rate**: Sample rate of the output audio file. scale_output_volume**: Scale normalization. If set to true, the output audio will be scaled according to the input sound if provided. seed**: Random seed to use for reproducibility. Outputs Output**: An audio file in .mp3 format containing the synthesized speech. Capabilities hierspeechpp can generate high-quality speech by leveraging a target voice clip. It is capable of zero-shot speech synthesis, meaning it can create speech in the voice of the target speaker without any additional training data. This allows for a wide range of applications, such as voice cloning, audiobook narration, and dubbing. What can I use it for? You can use hierspeechpp for various speech-related tasks, such as creating custom voice interfaces, generating audio content for podcasts or audiobooks, or even dubbing videos in different languages. The zero-shot nature of the model makes it particularly useful for projects where you need to generate speech in a specific voice without access to a large dataset of that speaker's recordings. Things to try One interesting thing to try with hierspeechpp is to experiment with the different input parameters, such as the denoise_ratio and text_to_vector_temperature. By adjusting these settings, you can fine-tune the output to your specific needs, such as reducing background noise or making the speech more natural-sounding. Additionally, you can try using different target voice clips to see how the model adapts to different speakers.

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Updated 5/19/2024