Adirik

Models by this creator

AI model preview image

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.

Read more

Updated 6/19/2024

AI model preview image

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.

Read more

Updated 6/19/2024

AI model preview image

t2i-adapter-sdxl-depth-midas

adirik

Total Score

156

The t2i-adapter-sdxl-depth-midas model is a text-to-image diffusion model that allows users to modify images using depth maps. It is an implementation of the T2I-Adapter-SDXL model, developed by TencentARC and the Diffuser team. This model is part of a series of similar models created by adirik, including t2i-adapter-sdxl-sketch, t2i-adapter-sdxl-lineart, and t2i-adapter-sdxl-openpose, each with their own unique capabilities. Model inputs and outputs The t2i-adapter-sdxl-depth-midas model takes several inputs, including an image, a prompt, a scheduler, the number of samples to generate, a random seed, a guidance scale, a negative prompt, the number of inference steps, an adapter conditioning scale, and an adapter conditioning factor. The model then generates an array of output images based on the provided inputs. Inputs Image**: The input image to be modified. Prompt**: The text prompt that describes the desired output image. Scheduler**: The scheduler to use for the diffusion process. Num Samples**: The number of output images to generate. Random Seed**: A random seed for reproducibility. Guidance Scale**: The scale to match the prompt. Negative Prompt**: Specify things to not see in the output. Num Inference Steps**: The number of diffusion steps. Adapter Conditioning Scale**: The conditioning scale for the adapter. Adapter Conditioning Factor**: The factor to scale the image by. Outputs Output**: An array of generated output images. Capabilities The t2i-adapter-sdxl-depth-midas model is capable of modifying images using depth maps, allowing users to create unique and visually striking outputs. By leveraging the T2I-Adapter-SDXL architecture, this model can generate images that closely match the provided prompt while incorporating the depth information from the input image. What can I use it for? The t2i-adapter-sdxl-depth-midas model can be used for a variety of creative applications, such as generating concept art, visualizing 3D scenes, or enhancing existing images. For example, you could use this model to create fantastical landscapes, surreal scenes, or even to modify portraits by adding depth-based effects. Additionally, adirik's other models, such as t2i-adapter-sdxl-sketch, t2i-adapter-sdxl-lineart, and t2i-adapter-sdxl-openpose, offer even more possibilities for image manipulation and transformation. Things to try One interesting thing to try with the t2i-adapter-sdxl-depth-midas model is to use it in combination with other image processing techniques, such as segmentation or edge detection. By layering different types of visual information, you can create truly unique and unexpected results. Additionally, experimenting with different prompts and input images can lead to a wide range of creative outcomes, from surreal to photorealistic.

Read more

Updated 6/19/2024

AI model preview image

grounding-dino

adirik

Total Score

119

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.

Read more

Updated 6/19/2024

AI model preview image

deforum-kandinsky-2-2

adirik

Total Score

107

The deforum-kandinsky-2-2 model is a powerful text-to-video generation tool developed by adirik. It utilizes the Kandinsky-2.2 model, which is a multilingual text-to-image latent diffusion model. This combination allows for the generation of videos from text prompts, opening up new creative possibilities. Similar models in this domain include kandinskyv22-adalab-ai, which focuses on generating images, and kandinskyvideo-cjwbw, a text-to-video generation model. These models all leverage the Kandinsky framework to explore the intersection of text, images, and video. Model inputs and outputs The deforum-kandinsky-2-2 model takes in a series of text prompts, animations, and configuration parameters to generate a video. The input prompts can be a mix of text and images, allowing for a diverse range of creative expressions. Inputs Animation Prompts**: The text prompts that will be used to generate the animation. Prompt Durations**: The duration (in seconds) for each animation prompt. Animations**: The type of animation to apply to each prompt, such as "right", "left", "spin_clockwise", etc. Max Frames**: The maximum number of frames to generate for the animation. Width and Height**: The dimensions of the output video. Fps**: The frames per second of the output video. Scheduler**: The diffusion scheduler to use for the generation process. Seed**: The random seed for generation. Steps**: The number of diffusion denoising steps to perform. Outputs Output Video**: The generated video, which can be saved and shared. Capabilities The deforum-kandinsky-2-2 model can generate unique and visually striking videos from text prompts. By combining the text-to-image capabilities of Kandinsky-2.2 with the animation features of Deforum, the model can create dynamic, evolving video scenes that bring the user's imagination to life. The results can range from dreamlike, surreal landscapes to stylized, abstract animations. What can I use it for? The deforum-kandinsky-2-2 model offers a wide range of potential applications, from creative, artistic endeavors to commercial use cases. Artists and content creators can utilize the model to generate unique, attention-grabbing videos for social media, music videos, or experimental art projects. Businesses and marketers can explore the model's capabilities to create captivating, dynamic visual content for advertising, product demonstrations, or immersive brand experiences. Things to try One interesting aspect of the deforum-kandinsky-2-2 model is its ability to seamlessly transition between different text prompts and animation styles within a single video. Users can experiment with mixing prompts that evoke contrasting moods, genres, or visual styles, and observe how the model blends these elements together. Additionally, playing with the various animation options, such as "spin_clockwise", "zoomin", or "around_left", can result in mesmerizing, fluid transitions that bring the prompts to life in unexpected ways.

Read more

Updated 6/19/2024

AI model preview image

t2i-adapter-sdxl-openpose

adirik

Total Score

73

The t2i-adapter-sdxl-openpose model is a text-to-image generation model that allows users to modify images using human pose. It is an implementation of the T2I-Adapter-SDXL model, developed by TencentARC and the diffuser team. The model is available through Replicate and can be accessed using the Cog interface. Similar models created by the same maintainer, adirik, include the t2i-adapter-sdxl-sketch model for modifying images using sketches, and the t2i-adapter-sdxl-lineart model for modifying images using line art. The maintainer has also created the t2i-adapter-sdxl-sketch model with a different creator, alaradirik, as well as the t2i-adapter-sdxl-depth-midas model for modifying images using depth maps. Model inputs and outputs The t2i-adapter-sdxl-openpose model takes in an input image, a prompt, and various optional parameters such as the number of samples, guidance scale, and number of inference steps. The output is an array of generated images based on the input prompt and the modifications made using the human pose. Inputs Image**: The input image to be modified. Prompt**: The text prompt describing the desired output. Scheduler**: The scheduler to use for the diffusion process. Num Samples**: The number of output images to generate. Random Seed**: A random seed for reproducibility. Guidance Scale**: The guidance scale to match the prompt. Negative Prompt**: Specifies things to not see in the output. Num Inference Steps**: The number of diffusion steps. Adapter Conditioning Scale**: The conditioning scale for the adapter. Adapter Conditioning Factor**: The factor to scale the image by. Outputs An array of generated images based on the input prompt and human pose modifications. Capabilities The t2i-adapter-sdxl-openpose model can be used to modify images by incorporating human pose information. This allows users to generate images that adhere to specific poses or body movements, opening up new creative possibilities for visual art and content creation. What can I use it for? The t2i-adapter-sdxl-openpose model can be used for a variety of applications, such as creating dynamic and expressive character illustrations, generating poses for animation or 3D modeling, and enhancing visual storytelling by incorporating human movement into the generated imagery. With the ability to fine-tune the model's parameters, users can explore a range of creative directions and experiment with different styles and aesthetics. Things to try One interesting aspect of the t2i-adapter-sdxl-openpose model is the ability to combine the human pose information with other modification techniques, such as sketches or line art. By leveraging the different adapters created by the maintainer, users can explore unique blends of visual elements and push the boundaries of what's possible with text-to-image generation.

Read more

Updated 6/19/2024

AI model preview image

realvisxl-v3.0-turbo

adirik

Total Score

70

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.

Read more

Updated 6/19/2024

AI model preview image

t2i-adapter-sdxl-lineart

adirik

Total Score

61

The t2i-adapter-sdxl-lineart model is a text-to-image generation model developed by Tencent ARC that can modify images using line art. It is an implementation of the T2I-Adapter model, which provides additional conditioning to the Stable Diffusion model. The T2I-Adapter-SDXL lineart model is trained on the StableDiffusionXL checkpoint and can generate images based on a text prompt while using line art as a conditioning input. The T2I-Adapter-SDXL lineart model is part of a family of similar models developed by Tencent ARC, including the t2i-adapter-sdxl-sketch and t2i-adapter-sdxl-sketch models, which use sketches as conditioning, and the masactrl-sdxl model, which provides editable image generation capabilities. Model inputs and outputs Inputs Image**: The input image, which will be used as the line art conditioning for the generation process. Prompt**: The text prompt that describes the desired image to generate. Scheduler**: The scheduling algorithm to use for the diffusion process, with the default being the K_EULER_ANCESTRAL scheduler. Num Samples**: The number of output images to generate, up to a maximum of 4. Random Seed**: An optional random seed to ensure reproducibility of the generated output. Guidance Scale**: A scaling factor that determines how closely the generated image will match the input prompt. Negative Prompt**: A text prompt that specifies elements that should not be present in the generated image. Num Inference Steps**: The number of diffusion steps to perform during the generation process, up to a maximum of 100. Adapter Conditioning Scale**: A scaling factor that determines the influence of the line art conditioning on the generated image. Adapter Conditioning Factor**: A scaling factor that determines the overall size of the generated image. Outputs Output**: An array of generated images in the form of image URIs. Capabilities The T2I-Adapter-SDXL lineart model can generate images based on text prompts while using line art as a conditioning input. This allows for more fine-grained control over the generated images, enabling the creation of artistic or stylized outputs that incorporate the line art features. What can I use it for? The T2I-Adapter-SDXL lineart model can be used for a variety of creative and artistic applications, such as generating concept art, illustrations, or stylized images for use in design projects, games, or other creative endeavors. The ability to incorporate line art as a conditioning input can be especially useful for generating images with a distinct artistic or technical style, such as comic book-style illustrations or technical diagrams. Things to try One interesting application of the T2I-Adapter-SDXL lineart model could be to generate images for use in educational or instructional materials, where the line art conditioning could be used to create clear, technical-looking diagrams or illustrations to accompany written content. Additionally, the model's ability to generate images based on text prompts could be leveraged to create personalized or customized artwork, such as character designs or scene illustrations for stories or games.

Read more

Updated 6/19/2024

AI model preview image

interior-design

adirik

Total Score

59

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.

Read more

Updated 6/19/2024

AI model preview image

t2i-adapter-sdxl-canny

adirik

Total Score

34

The t2i-adapter-sdxl-canny model is a text-to-image diffusion model that allows users to modify images using canny edge detection. It is an implementation of the T2I-Adapter-SDXL model developed by TencentARC and the diffuser team. The model is maintained by adirik and is available on Replicate. Similar models maintained by adirik include t2i-adapter-sdxl-sketch, t2i-adapter-sdxl-lineart, and t2i-adapter-sdxl-depth-midas, which allow users to modify images using sketches, line art, and depth maps, respectively. Another similar model, t2i-adapter-sdxl-sketch, is maintained by alaradirik. Model inputs and outputs The t2i-adapter-sdxl-canny model takes an input image and a text prompt, and generates a modified image based on the prompt and the canny edge representation of the input image. The model also allows users to customize various parameters, such as the number of samples, the guidance scale, and the number of inference steps. Inputs Image**: The input image to be modified. Prompt**: The text prompt describing the desired output image. Scheduler**: The scheduler to use for the diffusion process. Num Samples**: The number of output images to generate. Random Seed**: A random seed for reproducibility. Guidance Scale**: The scale to match the prompt. Negative Prompt**: Specify things to not see in the output. Num Inference Steps**: The number of diffusion steps. Adapter Conditioning Scale**: The conditioning scale for the adapter. Adapter Conditioning Factor**: The factor to scale the image by. Outputs An array of generated image URIs. Capabilities The t2i-adapter-sdxl-canny model can be used to modify input images in various ways, such as adding or removing elements, changing the style or composition, or applying artistic effects. The model leverages the canny edge representation of the input image to guide the generation process, allowing for more precise and controllable modifications. What can I use it for? The t2i-adapter-sdxl-canny model can be used for a variety of creative and artistic applications, such as photo editing, digital art, and image generation. It could be particularly useful for tasks that involve modifying or enhancing existing images, such as product visualization, architectural rendering, or character design. Things to try One interesting thing to try with the t2i-adapter-sdxl-canny model is to experiment with different combinations of the input parameters, such as the guidance scale, the number of inference steps, and the adapter conditioning scale. This can help you find the optimal settings for your specific use case and achieve more compelling results.

Read more

Updated 6/19/2024