Zsxkib

Models by this creator

3.2K

diffbir

zsxkib

diffbir is a versatile AI model developed by researcher Xinqi Lin and team that can tackle various blind image restoration tasks, including blind image super-resolution, blind face restoration, and blind image denoising. Unlike traditional image restoration models that rely on fixed degradation assumptions, diffbir leverages the power of generative diffusion models to handle a wide range of real-world image degradations in a blind manner. This approach enables diffbir to produce high-quality restored images without requiring prior knowledge about the specific degradation process. The model is similar to other powerful image restoration models like GFPGAN, which specializes in restoring old photos and AI-generated faces, and SuperIR, which practices model scaling for photo-realistic image restoration. However, diffbir distinguishes itself by its broad applicability and its ability to handle a wide range of real-world image degradations in a unified manner. Model inputs and outputs Inputs input**: Path to the input image you want to enhance. upscaling_model_type**: Choose the type of model best suited for the primary content of the image: 'faces' for portraits and 'general_scenes' for everything else. restoration_model_type**: Select the restoration model that aligns with the content of your image. This model is responsible for image restoration which removes degradations. super_resolution_factor**: Factor by which the input image resolution should be increased. For instance, a factor of 4 will make the resolution 4 times greater in both height and width. steps**: The number of enhancement iterations to perform. More steps might result in a clearer image but can also introduce artifacts. repeat_times**: Number of times the enhancement process is repeated by feeding the output back as input. This can refine the result but might also introduce over-enhancement issues. tiled**: Whether to use patch-based sampling. This can be useful for very large images to enhance them in smaller chunks rather than all at once. tile_size**: Size of each tile (or patch) when 'tiled' option is enabled. Determines how the image is divided during patch-based enhancement. tile_stride**: Distance between the start of each tile when the image is divided for patch-based enhancement. A smaller stride means more overlap between tiles. use_guidance**: Use latent image guidance for enhancement. This can help in achieving more accurate and contextually relevant enhancements. guidance_scale**: For 'general_scenes': Scale factor for the guidance mechanism. Adjusts the influence of guidance on the enhancement process. guidance_space**: For 'general_scenes': Determines in which space (RGB or latent) the guidance operates. 'latent' can often provide more subtle and context-aware enhancements. guidance_repeat**: For 'general_scenes': Number of times the guidance process is repeated during enhancement. guidance_time_start**: For 'general_scenes': Specifies when (at which step) the guidance mechanism starts influencing the enhancement. guidance_time_stop**: For 'general_scenes': Specifies when (at which step) the guidance mechanism stops influencing the enhancement. has_aligned**: For 'faces' mode: Indicates if the input images are already cropped and aligned to faces. If not, the model will attempt to do this. only_center_face**: For 'faces' mode: If multiple faces are detected, only enhance the center-most face in the image. background_upsampler**: For 'faces' mode: Model used to upscale the background in images where the primary subject is a face. face_detection_model**: For 'faces' mode: Model used for detecting faces in the image. Choose based on accuracy and speed preferences. background_upsampler_tile**: For 'faces' mode: Size of each tile used by the background upsampler when dividing the image into patches. background_upsampler_tile_stride**: For 'faces' mode: Distance between the start of each tile when the background is divided for upscaling. A smaller stride means more overlap between tiles. Outputs Output**: The enhanced image(s) produced by the diffbir model. Capabilities diffbir can handle a wide range of real-world image degradations, including low resolution, noise, and blur, without requiring prior knowledge about the specific degradation process. The model is capable of performing blind image super-resolution, blind face restoration, and blind image denoising, producing high-quality results that outperform traditional restoration methods. What can I use it for? You can use diffbir to enhance various types of images, from portraits and landscapes to old photos and AI-generated images. The model's versatility makes it a powerful tool for tasks such as: Upscaling low-resolution images while preserving details and avoiding artifacts Restoring degraded or low-quality facial images, such as those from old photos or AI-generated faces Removing noise and artifacts from images, improving their overall quality and clarity The broad applicability of diffbir makes it a valuable resource for photographers, digital artists, and anyone working with visual content that requires restoration or enhancement. Things to try One interesting aspect of diffbir is its ability to leverage latent image guidance for more accurate and context-aware enhancements. By specifying the appropriate guidance settings, you can explore how this feature affects the restoration results and find the right balance between quality and fidelity. Another feature worth experimenting with is the patch-based sampling approach, which can be useful for enhancing very large images. By dividing the image into smaller tiles and processing them individually, you can reduce the memory requirements and potentially achieve better results, especially for high upscaling factors. Overall, the versatility and performance of diffbir make it a compelling choice for a wide range of image restoration and enhancement tasks. By exploring the various options and capabilities of the model, you can unlock its full potential and achieve impressive results.

Updated 12/13/2024

Image-to-Image

956

blip-3

zsxkib

blip-3 is a series of large multimodal models (LMMs) developed by Salesforce AI Research. These models have been trained at scale on high-quality image caption datasets and interleaved image-text data. blip3-phi3-mini-instruct-r-v1 is a fine-tuned version of the pretrained blip3-phi3-mini-base-r-v1 model that achieves state-of-the-art performance among open-source and closed-source vision-language models under 5 billion parameters. It supports flexible high-resolution image encoding with efficient visual token sampling. The blip-3 model series is related to other multimodal models like SDXL-Lightning from ByteDance, which generates high-quality images in 4 steps, and the original BLIP model from Salesforce, which generates image captions. The BLIP-2 model from Andreas Jansson also answers questions about images. Model inputs and outputs Inputs Image**: The input image to generate captions or answer questions about. Question**: The question to ask about the input image. Context** (optional): Previous questions and answers to use as context for answering the current question. Miscellaneous parameters**: Options to control the output, such as the number of top tokens to consider, the temperature for sampling, and whether to use beam search. Outputs String**: The model's response to the input question, either a caption or an answer. Capabilities The blip-3 models excel at answering questions about images, with state-of-the-art performance on benchmarks like COCO, NoCaps, TextCaps, OKVQA, TextVQA, VizWiz, and VQAv2. They can provide detailed, polite, and helpful answers to a wide variety of image-related questions. What can I use it for? The blip-3 models can be useful for building applications that need to understand and reason about images, such as: Visual question answering systems Image captioning tools Multimodal search engines Automated image analysis for e-commerce or other domains The maintainer's profile also showcases their work on the related uform-gen model, which is a fast 1.5B image captioning and VQA multimodal language model. Things to try One interesting aspect of the blip-3 models is their ability to perform in-context learning, where they can leverage previous questions and answers to provide more contextual responses. You could experiment with different ways of providing context to the model and see how it affects the quality and relevance of the answers. Another area to explore is the model's performance on specialized tasks like document understanding, chart analysis, or OCR-related questions. The README mentions the model was trained on a mixture of academic VQA datasets covering these types of tasks, so it could be worth testing its capabilities in these domains.

Updated 12/13/2024

Image-to-Text

879

pulid

zsxkib

PuLID is a powerful text-to-image model developed by researchers at ByteDance Inc. Similar to other advanced models like Stable Diffusion, SDXL-Lightning, and BLIP, PuLID uses contrastive learning techniques to generate high-quality, customized images from textual prompts. Unlike traditional text-to-image models, PuLID has a unique focus on identity customization, allowing for fine-grained control over the appearance of generated faces and portraits. Model inputs and outputs PuLID takes in a textual prompt, as well as one or more reference images of a person's face. The model then generates a set of new images that match the provided prompt while retaining the identity and appearance of the reference face(s). Inputs Prompt**: A text description of the desired image, such as "portrait, color, cinematic, in garden, soft light, detailed face" Seed**: An optional integer value to control the randomness of the generated images CF Scale**: A scaling factor that controls the influence of the textual prompt on the generated image Num Steps**: The number of iterative refinement steps to perform during image generation Image Size**: The desired width and height of the output images Num Samples**: The number of unique images to generate Identity Scale**: A scaling factor that controls the influence of the reference face(s) on the generated images Mix Identities**: A boolean flag to enable mixing of multiple reference face images Main Face Image**: The primary reference face image Auxiliary Face Image(s)**: Additional reference face images (up to 3) to be used for identity mixing Outputs Images**: A set of generated images that match the provided prompt and retain the identity and appearance of the reference face(s) Capabilities PuLID excels at generating high-quality, customized portraits and face images. By leveraging contrastive alignment techniques, the model is able to faithfully preserve the identity and appearance of the reference face(s) while seamlessly blending them with the desired textual prompt. This makes PuLID a powerful tool for applications such as photo editing, character design, and virtual avatar creation. What can I use it for? PuLID can be used in a variety of creative and commercial applications. For example, artists and designers could use it to quickly generate concept art for characters or illustrations, while businesses could leverage it to create custom virtual avatars or product visualizations. The model's ability to mix and match different facial features also opens up possibilities for personalized image generation, such as creating unique profile pictures or avatars. Things to try One interesting aspect of PuLID is its ability to mix and match different facial features from multiple reference images. By experimenting with the "Mix Identities" setting, users can create unique hybrid faces that combine the characteristics of several individuals. This can be a powerful tool for creative expression or character design. Additionally, exploring the various input parameters, such as the prompt, CFG scale, and number of steps, can help users fine-tune the generated images to their specific needs and preferences.

Updated 12/13/2024

Text-to-Image

716

instant-id

zsxkib

instant-id is a state-of-the-art AI model developed by the InstantX team that can generate realistic images of real people instantly. It utilizes a tuning-free approach to achieve identity-preserving generation with only a single input image. The model is capable of various downstream tasks such as stylized synthesis, where it can blend the facial features and style of the input image. Compared to similar models like AbsoluteReality V1.8.1, Reliberate v3, Stable Diffusion, Photomaker, and Photomaker Style, instant-id achieves better fidelity and retains good text editability, allowing the generated faces and styles to blend more seamlessly. Model inputs and outputs instant-id takes a single input image of a face and a text prompt, and generates one or more realistic images that preserve the identity of the input face while incorporating the desired style and content from the text prompt. The model utilizes a novel identity-preserving generation technique that allows it to generate high-quality, identity-preserving images in a matter of seconds. Inputs Image**: The input face image used as a reference for the generated images. Prompt**: The text prompt describing the desired style and content of the generated images. Seed** (optional): A random seed value to control the randomness of the generated images. Pose Image** (optional): A reference image used to guide the pose of the generated images. Outputs Images**: One or more realistic images that preserve the identity of the input face while incorporating the desired style and content from the text prompt. Capabilities instant-id is capable of generating highly realistic images of people in a variety of styles and settings, while preserving the identity of the input face. The model can seamlessly blend the facial features and style of the input image, allowing for unique and captivating results. This makes the model a powerful tool for a wide range of applications, from creative content generation to virtual avatars and character design. What can I use it for? instant-id can be used for a variety of applications, such as: Creative Content Generation**: Quickly generate unique and realistic images for use in art, design, and multimedia projects. Virtual Avatars**: Create personalized virtual avatars that can be used in games, social media, or other digital environments. Character Design**: Develop realistic and expressive character designs for use in animation, films, or video games. Augmented Reality**: Integrate generated images into augmented reality experiences, allowing for the seamless blending of real and virtual elements. Things to try With instant-id, you can experiment with a wide range of text prompts and input images to generate unique and captivating results. Try prompts that explore different styles, genres, or themes, and see how the model can blend the facial features and aesthetics in unexpected ways. You can also experiment with different input images, from close-up portraits to more expressive or stylized faces, to see how the model adapts and responds. By pushing the boundaries of what's possible with identity-preserving generation, you can unlock a world of creative possibilities.

Updated 12/13/2024

Text-to-Image

459

realistic-voice-cloning

zsxkib

The realistic-voice-cloning model, created by zsxkib, is an AI model that can create song covers by cloning a specific voice from audio files. It builds upon the Realistic Voice Cloning (RVC v2) technology, allowing users to generate vocals in the style of any RVC v2 trained voice. This model offers an alternative to similar voice cloning models like create-rvc-dataset, openvoice, free-vc, train-rvc-model, and voicecraft, each with its own unique features and capabilities. Model inputs and outputs The realistic-voice-cloning model takes a variety of inputs that allow users to fine-tune the generated vocals, including the RVC model to use, pitch changes, reverb settings, and more. The output is a generated audio file in either MP3 or WAV format, containing the original song's vocals replaced with the cloned voice. Inputs Song Input**: The audio file to use as the source for the song RVC Model**: The specific RVC v2 model to use for the voice cloning Pitch Change**: Adjust the pitch of the AI-generated vocals Index Rate**: Control the balance between the AI's accent and the original vocals RMS Mix Rate**: Adjust the balance between the original vocal's loudness and a fixed loudness Filter Radius**: Apply median filtering to the harvested pitch results Pitch Detection Algorithm**: Choose between different pitch detection algorithms Protect**: Control the amount of original vocals' breath and voiceless consonants to leave in the AI vocals Reverb Size, Damping, Dryness, and Wetness**: Adjust the reverb settings Pitch Change All**: Change the pitch/key of the background music, backup vocals, and AI vocals Volume Changes**: Adjust the volume of the main AI vocals, backup vocals, and background music Outputs The generated audio file in either MP3 or WAV format, with the original vocals replaced by the cloned voice Capabilities The realistic-voice-cloning model can create high-quality song covers by replacing the original vocals with a cloned voice. Users can fine-tune the generated vocals to achieve their desired sound, adjusting parameters like pitch, reverb, and volume. This model is particularly useful for musicians, content creators, and audio engineers who want to create unique vocal covers or experiments with different voice styles. What can I use it for? The realistic-voice-cloning model can be used to create song covers, remixes, and other audio projects where you want to replace the original vocals with a different voice. This can be useful for musicians who want to experiment with different vocal styles, content creators who want to create unique covers, or audio engineers who need to modify existing vocal tracks. The model's ability to fine-tune the generated vocals also makes it suitable for professional audio production work. Things to try With the realistic-voice-cloning model, you can try creating unique song covers by cloning the voice of your favorite singers or even your own voice. Experiment with different RVC models, pitch changes, and reverb settings to achieve the desired sound. You could also explore using the model to create custom vocal samples or background vocals for your music productions. The versatility of the model allows for a wide range of creative possibilities.

Updated 12/13/2024

Audio-to-Audio

374

flux-pulid

zsxkib

flux-pulid is a powerful AI model developed by zsxkib that builds upon the FLUX-dev framework. It combines the capabilities of Pure and Lightning ID Customization with Contrastive Alignment to enable highly customizable and high-quality image generation. This model is closely related to PuLID, which uses a similar approach, as well as other FLUX-based models like SDXL-Lightning and FLUX-dev Inpainting. Model inputs and outputs The flux-pulid model takes a variety of inputs to guide the image generation process, including a text prompt, seed, image dimensions, and various parameters to control the style and quality of the output. The model can generate high-resolution images in a range of formats, such as PNG and JPEG. Inputs Prompt**: The text prompt that describes the desired image Seed**: A random seed value to ensure consistent generation Width/Height**: The desired dimensions of the output image True CFG Scale**: The weight of the text prompt in the generation process ID Weight**: The influence of an input face image on the generated image Num Steps**: The number of denoising steps to perform Start Step**: The timestep to start inserting the ID image Guidance Scale**: The strength of the text prompt guidance Main Face Image**: An input image to use for face generation Negative Prompt**: Additional prompts to guide what to avoid in the image Outputs Image**: The generated image in the specified format and quality Capabilities flux-pulid is capable of generating highly detailed and customizable images based on text prompts. It can seamlessly incorporate facial features from an input image, allowing for the creation of personalized portraits and characters. The model's use of Contrastive Alignment helps to ensure that the generated images closely match the desired style and content, while the FLUX-dev framework enables fast and efficient generation. What can I use it for? flux-pulid can be particularly useful for creating unique and expressive portraits, characters, and illustrations. The ability to customize the generated images with a specific face or style makes it a powerful tool for artists, designers, and creative professionals. The model's fast generation speed and high-quality outputs also make it suitable for applications like game development, concept art, and visual storytelling. Things to try One interesting aspect of flux-pulid is its ability to generate images with a strong sense of personality and individuality. By experimenting with different facial features, expressions, and styles, users can create a wide range of unique and compelling characters. Additionally, the model's flexibility in handling text prompts, combined with its capacity for fine-tuning, allows for the exploration of diverse visual narratives and creative concepts.

Updated 12/13/2024

Text-to-Image

182

flux-dev-inpainting

zsxkib

flux-dev-inpainting is an AI model developed by zsxkib that can fill in masked parts of images. This model is similar to other inpainting models like stable-diffusion-inpainting, sdxl-inpainting, and inpainting-xl, which use Stable Diffusion or other diffusion models to generate content that fills in missing regions of an image. Model inputs and outputs The flux-dev-inpainting model takes several inputs to control the inpainting process: Inputs Mask**: The mask image that defines the region to be inpainted Image**: The input image to be inpainted Prompt**: The text prompt that guides the inpainting process Strength**: The strength of the inpainting, ranging from 0 to 1 Seed**: The random seed to use for the inpainting process Output Format**: The format of the output image (e.g. WEBP) Output Quality**: The quality of the output image, from 0 to 100 Outputs Output**: The inpainted image Capabilities The flux-dev-inpainting model can generate realistic and visually coherent content to fill in masked regions of an image. It can handle a wide range of image types and prompts, and produces high-quality output. The model is particularly adept at preserving the overall style and composition of the original image while seamlessly integrating the inpainted content. What can I use it for? You can use flux-dev-inpainting for a variety of image editing and manipulation tasks, such as: Removing unwanted objects or elements from an image Filling in missing or damaged parts of an image Creating new image content by inpainting custom prompts Experimenting with different inpainting techniques and styles The model's capabilities make it a powerful tool for creative projects, photo editing, and visual content production. You can also explore using flux-dev-inpainting in combination with other FLUX-based models for more advanced image-to-image workflows. Things to try Try experimenting with different input prompts and masks to see how the model handles various inpainting challenges. You can also play with the strength and seed parameters to generate diverse output and explore the model's creative potential. Additionally, consider combining flux-dev-inpainting with other image processing techniques, such as segmentation or style transfer, to create unique visual effects and compositions.

Updated 12/13/2024

Image-to-Image

169

clip-age-predictor

zsxkib

The clip-age-predictor model is a tool that uses the CLIP (Contrastive Language-Image Pretraining) algorithm to predict the age of a person in an input image. This model is a patched version of the original clip-age-predictor model by andreasjansson that works with the new version of Cog. Similar models include clip-features, which returns CLIP features for the clip-vit-large-patch14 model, and stable-diffusion, a latent text-to-image diffusion model. Model inputs and outputs The clip-age-predictor model takes a single input - an image of a person whose age we want to predict. The model then outputs a string representing the predicted age of the person in the image. Inputs Image**: The input image of the person whose age we'd like to predict Outputs Predicted Age**: A string representing the predicted age of the person in the input image Capabilities The clip-age-predictor model uses the CLIP algorithm to analyze the input image and compare it to prompts of the form "this person is {age} years old". The model then outputs the age that has the highest similarity to the input image. What can I use it for? The clip-age-predictor model could be useful for applications that require estimating the age of people in images, such as demographic analysis, age-restricted content filtering, or even as a feature in photo editing software. For example, a marketing team could use this model to analyze the age distribution of their customer base from product photos. Things to try One interesting thing to try with the clip-age-predictor model is to experiment with different types of input images, such as portraits, group photos, or even images of people in different poses or environments. You could also try combining this model with other AI tools, like the gfpgan model for face restoration, to see if it can improve the accuracy of the age predictions.

Updated 12/13/2024

Image-to-Text

149

ic-light

zsxkib

ic-light is an AI model developed by zsxkib that can automatically relight images. It can manipulate the illumination of images, including adjusting the lighting conditions, adding shadows, and creating different moods and atmospheres. The model is capable of producing highly consistent relight results, even to the point of being able to estimate normal maps from the relighting. This consistency is achieved through a novel technique called "Imposing Consistent Light" which ensures that the blending of different light sources is mathematically equivalent to the appearance with mixed light sources. The ic-light model is similar to other image editing and enhancement models like GFPGAN, which focuses on face restoration, and LedNet, which handles joint low-light enhancement and deblurring. However, ic-light is specifically designed for relighting images, allowing users to adjust the lighting conditions in creative ways. Model inputs and outputs Inputs Prompt**: A text description guiding the relighting and generation process Subject Image**: The main foreground image to be relighted Lighting Preference**: The type and position of lighting to apply to the initial background latent Various hyperparameters**: Including number of steps, image size, denoising strength, etc. Outputs Relighted Images**: The generated images with the desired lighting conditions applied Capabilities The ic-light model can automatically relight images based on textual prompts and lighting preferences. It can add shadows, adjust the mood and atmosphere, and create cinematic lighting effects. The model's ability to maintain consistent lighting across different relighting conditions is a key strength, allowing users to experiment and iterate on the lighting without losing coherence. What can I use it for? ic-light can be used for a variety of image editing and enhancement tasks, such as: Enhancing portrait photography by adjusting the lighting to create a more flattering or artistic look Generating stylized images with specific lighting conditions, such as warm, moody bedroom scenes or bright, sunny outdoor settings Adjusting the lighting in product or architectural photography to better showcase the subject Experimenting with different lighting setups for CGI or 3D rendering projects The model's consistent relighting capabilities also make it useful for tasks like normal map estimation, which can be leveraged in 3D modeling and game development workflows. Things to try One interesting aspect of ic-light is its ability to generate normal maps from the relighting results, despite not being trained on any normal map data. This suggests the model has learned to maintain a consistent 3D lighting representation, which could be useful for a variety of applications beyond just image editing. Another interesting feature is the background-conditioned model, which allows for simple prompting without the need for careful text guidance. This could be useful for users who want to quickly generate relighted images without the overhead of fine-tuning the prompts. Overall, ic-light is a powerful tool for creative image manipulation and lighting experimentation, with potential applications in photography, digital art, and 3D modeling.

Updated 12/13/2024

Image-to-Image

animate-diff

zsxkib

animate-diff is a plug-and-play module developed by Yuwei Guo, Ceyuan Yang, and others that can turn most community text-to-image diffusion models into animation generators, without the need for additional training. It was presented as a spotlight paper at ICLR 2024. The model builds on previous work like Tune-a-Video and provides several versions that are compatible with Stable Diffusion V1.5 and Stable Diffusion XL. It can be used to animate personalized text-to-image models from the community, such as RealisticVision V5.1 and ToonYou Beta6. Model inputs and outputs animate-diff takes in a text prompt, a base text-to-image model, and various optional parameters to control the animation, such as the number of frames, resolution, camera motions, etc. It outputs an animated video that brings the prompt to life. Inputs Prompt**: The text description of the desired scene or object to animate Base model**: A pre-trained text-to-image diffusion model, such as Stable Diffusion V1.5 or Stable Diffusion XL, potentially with a personalized LoRA model Animation parameters**: Number of frames Resolution Guidance scale Camera movements (pan, zoom, tilt, roll) Outputs Animated video in MP4 or GIF format, with the desired scene or object moving and evolving over time Capabilities animate-diff can take any text-to-image model and turn it into an animation generator, without the need for additional training. This allows users to animate their own personalized models, like those trained with DreamBooth, and explore a wide range of creative possibilities. The model supports various camera movements, such as panning, zooming, tilting, and rolling, which can be controlled through MotionLoRA modules. This gives users fine-grained control over the animation and allows for more dynamic and engaging outputs. What can I use it for? animate-diff can be used for a variety of creative applications, such as: Animating personalized text-to-image models to bring your ideas to life Experimenting with different camera movements and visual styles Generating animated content for social media, videos, or illustrations Exploring the combination of text-to-image and text-to-video capabilities The model's flexibility and ease of use make it a powerful tool for artists, designers, and content creators who want to add dynamic animation to their work. Things to try One interesting aspect of animate-diff is its ability to animate personalized text-to-image models without additional training. Try experimenting with your own DreamBooth models or models from the community, and see how the animation process can enhance and transform your creations. Additionally, explore the different camera movement controls, such as panning, zooming, and rolling, to create more dynamic and cinematic animations. Combine these camera motions with different text prompts and base models to discover unique visual styles and storytelling possibilities.

Updated 12/13/2024

Text-to-Video