Kblueleaf
Models by this creator
🏅
Kohaku-XL-Delta
68
Kohaku-XL-Delta is the fourth major iteration of the Kohaku XL series of AI models developed by KBlueLeaf. This open-source, diffusion-based image-to-image model has been trained on a dataset of 3.6 million images and fine-tuned using LyCORIS, allowing it to generate high-quality anime-style artwork. Compared to similar models like loliDiffusion and Animagine XL, Kohaku-XL-Delta focuses on achieving a high fidelity in replicating specific artists' styles while also encouraging users to blend multiple artist tags to explore new styles. Model inputs and outputs Kohaku-XL-Delta is an image-to-image AI model, taking text prompts as inputs and generating corresponding anime-style artwork as outputs. The model has been trained to understand and interpret a wide range of Danbooru-style tags related to characters, series, artists, and various qualitative and contextual attributes. Inputs Text prompts**: Structured tags following the format: , , , , , Outputs Anime-style images**: High-quality, visually appealing artworks in the anime aesthetic, ranging from portraits to full-body scenes. Capabilities Kohaku-XL-Delta has demonstrated the ability to generate anime-inspired artwork with a high level of fidelity, accurately capturing the styles of various artists when provided with the appropriate tags. The model is capable of producing a diverse range of characters, scenes, and compositions, making it a valuable tool for artists, designers, and anime enthusiasts. What can I use it for? The Kohaku-XL-Delta model can be utilized for a variety of applications, including: Anime-style artwork generation**: Creating illustrations, character designs, and scene compositions for personal, commercial, or fan-art projects. Concept art and visualization**: Generating visual ideas and references for storytelling, game development, or other creative endeavors. Educational and research purposes**: Studying the model's capabilities, limitations, and potential applications in the field of AI-generated art. Things to try When working with Kohaku-XL-Delta, users are encouraged to experiment with blending multiple artist tags to explore new and unique artistic styles, rather than simply attempting to replicate the work of specific artists. Additionally, incorporating a diverse range of character, series, and contextual tags can lead to unexpected and interesting results, allowing for the discovery of novel anime-inspired creations.
Updated 5/28/2024
🌀
DanTagGen-beta
51
The DanTagGen-beta is a text-to-image AI model created by KBlueLeaf. It is similar to other text-to-image models like sdxl-lightning-4step by ByteDance, which can generate high-quality images from text descriptions in a few steps. Model inputs and outputs The DanTagGen-beta model takes text descriptions as input and generates corresponding images as output. This allows users to create images based on their ideas and written prompts, without the need for manual image editing or creation. Inputs Text descriptions or prompts that provide details about the desired image Outputs Generated images that match the provided text input Capabilities The DanTagGen-beta model is capable of generating a wide variety of images from text descriptions, including realistic scenes, abstract art, and imaginative concepts. It can produce high-quality results that capture the essence of the prompt. What can I use it for? The DanTagGen-beta model can be used for a range of applications, such as: Rapid prototyping and visualization of ideas Generating unique artwork and illustrations Creating custom images for social media, marketing, and other digital content Assisting creative professionals with ideation and image creation Things to try Experimenting with different levels of detail and specificity in the text prompts can produce quite varied results with the DanTagGen-beta model. Users may also want to try combining the model's outputs with other image editing tools to further refine and enhance the generated images.
Updated 8/7/2024
💬
Kohaku-XL-Zeta
48
The Kohaku-XL-Zeta model, developed by KBlueLeaf, is the latest iteration in the Kohaku XL series of text-to-image models. It builds upon the capabilities of previous models like Kohaku-XL-Delta and Kohaku-XL-Epsilon, offering improved stability, fidelity, and versatility in generating high-quality anime-style artwork. Compared to the previous Kohaku XL models, Kohaku-XL-Zeta has a larger and more diverse training dataset, spanning over 8.46 million images from sources like Danbooru, Pixiv, and PVC figure images. This has enabled the model to better capture a wide range of artistic styles and character designs, as evidenced by its significantly improved CCIP metric scores. Model inputs and outputs Inputs Textual prompts describing the desired image, including elements like character names, series, artists, and various tags Outputs High-quality, detailed anime-style images generated based on the input prompt Capabilities Kohaku-XL-Zeta excels at producing visually striking anime-inspired artwork with a high degree of fidelity and style consistency. The model can generate images depicting a wide range of characters, scenes, and artistic elements, from realistic portraits to fantastical, imaginative compositions. One of the key improvements in Kohaku-XL-Zeta is its ability to handle longer and more detailed prompts without compromising stability. The model can now generate images based on prompts up to 300 tokens in length, allowing for more nuanced and expressive descriptions. What can I use it for? The Kohaku-XL-Zeta model is a versatile tool that can be leveraged for a variety of creative and artistic applications. Artists and designers working in the anime and manga genres can use the model to quickly generate high-quality reference images, explore new ideas, and bring their visions to life. The model's capabilities also lend themselves well to the creation of illustrations, character designs, and even conceptual art for animations, games, and other multimedia projects. Additionally, the model's open-source nature and detailed documentation make it accessible to a wide range of users, from hobbyists to professional creators. By tapping into the rich artistic styles and techniques captured by Kohaku-XL-Zeta, users can unlock new possibilities for their own creative endeavors. Things to try One interesting aspect of the Kohaku-XL-Zeta model is its ability to handle a diverse range of artistic styles and character types. Experiment with blending different artist tags and style prompts to see how the model can combine elements in unique and unexpected ways. For example, try pairing traditional Japanese art styles with modern character designs, or explore the intersection of realistic and fantastical elements. Another area worth exploring is the model's behavior when faced with longer, more detailed prompts. Craft intricate descriptions that incorporate character backstories, complex settings, and layered emotional narratives to see how the model responds. This can open up new avenues for storytelling and world-building through the medium of generated imagery.
Updated 10/4/2024
📉
Kohaku-XL-Epsilon
48
The Kohaku-XL-Epsilon is the fifth major iteration in the Kohaku XL series, developed by the maintainer KBlueLeaf. This model features a 5.2 million image dataset, LyCORIS fine-tuning, and is trained on consumer-level hardware. It is a significant improvement over the previous Kohaku-XL-Delta model, as the CCIP score on 3600 characters shows. The Kohaku-XL-Epsilon has mastered more artists' styles than the Delta version, while also increasing the stability when combining multiple artist tags. Users are encouraged to experiment with their own style prompts, as the model performs well with a variety of inputs. Model inputs and outputs Inputs ``: Specifies the number of characters in the image ``: The name of the character(s) ``: The series the character(s) is from ``: The artist(s) whose style should be emulated ``: Additional tags to describe the desired image ``: Tags to indicate the desired quality level ``: Tags to indicate the desired time period ``: Tags to indicate additional metadata ``: Tags to indicate the desired rating (safe, sensitive, nsfw, explicit) Outputs The model generates high-quality anime-style images based on the provided input prompts. The output images showcase a variety of styles and subjects, ranging from detailed character portraits to dynamic scenes. Capabilities The Kohaku-XL-Epsilon model has demonstrated impressive capabilities in generating diverse and visually striking anime-style images. By leveraging the LyCORIS fine-tuning technique and a large dataset, the model has developed a deep understanding of various artistic styles and can seamlessly blend them to create unique and compelling outputs. What can I use it for? The Kohaku-XL-Epsilon model can be a valuable tool for a wide range of applications, from personal art projects to commercial endeavors. Artists and hobbyists can use it to explore new creative directions, generate reference images, or quickly prototype ideas. Businesses in the anime, manga, or gaming industries may find the model useful for rapid content generation, asset creation, or character design. Things to try One of the key strengths of the Kohaku-XL-Epsilon model is its ability to blend multiple artist styles seamlessly. Users are encouraged to experiment with combining various artist tags, such as ask (askzy), torino aqua, and migolu, to see how the model can generate unique and visually captivating results. Additionally, exploring the use of different quality, rating, and year tags can help users fine-tune the output to their specific preferences and needs.
Updated 9/6/2024
🏅
Stable-Cascade-FP16-fixed
41
The Stable-Cascade-FP16-fixed model is a modified version of the Stable-Cascade model that is compatible with FP16 inference. This model was created by KBlueLeaf to address issues with the original Stable-Cascade model generating NaNs during FP16 inference. The key modification was to scale down the weights and biases within the network to keep the final output the same while making the internal activation values smaller, preventing the NaNs. The Stable-Cascade model is a diffusion-based generative model that works at a much smaller latent space compared to Stable Diffusion, allowing for faster inference and cheaper training. It consists of three sub-models - Stage A, Stage B, and Stage C - that work together to generate images from text prompts. This Stable-Cascade-FP16-fixed variant maintains the same core architecture and capabilities, but with the FP16 compatibility fix. Model inputs and outputs Inputs Text prompt**: A text description of the desired image to generate. Outputs Generated image**: An image that matches the provided text prompt, generated through the Stable-Cascade diffusion process. Capabilities The Stable-Cascade-FP16-fixed model is capable of generating high-quality images from text prompts, with a focus on efficiency and speed compared to larger models like Stable Diffusion. The FP16 compatibility allows the model to run efficiently on hardware with limited VRAM, such as lower-end GPUs or edge devices. However, the model may have some limitations in accurately rendering certain types of content, such as faces and detailed human figures, as indicated in the maintainer's description. The autoencoding process can also result in some loss of fidelity compared to the original input. What can I use it for? The Stable-Cascade-FP16-fixed model is well-suited for use cases where efficiency and speed are important, such as in creative tools, educational applications, or on-device inference. Its smaller latent space and FP16 compatibility make it a good choice for deployment on resource-constrained platforms. Researchers and developers may also find the model useful for exploring the trade-offs between model size, speed, and quality in the context of diffusion-based image generation. The maintainer's description notes that the model is intended for research purposes, and it may not be suitable for all production use cases. Things to try One interesting aspect of the Stable-Cascade-FP16-fixed model is the potential to explore different quantization techniques, such as the FP8 quantization mentioned in the maintainer's description. Experimenting with various quantization approaches could help further improve the efficiency and deployment options for the model. Additionally, the model's smaller latent space and faster inference could make it a good candidate for integration with other AI systems, such as using it as a component in larger computer vision pipelines or incorporating it into interactive creative tools.
Updated 9/6/2024