Haor
Models by this creator
🛸
Evt_V3
73
The Evt_V3 model is an AI image generation model developed by the maintainer haor. It is based on the previous Evt_V2 model, with 20 epochs of fine-tuning using a dataset of 35,467 images. The model is capable of generating high-quality, highly detailed anime-style images featuring characters with intricate features, expressions, and environments. Compared to the Evt_V2 model, Evt_V3 has been further refined and trained on a larger dataset, resulting in improved quality and consistency of the generated outputs. The model can produce images with a wide range of styles, from detailed character portraits to complex, cinematic scenes. Model inputs and outputs Inputs Text prompts describing the desired image, including details about the subject, style, and composition. Outputs High-quality, highly detailed images in the anime-style format, with a resolution of 512x512 pixels. The model can generate a variety of scenes, characters, and environments, ranging from portraits to complex, multi-element compositions. Capabilities The Evt_V3 model is capable of generating detailed, visually striking anime-style images. It can produce characters with intricate facial features, hairstyles, and expressions, as well as complex environments and scenes with elements like detailed skies, water, and lighting. The model's ability to generate such high-quality, cohesive images is a testament to the quality of its training data and fine-tuning process. What can I use it for? The Evt_V3 model can be a valuable tool for a variety of creative projects, such as: Concept art and illustrations for anime, manga, or other visual media Character design and development for games, animations, or other storytelling media Generating inspirational or reference images for artists and creatives Producing high-quality, visually striking images for use in marketing, advertising, or social media As a powerful AI-driven image generation tool, Evt_V3 can help streamline and enhance the creative process, allowing users to quickly explore and refine ideas without the constraints of traditional media. Things to try One interesting aspect of the Evt_V3 model is its ability to generate images with a strong sense of atmosphere and mood. By carefully crafting prompts that incorporate elements like "cinematic lighting," "dramatic angle," or "beautiful detailed water," users can create breathtaking, almost cinematic scenes that evoke a particular emotional response or narrative. Another area to explore is the model's handling of character expressions and poses. The examples provided demonstrate the model's skill in rendering nuanced facial expressions and body language, which can be a crucial element in crafting compelling and believable characters. Experimenting with prompts that focus on these details can yield compelling and impactful results. Overall, the Evt_V3 model offers a rich and versatile set of capabilities that can enable a wide range of creative projects and applications. By exploring the model's strengths and pushing the boundaries of what it can do, users can unlock new possibilities in the world of AI-driven art and design.
Updated 5/28/2024
🖼️
Evt_V4-preview
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The Evt_V4-preview model is an experimental text-to-image diffusion model created by maintainer haor that is focused on generating animation-style images. It is part of the EVT series, which aims to fine-tune large datasets to produce diverse artistic styles. Compared to previous EVT models, Evt_V4-preview uses an even larger dataset, resulting in images that have a cosine similarity of 85% with the ACertainty model. Similar models include Stable Diffusion v1-4, a general-purpose text-to-image diffusion model, and Epic Diffusion, a highly customized version of Stable Diffusion aimed at producing high-quality results in a wide range of styles. Model inputs and outputs Inputs Prompt**: A text description of the desired image, which can include specific details about the content, style, and artistic references. Outputs Image**: A generated image that corresponds to the provided text prompt. The model can produce images in a variety of artistic styles, including animation-influenced aesthetics. Capabilities The Evt_V4-preview model is capable of generating diverse, artistically-styled images from text prompts. The model excels at producing anime-inspired artwork, as evidenced by the provided samples that feature detailed characters, fantastical environments, and a vibrant color palette. What can I use it for? The Evt_V4-preview model is well-suited for artistic and creative applications, such as generating concept art, character designs, and illustrations. It could be used to quickly produce draft images for creative projects or as a tool for ideation and exploration. However, the model's capabilities are not limited to animation-style art, and it may be able to generate images in a range of other artistic genres as well. Things to try One interesting aspect of the Evt_V4-preview model is its potential to generate unique animation-inspired styles that differ from traditional anime or manga aesthetics. Experimenting with different prompts that blend various artistic influences, such as combining anime elements with western comic book styles or surreal, dreamlike compositions, could yield intriguing results. Additionally, trying the model with prompts that focus on less common subject matter, such as sci-fi or fantasy settings, might uncover new creative directions for the model's animation-influenced capabilities.
Updated 5/28/2024
⚙️
Evt_V2
55
The Evt_V2 model is a text-to-image AI model developed by the maintainer haor. It is an experimental model fine-tuned on a dataset of 15,000 images, mostly from the Pixiv daily ranking and some NSFW anime images. The model is based on the animefull-latest base model and exhibits an artistic anime-influenced style. The model is capable of generating highly detailed images with attributes like "beautiful detailed eyes", "long hair", and "dramatic angle". The examples provided show a range of anime-style characters and scenes, with a focus on portraits and upper body shots. Similar models like Evt_V4-preview and Ekmix-Diffusion also explore anime-influenced text-to-image generation. Model inputs and outputs Inputs Textual prompts**: The model takes in textual prompts that describe the desired image, using a combination of specific attributes like character descriptions, scene elements, and artistic styles. Outputs Generated images**: The model outputs high-quality, artistic anime-style images that match the provided textual prompts. Capabilities The Evt_V2 model excels at generating highly detailed, visually striking anime-inspired images. The examples demonstrate the model's ability to produce portraits with expressive eyes, flowing hair, and cinematic lighting and composition. By leveraging the artistic style of the training data, the model is able to imbue the generated images with a distinct anime aesthetic. What can I use it for? The Evt_V2 model could be useful for a variety of applications, such as: Concept art and illustration**: The model's ability to generate visually compelling anime-style images makes it a valuable tool for artists and concept designers working on projects with an anime or manga-inspired aesthetic. Character design**: The model's skill in rendering detailed character portraits could aid in the development of unique, expressive anime-style characters for various creative projects. Anime-themed content generation**: The artistic flair of the Evt_V2 model makes it well-suited for generating images to be used in anime-themed media, such as fan art, webcomics, or promotional materials. Things to try Experimenting with different prompt styles and modifiers can help you get the most out of the Evt_V2 model. Try using detailed character descriptions, incorporating specific artistic styles, or combining various scene elements to see how the model responds. Additionally, exploring the interplay between the model's anime-influenced style and more realistic or surreal elements could lead to unique and unexpected results.
Updated 5/28/2024