Asiryan

Models by this creator

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reliberate-v3

asiryan

Total Score

323

reliberate-v3 is the third iteration of the Reliberate model, developed by asiryan. It is a versatile AI model that can perform text-to-image generation, image-to-image translation, and inpainting tasks. The model builds upon the capabilities of similar models like deliberate-v6, proteus-v0.2, blue-pencil-xl-v2, and absolutereality-v1.8.1, all of which were also created by asiryan. Model inputs and outputs reliberate-v3 takes a variety of inputs, including a text prompt, an optional input image, and various parameters to control the output. The model can generate multiple images in a single output, and the output images are returned as a list of URIs. Inputs Prompt**: The text prompt describing the desired output image. Image**: An optional input image for image-to-image or inpainting tasks. Mask**: A mask image for the inpainting task, specifying the region to be filled. Width and Height**: The desired dimensions of the output image. Seed**: An optional seed value for reproducible results. Strength**: The strength of the image-to-image or inpainting operation. Scheduler**: The scheduling algorithm to use during the inference process. Num Outputs**: The number of images to generate. Guidance Scale**: The scale of the guidance signal during the inference process. Negative Prompt**: An optional prompt to guide the model away from certain undesirable outputs. Num Inference Steps**: The number of inference steps to perform. Outputs A list of URIs pointing to the generated images. Capabilities reliberate-v3 is a powerful AI model that can generate high-quality images from text prompts, transform existing images using image-to-image tasks, and fill in missing regions of an image through inpainting. The model is particularly adept at producing detailed, photorealistic images with a high degree of fidelity. What can I use it for? The versatility of reliberate-v3 makes it suitable for a wide range of applications, such as visual content creation, product visualization, image editing, and more. For example, you could use the model to generate concept art for a video game, create product images for an e-commerce website, or restore and enhance old photographs. The model's ability to generate multiple outputs with a single input also makes it a useful tool for creative experimentation and ideation. Things to try One interesting aspect of reliberate-v3 is its ability to blend different visual styles and concepts in a single image. Try using prompts that combine elements from various genres, such as "a cyberpunk landscape with a whimsical fantasy creature" or "a surrealist portrait of a famous historical figure." Experiment with the various input parameters, such as guidance scale and number of inference steps, to see how they affect the output. You can also try using the image-to-image and inpainting capabilities to transform existing images in unexpected ways.

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Updated 5/21/2024

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blue-pencil-xl-v2

asiryan

Total Score

218

The blue-pencil-xl-v2 model is a text-to-image, image-to-image, and inpainting model created by asiryan. It is similar to other models such as deliberate-v6, reliberate-v3, and proteus-v0.2 in its capabilities. Model inputs and outputs The blue-pencil-xl-v2 model accepts a variety of inputs, including text prompts, input images, and masks for inpainting. It can generate high-quality images based on these inputs, with customizable parameters such as output size, number of images, and more. Inputs Prompt**: The text prompt that describes the desired image. Image**: An input image for image-to-image or inpainting mode. Mask**: A mask for the inpainting mode, where white areas will be inpainted. Seed**: A random seed to control the image generation. Strength**: The strength of the prompt when using image-to-image or inpainting. Scheduler**: The scheduler to use for the image generation. LoRA Scale**: The scale for any LoRA weights used in the model. Num Outputs**: The number of images to generate. LoRA Weights**: Optional LoRA weights to use. Guidance Scale**: The scale for classifier-free guidance. Negative Prompt**: A prompt to guide the model away from certain undesirable elements. Num Inference Steps**: The number of denoising steps to use in the image generation. Outputs One or more images generated based on the provided inputs. Capabilities The blue-pencil-xl-v2 model can generate a wide variety of images, from realistic scenes to fantastical, imaginative creations. It excels at tasks like character design, landscape generation, and abstract art. The model can also be used for image-to-image tasks, such as editing or inpainting existing images. What can I use it for? The blue-pencil-xl-v2 model can be used for various creative and artistic projects. For example, you could use it to generate concept art for a video game or illustration, create promotional images for a business, or explore new artistic styles and ideas. The model's inpainting capabilities also make it useful for tasks like object removal or image repair. Things to try One interesting thing to try with the blue-pencil-xl-v2 model is experimenting with the different input parameters, such as the prompt, strength, and guidance scale. Adjusting these settings can result in vastly different output images, allowing you to explore the model's creative potential. You could also try combining the model with other tools or techniques, such as using the generated images as a starting point for further editing or incorporating them into a larger creative project.

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Updated 5/21/2024

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kandinsky-3.0

asiryan

Total Score

100

Kandinsky 3.0 is a powerful text-to-image (T2I) and image-to-image (I2I) AI model developed by asiryan. It builds upon the capabilities of earlier Kandinsky models, such as Kandinsky 2 and Kandinsky 2.2, while introducing new features and improvements. Model Inputs and Outputs The Kandinsky 3.0 model accepts a variety of inputs, including a text prompt, an optional input image, and various parameters to control the output. The model can generate high-quality images based on the provided prompt, or it can perform image-to-image transformations using the input image and a new prompt. Inputs Prompt**: A text description of the desired image. Image**: An optional input image for the image-to-image mode. Width/Height**: The desired size of the output image. Seed**: A random seed value to control the image generation. Strength**: The strength or weight of the text prompt in the image-to-image mode. Negative Prompt**: A text description of elements to be avoided in the output image. Num Inference Steps**: The number of denoising steps used in the image generation process. Outputs Output Image**: The generated image based on the provided inputs. Capabilities The Kandinsky 3.0 model can create highly detailed and imaginative images from text prompts, ranging from fantastical landscapes to surreal scenes and photorealistic depictions. It also excels at image-to-image transformations, allowing users to seamlessly modify existing images based on new prompts. What Can I Use It For? The Kandinsky 3.0 model can be a valuable tool for a wide range of applications, such as art generation, concept design, product visualization, and even creative storytelling. Its capabilities could be leveraged by artists, designers, marketers, and anyone looking to bring their ideas to life through stunning visuals. Things to Try Experiment with various prompts, including specific details, emotions, and artistic styles, to see the range of images the Kandinsky 3.0 model can produce. Additionally, try using the image-to-image mode to transform existing images in unexpected and creative ways, opening up new possibilities for visual exploration and content creation.

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Updated 5/21/2024

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juggernaut-xl-v7

asiryan

Total Score

84

juggernaut-xl-v7 is a powerful AI model developed by asiryan that can handle a variety of image-related tasks, including text-to-image generation, image-to-image translation, and inpainting. It builds upon similar models like juggernaut-aftermath, counterfeit-xl-v2, and juggernaut-xl-v9 developed by the same team. Model inputs and outputs The juggernaut-xl-v7 model accepts a variety of inputs, including text prompts, input images, and masks for inpainting. It can generate high-quality images with a resolution of up to 1024x1024 pixels. The model supports features like seed control, guidance scale, and the ability to use LoRA (Low-Rank Adaptation) weights for fine-tuning. Inputs Prompt**: The text prompt that describes the desired output image. Image**: An input image for image-to-image translation or inpainting tasks. Mask**: A mask that defines the areas of the input image to be inpainted. Seed**: A random seed value to control the stochastic generation process. Scheduler**: The type of scheduler to use for the diffusion process. LoRA Scale**: The scaling factor for LoRA weights, if applicable. LoRA Weights**: The LoRA weights to use for fine-tuning, if any. Guidance Scale**: The scale for classifier-free guidance during the diffusion process. Negative Prompt**: A text prompt that describes undesirable features to avoid in the output image. Num Inference Steps**: The number of denoising steps to perform during the diffusion process. Outputs Generated Images**: One or more high-quality images generated based on the provided inputs. Capabilities The juggernaut-xl-v7 model excels at generating detailed, photorealistic images based on text prompts. It can also perform image-to-image translation, allowing users to modify existing images by applying various effects or transformations. The inpainting capabilities of the model make it useful for tasks like removing unwanted elements from images or restoring damaged areas. What can I use it for? The juggernaut-xl-v7 model can be used for a wide range of applications, such as creating concept art, illustrations, and visualizations for various industries. Its text-to-image generation capabilities make it useful for tasks like product visualization, interior design, and creative content creation. The image-to-image and inpainting features can be leveraged for photo editing, restoration, and enhancement tasks. Things to try With the juggernaut-xl-v7 model, you can experiment with different text prompts to generate unique and imaginative images. You can also try using the image-to-image translation feature to transform existing images in various ways, or use the inpainting capabilities to remove or restore specific elements within an image. Additionally, you can explore the use of LoRA weights and other advanced features to fine-tune the model for your specific needs.

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Updated 5/21/2024

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realistic-vision-v6.0-b1

asiryan

Total Score

43

realistic-vision-v6.0-b1 is a text-to-image, image-to-image, and inpainting AI model developed by asiryan. It is part of a series of similar models like deliberate-v6, absolutereality-v1.8.1, reliberate-v3, blue-pencil-xl-v2, and proteus-v0.2 that aim to generate high-quality, realistic images from textual prompts or existing images. Model inputs and outputs The realistic-vision-v6.0-b1 model accepts a variety of inputs, including text prompts, input images, masks, and various parameters to control the output. The model can then generate new images that match the provided prompt or inpaint/edit the input image. Inputs Prompt**: The textual prompt describing the desired image. Image**: An input image for image-to-image or inpainting tasks. Mask**: A mask image for the inpainting task, which specifies the region to be filled. Width/Height**: The desired width and height of the output image. Strength**: The strength or weight of the input image for image-to-image tasks. Scheduler**: The scheduling algorithm to use for the image generation. Guidance Scale**: The scale for the guidance of the image generation. Negative Prompt**: A prompt describing undesired elements to avoid in the output image. Seed**: A random seed value for reproducibility. Use Karras Sigmas**: A boolean flag to use the Karras sigmas during the image generation. Num Inference Steps**: The number of inference steps to perform during the image generation. Outputs Output Image**: The generated image that matches the provided prompt or edits the input image. Capabilities The realistic-vision-v6.0-b1 model can generate high-quality, photorealistic images from text prompts, edit existing images through inpainting, and perform image-to-image tasks. It is capable of handling a wide range of subjects and styles, from natural landscapes to abstract art. What can I use it for? The realistic-vision-v6.0-b1 model can be used for a variety of applications, such as creating custom artwork, generating product images, designing book covers, or enhancing existing images. It could be particularly useful for creative professionals, marketing teams, or hobbyists who want to quickly generate high-quality visuals without the need for extensive artistic skills. Things to try Some interesting things to try with the realistic-vision-v6.0-b1 model include generating images with detailed, imaginative prompts, experimenting with different scheduling algorithms and guidance scales, and using the inpainting capabilities to remove or replace elements in existing images. The model's versatility makes it a powerful tool for exploring the boundaries of AI-generated art.

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Updated 5/21/2024

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anything-v4.5

asiryan

Total Score

41

The anything-v4.5 model, created by asiryan, is a versatile AI model capable of text-to-image, image-to-image, and inpainting tasks. It builds upon previous models like deliberate-v4, deliberate-v5, and realistic-vision-v6.0-b1, offering enhanced capabilities and performance. Model inputs and outputs The anything-v4.5 model accepts a variety of inputs, including text prompts, images, and masks (for inpainting). The model can generate high-quality images based on the provided inputs, with options to control parameters like width, height, guidance scale, and number of inference steps. Inputs Prompt**: The text prompt that describes the desired output image. Negative Prompt**: A text prompt that specifies elements to be excluded from the generated image. Image**: An input image for image-to-image or inpainting tasks. Mask**: A mask image used for inpainting, specifying the areas to be filled. Seed**: A numerical seed value to control the randomness of the generated image. Scheduler**: The algorithm used for image generation. Strength**: The strength or weight of the image-to-image transformation. Guidance Scale**: The scale of the guidance used during image generation. Num Inference Steps**: The number of steps used in the image generation process. Outputs Output Image**: The generated image based on the provided inputs. Capabilities The anything-v4.5 model can produce high-quality, photorealistic images from text prompts, as well as perform image-to-image transformations and inpainting tasks. The model's versatility allows it to be used for a wide range of applications, from art generation to product visualization and more. What can I use it for? The anything-v4.5 model can be used for a variety of creative and commercial applications. For example, you could use it to generate concept art, product visualizations, or even personalized illustrations. The model's image-to-image and inpainting capabilities also make it useful for tasks like photo editing, scene manipulation, and image restoration. Things to try Experiment with the model's capabilities by trying different text prompts, image-to-image transformations, and inpainting tasks. You can also explore the model's various input parameters, such as guidance scale and number of inference steps, to see how they affect the generated output.

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Updated 5/21/2024

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dark-sushi-mix-225d

asiryan

Total Score

40

The dark-sushi-mix-225d model is a Stable Diffusion-based AI model created by asiryan. It is a 2.25D variation of the Dark Sushi Mix model, with a focus on text-to-image and image-to-image generation capabilities. This model shares similarities with other models in asiryan's portfolio, such as Meina Mix V11, Deliberate V6, and Counterfeit XL v2, all of which offer text-to-image, image-to-image, and inpainting capabilities. Model inputs and outputs The dark-sushi-mix-225d model accepts a variety of inputs, including text prompts, source images, and parameters such as seed, width, height, and guidance scale. The model can generate multiple output images based on the provided inputs. Inputs Prompt**: The text prompt that describes the desired image. Image**: An optional input image for image-to-image and inpainting modes. Width/Height**: The desired dimensions of the output image. Seed**: An optional seed value to control the randomness of the generated image. Strength**: The strength/weight of the input image for image-to-image and inpainting modes. Scheduler**: The scheduling algorithm used for the diffusion process. Num Outputs**: The number of images to generate. Guidance Scale**: The guidance scale, which controls the balance between the prompt and the noise. Negative Prompt**: An optional prompt that describes what should not be present in the output image. Num Inference Steps**: The number of diffusion steps to perform. Outputs Image(s)**: One or more generated images that match the provided prompt and input. Capabilities The dark-sushi-mix-225d model is capable of generating high-quality images from text prompts, as well as performing image-to-image and inpainting tasks. The model's 2.25D nature allows it to produce visually striking and detailed images, with a focus on realistic elements and coherent compositions. What can I use it for? The dark-sushi-mix-225d model can be used for a variety of creative and commercial applications, such as: Generating concept art and illustrations for various industries, including gaming, film, and advertising. Creating unique and personalized images for social media, marketing, and content creation. Exploring visual ideas and experimenting with different styles and compositions. Enhancing or modifying existing images through image-to-image and inpainting capabilities. Things to try With the dark-sushi-mix-225d model, you can experiment with a wide range of prompts and input images to see the diverse range of outputs it can produce. Try combining the model's capabilities with your own creative vision to generate unexpected and visually compelling results.

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Updated 5/21/2024

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absolutereality-v1.8.1

asiryan

Total Score

33

The absolutereality-v1.8.1 model is a text-to-image, image-to-image, and inpainting AI model developed by asiryan. This model is part of a suite of similar models created by asiryan, including reliberate-v3, realistic-vision-v6.0-b1, realistic-vision-v4, dreamshaper_v8, and proteus-v0.2. These models share similar capabilities in generating high-quality, photorealistic images from text prompts, editing existing images, and performing inpainting tasks. Model Inputs and Outputs The absolutereality-v1.8.1 model accepts a variety of inputs, including a text prompt, an optional input image, a mask for the inpainting mode, and various settings such as image size, seed, guidance scale, and number of inference steps. The model outputs one or more images based on the provided input. Inputs Prompt**: The text prompt that describes the desired image Image**: An optional input image for the img2img and inpainting modes Mask**: A mask image for the inpainting mode Width/Height**: The desired dimensions of the output image Seed**: An optional seed value for reproducible results Strength**: The strength/weight of the image-to-image transformation Num Outputs**: The number of images to generate Guidance Scale**: The guidance scale for the text-to-image generation Negative Prompt**: An optional prompt to exclude certain elements from the generated image Num Inference Steps**: The number of steps for the image generation process Outputs One or more images generated based on the provided inputs Capabilities The absolutereality-v1.8.1 model is capable of generating high-quality, photorealistic images from text prompts, editing existing images, and performing inpainting tasks. The model can handle a wide range of subjects and styles, from realistic scenes to fantastical and surreal compositions. What Can I Use It For? The absolutereality-v1.8.1 model can be used for a variety of creative and practical applications, such as: Generating concept art, character designs, and illustrations for books, games, or films Editing and enhancing existing images by combining them with new elements or correcting imperfections Inpainting images to remove unwanted objects or fill in missing areas Experimenting with different artistic styles and compositions The model's versatility and high-quality outputs make it a valuable tool for creative professionals, artists, and hobbyists alike. Things to Try With the absolutereality-v1.8.1 model, you can explore a wide range of creative possibilities. Try providing detailed, specific prompts to see how the model interprets and renders your ideas. Experiment with different image-to-image and inpainting techniques to transform your existing images in unique ways. Additionally, you can try varying the model's settings, such as the guidance scale and number of inference steps, to fine-tune the output and achieve your desired aesthetic.

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Updated 5/21/2024

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realistic-vision-v4

asiryan

Total Score

28

realistic-vision-v4 is a powerful text-to-image, image-to-image, and inpainting model created by the Replicate user asiryan. It is part of a family of similar models from the same maintainer, including realistic-vision-v6.0-b1, deliberate-v4, deliberate-v5, absolutereality-v1.8.1, and anything-v4.5. These models showcase asiryan's expertise in generating highly realistic and detailed images from text prompts, as well as performing advanced image manipulation tasks. Model inputs and outputs realistic-vision-v4 takes a text prompt as the main input, along with optional parameters like image, mask, and seed. It then generates a high-quality image based on the provided prompt and other inputs. The output is a URI pointing to the generated image. Inputs Prompt**: The text prompt that describes the desired image. Image**: An optional input image for image-to-image and inpainting tasks. Mask**: An optional mask image for inpainting tasks. Seed**: An optional seed value to control the randomness of the image generation. Width/Height**: The desired dimensions of the generated image. Strength**: The strength of the image-to-image or inpainting operation. Scheduler**: The type of scheduler to use for the image generation. Guidance Scale**: The guidance scale for the image generation. Negative Prompt**: An optional prompt that describes aspects to be excluded from the generated image. Use Karras Sigmas**: A boolean flag to control the use of Karras sigmas in the image generation. Num Inference Steps**: The number of inference steps to perform during image generation. Outputs Output**: A URI pointing to the generated image. Capabilities realistic-vision-v4 is capable of generating highly realistic and detailed images from text prompts, as well as performing advanced image manipulation tasks like image-to-image translation and inpainting. The model is particularly adept at producing natural-looking portraits, landscapes, and scenes with a high level of realism and visual fidelity. What can I use it for? The capabilities of realistic-vision-v4 make it a versatile tool for a wide range of applications. Content creators, designers, and artists can use it to quickly generate unique and custom visual assets for their projects. Businesses can leverage the model to create product visuals, advertisements, and marketing materials. Researchers and developers can experiment with the model's image generation and manipulation capabilities to explore new use cases and applications. Things to try One interesting aspect of realistic-vision-v4 is its ability to generate images with a strong sense of realism and attention to detail. Users can experiment with prompts that focus on specific visual elements, such as textures, lighting, or composition, to see how the model handles these nuances. Another intriguing area to explore is the model's inpainting capabilities, where users can provide a partially masked image and prompt the model to fill in the missing areas.

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Updated 5/21/2024

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deliberate-v5

asiryan

Total Score

10

The deliberate-v5 model is a text-to-image, image-to-image, and inpainting model created by asiryan. It is part of a series of Deliberate models, with the Deliberate V4, Deliberate V6, and Reliberate V3 as similar models. Model inputs and outputs The deliberate-v5 model accepts a variety of inputs, including text prompts, input images, masks, and parameters such as width, height, and guidance scale. The model can generate high-quality images based on the provided inputs. Inputs Prompt**: The text prompt that describes the desired image. Image**: An input image for image-to-image and inpainting modes. Mask**: A mask image for inpainting mode. Width**: The desired width of the output image. Height**: The desired height of the output image. Strength**: The strength/weight of the input image for image-to-image mode. Scheduler**: The scheduling algorithm to use for the diffusion process. Guidance Scale**: The guidance scale, which controls the influence of the text prompt on the generated image. Negative Prompt**: A text prompt that describes unwanted elements in the generated image. Use Karras Sigmas**: A boolean flag to use Karras sigmas or not. Num Inference Steps**: The number of inference steps to perform. Outputs The generated image, returned as a URI. Capabilities The deliberate-v5 model can generate high-quality, photorealistic images based on text prompts, as well as perform image-to-image and inpainting tasks. It can create a wide variety of images, from landscapes and scenes to portraits and abstract art. What can I use it for? The deliberate-v5 model can be used for various creative and practical applications, such as: Generating concept art or illustrations for digital media, games, or films. Designing product visualizations or mockups. Creating personalized images for social media, marketing, or other digital content. Inpainting and restoring damaged or incomplete images. Things to try Some ideas to explore with the deliberate-v5 model include: Experimenting with different prompts and parameters to see the range of images it can produce. Combining the model's text-to-image and image-to-image capabilities to create unique hybrid images. Exploring the model's inpainting capabilities by providing partial images and prompts to see how it can fill in missing or damaged areas. Comparing the results of deliberate-v5 with other similar models, such as Realistic Vision V6.0 B1 or Meina Mix V11, to understand their unique strengths and capabilities.

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Updated 5/21/2024