Ai-forever

kandinsky-2

6.1K

The kandinsky-2 model is a powerful text-to-image AI model developed by ai-forever. It is an improvement upon its predecessor, kandinsky-2.1, by incorporating a new and more capable image encoder, CLIP-ViT-G, as well as support for the ControlNet mechanism. These advancements enable the model to generate more aesthetically pleasing images and better understand text, leading to enhanced overall performance. The kandinsky-2 model stands out among similar text-to-image models like reliberate-v3, absolutereality-v1.8.1, and real-esrgan, as it offers a more comprehensive and versatile text-to-image generation experience. Model inputs and outputs The kandinsky-2 model takes a text prompt as input and generates corresponding high-quality images as output. The model's architecture includes a text encoder, a diffusion image prior, a CLIP image encoder, a latent diffusion U-Net, and a MoVQ encoder/decoder. Inputs Prompt**: A text prompt that describes the desired image. Seed**: An optional random seed to ensure reproducible results. Width/Height**: The desired dimensions of the output image. Scheduler**: The algorithm used to generate the images. Batch Size**: The number of images to generate at once. Prior Steps**: The number of steps used in the prior diffusion model. Output Format**: The format of the output images (e.g., WEBP). Guidance Scale**: The scale for classifier-free guidance, which controls the balance between the text prompt and the generated image. Output Quality**: The quality of the output images, ranging from 0 to 100. Prior Cf Scale**: The scale for the prior classifier-free guidance. Num Inference Steps**: The number of denoising steps used to generate the final image. Outputs Image(s)**: One or more high-quality images generated based on the input prompt. Capabilities The kandinsky-2 model excels at generating visually appealing, text-guided images across a wide range of subjects and styles. Its enhanced capabilities, including better text understanding and the addition of ControlNet support, allow for more accurate and customizable image generation. This model can be particularly useful for tasks such as product visualization, digital art creation, and image-based storytelling. What can I use it for? The kandinsky-2 model is a versatile tool that can be employed in various applications, such as: Creative content creation**: Generate unique and compelling images for art, illustrations, product design, and more. Visual marketing and advertising**: Create eye-catching visuals for promotional materials, social media, and advertising campaigns. Educational and informational content**: Produce visuals to support educational materials, tutorials, and explainer videos. Concept prototyping**: Quickly generate visual representations of ideas and concepts for further development. Things to try Experiment with the kandinsky-2 model's capabilities by trying different prompts, adjusting the input parameters, and leveraging the ControlNet support to fine-tune the generated images. Explore the model's ability to blend images and text, create imaginative scenes, and even perform inpainting tasks. The versatility of this model opens up a world of creative possibilities for users.

Updated 5/9/2024

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mGPT

227

The mGPT is a family of autoregressive GPT-like models with 1.3 billion parameters, trained on 61 languages from 25 language families using Wikipedia and Colossal Clean Crawled Corpus. The model was developed by ai-forever and the source code is available on Github. The model reproduces the GPT-3 architecture using GPT-2 sources and the sparse attention mechanism, leveraging the Deepspeed and Megatron frameworks to effectively parallelize the training and inference steps. The resulting models show performance on par with the recently released XGLM models, while covering more languages and enhancing NLP possibilities for low resource languages. Model inputs and outputs Inputs Sequence of text in any of the 61 supported languages Outputs Predicted next token in the input sequence Capabilities The mGPT model is capable of generating text in 61 languages across 25 language families, including low-resource languages. This makes it a powerful tool for multilingual and cross-lingual natural language processing tasks, such as machine translation, text generation, and language understanding. What can I use it for? The mGPT model can be used for a variety of natural language processing tasks, such as text generation, language translation, and language understanding. Researchers and practitioners can use this model as a foundation for building more advanced NLP applications, particularly for working with low-resource languages. For example, the model could be fine-tuned on domain-specific data to create specialized language models for applications in fields like healthcare, finance, or education. Things to try One interesting aspect of the mGPT model is its ability to handle a wide range of languages, including those with very different writing systems and linguistic structures. Researchers could explore the model's cross-lingual capabilities by evaluating its performance on tasks that require understanding and generating text across multiple languages, such as zero-shot or few-shot translation. Additionally, the model's multilingual nature could be leveraged to build language-agnostic NLP systems that can operate seamlessly across languages.

Updated 5/9/2024

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ruGPT-3.5-13B

223

The ruGPT-3.5-13B is a large language model developed by ai-forever that has been trained on a 300GB dataset of various domains, with an additional 100GB of code and legal documents. This 13 billion parameter model is the largest version in the ruGPT series and was used to train the GigaChat model. Similar models include the mGPT multilingual GPT model, the FRED-T5-1.7B Russian-focused T5 model, and the widely used GPT-2 English language model. Model Inputs and Outputs Inputs Raw Russian text prompts of varying length Outputs Continuation of the input text, generating new content in the Russian language Capabilities The ruGPT-3.5-13B model demonstrates strong text generation capabilities for the Russian language. It can be used to continue and expand on Russian text prompts, producing fluent and coherent continuations. The model has been trained on a diverse dataset, allowing it to generate text on a wide range of topics. What Can I Use It For? The ruGPT-3.5-13B model could be useful for a variety of Russian language applications, such as: Chatbots and conversational agents that can engage in open-ended dialogue in Russian Content generation for Russian websites, blogs, or social media Assistants that can help with Russian language tasks like summarization, translation, or question answering Things to Try One interesting thing to try with the ruGPT-3.5-13B model is to experiment with different generation strategies, such as adjusting the number of beams or sampling temperature. This can help produce more diverse or controlled outputs depending on the specific use case. Another idea is to fine-tune the model on a smaller, domain-specific dataset to adapt it for specialized tasks like generating legal or technical Russian text. The model's large size and broad training make it a strong starting point for further fine-tuning.

Updated 5/9/2024

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Kandinsky_2.1

181

Kandinsky 2.1 is a state-of-the-art text-to-image AI model developed by ai-forever. It builds upon the successes of models like DALL-E 2 and Latent Diffusion, while introducing new architectural innovations. Kandinsky 2.1 uses a CLIP model as its text and image encoder, along with a diffusion image prior to map between the latent spaces of the CLIP modalities. This approach enhances the visual performance of the model and enables new possibilities in text-guided image manipulation. The model architecture includes a text encoder (XLM-Roberta-Large-ViT-L-14), a Diffusion Image Prior, a CLIP image encoder (ViT-L/14), a Latent Diffusion U-Net, and a MoVQ encoder/decoder. This combination of components allows Kandinsky 2.1 to generate high-quality, visually striking images from text prompts. Similar models in the Kandinsky family include Kandinsky-2.2, a multilingual text-to-image latent diffusion model, and Kandinsky-3, a text-to-image diffusion model with enhancements to text understanding and visual quality. Model inputs and outputs Inputs Text prompt**: A textual description of the desired image, which the model uses to generate the corresponding visual output. Outputs Generated image**: The model's interpretation of the input text prompt, presented as a high-quality, visually compelling image. Capabilities Kandinsky 2.1 excels at generating diverse and detailed images from a wide range of text prompts, including scenes, objects, and abstract concepts. The model's ability to blend text and image information results in outputs that are both faithful to the input prompt and visually striking. For example, the model can generate photorealistic images of imaginary scenes, like "a subway train full of raccoons reading newspapers," or create surreal and dreamlike compositions, such as "a beautiful fairy-tale desert with a wave of sand merging into the Milky Way." What can I use it for? Kandinsky 2.1 can be a powerful tool for a variety of applications, such as creative content generation, visual design, and product visualization. Artists, designers, and marketing professionals can use the model to quickly generate unique and eye-catching visuals to support their work. Educators and researchers may also find the model useful for exploring the intersection of language and image understanding in AI systems. Things to try One interesting aspect of Kandinsky 2.1 is its ability to blend different artistic styles and techniques into the generated images. By incorporating prompts that reference specific artists, movements, or visual aesthetics, users can explore the model's capacity for creative and imaginative image generation. For example, trying prompts like "a landscape in the style of Vincent Van Gogh" or "a portrait in the style of Pablo Picasso" can result in unique and visually striking outputs.

Updated 5/9/2024

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Real-ESRGAN

Real-ESRGAN is an upgraded version of the ESRGAN model, designed to enhance real-world images by improving details and removing artifacts. It was trained on a custom dataset by the maintainer ai-forever, and shows better results on faces compared to the original ESRGAN model. This model is easier to integrate into projects than the original implementation. Similar models include real-esrgan by nightmareai, gfpgan by tencentarc, and realesrgan by lqhl. Model inputs and outputs Real-ESRGAN takes low-resolution real-world images as input and outputs high-resolution, enhanced versions of those images. The model is capable of 4x upscaling, and can remove common artifacts and improve details in the process. Inputs Low-resolution real-world images Outputs High-resolution, enhanced versions of the input images with improved details and removed artifacts Capabilities Real-ESRGAN is designed to enhance the quality of real-world images, particularly those with faces. It can remove common issues like blurriness, JPEG artifacts, and missing details, while preserving the overall integrity of the image. What can I use it for? You can use Real-ESRGAN to improve the visual quality of images for a variety of applications, such as social media, photography, and content creation. The model's ability to upscale and enhance images makes it a valuable tool for tasks like restoring old photos, improving the quality of AI-generated images, and enhancing the visuals in your projects. Things to try One interesting thing to try with Real-ESRGAN is using it in combination with face restoration models like gfpgan to achieve even better results on images with human faces. You could also experiment with different input resolutions and upscaling factors to find the optimal settings for your specific use case.

Updated 5/9/2024

Image-to-Image

kandinsky-2-1

kandinsky-2-1 is a text-to-image diffusion model developed by ai-forever. It builds on the capabilities of models like Stable Diffusion and earlier versions of the Kandinsky model, incorporating advancements in text-image alignment and latent diffusion. The model can generate photorealistic images from textual descriptions, with the ability to fine-tune the output based on input parameters. Model inputs and outputs kandinsky-2-1 takes a variety of inputs to control the generated image, including a text prompt, image seed, size, and strength of image transformation. The model outputs one or more images based on the provided inputs. Inputs Prompt**: A textual description of the desired image Seed**: A random seed value to initialize image generation Task**: The type of image generation task (e.g. text-to-image, image-to-image) Image**: An input image for tasks like text-guided image-to-image Width/Height**: The desired size of the generated image Strength**: The strength of the image transformation for text-guided image-to-image Num Outputs**: The number of images to generate Guidance Scale**: The scale for classifier-free guidance Negative Prompt**: A prompt describing undesirable elements to avoid in the output Outputs Image(s)**: One or more generated images in URI format Capabilities kandinsky-2-1 can generate a wide variety of photorealistic images from textual descriptions, including scenes, objects, and abstract concepts. The model's ability to blend text and image inputs for text-guided image-to-image tasks opens up possibilities for creative image manipulation and editing. What can I use it for? kandinsky-2-1 could be used for a range of applications, such as: Generating custom artwork, illustrations, or images for marketing, design, or personal use Aiding in the creative process by providing visual inspiration from textual descriptions Enhancing existing images through text-guided image-to-image transformations Exploring the boundaries of machine-generated art and creativity Things to try One interesting aspect of kandinsky-2-1 is its ability to blend text and image inputs for tasks like text-guided image-to-image generation. This could be used to transform existing images in creative ways, such as adding new elements, changing the style, or combining multiple visual concepts.

Updated 5/9/2024

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rugpt3large_based_on_gpt2

The rugpt3large_based_on_gpt2 is a large language model developed by the SberDevices team at Sber. It was trained on 80 billion tokens of Russian text over 3 epochs, with a final perplexity of 13.6 on the test set. The model architecture is based on GPT-2, but the training focused on Russian language data. Similar models include the FRED-T5-1.7B, a 1.7B parameter model also developed by the AI-Forever team and trained on Russian text, and the ruGPT-3.5-13B, a large 13B parameter Russian language model. Another related model is the mGPT, a multilingual GPT-like model covering 61 languages. Model inputs and outputs The rugpt3large_based_on_gpt2 model is a text-to-text transformer that can be used for a variety of natural language processing tasks. It takes in a sequence of text as input and generates a sequence of text as output. Inputs Text sequence**: A sequence of text to be processed by the model. Outputs Generated text**: The model will generate a sequence of text, continuing or completing the input sequence. Capabilities The rugpt3large_based_on_gpt2 model is capable of generating human-like Russian text given a prompt. It can be used for tasks like story generation, dialogue, and text summarization. The model has also been shown to perform well on language modeling benchmarks for Russian. What can I use it for? The rugpt3large_based_on_gpt2 model could be used for a variety of Russian language applications, such as: Content generation**: Automatically generating Russian text for stories, articles, or dialogues. Text summarization**: Condensing long Russian documents into concise summaries. Dialogue systems**: Building conversational agents that can engage in natural Russian discussions. Language modeling**: Evaluating the probability of Russian text sequences for applications like machine translation or speech recognition. Things to try One interesting aspect of the rugpt3large_based_on_gpt2 model is its ability to generate coherent and contextual Russian text. Experimenting with different prompts and generation settings can yield creative and unexpected outputs. For example, trying prompts that combine different topics or styles could result in unique and imaginative text. Additionally, fine-tuning the model on specific Russian language datasets or tasks could further enhance its capabilities for targeted applications. The large scale of the original training corpus suggests the model has learned rich representations of the Russian language that could be leveraged in novel ways.

Updated 5/9/2024

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FRED-T5-1.7B

The FRED-T5-1.7B (Full-scale Russian Enhanced Denoisers T5) is a language model developed by SberDevices and based on the T5 architecture. It was trained on a 300GB Russian language corpus and has 24 layers and 1536 hidden size. The model was trained on a mixture of 7 different denoisers, similar to the UL2 model, with several differences. It uses a BBPE tokenizer with 50,257 tokens plus 107 special tokens. The FRED-T5-1.7B model is part of a family of Russian language models developed by the SberDevices team, similar to models like the mGPT which covers 61 languages. The FRED-T5-1.7B focuses specifically on the Russian language and has been enhanced with additional denoising capabilities. Model inputs and outputs Inputs Text**: The model accepts various types of text input, including prompts, tasks, and other natural language text. Prefix tokens**: The model uses a set of six prefix tokens (`, , ..., `) to specify the type of task or output desired. Outputs Text**: The model generates coherent, fluent text outputs in Russian based on the provided inputs and prefix tokens. Capabilities The FRED-T5-1.7B model is capable of a variety of text-to-text tasks in the Russian language, such as language modeling, text generation, and other natural language processing applications. The model's denoising capabilities allow it to generate high-quality, fluent Russian text even when the input is noisy or incomplete. What can I use it for? The FRED-T5-1.7B model can be used for a wide range of Russian language applications, including: Content generation**: Creating Russian-language articles, stories, or other text-based content. Language modeling**: Evaluating and scoring the grammaticality and fluency of Russian text. Text summarization**: Generating concise summaries of longer Russian-language documents. Machine translation**: Translating text between Russian and other languages. The model's versatility and strong performance on a variety of Russian language tasks make it a valuable resource for researchers, developers, and businesses working with Russian text. Things to try One interesting aspect of the FRED-T5-1.7B model is its use of prefix tokens to specify different tasks or output formats. By experimenting with different prefix tokens, you can explore the model's capabilities in areas like language modeling, text generation, and more. For example, you could try using the ` prefix to generate text with a particular style or tone, or the ` prefix to produce text with a specific structure or formatting. Another interesting area to explore is the model's denoising capabilities. By intentionally introducing noise or errors into your input text, you can see how the model handles and corrects these issues, producing high-quality, fluent Russian output.

Updated 5/9/2024