AI Models
Browse and discover AI models across various categories.
New!kolors
3.5K
Kolors is a large-scale text-to-image generation model based on latent diffusion, developed by the Kuaishou Kolors team. Trained on billions of text-image pairs, Kolors exhibits significant advantages over both open-source and proprietary models in visual quality, complex semantic accuracy, and text rendering for both Chinese and English characters. Furthermore, Kolors supports both Chinese and English inputs, demonstrating strong performance in understanding and generating Chinese-specific content. Model inputs and outputs Kolors takes a text prompt as input and generates a high-quality, photorealistic image based on that prompt. The model supports a wide range of content, from realistic portraits to fantastical scenes, and can handle complex semantic concepts with impressive accuracy. Inputs Prompt**: The text prompt that describes the desired image. Kolors can understand a variety of prompts in both Chinese and English. Outputs Image**: The generated image that corresponds to the input prompt. The model produces images with a resolution of 1024x1024 pixels by default. Capabilities Kolors shines in its ability to generate high-quality, photorealistic images that faithfully capture the intent of the input prompt. The model can render intricate details, complex scenes, and diverse subject matter with impressive accuracy. For example, Kolors can generate stunning portraits with realistic facial features, as well as imaginative scenes with detailed Chinese elements or futuristic technology. What can I use it for? Kolors can be a powerful tool for a variety of applications, from creative content generation to product visualization. Artists and designers can use the model to quickly generate concept art or explore new ideas. Marketers and e-commerce businesses can leverage Kolors to create high-quality product images or generate custom visuals for their campaigns. Educators and researchers may find the model useful for data augmentation or visual storytelling. Things to try One interesting aspect of Kolors is its ability to handle complex semantic concepts and generate images that go beyond simple object recognition. For example, the model can understand prompts that describe intricate emotions, moods, or artistic styles, and generate images that faithfully capture those nuances. Experimenting with prompts that push the boundaries of the model's understanding can lead to unexpected and fascinating results.
Updated 9/16/2024
sd-inpaint
1.1K
The sd-inpaint model is a powerful AI tool developed by zf-kbot that allows users to fill in masked parts of images using Stable Diffusion. It is similar to other inpainting models like stable-diffusion-inpainting, stable-diffusion-wip, and flux-dev-inpainting, all of which aim to provide users with the ability to modify and enhance existing images. Model inputs and outputs The sd-inpaint model takes a number of inputs, including the input image, a mask, a prompt, and various settings like the seed, guidance scale, and scheduler. The model then generates one or more output images that fill in the masked areas based on the provided prompt and settings. Inputs Image**: The input image to be inpainted Mask**: The mask that defines the areas to be inpainted Prompt**: The text prompt that guides the inpainting process Seed**: The random seed to use for the image generation Guidance Scale**: The scale for the classifier-free guidance Scheduler**: The scheduler to use for the image generation Outputs Output Images**: One or more images that have been inpainted based on the input prompt and settings Capabilities The sd-inpaint model is capable of generating high-quality inpainted images that seamlessly blend the generated content with the original image. This can be useful for a variety of applications, such as removing unwanted elements from photos, completing partially obscured images, or creating new content within existing images. What can I use it for? The sd-inpaint model can be used for a wide range of creative and practical applications. For example, you could use it to remove unwanted objects from photos, fill in missing portions of an image, or even create new art by generating content within a specified mask. The model's versatility makes it a valuable tool for designers, artists, and content creators who need to modify and enhance existing images. Things to try One interesting thing to try with the sd-inpaint model is to experiment with different prompts and settings to see how they affect the generated output. You could try varying the prompt complexity, adjusting the guidance scale, or using different schedulers to see how these factors influence the inpainting results. Additionally, you could explore using the model in combination with other image processing tools to create more complex and sophisticated image manipulations.
Updated 9/16/2024
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pixtral-12b-240910
330
pixtral-12b-240910 This model checkpoint is provided as-is and might not be up-to-date. It mirrors the torrent released by Mistral AI and uploaded by the community. Downloaded from the magnet link: magnet:?xt=urn:btih:7278e625de2b1da598b23954c13933047126238a&dn=pixtral-12b-240910&tr=udp%3A%2F%http://2Ftracker.opentrackr.org%3A1337%2Fannounce&tr=udp%3A%2F%http://2Fopen.demonii.com%3A1337%2Fannounce&tr=http%3A%2F%http://2Ftracker.ipv6tracker.org%3A80%2Fannounce Published by MistralAI in twitter/X: https://x.com/MistralAI/status/1833758285167722836 Release information: https://github.com/mistralai/mistral-common/releases/tag/v1.4.0 Pixtral is out! Mistral common has image support! You can now pass images and URLs alongside text into the user message. pip install --upgrade mistral_common To use the model checkpoint: pip install huggingface-hub from huggingface_hub import snapshot_download snapshot_download(repo_id="mistral-community/pixtral-12b-240910", local_dir="...") PIXTRAL - 12B - v0.1 10/09/24 md5sum b8e9126ef0c15a1130c14b15e8432a67 consolidated.safetensors 68b39355a7b14a7d653292dab340a0be params.json 10229adc84036ff8fe44a2a8e2ad9ba9 tekken.json Released by the Mistral AI team - Use GELU for the vision adapter - Use 2D ROPE for the vision encoder Images You can encode images as follows from mistral_common.protocol.instruct.messages import ( UserMessage, TextChunk, ImageURLChunk, ImageChunk, ) from PIL import Image from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer tokenizer = MistralTokenizer.from_model("pixtral") image = Image.new('RGB', (64, 64)) tokenize images and text tokenized = tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ UserMessage( content=[ TextChunk(text="Describe this image"), ImageChunk(image=image), ] ) ], model="pixtral", ) ) tokens, text, images = tokenized.tokens, tokenized.text, tokenized.images Count the number of tokens print("# tokens", len(tokens)) print("# images", len(images)) Image URLs You can pass image url which will be automatically downloaded url_dog = "https://picsum.photos/id/237/200/300" url_mountain = "https://picsum.photos/seed/picsum/200/300" tokenize image urls and text tokenized = tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ UserMessage( content=[ TextChunk(text="Can this animal"), ImageURLChunk(image_url=url_dog), TextChunk(text="live here?"), ImageURLChunk(image_url=url_mountain), ] ) ], model="pixtral", ) ) tokens, text, images = tokenized.tokens, tokenized.text, tokenized.images Count the number of tokens print("# tokens", len(tokens)) print("# images", len(images)) ImageData You can also pass image encoded as base64 tokenized = tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ UserMessage( content=[ TextChunk(text="What is this?"), 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"), ] ) ], model="pixtral", ) ) tokens, text, images = tokenized.tokens, tokenized.text, tokenized.images Count the number of tokens print("# tokens", len(tokens)) print("# images", len(images))
Updated 9/16/2024
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solar-pro-preview-instruct
295
The solar-pro-preview-instruct model is an advanced 22 billion parameter large language model (LLM) developed by upstage. It is designed to run efficiently on a single GPU, delivering performance comparable to much larger models like Llama 3.1 with 70 billion parameters. The model was developed using an enhanced version of upstage's depth up-scaling method, which scales a smaller 14 billion parameter model to 22 billion parameters. Compared to the SOLAR-10.7B-Instruct-v1.0 model, the solar-pro-preview-instruct demonstrates enhanced performance, particularly on the MMLU-Pro and IFEval benchmarks which test a model's knowledge and instruction-following abilities. It is a pre-release version of the official Solar Pro model, with limitations on language coverage and context length, but with the potential for further expansion. Model inputs and outputs Inputs Instruction prompts**: The model is designed to excel at following instructions and engaging in conversational tasks. It uses the ChatML prompt template for optimal performance. Outputs Conversational responses**: The model generates coherent and relevant responses to instruction-based prompts, demonstrating strong task-completion abilities. Capabilities The solar-pro-preview-instruct model shows superior performance compared to LLMs with under 30 billion parameters. It is capable of engaging in a wide variety of instruction-following tasks, from answering questions to generating summaries and completing multi-step workflows. The model's depth up-scaling approach allows it to pack a lot of capability into a relatively compact size, making it an efficient choice for deployment. What can I use it for? The solar-pro-preview-instruct model is well-suited for building AI assistants and chatbots that need to understand and follow complex instructions. It could be used to power virtual assistants, content generation tools, code completion applications, and more. Its small footprint makes it a compelling choice for edge deployments or other scenarios where compute resources are constrained. Things to try One interesting aspect of the solar-pro-preview-instruct model is its ability to handle long-form instruction-based prompts, thanks to the RoPE scaling techniques used in its development. Try providing the model with multi-step workflows or intricate task descriptions and see how it responds. You can also experiment with fine-tuning the model on your own datasets to adapt it to specialized domains or use cases.
Updated 9/16/2024
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fish-speech-1.4
255
fish-speech-1.4 is a leading text-to-speech (TTS) model developed by fishaudio. It is trained on over 700k hours of audio data across multiple languages, including English, Chinese, German, Japanese, French, Spanish, Korean, and Arabic. This makes it one of the most comprehensive multilingual TTS models available. In comparison, earlier versions like fish-speech-1.2 and fish-speech-1 were trained on smaller datasets of 300k and 150k hours respectively, focusing primarily on English, Chinese, and Japanese. Model inputs and outputs fish-speech-1.4 is a text-to-speech model, taking text input and generating high-quality audio output. The model supports a wide range of languages, allowing users to generate speech in their language of choice. Inputs Text in one of the supported languages: English, Chinese, German, Japanese, French, Spanish, Korean, or Arabic Outputs Synthesized audio in the corresponding language Capabilities fish-speech-1.4 is capable of generating highly natural-sounding speech across multiple languages. The model leverages extensive training data and advanced deep learning techniques to produce realistic intonation, rhythm, and timbre. This makes it suitable for a variety of applications, from text-to-speech assistants to audio book narration. What can I use it for? fish-speech-1.4 can be used in a wide range of applications that require text-to-speech functionality. This includes virtual assistants, audiobook creation, language learning tools, and multimedia content production. The model's multilingual capabilities make it particularly useful for reaching global audiences or creating content in multiple languages. Things to try One interesting aspect of fish-speech-1.4 is its ability to handle code-switching between languages. This means the model can generate speech that seamlessly transitions between different languages within the same audio, which can be useful for content creators working with multilingual audiences. Experimenting with this feature can lead to unique and engaging audio experiences.
Updated 9/16/2024
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Llama-3.1-8B-Omni
240
LLaMA-Omni is a speech-language model built upon the Llama-3.1-8B-Instruct model. Developed by ICTNLP, it supports low-latency and high-quality speech interactions, simultaneously generating both text and speech responses based on speech instructions. Compared to the original Llama-3.1-8B-Instruct model, LLaMA-Omni ensures high-quality responses with low-latency speech interaction, reaching a latency as low as 226ms. It can generate both text and speech outputs in response to speech prompts, making it a versatile model for seamless speech-based interactions. Model inputs and outputs Inputs Speech audio**: The model takes speech audio as input and processes it to understand the user's instructions. Outputs Text response**: The model generates a textual response to the user's speech prompt. Audio response**: Simultaneously, the model produces a corresponding speech output, enabling a complete speech-based interaction. Capabilities LLaMA-Omni demonstrates several key capabilities that make it a powerful speech-language model: Low-latency speech interaction**: With a latency as low as 226ms, LLaMA-Omni enables responsive and natural-feeling speech-based dialogues. Simultaneous text and speech output**: The model can generate both textual and audio responses, allowing for a seamless and multimodal interaction experience. High-quality responses**: By building upon the strong Llama-3.1-8B-Instruct model, LLaMA-Omni ensures high-quality and coherent responses. Rapid development**: The model was trained in less than 3 days using just 4 GPUs, showcasing the efficiency of the development process. What can I use it for? LLaMA-Omni is well-suited for a variety of applications that require seamless speech interactions, such as: Virtual assistants**: The model's ability to understand and respond to speech prompts makes it an excellent foundation for building intelligent virtual assistants that can engage in natural conversations. Conversational interfaces**: LLaMA-Omni can power intuitive and multimodal conversational interfaces for a wide range of products and services, from smart home devices to customer service chatbots. Language learning applications**: The model's speech understanding and generation capabilities can be leveraged to create interactive language learning tools that provide real-time feedback and practice opportunities. Things to try One interesting aspect of LLaMA-Omni is its ability to rapidly handle speech-based interactions. Developers could experiment with using the model to power voice-driven interfaces, such as voice commands for smart home automation or voice-controlled productivity tools. The model's simultaneous text and speech output also opens up opportunities for creating unique, multimodal experiences that blend spoken and written interactions.
Updated 9/16/2024
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reader-lm-1.5b
223
reader-lm-1.5b is a series of models developed by Jina AI that convert HTML content to Markdown content. The models are trained on a curated collection of HTML content and its corresponding Markdown content, allowing them to effectively perform content conversion tasks. There are two main models in the reader-lm series: reader-lm-0.5b with a context length of 256K reader-lm-1.5b with a context length of 256K These models can be used to convert HTML content to Markdown format, which is useful for tasks like content migration, blog post formatting, and more. Model inputs and outputs Inputs HTML content: The model takes raw HTML content as input, with no prefix instruction required. Outputs Markdown content: The model outputs the corresponding Markdown version of the input HTML content. Capabilities The reader-lm models are capable of effectively converting HTML content to Markdown format, leveraging their training on a curated dataset of HTML-Markdown pairs. This allows them to accurately preserve the structure and formatting of the original HTML content when generating the Markdown output. What can I use it for? The reader-lm models can be a valuable tool for a variety of content-related tasks, such as: Content migration**: Easily convert HTML content to Markdown format when moving content between platforms or websites. Blog post formatting**: Automatically convert HTML blog posts to Markdown, which is a common format for many blogging and publishing platforms. Document conversion**: Convert HTML documentation or reports to Markdown for better readability and portability. Things to try One interesting thing to try with the reader-lm models is to explore their performance on different types of HTML content, such as complex web pages, long-form articles, or even code-heavy documentation. You can also experiment with the models' ability to preserve formatting, links, and other HTML elements when generating the Markdown output.
Updated 9/16/2024
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New!GOT-OCR2_0
169
The GOT-OCR2_0 model, created by maintainer ucaslcl, is an end-to-end optical character recognition (OCR) model that can handle a wide range of text formats, including plain text, formatted text, fine-grained OCR, and multi-crop OCR. This model is an advancement in OCR technology, building upon the previous "OCR 1.0" approaches by providing a more unified and robust solution. The GOT-OCR2_0 model is trained on a large dataset of cultural heritage archives, allowing it to accurately recognize and correct text from historical documents. It can handle a variety of input types, including images with noisy or degraded text, and provides high-quality output in markdown format. The model's capabilities are highlighted in its strong performance on benchmarks like TextVQA, DocVQA, ChartQA, and OCRbench, where it outperforms other open-source and commercial models. Model inputs and outputs Inputs Image file**: The model takes an image file as input, which can contain text in various formats, such as plain text, formatted text, or a mixture of text and other elements. Outputs Markdown-formatted text**: The model's primary output is the text content of the input image, formatted in Markdown syntax. This includes: Detected text, with headers marked by ## Mathematical expressions wrapped in \( inline math \) and \[ display math \] Formatting elements like bold, italic, and code blocks The model can also provide additional outputs, such as: Fine-grained OCR**: Bounding boxes and text annotations for individual text elements in the image. Multi-crop OCR**: Detection and recognition of multiple text regions within the input image. Rendered HTML**: The formatted text output can be rendered as an HTML document for easy visualization. Capabilities The GOT-OCR2_0 model excels at handling a wide range of text formats, including plain text, formatted text, mathematical expressions, and mixed-content documents. It can accurately detect and recognize text, even in noisy or degraded images, and provide high-quality Markdown-formatted output. One of the key strengths of the GOT-OCR2_0 model is its ability to handle historical documents. Thanks to its training on a large dataset of cultural heritage archives, the model can accurately recognize and correct text from old, damaged, or low-quality sources. This makes it a valuable tool for researchers and archivists working with historical documents. What can I use it for? The GOT-OCR2_0 model is well-suited for a variety of applications, including: Document digitization and archiving**: Convert physical documents into searchable, structured digital formats, making it easier to preserve and access historical records. Automated data extraction**: Extract structured data from scanned forms, invoices, or other business documents, reducing manual data entry tasks. Assistive technology**: Improve accessibility by providing accurate text recognition for people with visual impairments or other disabilities. Academic and research applications**: Enhance text analysis and information retrieval tasks for historical, scientific, or other specialized domains. Things to try One interesting application of the GOT-OCR2_0 model is its ability to handle mathematical expressions. By wrapping detected equations in Markdown syntax, the model makes it easier to process and analyze the mathematical content of documents. This could be particularly useful for researchers in fields like physics, engineering, or finance, where accurate extraction of formulas and equations is crucial. Another area to explore is the model's fine-grained OCR capabilities. By providing bounding boxes and text annotations for individual elements, the GOT-OCR2_0 model can enable more advanced document analysis, such as layout reconstruction, table extraction, or figure captioning. This could be valuable for applications like automated document processing or information retrieval. Overall, the GOT-OCR2_0 model represents a significant advancement in OCR technology, delivering robust and versatile text recognition capabilities that can benefit a wide range of industries and applications.
Updated 9/16/2024
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Pixtral-12B-2409
153
The Pixtral-12B-2409 is a large language model developed by mistralai. It is a powerful image-to-text model capable of generating detailed descriptions of images. Similar models include the Mixtral-8x7B-v0.1 and MistralLite, which are also large language models developed by Mistral AI. The Mixtral-8x7B is a Sparse Mixture of Experts model that outperforms Llama 2 70B on most benchmarks, while MistralLite is a fine-tuned version of the Mistral-7B model with enhanced capabilities for long-context tasks. Model inputs and outputs Inputs Text prompt**: A text prompt describing what the model should generate an image description for. Image URL**: A URL pointing to the image that the model should generate a description for. Outputs Generated text**: A detailed, coherent description of the image provided as input. Capabilities The Pixtral-12B-2409 model is capable of generating high-quality, contextual image descriptions from a given text prompt and image URL. It can capture details about the contents, objects, and scenes depicted in the image, and produce natural language descriptions that flow well and provide meaningful insights. What can I use it for? The Pixtral-12B-2409 model could be used in a variety of applications that require converting images to text, such as: Image captioning**: Automatically generating captions for images in social media, online galleries, or other visual content. Image search and retrieval**: Enabling users to search for images based on textual descriptions, and retrieve relevant images from a database. Accessibility**: Providing text descriptions of images for users who are visually impaired or have other accessibility needs. Multimodal AI assistants**: Integrating the model into AI assistants that can understand and respond to both text and image inputs. Things to try One interesting aspect of the Pixtral-12B-2409 model is its ability to handle multiple images within a single prompt. By passing in a list of image URLs, the model can generate a cohesive description that ties together the contents of all the provided images. This could be useful for tasks like summarizing a set of related images, or describing the progression of a story or sequence of events. Another thing to explore is the model's performance on specialized or domain-specific image types, such as medical images, technical diagrams, or artistic compositions. The model's ability to understand and describe these more complex or niche image categories could be an important factor in certain applications.
Updated 9/16/2024
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New!octo-net
123
octo-net is an advanced open-source language model with 3 billion parameters, developed by NexaAIDev. It serves as the master node in Nexa AI's envisioned graph of language models, efficiently translating user queries into formats that specialized models can effectively process. octo-net excels at directing queries to the appropriate specialized model, ensuring precise and effective query handling. Compared to similar models like Octopus-v4 and Octopus-v2, octo-net is compact in size, enabling it to operate on smart devices efficiently and swiftly. It also accurately maps user queries to specialized models using a functional token design, enhancing its precision. Additionally, octo-net assists in converting natural human language into a more professional format, improving query description and resulting in more accurate responses. Model inputs and outputs octo-net is a text-to-text model that takes user queries as input and generates responses that direct the query to the appropriate specialized model for processing. Inputs User query**: The natural language query provided by the user. Outputs Reformatted query**: The user query converted into a more professional format that can be effectively processed by specialized models. Specialized model call**: The instructions to call the specialized model that can best handle the given query. Capabilities octo-net demonstrates impressive capabilities in translating user queries into a format that can be efficiently processed by specialized models. For example, when provided with the query "Tell me the result of derivative of x^3 when x is 2?", octo-net generates a response that calls the appropriate math-focused model to determine the derivative of the function f(x) = x^3 at x = 2. What can I use it for? octo-net can be particularly useful in building intelligent systems that require seamless integration of multiple specialized models. For example, a virtual assistant application could leverage octo-net to route user queries to the appropriate domain-specific models for tasks like answering math questions, providing medical advice, or retrieving business insights. By automating the process of selecting the right model for a given query, octo-net can help streamline the development of such complex AI-powered applications. Things to try One interesting aspect of octo-net is its ability to reformat user queries into a more professional format. Developers could experiment with providing octo-net with a variety of natural language queries and observe how it translates them into a format that is more easily processed by specialized models. This could lead to insights on how to improve the natural language understanding and query reformatting capabilities of the model. Additionally, exploring the model's performance on specialized tasks like math, science, or business-related queries could provide valuable feedback on the strengths and limitations of the octo-net approach. Developers could also investigate ways to fine-tune or customize octo-net to better suit their specific use cases.
Updated 9/16/2024
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Llama-3.1-SuperNova-Lite
121
Llama-3.1-SuperNova-Lite is an 8B parameter model developed by Arcee.ai, based on the Llama-3.1-8B-Instruct architecture. It is a distilled version of the larger Llama-3.1-405B-Instruct model, leveraging offline logits extracted from the 405B parameter variant. This 8B variation of Llama-3.1-SuperNova maintains high performance while offering exceptional instruction-following capabilities and domain-specific adaptability. The model was trained using a state-of-the-art distillation pipeline and an instruction dataset generated with EvolKit, ensuring accuracy and efficiency across a wide range of tasks. Llama-3.1-SuperNova-Lite excels in both benchmark performance and real-world applications, providing the power of large-scale models in a more compact, efficient form ideal for organizations seeking high performance with reduced resource requirements. Model inputs and outputs Inputs Text Outputs Text Capabilities Llama-3.1-SuperNova-Lite excels at a variety of text-to-text tasks, including instruction-following, open-ended question answering, and knowledge-intensive applications. The model's distilled architecture maintains the strong performance of its larger counterparts while being more resource-efficient. What can I use it for? The compact and powerful nature of Llama-3.1-SuperNova-Lite makes it an excellent choice for organizations looking to leverage the capabilities of large language models without the resource requirements. Potential use cases include chatbots, content generation, question-answering systems, and domain-specific applications that require high-performing text-to-text capabilities. Things to try Explore how Llama-3.1-SuperNova-Lite performs on your specific text-to-text tasks, such as generating coherent and informative responses to open-ended prompts, following complex instructions, or answering knowledge-intensive questions. The model's strong instruction-following abilities and domain-specific adaptability make it a versatile tool for a wide range of applications.
Updated 9/16/2024
🛠️
FilmPortrait
106
The FilmPortrait model is a LoRA model finetuned on the FLUX.1-dev dataset, specifically designed to enhance the film texture of generated images. The model embodies a subdued, low-saturation color palette reminiscent of classic Japanese cinema, which is particularly evident in its portrayal of characters (with a subtle bias towards Asian features), serene still lifes, and sweeping landscapes. The model delivers an exceptional aesthetic experience, capturing the essence of a bygone era with modern precision. Compared to the base FLUX.1-dev model, the FilmPortrait model produces images with a more muted, film-like quality. As shown in the comparison section, the FilmPortrait model results in a softer, more nostalgic appearance compared to the standard FLUX.1-dev output. The AWPortrait-FL model is another similar LoRA model finetuned on FLUX.1-dev, but with a focus on improving composition and details in portrait photography. The FLUX.1-dev-LoRA-blended-realistic-illustration model blends realistic and illustrated elements, creating a unique mixed media effect. Model inputs and outputs The FilmPortrait model takes text prompts as input and generates corresponding images. The model is particularly well-suited for prompts related to film, photography, and nostalgic aesthetics. Inputs Text prompts**: Descriptive text that informs the content and style of the generated image, such as "a young girl, filmfotos, film grain, reversal film photography" Outputs Images**: The model generates 2D images that reflect the provided text prompt, with a subdued, film-like appearance. Capabilities The FilmPortrait model excels at producing images with a classic, cinematic aesthetic. The model's strength lies in its ability to capture the essence of traditional film photography, with its muted colors, soft textures, and subtle biases towards certain subject matter. As demonstrated in the comparison section, the FilmPortrait model can transform a standard FLUX.1-dev output into a more evocative, nostalgic image. The model's unique style is particularly well-suited for scenes depicting characters, landscapes, and still lifes. What can I use it for? The FilmPortrait model is an excellent choice for projects that require a vintage, film-inspired aesthetic. This could include: Concept art and mood boards for film, television, or game productions Illustrations and cover art for books, magazines, or albums with a retro feel Social media content and marketing materials with a nostalgic, analog aesthetic Personal art projects that aim to capture the essence of classic photography By leveraging the FilmPortrait model, creators can add a touch of cinematic charm to their digital creations, transporting viewers to a bygone era. Things to try To get the most out of the FilmPortrait model, try experimenting with prompts that evoke a sense of nostalgia or classic film. Keywords like "filmfotos", "film grain", and "reversal film photography" can help the model achieve the desired aesthetic. Additionally, consider combining the FilmPortrait model with other LoRA models, such as the AWPortrait-FL or FLUX.1-dev-LoRA-blended-realistic-illustration models, to create unique and compelling hybrid styles.
Updated 9/16/2024