Alternatives to Decart Mirage

The official launch of the Seedance 1.0 API makes ByteDance’s industry-leading video generation technology accessible to creators worldwide. Recently ranked #1 globally in the Artificial Analysis benchmark for both T2V and I2V tasks, Seedance is recognized for its cinematic realism, smooth motion, and advanced multi-shot storytelling capabilities. Unlike single-scene models, it maintains subject identity, atmosphere, and style across multiple shots, enabling narrative video production at scale. Users benefit from precise instruction following, diverse stylistic expression, and studio-grade 1080p video output in just seconds. Pricing is transparent and cost-effective, with 2 million free tokens to start and affordable tiers at $1.8–$2.5 per million tokens, depending on whether you use the Lite or Pro model. For a 5-second 1080p video, the cost is under a dollar, making high-quality AI content creation both accessible and scalable. Beyond affordability, Seedance is optimized for high concurrency, meaning developers and teams can generate large volumes of videos simultaneously without performance loss. Designed for film production, marketing campaigns, storytelling, and product pitches, the Seedance API empowers businesses and individuals to scale their creativity with enterprise-grade tools.

Mirage 2 is an innovative Generative World Engine powered by AI, allowing users to effortlessly convert images or textual descriptions into dynamic, interactive game environments right within their browser. Whether you upload sketches, concept art, photographs, or prompts like “Ghibli-style village” or “Paris street scene,” Mirage 2 crafts rich, immersive worlds for you to explore in real time. This interactive experience is not bound by pre-defined scripts; users can alter their environments during gameplay through natural-language chat, enabling the settings to shift fluidly from a cyberpunk metropolis to a lush rainforest or a majestic mountaintop castle, all while maintaining low latency (approximately 200 ms) on a standard consumer GPU. Furthermore, Mirage 2 boasts smooth rendering and offers real-time prompt control, allowing for extended gameplay durations that go beyond ten minutes. Unlike previous world-modeling systems, it excels in general-domain generation, eliminating restrictions on styles or genres, and provides seamless world adaptation alongside sharing capabilities, which enhances collaborative creativity among users. This transformative platform not only redefines game development but also encourages a vibrant community of creators to engage and explore together.

Ray2 represents a cutting-edge video generation model that excels at producing lifelike visuals combined with fluid, coherent motion. Its proficiency in interpreting text prompts is impressive, and it can also process images and videos as inputs. This advanced model has been developed using Luma’s innovative multi-modal architecture, which has been enhanced to provide ten times the computational power of its predecessor, Ray1. With Ray2, we are witnessing the dawn of a new era in video generation technology, characterized by rapid, coherent movement, exquisite detail, and logical narrative progression. These enhancements significantly boost the viability of the generated content, resulting in videos that are far more suitable for production purposes. Currently, Ray2 offers text-to-video generation capabilities, with plans to introduce image-to-video, video-to-video, and editing features in the near future. The model elevates the quality of motion fidelity to unprecedented heights, delivering smooth, cinematic experiences that are truly awe-inspiring. Transform your creative ideas into stunning visual narratives, and let Ray2 help you create mesmerizing scenes with accurate camera movements that bring your story to life. In this way, Ray2 empowers users to express their artistic vision like never before.

HunyuanVideo-Avatar allows for the transformation of any avatar images into high-dynamic, emotion-responsive videos by utilizing straightforward audio inputs. This innovative model is based on a multimodal diffusion transformer (MM-DiT) architecture, enabling the creation of lively, emotion-controllable dialogue videos featuring multiple characters. It can process various styles of avatars, including photorealistic, cartoonish, 3D-rendered, and anthropomorphic designs, accommodating different sizes from close-up portraits to full-body representations. Additionally, it includes a character image injection module that maintains character consistency while facilitating dynamic movements. An Audio Emotion Module (AEM) extracts emotional nuances from a source image, allowing for precise emotional control within the produced video content. Moreover, the Face-Aware Audio Adapter (FAA) isolates audio effects to distinct facial regions through latent-level masking, which supports independent audio-driven animations in scenarios involving multiple characters, enhancing the overall experience of storytelling through animated avatars. This comprehensive approach ensures that creators can craft richly animated narratives that resonate emotionally with audiences.

Captions has introduced Mirage, the revolutionary AI model that creates user-generated content (UGC) seamlessly. This innovative tool crafts original actors equipped with authentic expressions and body language, entirely free from licensing hurdles. With Mirage, video production becomes faster than ever before; simply provide a prompt to generate a complete video from beginning to end. You can quickly create an actor, set, voiceover, and script, all in one go. Mirage breathes life into distinctive AI-generated characters, removing any rights limitations and enabling boundless, expressive narratives. The process of scaling video advertisement production is now remarkably straightforward. With the advent of Mirage, marketing teams can significantly shorten expensive production timelines, decrease dependence on outside creators, and redirect their efforts towards strategic planning. There's no need for traditional actors, studios, or filming; you only need to enter a prompt, and Mirage will produce a fully-realized video, from script to screen. This advancement allows you to avoid the typical legal and logistical challenges associated with conventional video production, paving the way for a more creative and efficient approach to video content.

The Gemini Live API is an advanced preview feature designed to facilitate low-latency, bidirectional interactions through voice and video with the Gemini system. This innovation allows users to engage in conversations that feel natural and human-like, while also enabling them to interrupt the model's responses via voice commands. In addition to handling text inputs, the model is capable of processing audio and video, yielding both text and audio outputs. Recent enhancements include the introduction of two new voice options and support for 30 additional languages, along with the ability to configure the output language as needed. Furthermore, users can adjust image resolution settings (66/256 tokens), decide on turn coverage (whether to send all inputs continuously or only during user speech), and customize interruption preferences. Additional features encompass voice activity detection, new client events for signaling the end of a turn, token count tracking, and a client event for marking the end of the stream. The system also supports text streaming, along with configurable session resumption that retains session data on the server for up to 24 hours, and the capability for extended sessions utilizing a sliding context window for better conversation continuity. Overall, Gemini Live API enhances interaction quality, making it more versatile and user-friendly.

Seaweed, an advanced AI model for video generation created by ByteDance, employs a diffusion transformer framework that boasts around 7 billion parameters and has been trained using computing power equivalent to 1,000 H100 GPUs. This model is designed to grasp world representations from extensive multi-modal datasets, which encompass video, image, and text formats, allowing it to produce videos in a variety of resolutions, aspect ratios, and lengths based solely on textual prompts. Seaweed stands out for its ability to generate realistic human characters that can exhibit a range of actions, gestures, and emotions, alongside a diverse array of meticulously detailed landscapes featuring dynamic compositions. Moreover, the model provides users with enhanced control options, enabling them to generate videos from initial images that help maintain consistent motion and aesthetic throughout the footage. It is also capable of conditioning on both the opening and closing frames to facilitate smooth transition videos, and can be fine-tuned to create content based on specific reference images, thus broadening its applicability and versatility in video production. As a result, Seaweed represents a significant leap forward in the intersection of AI and creative video generation.

Gemini Diffusion represents our cutting-edge research initiative aimed at redefining the concept of diffusion in the realm of language and text generation. Today, large language models serve as the backbone of generative AI technology. By employing a diffusion technique, we are pioneering a new type of language model that enhances user control, fosters creativity, and accelerates the text generation process. Unlike traditional models that predict text in a straightforward manner, diffusion models take a unique approach by generating outputs through a gradual refinement of noise. This iterative process enables them to quickly converge on solutions and make real-time corrections during generation. As a result, they demonstrate superior capabilities in tasks such as editing, particularly in mathematics and coding scenarios. Furthermore, by generating entire blocks of tokens simultaneously, they provide more coherent responses to user prompts compared to autoregressive models. Remarkably, the performance of Gemini Diffusion on external benchmarks rivals that of much larger models, while also delivering enhanced speed, making it a noteworthy advancement in the field. This innovation not only streamlines the generation process but also opens new avenues for creative expression in language-based tasks.

Seed Diffusion Preview is an advanced language model designed for code generation that employs discrete-state diffusion, allowing it to produce code in a non-sequential manner, resulting in significantly faster inference times without compromising on quality. This innovative approach utilizes a two-stage training process that involves mask-based corruption followed by edit-based augmentation, enabling a standard dense Transformer to achieve an optimal balance between speed and precision while avoiding shortcuts like carry-over unmasking, which helps maintain rigorous density estimation. The model impressively achieves an inference rate of 2,146 tokens per second on H20 GPUs, surpassing current diffusion benchmarks while either matching or exceeding their accuracy on established code evaluation metrics, including various editing tasks. This performance not only sets a new benchmark for the speed-quality trade-off in code generation but also showcases the effective application of discrete diffusion methods in practical coding scenarios. Its success opens up new avenues for enhancing efficiency in coding tasks across multiple platforms.

Qwen3-Omni is a comprehensive multilingual omni-modal foundation model designed to handle text, images, audio, and video, providing real-time streaming responses in both textual and natural spoken formats. Utilizing a unique Thinker-Talker architecture along with a Mixture-of-Experts (MoE) framework, it employs early text-centric pretraining and mixed multimodal training, ensuring high-quality performance across all formats without compromising on text or image fidelity. This model is capable of supporting 119 different text languages, 19 languages for speech input, and 10 languages for speech output. Demonstrating exceptional capabilities, it achieves state-of-the-art performance across 36 benchmarks related to audio and audio-visual tasks, securing open-source SOTA on 32 benchmarks and overall SOTA on 22, thereby rivaling or equaling prominent closed-source models like Gemini-2.5 Pro and GPT-4o. To enhance efficiency and reduce latency in audio and video streaming, the Talker component leverages a multi-codebook strategy to predict discrete speech codecs, effectively replacing more cumbersome diffusion methods. Additionally, this innovative model stands out for its versatility and adaptability across a wide array of applications.

NVIDIA Cosmos serves as a cutting-edge platform tailored for developers, featuring advanced generative World Foundation Models (WFMs), sophisticated video tokenizers, safety protocols, and a streamlined data processing and curation system aimed at enhancing the development of physical AI. This platform empowers developers who are focused on areas such as autonomous vehicles, robotics, and video analytics AI agents to create highly realistic, physics-informed synthetic video data, leveraging an extensive dataset that encompasses 20 million hours of both actual and simulated footage, facilitating the rapid simulation of future scenarios, the training of world models, and the customization of specific behaviors. The platform comprises three primary types of WFMs: Cosmos Predict, which can produce up to 30 seconds of continuous video from various input modalities; Cosmos Transfer, which modifies simulations to work across different environments and lighting conditions for improved domain augmentation; and Cosmos Reason, a vision-language model that implements structured reasoning to analyze spatial-temporal information for effective planning and decision-making. With these capabilities, NVIDIA Cosmos significantly accelerates the innovation cycle in physical AI applications, fostering breakthroughs across various industries.

VideoPoet is an innovative modeling technique that transforms any autoregressive language model or large language model (LLM) into an effective video generator. It comprises several straightforward components. An autoregressive language model is trained across multiple modalities—video, image, audio, and text—to predict the subsequent video or audio token in a sequence. The training framework for the LLM incorporates a range of multimodal generative learning objectives, such as text-to-video, text-to-image, image-to-video, video frame continuation, inpainting and outpainting of videos, video stylization, and video-to-audio conversion. Additionally, these tasks can be combined to enhance zero-shot capabilities. This straightforward approach demonstrates that language models are capable of generating and editing videos with impressive temporal coherence, showcasing the potential for advanced multimedia applications. As a result, VideoPoet opens up exciting possibilities for creative expression and automated content creation.

Runway Aleph represents a revolutionary advancement in in-context video modeling, transforming the landscape of multi-task visual generation and editing by allowing extensive modifications on any video clip. This model can effortlessly add, delete, or modify objects within a scene, create alternative camera perspectives, and fine-tune style and lighting based on either natural language commands or visual cues. Leveraging advanced deep-learning techniques and trained on a wide range of video data, Aleph functions entirely in context, comprehending both spatial and temporal dynamics to preserve realism throughout the editing process. Users are empowered to implement intricate effects such as inserting objects, swapping backgrounds, adjusting lighting dynamically, and transferring styles without the need for multiple separate applications for each function. The user-friendly interface of this model is seamlessly integrated into Runway's Gen-4 ecosystem, providing an API for developers alongside a visual workspace for creators, making it a versatile tool for both professionals and enthusiasts in video editing. With its innovative capabilities, Aleph is set to revolutionize how creators approach video content transformation.

Inception Labs is at the forefront of advancing artificial intelligence through the development of diffusion-based large language models (dLLMs), which represent a significant innovation in the field by achieving performance that is ten times faster and costs that are five to ten times lower than conventional autoregressive models. Drawing inspiration from the achievements of diffusion techniques in generating images and videos, Inception's dLLMs offer improved reasoning abilities, error correction features, and support for multimodal inputs, which collectively enhance the generation of structured and precise text. This innovative approach not only boosts efficiency but also elevates the control users have over AI outputs. With its wide-ranging applications in enterprise solutions, academic research, and content creation, Inception Labs is redefining the benchmarks for speed and effectiveness in AI-powered processes. The transformative potential of these advancements promises to reshape various industries by optimizing workflows and enhancing productivity.

Runway Gen-4 offers a powerful AI tool for generating consistent media, allowing creators to produce videos, images, and interactive content with ease. The model excels in creating consistent characters, objects, and scenes across varying angles, lighting conditions, and environments, all with a simple reference image or description. It supports a wide range of creative applications, from VFX and product photography to video generation with dynamic and realistic motion. With its advanced world understanding and ability to simulate real-world physics, Gen-4 provides a next-level solution for professionals looking to streamline their production workflows and enhance storytelling.

Janus-Pro-7B is a groundbreaking open-source multimodal AI model developed by DeepSeek, expertly crafted to both comprehend and create content involving text, images, and videos. Its distinctive autoregressive architecture incorporates dedicated pathways for visual encoding, which enhances its ability to tackle a wide array of tasks, including text-to-image generation and intricate visual analysis. Demonstrating superior performance against rivals such as DALL-E 3 and Stable Diffusion across multiple benchmarks, it boasts scalability with variants ranging from 1 billion to 7 billion parameters. Released under the MIT License, Janus-Pro-7B is readily accessible for use in both academic and commercial contexts, marking a substantial advancement in AI technology. Furthermore, this model can be utilized seamlessly on popular operating systems such as Linux, MacOS, and Windows via Docker, broadening its reach and usability in various applications.

Marey serves as the cornerstone AI video model for Moonvalley, meticulously crafted to achieve exceptional cinematography, providing filmmakers with unparalleled precision, consistency, and fidelity in every single frame. As the first video model deemed commercially safe, it has been exclusively trained on licensed, high-resolution footage to mitigate legal ambiguities and protect intellectual property rights. Developed in partnership with AI researchers and seasoned directors, Marey seamlessly replicates authentic production workflows, ensuring that the output is of production-quality, devoid of visual distractions, and primed for immediate delivery. Its suite of creative controls features Camera Control, which enables the transformation of 2D scenes into adjustable 3D environments for dynamic cinematic movements; Motion Transfer, which allows the timing and energy from reference clips to be transferred to new subjects; Trajectory Control, which enables precise paths for object movements without the need for prompts or additional iterations; Keyframing, which facilitates smooth transitions between reference images along a timeline; and Reference, which specifies how individual elements should appear and interact. By integrating these advanced features, Marey empowers filmmakers to push creative boundaries and streamline their production processes.

Wan2.2 marks a significant enhancement to the Wan suite of open video foundation models by incorporating a Mixture-of-Experts (MoE) architecture that separates the diffusion denoising process into high-noise and low-noise pathways, allowing for a substantial increase in model capacity while maintaining low inference costs. This upgrade leverages carefully labeled aesthetic data that encompasses various elements such as lighting, composition, contrast, and color tone, facilitating highly precise and controllable cinematic-style video production. With training on over 65% more images and 83% more videos compared to its predecessor, Wan2.2 achieves exceptional performance in the realms of motion, semantic understanding, and aesthetic generalization. Furthermore, the release features a compact TI2V-5B model that employs a sophisticated VAE and boasts a remarkable 16×16×4 compression ratio, enabling both text-to-video and image-to-video synthesis at 720p/24 fps on consumer-grade GPUs like the RTX 4090. Additionally, prebuilt checkpoints for T2V-A14B, I2V-A14B, and TI2V-5B models are available, ensuring effortless integration into various projects and workflows. This advancement not only enhances the capabilities of video generation but also sets a new benchmark for the efficiency and quality of open video models in the industry.

ALBERT is a self-supervised Transformer architecture that undergoes pretraining on a vast dataset of English text, eliminating the need for manual annotations by employing an automated method to create inputs and corresponding labels from unprocessed text. This model is designed with two primary training objectives in mind. The first objective, known as Masked Language Modeling (MLM), involves randomly obscuring 15% of the words in a given sentence and challenging the model to accurately predict those masked words. This approach sets it apart from recurrent neural networks (RNNs) and autoregressive models such as GPT, as it enables ALBERT to capture bidirectional representations of sentences. The second training objective is Sentence Ordering Prediction (SOP), which focuses on the task of determining the correct sequence of two adjacent text segments during the pretraining phase. By incorporating these dual objectives, ALBERT enhances its understanding of language structure and contextual relationships. This innovative design contributes to its effectiveness in various natural language processing tasks.

OmniHuman-1 is an innovative AI system created by ByteDance that transforms a single image along with motion cues, such as audio or video, into realistic human videos. This advanced platform employs multimodal motion conditioning to craft lifelike avatars that exhibit accurate gestures, synchronized lip movements, and facial expressions that correspond with spoken words or music. It has the flexibility to handle various input types, including portraits, half-body, and full-body images, and can generate high-quality videos even when starting with minimal audio signals. The capabilities of OmniHuman-1 go beyond just human representation; it can animate cartoons, animals, and inanimate objects, making it ideal for a broad spectrum of creative uses, including virtual influencers, educational content, and entertainment. This groundbreaking tool provides an exceptional method for animating static images, yielding realistic outputs across diverse video formats and aspect ratios, thereby opening new avenues for creative expression. Its ability to seamlessly integrate various forms of media makes it a valuable asset for content creators looking to engage audiences in fresh and dynamic ways.

FLUX.1 Krea [dev] is a cutting-edge, open-source diffusion transformer with 12 billion parameters, developed through the collaboration of Krea and Black Forest Labs, aimed at providing exceptional aesthetic precision and photorealistic outputs while avoiding the common “AI look.” This model is fully integrated into the FLUX.1-dev ecosystem and is built upon a foundational model (flux-dev-raw) that possesses extensive world knowledge. It utilizes a two-phase post-training approach that includes supervised fine-tuning on a carefully selected combination of high-quality and synthetic samples, followed by reinforcement learning driven by human feedback based on preference data to shape its stylistic outputs. Through the innovative use of negative prompts during pre-training, along with custom loss functions designed for classifier-free guidance and specific preference labels, it demonstrates substantial enhancements in quality with fewer than one million examples, achieving these results without the need for elaborate prompts or additional LoRA modules. This approach not only elevates the model's output but also sets a new standard in the field of AI-driven visual generation.

Gen-3 Alpha marks the inaugural release in a new line of models developed by Runway, leveraging an advanced infrastructure designed for extensive multimodal training. This model represents a significant leap forward in terms of fidelity, consistency, and motion capabilities compared to Gen-2, paving the way for the creation of General World Models. By being trained on both videos and images, Gen-3 Alpha will enhance Runway's various tools, including Text to Video, Image to Video, and Text to Image, while also supporting existing functionalities like Motion Brush, Advanced Camera Controls, and Director Mode. Furthermore, it will introduce new features that allow for more precise manipulation of structure, style, and motion, offering users even greater creative flexibility.

Magi AI is an innovative open-source video generation platform that converts single images into infinitely extendable, high-quality videos using a pioneering autoregressive model. Developed by Sand.ai, it offers users seamless video extension capabilities, enabling smooth transitions and continuous storytelling without interruptions. With a user-friendly canvas editing interface and support for realistic and 3D semi-cartoon styles, Magi AI empowers creators across film, advertising, and social media to generate videos rapidly—usually within 1 to 2 minutes. Its advanced timeline control and AI-driven precision allow users to fine-tune every frame, making Magi AI a versatile tool for professional and hobbyist video production.

AudioLM is an innovative audio language model designed to create high-quality, coherent speech and piano music by solely learning from raw audio data, eliminating the need for text transcripts or symbolic forms. It organizes audio in a hierarchical manner through two distinct types of discrete tokens: semantic tokens, which are derived from a self-supervised model to capture both phonetic and melodic structures along with broader context, and acoustic tokens, which come from a neural codec to maintain speaker characteristics and intricate waveform details. This model employs a series of three Transformer stages, initiating with the prediction of semantic tokens to establish the overarching structure, followed by the generation of coarse tokens, and culminating in the production of fine acoustic tokens for detailed audio synthesis. Consequently, AudioLM can take just a few seconds of input audio to generate seamless continuations that effectively preserve voice identity and prosody in speech, as well as melody, harmony, and rhythm in music. Remarkably, evaluations by humans indicate that the synthetic continuations produced are almost indistinguishable from actual recordings, demonstrating the technology's impressive authenticity and reliability. This advancement in audio generation underscores the potential for future applications in entertainment and communication, where realistic sound reproduction is paramount.

Act-Two allows for the animation of any character by capturing and transferring movements, facial expressions, and dialogue from a performance video onto a static image or reference video of the character. To utilize this feature, you can choose the Gen‑4 Video model and click on the Act‑Two icon within Runway’s online interface, where you will need to provide two key inputs: a video showcasing an actor performing the desired scene and a character input, which can either be an image or a video clip. Additionally, you have the option to enable gesture control to effectively map the actor's hand and body movements onto the character images. Act-Two automatically integrates environmental and camera movements into static images, accommodates various angles, non-human subjects, and different artistic styles, while preserving the original dynamics of the scene when using character videos, although it focuses on facial gestures instead of full-body movement. Users are given the flexibility to fine-tune facial expressiveness on a scale, allowing them to strike a balance between natural motion and character consistency. Furthermore, they can preview results in real time and produce high-definition clips that last up to 30 seconds, making it a versatile tool for animators. This innovative approach enhances the creative possibilities for animators and filmmakers alike.

The Goku AI system, crafted by ByteDance, is a cutting-edge open source artificial intelligence platform that excels in generating high-quality video content from specified prompts. Utilizing advanced deep learning methodologies, it produces breathtaking visuals and animations, with a strong emphasis on creating lifelike, character-centric scenes. By harnessing sophisticated models and an extensive dataset, the Goku AI empowers users to generate custom video clips with remarkable precision, effectively converting text into captivating and immersive visual narratives. This model shines particularly when rendering dynamic characters, especially within the realms of popular anime and action sequences, making it an invaluable resource for creators engaged in video production and digital media. As a versatile tool, Goku AI not only enhances creative possibilities but also allows for a deeper exploration of storytelling through visual art.

Veo 2 is an advanced model for generating videos that stands out for its realistic motion and impressive output quality, reaching resolutions of up to 4K. Users can experiment with various styles and discover their unique preferences by utilizing comprehensive camera controls. This model excels at adhering to both simple and intricate instructions, effectively mimicking real-world physics while offering a diverse array of visual styles. In comparison to other AI video generation models, Veo 2 significantly enhances detail, realism, and minimizes artifacts. Its high accuracy in representing motion is a result of its deep understanding of physics and adeptness in interpreting complex directions. Additionally, it masterfully creates a variety of shot styles, angles, movements, and their combinations, enriching the creative possibilities for users. Ultimately, Veo 2 empowers creators to produce visually stunning content that resonates with authenticity.

SEELE AI serves as a comprehensive multimodal platform that converts straightforward text prompts into captivating, interactive 3D gaming environments, empowering users to create dynamic settings, assets, characters, and interactions which they can remix and enhance in real-time. It facilitates the generation of assets and spatial designs, offering endless possibilities for users to craft natural landscapes, parkour courses, or racing tracks purely through descriptive input. Leveraging advanced models, including innovations from Baidu, SEELE AI simplifies the complexities typically associated with traditional 3D game development, allowing creators to swiftly prototype and delve into virtual worlds without requiring extensive technical knowledge. Among its standout features are text-to-3D generation, limitless remixing capabilities, interactive editing of worlds, and the production of game content that remains both playable and customizable. This powerful platform not only enhances creativity but also democratizes game development, making it accessible to a broader range of users.

Genie 3 represents DeepMind's innovative leap in general-purpose world modeling, capable of real-time generation of immersive 3D environments at 720p resolution and 24 frames per second, maintaining consistency for several minutes. When provided with textual prompts, this advanced system fabricates interactive virtual landscapes that allow users and embodied agents to explore and engage with natural occurrences from various viewpoints, including first-person and isometric perspectives. One of its remarkable capabilities is the emergent long-horizon visual memory, which ensures that environmental details remain consistent even over lengthy interactions, retaining off-screen elements and spatial coherence when revisited. Additionally, Genie 3 features “promptable world events,” granting users the ability to dynamically alter scenes, such as modifying weather conditions or adding new objects as desired. Tailored for research involving embodied agents, Genie 3 works in harmony with systems like SIMA, enhancing navigation based on specific goals and enabling the execution of intricate tasks. This level of interactivity and adaptability marks a significant advancement in how virtual environments can be experienced and manipulated.

In honor of Cleopatra, whose magnificent fate concluded amidst the tragic incident involving a snake, we are excited to introduce Codestral Mamba, a Mamba2 language model specifically designed for code generation and released under an Apache 2.0 license. Codestral Mamba represents a significant advancement in our ongoing initiative to explore and develop innovative architectures. It is freely accessible for use, modification, and distribution, and we aspire for it to unlock new avenues in architectural research. The Mamba models are distinguished by their linear time inference capabilities and their theoretical potential to handle sequences of infinite length. This feature enables users to interact with the model effectively, providing rapid responses regardless of input size. Such efficiency is particularly advantageous for enhancing code productivity; therefore, we have equipped this model with sophisticated coding and reasoning skills, allowing it to perform competitively with state-of-the-art transformer-based models. As we continue to innovate, we believe Codestral Mamba will inspire further advancements in the coding community.

Pythia integrates the examination of interpretability and scaling principles to gain insights into the progression and transformation of knowledge throughout the training of autoregressive transformer models. This approach enables a deeper understanding of the mechanisms behind model learning and adaptation.

Mercury, the groundbreaking creation from Inception Labs, represents the first large language model at a commercial scale that utilizes diffusion technology, achieving a remarkable tenfold increase in processing speed while also lowering costs in comparison to standard autoregressive models. Designed for exceptional performance in reasoning, coding, and the generation of structured text, Mercury can handle over 1000 tokens per second when operating on NVIDIA H100 GPUs, positioning it as one of the most rapid LLMs on the market. In contrast to traditional models that produce text sequentially, Mercury enhances its responses through a coarse-to-fine diffusion strategy, which boosts precision and minimizes instances of hallucination. Additionally, with the inclusion of Mercury Coder, a tailored coding module, developers are empowered to take advantage of advanced AI-assisted code generation that boasts remarkable speed and effectiveness. This innovative approach not only transforms coding practices but also sets a new benchmark for the capabilities of AI in various applications.

The Gemini 2.5 Flash Image is Google's cutting-edge model for image creation and modification, now available through the Gemini API, build mode in Google AI Studio, and Vertex AI. This model empowers users with remarkable creative flexibility, allowing them to seamlessly merge various input images into one cohesive visual, ensure character or product consistency throughout edits for enhanced storytelling, and execute detailed, natural-language transformations such as object removal, pose adjustments, color changes, and background modifications. Drawing from Gemini’s extensive knowledge of the world, the model can comprehend and reinterpret scenes or diagrams contextually, paving the way for innovative applications like educational tutors and scene-aware editing tools. Showcased through customizable template applications in AI Studio, which includes features such as photo editors, multi-image merging, and interactive tools, this model facilitates swift prototyping and remixing through both prompts and user interfaces. With its advanced capabilities, Gemini 2.5 Flash Image is set to revolutionize the way users approach creative visual projects.

This repository showcases an implementation of model parallel autoregressive transformers utilizing GPUs, leveraging the capabilities of the DeepSpeed library. It serves as a record of EleutherAI's framework designed for training extensive language models on GPU architecture. Currently, it builds upon NVIDIA's Megatron Language Model, enhanced with advanced techniques from DeepSpeed alongside innovative optimizations. Our goal is to create a centralized hub for aggregating methodologies related to the training of large-scale autoregressive language models, thereby fostering accelerated research and development in the field of large-scale training. We believe that by providing these resources, we can significantly contribute to the progress of language model research.

Gen-2: Advancing the Frontier of Generative AI. This innovative multi-modal AI platform is capable of creating original videos from text, images, or existing video segments. It can accurately and consistently produce new video content by either adapting the composition and style of a source image or text prompt to the framework of an existing video (Video to Video), or by solely using textual descriptions (Text to Video). This process allows for the creation of new visual narratives without the need for actual filming. User studies indicate that Gen-2's outputs are favored over traditional techniques for both image-to-image and video-to-video transformation, showcasing its superiority in the field. Furthermore, its ability to seamlessly blend creativity and technology marks a significant leap forward in generative AI capabilities.

Qwen-Image is a cutting-edge multimodal diffusion transformer (MMDiT) foundation model that delivers exceptional capabilities in image generation, text rendering, editing, and comprehension. It stands out for its proficiency in integrating complex text, effortlessly incorporating both alphabetic and logographic scripts into visuals while maintaining high typographic accuracy. The model caters to a wide range of artistic styles, from photorealism to impressionism, anime, and minimalist design. In addition to creation, it offers advanced image editing functionalities such as style transfer, object insertion or removal, detail enhancement, in-image text editing, and manipulation of human poses through simple prompts. Furthermore, its built-in vision understanding tasks, which include object detection, semantic segmentation, depth and edge estimation, novel view synthesis, and super-resolution, enhance its ability to perform intelligent visual analysis. Qwen-Image can be accessed through popular libraries like Hugging Face Diffusers and is equipped with prompt-enhancement tools to support multiple languages, making it a versatile tool for creators across various fields. Its comprehensive features position Qwen-Image as a valuable asset for both artists and developers looking to explore the intersection of visual art and technology.

BLOOM is a sophisticated autoregressive language model designed to extend text based on given prompts, leveraging extensive text data and significant computational power. This capability allows it to generate coherent and contextually relevant content in 46 different languages, along with 13 programming languages, often making it difficult to differentiate its output from that of a human author. Furthermore, BLOOM's versatility enables it to tackle various text-related challenges, even those it has not been specifically trained on, by interpreting them as tasks of text generation. Its adaptability makes it a valuable tool for a range of applications across multiple domains.

Wan2.1 represents an innovative open-source collection of sophisticated video foundation models aimed at advancing the frontiers of video creation. This state-of-the-art model showcases its capabilities in a variety of tasks, such as Text-to-Video, Image-to-Video, Video Editing, and Text-to-Image, achieving top-tier performance on numerous benchmarks. Designed for accessibility, Wan2.1 is compatible with consumer-grade GPUs, allowing a wider range of users to utilize its features, and it accommodates multiple languages, including both Chinese and English for text generation. The model's robust video VAE (Variational Autoencoder) guarantees impressive efficiency along with superior preservation of temporal information, making it particularly well-suited for producing high-quality video content. Its versatility enables applications in diverse fields like entertainment, marketing, education, and beyond, showcasing the potential of advanced video technologies.

Imagen 3 represents the latest advancement in Google's innovative text-to-image AI technology. It builds upon the strengths of earlier versions and brings notable improvements in image quality, resolution, and alignment with user instructions. Utilizing advanced diffusion models alongside enhanced natural language comprehension, it generates highly realistic, high-resolution visuals characterized by detailed textures, vibrant colors, and accurate interactions between objects. In addition, Imagen 3 showcases improved capabilities in interpreting complex prompts, which encompass abstract ideas and scenes with multiple objects, all while minimizing unwanted artifacts and enhancing overall coherence. This powerful tool is set to transform various creative sectors, including advertising, design, gaming, and entertainment, offering artists, developers, and creators a seamless means to visualize their ideas and narratives. The impact of Imagen 3 on the creative process could redefine how visual content is produced and conceptualized across industries.

Command A Vision is an enterprise-focused multimodal AI solution from Cohere that merges image interpretation with language processing to enhance business results while minimizing computing expenses; this addition to the Command suite introduces vision analysis, enabling companies to decode and respond to visual materials alongside textual information. Seamlessly integrating with workplace systems, it helps uncover insights, enhance productivity, and facilitate smarter search and discovery, firmly placing itself within Cohere’s extensive AI ecosystem. The solution is designed to leverage real-world workflows, aiding teams in harmonizing various multimodal signals, deriving meaningful insights from visual data and its accompanying metadata, and presenting pertinent business intelligence without incurring heavy infrastructure costs. Command A Vision is particularly adept at interpreting and examining a diverse array of visual and multilingual information, such as charts, graphs, tables, and diagrams, showcasing its versatility for various business applications. As a result, organizations can maximize their operational efficiency and make informed decisions based on a comprehensive understanding of both visual and textual data.

HunyuanCustom is an advanced framework for generating customized videos across multiple modalities, focusing on maintaining subject consistency while accommodating conditions related to images, audio, video, and text. This framework builds on HunyuanVideo and incorporates a text-image fusion module inspired by LLaVA to improve multi-modal comprehension, as well as an image ID enhancement module that utilizes temporal concatenation to strengthen identity features throughout frames. Additionally, it introduces specific condition injection mechanisms tailored for audio and video generation, along with an AudioNet module that achieves hierarchical alignment through spatial cross-attention, complemented by a video-driven injection module that merges latent-compressed conditional video via a patchify-based feature-alignment network. Comprehensive tests conducted in both single- and multi-subject scenarios reveal that HunyuanCustom significantly surpasses leading open and closed-source methodologies when it comes to ID consistency, realism, and the alignment between text and video, showcasing its robust capabilities. This innovative approach marks a significant advancement in the field of video generation, potentially paving the way for more refined multimedia applications in the future.

WeatherNext represents a suite of AI-driven models developed by Google DeepMind and Google Research, designed to deliver cutting-edge weather predictions. These advanced models surpass conventional physics-based approaches in both speed and efficiency, leading to enhanced reliability in forecasts. By improving the accuracy of weather predictions, these innovations could significantly aid in disaster preparedness, ultimately saving lives during severe weather scenarios and bolstering the dependability of renewable energy sources and supply chains. WeatherNext Graph stands out by providing more precise and efficient deterministic forecasts than existing systems, producing a single forecast for each specified time and location with a 6-hour temporal resolution and a 10-day lead time. In addition, WeatherNext Gen excels at generating ensemble forecasts that outshine the current predominant models, thereby equipping decision-makers with a clearer understanding of weather uncertainties and the associated risks of extreme weather conditions. This leap in forecasting capability promises to transform how we respond to and manage the impacts of climate variability.

Microsoft has introduced Muse, an innovative generative AI model poised to transform the way gameplay concepts are developed. In partnership with Ninja Theory, this World and Human Action Model (WHAM) draws training data from the game Bleeding Edge, granting it a profound grasp of 3D game landscapes, including the intricacies of physics and player interactions. This capability allows Muse to generate varied and coherent gameplay sequences, which can enhance the creative process for developers. Additionally, the AI is capable of creating game visuals and anticipating controller actions, streamlining prototyping and artistic exploration in game design. By leveraging an analysis of over 1 billion images and actions, Muse showcases its potential not only for game creation but also for game preservation, as it can recreate classic titles for contemporary gaming platforms. Despite being in its initial phases, with output currently limited to a resolution of 300×180 pixels, Muse signifies a pivotal step forward in harnessing AI to support game development, with the goal of amplifying human creativity rather than supplanting it. As Muse evolves, it may open up new avenues for both game innovation and the revival of beloved gaming classics.

Amazon Nova Lite is a versatile AI model that supports multimodal inputs, including text, image, and video, and provides lightning-fast processing. It offers a great balance of speed, accuracy, and affordability, making it ideal for applications that need high throughput, such as customer engagement and content creation. With support for fine-tuning and real-time responsiveness, Nova Lite delivers high-quality outputs with minimal latency, empowering businesses to innovate at scale.

OpenAI’s GPT-5 nano is the most cost-effective and rapid variant of the GPT-5 series, tailored for tasks like summarization, classification, and other well-defined language problems. Supporting both text and image inputs, GPT-5 nano can handle extensive context lengths of up to 400,000 tokens and generate detailed outputs of up to 128,000 tokens. Its emphasis on speed makes it ideal for applications that require quick, reliable AI responses without the resource demands of larger models. With highly affordable pricing — just $0.05 per million input tokens and $0.40 per million output tokens — GPT-5 nano is accessible to a wide range of developers and businesses. The model supports key API functionalities including streaming responses, function calling, structured output, and fine-tuning capabilities. While it does not support web search or audio input, it efficiently handles code interpretation, image generation, and file search tasks. Rate limits scale with usage tiers to ensure reliable access across small to enterprise deployments. GPT-5 nano offers an excellent balance of speed, affordability, and capability for lightweight AI applications.