LTX-2.3 is an open-source audio-video foundation model released by Lightricks. Its core feature is not simply generating video alone or producing video first and adding audio later. Instead, it places both video and audio within a single generation framework, directly producing synchronized visuals and sound. Officially, it is described as a DiT-based audio-video foundation model, meaning a joint audio-video generation model built on Diffusion Transformer architecture.Compared with many traditional video generation approaches, the biggest difference of LTX-2.3 is its native audio-visual synchronization. If a prompt includes speaking, singing, ambient sound, or rhythmic motion, the model attempts to align lip movements, actions, and sound within a single generation process, rather than relying on post-processing to dub audio or correct lip sync afterward. This makes it especially valuable for dialogue videos, character singing, and short narrative scenes.

Lens is a newly open-sourced text-to-image foundation model developed by the Microsoft Lens Team. It is designed for scenarios such as high-quality image generation, design creation, visual content production, and generative AI research. Rather than simply scaling up model size, Lens focuses on achieving comparable or even superior generation quality to larger text-to-image models through more efficient training, while maintaining a relatively compact parameter scale.Lens has approximately 3.8 billion parameters and is systematically optimized around training efficiency. The model is trained on the Lens-800M dataset, which contains around 800 million image-text pairs and uses longer, denser image captions to increase the information density of each training batch. At the same time, Lens introduces mixed-resolution and multi-aspect-ratio training strategies, allowing the model to learn from a richer variety of visual layouts and compositions during a single training process. This enhances its ability to handle complex prompts, different aspect ratios, and high-resolution generation tasks.

Ideogram 4 is Ideogram’s first open-weight text-to-image foundation model. It is not positioned as merely a model that “can generate images,” but as one more focused on design, layout, posters, brand visuals, and image generation involving text content.Compared with many traditional text-to-image models, Ideogram 4’s biggest strengths are its stronger control over text, typography, and spatial layout. It is especially suitable for creating design-oriented content such as posters, logos, covers, product images, advertising visuals, and social media graphics.

HiDream‑O1‑Image is a next-generation open-source image generation model. It demonstrates strong performance in image generation and editing tasks and achieves competitive results on multiple standard benchmarks with an 8 B parameter scale.🖌 Image Editing: Modify images using a given original image and instructions, such as adjusting content.📸 Reference Image-Based Reconstruction: Supports defining characters using multiple reference images and then reconstructing them in a new scene.🧩 Long Text Rendering and Layout Control: For complex cues containing multiple regions, long descriptions, and multiple languages, the UiT architecture demonstrates strong semantic consistency and layout understanding capabilities.

The user wants me to translate the Chinese text about GPT Image 2 into English.GPT Image 2 Model IntroductionGPT Image 2 is OpenAI's latest image generation model, released on April 21, 2026. It claimed the #1 spot on the Image Arena leaderboard with a +242 point margin.Core CapabilitiesFeatureDescriptionText RenderingAchieves up to 99% accuracy in generating clear text, signs, and labelsResolutionSupports up to 4K native resolutionCharacter ConsistencyGenerates multiple images with consistent characters and objects from a single promptNative ReasoningBuilt-in "thinking" capabilities, supporting layout reasoning and multi-image batchingColor AccuracyResolved the yellow color cast issues from previous models

Core Highlights1️⃣ Beyond the "AI Standard Face" Supports full customization from bone structure and eye details to facial features, generating unique characters with a "living person" feel — no more cookie-cutter designs.2️⃣ Powerful Text Rendering Handles up to 3K tokens of ultra-long text input, rendering with clarity:Complex tablesMathematical formulasMultilingual long textSupports 12 languages3️⃣ Color Palette Function One-click color adjustment via Hex codes for pixel-perfect color control.4️⃣ Full-Chain Editing CapabilitiesText-to-ImageImage-to-Image (batch generation up to 12 images)Instruction-based image editingRegional selective editing

Nano Banana 2 is an image generation and editing model focused on high quality + high speed.It supports text-to-image, image-to-image, and multi-reference image editing (useful for style transfer, subject consistency, poster redesign, and more), with strong prompt understanding and text rendering capabilities.Overall, its key strengths are fast output, rich detail, and more controllable edits, making it well-suited for e-commerce assets, social media visuals, and rapid creative iteration

Model OverviewERNIE-Image is an open-source text-to-image generation model developed by Baidu's Wenxin (ERNIE) team. Built on a single-stream Diffusion Transformer (DiT) architecture with 8 billion parameters, it operates within a Latent Diffusion Model (LDM) framework.The model's core philosophy emphasizes not only visual aesthetics but also controllability. In content creation scenarios such as commercial posters, comics, and multi-panel layouts, accurate content realization matters just as much as visual appeal. Core CapabilitiesNative Multilingual SupportNatively understands Chinese, English, and Japanese, supporting culturally authentic outputs and idiomatic expressionsParticularly well-suited for East Asian content creationPrecise Text RenderingStrongest text rendering among all open-source modelsSupports dense typography, long-form text, and layout-sensitive content in both Chinese and EnglishIdeal for text-heavy imagery such as poster titles, comic dialogue boxes, and UI interfacesComplex Instruction FollowingReliably handles multi-object relationships, complex descriptions, and knowledge-intensive content

Kling V3 (Kling 3.0) is an updated version of KlingAI’s (Kuaishou’s Kling Team) video generation capability, positioned as an integrated, multimodal generation engine for video creation. It supports combining text instructions and reference inputs within a single creative workflow to unify generation and editing.This workflow collection covers the following use cases:Text-to-VideoImage-to-VideoImage Generation

Image editing model with higher-quality generation, supporting features such as text-to-image and image editing. Credit cost: 55 credits per image.

ViduQ3Turbo is a video generation model focused on high speed and high stability, making it ideal for quickly moving from idea to final output. While maintaining smooth motion and solid visual detail, it follows prompt intent effectively, making it suitable for short-form video creation, ad storyboard previsualization, and content production workflows.

Z-Image is the foundational model of the Image family, designed with high quality, robust generation versatility, broad style coverage, and precise cue adherence in mind. While Z-Image-Turbo is designed for speed, Z-Image is a full-capacity, unrefined transformer.

FLUX.2-klein- is a high-speed image generation and editing model released by Black Forest Labs. It unifies text-to-image and image-to-image/image editing in a single architecture, focusing on low latency: with the right settings, inference can be extremely fast, making it well-suited for interactive “tweak-and-preview” workflows and production deployment. It can also run on consumer GPUs; according to the official specs, it requires about 13GB of VRAM.What it can do: text-to-image (T2I), editing/redrawing from uploaded reference images (I2I), and multi-reference editing.Best for: real-time preview, rapid image generation, application integration, and edge deployment.

This workflow is providing access to two distinct versions: FLUX.2 Pro and FLUX.2 Flex. You can switch between them based on your specific needs for image precision and cost efficiency.🧩 Versions & Capabilities1. FLUX.2 ProCapabilities: Capable of generating high-quality images. Ideal for most standard creative tasks, style exploration, and rapid generation.Pricing (Credits):Text Only: 55 (≤1024px) / 70 (>1024px)Image Input: 80 (≤1024px) / 100 (>1024px)2. FLUX.2 FlexCapabilities: Compared to Pro, Flex excels in handling complex lighting, intricate textures, and adherence to long, complex prompts. It is the premier choice for ultimate image quality, commercial poster output, and high-precision editing tasks.Pricing (Credits):Text Only: 110 (≤1024px) / 140 (>1024px)Image Input: 220 (≤1024px) / 260 (>1024px)

For image-to-video (I2V) tasks, Wan2.2 transforms a single static image into a high-resolution, smoothly animated video sequence. Thanks to an efficient MoE structure and region-aware design, the model preserves the core composition, subject details, and atmosphere of the reference image, while introducing natural motion and stylistic coherence throughout the video.Wan2.2 accommodates a variety of aesthetic preferences, making it suitable for anime, illustration animation, and expressive character scenes. It offers high inference efficiency and is resource-friendly, supporting high-quality and scalable content creation on diverse hardware.

Continue the “unified editing/control” paradigm on the 2.2 backbone. The 2.2 backbone adopts a Mixture‑of‑Experts (MoE) design—high‑noise and low‑noise experts operating at different denoising stages—to improve quality and detail while keeping inference costs manageable. A representative controllable variant is Wan2.2‑VACE‑Fun‑A14B, which supports multi‑modal control conditions (Canny, Depth, OpenPose, MLSD, Trajectory, etc.). A typical workflow is: provide a reference image (to preserve identity/appearance) plus a driving video or its parsed control signals (e.g., pose sequence, trajectory, time‑varying depth/edges) to generate a video driven by that reference image. The VACE/Fun family provides these temporal control interfaces and the unified task support.

Wan2.1_SCAIL_2 is an enhanced, fine-tuned model version based on the Wan2.1 video generation system, optimized for high-quality video generation and controllable visual synthesis tasks.Building upon the original Wan2.1 Diffusion Transformer architecture, this model improves performance in the following areas through data augmentation strategies, motion consistency optimization, and style alignment fine-tuning (SCAIL pipeline):Stability in generating complex dynamic scenesCharacter consistency and structure preservationVisual style uniformity (cinematic/anime/concept art)Prompt-following accuracyMulti-frame motion coherence

SkyReels-V3-R2V-14B is a “reference-image-to-video (R2V)” model from Skywork’s SkyReels V3 series, built on a unified multimodal in-context learning framework. It supports 1–4 reference images (e.g., characters, objects, backgrounds) combined with a text prompt to generate videos with more stable subject consistency, scene coherence, and narrative consistency.

SkyReels-V3-A2V-19B is a “Talking Avatar” (audio-driven portrait video) model from Skywork’s SkyReels V3 series, built on a unified multimodal in-context learning framework. It generates a talking video with natural expressions and lip-sync from a single portrait image plus an audio clip, emphasizing high visual quality and strong audio-visual alignment.Core capability: Generate talking videos from one portrait image + audio (Talking Avatar / A2V), with strong lip-sync and expressiveness.Inputs: Images in jpg/jpeg/png/gif/bmp; audio in mp3/wav (the official note says audio duration must be <= 200s).Recommended output: 720p at 24 fps.

FlashVSR is a video super-resolution model that upscales low-resolution videos to high-resolution in real time, aiming for "clarity, stability, and speed."It can perform super-resolution/deblurring on long videos, resulting in clearer details, more stable textures, and significantly reduced jagged edges and flicker. Traditional super-resolution models require multiple iterations, which is slow; FlashVSR uses a "single-step diffusion" approach, which is very fast, achieving both good results and speed. One-step diffusion (reducing dozens of steps) + a streamlined decoder → faster inference.

Ovi is a veo-3-like, video + audio generation model that simultaneously generates both video and audio content from text or text + image inputs.🎬 Video+Audio Generation: Generate synchronized video and audio content simultaneously🎵 High-Quality Audio Branch: Designed and pretrained a 5B audio branch from scratch using our high quality in-house audio datasets📝 Flexible Input: Supports text-only or text+image conditioning⏱️ 10-second (or 5-second) Videos: Generates 10-second or 5-second videos at 24 FPS, at various aspect ratios

Wan2.2‑Fun‑InP is part of the Wan2.2‑Fun series. It supports conditioning on a start frame and an end frame to estimate the in‑between transition and produce temporally consistent video results for controllable image‑to‑video applications.What it addresses:Traditional image‑to‑video workflows typically extend motion from a single starting image. By adding an optional end keyframe, Fun‑InP helps the motion, composition, and overall content progress toward a specified target, making transitions easier to control and the sequence more coherent.Inputs: start‑frame image, end‑frame image (optional text prompt / control signals).Output: a video clip made up of interpolated middle frames, with the first and last frames visually consistent with the provided keyframes.

Core Focus: Video-level object removal. Given a sequence of video frames and a corresponding mask, it seamlessly removes the masked object and fills in the background while maintaining temporal consistency, minimizing artifacts or remnants.Method Highlights:Minimum-Maximum Optimization: Tames bad noise during training and inference, improving the model's robustness to masked regions and reducing the probability of object regeneration.Two-Stage Architecture: First, a simplified DiT (Diffusion Transformer) structure is used to learn the removal capability; then, a version with fewer sampling steps and faster inference is obtained through "CFG de-distillation."Efficiency Features: Extremely low inference steps (approximately 6 steps in the official example), and does not rely on CFG, resulting in high speed and low resource consumption, suitable for long videos/batch processing. References

🐱 LongCat-Video: Infinite Video Extension Workflow【One-Sentence Intro】Break the duration limit of AI video generation 🚀What Can It Do?This is an advanced workflow based on the **Wan2.1** model, designed to solve the core pain points of AI videos being "too short" and "disjointed when extended."♾️ Infinite Extension Just provide an image or a short video clip, and the workflow will automatically generate subsequent frames like a "relay race," theoretically allowing for infinite generation.Seamless "Invisible" Stitching It automatically trims the awkward beginnings of extended segments, making the transition between clips as smooth as silk, with absolutely no visible stitching marks.【Use Cases】Creating ultra-long looping landscape videos.Producing coherent narrative shorts, no longer limited by the 5-second barrier.

Powered by the experimental SeaArt Wan2.1 node, this workflow enables fast, high-quality video generation from a single image with streamlined settings. Enjoy ultra-fast processing, simple controls, and full compatibility with Lora styles—perfect for creators who value both efficiency and creativity

Ltx2.3 - VideoEdit

Anima is a 2B-parameter text-to-image model created through a collaboration between CircleStone Labs and Comfy Org. It’s positioned for anime/illustration/non-photorealistic creation: it excels at anime characters, illustration aesthetics, and a wide range of artistic styles, but it’s not suitable for photorealistic results.

A video generation workflow that can generate a video using three images: the first frame, the middle frame, and the end frame.

this workflow is a hybrid where the standard Z-anime AIO enhanced by QWEN 2511 Anime.upcaled !

LTX 2.3 IMG TO VIDEO FASTT BETTER

Kling 3 : MotionControl is the motion control capability within the Kling video generation system. It is designed to accurately transfer the character movement, expressions, and rhythm from a reference video onto a target character image. Its core purpose is not simply to “generate a video,” but to let you use a motion video to drive a new character, scene, and style, resulting in a video with consistent motion, stable subject identity, and coherent camera continuity.

Transform low-quality images into clean, high-resolution results in seconds.This AI-powered upscaler enhances clarity, improves fine details, and increases overall sharpness while keeping the original look and composition intact.✔ Improves image clarity✔ Adds natural detail and texture✔ Keeps the original appearance✔ High-resolution output✔ Ideal for photos, AI art, and digital imagesSimple. Fast. High quality.

sdxl二次元生图以及1.5倍高清放大

smart prompt generator, higher resolution, and of course cheaper.enjoy super simple workflow.

SeedVR2 2x2 block-based high-definition restoration

2025の恩返し自分のフォロアーに送る動画生成（無料）動画アプリ製作支援ツール質問要望何でも来い！機能追加予定の進化するワークフローコメントに希望を書いたら機能が追加される？進化するワークフロー爆誕！！

Powered by the experimental SeaArt Wan2.2 node, this workflow enables fast, high-quality video generation from a single image with streamlined settings. Enjoy ultra-fast processing, simple controls, and full compatibility with Lora styles—perfect for creators who value both efficiency and creativity