LTX-2.5 is a new-generation open-weight audiovisual generation model from LTX, capable of simultaneously generating high-quality video and synchronized audio in a single pass. It supports text-to-video, image-to-video, and start/end-frame-guided video generation, while delivering enhanced visual detail, motion stability, comprehension of complex prompts, and consistency across long-sequence content.Compared to LTX-2.3, LTX-2.5 introduces an all-new Diffusion Video Decoder that effectively mitigates issues such as blur, texture degradation, and loss of character detail during high-speed motion. It also incorporates native multi-shot generation capabilities, enabling the creation of multiple consecutive shots in a single run while maintaining—to the greatest extent possible—consistency in characters, scenes, lighting, style, and audio across shot transitions.The model utilizes an LTX-customized Gemma 4 12B text encoder, offering superior comprehension of multiple characters, actions, cinematic language, lighting, and complex scene descriptions. It also supports automatic duration prediction, dynamically matching the video length to the actions and scene content specified in the prompt. Furthermore, LTX-2.5 supports native 4K HDR, frame rates up to 50 FPS, and RAW workflows, providing greater flexibility for post-production in film, television, and high-quality video creation.As an open-weight model, LTX-2.5 supports local deployment, LoRA, and fine-tuning. Balancing generation speed, controllability, and openness, it is well-suited for applications such as short films, advertisements, character animation, multi-shot storytelling, and professional video production.

MiniMax H3 is an open-source omni-modal video and audio generation model from MiniMax. It can jointly understand multiple types of reference information, including text, images, video, and audio, while generating video and native stereo audio in sync. The currently open-sourced H3-Base includes two model variants, FL2VA and Ref2VA, supporting text-to-video, first-and-last-frame-to-video, and generation with multiple image, video, and audio references. The local version supports resolutions up to 768p and video durations from 4 to 15 seconds.H3 performs strongly in complex instruction following, multimodal reference understanding, character and scene consistency, motion transfer, and audio-video synchronization, making it suitable for advertising, e-commerce, animation, film and video production, and creative video generation. The model is built on a 33B-parameter H3-Omni-Transformer and simultaneously predicts video and audio latents through a unified multimodal sequence.

Boogu-Image--Edit is an open-source image editing model designed for instruction-based image transformation. It can modify existing images while preserving the main subject, structure, and visual details, making it suitable for style changes, object replacement, background editing, character transformation, and other creative image-editing tasks. The model currently supports a single reference image and delivers its most stable results at around 1K resolution

Krea 2 is Krea’s next-generation open-source text-to-image model, designed for strong visual aesthetics, expressive styles, and natural-looking image quality. It excels at photography, portraits, posters, illustrations, and cinematic scenes, with improved rendering of lighting, materials, composition, and atmosphere while reducing the common “AI plastic” look.

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.

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.

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.

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)

minimax-H3 ref2va motion referenceLeverage MiniMax-H3's robust video reference feature for character motion transfer.Simply upload your reference video and character image, then click Run.

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Fast, optimized, ez, stop wasting your credits on slow outdated WF ;)Full Minimax H3 Turbo - Ref 2 VA - Director , with multiple image / video / audio referencesMinimax H3 Reference to video Turbo director rewrite your prompt in correct H3 format.Turbo 4-steps , sage attention, cache etc..., prompt director with H3 prompt doc will rewrite your idea in a correct format.Roooooaaaaaar 💙💛

# Bernini 4-Step Character Replace Workflow## DescriptionA fast AI video workflow for character replacement based on Bernini 4-Step sampling.Simply upload a **reference video** and a **character reference image** to generate a new video with the target character replaced into the original footage.Suitable for:- Character replacement- Dance video remake- Movie / scene face swap- Short video editing- Creative AI video generationYou can freely adjust the prompt and reference inputs to control the final style and motion result.---## Recommended Settings- **Video Length:** 80 + 1 frames (≈ 5 seconds)- **Frame Limit:** 16 frames- **Recommended Resolution:** 480p (shortest side)- **Resolution:** Adjustable according to VRAM⚠️ To avoid VRAM overflow, it is recommended to keep the shortest side at **480p**.---## Input- **Reference Video:** the source motion / original footage- **Character Reference Image:** the target character or person to replace into the video---## Features- Character replacement from video + image- Fast 4-step generation- Supports dance and motion scenes- Suitable for video editing and remake- Flexible prompt control

Storyboard-to-Image Generation: Multi-view, Multi-angle, Multi-perspective (Qwen_Edit_2511)

This is a high-quality workflow designed for anime-to-real conversion.Powered by Qwen’s strong image editing capability and enhanced with xiaozhi’s LoRA, it can stably redraw anime characters into realistic human portraits while preserving core character identity. At the same time, it helps reduce the awkward proportion and perspective issues that often happen when the result follows the original anime structure too rigidly, making the final image look more natural and visually convincing.After the initial realistic redraw, the workflow uses Z-Image to refine the texture and reduce the plasticky skin look that can sometimes appear in Qwen-generated results. This helps produce more natural skin, cleaner facial texture, and a more photographic overall finish.Finally, SeedVR2 is used for super-resolution upscaling to further enhance sharpness, restore fine details, and reduce the slightly dirty or noisy look that may occasionally appear after Z-Image refinement.The full node setup and parameters have already been organized and prepared, so there is no need to build the workflow manually. Just upload your image and click generate to experience a clean and efficient anime-to-real conversion pipeline.Perfect for:Anime to real portrait conversionStylized character to realistic human redrawCharacter redesignPoster-quality portrait generationAI image transformation and refinementIf you want a workflow that delivers more stable, more natural, and more detailed anime-to-real results, this one is definitely worth trying.

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.

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