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 is standard QWEN 2512 workflow, but i use my simple automation system to generate both positive and negative prompts, so you just need to write your request, let my system generate the prompt for you.

Qwen-Image-Edit-2511 with multi-image inputs. Supports 1 to 3 input images for various editing tasks:• Person + Person (face swap, person combination)• Person + Outfit (clothing transfer, style change) • Person + Scene (background replacement, environment blend)• Texture Transfer (apply textures from one image to objects in another)• Relighting (change lighting conditions and atmosphere)• Viewpoint Changes (modify camera angles and perspectives)Flexible input combinations for creative image editing and composition.Generation time: ~1-2 minute

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)

This is a straightforward workflow for Z-image with LoRA, without an upscaler involved.

This workflow utilizes the Flux 2 model to generate images with text or multiple reference images. Ideal for character design, fashion, and creative photography scenarios.This workflow comes pre-configured with up to 10 reference options that you can use in your projects.And even if you disable all reference images, you can still use it with just text to image.You can learn more about how it works in the official Flux 2 documentation. https://docs.bfl.ai/guides/prompting_guide_flux2 Visit my profile to see other projects I've developed:👍Laikezando

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.

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

BRING YOUR CINEMATIC IDEA TO LIFEHYBRID WORKFLOW that combing newest QWEN 2512 T2I with WAN2.2 2xthis workflow is for fun. you just need to put your idea, and let the workflow do the magic for you.get 25 fps, 10s duration smooth video result,why i use 25fps ? so you can add MMAudio Sampler nodes if you want, or you can use BGM for your results.and i use 1280x720 as the image output, enjoy!

A next-gen multimodal pipeline using DeepSeek Janus-Pro 7B for visual reasoning and Flux.2 GGUF for high-fidelity diffusion.How it Works:Analyze: Janus-Pro "sees" your input image and writes a professional prompt.Refine: Your manual prompt is blended with Janus's intelligence.Synthesize: Flux.2 generates the final image with local-to-cloud parity.Created by: [Your Name/Handle]License: Creative ML Open RAIL-M

A fully automatic Illustrious AIO workflow supporting both Text-to-Image and Image-to-Image. Switch between modes instantly using simple toggles — no need to reconfigure or rebuild the workflow.

This workflow aims to create high-quality images without being a turtle slow. It consists of a USDU acting as a refiner and a chain of detailers. The result is very good quality images with an execution time of less than one minute and thirty seconds. Times range from 1:10 to 1:30 minutes.It is optimized to work with the recommended latent resolutions for Illustrious-XL, which are close to 832x1216. These resolutions avoid long, deformed bodies, elongated faces, broken columns, etc. Don't worry, the workflow refinement leaves the images with tremendous quality.I left a Preview Image from the initial Ksmapler so you can see if your Checkpoint, LoRA, and Prompt are causing problems (if your problem comes from here, it's a problem with your own model configuration, LoRA, and prompt; don't blame the workflow!).If you have questions, suggestions, or want to point out errors, feel free to comment. Oh, and don't forget to post your artwork! :3

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