Wan2.2 Animate 14B Alternatives

Wan2.2-T2V-A14B-GGUF is a quantized version in GGUF format of the 14-billion parameter Wan2.2 text-to-video model. It enables local inference of text-to-video generation on compatible hardware through reduced memory requirements offered by multiple quantization levels. The repository provides GGUF files at quantization levels ranging from Q2_K at 5.3 GB to Q8_0 at 15.4 GB. Specific variants include Q3_K_S at 6.51 GB, Q3_K_M at 7.17 GB, Q4_K_S at 8.75 GB, Q4_0 at 8.56 GB, Q4_1 at 9.26 GB, Q4_K_M at 9.65 GB, Q5_K_S at 10.1 GB, Q5_0 at 10.3 GB, Q5_1 at 11 GB, Q5_K_M at 10.8 GB, and Q6_K at 12 GB. This model is a direct conversion of the original Wan-AI/Wan2.2-T2V-A14B, so all original licensing terms and usage restrictions apply. It integrates with the ComfyUI custom node ComfyUI-GGUF developed by city96. Model files are placed in the ComfyUI/models/unet directory, with further setup details available in the associated GitHub readme. The underlying architecture is listed as wan. The repository records 94,545 downloads in the last month and carries an apache-2.0 license.

Wan2.2-I2V-A14B-GGUF is an open-source, quantized image-to-video generative AI model distributed in GGUF format. It allows developers and researchers to generate videos from images locally, supporting various hardware configurations. The model is suitable for integration into custom AI pipelines and experimentation.

Wan2.2BasedModels is a repository containing GGUF quantized versions of models based on the Wan2.2 architecture. It offers multiple quantization levels (Q4_K_M through Q8_0) for a 14B parameter model, enabling flexible local inference across different hardware setups.

Wan2.2-S2V-14B is a 14-billion parameter open-weight diffusion model for image-to-video and audio-driven video generation. It enables developers to create videos from still images and audio prompts using the Diffusers library. The model is hosted on Hugging Face, comes with Apache-2.0 licensing, and supports local inference on compatible hardware.

Wan_GGUF is a repository containing GGUF quantized model files for use with Diffusers and other inference frameworks. Created by Dr. Furkan Gözükara of SECourses, it enables efficient local image generation. The models support prompts for high-quality image synthesis and are optimized for various hardware configurations.

Wan2.1-T2V-14B is a 14 billion parameter text-to-video model developed by Wan-AI. It uses a diffusion-based architecture and is distributed with full open weights under the Apache 2.0 license. The model can be run locally or via Hugging Face inference providers using the Diffusers library and supports high-resolution video generation from natural language prompts.

Wan2.2 I2V A14B is an image-to-video diffusion model hosted on Hugging Face. It accepts a starting image and a text prompt to produce a short video clip. The model is provided in Diffusers format and uses the WanImageToVideoPipeline. Installation proceeds through the command pip install -U diffusers transformers accelerate. Code examples load the pipeline with bfloat16 precision, map it to a CUDA device, pass an image loaded via load_image and a prompt string such as "A man with short gray hair plays a red electric guitar," then retrieve the generated frames and export them with export_to_video. The repository supplies the model in Safetensors format and carries an Apache-2.0 license. An arXiv paper numbered 2503.20314 is referenced in the repository metadata. It is delivered as a downloadable model card on the Hugging Face platform. The repository lists support for English and Chinese. Users run it locally or on compatible inference providers after installing the listed libraries. The page indicates 11.7k likes and 280 followers for the Wan-AI organization.

Qwen2.5-14B-Instruct-GGUF is a quantized variant of the Qwen2.5 14B Instruct model provided on Hugging Face. It supplies GGUF format files that enable local inference using compatible engines such as llama.cpp. The repository includes a specific chat template for the model. This template defines behavior for system prompts and supports tool calling through an XML-based format that supplies function signatures and expects JSON-structured calls wrapped in designated tags. When no system message is supplied the template defaults to identifying the model as Qwen created by Alibaba Cloud and positioning it as a helpful assistant. The files are hosted under the bartowski organization on the Hugging Face platform. This delivery method allows users to download the quantized weights directly and run them on consumer hardware without relying on remote API services. The presence of the GGUF extension indicates compatibility with the ecosystem of tools that consume this standardized format for on-device or self-hosted execution. No pricing information appears in the repository metadata. The model is distributed through the open platform that supports open-source and open-science initiatives.

This repository contains GGUF quantized versions of Alibaba's Qwen2.5-Coder-14B-Instruct model, optimized for use with LM Studio and other local LLM tools. It supports code generation, reasoning, and general instruction following with multiple quantization levels for different hardware.

Wan2.2 Animate is a web app that enables users to upload a video and an image, then generate a new animated video by transferring the original motion to the new appearance. Users can add mask points to refine the animation. It's designed for content creators seeking easy video animation tools.

Wan2.1-2.2 contains open-source video generative models from the Wan family, optimized for low VRAM usage (as low as 6GB). It supports image-to-video generation and is compatible with the WanGP toolkit. The models are designed to run on older GPUs such as the RTX 10-series.

Wan2.1 I2V 14B 480P is an image-to-video diffusion model hosted on Hugging Face. It generates short video clips from a starting image combined with a text prompt and is provided as an open-source model under the Apache 2.0 license. The model carries 14 billion parameters and produces 480p output. It is tagged for i2v and video-generation tasks and supports both English and Chinese. Integration with the Diffusers library allows users to load the pipeline, supply an image and prompt, and export the resulting frames as video. Example code demonstrates installation via pip, use of bfloat16 precision on CUDA devices, and conversion of output to an mp4 file. Distribution occurs through the Hugging Face repository, where the model card supplies instructions for local execution and references to inference providers. The repository includes Safetensors format files and supports deployment options such as notebooks or local applications. No pricing information appears for the model itself, which remains freely available for download and use under its stated license.

Wan2.2 14B Preview is a web app that generates short videos from uploaded images and user-defined movement prompts. It is designed for animators, video creators, and designers seeking to animate static images easily.

Wan2.2 14B Fast is a web app that lets users upload images and generate short video clips by describing the desired motion. It provides controls for video length, steps, and seed, serving digital artists and creators.

Wan2.2 14B Fast is a web app that generates animated videos from uploaded images and user-provided motion descriptions. It is designed for content creators and animators seeking to bring static visuals to life using AI.

Wan2.2 14B Fast Preview is a web app that animates user-uploaded images into videos based on descriptive prompts. Users can control video length, frame rate, and quality, making it suitable for content creators and animators.

This repository hosts GGUF quantized files for Qwen2.5-Coder-14B-Instruct, a specialized 14-billion parameter model for code generation and software development tasks. It supports advanced features such as tool calling and follows a chat template optimized for coding assistance. Ideal for local development environments and offline coding agents.

A GGUF quantized version of Alibaba's Qwen2.5-Coder 14B Instruct model. It is optimized for code generation, completion, and reasoning tasks. The GGUF format allows efficient local execution using tools such as llama.cpp, LM Studio, and Ollama.