Wan2.2 T2V A14B Alternatives

Wan2.2-Animate-14B-GGUF is a quantized version of the Wan2.2-Animate-14B video-to-video model provided in GGUF format. It is hosted on Hugging Face by QuantStack and is intended for use in video-to-video generation tasks. The repository supplies the main model files along with guidance on complementary components including the Umt5-xxl text encoder and Wan2.1_VAE. It lists multiple quantization levels that allow users to trade off between model size and performance on available hardware. Available options range from 2-bit Q2_K at 6.46 GB through 8-bit Q8_0 at 18.7 GB. An example workflow for integration is referenced. The model is delivered as downloadable GGUF files placed in the ComfyUI/models/unet directory. It is designed for operation with the ComfyUI-GGUF custom node. The original model originates from Wan-AI/Wan2.2-Animate-14B and retains all original licensing terms and usage restrictions. The repository itself is released under the Apache-2.0 license. It has recorded 116000 downloads in the past month. The tool belongs to the class of video-to-video models and supports English and Chinese. No pricing information is stated because the files are offered for direct download.

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.

Wan2.1-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-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.

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.

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-T2V-A14B is a large-scale text-to-video generation model with 14 billion parameters. It uses a diffusion-based approach and is distributed in a format compatible with the Hugging Face Diffusers library. The model supports detailed prompt-based video synthesis and includes computational efficiency optimizations for different GPU configurations.

Wan2.1-T2V-1.3B is a compact open-weight text-to-video diffusion model. It converts text prompts into short video sequences and is distributed in Diffusers format on Hugging Face. The model can be run locally with PyTorch or through cloud inference providers, making high-quality video generation accessible to developers.

WAN2.1-t2v-14B-int4-ConvRot is an open-source checkpoint for a text-to-video generation model, available on Hugging Face. It supports video generation from text prompts, model quantization, and fine-tuning for research and development in AI and multimedia applications.

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.

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.

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.

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.

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.

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.