Qwen Image Edit 2511 Alternatives

Qwen-Image-Edit-2509-GGUF is a quantized GGUF conversion of the Qwen image editing model. It includes components for the main UNet, text encoders, and VAE. The model is designed for use with ComfyUI-GGUF custom nodes and supports various quantization levels for local inference. It enables high-quality image editing on a range of hardware.

Qwen-Image-Edit-2511 is an open-weight diffusion model for image-to-image editing. Users provide an input image and a text prompt (e.g. "turn this cat into a dog") to generate edited outputs. It integrates with the Diffusers library and supports high-quality, instruction-based visual transformations.

Qwen-Image-Edit-2509 is a diffusion-based model that performs text-guided image editing. Users provide an input image and a prompt (e.g. "turn this cat into a dog"), and the model generates the corresponding edited image. It is distributed on Hugging Face and integrates with the Diffusers library.

Qwen-Image-Edit-2511 is an open-source image-to-image editing model that allows users to generate and modify images based on text prompts. It integrates with the Diffusers library and supports both English and Chinese, making it suitable for creative developers and AI researchers.

Qwen-Image-Edit is an image-to-image diffusion model developed by the Qwen team. It allows users to modify existing images using text prompts, such as changing a cat into a dog. The model integrates with the Diffusers library and supports both research and practical image editing workflows.

Qwen-Image-Edit-2509 is an open-source image-to-image editing model that enables developers and researchers to perform advanced image manipulation tasks. It supports integration with popular ML libraries and offers multilingual documentation. Distributed under the Apache 2.0 license.

Qwen-Image-2512 is an open-source text-to-image diffusion model developed by Qwen. It enables the generation of images from textual prompts, supporting both English and Chinese. The model is suitable for AI researchers and developers seeking customizable image generation tools.

GGUF quantized versions of the Qwen3.5-2B model, optimized by Unsloth for fast local inference. Compatible with llama.cpp, Ollama, and other GGUF runtimes. Suitable for edge devices or low-memory environments while retaining strong language modeling performance.

Qwen3.5-9B-GGUF is a community-provided collection of GGUF-quantized files for the Qwen3.5-9B language model. It enables efficient local execution using tools such as LM Studio, llama.cpp, or Ollama. The repository is targeted at developers and enthusiasts who want to run a capable open-source LLM on their own machines without relying on cloud APIs.

Qwen-Image is a text-to-image generation model from the Qwen team at Alibaba. It uses a diffusion-based approach and is compatible with the Hugging Face Diffusers library. The model supports both English and Chinese prompts and can generate detailed images based on textual descriptions.

Qwen2.5-VL-7B-Instruct-GGUF is a repository on Hugging Face that supplies GGUF quantized files of the Qwen2.5-VL 7B vision-language model. It is intended for local inference of a multimodal model capable of processing both images and video alongside text. The repository is provided by unsloth. The files include variants such as Qwen2.5-VL-7B-Instruct-BF16.gguf and Qwen2.5-VL-7B-Instruct-IQ4_NL.gguf. A chat template is defined that handles messages containing text, image, or video content by inserting specific vision and image or video pad tokens. The template supports counting multiple images or videos in a conversation and adds optional identifiers such as "Picture 1:" or "Video 1:" before the visual tokens. It uses special tokens including vision_start, vision_end, image_pad, video_pad, im_start, and im_end. The total file size across the GGUF files is 15237851776 bytes. These quantized models are distributed for use with compatible inference engines that accept the GGUF format. The repository forms part of the broader collection of foundation models available on the platform. No pricing, licensing terms, or additional deployment details beyond the GGUF files and chat template are stated.

This GGUF-quantized version of Qwen3-VL-2B-Instruct enables efficient on-device or local-server multimodal inference. The model can process both text and images, supports tool calling, and is optimized for local use with tools such as llama.cpp. It is ideal for developers building vision-enhanced AI applications without relying on cloud APIs.

Qwen Image Edit 2509 enables users to upload photos and describe the changes they want, such as adding or removing objects or applying new styles. The app interprets instructions and generates edited images accordingly, ideal for content creators and designers.

This repository contains GGUF quantized versions of the Qwen3-VL-8B-Instruct model, enabling efficient local inference on CPUs and GPUs. It supports vision-language tasks including image understanding, visual question answering, and document analysis. The GGUF format makes it compatible with popular local LLM runners like llama.cpp.

This is the GGUF quantized format of Alibaba's Qwen3-4B large language model. It enables efficient local inference using tools like llama.cpp or LM Studio. The model includes support for tool calling and follows a specific chat template for structured interactions.

Qwen3-Coder-Next-GGUF is a collection of GGUF quantized files for the Qwen3-Coder-Next series of coding models, published by Unsloth. It enables efficient local inference of powerful coding LLMs on CPUs and GPUs using tools like llama.cpp. The models support advanced code generation, tool use, and follow a specialized chat template for programming tasks.

Qwen3-14B-GGUF contains GGUF format files for the 14 billion parameter Qwen3 model, enabling efficient local execution using tools like llama.cpp. It supports advanced features such as tool calling and follows a specific chat template for multi-turn conversations. This allows developers to run a powerful open LLM on consumer hardware without relying on cloud APIs.

Qwen3-8B-GGUF is the GGUF-quantized format of Alibaba's Qwen3-8B model. This version enables efficient local inference on consumer hardware using tools like llama.cpp or Ollama. It retains strong language understanding and generation capabilities while supporting tool calling and structured prompting formats.

Qwen3.6-27B-GGUF is an open-source large language model distributed in GGUF format for local inference. It is designed for AI researchers and developers who require a powerful language model that can be run on local hardware for experimentation and development.

Qwen3-4B-GGUF provides a quantized version of the Qwen3 4B parameter model in GGUF format, optimized for efficient inference and fine-tuning using Unsloth. It supports local execution on consumer hardware with features like chat templates and tool calling capabilities. Primarily used by developers and researchers looking to run or customize open-weight language models without relying on cloud APIs.

Qwen3.5-27B-GGUF is an open-source large language model released in GGUF format for local inference and experimentation. It is suitable for AI researchers and developers who require access to model weights and the ability to run models on their own hardware.

Qwen3-30B-A3B-GGUF provides GGUF quantized weights for the Qwen3 30B-A3B model, enabling efficient local execution with tools like llama.cpp. It supports advanced features including tool calling and follows a specific chat template for multi-turn conversations. The model is intended for developers who want to run powerful language models offline.