Qwen Image 2512 Alternatives

Qwen-Image-Edit-2511-GGUF is a quantized GGUF version of a Qwen image-to-image model hosted by Unsloth AI on Hugging Face. It is provided for local inference of image editing tasks that take an input image and text instructions to produce a modified output. The model card lists it under the image-to-image class and specifies support for English and Chinese. It carries an Apache-2.0 license and is based on work referenced by arxiv:2508.02324. Quantization enables the model to run on consumer hardware through compatible local tools. Delivery occurs via the Hugging Face repository, where users can obtain the GGUF files. The primary documented interface is Unsloth Studio, a desktop application available for macOS, Linux, and WSL via a one-line curl installer or for Windows via a PowerShell command. After installation the studio is launched on a local port and the model is located by name to begin use. A partial reference to Hugging Face Spaces appears but supplies no complete usage details. The repository is maintained under the Unsloth AI organization, which has 28.2k followers on the platform. No pricing information is stated for the model itself.

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.

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.

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 is a web application that generates detailed images from text descriptions, allowing users to adjust settings and optionally upload images. It is designed for artists and creative users seeking flexible AI-powered image creation.

A collection of Qwen image and video generation models designed to work with the WanGP framework. These models support very low VRAM requirements (as low as 6GB) and older GPUs. They enable high-quality image and video generation for users with modest hardware through specialized optimizations and compatibility with multiple base models including Flux and Hunyuan.

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.

Qwen-Image-2512-Lightning is a LoRA weight adaptation that speeds up the Qwen-Image text-to-image diffusion model. It enables faster inference while maintaining quality and is compatible with Diffusers and ComfyUI. The model is available on Hugging Face for local and cloud image generation workflows.

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-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.

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.

This repository contains GGUF quantized weights for the Qwen3-8B model, designed to work seamlessly with the Unsloth library for fast inference and fine-tuning. It includes optimized formats for local execution on consumer hardware. The models support advanced prompting and tool-calling capabilities as defined in the provided chat templates.

Qwen Image 2512 is a web app that converts short image descriptions in any language into richly detailed English prompts suitable for image generation. It automatically classifies scenes and enhances prompts, helping digital artists and creators improve their AI-generated artwork.

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_ComfyUI is an open-source diffusion model for image generation and editing, designed to work with the ComfyUI interface. It enables developers and digital artists to create and modify images using advanced AI techniques. The model is suitable for creative and research applications.

This repository provides GGUF quantized weights for the 0.6 billion parameter Qwen3 model. It is optimized for use with Unsloth, supporting fast fine-tuning and inference. The model includes advanced features such as tool calling and is designed for users who want a lightweight yet powerful open LLM that runs locally.

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 is an AI image generator built around a 20B MMDiT image foundation model. It is described as designed for both image generation and precise image editing, with an emphasis on rendering text inside images accurately, including support for English and Chinese characters. Its stated capabilities include native text rendering with complex layouts and multi-line arrangements, as well as multiple artistic styles such as photorealistic, impressionist, anime, and minimalist. The page also lists image editing functions including style transfer, object manipulation, detail enhancement, and pose adjustment. A section on use mentions image description prompts, aspect ratio selection, and image preview before generation. The feature list also mentions local deployment with multi-GPU support, a Gradio interface, queue management, and automatic prompt optimization. Qwen Image is presented for use through a web interface or API, and the page says users can clone the repository from GitHub, install dependencies, and set up a local deployment or API server. It also says the model is open source and customizable, and that it can be fine-tuned and integrated into applications or workflows. The page names Kohya Tech in a gallery credit and says development is open source with active community support and transparent development on GitHub.

A set of LoRA weights that accelerate and improve the Qwen-Image text-to-image model. It enables faster inference while maintaining quality. The model can be used with the Diffusers library and is compatible with various local apps and notebooks. It supports both English and Chinese prompts.

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 is a GGUF-quantized version of the Qwen3.6-35B-A3B model, optimized for efficient local inference using tools like llama.cpp. It supports multimodal inputs and is designed for developers who want to run powerful language models on standard hardware without relying on cloud APIs.

This is a GGUF-quantized release of Qwen3-235B-A22B, a massive mixture-of-experts language model optimized for local execution. It supports advanced reasoning, tool calling, and follows a specific chat template. Provided by Unsloth, it enables efficient inference of one of the largest open models using tools like llama.cpp.

Qwen3.6-14B-A3B-FableVibes-GGUF is a GGUF quantized fine-tune of the Qwen3.6 model tuned for fable and storytelling tasks. It allows local execution using tools like llama.cpp or LM Studio. The model is available on Hugging Face for creative text generation applications.