Tools, frameworks, and models for local AI
23 resources found for "Immagini"
Black Forest Labs (Munich, Germany): the FLUX models, today's reference point for high-quality open image generation. The FLUX models in the catalog are the child resources.
Cloudflare's 27B multimodal decision model (base Qwen3.8-27B): reads a state (text, JSON, images, video) and a schema of typed questions and returns a probability per option in a single pass, with no text generation. Jev and SystemOne API compatible.
A 5.6B open-weights image model for text-to-image, single-reference editing and multi-reference composition in one model, with placement boxes in the prompt. Qwen3-VL-4B text encoder. In BF16 (11.1GB), FP8 and NVFP4 (5.3GB). FLUX Kommunity license (non-commercial + qualified-user exception).
The most advanced open image model: excellent prompt adherence, solid anatomy, full fine-tuning. The standard for serious work.
Very few steps, high quality, and strong text rendering. The distilled FLUX variant, built for speed.
One-click SDXL image generation, Midjourney-style: zero parameters to manage. The simplest way to produce quality images.
Google's open model family in 1B, 4B, 12B and 27B sizes. 128K-token context, 140+ supported languages and multimedia input (text and images). An excellent choice for RAG and long documents on consumer hardware.
La taglia compatta della famiglia Gemma 3 di Google: 4,32B di parametri, contesto di 128K token e supporto nativo a 140+ lingue (incluso l'italiano), con input testo+immagini. Licenza Gemma (uso commerciale con condizioni). In Q4_K_M occupa circa 2,9GB: gira su GPU da 8GB e su CPU moderne.
La taglia 12B della famiglia Gemma 4 di Google: 11,95B dense, contesto 256K e multimodalità integrata senza encoder separati (testo+immagini+audio). Licenza Apache 2.0. In Q4_K_M occupa circa 7,4GB: il punto di equilibrio tra qualità e requisiti hardware per l'uso locale su 8-16GB.
Il modello MoE della famiglia Gemma 4: 25,2B di parametri totali con 3,8B attivi per token. Contesto 256K, input testo+immagini, oltre 140 lingue, Apache 2.0. In Q4_K_M pesa circa 16,8GB: la scelta per chi ha 24GB di VRAM e vuole qualità da modello grande con velocità da modello piccolo.
Compact model from Google's Gemma 4 family: 8B total parameters, of which 4.5B effective thanks to per-layer embeddings (PLE). 131K-token context, 140+ languages, text+image and audio input. The entry size of the Gemma 4 family for local use on 8-16GB.
Hugging Face's Python library for running the pipelines of generative models: image, video, and audio. The standard way to run models like Qwen-Image locally, with CUDA support and CPU offload for lower-memory GPUs.
Il modello MoE grande della famiglia Llama 4 di Meta: 400B di parametri totali con 17B attivi (128 esperti). Contesto 1M di token, nativamente multimodale (testo+immagini), licenza comunitaria Meta. Richiede multi-GPU o macchine con memoria unificata 128GB+ anche quantizzato.
The first open-source MoE of OpenBMB's MiniCPM-V series: 35.2B total parameters, ~3B active per token, multi-image and video understanding with early visual-token compression (LLaVA-UHD v4). First open-source MoE with fluid on-device decoding — 13+ tok/s on 3GB of RAM on an iPhone 17 Pro Max — thanks to Lookahead Expert Scheduling (llama.cpp-omni), fine per-level quantization and DSpark speculative sampling. Day-0 support for Rockchip, Houmo, Intel, AMD and Arm; runs on Ollama, llama.cpp, vLLM and SGLang.
Il primo 'modello unificato' di Mistral: dense da 128B che unisce instruction-following, reasoning e coding in un'unica serie di pesi, sostituendo Medium 3.1, Magistral e Devstral 2. Multimodale (testo+immagini), contesto 256K, ragionamento configurabile. Open weights con licenza Modified MIT: uso libero fino a una soglia di fatturato per le grandi aziende.
Il modello della linea Small di Mistral, ora MoE: 119B di parametri totali con 6,5B attivi per token (128 esperti, 4 attivi). Unifica instruct, reasoning e coding in un unico modello multimodale (testo+immagini) con contesto 256K. Licenza Apache 2.0.
GTK4/libadwaita app for GNOME to chat with local models via Ollama, in Rust under GPLv3: it handles Ollama setup, models, and loading local documents and images.
Alibaba's 20B (MMDiT) image-generation model: excellent in-image text comprehension, precise editing, and an Apache 2.0 license. The base of the Qwen-Image family, of which 2.1 is the compact 7B variant.
7B open-weight model (32-layer DiT) that unifies text-to-image generation up to 2048x2048, editing with up to 10 reference images, and native RGBA transparency. Designed to run locally on 16GB+ VRAM; supported by diffusers and ComfyUI.
Il modello dense multimodale della generazione Qwen3.8 di Alibaba: 27,78B di parametri, input testo+immagini+video e contesto nativo di 262K token. Licenza Apache 2.0. L'attenzione ibrida (DeltaNet lineare + GQA) riduce la KV cache di circa il 75% rispetto a un transformer standard: in Q4_K_M sta su una singola GPU da 24GB.
Stability AI (London, UK): the Stable Diffusion models for image, video and audio generation, open-weight with permissive licenses. The Stable models in the catalog are the child resources.
The image-generation classic: fast, light, and with the largest LoRA/ControlNet ecosystem of all. Perfect for 6-8GB GPUs.
The current standard for photoreal and artistic images at 1024px. Better prompt adherence and more coherent composition than SD 1.5.
