Every local setup I tried for Qwen-Image 2.1 meant a Python env, a node graph and an evening of picking quantization, offloading and resolution for my card. Kokon is a free, open-source desktop app that does all of that for you and gives you a plain photo editor on top. Zero setup: first run detects your GPU and memory and downloads the matching GGUFs (Qwen-Image 2.1 Turbo Q4_K + Qwen3-VL 8B text encoder, ~12 GB, checksummed). Each job is sized automatically: working resolution, VRAM/RAM split, tiled VAE, retry if it runs out of memory. Engine: stable-diffusion.cpp (ggml), so no Python, no PyTorch. CUDA, Vulkan and Metal builds. An editor, not a workflow: type the change, or brush an area with Change / Remove / Replace / Add. The brushed area is marked on a second reference image, so the model actually sees it. Versions with undo, before/after, upscaler comparison. Hardware: NVIDIA 12 GB+ (CUDA). Apple Silicon (Metal, beta, slow: ~4 min per image on a 16 GB M2 Pro). AMD / Intel Arc 12 GB+ via Vulkan (beta, only tested on NVIDIA so far, reports very welcome). Speed: 5070 Ti 16 GB: ~24 s per edit at 1.5K, ~40 s for a native 2K edit. Agents: built-in MCP server (generate_image, edit_image, inpaint_image, outpaint_image, upscale_image…) and a local HTTP API: npx -y kokon-mcp, also in the official MCP Registry. Not yet: Linux; code signing (SmartScreen warns on first run); reusing model files you already have is a manual workaround for now (#14). Repo: https://github.com/GabrieleLozupone/kokon · Site: https://kokon.app Code is AGPL-3.0; the Qwen weights are under the Qwen Research License (non-commercial).   submitted by   /u/Ok-Director4174 [link]   [comments]