GPT-Image-style photo editing on your own GPU (12 GB+): I built a zero-setup app for Qwen-Image 2.1 on stable-diffusion.cpp

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포스트 요지

• 목표: 자체 GPU(12GB+) 에서 GPT-Image 비슷한 사진 편집
• Qwen 이미지 계열, zero-setup 앱 주장
• 클라우드 이미지 API 대신 로컬 파이프라인

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