{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "leejet-stable-diffusion-cpp--run-newer-image-models-on-modest-hardware",
 "repo": "leejet/stable-diffusion.cpp",
 "repo_url": "https://github.com/leejet/stable-diffusion.cpp",
 "repo_slug": "leejet-stable-diffusion-cpp",
 "title": "Run FLUX or Qwen Image on modest hardware with GGUF",
 "page_title": "How to run FLUX or Qwen Image on modest hardware with GGUF with Stable Diffusion CPP",
 "summary": "Use GGUF quantized weights and the right backend so newer image models fit in limited memory.",
 "who": "Someone with a mid-range GPU, an Apple Silicon Mac or no GPU who wants to try newer models like FLUX.",
 "problem": "Newer image models are huge in full precision and simply do not fit on ordinary machines.",
 "outcome": "A newer model generating images locally on the backend your hardware supports.",
 "steps": [
  "Get the sd binary for your backend: CPU, CUDA, Vulkan, Metal, OpenCL or SYCL",
  "Follow the docs page for your model, such as FLUX.1-dev/FLUX.1-schnell or Qwen Image, to download the weights it needs",
  "Prefer GGUF weights and read the Quantization and GGUF guide to pick a size that fits your memory",
  "Use VAE tiling and Flash Attention, both listed features, to cut memory use further",
  "Read the performance guide and backend selection guide if generation is slow or runs out of memory"
 ],
 "agent_prompt": "Help me run <FLUX or Qwen Image> with leejet/stable-diffusion.cpp on my <hardware>. Follow the model's docs page and the Quantization and GGUF guide to choose weights that fit in <memory>, then generate one test image of <prompt> and tell me the time and memory used.",
 "agents": [
  "claude-code",
  "codex",
  "cursor"
 ],
 "difficulty": "Intermediate",
 "time": "1 hour",
 "basis": "readme",
 "caveat": "Each model needs its own set of files, explained in its docs page rather than the README, so expect some reading.",
 "category": "Content",
 "tags": [
  "Content"
 ],
 "lang": "c++",
 "requirements": [
  "The sd executable from the releases page, or a build from source following the build guide",
  "Model weights in .ckpt, .safetensors or .gguf format",
  "Linux, macOS or Windows; Android works through Termux",
  "Enough RAM or VRAM for the model; the performance guide covers reducing it"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.9,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/leejet-stable-diffusion-cpp/run-newer-image-models-on-modest-hardware/",
 "md": "https://dedupedai.com/use-cases/leejet-stable-diffusion-cpp/run-newer-image-models-on-modest-hardware/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/leejet-stable-diffusion-cpp--run-newer-image-models-on-modest-hardware.json",
 "api_version": "v1"
}