NMKD Stable Diffusion GUI vs mo-di-diffusion on Hugging Face

NMKD Stable Diffusion GUI runs text-to-image and image-to-image generation locally on your Windows PC using your own GPU. The itch.io download bundles dependencies so you skip manual Python setup, and version 1.11.0 ships with a Stable Diffusion 1.5 model in a 3.3 GB package or a 1.5 GB build if you bring your own checkpoints.

Unlike browser-based generators that queue jobs on remote servers, NMKD keeps everything on your hardware with no data collection. It adds LoRA training on 8 GB GPUs, InstructPix2Pix editing, RealESRGAN upscaling, and CodeFormer or GFPGAN face restoration in one desktop app. The author reports sub-2-second generations on an RTX 3090 and under one second on an RTX 4090.

Windows hobbyists, game texture artists, and creators who want uncensored local control are the main audience. Built-in prompt queues, tileable output for game assets, custom VAE and checkpoint loading, and a malware scan on downloaded models make it a full workstation tool rather than a quick online demo.

mo-di-diffusion is a fine-tuned Stable Diffusion 1.5 checkpoint on Hugging Face that renders characters and scenes in a modern animated-film look. Creator nitrosocke trained it on screenshots from a popular animation studio using DreamBooth with prior-preservation loss and text-encoder training over 9,000 steps. Add the token modern disney style to your prompt to trigger the aesthetic.

General SD 1.5 checkpoints aim for photorealism or broad illustration styles. mo-di-diffusion narrows hard on that glossy character-animation look, which is why sample prompts for game heroes, animals, and landscapes all lean cinematic rather than photographic. The weights load through the standard Diffusers StableDiffusionPipeline, so you can run it locally, in Colab, or inside Hugging Face Spaces without a proprietary API.

Artists, hobbyists, and developers experimenting with character concepts use mo-di-diffusion when they want Disney-adjacent renders without commissioning custom model training. The model card ships Python sample code, links to Gradio demos, and notes ONNX, MPS, and FLAX export options. It is free to download under the CreativeML OpenRAIL-M license with commercial use allowed subject to the license harm restrictions.