ICBINP | Civitai vs mo-di-diffusion on Hugging Face

ICBINP is a Stable Diffusion 1.5 checkpoint merge built to push photorealistic image generation as far as a local SD 1.5 setup allows. The Civitai model page hosts multiple versions, with Mid 2024 as the latest major release updated in February 2025. Each variant ships as a pruned fp16 SafeTensor file with the SD-v2 840k VAE baked in, so you can load it in Automatic1111, ComfyUI, or any standard SD 1.5 interface without swapping VAEs.

Where most photorealistic checkpoints chase a single aesthetic, ICBINP grew through successive merges. It started from the RCNZ Gorilla With A Brick base, then folded in ten more photorealistic models at varying weights plus noise offset tuning. The Mid 2024 version adds a LoRA trained on Pexels stock photos merged with ICBINP Final, which tightens skin and lighting realism but still shows typical SD 1.5 quirks like cleft chin bias.

The author recommends DPM++ 3M SDE Karras or DPM++ 2M Karras at 20 to 30 steps with CFG between 2.5 and 5. Resolution sweet spot is 640x960 with a hires fix pass, though 768x1152 works if you hunt seeds to avoid duplicate heads. Beyond portraits, the checkpoint handles CGI characters and landscapes when prompted accordingly.

Downloads are free from Civitai under CreativeML Open RAIL-M. The Mid 2024 file (ICBINP_midYear.safetensors) weighs 2.21 GB and needs about 3.4 GB recommended VRAM. With 163,500+ downloads and 285 Very Positive reviews, it remains one of the most downloaded photorealistic SD 1.5 merges on the platform.

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.