Stable Diffusion 2.1 Base (CoreML) is a lightweight version of the popular text-to-image generation model developed by Stability AI, optimized for Apple devices through CoreML. With just 0.86 billion parameters, this model is designed to generate high-quality images from textual descriptions while maintaining efficient performance on local hardware. It excels in producing detailed and coherent images, making it a solid choice for users who need a balance between image quality and computational resources. Despite its smaller size, it manages to retain much of the creative potential and versatility of larger models, which is particularly impressive given its reduced parameter count.
Compared to other models in its size class, Stable Diffusion 2.1 Base (CoreML) punches well above its weight. It offers good efficiency, requiring only 1.6 GB of VRAM, which makes it accessible on a wide range of Apple devices, including older Macs and iPads. This efficiency is a significant advantage for users who want to experiment with AI-generated art without investing in high-end hardware. The model is ideal for hobbyists, artists, and developers who are looking for a powerful yet lightweight solution for text-to-image tasks. Realistically, anyone with an Apple device that meets the VRAM requirements can benefit from this model, making it a versatile and user-friendly option for local deployment.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| CoreML-Palettized | 6 | 1.063 GB | 1.56 GB | 2.06 GB | 85% |
How to run Stable Diffusion 2.1 Base (CoreML)
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
Official Hugging Face pipeline. Best quality & sampler control.
🤗 Diffusers home →- 1
Install
pip install diffusers transformers accelerate torch - 2
Run
from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained("apple/coreml-stable-diffusion-2-1-base-palettized").to("cuda") img = pipe("a futuristic city").images[0]Pipeline class auto-detects (StableDiffusion, FluxPipeline, etc.).
Community benchmarks
Real seconds-per-image reports from people running Stable Diffusion 2.1 Base (CoreML) on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Try It — Diffusion Generation Demo
Click "Generate" to watch how Flux.1 creates an image from noise. Real outputs from RunThisModel.com.
"A cozy wooden cabin in snowy mountains at golden hour sunset"
"A friendly humanoid robot reading a book in a library"
"Gourmet sushi platter, professional food photography"
"Woman scientist in a modern lab, natural lighting"
"Snow leopard on mountain peak at dawn, golden rim light"
"Cyberpunk city at night, neon signs, rain reflections"
Animation simulates the diffusion denoising process at recorded generation speed. Actual generation requires GPU hardware or cloud service.
how much VRAM do I need to run Stable Diffusion 2.1 Base (CoreML)?
Stable Diffusion 2.1 Base (CoreML) requires 1.56 GB VRAM minimum with CoreML-Palettized quantization. For full precision you need 1.56 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
What GPU do I need to run Stable Diffusion 2.1 Base (CoreML)?
Stable Diffusion 2.1 Base (CoreML) is optimized for Apple devices and runs on the GPU of any iPhone with 6GB RAM or higher.
Is Stable Diffusion 2.1 Base (CoreML) good for coding?
Stable Diffusion 2.1 Base (CoreML) is primarily designed for image generation and is not suitable for coding tasks.
Stable Diffusion 2.1 Base (CoreML) vs Llama 3.1 8B?
Stable Diffusion 2.1 Base (CoreML) is an image generation model, while Llama 3.1 8B is a text generation model. They serve different purposes and are not directly comparable.
Can I run Stable Diffusion 2.1 Base (CoreML) on a Mac?
Yes, you can run Stable Diffusion 2.1 Base (CoreML) on a Mac with Apple Silicon and at least 6GB of RAM.
How much VRAM does Stable Diffusion 2.1 Base (CoreML) need?
Stable Diffusion 2.1 Base (CoreML) requires 1.6 GB of VRAM to run smoothly.
Is Stable Diffusion 2.1 Base (CoreML) censored?
The model is not inherently censored, but it adheres to the CreativeML OpenRail-M license which may include content guidelines.
Is Stable Diffusion 2.1 Base (CoreML) commercial-use allowed?
Yes, Stable Diffusion 2.1 Base (CoreML) can be used commercially under the terms of the CreativeML OpenRail-M license.
Stable Diffusion 2.1 Base (CoreML) context length?
The context length for Stable Diffusion 2.1 Base (CoreML) is not explicitly defined as it is an image generation model, not a text model.
Does Stable Diffusion 2.1 Base (CoreML) support function calling?
No, Stable Diffusion 2.1 Base (CoreML) does not support function calling as it is an image generation model.
Stable Diffusion 2.1 Base (CoreML) quantization options?
Stable Diffusion 2.1 Base (CoreML) supports quantization, but the specific options depend on the implementation and are typically optimized for mobile devices.
Can Stable Diffusion 2.1 Base (CoreML) run on CPU?
While it can run on CPU, the performance will be significantly slower compared to running on the GPU of supported Apple devices.
Stable Diffusion 2.1 Base (CoreML) fine-tuning?
Fine-tuning is possible but requires advanced knowledge and additional resources. It is generally more complex than running the model out-of-the-box.
Stable Diffusion 2.1 Base (CoreML) system requirements?
The system requirements include an Apple device with at least 6GB of RAM and a compatible GPU. The model is optimized for iOS and macOS with Apple Silicon.
Stable Diffusion 2.1 Base (CoreML) performance benchmark?
Performance benchmarks vary, but the model is optimized for efficient image generation on Apple devices, typically generating images in seconds.
Stable Diffusion 2.1 Base (CoreML) for RAG?
Stable Diffusion 2.1 Base (CoreML) is not designed for Retrieval-Augmented Generation (RAG), which is more suited for text models.
Stable Diffusion 2.1 Base (CoreML) for agents?
While it can be integrated into agent systems, its primary function is image generation, not decision-making or interaction.
Stable Diffusion 2.1 Base (CoreML) for coding vs general?
Stable Diffusion 2.1 Base (CoreML) is not suitable for coding tasks; it is designed for general image generation purposes.
Stable Diffusion 2.1 Base (CoreML) vs ChatGPT?
Stable Diffusion 2.1 Base (CoreML) generates images, while ChatGPT is a language model designed for text-based conversations and tasks.
Stable Diffusion 2.1 Base (CoreML) download size?
The download size for Stable Diffusion 2.1 Base (CoreML) is approximately 1.14GB, making it one of the smallest CoreML image generation models.
Best quant for Stable Diffusion 2.1 Base (CoreML)?
The best quantization option depends on your specific needs, but the model is optimized for efficient performance on Apple devices with minimal impact on image quality.