Can the Apple M2 (16 GB) run Kolors?

Yes, but slowly (encoder unloaded after use)

Slow (encoder unloaded after use). At FP16 it needs about 7.3 GB of 10.7 GB usable and takes about 84 s per image.

Best precision: FP16
Memory mode: Text encoder offloaded
Time per image: 84 s
Output: 1024×1024, 25 steps

Best app to run it on the Apple M2 (16 GB)

On macOS

Draw Things

Desktop app · runs on Draw Things (Metal FlashAttention)

Native Mac app with its own fast Metal engine and quantized downloads.

Open Kolors in Draw Things

ComfyUI Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.
SwarmUI

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Kolors

Original weights · Hugging Face

Full-precision files from Kuaishou, for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/Kwai-Kolors/Kolors-diffusers (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Kuaishou. The weights themselves download from Hugging Face.

github.com/Kwai-Kolors/Kolors (opens in a new tab)

git clone https://github.com/Kwai-Kolors/Kolors

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 60Good quality (B) · 60 of 100: how capable the model is at FP16, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at FP16, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Slowencoder unloaded after use: How well it runs on the Apple M2 (16 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	27.0 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.	20.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	5.2 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	15.0 / 15
Match score	67.2 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every precision on the Apple M2 (16 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	7.3 GB	10.7 GB	84 s	Text encoder offloaded	Slowencoder unloaded after use	60Good quality (B) · 60 of 100: how capable the model is at FP16, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`FP8`	4.8 GB	10.7 GB	75 s	Text encoder offloaded	Slowencoder unloaded after use	58Good quality (B) · 58 of 100: how capable the model is at FP8, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Other hardware that runs it

60Good quality (B) · 60 of 100: how capable the model is at FP16, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX FP16 · 4.5 s · Runs great
60Good quality (B) · 60 of 100: how capable the model is at FP16, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 FP16 · 7.5 s · Runs great
60Good quality (B) · 60 of 100: how capable the model is at FP16, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 FP16 · 4.0 s · Runs great
58Good quality (B) · 58 of 100: how capable the model is at FP8, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT FP8 · 4.8 s · Runs great
58Good quality (B) · 58 of 100: how capable the model is at FP8, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 Ti 16GB FP8 · 9.7 s · Runs great
58Good quality (B) · 58 of 100: how capable the model is at FP8, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPER FP8 · 5.8 s · Runs great

Quick answers

How much memory does Kolors need on the M2 16GB?

At FP16 it needs about 7.3 GB. The M2 16GB has 10.7 GB usable.

How long does Kolors take on the M2 16GB?

About 84 s per image at FP16 and default settings, an estimate scaled from a reference timing by this hardware’s compute speed.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.