LCM DreamShaper v7 on Raspberry Pi 4 (4 GB)

Can the Raspberry Pi 4 (4 GB) run LCM DreamShaper v7?

Yes, but slowly (tight fit · encoder unloaded after use)

Slow (tight fit · encoder unloaded after use). At FP16 it needs about 2.5 GB of 2.8 GB usable, with an estimated speed of 65 s.

Best precision: FP16
Memory mode: Text encoder offloaded
Time per image: 65 s
Output: 512×512, 4 steps

Best app to run it on the Raspberry Pi 4 (4 GB)

On Linux on ARM boards

ComfyUI

Desktop app · runs on PyTorch (ComfyUI), GGUF via ComfyUI-GGUF

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

Get ComfyUI

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 LCM DreamShaper v7

Original weights · Hugging Face

Full-precision files from Tsinghua (LCM), for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/SimianLuo/LCM_Dreamshaper_v7 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Tsinghua (LCM). The weights themselves download from Hugging Face.

github.com/luosiallen/latent-consistency-model (opens in a new tab)

git clone https://github.com/luosiallen/latent-consistency-model

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 30Basic quality (D) · 30 of 100: how capable the model is at FP16, whatever it runs on. Match score 52/100 (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 Slowtight fit · encoder unloaded after use: How well it runs on the Raspberry Pi 4 (4 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.	13.5 / 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.	18.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	5.9 / 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	52.4 / 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 Raspberry Pi 4 (4 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	2.5 GB	2.8 GB	65 s	Text encoder offloaded	Slowtight fit · encoder unloaded after use	30Basic quality (D) · 30 of 100: how capable the model is at FP16, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Other hardware that runs it

30Basic quality (D) · 30 of 100: how capable the model is at FP16, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 FP16 · 0.9 s · Runs great
30Basic quality (D) · 30 of 100: how capable the model is at FP16, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. Apple M1 (16 GB) FP16 · 6.4 s · Runs great
30Basic quality (D) · 30 of 100: how capable the model is at FP16, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 (16 GB) FP16 · 4.4 s · Runs great
30Basic quality (D) · 30 of 100: how capable the model is at FP16, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. Intel Arc B580 FP16 · 1.0 s · Runs great
30Basic quality (D) · 30 of 100: how capable the model is at FP16, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3060 12GB FP16 · 1.0 s · Runs great
30Basic quality (D) · 30 of 100: how capable the model is at FP16, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 SUPER FP16 · 0.5 s · Runs great

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.