FireRed-Image-Edit 1.1 on Apple M1 Pro (32 GB)

Can the Apple M1 Pro (32 GB) run FireRed-Image-Edit 1.1?

Barely: it runs very slowly (encoder unloaded after use)

Very slow (encoder unloaded after use). At Q4 GGUF it needs about 16.0 GB of 21.4 GB usable and takes about 25.4 min per image.

Best precision: Q4 GGUF
Memory mode: Text encoder offloaded
Time per image: 25.4 min
Output: 1024×1024, 40 steps

Best app to run it on the Apple M1 Pro (32 GB)

On macOS

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 FireRed-Image-Edit 1.1

Recommended for the Apple M1 Pro (32 GB): q4_gguf. Every other precision has its own download link in the table below.

Quantized files · GGUF for ComfyUI

Smaller GGUF versions for ComfyUI (with the ComfyUI-GGUF node). No sign-in needed; the model’s license still applies.

Download the q4_gguf (Q4_1) file (opens in a new tab)

huggingface.co/FireRedTeam/FireRed-Image-Edit-1.1-ComfyUI (opens in a new tab)

Original weights · Hugging Face

Full-precision files from rednote (FireRed Team), for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/FireRedTeam/FireRed-Image-Edit-1.1 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from rednote (FireRed Team). The weights themselves download from Hugging Face.

github.com/FireRedTeam/FireRed-Image-Edit (opens in a new tab)

git clone https://github.com/FireRedTeam/FireRed-Image-Edit

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 80Great quality (A) · 80 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q4 GGUF, 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 Very slowencoder unloaded after use: How well it runs on the Apple M1 Pro (32 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.	36.4 / 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.	1.2 / 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	72.6 / 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 M1 Pro (32 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	17.8 GB	21.4 GB	26.2 min	Sequential CPU offload	Very slowCPU offload	87Excellent quality (A+) · 87 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`	10.7 GB	21.4 GB	23.6 min	Sequential CPU offload	Very slowCPU offload	85Excellent quality (A+) · 85 of 100: how capable the model is at FP8, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`Q8 GGUF`	11.2 GB	21.4 GB	30.1 min	Sequential CPU offload	Very slowCPU offload	86Excellent quality (A+) · 86 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4 GGUF`	16.0 GB	21.4 GB	25.4 min	Text encoder offloaded	Very slowencoder unloaded after use	80Great quality (A) · 80 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)

Other hardware that runs it

87Excellent quality (A+) · 87 of 100: how capable the model is at FP16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell FP16 · 59 s · Slow
80Great quality (A) · 80 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 Q4 GGUF · 80 s · Slow
80Great quality (A) · 80 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 Q4 GGUF · 1.7 min · Slow (encoder in RAM)
80Great quality (A) · 80 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX Q4 GGUF · 1.9 min · Slow (encoder in RAM)
85Excellent quality (A+) · 85 of 100: how capable the model is at FP8, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT FP8 · 2.1 min · Slow (CPU offload)
85Excellent quality (A+) · 85 of 100: how capable the model is at FP8, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5080 FP8 · 2.1 min · Slow (CPU offload)

Quick answers

How much memory does FireRed-Image-Edit 1.1 need on the M1 Pro 32GB?

At Q4 GGUF it needs about 16.0 GB. The M1 Pro 32GB has 21.4 GB usable.

How long does FireRed-Image-Edit 1.1 take on the M1 Pro 32GB?

About 25.4 min per image at Q4 GGUF 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.