Best stable diffusion mac m2 performance reddit. Thanks
A mix of Automatic1111 and ComfyUI.
● Best stable diffusion mac m2 performance reddit Thanks A mix of Automatic1111 and ComfyUI. Among the several issues I'm having now, the one below is making it very difficult to use Stable Diffusion. But I've been using a Mac since the 90s and I love being able I'd like some thoughts about the real performance difference between Tesla P40 24GB vs RTX 3060 12GB in Stable Diffusion and Image Creation in general. What was discovered. On Mac, as far as i can tell and have testet with different Mac Studios, the amount of available RAM is important. 0 model, the speed 🚀 Introducing SALL-E V1. The developer is very active and involved, and there have been great updates for compatibility and optimization (you can even run SDXL on an iPhone X, I believe). 1; 2; Next. there so many simple people that failed school but are good at art thinking AI steals art and have no clue at all. Yes i know the Tesla's graphics card are the best when we talk about anything around Artificial Intelligence, but when i click "generate" how much difference will it make to have a Tesla one instead of RTX? The N VIDIA 5090 is the Stable Diffusion Champ!This $5000 card processes images so quickly that I had to switch to a log scale. Download and install it. I was stoked to test it out so i tried stable diffusion and was impressed that it could generate images (i didn't know what benchmark numbers to expect in terms of speed so the fact it could do it at in a reasonable time was impressive). Install Stable Diffusion on a Mac M1, M2, M3 or M4 (Apple Silicon) This guide will show you how to easily install Stable Diffusion on your Apple Silicon Mac in just a few steps. Another way to compare (although not all inclusive) using the Metal benchmarks from Geekbench. I'm pretty sure Apple will introduce the M4 Ultra at the WWDC 2024, and the M4 Mac lineup will be released in September. Can you recommend it performance-wise for normal SD inference? I am thinking of getting such a RAM beast as I am contemplating running a local LLM on it as well and they are quite RAM hungry. Click Discover on the top menu. If you're using AUTOMATIC1111, leave your SD on the SSD and only keep models that you use very often in . My priority is towards smooth timeline editing performance. PromptToImage is a free and open source Stable Diffusion app for macOS. Can anyone help me to find out what is causing such images using SD3? I am using the standard Basic Demo, with the included clips model. Is there any other solution out there for M1 Macs which does not cause these issues? Posted by u/akasaka99 - 1 vote and no comments As CPU shares the workload during batch conversion and probably other tasks I'm skeptical. And before you as, no, I I've read there are issues with Macs and Stable Diffusion because of the Nvidia source. Model is on @huggingface Well maybe then, you should recheck. 1:7827 from imac or macbook pro? This community was originally created to provide information about and support for the discontinued Vanced apps on Android. What board would you all recommend? Would a 4090 make a big difference over a 3090? Apple computers cost more than the average Windows PC. If Stable Diffusion is ported to If Stable Diffusion is just one consideration among many, then an M2 should be fine. 5, a Stable Diffusion V1. How to run Stable Diffusion on a MacBook M1, MacBook M2 and other apple silicon models? View community ranking In the Top 1% of largest communities on Reddit. Test the function. However, since I have plenty of downtime during work hours, I'm eager to There's no big performance difference. Remove the old or bkup it. Agree. 5 model fine-tuned on DALL-E 3 generated samples! Our tests reveal significant improvements in performance, including better textual alignment and aesthetics. Free and open Yes, it's really fast, specially using the Neural Engine on arm Macs with poor GPU performance (M1, M2). Using Stable Diffusion on Mac M3 Pro, extremely slow Question - Help I’m running a workflow through ComfyUI using inpainting that allows me to replace areas of the image with new things based on my prompts but I’m getting terrible speeds! From what I can tell the camera movement drastically impacts the final output. M2 CPUs perform noticeably better but are still very overpriced when all you care about is Stable Diffusion. The ancestral doesn't look any better than the non-ancestral, and when you compare the non-ancestral to other samplers (aka, to generate the same output), the only real difference is just that Euler takes more steps than the others. Unless the GPU and CPU can't run their tasks mostly in parallel, or the CPU time exceeds the GPU time, so the CPU is the bottleneck, the CPU performance shouldn't matter much. 5GB + 5. But just to get this out of the way: the tools are overwhelmingly NVidia-centric, you’re going to have to learn to do conversion of models with python, and performance is pale compared to a M1 Max, 24 cores, 32 GB RAM, and running the latest Monterey 12. With these numbers, do you think I'll get a big advantage with the Base M2 Max Studio or are the decoders the same on the M1 Pro as the M2 Max. native Swift/AppKit Stable Diffusion App for macOS, uses CoreML models for best performance. A1111 takes about 10-15 sec and Vlad and Comfyui about 6-8 seconds for a Euler A 20 step 512x512 generation. I am trying to workout a workflow to go from stability diffusion to a blender 3D object. 5 to 2. Downsides: closed source, missing some exotic features, has an idiosyncratic UI. I've been very successful with the txt2img script with the command below. Download Here. Suggestions? Going to get an M2 nvme for storage. Some friends and I are building a Mac app that lets you connect different generative AI models in a single platform. SD Performance Data. This is not a tutorial just some personal experience. I find Stable Diffusion is a text-to-image AI that can be run on personal computers like Mac M1 or M2. \stable-diffusion-webui\models\Stable-diffusion. Audio reactive stable diffusion music video for Watching Us by YEOMAN and STATEOFLIVING. What do you guys think? I am tempted by the Acer, but I'm not sure about the quality of its build. Leave all your other models on the external drive, and use the command line argument --ckpt-dir to point to the models on the external drive (SD will always look in both locations). We're looking for alpha testers to try out the app and give us feedback - especially around how we're structuring Stable Diffusion/ControlNet workflows. (Or in my case, my 64GB M1 Max) I'm using a MacBook Pro 16, M1 Pro, 16G RAM, use a 4G model to get a 512*768 pic, but it costs me about 7s/it ,much more slower than I expect. 1 in resolutions up to 960x960 with different samplers and upscalers. This got me thinking about the better deal. More posts you may like. Can someone explain if/ how this may be better/ different than running an app like diffusion bee or mochi diffusion? Especially mochi diffusion & similar apps that appear use the same optimizations in macOS 13. Please share your tips, tricks, and workflows for using this /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, do i use stable diffusion if i bought m2 mac mini? Locked post. Realistic Vision: Best realistic model for Stable Diffusion, capable of generating realistic humans. I am interested in trying out the img2img script, but not sure what the syntax should be. The Draw Things app is the best way to use Stable Diffusion on Mac and iOS. This ability emerged during the training phase of the AI, and was not programmed by people. It runs SD like complete garbage however, as unlike with ollama, there's barely anything utilizing it's custom hardware to make things faster. Might not be best bang for the buck for current stable diffusion, but as soon as a much larger model is released, be it a stable diffusion, or other model, you will be able to run it on a 192GB M2 Ultra. 6 OS. Not a studio, but I’ve been using it on a MacBook Pro 16 M2 Max. 0 from pyTorch to Core ML. 5-2. sh. For people who don't know: Draw Things is the only app that supports from iPhone Xs and up, macOS 12. Free & open source Exclusively for Apple Silicon Mac users (no web apps) Native Mac app using Core ML (rather than PyTorch, etc) So i have been using Stable Diffusion for quite a while as a hobby (I used websites that let you use Stable Diffusion) and now i need to buy a laptop for work and college and i've been wondering if Stable Diffusion works on MacBook like Welcome to the unofficial ComfyUI subreddit. It doesn't offer every model but it does have some great ones: Juggernaut v9 The only thing I regret is that it takes so long to get it, but everybody's that way except for Apple. 25 leads to way different results both in the images created and how they blend together over time. Stable Diffusion Benchmarked: Which GPU Runs AI Fastest (Updated) | Tom's Hardware (tomshardware. I'm trying to run Stable Diffusion A1111 on my Macbook Pro and it doesn't seem to be using the GPU at all. comments sorted by Best Top New Controversial Q&A Add a Comment. 2. Currently using an M1 Mac Studio. I agree that buying a Mac to use Stable Diffusion is not the best choice. Generating 42 frames took me about 1,5 hour. Someone had similar problem, and there's a workaround described here. Yes. Got the stable diffusion WebUI Running on my Mac (M2). That will be the actual limitation on Mac unless you have an M1+ or M2 with at least 32gb ram, which most Mac users don't have lol. My only fear is that the M4 Ultra will be reserved for the Mac Pro, but in the meantime I'm hoping to see some Mac Pro specific hardware like a dedicated GPU/ML extension card. How to install and run Stable Diffusion on Apple Silicon M1/M2 Macs. So, essentially the question is why even do it if I can't train it? As a side note I have gotten the same setup/compile to work on my bootcamp partition with windows 11, its much much slower due to windows being an 'everything' hog. Pretty sure I want a Ryzen processor but not sure which one is adequate and which would be overkill. 23 to 0. Same kinds of performance with M2 iPads. I'm quite impatient but generation is fast enough to make 15-25 step images without too much frustration. Even if it's a custom build. I've heard that performance is upwards of How to install and run Stable Diffusion on Apple Silicon M1/M2 Macs Tutorial | Guide stable What is the best GUI to install to use Stable Diffusion locally /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and We have mostly Macs at work and I would gravitate towards the Mac Studio M2 Ultra 192GB, but maybe a PC with a 4090 is just better suited for the job? I assume we would hold onto the PC/Mac for a few years, so I’m wondering if a Mac with 192GB RAM might be better in the long run, if they keep optimising for it. Since those no longer work, we now provide information about and support for all YouTube client alternatives, primarily on Android, but also on other mobile and desktop operating systems. But while getting Stable Diffusion working on Linux and Windows is a breeze, getting it working on macOS appears to be a lot more difficult — at least based the experiences of others. For SD 1. It's a complete redesign of the user interface from vanilla gradio with a big focus on usability. Please dont judge 😅 it's also known for being more stable and less prone to crashing. i'm currently attempting a Lensa work around with image to image (insert custom faces into trained models). I don't know why. You're much better off with a pc you can stuff a bunch of m2 drives and shitloads of ram in. I want to know if using ComfyUI: The performance is better? The image size can be larger? How can UI make a difference in speed, mem usage? Are workflows like mov2mov, infizoom possible in With the help of a sample project I decided to use this opportunity to learn SwiftUI to create a simple app to use Stable Diffusion, all while fighting COVID (bad idea in hindsight. In this article, you will find a step-by-step guide for I'm planning on buying a new Mac, and will be using UE on it. S. M1 is for sure more efficient, but it can't be cranked up to power levels and performance anywhere near a beefy cpu/gpu. I've got an m2 max with 64gb of ram. To use the Flux. View community ranking In the Top 1% of largest communities on Reddit. If you want speed and memory efficiency, you can’t use lora, ti, or pick your own custom model unless you know what you are doing with CoreML and quantization. Stable requires a good Nvidia video card to be really fast. It is nowhere near it/s that some guys report here. Recommend MochiDiffusion (really, really good and well maintained app by a great developer) as it runs natively and with CoreML models. However, the MacBook Pro might offer more benefits for coding and portability. But I began learning AI gen art with it and after investing so much time and efforts into developing a work process it's hard to quit it. I am benchmarking these 3 devices: macbook Air M1, macbook Air M2 and macbook Pro M2 using ml-stable-diffusion. Works fine after that. Different Stable Diffusion implementations report performance differently, some display s/it and others it/s. io) Even the M2 Ultra can only do about 1 iteration per second at 1024x1024 on SDXL, where the 4090 runs around 10-12 iterations per second from what I can see from the vladmandic collected data. If I want to stay with MacOS for simplicity, do I really need to spend 5k for the Studio version? If Stable Diffusion is just one consideration among many, then an M2 should be fine. However, if SD is your primary consideration, go with a PC and dedicated NVIDIA graphics card. I wanted to see if it's practical to use an 8 gb M1 Mac Air for SD (the specs recommend at least 16 gb). I use it for some video editing and photoshop and I will continue to do some. New comments cannot be posted. It’s fast, free, and frequently updated. DiffusionBee is a Stable Diffusion App for MacOS. My daily driver is an M1, and Draw Things is a great app for running Stable Diffusion. Mochi Diffusion crashes as soon as I click generate. For now I am working on a Mac Studio (M1 Max, 64 Gig) and it's okay-ish. This actual makes a Mac more affordable in this category Just updated and now running SD for first time and have done from about 2s/it to 20s/it. To optimize Stable Diffusion on Mac Hi! I'm a complete beginner and today I installed fooocus and DiffusionBee versions of SD. current setup seems to work fine for a 10 min test edit with some color grading. There are many old threads on the Internet discussing that TOS doesn’t run well natively on M1 and that people had to resort to use virtual windows machines, that’s not the case with M2 as I'm planning to upgrade my HP laptop for hosting local LLMs and Stable Diffusion and considering two options: A Windows PC with an i9-14900K processor and NVidia RTX 4080 (16 GB RAM) (Desktop) A MacBook Pro Pricewise, both options are similar. Is anyone using Mac Studio Ultra for machine learning? My data is fairly heavy so I just am wondering if I should keep it or return for a PC once I get it. What's your it/s for sd now? Oh! And have you benchmarked it? I'd love to know what the score is. Been playing with it a bit and I found a way to get ~10-25% speed improvement (tested on various output resolutions and SD v1. The contenders are 1) Mac Mini M2 Pro 32GB Shared Memory, 19 Core GPU, 16 Core Neural Engine -vs-2) Studio M1 Max, 10 Core, with 64GB Shared RAM. It already supports SDXL. P. 12 votes, 17 comments. Apple Silicon Mac is very limited. A Mac mini is a very affordable way to efficiently run Stable Diffusion locally. Is there anything Draw Things (available from Apple App Store) is powerful and with that power comes some complexity. If base M2, use neural engine. do m2 mac for stable diffusion or not? if i am running sd at win pc, can open 127. To give you some perspective it is perfectly usable, for instance I can get a 512*512 image between 15s and 30s depending on the diffuser (DDIM is faster than Euler or Karras for instance). Hey, i'm little bit new to SD, but i have been using Automatic 1111 to run stable diffusion. Hi guys, im planning to get mac mini m2 base model, is it good for running automatic 1111 stable diffusion? im running it on an M1 16g ram mac mini. The new M2 Ultra in the updated Mac Studio supports a whopping 192 GB of VRAM due to its unified memory. The Mac mini m2 pro is apparently beating the mbp m2 max on benchmarks! I'd love to know if that's accurate. As I type this from my M1 Mac Book Pro, I gave up and bought a NVIDIA 12GB 3060 and threw it into a Ubuntu box. I can't even fathom the cost of an Nvidia GPU with 192 GB of VRAM, but Nvidia is renowned for its AI support and offers greater flexibility, based on my experience. What's interesting is that I just linked diffusers from InvokeAI to Vlad's Automatic UI and image generation seems to be up to 40% faster with Euler A sampler. 5 yet, but it should be a lot faster. The benchmark table is as below. You can see this easily in tasks like 3D rendering or stable diffusion renders or ML training. 1 dev and Flux. I have an older Mac and it takes about 6-10 minutes to generate one 1024x1024 image, and I have to use --medvram and high watermark ratio 0. Is there any reasonable way to do LoRA or other model training on a Mac? I’ve searched for an answer and seems like the answer is no, but this space changes so quickly I wondered if anything new is available, even in beta. I checked on the GitHub and it appears there are a huge number of outstanding issues and not many recent commits. I'm in construction so I have to move around a lot, so I can't get a PC. 13 votes, 18 comments. Macs are pretty far down the price-to-performance chart, at least the older M1 models. Paper: "Generative Models: What do they know? I've run SD on an M1 Pro and while performance is acceptable, it's not great - I would imagine the main advantage would be the size of the images you could make with that much memory available, but each iteration would be slower than it would be on even something like a GTX 1070, which can be had for ~$100 or less if you shop around. Welcome to the unofficial ComfyUI subreddit. so which GUI in your opinion is the best (user friendly, has the most utilities, less buggy etc) personally, i am using 11 votes, 21 comments. py, for example: export COMMANDLINE_ARGS="--medvram --opt-split-attention" 13 #export COMMANDLINE_ARGS="" So I'm a complete noob and I would like to request for help and guidance on what would be the best laptop to buy if I want to start using stable diffusion, especially high end uses like training models and making the video types of outputs. Hi, I am trying to pace my updates about the app posted here so it didn't clutter this subreddit. github. I have tried with separate clips too. Stable Diffusion is like having a mini art studio powered by generative AI, capable of whipping up stunning photorealistic images from just a few words or an image prompt. I was looking into getting a Mac Studio with the M1 chip but had several people tell me that if I wanted to run Stable Diffusion a mac wouldn't work, and I should really get a PC with a nvidia GPU. Researchers discover that Stable Diffusion v1 uses internal representations of 3D geometry when generating an image. There's a thread on Reddit about my GUI where others have gotten it to work too. I own these The thing is if you look at how stable diffusion is going, there's A TON of value in having people out there running and customizing their own open source models. Features. Hi guys, I'm currently use sd on my RTX 3080 10GB. 0. When I just started out using stable diffusion on my intel AMD Mac, I got a decent speed of 1. Reddit . Hi, How feasible is it to run various Stable Diffusion models from an external SSD? How badly will it affect the drive's lifespan? What is the First Part- Using Stable Diffusion in Linux. My Mac is a M2 Mini upgraded to almost the max. But hey, I still have 16gb of vram, so can do almost all of the things, even if slower. " but where do I find the file that contains "launch" or Welcome to the unofficial ComfyUI subreddit. I'm using SD with Automatic1111 on M1Pro, 32GB, 16" MacBook Pro. I don't like it, it's too simple and so on but holy cow it did it in 10 seconds! So there's performance stil on the table. I am thinking of upgrading my Mac to a Studio and have the choice between M2 Max and M2 Ultra. It’s ok. Is a Max sufficient or should I go for the Ultra for creating LORAs? And how much RAM do you recommend? Copy the folder "stable-diffusion-webui" to the external drive's folder. Right now I am using the experimental build of A1111 and it takes ~15 mins to generate a single SDXL image without refiner. Please share your tips, tricks, and workflows for using this software to create your AI art. Do you think a M2 max would be sufficient or should Evidence has been found that generative image models - including Stable Diffusion - have representations of these scene characteristics: surface normals, depth, albedo, and shading. keep in mind, you're also using a Mac M2 and AUTOMATIC1111 has been noted to work quite A few months ago I got an M1 Max Macbook pro with 64GB unified RAM and 24 GPU cores. You have summed it up with Automatic 1111. I'll root for the Ui-UX fork by Ananope. It does allow for bigger batch sizes which does improve performance - but only if you're generating large batches of images, does not improve single image generation speed. My GPU is an AMD Radeon RX 6600 (8 Gb VRAM) and CPU is an AMD Ryzen 5 3600, running on Windows 10 and Opera GX if that matters. Up until now, I've exclusively run SD on my personal computer at home. I do appreciate the list of available models downloadable from the models menu, that's a real convenience as you don't need to jump thru any hoops downloading them and getting them working. Anyone have any success with this on a mac and can share the correct commands? stable-diffusion % python scripts/txt2img. I'm using some optimisations on the webui_user script to get better performance Mac is good for final retouch and image workflow in general, but for example in a normal pc with ryzen 5600 and rtx 3060 12 gb, the same generate only take 30 second. stable-diffusion-art. Euler - ancestral or not - is slow to converge. Best Stable Diffusion Models of All Time SDXL: Best overall Stable Diffusion model, excellent in generating highly detailed, realistic images. I started working with Stable Diffusion some days ago and really enjoy all the possibilities. i have models downloaded from civitai. I require a Mac for other software, so please don't suggest Windows :) I'm wondering how much to throw at it, basically. If you are looking for speed and optimization, I recommend Draw Things. We're talking 8-12 times slower than a decent nvidia card. My M1 MBA doesn’t heat up at all when I use neural engine with optimized sampler and model for Mac. 6GB models). TL;DR Stable Diffusion runs great on my M1 Macs. I copied his settings and just like him made a 512*512 image with 30 steps, it took 3 seconds flat (no joke) I am benchmarking these 3 devices: macbook Air M1, macbook Air M2 and macbook Pro M2 using ml-stable-diffusion. ). I am currently setup on MacBook Pro M2, 16gb unified memory. Laptop GPUs work fine as well, but are often more VRAM limited and you essentially pay a huge premium over a similar desktop machine. The AI Diffusion plugin is fantastic and the firefly person that made it who if on reddit needs a lot of support. Enjoy the saved space of 350G(my case) and faster performance. It takes up all of my memory and sometime causes memory leak as well. Paper: "Beyond Surface Statistics: Scene Representations in a Latent Diffusion Model". Like even changing the strength multiplier from 0. The Draw Things app makes it really easy to run too. Titan = Prosumer cards ~1. You have proper memory management when switching models. This is only a magnitude slower than NVIDIA GPUs, if we compare with batch processing capabilities (from my experience, I can get a batch of 10-20 images generated in To optimize Stable Diffusion on Mac M2, it is essential to leverage Apple's Core ML optimizations, which significantly enhance performance. I would like to speed up the whole processes without buying me a new system (like Windows). Samples in 🧵. I generated a few images and noticed a significant It's fine. You also can’t disregard that Apple’s M chips actually have dedicated neural processing for ML/AI. Using Kosinkadink's AnimateDiff-Evolved, I was getting black frames at first. I have a lenovo legion 7 with 3080 16gb, and while I'm very happy with it, using it for stable diffusion inference showed me the real gap in performance between laptop and regular GPUs. old" and execute a1111 on external one) if it works or not. I can generate a 20 step image in 6 seconds or less with a web browser plus I have access to all the plugins, in-painting, out-painting, and soon dream booth. r Or maybe they'll even have an m series Mac Pro that isn't crazy expensive. ai, no issues. Stable diffusion speed on M2 Pro Mac is insane! I mean, is it though? It costs like 7k$ But my 1500€ pc with an rtx3070ti is way faster. Hi ! I just got into Stable diffusion (mainly to produce resources for DnD) and am still trying to figure things out. A group of open source hackers forked Stable Diffusion on GitHub and optimized the model to run on Apple's M1 chip, enabling images to be generated in ~ 15 seconds (512x512 pixels, 50 diffusion steps). Remember, apple's graphs showing how great their chip is relative to intel/nvidia, are relative to power window. I am thinking of buying a Mac Studio and would like to use Draw Things for creating my own LORAs. 5 GHz (12 cores)" but don't want to spend that money unless I get blazing SD performance. I'm looking to buy the M2 Mac Studio with 64GB ram, and 12core cpu, 38core gpu. DreamShaper: Best Stable Diffusion model for fantastical and illustration realms and sci-fi scenes. I have an M2 Pro with 32GB RAM. I’ve heard a lot of people hating on the Mac studio bc their numbers were not what they said they were. All credits go to Apple for releasing Background: I love making AI-generated art, made an entire book with Midjourney AI, but my old MacBook cannot run Stable Diffusion. Will I I've looked at the "Mac mini (2023) Apple M2 Pro @ 3. com) SD WebUI Benchmark Data (vladmandic. But 16 GB of RAM with Stable Diffusion on a Mac is just not enough. VRAM basically is a threshold and limits resolution. runs solid. Also a decent update even if you were already on an M1/M2 Mac, since it adds the ability to queue up to 14 takes on a given prompt in the “advanced options” popover, as well as a gallery view of your history so it doesn’t immediately discard anything you didn’t save right away. I found the macbook Air M1 is fastest. It's not the standard approach mixing generation and image to image working on one image as a project. Don't get a mac haha. I found "Running MIL default pipeline" the Pro M2 macbook will become slower than M1. 5 based models, Euler a sampler, with and without hypernetwork attached). And for LLM, M1 Max shows similar performance against 4060 Ti for token generations, but 3 or 4 times slower than 4060 Ti for input prompt evaluations. I am on a Mac M2, with 24GB memory. Everything from the parameter boxes to the image output to the tab navigation has been either overhauled or tweaked. maybe you can buy a Mac mini m2 for all general graphics workflow and ai, and a simple pc just for generate fast images, the rtx 3060 12 gb work super fast for ai. Enter the search term “flux”. I'm really looking forward to using this one. (rename the original folder adding ". We'll see that next month! I have both M1 Max (Mac Studio) maxed out options except SSD and 4060 Ti 16GB of VRAM Linux machine. Am going to try to roll back OS this is madness. Going to be doing a lot of generating this weekend, I always miss good models so I thought I would share my favorites as of Since you seem to have experience with creating LORAs using Draw Things I would like to know which hardware you use. Yeah I know SD is compatible with M1/M2 Mac but not sure if the cheapest M1/M2 MBP would be enough to Stable Diffusion runs on under 10 GB of VRAM on consumer Also, I had a dozen apps open with a couple hundred windows and over a thousand tabs in Safari, so not exactly a best-case benchmarking scenario. To the best of my knowledge, the WebUI install checks for updates at each startup. I am currently using a base macbook pro M2 (16gb + 512go) for stable diffusion. 2-1. So if we can do this for high performance llms it will open up so many creative uses. A1111 barely runs, takes way too long to make a single image and crashes with any resolution other than 512x512. The way i went down deep after i switches to a Nvidia/Win box is not comparable. 7 or it will crash before it finishes. Comfy is great for VRAM intensive tasks including SDXL but it is a pain for Inpainting and outpainting. 5 I generate in A1111 and complete any Inpainting or Outpainting, Then I use Comfy to upscale and face restore. I know Macs aren't the best for this kind of stuff but I just want to know how it performs out of curiosity. however, it completely depends on your requirements and what you prioritize - ease of use or performance. Posted by u/Motor-Association755 - 7 votes and 8 comments Running an M3 Max MacBook with 128gb RAM Thought I would see faster text to image renders with DiffusionBee and Draw Things apps running locally. My intention is to use Generating a 512x512 image now puts the iteration speed at about 3it/s, which is much faster than the M2 Pro, which gave me speeds at 1it/s or 2s/it, depending on the mood of the machine. when launching SD via Terminal it says: "To create a public link, set `share=True` in `launch()`. I tried comfyUI and it takes about 30s to generate 768*1048 images (i have a RTX2060, 6GB vram). It now supports all models including XL, VAE, loras, embedding, upscalers and refiner . How fast is an M1 Max 32 gb ram to generate images? My M1 takes roughly 30 seconds for one image with DiffusionBee. it/s are still around 1. I have Automatic1111 installed. The m2 runs LLMs surprisingly well with apps like ollama, assuming you get enough ram to hold the model. What affects performance a lot is VRAM quality / generation / speed. There are threads here already where you find probably I am benchmarking Stable Diffusion on MacBook Pro M2, MacBook Air M2 and MacBook Air M1. Select the flux-webui app. Looking to build a pc for stable diffusion. If I have a set of 4-5 photos and I'd like to train them on my Mac M1 Max, and go for textual inversion - and without Diffusion bee running great for me on MacBook Air with 8gb. Contribute to apple/ml-stable-diffusion development by creating an account on GitHub. The first image I run after starting the UI goes normally. Go to your SD directory /stable-diffusion-webui and find the file webui. However GPU to GPU, the M2 Ultra even at it's max config is considerably beneath the top end of PCs in pure GPU tasks. For A1111, it's not really fast compared to what I've seen in youtube vids, but it's decent. I found the macbook Stable Diffusion with Core ML on Apple Silicon. 1 Schnell models, you will need an Apple Silicon (M1/M2/M3/M4) machine with at least 16 GB RAM. Step 1: Download DiffusionBee. Can I download and run stable diffusion on MacBook Air m2 16gb ram 1tb ssd Question - Help I don’t know too much about stable diffusion but I have it installed on my windows computer and use it text to image pictures and image to image pictures Hello, just recently installed Fooocus on my M1 Pro macbook, and I'm getting around 130s/it, which is just sad to say the least. in using Stable Diffusion for a number of professional and personal (ha, ha) applications. I'm using lshqqytiger's fork of webui and I'm trying to optimize everything as best I can. py \ Welcome to the unofficial ComfyUI subreddit. I am thinking of getting a Mac Studio M2 Ultra with 192GB RAM for our company. I tried SDXL in A1111, but even after updating the UI, the images take veryyyy long time and don't finish, like they stop at 99% every time. Nonetheless, from this experience, having Stable Diffusion (ComfyUi) on NVME SSD, even the cheap Pcie 3. It works except when it doesn't. Why is Mac still behind? I know that That’s why we’ve seen much more performance gains with AMD on Linux than with Metal on Mac. Like on Win PC where VRAM is King - on Mac RAM is King. Can use any of the checkpoints from Civit. There even I have a Mac Mini M2 (8GB) and it works fine. I haven't tried with SD 1. However, if SD is According to Apple's benchmarks, the performance of Stable Diffusion on M1 and M2 chips has seen remarkable improvements: M1 Chip: Generates a 512×512 image at 50 steps in Explore stable diffusion techniques optimized for Mac M2, leveraging top open-source AI diffusion models for enhanced performance. Since I regulary see the limitations of 10 GB VRAM, especially when it Just posted a YT-video, comparing the performance of Stable Diffusion Automatic1111 on a Mac M1, a PC with an NVIDIA RTX4090, another one with a RTX3060 and Google Colab. It does really heat up for a while with a large batch size, complicated xyz plot, or multi-controlnet. But I have a MacBook Pro M2. Edit- If anyone sees this, just reinstall Automatic1111 from scratch. much like half of the people i’m very much interested if anyone has real world experience from running any stable diffusion models on M2 Ultra? i’m contemplating on getting one for work, and just trying to figure out whether it could speed up a project I have regarding image generation (up to million images). I have a M1 so it takes quite a bit too, with upscale and faceteiler around 10 min but ComfyUI is great for that. now I wanna be able to use my phones browser to play around. I never had a MacBook so i can't say its solved. 8it/s, which takes 30-40s for a 512x512 image| 25 steps| no control net, is fine for an AMD 6800xt, I guess. Yes 🙂 I use it daily. Having a laptop like this also gives me the freedom to travel and continue to work on my AI projects. Hello everybody! I am trying out the WebUI Forge app on my Macbook Air M1 16GB, and after installing following the instructions, adding a model and some LoRas, and generating image, I am getting processing times up to 60min! A stable diffusion model, say, takes a lot less memory than a LLM. 1 of 2 Go 10K subscribers in the comfyui community. I do both, and memory, GPU and local storage are going to be the three factors which have the most impact on performance. Share Top posts of March 3, 2023. Now, if you look in the Mac App Store there's also "Diffusers". 4 and above, runs Stable Diffusion from 1. The img2img tab is still a placeholder, sadly. This image took about 5 minutes, which is slow for my taste. . Hi Everyone, Can someone please tell me the best Stable Diffusion install that will allow plugins on Mac that is not M1 or M2 chips as my macs a 2019 version. Given that Apple M2 Max with 12‑core CPU, 38‑core GPU, 16‑core Neural Engine with 96GB unified memory and 1TB SSD storage is currently $4,299, would that be a much better choice? How does the performance compare I spent months limiting my experience to one sampler and mostly 512x512 base work on my Studio Ultra. Chip Apple Silicone M2 Max Pro Hi All I'm a photographer hoping to train Stable Diffusion on some of my own images to see if I can capture my own style /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt not that many MAC M2 peoepl out there trying to make M1 or M2 work as fast as they maybe are I’m not sure what soft you use, but I run TOS natively on my M2 Max 32Gb and so far the performance was amazing (compared to my 2016 old Windows laptop with i7 and 16Gb RAM). 206 votes, 30 comments. 0, with BIG files (6. This is for SDXL 1. it's based on rigorous testing & refactoring, hence most users find it more reliable. Most of the M1 Max posts I found are more than half a year old. 1 & don’t need the user to use the terminal. Running pifuhd on an m2 Mac. I think it will work with te possibility of 95% over. With that, I managed to run basic vid2vid workflow (linked in this guide, I believe), but the input video I used was scaled down to 512x288 @ 8fps. The M2 chip can generate a 512×512 image at 50 steps in just 23 seconds, a remarkable improvement over previous models. Why I bought 4060 Ti machine is that M1 Max is too slow for Stable Diffusion image generation. Python / SD is using max 16G ram, not sure what it was before the update. Mac Min M2 16RAM. Apple gets your laptop the next day. My assumption is the ml-stable-diffusion project may only use CPU cores to If it does not use CoreML, it is normal for Stable Diffusion to be slow on Apple hardware because Pytorch has an experimental Metal backend. Use whatever script editor you have to open the file (I use Sublime Text) You will find two lines of codes: 12 # Commandline arguments for webui. I convert Stable Diffusion Models DreamShaper XL1. 5x+ the price of the top of line consumer card of it's generation, about specs (#cuda cores/tensor codes/ shaders/ vrams) are usually 30%-50% higher but the performance rarely scales linearly to the specs I'm currently using Automatic on a MAC OS, but having numerous problems. Please keep posted images SFW. kdfnmcbzkjerwlxqefnkimittjccayquqlijgvrsnbdccidgu