Best stable diffusion mac m2 performance reddit. My Mac is a M2 Mini upgraded to almost the max.
● Best stable diffusion mac m2 performance reddit Most of the M1 Max posts I found are more than half a year old. 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. 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. My daily driver is an M1, and Draw Things is a great app for running Stable Diffusion. Select the flux-webui app. Up until now, I've exclusively run SD on my personal computer at home. 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. A1111 takes about 10-15 sec and Vlad and Comfyui about 6-8 seconds for a Euler A 20 step 512x512 generation. However, since I have plenty of downtime during work hours, I'm eager to There's no big performance difference. The Draw Things app is the best way to use Stable Diffusion on Mac and iOS. I am benchmarking these 3 devices: macbook Air M1, macbook Air M2 and macbook Pro M2 using ml-stable-diffusion. Download and install it. 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. I am interested in trying out the img2img script, but not sure what the syntax should be. when launching SD via Terminal it says: "To create a public link, set `share=True` in `launch()`. Best Stable Diffusion Models of All Time SDXL: Best overall Stable Diffusion model, excellent in generating highly detailed, realistic images. 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. Running pifuhd on an m2 Mac. The benchmark table is as below. 13 votes, 18 comments. 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. I do both, and memory, GPU and local storage are going to be the three factors which have the most impact on performance. Python / SD is using max 16G ram, not sure what it was before the update. To use the Flux. You also can’t disregard that Apple’s M chips actually have dedicated neural processing for ML/AI. now I wanna be able to use my phones browser to play around. I found the macbook Stable Diffusion with Core ML on Apple Silicon. 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. 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. Recommend MochiDiffusion (really, really good and well maintained app by a great developer) as it runs natively and with CoreML models. 0 model, the speed 🚀 Introducing SALL-E V1. Click Discover on the top menu. 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. The new M2 Ultra in the updated Mac Studio supports a whopping 192 GB of VRAM due to its unified memory. Hi ! I just got into Stable diffusion (mainly to produce resources for DnD) and am still trying to figure things out. " but where do I find the file that contains "launch" or Welcome to the unofficial ComfyUI subreddit. 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. Using Kosinkadink's AnimateDiff-Evolved, I was getting black frames at first. It's not the standard approach mixing generation and image to image working on one image as a project. Please share your tips, tricks, and workflows for using this software to create your AI art. PromptToImage is a free and open source Stable Diffusion app for macOS. 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. I’ve heard a lot of people hating on the Mac studio bc their numbers were not what they said they were. 0 from pyTorch to Core ML. I am currently using a base macbook pro M2 (16gb + 512go) for stable diffusion. 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. It is nowhere near it/s that some guys report here. I found the macbook Air M1 is fastest. 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. 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). 8it/s, which takes 30-40s for a 512x512 image| 25 steps| no control net, is fine for an AMD 6800xt, I guess. 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. native Swift/AppKit Stable Diffusion App for macOS, uses CoreML models for best performance. Test the function. 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. 5, a Stable Diffusion V1. 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. So if we can do this for high performance llms it will open up so many creative uses. 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. Generating 42 frames took me about 1,5 hour. Samples in 🧵. 6 OS. The img2img tab is still a placeholder, sadly. Paper: "Beyond Surface Statistics: Scene Representations in a Latent Diffusion Model". 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. 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. 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. Mochi Diffusion crashes as soon as I click generate. 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. \stable-diffusion-webui\models\Stable-diffusion. 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. Same kinds of performance with M2 iPads. 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. 2-1. py \ Welcome to the unofficial ComfyUI subreddit. it/s are still around 1. Hi guys, I'm currently use sd on my RTX 3080 10GB. You have proper memory management when switching models. Apple Silicon Mac is very limited. 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. View community ranking In the Top 1% of largest communities on Reddit. But hey, I still have 16gb of vram, so can do almost all of the things, even if slower. 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. 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. 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. Contribute to apple/ml-stable-diffusion development by creating an account on GitHub. This got me thinking about the better deal. Looking to build a pc for stable diffusion. It now supports all models including XL, VAE, loras, embedding, upscalers and refiner . I convert Stable Diffusion Models DreamShaper XL1. I'm quite impatient but generation is fast enough to make 15-25 step images without too much frustration. 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. What's your it/s for sd now? Oh! And have you benchmarked it? I'd love to know what the score is. The way i went down deep after i switches to a Nvidia/Win box is not comparable. 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. Yes. It’s fast, free, and frequently updated. It already supports SDXL. I'm using lshqqytiger's fork of webui and I'm trying to optimize everything as best I can. Macs are pretty far down the price-to-performance chart, at least the older M1 models. 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. 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. There even I have a Mac Mini M2 (8GB) and it works fine. 5 yet, but it should be a lot faster. Having a laptop like this also gives me the freedom to travel and continue to work on my AI projects. 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. 1 & don’t need the user to use the terminal. I am thinking of getting a Mac Studio M2 Ultra with 192GB RAM for our company. it's based on rigorous testing & refactoring, hence most users find it more reliable. Everything from the parameter boxes to the image output to the tab navigation has been either overhauled or tweaked. 5GB + 5. i have models downloaded from civitai. 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. 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. Why I bought 4060 Ti machine is that M1 Max is too slow for Stable Diffusion image generation. Step 1: Download DiffusionBee. I don't know why. Thanks A mix of Automatic1111 and ComfyUI. I generated a few images and noticed a significant It's fine. com) SD WebUI Benchmark Data (vladmandic. New comments cannot be posted. Someone had similar problem, and there's a workaround described here. Like on Win PC where VRAM is King - on Mac RAM is King. A Mac mini is a very affordable way to efficiently run Stable Diffusion locally. 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. stable-diffusion-art. I would like to speed up the whole processes without buying me a new system (like Windows). Stable Diffusion Benchmarked: Which GPU Runs AI Fastest (Updated) | Tom's Hardware (tomshardware. 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. Researchers discover that Stable Diffusion v1 uses internal representations of 3D geometry when generating an image. current setup seems to work fine for a 10 min test edit with some color grading. P. 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. Nonetheless, from this experience, having Stable Diffusion (ComfyUi) on NVME SSD, even the cheap Pcie 3. How to install and run Stable Diffusion on Apple Silicon M1/M2 Macs. 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. What do you guys think? I am tempted by the Acer, but I'm not sure about the quality of its build. I'm using SD with Automatic1111 on M1Pro, 32GB, 16" MacBook Pro. there so many simple people that failed school but are good at art thinking AI steals art and have no clue at all. We're talking 8-12 times slower than a decent nvidia card. This is not a tutorial just some personal experience. For SD 1. TL;DR Stable Diffusion runs great on my M1 Macs. 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. Features. Enter the search term “flux”. 23 to 0. 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. i'm currently attempting a Lensa work around with image to image (insert custom faces into trained models). 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. ). 1; 2; Next. Anyone have any success with this on a mac and can share the correct commands? stable-diffusion % python scripts/txt2img. The first image I run after starting the UI goes normally. I am thinking of buying a Mac Studio and would like to use Draw Things for creating my own LORAs. I've got an m2 max with 64gb of ram. SD Performance Data. (rename the original folder adding ". I tried comfyUI and it takes about 30s to generate 768*1048 images (i have a RTX2060, 6GB vram). 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. 1 in resolutions up to 960x960 with different samplers and upscalers. But 16 GB of RAM with Stable Diffusion on a Mac is just not enough. I'm trying to run Stable Diffusion A1111 on my Macbook Pro and it doesn't seem to be using the GPU at all. 12 votes, 17 comments. M2 CPUs perform noticeably better but are still very overpriced when all you care about is Stable Diffusion. 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. 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. 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. do m2 mac for stable diffusion or not? if i am running sd at win pc, can open 127. I'm in construction so I have to move around a lot, so I can't get a PC. I started working with Stable Diffusion some days ago and really enjoy all the possibilities. Is there anything Draw Things (available from Apple App Store) is powerful and with that power comes some complexity. I use it for some video editing and photoshop and I will continue to do some. Among the several issues I'm having now, the one below is making it very difficult to use Stable Diffusion. Remove the old or bkup it. For people who don't know: Draw Things is the only app that supports from iPhone Xs and up, macOS 12. I found "Running MIL default pipeline" the Pro M2 macbook will become slower than M1. 5 I generate in A1111 and complete any Inpainting or Outpainting, Then I use Comfy to upscale and face restore. 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. 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. 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. Agree. old" and execute a1111 on external one) if it works or not. If base M2, use neural engine. For A1111, it's not really fast compared to what I've seen in youtube vids, but it's decent. Mac Min M2 16RAM. Even if it's a custom build. If Stable Diffusion is ported to If Stable Diffusion is just one consideration among many, then an M2 should be fine. 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). What was discovered. My Mac is a M2 Mini upgraded to almost the max. It does really heat up for a while with a large batch size, complicated xyz plot, or multi-controlnet. I have Automatic1111 installed. DreamShaper: Best Stable Diffusion model for fantastical and illustration realms and sci-fi scenes. Got the stable diffusion WebUI Running on my Mac (M2). I have an M2 Pro with 32GB RAM. Share Top posts of March 3, 2023. The Mac mini m2 pro is apparently beating the mbp m2 max on benchmarks! I'd love to know if that's accurate. 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. Euler - ancestral or not - is slow to converge. 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. 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. However, the MacBook Pro might offer more benefits for coding and portability. 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. Hey, i'm little bit new to SD, but i have been using Automatic 1111 to run stable diffusion. 1 Schnell models, you will need an Apple Silicon (M1/M2/M3/M4) machine with at least 16 GB RAM. 5-2. On Mac, as far as i can tell and have testet with different Mac Studios, the amount of available RAM is important. 5 GHz (12 cores)" but don't want to spend that money unless I get blazing SD performance. But I have a MacBook Pro M2. 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. If you're using AUTOMATIC1111, leave your SD on the SSD and only keep models that you use very often in . Now, if you look in the Mac App Store there's also "Diffusers". And before you as, no, I I've read there are issues with Macs and Stable Diffusion because of the Nvidia source. Please dont judge 😅 it's also known for being more stable and less prone to crashing. You're much better off with a pc you can stuff a bunch of m2 drives and shitloads of ram in. 4 and above, runs Stable Diffusion from 1. 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. 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. Like even changing the strength multiplier from 0. 1 of 2 Go 10K subscribers in the comfyui community. 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. Am going to try to roll back OS this is madness. Not a studio, but I’ve been using it on a MacBook Pro 16 M2 Max. I am trying to workout a workflow to go from stability diffusion to a blender 3D object. 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. Pretty sure I want a Ryzen processor but not sure which one is adequate and which would be overkill. 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 never had a MacBook so i can't say its solved. 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. I think it will work with te possibility of 95% over. Comfy is great for VRAM intensive tasks including SDXL but it is a pain for Inpainting and outpainting. I find Stable Diffusion is a text-to-image AI that can be run on personal computers like Mac M1 or M2. 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. 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. 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 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. Don't get a mac haha. I am on a Mac M2, with 24GB memory. It runs SD like complete garbage however, as unlike with ollama, there's barely anything utilizing it's custom hardware to make things faster. Audio reactive stable diffusion music video for Watching Us by YEOMAN and STATEOFLIVING. 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. You can see this easily in tasks like 3D rendering or stable diffusion renders or ML training. I am currently setup on MacBook Pro M2, 16gb unified memory. This ability emerged during the training phase of the AI, and was not programmed by people. To the best of my knowledge, the WebUI install checks for updates at each startup. Model is on @huggingface Well maybe then, you should recheck. 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. I'll root for the Ui-UX fork by Ananope. 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. Free and open Yes, it's really fast, specially using the Neural Engine on arm Macs with poor GPU performance (M1, M2). r Or maybe they'll even have an m series Mac Pro that isn't crazy expensive. My M1 MBA doesn’t heat up at all when I use neural engine with optimized sampler and model for Mac. Reddit . 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. Will I I've looked at the "Mac mini (2023) Apple M2 Pro @ 3. ai, no issues. M1 is for sure more efficient, but it can't be cranked up to power levels and performance anywhere near a beefy cpu/gpu. Hi, I am trying to pace my updates about the app posted here so it didn't clutter this subreddit. For now I am working on a Mac Studio (M1 Max, 64 Gig) and it's okay-ish. 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. 7 or it will crash before it finishes. 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. Different Stable Diffusion implementations report performance differently, some display s/it and others it/s. Enjoy the saved space of 350G(my case) and faster performance. in using Stable Diffusion for a number of professional and personal (ha, ha) applications. DiffusionBee is a Stable Diffusion App for MacOS. Go to your SD directory /stable-diffusion-webui and find the file webui. The AI Diffusion plugin is fantastic and the firefly person that made it who if on reddit needs a lot of support. I agree that buying a Mac to use Stable Diffusion is not the best choice. S. VRAM basically is a threshold and limits resolution. To optimize Stable Diffusion on Mac Hi! I'm a complete beginner and today I installed fooocus and DiffusionBee versions of SD. My priority is towards smooth timeline editing performance. 0. 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. 1 dev and Flux. I checked on the GitHub and it appears there are a huge number of outstanding issues and not many recent commits. 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). 5 model fine-tuned on DALL-E 3 generated samples! Our tests reveal significant improvements in performance, including better textual alignment and aesthetics. 25 leads to way different results both in the images created and how they blend together over time. 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. I haven't tried with SD 1. 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. github. You have summed it up with Automatic 1111. I've been very successful with the txt2img script with the command below. How fast is an M1 Max 32 gb ram to generate images? My M1 takes roughly 30 seconds for one image with DiffusionBee. Titan = Prosumer cards ~1. Download Here. 5 based models, Euler a sampler, with and without hypernetwork attached). 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. Works fine after that. Apple gets your laptop the next day. 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. 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). 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. Laptop GPUs work fine as well, but are often more VRAM limited and you essentially pay a huge premium over a similar desktop machine. Currently using an M1 Mac Studio. 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. Stable requires a good Nvidia video card to be really fast. 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. 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. 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. 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. It takes up all of my memory and sometime causes memory leak as well. 6GB models). Yes 🙂 I use it daily. There's a thread on Reddit about my GUI where others have gotten it to work too. This image took about 5 minutes, which is slow for my taste. I'm pretty sure Apple will introduce the M4 Ultra at the WWDC 2024, and the M4 Mac lineup will be released in September. More posts you may like. I have a M1 so it takes quite a bit too, with upscale and faceteiler around 10 min but ComfyUI is great for that. Downsides: closed source, missing some exotic features, has an idiosyncratic UI. Edit- If anyone sees this, just reinstall Automatic1111 from scratch. 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. Suggestions? Going to get an M2 nvme for storage. 5 to 2. I'm looking to buy the M2 Mac Studio with 64GB ram, and 12core cpu, 38core gpu. I'm really looking forward to using this one. Some friends and I are building a Mac app that lets you connect different generative AI models in a single platform. 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). I have tried with separate clips too. 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. It’s ok. It's a complete redesign of the user interface from vanilla gradio with a big focus on usability. 2. Can use any of the checkpoints from Civit. A1111 barely runs, takes way too long to make a single image and crashes with any resolution other than 512x512. However, if SD is your primary consideration, go with a PC and dedicated NVIDIA graphics card. 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. 206 votes, 30 comments. I am thinking of upgrading my Mac to a Studio and have the choice between M2 Max and M2 Ultra. sh. Remember, apple's graphs showing how great their chip is relative to intel/nvidia, are relative to power window. The M2 chip can generate a 512×512 image at 50 steps in just 23 seconds, a remarkable improvement over previous models. 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). . 0, with BIG files (6. The Draw Things app makes it really easy to run too. 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. Realistic Vision: Best realistic model for Stable Diffusion, capable of generating realistic humans. When I just started out using stable diffusion on my intel AMD Mac, I got a decent speed of 1. runs solid. (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. 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. 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). 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. This is for SDXL 1. Another way to compare (although not all inclusive) using the Metal benchmarks from Geekbench. however, it completely depends on your requirements and what you prioritize - ease of use or performance. It works except when it doesn't. I require a Mac for other software, so please don't suggest Windows :) I'm wondering how much to throw at it, basically. Right now I am using the experimental build of A1111 and it takes ~15 mins to generate a single SDXL image without refiner. 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. I know Macs aren't the best for this kind of stuff but I just want to know how it performs out of curiosity. Please keep posted images SFW. comments sorted by Best Top New Controversial Q&A Add a Comment. Welcome to the unofficial ComfyUI subreddit. 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. If you are looking for speed and optimization, I recommend Draw Things. 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. eodjunxpqtakasdqsqxkcaxvubzyoouyyfeozpakmxmfvag