Last month, Stability AI released Stable Diffusion XL 1. They may just give the 20* bar as a performance metric, instead of the requirement of tensor cores. Stability AI API and DreamStudio customers will be able to access the model this Monday,. 3. (This is running on Linux, if I use Windows and diffusers etc then it’s much slower, about 2m30 per image) 1. It shows that the 4060 ti 16gb will be faster than a 4070 ti when you gen a very big image. For a beginner a 3060 12GB is enough, for SD a 4070 12GB is essentially a faster 3060 12GB. We have seen a double of performance on NVIDIA H100 chips after. To use the Stability. As the title says, training lora for sdxl on 4090 is painfully slow. 10 k+. Maybe take a look at your power saving advanced options in the Windows settings too. Thanks for. 5 examples were added into the comparison, the way I see it so far is: SDXL is superior at fantasy/artistic and digital illustrated images. Devastating for performance. 0 is supposed to be better (for most images, for most people running A/B test on their discord server. System RAM=16GiB. 9 can run on a modern consumer GPU, requiring only a Windows 10 or 11 or Linux operating system, 16 GB of RAM, and an Nvidia GeForce RTX 20 (equivalent or higher) graphics card with at least 8 GB of VRAM. 🧨 Diffusers SDXL GPU Benchmarks for GeForce Graphics Cards. The RTX 4090 costs 33% more than the RTX 4080, but its overall specs far exceed that 33%. The first invocation produces plan files in engine. The answer from our Stable […]29. The Collective Reliability Factor Chance of landing tails for 1 coin is 50%, 2 coins is 25%, 3. Description: SDXL is a latent diffusion model for text-to-image synthesis. It was trained on 1024x1024 images. 51. torch. Next select the sd_xl_base_1. 5 and SDXL (1. ☁️ FIVE Benefits of a Distributed Cloud powered by gaming PCs: 1. true. For our tests, we’ll use an RTX 4060 Ti 16 GB, an RTX 3080 10 GB, and an RTX 3060 12 GB graphics card. Consider that there will be future version after SDXL, which probably need even more vram, it seems wise to get a card with more vram. But this bleeding-edge performance comes at a cost: SDXL requires a GPU with a minimum of 6GB of VRAM,. 5). 0 release is delayed indefinitely. 我们也可以更全面的分析不同显卡在不同工况下的AI绘图性能对比。. It's a single GPU with full access to all 24GB of VRAM. PyTorch 2 seems to use slightly less GPU memory than PyTorch 1. LCM 模型 通过将原始模型蒸馏为另一个需要更少步数 (4 到 8 步，而不是原来的 25 到 50 步. e. 60s, at a per-image cost of $0. 2. It would be like quote miles per gallon for vehicle fuel. Generate an image of default size, add a ControlNet and a Lora, and AUTO1111 becomes 4x slower than ComfyUI with SDXL. 9 brings marked improvements in image quality and composition detail. Next WebUI: Full support of the latest Stable Diffusion has to offer running in Windows or Linux;. 0 is particularly well-tuned for vibrant and accurate colors, with better contrast, lighting, and shadows than its predecessor, all in native 1024×1024 resolution. Stable Diffusion XL (SDXL) GPU Benchmark Results . SDXL 1. Follow the link below to learn more and get installation instructions. Stable Diffusion XL (SDXL) Benchmark A couple months back, we showed you how to get almost 5000 images per dollar with Stable Diffusion 1. 5 and 2. 939. SD WebUI Bechmark Data. 0 aesthetic score, 2. The RTX 3060. I have no idea what is the ROCM mode, but in GPU mode my RTX 2060 6 GB can crank out a picture in 38 seconds with those specs using ComfyUI, cfg 8. Originally Posted to Hugging Face and shared here with permission from Stability AI. x and SD 2. 5GB vram and swapping refiner too , use --medvram-sdxl flag when starting r/StableDiffusion • Making Game of Thrones model with 50 characters4060Ti, just for the VRAM. 6k hi-res images with randomized prompts, on 39 nodes equipped with RTX 3090 and RTX 4090 GPUs - getting . What does matter for speed, and isn't measured by the benchmark, is the ability to run larger batches. 8, 2023. keep the final output the same, but. No way that's 1. keep the final output the same, but. The chart above evaluates user preference for SDXL (with and without refinement) over Stable Diffusion 1. 6. While SDXL already clearly outperforms Stable Diffusion 1. The beta version of Stability AI’s latest model, SDXL, is now available for preview (Stable Diffusion XL Beta). AI is a fast-moving sector, and it seems like 95% or more of the publicly available projects. Performance gains will vary depending on the specific game and resolution. On my desktop 3090 I get about 3. 0 should be placed in a directory. In this Stable Diffusion XL (SDXL) benchmark, consumer GPUs (on SaladCloud) delivered 769 images per dollar - the highest among popular clouds. NVIDIA RTX 4080 – A top-tier consumer GPU with 16GB GDDR6X memory and 9,728 CUDA cores providing elite performance. Yes, my 1070 runs it no problem. 0 Alpha 2. Unfortunately, it is not well-optimized for WebUI Automatic1111. 15. We collaborate with the diffusers team to bring the support of T2I-Adapters for Stable Diffusion XL (SDXL) in diffusers! It achieves impressive results in both performance and efficiency. 0 alpha. Stay tuned for more exciting tutorials!HPS v2: Benchmarking Text-to-Image Generative Models. 9. The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance. Despite its powerful output and advanced model architecture, SDXL 0. Did you run Lambda's benchmark or just a normal Stable Diffusion version like Automatic's? Because that takes about 18. For example, in #21 SDXL is the only one showing the fireflies. SDXL outperforms Midjourney V5. Normally you should leave batch size at 1 for SDXL, and only increase batch count (since batch size increases VRAM usage, and if it starts using system RAM instead of VRAM because VRAM is full, it will slow down, and SDXL is very VRAM heavy) I use around 25 iterations with SDXL, and SDXL refiner enabled with default settings. These settings balance speed, memory efficiency. Both are. Funny, I've been running 892x1156 native renders in A1111 with SDXL for the last few days. I the past I was training 1. With further optimizations such as 8-bit precision, we. 5 examples were added into the comparison, the way I see it so far is: SDXL is superior at fantasy/artistic and digital illustrated images. SDXL GPU Benchmarks for GeForce Graphics Cards. weirdly. For our tests, we’ll use an RTX 4060 Ti 16 GB, an RTX 3080 10 GB, and an RTX 3060 12 GB graphics card. --api --no-half-vae --xformers : batch size 1 - avg 12. The bigger the images you generate, the worse that becomes. The latest result of this work was the release of SDXL, a very advanced latent diffusion model designed for text-to-image synthesis. The A100s and H100s get all the hype but for inference at scale, the RTX series from Nvidia is the clear winner delivering at. In #22, SDXL is the only one with the sunken ship, etc. We cannot use any of the pre-existing benchmarking utilities to benchmark E2E stable diffusion performance,","# because the top-level StableDiffusionPipeline cannot be serialized into a single Torchscript object. Benchmarks exist for classical clone detection tools, which scale to a single system or a small repository. Tried SDNext as its bumf said it supports AMD/Windows and built to run SDXL. I have a 3070 8GB and with SD 1. When fps are not CPU bottlenecked at all, such as during GPU benchmarks, the 4090 is around 75% faster than the 3090 and 60% faster than the 3090-Ti, these figures are approximate upper bounds for in-game fps improvements. Despite its advanced features and model architecture, SDXL 0. finally , AUTOMATIC1111 has fixed high VRAM issue in Pre-release version 1. The mid range price/performance of PCs hasn't improved much since I built my mine. 5: SD v2. It should be noted that this is a per-node limit. . At 4k, with no ControlNet or Lora's it's 7. Thanks to specific commandline arguments, I can handle larger resolutions, like 1024x1024, and use still ControlNet smoothly and also use. I'm still new to sd but from what I understand xl is supposed to be a better more advanced version. GPU : AMD 7900xtx , CPU: 7950x3d (with iGPU disabled in BIOS), OS: Windows 11, SDXL: 1. 5: Options: Inputs are the prompt, positive, and negative terms. 5 base model. You'll also need to add the line "import. SDXL’s performance is a testament to its capabilities and impact. So it takes about 50 seconds per image on defaults for everything. A meticulous comparison of images generated by both versions highlights the distinctive edge of the latest model. While for smaller datasets like lambdalabs/pokemon-blip-captions, it might not be a problem, it can definitely lead to memory problems when the script is used on a larger dataset. It features 16,384 cores with base / boost clocks of 2. My advice is to download Python version 10 from the. Despite its powerful output and advanced model architecture, SDXL 0. SDXL GPU Benchmarks for GeForce Graphics Cards. By the end, we’ll have a customized SDXL LoRA model tailored to. 5, and can be even faster if you enable xFormers. 5 over SDXL. 1,871 followers. The abstract from the paper is: We present SDXL, a latent diffusion model for text-to-image synthesis. 16GB VRAM can guarantee you comfortable 1024×1024 image generation using the SDXL model with the refiner. That's what control net is for. previously VRAM limits a lot, also the time it takes to generate. Stable Diffusion XL, an upgraded model, has now left beta and into "stable" territory with the arrival of version 1. Dubbed SDXL v0. 5 and 2. Generate image at native 1024x1024 on SDXL, 5. The performance data was collected using the benchmark branch of the Diffusers app; Swift code is not fully optimized, introducing up to ~10% overhead unrelated to Core ML model execution. April 11, 2023. This powerful text-to-image generative model can take a textual description—say, a golden sunset over a tranquil lake—and render it into a. 9. 5: Options: Inputs are the prompt, positive, and negative terms. Close down the CMD and. Exciting SDXL 1. 85. 0 created in collaboration with NVIDIA. Results: Base workflow results. like 838. Your Path to Healthy Cloud Computing ~ 90 % lower cloud cost. I believe that the best possible and even "better" alternative is Vlad's SD Next. ago. DPM++ 2M, DPM++ 2M SDE Heun Exponential (these are just my usuals, but I have tried others) Sampling steps: 25-30. Stable Diffusion 1. They can be run locally using Automatic webui and Nvidia GPU. For our tests, we’ll use an RTX 4060 Ti 16 GB, an RTX 3080 10 GB, and an RTX 3060 12 GB graphics card. The chart above evaluates user preference for SDXL (with and without refinement) over Stable Diffusion 1. I selected 26 images of this cat from Instagram for my dataset, used the automatic tagging utility, and further edited captions to universally include "uni-cat" and "cat" using the BooruDatasetTagManager. 9: The weights of SDXL-0. Notes: ; The train_text_to_image_sdxl. 9 includes a minimum of 16GB of RAM and a GeForce RTX 20 (or higher) graphics card with 8GB of VRAM, in addition to a Windows 11, Windows 10, or Linux operating system. This GPU handles SDXL very well, generating 1024×1024 images in just. Here is what Daniel Jeffries said to justify Stability AI takedown of Model 1. 16GB VRAM can guarantee you comfortable 1024×1024 image generation using the SDXL model with the refiner. (close-up editorial photo of 20 yo woman, ginger hair, slim American. 10 Stable Diffusion extensions for next-level creativity. "finally , AUTOMATIC1111 has fixed high VRAM issue in Pre-release version 1. Cheaper image generation services. 0 is expected to change before its release. Building upon the foundation of Stable Diffusion, SDXL represents a quantum leap in performance, achieving results that rival state-of-the-art image generators while promoting openness. SDXL consists of a two-step pipeline for latent diffusion: First, we use a base model to generate latents of the desired output size. I solved the problem. The chart above evaluates user preference for SDXL (with and without refinement) over SDXL 0. Besides the benchmark, I also made a colab for anyone to try SD XL 1. r/StableDiffusion. On a 3070TI with 8GB. AMD, Ultra, High, Medium & Memory Scaling r/soccer • Bruno Fernandes: "He [Nicolas Pépé] had some bad games and everyone was saying, ‘He still has to adapt’ [to the Premier League], but when Bruno was having a bad game, it was just because he was moaning or not focused on the game. Learn how to use Stable Diffusion SDXL 1. MASSIVE SDXL ARTIST COMPARISON: I tried out 208 different artist names with the same subject prompt for SDXL. 5 negative aesthetic score Send refiner to CPU, load upscaler to GPU Upscale x2 using GFPGANSDXL (ComfyUI) Iterations / sec on Apple Silicon (MPS) currently in need of mass producing certain images for a work project utilizing Stable Diffusion, so naturally looking in to SDXL. Get started with SDXL 1. But these improvements do come at a cost; SDXL 1. 使用 LCM LoRA 4 步完成 SDXL 推理 . The key to this success is the integration of NVIDIA TensorRT, a high-performance, state-of-the-art performance optimization framework. 0, an open model representing the next evolutionary step in text-to-image generation models. One way to make major improvements would be to push tokenization (and prompt use) of specific hand poses, as they have more fixed morphology - i. 0, anyone can now create almost any image easily and. ) and using standardized txt2img settings. The animal/beach test. Overall, SDXL 1. Without it, batches larger than one actually run slower than consecutively generating them, because RAM is used too often in place of VRAM. 94, 8. SDXL-VAE-FP16-Fix was created by finetuning the SDXL-VAE to: 1. cudnn. Omikonz • 2 mo. The Best Ways to Run Stable Diffusion and SDXL on an Apple Silicon Mac The go-to image generator for AI art enthusiasts can be installed on Apple's latest hardware. arrow_forward. It’s perfect for beginners and those with lower-end GPUs who want to unleash their creativity. This means that you can apply for any of the two links - and if you are granted - you can access both. System RAM=16GiB. ; Prompt: SD v1. For those purposes, you. x models. batter159. 0. ago. Conclusion: Diving into the realm of Stable Diffusion XL (SDXL 1. Updates [08/02/2023] We released the PyPI package. I was going to say. 9, produces visuals that are more realistic than its predecessor. After the SD1. Static engines provide the best performance at the cost of flexibility. Use TAESD; a VAE that uses drastically less vram at the cost of some quality. 1, and SDXL are commonly thought of as "models", but it would be more accurate to think of them as families of AI. Stable Diffusion XL (SDXL) Benchmark – 769 Images Per Dollar on Salad. 9 Release. The 8GB 3060ti is quite a bit faster than the12GB 3060 on the benchmark. SDXL GeForce GPU Benchmarks. Würstchen V1, introduced previously, shares its foundation with SDXL as a Latent Diffusion model but incorporates a faster Unet architecture. Create models using more simple-yet-accurate prompts that can help you produce complex and detailed images. From what i have tested, InvokeAi (latest Version) have nearly the same Generation Times as A1111 (SDXL, SD1. Benchmarking: More than Just Numbers. Floating points are stored as 3 values: sign (+/-), exponent, and fraction. . 5 is superior at human subjects and anatomy, including face/body but SDXL is superior at hands. 1. 9: The weights of SDXL-0. Your Path to Healthy Cloud Computing ~ 90 % lower cloud cost. Both are. As for the performance, the Ryzen 5 4600G only took around one minute and 50 seconds to generate a 512 x 512-pixel image with the default setting of 50 steps. For our tests, we’ll use an RTX 4060 Ti 16 GB, an RTX 3080 10 GB, and an RTX 3060 12 GB graphics card. 私たちの最新モデルは、StabilityAIのSDXLモデルをベースにしていますが、いつものように、私たち独自の隠し味を大量に投入し、さらに進化させています。例えば、純正のSDXLよりも暗いシーンを生成するのがはるかに簡単です。SDXL might be able to do them a lot better but it won't be a fixed issue. If you would like to access these models for your research, please apply using one of the following links: SDXL-base-0. 在过去的几周里，Diffusers 团队和 T2I-Adapter 作者紧密合作，在 diffusers 库上为 Stable Diffusion XL (SDXL) 增加 T2I-Adapter 的支持. 5 has developed to a quite mature stage, and it is unlikely to have a significant performance improvement. SDXL consists of a two-step pipeline for latent diffusion: First, we use a base model to generate latents of the desired output size. The LoRA training can be done with 12GB GPU memory. ptitrainvaloin. Compared to previous versions, SDXL is capable of generating higher-quality images. A new version of Stability AI’s AI image generator, Stable Diffusion XL (SDXL), has been released. ","#Lowers performance, but only by a bit - except if live previews are enabled. a 20% power cut to a 3-4% performance cut, a 30% power cut to a 8-10% performance cut, and so forth. Everything is. Then, I'll go back to SDXL and the same setting that took 30 to 40 s will take like 5 minutes. Wiki Home. Instructions:. Also memory requirements—especially for model training—are disastrous for owners of older cards with less VRAM (this issue will disappear soon as better cards will resurface on second hand. Yeah as predicted a while back, I don't think adoption of SDXL will be immediate or complete. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder. You can also vote for which image is better, this. In particular, the SDXL model with the Refiner addition achieved a win rate of 48. Mine cost me roughly $200 about 6 months ago. In addition, the OpenVino script does not fully support HiRes fix, LoRa, and some extenions. Results: Base workflow results. Also it is using full 24gb of ram, but it is so slow that even gpu fans are not spinning. 9, the newest model in the SDXL series!Building on the successful release of the Stable Diffusion XL beta, SDXL v0. This suggests the need for additional quantitative performance scores, specifically for text-to-image foundation models. 5 is version 1. OS= Windows. Pertama, mari mulai dengan komposisi seni yang simpel menggunakan parameter default agar GPU kami mulai bekerja. Get up and running with the most cost effective SDXL infra in a matter of minutes, read the full benchmark here 11 3 Comments Like CommentThe SDXL 1. 0. [8] by. If you're just playing AAA 4k titles either will be fine. There are slight discrepancies between the output of SDXL-VAE-FP16-Fix and SDXL-VAE, but the decoded images should be close. To stay compatible with other implementations we use the same numbering where 1 is the default behaviour and 2 skips 1 layer. I guess it's a UX thing at that point. 61. 3gb of vram at 1024x1024 while sd xl doesn't even go above 5gb. 1 OS Loader Version: 8422. Single image: < 1 second at an average speed of ≈27. First, let’s start with a simple art composition using default parameters to give our GPUs a good workout. 5 base, juggernaut, SDXL. In a groundbreaking advancement, we have unveiled our latest optimization of the Stable Diffusion XL (SDXL 1. 2. To harness the full potential of SDXL 1. SDXL GPU Benchmarks for GeForce Graphics Cards. But in terms of composition and prompt following, SDXL is the clear winner. SytanSDXL [here] workflow v0. 1 in all but two categories in the user preference comparison. AI Art using SDXL running in SD. For direct comparison, every element should be in the right place, which makes it easier to compare. 1 is clearly worse at hands, hands down. ) Cloud - Kaggle - Free. This architectural finesse and optimized training parameters position SSD-1B as a cutting-edge model in text-to-image generation. Then, I'll change to a 1. Dhanshree Shripad Shenwai. We design. With 3. This is the image without control net, as you can see, the jungle is entirely different and the person, too. If you want to use more checkpoints: Download more to the drive or paste the link / select in the library section. Get up and running with the most cost effective SDXL infra in a matter of minutes, read the full benchmark here 11 3 Comments Like CommentPerformance Metrics. Running on cpu upgrade. 这次我们给大家带来了从RTX 2060 Super到RTX 4090一共17款显卡的Stable Diffusion AI绘图性能测试。. First, let’s start with a simple art composition using default parameters to give our GPUs a good workout. bat' file, make a shortcut and drag it to your desktop (if you want to start it without opening folders) 10. Aesthetic is very subjective, so some will prefer SD 1. Moving on to 3D rendering, Blender is a popular open-source rendering application, and we're using the latest Blender Benchmark, which uses Blender 3. ago. 1. 5 it/s. Empty_String. My workstation with the 4090 is twice as fast. 既にご存じの方もいらっしゃるかと思いますが、先月Stable Diffusionの最新かつ高性能版である Stable Diffusion XL が発表されて話題になっていました。. 0 is still in development: The architecture of SDXL 1. NVIDIA GeForce RTX 4070 Ti (1) (compute_37) (8, 9) cuda: 11. 6. ; Prompt: SD v1. it's a bit slower, yes. Sep 03, 2023. when fine-tuning SDXL at 256x256 it consumes about 57GiB of VRAM at a batch size of 4. If you're just playing AAA 4k titles either will be fine. Everything is. 5, Stable diffusion 2. August 27, 2023 Imraj RD Singh, Alexander Denker, Riccardo Barbano, Željko Kereta, Bangti Jin,. 5 in ~30 seconds per image compared to 4 full SDXL images in under 10 seconds is just HUGE!It features 3,072 cores with base / boost clocks of 1. 54. Network latency can add a second or two to the time it. Asked the new GPT-4-Vision to look at 4 SDXL generations I made and give me prompts to recreate those images in DALLE-3 - (First. 0 (SDXL) and open-sourced it without requiring any special permissions to access it. 3. The RTX 2080 Ti released at $1,199, the RTX 3090 at $1,499, and now, the RTX 4090 is $1,599. SD1. 0 outshines its predecessors and is a frontrunner among the current state-of-the-art image generators. Resulted in a massive 5x performance boost for image generation. Salad. 0, while slightly more complex, offers two methods for generating images: the Stable Diffusion WebUI and the Stable AI API. It's slow in CompfyUI and Automatic1111. git 2023-08-31 hash:5ef669de. 我们也可以更全面的分析不同显卡在不同工况下的AI绘图性能对比。. First, let’s start with a simple art composition using default parameters to. For our tests, we’ll use an RTX 4060 Ti 16 GB, an RTX 3080 10 GB, and an RTX 3060 12 GB graphics card. 这次我们给大家带来了从RTX 2060 Super到RTX 4090一共17款显卡的Stable Diffusion AI绘图性能测试。. It'll most definitely suffice. I'm getting really low iterations per second a my RTX 4080 16GB. -. 5 it/s. On a 3070TI with 8GB. 0, iPadOS 17. 🔔 Version : SDXL. (6) Hands are a big issue, albeit different than in earlier SD. Found this Google Spreadsheet (not mine) with more data and a survey to fill. But these improvements do come at a cost; SDXL 1. The realistic base model of SD1. The abstract from the paper is: We present SDXL, a latent diffusion model for text-to-image synthesis. Join. It supports SD 1. 10 Stable Diffusion extensions for next-level creativity. 0 and Stability AI open-source language models and determine the best use cases for your business. There have been no hardware advancements in the past year that would render the performance hit irrelevant. From what I've seen, a popular benchmark is: Euler a sampler, 50 steps, 512X512. In contrast, the SDXL results seem to have no relation to the prompt at all apart from the word "goth", the fact that the faces are (a bit) more coherent is completely worthless because these images are simply not reflective of the prompt . At higher (often sub-optimal) resolutions (1440p, 4K etc) the 4090 will show increasing improvements compared to lesser cards. 0 model was developed using a highly optimized training approach that benefits from a 3. ashutoshtyagi. This is an order of magnitude faster, and not having to wait for results is a game-changer. The SDXL model incorporates a larger language model, resulting in high-quality images closely matching the provided prompts. The Results. You can not prompt for specific plants, head / body in specific positions. The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance. scaling down weights and biases within the network. Following up from our Whisper-large-v2 benchmark, we recently benchmarked Stable Diffusion XL (SDXL) on consumer GPUs. Join. SDXL-VAE-FP16-Fix was created by finetuning the SDXL-VAE to: 1. ago. It's a small amount slower than ComfyUI, especially since it doesn't switch to the refiner model anywhere near as quick, but it's been working just fine. Running on cpu upgrade. In Brief. It's just as bad for every computer. 9 is now available on the Clipdrop by Stability AI platform. 5 will likely to continue to be the standard, with this new SDXL being an equal or slightly lesser alternative. 5 so SDXL could be seen as SD 3. To use SD-XL, first SD. It is important to note that while this result is statistically significant, we must also take into account the inherent biases introduced by the human element and the inherent randomness of generative models. 1: SDXL ; 1: Stunning sunset over a futuristic city, with towering skyscrapers and flying vehicles, golden hour lighting and dramatic clouds, high detail, moody atmosphereGoogle Cloud TPUs are custom-designed AI accelerators, which are optimized for training and inference of large AI models, including state-of-the-art LLMs and generative AI models such as SDXL. In this Stable Diffusion XL (SDXL) benchmark, consumer GPUs (on SaladCloud) delivered 769 images per dollar - the highest among popular clouds. 5 negative aesthetic score Send refiner to CPU, load upscaler to GPU Upscale x2 using GFPGAN SDXL (ComfyUI) Iterations / sec on Apple Silicon (MPS) currently in need of mass producing certain images for a work project utilizing Stable Diffusion, so naturally looking in to SDXL. Next supports two main backends: Original and Diffusers which can be switched on-the-fly: Original: Based on LDM reference implementation and significantly expanded on by A1111. 19it/s (after initial generation).