However, there are still limitations to address, and we hope to see further improvements. 0: Guidance, Schedulers, and. There are slight discrepancies between the output of SDXL-VAE-FP16-Fix and SDXL-VAE, but the decoded images should be close. google / sdxl. 9 model, and SDXL-refiner-0. This repository hosts the TensorRT versions of Stable Diffusion XL 1. In. We release T2I-Adapter-SDXL models for sketch, canny, lineart, openpose, depth-zoe, and depth-mid. To use the Stability. Understanding Classifier-Free Diffusion Guidance We haven't tested SDXL, yet, mostly because the memory demands and getting it running properly tend to be even higher than 768x768 image generation. M. Problem is a giant big Gorilla in our tiny little AI world called 'Midjourney. No way that's 1. Disclaimer: Even though train_instruct_pix2pix_sdxl. keep the final output the same, but. Floating points are stored as 3 values: sign (+/-), exponent, and fraction. 5, Stable diffusion 2. Here is one 1024x1024 benchmark, hopefully it will be of some use. cudnn. 5 base, juggernaut, SDXL. I just built a 2080 Ti machine for SD. Sep 03, 2023. Or drop $4k on a 4090 build now. 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. SDXL is now available via ClipDrop, GitHub or the Stability AI Platform. stability-ai / sdxl A text-to-image generative AI model that creates beautiful images Public; 20. SDXL’s performance is a testament to its capabilities and impact. You’ll need to have: macOS computer with Apple silicon (M1/M2) hardware. Right click the 'Webui-User. I also tried with the ema version, which didn't change at all. Found this Google Spreadsheet (not mine) with more data and a survey to fill. SDXL Benchmark with 1,2,4 batch sizes (it/s): SD1. 6. If you don't have the money the 4080 is a great card. 9 の記事にも作例. 1mo. 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. The SDXL 1. Scroll down a bit for a benchmark graph with the text SDXL. 5 model and SDXL for each argument. 6B parameter refiner model, making it one of the largest open image generators today. Salad. 939. make the internal activation values smaller, by. SD WebUI Bechmark Data. This checkpoint recommends a VAE, download and place it in the VAE folder. You can not prompt for specific plants, head / body in specific positions. Metal Performance Shaders (MPS) 🤗 Diffusers is compatible with Apple silicon (M1/M2 chips) using the PyTorch mps device, which uses the Metal framework to leverage the GPU on MacOS devices. The RTX 3060. You can use Stable Diffusion locally with a smaller VRAM, but you have to set the image resolution output to pretty small (400px x 400px) and use additional parameters to counter the low VRAM. 9 is now available on the Clipdrop by Stability AI platform. They may just give the 20* bar as a performance metric, instead of the requirement of tensor cores. Read More. Animate Your Personalized Text-to-Image Diffusion Models with SDXL and LCM Updated 3 days, 20 hours ago 129 runs petebrooks / abba-8bit-dancing-queenIn addition to this, with the release of SDXL, StabilityAI have confirmed that they expect LoRA's to be the most popular way of enhancing images on top of the SDXL v1. Devastating for performance. We are proud to host the TensorRT versions of SDXL and make the open ONNX weights available to users of SDXL globally. 10 k+. , have to wait for compilation during the first run). It needs at least 15-20 seconds to complete 1 single step, so it is impossible to train. x models. Install Python and Git. AdamW 8bit doesn't seem to work. 5 did, not to mention 2 separate CLIP models (prompt understanding) where SD 1. [8] by. Install the Driver from Prerequisites above. compile will make overall inference faster. The images generated were of Salads in the style of famous artists/painters. In addition, the OpenVino script does not fully support HiRes fix, LoRa, and some extenions. The beta version of Stability AI’s latest model, SDXL, is now available for preview (Stable Diffusion XL Beta). 5, non-inbred, non-Korean-overtrained model this is. keep the final output the same, but. I have tried putting the base safetensors file in the regular models/Stable-diffusion folder. Image: Stable Diffusion benchmark results showing a comparison of image generation time. 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. To gauge the speed difference we are talking about, generating a single 1024x1024 image on an M1 Mac with SDXL (base) takes about a minute. Originally Posted to Hugging Face and shared here with permission from Stability AI. Wurzelrenner. a fist has a fixed shape that can be "inferred" from. 5 - Nearly 40% faster than Easy Diffusion v2. 5 so SDXL could be seen as SD 3. Can generate large images with SDXL. This is an order of magnitude faster, and not having to wait for results is a game-changer. 10 in series: ≈ 10 seconds. The Collective Reliability Factor Chance of landing tails for 1 coin is 50%, 2 coins is 25%, 3. Details: A1111 uses Intel OpenVino to accelate generation speed (3 sec for 1 image), but it needs time for preparation and warming up. Or drop $4k on a 4090 build now. Faster than v2. 6k hi-res images with randomized prompts, on 39 nodes equipped with RTX 3090 and RTX 4090 GPUs - getting . SDXL 1. 0) foundation model from Stability AI is available in Amazon SageMaker JumpStart, a machine learning (ML) hub that offers pretrained models, built-in algorithms, and pre-built solutions to help you quickly get started with ML. 9 の記事にも作例. 8. 1. 16GB VRAM can guarantee you comfortable 1024×1024 image generation using the SDXL model with the refiner. 5 and 2. The current benchmarks are based on the current version of SDXL 0. 217. SDXL-0. Untuk pengetesan ini, kami menggunakan kartu grafis RTX 4060 Ti 16 GB, RTX 3080 10 GB, dan RTX 3060 12 GB. In particular, the SDXL model with the Refiner addition achieved a win rate of 48. 9. 2 / 2. Use TAESD; a VAE that uses drastically less vram at the cost of some quality. --lowvram: An even more thorough optimization of the above, splitting unet into many modules, and only one module is kept in VRAM. 47 seconds. This architectural finesse and optimized training parameters position SSD-1B as a cutting-edge model in text-to-image generation. 0 and stable-diffusion-xl-refiner-1. x models. 0, an open model representing the next evolutionary step in text-to-image generation models. 5 and 2. IP-Adapter can be generalized not only to other custom models fine-tuned from the same base model, but also to controllable generation using existing controllable tools. Using my normal Arguments --xformers --opt-sdp-attention --enable-insecure-extension-access --disable-safe-unpickle Scroll down a bit for a benchmark graph with the text SDXL. Join. (close-up editorial photo of 20 yo woman, ginger hair, slim American. 56, 4. 9 is able to be run on a fairly standard PC, needing only a Windows 10 or 11, or Linux operating system, with 16GB RAM, an Nvidia GeForce RTX 20 graphics card (equivalent or higher standard) equipped with a minimum of 8GB of VRAM. Note | Performance is measured as iterations per second for different batch sizes (1, 2, 4, 8. 0, the base SDXL model and refiner without any LORA. Base workflow: Options: Inputs are only the prompt and negative words. We present SDXL, a latent diffusion model for text-to-image synthesis. 4070 uses less power, performance is similar, VRAM 12 GB. OS= Windows. 0. 100% free and compliant. For those purposes, you. But in terms of composition and prompt following, SDXL is the clear winner. . it's a bit slower, yes. 4K resolution: RTX 4090 is 124% faster than GTX 1080 Ti. Thanks Below are three emerging solutions for doing Stable Diffusion Generative AI art using Intel Arc GPUs on a Windows laptop or PC. 47 it/s So a RTX 4060Ti 16GB can do up to ~12 it/s with the right parameters!! Thanks for the update! That probably makes it the best GPU price / VRAM memory ratio on the market for the rest of the year. Like SD 1. Auto Load SDXL 1. At 7 it looked like it was almost there, but at 8, totally dropped the ball. The sheer speed of this demo is awesome! compared to my GTX1070 doing a 512x512 on sd 1. apple/coreml-stable-diffusion-mixed-bit-palettization contains (among other artifacts) a complete pipeline where the UNet has been replaced with a mixed-bit palettization recipe that achieves a compression equivalent to 4. Opinion: Not so fast, results are good enough. Stay tuned for more exciting tutorials!HPS v2: Benchmarking Text-to-Image Generative Models. Then, I'll change to a 1. Yesterday they also confirmed that the final SDXL model would have a base+refiner. To put this into perspective, the SDXL model would require a comparatively sluggish 40 seconds to achieve the same task. I was expecting performance to be poorer, but not by. Aug 30, 2023 • 3 min read. heat 1 tablespoon of olive oil in a skillet over medium heat ', ' add bell pepper and saut until softened slightly , about 3 minutes ', ' add onion and season with salt and pepper ', ' saut until softened , about 7 minutes ', ' stir in the chicken ', ' add heavy cream , buffalo sauce and blue cheese ', ' stir and cook until heated through , about 3-5 minutes ',. The answer from our Stable Diffusion XL (SDXL) Benchmark: a resounding yes. Specs n numbers: Nvidia RTX 2070 (8GiB VRAM). First, let’s start with a simple art composition using default parameters to. Looking to upgrade to a new card that'll significantly improve performance but not break the bank. 13. Your Path to Healthy Cloud Computing ~ 90 % lower cloud cost. 24GB VRAM. make the internal activation values smaller, by. In this Stable Diffusion XL (SDXL) benchmark, consumer GPUs (on SaladCloud) delivered 769 images per dollar - the highest among popular clouds. The SDXL model incorporates a larger language model, resulting in high-quality images closely matching the provided prompts. backends. ago. Let's create our own SDXL LoRA! For the purpose of this guide, I am going to create a LoRA on Liam Gallagher from the band Oasis! Collect training imagesSDXL 0. If you want to use more checkpoints: Download more to the drive or paste the link / select in the library section. 1 in all but two categories in the user preference comparison. 19it/s (after initial generation). 5GB vram and swapping refiner too , use --medvram-sdxl flag when starting. 5 billion parameters, it can produce 1-megapixel images in different aspect ratios. Eh that looks right, according to benchmarks the 4090 laptop GPU is going to be only slightly faster than a desktop 3090. Download the stable release. 5, SDXL is flexing some serious muscle—generating images nearly 50% larger in resolution vs its predecessor without breaking a sweat. Specs: 3060 12GB, tried both vanilla Automatic1111 1. 3. --api --no-half-vae --xformers : batch size 1 - avg 12. 0-RC , its taking only 7. 8 cudnn: 8800 driver: 537. Stability AI claims that the new model is “a leap. 0 aesthetic score, 2. 9 model, and SDXL-refiner-0. I tried --lovram --no-half-vae but it was the same problem. ) Cloud - Kaggle - Free. But these improvements do come at a cost; SDXL 1. I was having very poor performance running SDXL locally in ComfyUI to the point where it was basically unusable. You should be good to go, Enjoy the huge performance boost! Using SD-XL. That's still quite slow, but not minutes per image slow. Stable Diffusion XL（通称SDXL）の導入方法と使い方. ☁️ FIVE Benefits of a Distributed Cloud powered by gaming PCs: 1. The RTX 2080 Ti released at $1,199, the RTX 3090 at $1,499, and now, the RTX 4090 is $1,599. On a 3070TI with 8GB. Yeah 8gb is too little for SDXL outside of ComfyUI. Run time and cost. This also somtimes happens when I run dynamic prompts in SDXL and then turn them off. tl;dr: We use various formatting information from rich text, including font size, color, style, and footnote, to increase control of text-to-image generation. 6. The way the other cards scale in price and performance with the last gen 3xxx cards makes those owners really question their upgrades. I believe that the best possible and even "better" alternative is Vlad's SD Next. HumanEval Benchmark Comparison with models of similar size(3B). The key to this success is the integration of NVIDIA TensorRT, a high-performance, state-of-the-art performance optimization framework. Expressive Text-to-Image Generation with. 0 to create AI artwork. --lowvram: An even more thorough optimization of the above, splitting unet into many modules, and only one module is kept in VRAM. Next needs to be in Diffusers mode, not Original, select it from the Backend radio buttons. Follow the link below to learn more and get installation instructions. 5 seconds. 2. It'll most definitely suffice. Despite its powerful output and advanced model architecture, SDXL 0. 5. App Files Files Community . But these improvements do come at a cost; SDXL 1. Stability AI. 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. SD-XL Base SD-XL Refiner. 2. Now, with the release of Stable Diffusion XL, we’re fielding a lot of questions regarding the potential of consumer GPUs for serving SDXL inference at scale. Learn how to use Stable Diffusion SDXL 1. One is the base version, and the other is the refiner. SD. If you would like to access these models for your research, please apply using one of the following links: SDXL-base-0. I'm getting really low iterations per second a my RTX 4080 16GB. Total Number of Cores: 12 (8 performance and 4 efficiency) Memory: 32 GB System Firmware Version: 8422. Please share if you know authentic info, otherwise share your empirical experience. Stability AI, the company behind Stable Diffusion, said, "SDXL 1. 6B parameter refiner model, making it one of the largest open image generators today. Building a great tech team takes more than a paycheck. 0 が正式リリースされました この記事では、SDXL とは何か、何ができるのか、使ったほうがいいのか、そもそも使えるのかとかそういうアレを説明したりしなかったりします 正式リリース前の SDXL 0. 3 strength, 5. First, let’s start with a simple art composition using default parameters to give our GPUs a good workout. If you have the money the 4090 is a better deal. (This is running on Linux, if I use Windows and diffusers etc then it’s much slower, about 2m30 per image) 1. SDXL-VAE-FP16-Fix was created by finetuning the SDXL-VAE to: 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. In order to test the performance in Stable Diffusion, we used one of our fastest platforms in the AMD Threadripper PRO 5975WX, although CPU should have minimal impact on results. Same reason GPT4 is so much better than GPT3. If you're using AUTOMATIC1111, then change the txt2img. Funny, I've been running 892x1156 native renders in A1111 with SDXL for the last few days. The answer from our Stable […]29. Model weights: Use sdxl-vae-fp16-fix; a VAE that will not need to run in fp32. It's also faster than the K80. Your card should obviously do better. Show benchmarks comparing different TPU settings; Why JAX + TPU v5e for SDXL? Serving SDXL with JAX on Cloud TPU v5e with high performance and cost. The SDXL model will be made available through the new DreamStudio, details about the new model are not yet announced but they are sharing a couple of the generations to showcase what it can do. At 769 SDXL images per dollar, consumer GPUs on Salad’s distributed. ago. For example turn on Cyberpunk 2077's built in Benchmark in the settings with unlocked framerate and no V-Sync, run a benchmark on it, screenshot + label the file, change ONLY memory clock settings, rinse and repeat. The disadvantage is that slows down generation of a single image SDXL 1024x1024 by a few seconds for my 3060 GPU. Image created by Decrypt using AI. Optimized for maximum performance to run SDXL with colab free. 由于目前SDXL还不够成熟，模型数量和插件支持相对也较少，且对硬件配置的要求进一步提升，所以. Even less VRAM usage - Less than 2 GB for 512x512 images on ‘low’ VRAM usage setting (SD 1. 6 and the --medvram-sdxl. like 838. Stable Diffusion XL (SDXL) Benchmark – 769 Images Per Dollar on Salad. 10. Segmind's Path to Unprecedented Performance. • 6 mo. 16GB VRAM can guarantee you comfortable 1024×1024 image generation using the SDXL model with the refiner. How To Do SDXL LoRA Training On RunPod With Kohya SS GUI Trainer & Use LoRAs With Automatic1111 UI. 4090 Performance with Stable Diffusion (AUTOMATIC1111) Having issues with this, having done a reinstall of Automatic's branch I was only getting between 4-5it/s using the base settings (Euler a, 20 Steps, 512x512) on a Batch of 5, about a third of what a 3080Ti can reach with --xformers. because without that SDXL prioritizes stylized art and SD 1 and 2 realism so it is a strange comparison. Large batches are, per-image, considerably faster. 11 on for some reason when i uninstalled everything and reinstalled python 3. Base workflow: Options: Inputs are only the prompt and negative words. タイトルは釣りです 日本時間の7月27日早朝、Stable Diffusion の新バージョン SDXL 1. And I agree with you. Aesthetic is very subjective, so some will prefer SD 1. 5 guidance scale, 6. (I’ll see myself out. Along with our usual professional tests, we've added Stable Diffusion benchmarks on the various GPUs. By Jose Antonio Lanz. I use gtx 970 But colab is better and do not heat up my room. 0 alpha. 0 should be placed in a directory. We are proud to. This repository comprises: python_coreml_stable_diffusion, a Python package for converting PyTorch models to Core ML format and performing image generation with Hugging Face diffusers in Python. 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 path of the directory should replace /path_to_sdxl. comparative study. mechbasketmk3 • 7 mo. During inference, latent are rendered from the base SDXL and then diffused and denoised directly in the latent space using the refinement model with the same text input. 2, along with code to get started with deploying to Apple Silicon devices. 0) stands at the forefront of this evolution. After that, the bot should generate two images for your prompt. Insanely low performance on a RTX 4080. SDXL GPU Benchmarks for GeForce Graphics Cards. 0 and Stability AI open-source language models and determine the best use cases for your business. Unless there is a breakthrough technology for SD1. Notes: ; The train_text_to_image_sdxl. With upgrades like dual text encoders and a separate refiner model, SDXL achieves significantly higher image quality and resolution. SD 1. [08/02/2023]. The result: 769 hi-res images per dollar. 0) Benchmarks + Optimization Trick self. ai Discord server to generate SDXL images, visit one of the #bot-1 – #bot-10 channels. 5 base model: 7. The animal/beach test. While these are not the only solutions, these are accessible and feature rich, able to support interests from the AI art-curious to AI code warriors. SDXL-0. SD1. The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance. 9. The current benchmarks are based on the current version of SDXL 0. They could have provided us with more information on the model, but anyone who wants to may try it out. System RAM=16GiB. e. Step 3: Download the SDXL control models. I cant find the efficiency benchmark against previous SD models. 5 Vs SDXL Comparison. 5 guidance scale, 50 inference steps Offload base pipeline to CPU, load refiner pipeline on GPU Refine image at 1024x1024, 0. 5 GHz, 24 GB of memory, a 384-bit memory bus, 128 3rd gen RT cores, 512 4th gen Tensor cores, DLSS 3 and a TDP of 450W. このモデル. Step 1: Update AUTOMATIC1111. UsualAd9571. Conclusion. 3. It can be set to -1 in order to run the benchmark indefinitely. The time it takes to create an image depends on a few factors, so it's best to determine a benchmark, so you can compare apples to apples. Originally Posted to Hugging Face and shared here with permission from Stability AI. , SDXL 1. Your Path to Healthy Cloud Computing ~ 90 % lower cloud cost. . This checkpoint recommends a VAE, download and place it in the VAE folder. ; Use the LoRA with any SDXL diffusion model and the LCM scheduler; bingo! You get high-quality inference in just a few. 5B parameter base model and a 6. 3 strength, 5. Benchmark Results: GTX 1650 is the Surprising Winner As expected, our nodes with higher end GPUs took less time per image, with the flagship RTX 4090 offering the best performance. I'm sharing a few I made along the way together with some detailed information on how I. Finally, Stable Diffusion SDXL with ROCm acceleration and benchmarks Aug 28, 2023 3 min read rocm Finally, Stable Diffusion SDXL with ROCm acceleration. It was awesome, super excited about all the improvements that are coming! Here's a summary: SDXL is easier to tune. It was trained on 1024x1024 images. And that kind of silky photography is exactly what MJ does very well. Below we highlight two key factors: JAX just-in-time (jit) compilation and XLA compiler-driven parallelism with JAX pmap. 5 and 2. 44%. Today, we are excited to release optimizations to Core ML for Stable Diffusion in macOS 13. This benchmark was conducted by Apple and Hugging Face using public beta versions of iOS 17. For instance, the prompt "A wolf in Yosemite. A new version of Stability AI’s AI image generator, Stable Diffusion XL (SDXL), has been released. --network_train_unet_only. 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. It's an excellent result for a $95. 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. Description: SDXL is a latent diffusion model for text-to-image synthesis. Horrible performance. Network latency can add a second or two to the time it. 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. Score-Based Generative Models for PET Image Reconstruction. 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. Results: Base workflow results. Stable Diffusion XL (SDXL) Benchmark. SytanSDXL [here] workflow v0. 0 Alpha 2. 5 it/s. Excitingly, the model is now accessible through ClipDrop, with an API launch scheduled in the near future. It’ll be faster than 12GB VRAM, and if you generate in batches, it’ll be even better. 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. Has there been any down-level optimizations in this regard. 3gb of vram at 1024x1024 while sd xl doesn't even go above 5gb. By the end, we’ll have a customized SDXL LoRA model tailored to. Use the optimized version, or edit the code a little to use model. 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. Run SDXL refiners to increase the quality of output with high resolution images. A meticulous comparison of images generated by both versions highlights the distinctive edge of the latest model. 5 base model: 7. Stability AI is positioning it as a solid base model on which the. r/StableDiffusion. The SDXL base model performs significantly. 6k hi-res images with randomized prompts, on 39 nodes equipped with RTX 3090 and RTX 4090 GPUs - getting . py in the modules folder. The BENCHMARK_SIZE environment variables can be adjusted to change the size of the benchmark (total images to generate). Did you run Lambda's benchmark or just a normal Stable Diffusion version like Automatic's? Because that takes about 18. In your copy of stable diffusion, find the file called "txt2img. 1 at 1024x1024 which consumes about the same at a batch size of 4. 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. 50 and three tests. In this benchmark, we generated 60. SDXL’s performance has been compared with previous versions of Stable Diffusion, such as SD 1. 99% on the Natural Questions dataset. Researchers build and test a framework for achieving climate resilience across diverse fisheries. So yes, architecture is different, weights are also different. Access algorithms, models, and ML solutions with Amazon SageMaker JumpStart and Amazon. a 20% power cut to a 3-4% performance cut, a 30% power cut to a 8-10% performance cut, and so forth. Empty_String. 61. Stable Diffusion XL delivers more photorealistic results and a bit of text. I figure from the related PR that you have to use --no-half-vae (would be nice to mention this in the changelog!). 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. For users with GPUs that have less than 3GB vram, ComfyUI offers a. The result: 769 hi-res images per dollar. NansException: A tensor with all NaNs was produced in Unet. 0 Seed 8 in August 2023. ago. 10 Stable Diffusion extensions for next-level creativity. Hires. 0 is still in development: The architecture of SDXL 1. SDXL GPU Benchmarks for GeForce Graphics Cards. That made a GPU like the RTX 4090 soar far ahead of the rest of the stack, and gave a GPU like the RTX 4080 a good chance to strut. Your Path to Healthy Cloud Computing ~ 90 % lower cloud cost. AMD RX 6600 XT SD1. Single image: < 1 second at an average speed of ≈27.