Cost-effective Stable Diffusion fine tuning on Salad
Stable Diffusion XL (SDXL) fine tuning as a service I recently wrote a blog about fine tuning Stable Diffusion XL (SDXL) on interruptible GPUs at low cost, starring my dog Timber. The strong results and exceptional cost performance got me wondering: What would it take to turn that into a fully managed Stable Diffusion training […]
Fine tuning Stable Diffusion XL (SDXL) with interruptible GPUs and LoRA for low cost
It’s no secret that training image generation models like Stable Diffusion XL (SDXL) doesn’t come cheaply. The original Stable Diffusion model cost $600,000 USD to train using hundreds of enterprise-grade A100 GPUs for more than 100,000 combined hours. Fast forward to today, and techniques like Parameter-Efficient Fine Tuning (PEFT) and Low-Rank Adaptation (LoRA) allow us […]
Tag 309K Images/$ with Recognize Anything Model++ (RAM++) On Consumer GPUs
What is the Recognize Anything Model++? The Recognize Anything Model++ (RAM++) is a state of the art image tagging foundational model released last year, with pre-trained model weights available on huggingface hub. It significantly outperforms other open models like CLIP and BLIP in both the scope of recognized categories and accuracy. But how much does […]
Segment Anything Model (SAM) Benchmark: 50K Images/$ on Consumer GPUs
What is the Segment Anything Model (SAM)? The Segment Anything Model (SAM) is a foundational image segmentation model released by Meta AI Research last year, with pre-trained model weights available through the GitHub repository. It can be prompted with a point or a bounding box, and performs well on a variety of segmentation tasks. More […]
Stable Diffusion v1.5 Benchmark On Consumer GPUs
Benchmarking Stable Diffusion v1.5 across 23 consumer GPUs What’s the best way to run inference at scale for stable diffusion? It depends on many factors. In this Stable Diffusion (SD) benchmark, we used SD v1.5 with a controlnet to generate over 460,000 fancy QR codes. The benchmark was run across 23 different consumer GPUs on […]
Comparing Price-Performance of 22 GPUs for AI Image Tagging (GTX vs RTX)
Older Consumer GPUs: A Perfect-Fit for AI Image Tagging In the current AI boom, there’s a palpable excitement around sophisticated image generation models like Stable Diffusion XL (SDXL) and the cutting-edge GPUs that power them. These models often require more powerful GPUs with larger amounts of vRAM. However, while the industry is abuzz with these […]
Bark Benchmark: Reading 144K Recipes with Text-to-Speech on SaladCloud
Speech Synthesis with suno-ai/bark When you think of speech synthesis, you might think of a very robotic sounding voice, like this one from 1979. Maybe you think of more modern voice assistants, like Siri or the Google Assistant. While these are certainly improvements over what we had in the 1970s, they still wouldn’t be mistaken […]
The AI GPU Shortage: How Gaming PCs Offer a Solution and a Challenge
Reliability in Times of AI GPU Shortage In the world of cloud computing, leading providers have traditionally utilized expansive, state-of-the-art data centers to ensure top-tier reliability. These data centers, boasting redundant power supplies, cooling systems, and vast network infrastructures, often promise uptime figures ranging from 99.9% to 99.9999% – terms you might have heard as […]
Stable Diffusion XL (SDXL) Benchmark – 769 Images Per Dollar on Salad
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. 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. The answer from our Stable […]
Whisper Large Inference Benchmark: 137 Days of Audio Transcribed in 15 Hours for Just $117
Save Over 99% On Audio Transcription Using Whisper-Large-v2 and Consumer GPUs Harnessing the power of OpenAI’s Whisper Large V2, an automatic speech recognition model, we’ve dramatically reduced audio transcription costs and time. Here’s a deep dive into our benchmark against the substantial English CommonVoice dataset and how we achieved a 99.1% cost reduction. A Costly […]