Reddit Tech VN Bot😂 Một người dùng thắc mắc: Làm sao quản lý 100+ cuộc trò chuyện ChatGPT? Lưu KV cache (tốn RAM) hay tính toán lại khi tiếp tục (tốn CPU)? Đang tìm giải pháp cân bằng từ các dev tự phát triển chatbot LLM. #MachineLearning #KVcache #ComputationalTradeoff #ChatbotDevelopment #MemoryOptimization #TríTuệNhânTạo #TốiƯuHiệuSuất #TransformerModel #GiaoTiếpAI
Dave SpectorHad perhaps the #geekiest #tshirt ever printed up ..because well, we are going to be living with this, for better or worse, for a while. #TransformerModel #AttentionIsAllYouNeed #aihype #AIpocalypse
Gareth Emslie 🇿🇦 🇪🇦 🇨🇭The post discusses the impact of GPT-4, the most advanced version of a transformer model, on the development of Generative AI tools. These tools create content mimicking a particular style using a self-attention mechanism. The post also highlights the potential fears associated with such tools. https://blog.cloudflare.com/secure-generative-ai-applications/ #GenerativeAI #GPT4 #TransformerModel #softcorpremium
#ChatGPT is all the rage. What's this all about and what's the wider context? This paper gives a nice and thorough survey of #TransformerModel
Transformer models: an introduction and catalog
In the past few years we have seen the meteoric appearance of dozens of foundation models of the Transformer family, all of which have memorable and sometimes funny, but not self-explanatory, names. The goal of this paper is to offer a somewhat comprehensive but simple catalog and classification of the most popular Transformer models. The paper also includes an introduction to the most important aspects and innovations in Transformer models. Our catalog will include models that are trained using self-supervised learning (e.g., BERT or GPT3) as well as those that are further trained using a human-in-the-loop (e.g. the InstructGPT model used by ChatGPT).
Elio CampitelliInteresting preprint about image retrieval in image generation models.
They find that #StableDiffusion generates the same sofa 20% of the time when prompted with "Canvas Wall Art Print".
The problem seems to be that the training dataset has many repeated images from printshops.
Jelle ZuidemaInteresting investigation of how close generated images (Stable D) are to training set images.
(I'd avoid terms like "stealing" and "blatantly copy" -- results speak for themselves).
RT: @HxxxKxxx
Do diffusion models create unique works of art, or are they stealing content directly from their training sets?
📑Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models
Via @akhaliq @zentralwerkstatt
#transformermodel #generativeart #aiart
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models
Cutting-edge diffusion models produce images with high quality and customizability, enabling them to be used for commercial art and graphic design purposes. But do diffusion models create unique works of art, or are they replicating content directly from their training sets? In this work, we study image retrieval frameworks that enable us to compare generated images with training samples and detect when content has been replicated. Applying our frameworks to diffusion models trained on multiple datasets including Oxford flowers, Celeb-A, ImageNet, and LAION, we discuss how factors such as training set size impact rates of content replication. We also identify cases where diffusion models, including the popular Stable Diffusion model, blatantly copy from their training data.
Harald KlinkeDo diffusion models create unique works of art, or are they stealing content directly from their training sets?
📑Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models
Via @akhaliq @zentralwerkstatt
#transformermodel #generativeart #aiart
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models
Cutting-edge diffusion models produce images with high quality and customizability, enabling them to be used for commercial art and graphic design purposes. But do diffusion models create unique works of art, or are they replicating content directly from their training sets? In this work, we study image retrieval frameworks that enable us to compare generated images with training samples and detect when content has been replicated. Applying our frameworks to diffusion models trained on multiple datasets including Oxford flowers, Celeb-A, ImageNet, and LAION, we discuss how factors such as training set size impact rates of content replication. We also identify cases where diffusion models, including the popular Stable Diffusion model, blatantly copy from their training data.
If text-to-image models such as #dalle2 can be thought of as searches on large amounts of image data, is it then theoretically possible, given the right input, to find/generate *exactly* one of the input images?
#transformermodel #GAN #generativeart #AIart #latentspace @Bildoperationen @Quasimondo