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An automated website-building platform composed of multiple AI Agents","Your 24/7 AI Team | Dream, Chat, Create.",{"title":182,"list":183},"OPENAI",[184,191,199,206],{"id":185,"publish_date":167,"is_original":23,"collection":186,"cover_url":187,"cover_url_1_1":188,"title":189,"summary":190,"author":28},619,"#OpenAI #AI Image Generator #4o #MMM #AR Transformer","article_res/cover/2faffc97fcecf3151552cb0fd3206d89.jpeg","article_res/cover/1133cb4948af44cee2e7fbe79efb69e5.jpeg","The native image function of GPT-4o is officially launched","Introducing 4o Image Generation",{"id":192,"publish_date":193,"is_original":4,"collection":194,"cover_url":195,"cover_url_1_1":196,"title":197,"summary":198,"author":28},434,"2023-07-15","#Anthropic #OpenAI #Google #AI Code Generator #Claude","article_res/cover/e1b6f600a2b9f262a4392684e5f2ce25.jpeg","article_res/cover/6e1772e83f78f9a351ab23d3e414adee.jpeg","Latest Updates on Google Bard /Anthropic Claude2 / ChatGPT Code Interpreter","We want our models to use their programming skills to provide more natural interfaces to the basic functions of our computers.  \n - 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A town with 25 virtual residents","Believable proxies of human behavior can empower interactive apps: Immersive environment, Rehearsal space, Prototyping tool",{"title":242,"list":243},"NVIDIA",[],true,{"code":4,"msg":5,"data":246},{"id":247,"publish_date":248,"is_original":4,"collection":249,"articles_id":250,"cover_url":251,"cover_url_1_1":252,"title":253,"summary":254,"author":53,"content":255,"popular":256,"list":321,"category":378,"tag":379},424,"2023-08-05","#Stable Diffusion #AI Image Generator #Tencent #ControlNet","2G95LteElvBxD5VRd8Y73g","article_res/cover/df9e0e269868add9a42b2a15ea30c74b.jpeg","article_res/cover/b064c9157c194fb1ba6d8631e9f56364.jpeg","Controlnet and T2I-Adapter","T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models","\u003Cdiv class=\"rich_media_content js_underline_content\n                       defaultNoSetting\n            \" id=\"js_content\">\u003Csection data-tool=\"markdown编辑器\" data-website=\"https://markdown.com.cn/editor\" style='font-size: 16px;font-style: normal;font-variant-caps: normal;font-weight: 400;letter-spacing: 0px;orphans: auto;text-indent: 0px;text-transform: none;white-space: normal;widows: auto;word-spacing: 0px;-webkit-tap-highlight-color: rgba(26, 26, 26, 0.3);-webkit-text-size-adjust: auto;-webkit-text-stroke-width: 0px;text-decoration: none;color: black;padding: 25px 30px;line-height: 1.6;word-break: break-word;overflow-wrap: break-word;text-align: justify;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;margin-top: -10px;'>\u003Cp data-tool=\"markdown.com.cn编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0px;line-height: 26px;color: black;\">T2I-Adapter is an adapter for text-to-image generation developed by Tencent's ARC team. The paper was released in February this year, and the code has been open-sourced on GitHub.\u003C/p>\u003Csection>\u003Csection style=\"display: inline-block;\">\u003Cimg data-ratio=\"0.6219205630970337\" data-type=\"jpg\" data-w=\"1989\" src=\"./assets/17434959819880.9064000142453892.jpeg\">\u003C/section>​\u003C/section>\u003Cp data-tool=\"markdown.com.cn编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0px;line-height: 26px;color: black;\">Paper address: https://arxiv.org/pdf/2302.08453.pdf\u003C/p>\u003Cp data-tool=\"markdown.com.cn编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0px;line-height: 26px;color: black;\">Code address: https://github.com/TencentARC/T2I-Adapter\u003C/p>\u003Cp data-tool=\"markdown.com.cn编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0px;line-height: 26px;color: black;\">It is a small model that can be attached to any large text-to-image diffusion model to enhance its controllability. T2I-Adapter works by learning to align textual prompts with the internal states of image generators. This allows users to more finely control the generated images by adjusting the textual prompts.\u003C/p>\u003Cp data-tool=\"markdown.com.cn编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0px;line-height: 26px;color: black;\">Some advantages of T2I-Adapter mentioned in the official paper:\u003C/p>\u003Cul data-tool=\"markdown.com.cn编辑器\" style=\"margin-top: 8px;margin-bottom: 8px;padding-left: 25px;color: black;list-style-type: disc;\">\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1, 1, 1);font-weight: 500;\">Plug-and-play. Does not affect the original network topology or generative capabilities.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1, 1, 1);font-weight: 500;\">Simple and compact. Approximately 77M parameters and about 300M storage.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1, 1, 1);font-weight: 500;\">Flexible. Suitable for various adapters under different control conditions.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1, 1, 1);font-weight: 500;\">Combinable. Multiple adapters can be used to achieve multi-condition control.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1, 1, 1);font-weight: 500;\">Universal. Can be directly applied to custom models.\u003C/section>\u003C/li>\u003C/ul>\u003Cp data-tool=\"markdown.com.cn编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0px;line-height: 26px;color: black;\">Both ControlNet and today’s T2I-Adapter are technologies used for text-to-image generation. They both use small models to control the generation of large diffusion models. However, there are some differences between them:\u003C/p>\u003Cul data-tool=\"markdown.com.cn编辑器\" style=\"margin-top: 8px;margin-bottom: 8px;padding-left: 25px;color: black;list-style-type: disc;\">\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1, 1, 1);font-weight: 500;\">ControlNet uses a multimodal transformer model, while T2I-Adapter uses a simple linear model. (This may not be entirely accurate—it was derived from unknown sources by Google Bard.)\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1, 1, 1);font-weight: 500;\">ControlNet can provide finer control over the generated images, whereas T2I-Adapter is more lightweight.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1, 1, 1);font-weight: 500;\">ControlNet requires more training data and computational resources, while T2I-Adapter is easier to train.\u003C/section>\u003C/li>\u003C/ul>\u003Cp data-tool=\"markdown.com.cn编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0px;line-height: 26px;color: black;\">The author of ControlNet, Lvmin Zhang, is a PhD student in Stanford University's CS department since 2022. He graduated from Soochow University in 2021 with a Bachelor's degree in Engineering. His research areas include computational art and design, interactive content creation, computer graphics, image and video processing, as well as anime. He organized a special interest research group called Style2Paints Research and also developed an anime drawing software named Style2Paints.\u003C/p>\u003Cp data-tool=\"markdown.com.cn编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0px;line-height: 26px;color: black;\">Under the extensions section of the Stable Diffusion webUI, there are models for both ControlNet and T2I-Adapter.\u003C/p>\u003Csection>\u003Csection style=\"display: inline-block;\">\u003Cimg data-ratio=\"1.4126778783958602\" data-type=\"jpg\" data-w=\"773\" src=\"./assets/17434959819430.6741569161585368.jpeg\">\u003C/section>​\u003C/section>\u003Csection>\u003Csection style=\"display: inline-block;\">\u003Cimg data-ratio=\"1.3225806451612903\" data-type=\"jpg\" data-w=\"837\" src=\"./assets/17434959819970.7558578275090457.jpeg\">\u003C/section>​\u003C/section>\u003Cp data-tool=\"markdown.com.cn编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0px;line-height: 26px;color: black;\">In terms of effect comparison, theoretically ControlNet should perform better, but many netizens have found that there is no significant difference in output quality. However, T2I-Adapter generates images approximately three times faster than ControlNet.\u003C/p>\u003Csection>\u003Csection style=\"display: inline-block;\">\u003Cimg data-ratio=\"0.5138954309938766\" data-type=\"jpg\" data-w=\"2123\" src=\"./assets/17434959817400.7738846806927846.jpeg\">\u003C/section>​\u003C/section>\u003Csection>\u003Csection style=\"display: inline-block;\">\u003Cimg data-ratio=\"0.4558758314855876\" data-type=\"jpg\" data-w=\"2255\" src=\"./assets/17434959818210.44986612600536247.jpeg\">\u003C/section>​\u003C/section>\u003Cp data-tool=\"markdown.com.cn编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0px;line-height: 26px;color: black;\">Previously, it was said that T2I-Adapter had fewer model types, but recently, more T2I-Adapter models have appeared on C station: https://civitai.com/models/17220?modelVersionId=20330\u003C/p>\u003Cp data-tool=\"markdown.com.cn编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0px;line-height: 26px;color: black;\">You can install according to the instructions on the website based on your needs.\u003C/p>\u003Cp data-tool=\"markdown.com.cn编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0px;line-height: 26px;color: black;\">Of course, ControlNet and T2I-Adapter can also be used together.\u003C/p>\u003Csection>\u003Csection style=\"display: inline-block;\">\u003Cimg data-ratio=\"0.5039370078740157\" data-type=\"jpg\" data-w=\"2540\" src=\"./assets/17434959821140.3140911427879063.jpeg\">\u003C/section>​\u003C/section>\u003Cp data-tool=\"markdown.com.cn编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0px;line-height: 26px;color: black;\">\u003Cbr>\u003C/p>\u003C/section>\u003Cp style=\"display: none;\">\u003Cmp-style-type data-value=\"10000\">\u003C/mp-style-type>\u003C/p>\u003C/div>",[257,266,274,282,290,297,305,313],{"id":258,"title_md5":259,"publish_date":260,"author_md5":261,"is_original":23,"collection":262,"summary_md5":263,"cover_url":264,"cover_url_1_1":265},80,"63550e3e817d4b445c2e19430473c52c","2025-01-04","bc27fa490c4d0d525bac812fc0793534","#Google #AI Video Editor","3a4869b9a8e67f642938738ca18b3a22","article_res/cover/244ba813af9084ab8b2fb42748da185d.jpeg","article_res/cover/4af914a0a7176eac1a45541f144a623a.jpeg",{"id":267,"title_md5":268,"publish_date":269,"author_md5":261,"is_original":23,"collection":270,"summary_md5":271,"cover_url":272,"cover_url_1_1":273},59,"ccc68132a7aef4b447d6fe7ff6b58c61","2025-01-25","#LLM #DeepSeek #RL 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integrates",[364,370,376],{"title":10,"list":365},[366,367,368,369],{"id":96,"publish_date":97,"is_original":23,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28},{"id":104,"publish_date":105,"is_original":23,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":28},{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"title":222,"list":371},[372,373,374,375],{"id":120,"publish_date":113,"is_original":23,"collection":121,"cover_url":122,"cover_url_1_1":123,"title":124,"summary":125,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"id":227,"publish_date":228,"is_original":23,"collection":229,"cover_url":230,"cover_url_1_1":231,"title":232,"summary":233,"author":28},{"id":235,"publish_date":236,"is_original":23,"collection":73,"cover_url":237,"cover_url_1_1":238,"title":239,"summary":240,"author":28},{"title":242,"list":377},[],[8,9,10],[8,12,13,14,9,10,15,16,17,18],["Reactive",245],1754646411722]