Скачать с ютуб SANA: Ultra HD Fast Text to Image Model from NVIDIA Step by Step Tutorial on Windows, Cloud & Kaggle в хорошем качестве

SANA: Ultra HD Fast Text to Image Model from NVIDIA Step by Step Tutorial on Windows, Cloud & Kaggle

Step by step tutorial and a 1-Click to installer having very advanced Gradio APP to use newest Text-to-Image SANA Model on your Windows PC locally and also on cloud services such as Massed Compute, RunPod and free Kaggle. SANA's most powerful feature is being able to generate 4 Megapixel resolution (2048x2048) very fast natively. 🔗 Full Instructions, Configs, Installers, Information and Links Shared Post (the one used in the tutorial) ⤵️ ▶️   / click-to-open-post-used-in-tutorial-116474081   🔗 SECourses Official Discord 9500+ Members ⤵️ ▶️   / discord   🔗 Stable Diffusion, FLUX, Generative AI Tutorials and Resources GitHub ⤵️ ▶️ https://github.com/FurkanGozukara/Sta... 🔗 SECourses Official Reddit - Stay Subscribed To Learn All The News and More ⤵️ ▶️   / secourses   🔗 Official Repository of NVIDIA Labs SANA Model ⤵️ ▶️ https://github.com/NVlabs/Sana 0:00 Introduction to the published by NVIDIA SANA model step by step tutorial 2:48 How to install SANA model on Windows and start using 5:35 How to verify installation and save installation logs in case of an error to report back to us 6:03 How to start the APP after installation on Windows and how to use the SANA model properly 9:38 Where the generated images are saved in which folder 12:11 How to edit the styles that the APP has - prompting styles 12:59 How to install and use SANA APP and any of SECourses published AI apps on Massed Compute 14:17 How to select accurate category and the template image on Massed Compute cloud service 14:25 How to apply our SECourses coupon to get 50% price discount on Massed Compute - permanently working 14:46 How to install and setup ThinLinc client to transfer files and use Massed Compute cloud desktop PC 15:51 How to connect Massed Compute after initialized and install any AI scripts that we publish e.g. SANA model 19:05 How to start the application after it has been installed and use it on your PC (but it will run in Massed Compute server) 20:31 How to download individually and as a folder the generated images on Massed Compute to your computer 21:30 How to terminate Massed Compute to not spend any credits / money 22:03 How to install and use SANA APP and any of SECourses published AI apps on RunPod cloud service 24:43 How to start the SANA APP after installation has been completed on RunPod 26:34 The speed of RTX 4090 on RunPod for SANA 2K model 4 MegaPixel image generation 26:44 How to download individually and as a folder the generated images on RunPod to your computer 27:09 How to stop the pod and terminate to not waste any credits / money on RunPod 27:24 How to start the APP again that was previously installed on RunPod (not terminated only stopped pod) 27:34 How to use SANA APP on a free Kaggle account and any of my developed Kaggle notebooks 28:38 Selecting accurate session options on Kaggle like GPUs, accelerator and Internet On 29:06 How to run cells and install SANA APP or any APP on Kaggle 29:44 How to get Ngrok token and set it up and use it to connect SANA APP from Kaggle 30:57 How to download all generates images as a zip file on Kaggle 31:46 How to restart the SANA app on Kaggle or any AI APPs same logic 32:11 How to see how much GPU time you have left for free on Kaggle - 30 hours every week Sana: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformer We introduce Sana, a text-to-image framework that can efficiently generate images up to 4096 × 4096 resolution. Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed, deployable on laptop GPU. Core designs include: (1) DC-AE: unlike traditional AEs, which compress images only 8×, we trained an AE that can compress images 32×, effectively reducing the number of latent tokens. (2) Linear DiT: we replace all vanilla attention in DiT with linear attention, which is more efficient at high resolutions without sacrificing quality. (3) Decoder-only text encoder: we replaced T5 with modern decoder-only small LLM as the text encoder and designed complex human instruction with in-context learning to enhance the image-text alignment. (4) Efficient training and sampling: we propose Flow-DPM-Solver to reduce sampling steps, with efficient caption labeling and selection to accelerate convergence. As a result, Sana-0.6B is very competitive with modern giant diffusion model (e.g. Flux-12B), being 20 times smaller and 100+ times faster in measured throughput. Moreover, Sana-0.6B can be deployed on a 16GB laptop GPU, taking less than 1 second to generate a 1024 × 1024 resolution image. Sana enables content creation at low cost.