Kadir Nar

kadirnar

AI & ML interests

Computer Vision, Open Source, Generative AI

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kadirnar's activity

posted an update 2 months ago
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3773
I am training a controlnet model for Flux. And some of my experiences:

Checkpoint-10000:

https://x.com/kadirnar_ai/status/1829831750471606668

Checkpoint-12000:

https://x.com/kadirnar_ai/status/1829889524962640001

Checkpoint-14000:

https://x.com/kadirnar_ai/status/1829989622878744711

Checkpoint (16000-18000):

https://x.com/kadirnar_ai/status/1830179551407665654

Dataset: kadirnar/fluxdev_controlnet_16k
GPU: 1xA100(80GB)
GPU Hours: 65
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posted an update 5 months ago
posted an update 5 months ago
reacted to DmitryRyumin's post with ๐Ÿ”ฅ 6 months ago
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1479
๐Ÿ”ฅ๐Ÿš€๐ŸŒŸ New Research Alert - YOLOv10! ๐ŸŒŸ๐Ÿš€๐Ÿ”ฅ
๐Ÿ“„ Title: YOLOv10: Real-Time End-to-End Object Detection ๐Ÿ”

๐Ÿ“ Description: YOLOv10 improves real-time object recognition by eliminating non-maximum suppression and optimizing the model architecture to achieve state-of-the-art performance with lower latency and computational overhead.

๐Ÿ‘ฅ Authors: Ao Wang et al.

๐Ÿ“„ Paper: YOLOv10: Real-Time End-to-End Object Detection (2405.14458)

๐Ÿค— Demo: kadirnar/Yolov10 curated by @kadirnar
๐Ÿ”ฅ Model ๐Ÿค–: kadirnar/Yolov10

๐Ÿ“ Repository: https://github.com/THU-MIG/yolov10

๐Ÿ“ฎ Post about YOLOv9 - https://huggingface.co./posts/DmitryRyumin/519784698531054

๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

๐Ÿ” Keywords: #YOLOv10 #ObjectDetection #RealTimeAI #ModelOptimization #MachineLearning #DeepLearning #ComputerVision #Innovation
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replied to DmitryRyumin's post 6 months ago
posted an update 6 months ago
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1900
BLACK HOLE SDXL Lightning:

Prompt: a photo of a baby dragon
Steps: 4
replied to their post 6 months ago
posted an update 6 months ago
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1953
Midjourney + Custom SDXL-Lightning:
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posted an update 7 months ago
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2704
New SDXL model:
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reacted to MonsterMMORPG's post with ๐Ÿ”ฅโค๏ธ 7 months ago
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3682
Watch the full tutorial here : https://youtu.be/0t5l6CP9eBg

The tutorial is over 2 hours literally with manually fixed captions and perfect video chapters.

Most Awaited Full Fine Tuning (with DreamBooth effect) Tutorial Generated Images - Full Workflow Shared In The Comments - NO Paywall This Time - Explained OneTrainer - Cumulative Experience of 16 Months Stable Diffusion

In this tutorial, I am going to show you how to install OneTrainer from scratch on your computer and do a Stable Diffusion SDXL (Full Fine-Tuning 10.3 GB VRAM) and SD 1.5 (Full Fine-Tuning 7GB VRAM) based models training on your computer and also do the same training on a very cheap cloud machine from MassedCompute if you don't have such computer.

Tutorial Readme File โคต๏ธ
https://github.com/FurkanGozukara/Stable-Diffusion/blob/main/Tutorials/OneTrainer-Master-SD-1_5-SDXL-Windows-Cloud-Tutorial.md

Register Massed Compute From Below Link (could be necessary to use our Special Coupon for A6000 GPU for 31 cents per hour) โคต๏ธ
https://bit.ly/Furkan-Gรถzรผkara

Coupon Code for A6000 GPU is : SECourses


0:00 Introduction to Zero-to-Hero Stable Diffusion (SD) Fine-Tuning with OneTrainer (OT) tutorial
3:54 Intro to instructions GitHub readme
4:32 How to register Massed Compute (MC) and start virtual machine (VM)
5:48 Which template to choose on MC
6:36 How to apply MC coupon
8:41 How to install OT on your computer to train
9:15 How to verify your Python, Git, FFmpeg and Git installation
12:00 How to install ThinLinc and start using your MC VM
12:26 How to setup folder synchronization and file sharing between your computer and MC VM
13:56 End existing session in ThinClient
14:06 How to turn off MC VM
14:24 How to connect and start using VM
14:41 When use end existing session
16:38 How to download very best OT preset training configuration for SD 1.5 & SDXL models
18:00 How to load configuration preset
18:38 Full explanation of OT configuration and best hyper parameters for SDXL
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reacted to DmitryRyumin's post with ๐Ÿค—โค๏ธ 9 months ago
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๐ŸŽ‰โœจ Exciting Research Alert! YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information ๐Ÿš€

YOLOv9 is the latest breakthrough in object detection!

๐Ÿ“„ Title: YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

๐Ÿ‘ฅ Authors: Chien-Yao Wang et al.
๐Ÿ“… Published: ArXiv, February 2024

๐Ÿ”— Paper: YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information (2402.13616)
๐Ÿ”— Model ๐Ÿค–: adonaivera/yolov9
๐Ÿ”— Repo: https://github.com/WongKinYiu/yolov9

๐Ÿš€ Don't miss out on this cutting-edge research! Explore YOLOv9 today and stay ahead of the curve in the dynamic world of computer vision. ๐ŸŒŸ

๐Ÿ” Keywords: #YOLOv9 #ObjectDetection #DeepLearning #ComputerVision #Innovation #Research #ArtificialIntelligence
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replied to DmitryRyumin's post 9 months ago
reacted to merve's post with โค๏ธ 9 months ago
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There's a new leaderboard for vision language models ๐Ÿคฉ
The models are ranked based on ELO, you can rate the responses to preselected examples or try with your input ๐Ÿค—
WildVision/vision-arena