fal.ai
Kadir Nar PRO
kadirnar
AI & ML interests
Computer Vision, Open Source, Generative AI
Recent Activity
replied to
their
post
about 11 hours ago
I created my own AI image and video from scratch using the fal.ai platform 💫
Workflow: Flux Lora Training + Upscale + Kling AI(1.6)
Organizations
kadirnar's activity
replied to
their
post
about 11 hours ago
replied to
their
post
1 day ago
fal.ai üzerinden sync-lip var fakat onu kullanınca videonun kalitesi bozuluyor. Bu çalışma da onu kullanmadım. Onu yaptığım sesli bir çalışma var. Fakat tts modellerinden iyi çıktı alamadım.
posted
an
update
2 days ago
Post
1930
I created my own AI image and video from scratch using the fal.ai platform 💫
Workflow: Flux Lora Training + Upscale + Kling AI(1.6)
Workflow: Flux Lora Training + Upscale + Kling AI(1.6)
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- 5 replies
posted
an
update
5 months ago
Post
4220
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
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
- 1 reply
posted
an
update
7 months ago
Post
4431
Stable Diffusion 3: Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Demo(Zero A100): kadirnar/Stable-Diffusion-3
Paper: https://arxiv.org/pdf/2403.03206
Model Page: stabilityai/stable-diffusion-3-medium
Thanks ❤️ @dn6 @sayakpaul
Demo(Zero A100): kadirnar/Stable-Diffusion-3
Paper: https://arxiv.org/pdf/2403.03206
Model Page: stabilityai/stable-diffusion-3-medium
Thanks ❤️ @dn6 @sayakpaul
posted
an
update
7 months ago
Post
3925
Fast Tachyon SDXL Demo:
Demo(Zero A100): kadirnar/BlackHole-Lightning
Model Page: https://civitai.com/models/414108/black-hole
Demo(Zero A100): kadirnar/BlackHole-Lightning
Model Page: https://civitai.com/models/414108/black-hole
reacted to
DmitryRyumin's
post with 🔥
8 months ago
Post
1496
🔥🚀🌟 New Research Alert - YOLOv10! 🌟🚀🔥
📄 Title: YOLOv10: Real-Time End-to-End Object Detection 🔝
📝 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.
👥 Authors: Ao Wang et al.
📄 Paper: YOLOv10: Real-Time End-to-End Object Detection (2405.14458)
🤗 Demo: kadirnar/Yolov10 curated by @kadirnar
🔥 Model 🤖: kadirnar/Yolov10
📁 Repository: https://github.com/THU-MIG/yolov10
📮 Post about YOLOv9 - https://huggingface.co/posts/DmitryRyumin/519784698531054
📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
🔍 Keywords: #YOLOv10 #ObjectDetection #RealTimeAI #ModelOptimization #MachineLearning #DeepLearning #ComputerVision #Innovation
📄 Title: YOLOv10: Real-Time End-to-End Object Detection 🔝
📝 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.
👥 Authors: Ao Wang et al.
📄 Paper: YOLOv10: Real-Time End-to-End Object Detection (2405.14458)
🤗 Demo: kadirnar/Yolov10 curated by @kadirnar
🔥 Model 🤖: kadirnar/Yolov10
📁 Repository: https://github.com/THU-MIG/yolov10
📮 Post about YOLOv9 - https://huggingface.co/posts/DmitryRyumin/519784698531054
📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
🔍 Keywords: #YOLOv10 #ObjectDetection #RealTimeAI #ModelOptimization #MachineLearning #DeepLearning #ComputerVision #Innovation
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- 1 reply
replied to
DmitryRyumin's
post
8 months ago
Thanks🤗
posted
an
update
8 months ago
Post
1910
BLACK HOLE SDXL Lightning:
Prompt: a photo of a baby dragon
Steps: 4
Prompt: a photo of a baby dragon
Steps: 4
replied to
their
post
8 months ago
Thanks 😍
reacted to
MonsterMMORPG's
post with 🔥❤️
9 months ago
Post
3694
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 ⤵️
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) ⤵️
https://bit.ly/Furkan-Gözükara
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
.
.
.
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 ⤵️
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) ⤵️
https://bit.ly/Furkan-Gözükara
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
.
.
.
reacted to
DmitryRyumin's
post with 🤗❤️
11 months ago
Post
🎉✨ Exciting Research Alert! YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information 🚀
YOLOv9 is the latest breakthrough in object detection!
📄 Title: YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
👥 Authors: Chien-Yao Wang et al.
📅 Published: ArXiv, February 2024
🔗 Paper: YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information (2402.13616)
🔗 Model 🤖: adonaivera/yolov9
🔗 Repo: https://github.com/WongKinYiu/yolov9
🚀 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. 🌟
🔍 Keywords: #YOLOv9 #ObjectDetection #DeepLearning #ComputerVision #Innovation #Research #ArtificialIntelligence
YOLOv9 is the latest breakthrough in object detection!
📄 Title: YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
👥 Authors: Chien-Yao Wang et al.
📅 Published: ArXiv, February 2024
🔗 Paper: YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information (2402.13616)
🔗 Model 🤖: adonaivera/yolov9
🔗 Repo: https://github.com/WongKinYiu/yolov9
🚀 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. 🌟
🔍 Keywords: #YOLOv9 #ObjectDetection #DeepLearning #ComputerVision #Innovation #Research #ArtificialIntelligence
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- 1 reply
replied to
DmitryRyumin's
post
11 months ago
I developed the package code for the Yolov9 model.
reacted to
merve's
post with ❤️
11 months ago
Post
There's a new leaderboard for vision language models 🤩
The models are ranked based on ELO, you can rate the responses to preselected examples or try with your input 🤗
WildVision/vision-arena
The models are ranked based on ELO, you can rate the responses to preselected examples or try with your input 🤗
WildVision/vision-arena