Open FurkanGozukara opened 1 year ago
I believe this is a great tutorial for newbies. I spent over 24 hours to prepare it :) I hope you consider this adding into wiki and FAQ and perhaps other places where people actually reads for help and guides. Thank you so much. @cloneofsimo @oscarnevarezleal @laksjdjf @milyiyo @2kpr @AK391 @hdeezy
Nice! Thank you for this awesome material!
Nice! Thank you for this awesome material!
Thank you so much. However I learnt that their Lora version is not as up-to-date as your. How can I update web UI dreambooth lora version to make more fair comparison?
I hope this video gets added to the FAQ, wiki and stickies.
Appreciate very much.
https://youtu.be/mfaqqL5yOO4
content of the video
0:00 Introduction speech 1:07 How to install the LoRA extension to the Stable Diffusion Web UI 2:36 Preparation of training set images by properly sized cropping 2:54 How to crop images using Paint .NET, an open-source image editing software 5:02 What is Low-Rank Adaptation (LoRA) 5:35 Starting preparation for training using the DreamBooth tab - LoRA 6:50 Explanation of all training parameters, settings, and options 8:27 How many training steps equal one epoch 9:09 Save checkpoints frequency 9:48 Save a preview of training images after certain steps or epochs 10:04 What is batch size in training settings 11:56 Where to set LoRA training in SD Web UI 13:45 Explanation of Concepts tab in training section of SD Web UI 14:00 How to set the path for training images 14:28 Classification Dataset Directory 15:22 Training prompt - how to set what to teach the model 15:55 What is Class and Sample Image Prompt in SD training 17:57 What is Image Generation settings and why we need classification image generation in SD training 19:40 Starting the training process 21:03 How and why to tune your Class Prompt (generating generic training images) 22:39 Why we generate regularization generic images by class prompt 23:27 Recap of the setting up process for training parameters, options, and settings 29:23 How much GPU, CPU, and RAM the class regularization image generation uses 29:57 Training process starts after class image generation has been completed 30:04 Displaying the generated class regularization images folder for SD 2.1 30:31 The speed of the training process - how many seconds per iteration on an RTX 3060 GPU 31:19 Where LoRA training checkpoints (weights) are saved 32:36 Where training preview images are saved and our first training preview image 33:10 When we will decide to stop training 34:09 How to resume training after training has crashed or you close it down 36:49 Lifetime vs. session training steps 37:54 After 30 epochs, resembling images start to appear in the preview folder 38:19 The command line printed messages are incorrect in some cases 39:05 Training step speed, a certain number of seconds per iteration (IT) 39:25 Results after 5600 steps (350 epochs) - it was sufficient for SD 2.1 39:44 How I'm picking a checkpoint to generate a full model .ckpt file 40:23 How to generate a full model .ckpt file from a LoRA checkpoint .pt file 41:17 Generated/saved file name is incorrect, but it is generated from the correct selected .pt file 42:01 Doing inference (generating new images) using the text2img tab with our newly trained and generated model 42:47 The results of SD 2.1 Version 768 pixel model after training with the LoRA method and teaching a human face 44:38 Setting up the training parameters/options for SD version 1.5 this time 48:35 Re-generating class regularization images since SD 1.5 uses 512 pixel resolution 49:11 Displaying the generated class regularization images folder for SD 1.5 50:16 Training of Stable Diffusion 1.5 using the LoRA methodology and teaching a face has been completed and the results are displayed 51:09 The inference (text2img) results with SD 1.5 training 51:19 You have to do more inference with LoRA since it has less precision than DreamBooth 51:39 How to give more attention/emphasis to certain keywords in the SD Web UI 52:51 How to generate more than 100 images using the script section of the Web UI 54:46 How to check PNG info to see used prompts and settings 55:24 How to upscale using AI models 56:12 Fixing face image quality, especially eyes, with GFPGAN visibility 56:32 How to batch post-process 57:00 Where batch-generated images are saved 57:18 Conclusion and ending speech