Qwen Image Models Training - 0 to Hero Level Tutorial - LoRA and Fine Tuning - Base and Edit Model

Qwen Image Models Training - 0 to Hero Level Tutorial - LoRA and Fine Tuning - Base and Edit Model

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29:10 New Tool: Internal Image Pre-Processing Preview

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29:10 New Tool: Internal Image Pre-Processing Preview

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Classroom Contents

Qwen Image Models Training - 0 to Hero Level Tutorial - LoRA and Fine Tuning - Base and Edit Model

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  1. 1 0:00 Introduction & Tutorial Goals
  2. 2 0:59 Showcase: Realistic vs. Style Training GTA 5 Example
  3. 3 1:26 Showcase: High-Quality Product Training
  4. 4 1:40 Showcase: Qwen Image Edit Model Capabilities
  5. 5 1:57 Effort & Cost Behind The Tutorial
  6. 6 2:19 Introducing The Custom Training Application & Presets
  7. 7 3:09 Power of Qwen Models: High-Quality Results from a Small Dataset
  8. 8 3:58 Detailed Tutorial Outline & Chapter Flow
  9. 9 4:36 Part 4: Dataset Preparation Critical Section
  10. 10 5:05 Part 5: Monitoring Training & Performance
  11. 11 5:23 Part 6: Generating High-Quality Images with Presets
  12. 12 5:44 Part 7: Specialized Training Scenarios
  13. 13 6:07 Why You Should Watch The Entire Tutorial
  14. 14 7:15 Part 1 Begins: Finding Resources & Downloading The Zip File
  15. 15 7:50 Mandatory Prerequisites Python, CUDA, FFmpeg
  16. 16 8:30 Core Application Installation on Windows
  17. 17 9:47 Part 2: Downloading The Qwen Training Models
  18. 18 10:28 Features of The Custom Downloader Fast & Resumable
  19. 19 11:24 Verifying Model Downloads & Hash Check
  20. 20 12:41 Part 3 Begins: Starting The Application & UI Overview
  21. 21 13:16 Crucial First Step: Selecting & Loading a Training Preset
  22. 22 13:43 Understanding The Preset Structure LoRA/Fine-Tune, Epochs, Tiers
  23. 23 15:01 System & VRAM Preparation: Checking Your Free VRAM
  24. 24 16:07 How to Minimize VRAM Usage Before Training
  25. 25 17:06 Setting Checkpoint Save Path & Frequency
  26. 26 19:05 Saving Your Custom Configuration File
  27. 27 19:52 Part 4 Begins: Dataset Preparation Introduction
  28. 28 20:10 Using The Ultimate Batch Image Processing Tool
  29. 29 20:53 Stage 1: Auto-Cropping & Subject Focusing
  30. 30 23:37 Stage 2: Resizing Images to Final Training Resolution
  31. 31 25:49 Critical: Dataset Quality Guidelines & Best Practices
  32. 32 27:19 The Importance of Variety Clothing, Backgrounds, Angles
  33. 33 29:10 New Tool: Internal Image Pre-Processing Preview
  34. 34 31:21 Using The Debug Mode to See Each Processed Image
  35. 35 32:21 How to Structure The Dataset Folder For Training
  36. 36 34:31 Pointing The Trainer to Your Dataset Folder
  37. 37 35:19 Captioning Strategy: Why a Single Trigger Word is Best
  38. 38 36:30 Optional: Using The Built-in Detailed Image Captioner
  39. 39 39:56 Finalizing Model Paths & Settings
  40. 40 40:34 Setting The Base Model, VAE, and Text Encoder Paths
  41. 41 41:59 Training Settings: How Many Epochs Should You Use?
  42. 42 43:45 Part 5 Begins: Starting & Monitoring The Training
  43. 43 46:41 Performance Optimization: How to Improve Training Speed
  44. 44 48:35 Tip: Overclocking with MSI Afterburner
  45. 45 49:25 Part 6 Begins: Testing & Finding The Best Checkpoint
  46. 46 51:35 Using The Grid Generator to Compare Checkpoints
  47. 47 55:33 Analyzing The Comparison Grid to Find The Best Checkpoint
  48. 48 57:21 How to Resume an Incomplete LoRA Training
  49. 49 59:02 Generating Images with Your Best LoRA
  50. 50 1:00:21 Workflow: Generate Low-Res Previews First, Then Upscale
  51. 51 1:01:26 The Power of Upscaling: Before and After
  52. 52 1:02:08 Fixing Faces with Automatic Segmentation Inpainting
  53. 53 1:04:28 Manual Inpainting for Maximum Control
  54. 54 1:06:31 Batch Generating Images with Wildcards
  55. 55 1:08:49 How to Write Excellent Prompts with Google AI Studio Gemini
  56. 56 1:10:04 Quality Comparison: Tier 1 BF16 vs Tier 2 FP8 Scaled
  57. 57 1:12:10 Part 7 Begins: Fine-Tuning DreamBooth Explained
  58. 58 1:13:36 Converting 40GB Fine-Tuned Models to FP8 Scaled
  59. 59 1:15:15 Testing Fine-Tuned Checkpoints
  60. 60 1:16:27 Training on The Qwen Image Edit Model
  61. 61 1:17:39 Using The Trained Edit Model for Prompt-Based Editing
  62. 62 1:24:22 Advanced: Teaching The Edit Model New Commands Control Images
  63. 63 1:27:01 Performance Impact of Training with Control Images
  64. 64 1:31:41 How to Resume an Incomplete Fine-Tuning Training
  65. 65 1:33:08 Recap: How to Use Your Trained Models
  66. 66 1:35:36 Using Fine-Tuned Models in SwarmUI
  67. 67 1:37:16 Specialized Scenario: Style Training
  68. 68 1:38:20 Style Dataset Guidelines: Consistency & No Repeating Elements
  69. 69 1:40:25 Generating Prompts for Your Trained Style with Gemini
  70. 70 1:44:45 Generating Images with Your Trained Style Model
  71. 71 1:46:41 Specialized Scenario: Product Training
  72. 72 1:47:34 Product Dataset Guidelines: Proportions & Detail Shots
  73. 73 1:48:56 Generating Prompts for Your Trained Product with Gemini
  74. 74 1:50:52 Conclusion & Community Links Discord, GitHub, Reddit

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