🛠️ Module 6: Practical Project — Generate Your Personalized Image Collection

6.1 Project Objective

Apply everything learned to generate a collection of 5-10 personalized images, using both advanced prompts and fine-tuning techniques (LoRA or Dreambooth). Images must share a common theme (e.g., “my pet in different mythological scenarios”, “my artistic style applied to urban landscapes”, “my brand’s products in surreal contexts”).

6.2 Tools and Environment

• Google Colab (T4 or A100 GPU) for training and generation.
• Libraries: diffusers, transformers, accelerate, torch, xformers (optional for optimization).
• Base Model: Stable Diffusion v1.5, v2.1, or SDXL (depending on available resources).
• Optional Interface: Automatic1111 WebUI (for those preferring a local graphical environment).

6.3 Project Phases

➤ Phase 1: Environment Setup

• Install dependencies in Colab.
• Authenticate with Hugging Face Hub (to download models and upload results).
• Load the Stable Diffusion pipeline (StableDiffusionPipeline).

➤ Phase 2: Generation with Advanced Prompts

• Experiment with different prompts and negative prompts.
• Adjust parameters: guidance_scale, num_inference_steps, seed.
• Generate 3 baseline images without fine-tuning.

➤ Phase 3: Dataset Preparation for Fine-Tuning

• Collect 5-10 high-quality images of the concept to personalize (object, style, character).
• Preprocess: resize to 512x512 (or 1024x1024 for SDXL), crop, enhance contrast if needed.
• Upload to Google Drive or Hugging Face Dataset Hub.

➤ Phase 4: Training with LoRA

• Configure LoraConfig with low rank (r=4, 8, or 16).
• Define target_modules: ["to_q", "to_v", "to_k", "to_out.0"] in attention layers.
• Train for 500-2000 steps with batch size 1-2 (depending on VRAM).
• Save the LoRA adapter.

➤ Phase 5: Generation with the Customized Model

• Load the base model + LoRA adapter.
• Generate new images using the learned concept in varied contexts.
• Compare with images generated without fine-tuning.

➤ Phase 6: Documentation and Presentation

• Create a brief report (in notebook or PDF) including:
  • Prompts used.
  • Generation parameters.
  • Before/after fine-tuning images.
  • Reflection on results: what worked? what didn’t? how could it be improved?