🎨 Image Generation using Stable Diffusion Explore the power of Stable Diffusion for creating AI-generated images using text prompts, image manipulation, inpainting, ControlNet, and more.
📦 Part 1: Stable Diffusion Basics ✅ Setup Install required libraries (with xformers for memory optimization)
🧠 Image Generation Pipeline Create Prompt
Generate Image
Save Result
🖼️ Generate Multiple Images ⚙️ Key Parameters seed: Ensures reproducibility
inference_steps: Controls generation detail
guidance_scale (CFG): Controls adherence to prompt
image_size: Width x Height
negative_prompt: Avoid unwanted elements
🧩 Model Variants SD v1.5, SD v2.x
Fine-tuned models with specific aesthetics
🌀 Changing Schedulers PNDM (default)
DDIM
K-LMS
Euler A
DPM
✏️ Part 2: Prompt Engineering 🧩 Prompt Structure Subject / Object
Action / Location
Type & Style
Colors, Artists
Resolution, Site
Lighting, Negative Prompts
🎨 Use Cases Art & Paintings
Photorealistic Images
Landscapes & Architecture
3D Concepts & Drawings
🔧 Advanced Models for Enhanced Output Anything v3.1
DreamShaper
Realistic Vision
Analog Diffusion
Protogen
Mitsua Diffusion One
🧪 Part 3: Fine-Tuning Models 🛠️ Install Dependencies bash Copy Edit accelerate transformers ftfy bitsandbytes==0.35.0 gradio natsort safetensors xformers 🔄 Workflow Load model
Prepare dataset (images + unique token + class name)
Train with new concepts
Convert weights to .ckpt
🔍 Inference Test with custom prompts
Example prompts: in the forest, in Cairo desert, in a western scene, in Star Wars style, in Mount Fuji, etc.
Save results
🖼️ Part 4: Image-to-Image 🔧 Install Libraries Same as Fine-Tuning section
🔁 Steps Use an input image
Adjust strength for transformation intensity
Test with different styles, schedulers, and input images
✍️ Image Editing Use InstructPix2Pix for editable transformations
🎯 Part 5: Inpainting 📦 Setup Same libraries as before
🧙♂️ Magic Eraser via Prompt Mask and replace objects
Create new elements in existing images
Compare outputs across variations
🧠 Part 6: ControlNet ⚙️ Setup bash Copy Edit accelerate transformers xformers 📏 Edge-to-Image Detect edges with Canny Edge
Generate new images using the edge map and ControlNet
🤸 Pose-to-Image Use human poses to guide image generation
Combine with emojis for fun visual effects
📝 Final Tips Use prompt engineering creatively
Experiment with fine-tuned models for better realism or stylization
Adjust schedulers and parameters for unique results
Combine features: img2img + ControlNet + inpainting = 🔥