stablediffusion
5/5/25About 1 min
Stable Diffusion
Basic SDXL Refiner Workflow
What is SDXL?
SDXL (Stable Diffusion XL) is an advanced AI image generation model that creates higher quality images than previous versions. It's known for better composition, details, and understanding of prompts.
Base + Refiner Approach
A typical SDXL workflow uses two models:
- Base Model: Creates the initial image structure and composition
- Refiner Model: Enhances details and quality of the base image
Typical Steps in an SDXL Refiner Workflow:
Load Models:
- Base Model Loader - Loads the SDXL base model
- Refiner Model Loader - Loads the SDXL refiner model
Text Prompting:
- Positive Prompt - Describes what you want in the image
- Negative Prompt - Describes what you want to avoid
Create Empty Latent Image:
- Sets dimensions (width/height) of your output image
- Sets batch size (how many images to generate at once)
Base Model Sampling:
- Takes your prompt and uses the base model to create the initial image
- Parameters include seed (controls randomness), steps (how many iterations), and CFG (how closely to follow your prompt)
Refiner Model Sampling:
- Takes the output from the base model and refines it
- Usually runs fewer steps than the base model
- Uses a "denoise" parameter to control how much refinement to apply
Decode Image:
- VAE Decoder - Converts the mathematical representation back into a viewable image
Save/Preview Image:
- Displays and/or saves the final refined image
Key Parameters:
Seed: Controls randomness - same seed produces similar results
Steps: How many iterations - more steps generally means more detail
CFG Scale: How closely to follow the prompt - higher values mean stricter adherence
Sampler: Algorithm used (like Euler, DPM++, etc.)
Denoise: For refiners, controls how much of the original image to modify
If you'd like me to explain any of these concepts in more detail, or if you manage to upload the specific workflow file, I'd be happy to provide a more customized explanation based on your workflow.