Improving Image Generations

Modified on Thu, 21 Sep 2023 at 10:24 AM

If you're images generation results are not as you expected, here's a couple things you can do to improve the quality of your generations: 

  • Fine tuning your prompt - the more specific you are, the closer the image will turn out to what is in your head! Adding more details in the Positive Prompt or Negative Prompt can help add / remove pieces of your image to improve it - You can also use advanced techniques like upweighting and downweighting to control the influence of certain words. Learn more here.
    • Tip: If you’re seeing poor results, try adding the things you don’t like about the image to your negative prompt may help. E.g. distorted, low quality, unrealistic, etc
  • Explore different models - Other models can produce different results due to the data they’ve been trained on. Each model has specific language and settings it works best with; a model’s documentation is your friend here. Play around with some and see what works best for you!
  • Ensure you're creating images with the ideal resolution for the model type in use; 512x512 for SD1.5,  768x768 for SD2.1 and 1024x1024 for SDXL
  • Increasing Steps - The number of steps used controls how much time the model is given to produce an image, and depends on the “Scheduler” used. The schedule controls how each step is processed by the model. More steps tends to mean better results, but will take longer - We recommend at least 30 steps for most
  • Tweak and Iterate - Remember, it’s best to change one thing at a time so you know what is working and what isn't. Sometimes you just need to try a new image, and other times using a new prompt might be the ticket. For testing, consider turning off the “random” Seed - Using the same seed with the same settings will produce the same image, which makes it the perfect way to learn exactly what your changes are doing.
  • Utilize ControlNets - ControlNets help guide the model during the image generation process. Applying a ControlNet during the early steps of the denoising process will help provide structure to the composition of the image, while ControlNets applied to the later steps will provide control over the details in the final image. 
  • Explore Advanced Settings - InvokeAI has a full suite of tools available to allow you complete control over your image creation process 

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