[Stable Diffusion Repair] Explanation of inpainting parameters
Denoising strength controls how much respect the final image should pay to the original content. Setting it to 0 changes nothing. Setting to 1 you got an unrelated image.
Set to a low value if you want small change and a high value if you want big change.
Changing denoising strength. Set to low value if you want small change and high value if you want big change.
Similar to usage in text-to-image, the Classifier Free Guidance scale is a parameter to control how much the model should respect your prompt.
1 – Mostly ignore your prompt. 3 – Be more creative. 7 – A good balance between following the prompt and freedom. 15 – Adhere more to the prompt. 30 – Strictly follow the prompt.
Masked content controls how the masked area is initialized.
Fill: Initialize with a highly blurred of the original image. Original: Unmodified. Latent noise: Masked area initialized with fill and random noise is added to the latent space. Latent nothing: Like latent noise except no noise is added to the latent space.
Below are the initial mask content before any sampling steps. This gives you some idea of what they are.
Tips for inpainting
Successful inpainting requires patience and skill. Here are some take homes for using inpainting
One small area at a time. Keep masked content at Original and adjust denoising strength works 90% of the time. Play with masked content to see which one works the best. If nothing works well within AUTOMATIC1111’s settings, use photo editing software like Photoshop or GIMP to paint the area of interest with the rough shape and color you wanted. Upload that image and inpaint with original content.
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