โš™๏ธImage Gen Settings

Visit Image Generation Page: https://moescape.ai/models/

At the right side is the Settings! These are very crucial in generating the image that youโ€™re looking for.

Here is each one in further detail:

  • Sampling Method: It refers to the technique used to generate images by gradually refining noise into clear, detailed visuals based on the modelโ€™s understanding. Different sampling methods control the way this refinement process unfolds, affecting the speed and quality of the final image

  • Steps: Steps affect generation time; recommended range: 25-50.

  • Scale: How 'literally' the text input should be treated. Higher would be more faithful to the text while lower would have more artistic liberty

  • Seed: Seed value determines how image generation starts. The same seed will generate the same image.

    • โ™ป๏ธ โ€” use the same seed number to get the same character.

    • ๐ŸŽฒ โ€” use a random seed for different character.

    If no image is selected, โ™ป๏ธ button will use the seed of the latest image.

  • Canvas Size: The size of the generated image. Use Enhance button to increase the size of the final image.

  • CLIP Skip: CLIP skip only works in models that use CLIP text embedding and/or are based on models that use CLIP. Your prompt is digitized and then fed through the layers of the model and the output is used in the image-generation. At the slider value of 1, it goes through all 12 layers of the model. CLIP skip setting allows you to stop the process early and use the output from a different layer. For example, the slider value of 2 will use the output of the next to last layer (layer 11). The higher the value, the less layers of neural network have worked on the prompt

  • VAE: For txt2img, VAE (variational autoencoder) is used to create a resulting image after the sampling is finished. For img2img, it is used to process user's input image before the sampling, and to create an image after sampling

  • ENSD: Set seed offset for consistent outputs and image comparisons, while keeping the main seed random

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