

Fouriscale
Overview :
FouriScale explores high-resolution image generation from pre-trained diffusion models from a frequency analysis perspective. Through an innovative, no-training method, it replaces the original convolutional layers in pre-trained diffusion models with a combination of dilation techniques and low-pass operations, further enhanced by a fill-and-crop strategy. This allows for flexible handling of various aspect ratios in text-to-image generation. Guided by FouriScale, this method successfully balances the structural integrity and fidelity of generated images, achieving remarkable capabilities for arbitrary-sized, high-resolution, and high-quality generation. With its simplicity and compatibility, this method provides valuable insights for future explorations in ultra-high-resolution image synthesis.
Target Users :
Used for generating high-resolution images and text-to-image synthesis.
Use Cases
Generate high-quality anime-style avatars
Text-to-high-resolution image generation
Handle large-size image generation requirements
Features
Generate high-resolution images from pre-trained diffusion models
Handle repetitive patterns and structural distortions
Flexibly handle different aspect ratios in generation
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