Search Results for "level set method"

StyleAligned is a diffusion-model editing technique and codebase that preserves the visual “style” of an original image while applying new semantic edits driven by text. Instead of fully re-generating an image—and risking changes to lighting, texture, or rendering choices—the method aligns internal features across denoising steps so the target edit inherits the source style. This alignment acts like a constraint on the model’s evolution, steering composition, palette, and brushwork even as objects or attributes change. The result is more consistent edits across a set, which is crucial for workflows like product variations, character sheets, or brand-coherent art. ...

Prompt-to-Prompt is a research codebase that demonstrates how to edit images generated by diffusion models using only changes to the text prompt. Instead of retraining or heavy fine-tuning, it manipulates the model’s cross-attention maps so the structure of the original image is largely preserved while semantics shift according to the revised prompt. The method supports gentle edits (e.g., style, color, lighting) as well as stronger semantic substitutions, and it can localize edits to...

BLEURT-20-D12 is a PyTorch implementation of BLEURT, a model designed to assess the semantic similarity between two text sequences. It serves as an automatic evaluation metric for natural language generation tasks like summarization and translation. The model predicts a score indicating how similar a candidate sentence is to a reference sentence, with higher scores indicating greater semantic overlap. Unlike standard BLEURT models from TensorFlow, this version is built from a custom PyTorch...