deep-floyd/IF

Features

• Text-to-Image Generators - Translates natural language text prompts into high-resolution images through a diffusion pipeline.
• Diffusion Pipelines - Implements a sequence of diffusion models that progressively refine noise into detailed images.
• Image Inpainting - Provides generative filling of specific image regions based on text prompts and masks.
• Text-Guided Inpainting - Regenerates specific image regions by blending user-defined masks with text prompts and existing context.
• Image Super Resolution Models - Increases output clarity and pixel density using a dedicated super-resolution model.
• Model Fine-Tuning - Adapts pre-trained generative models to new visual concepts by updating specific parameters.
• Low-Rank Adaptation - Employs low-rank adaptation to teach new visual concepts by updating a small subset of model weights.
• Parameter Efficient Fine-Tuning - Implements parameter-efficient fine-tuning to teach the model new concepts without full retraining.
• Latent Space Generative Models - Processes visual data in a compressed mathematical space to reduce the computational cost of high-resolution generation.
• Cross-Attention Conditioning - Uses cross-attention mechanisms to map text embeddings to specific spatial regions in the latent grid.
• Style Transfers - Applies the aesthetic characteristics of a reference image to new visual outputs.
• Neural Style Transfer - Applies the aesthetic characteristics and visual mood of a reference image to new outputs.
• Image Synthesis - Photorealistic text-to-image generation model.
• Large Language Models - High-fidelity open-source text-to-image diffusion model.
• Natural Language Processing - Listed in the “Natural Language Processing” section of the FunNLP awesome list.

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