Conditioning is the process of guiding an AI model to generate outputs based on specific inputs.
Conditioning is a fundamental concept that defines how generative AI models are guided. The most common type is text conditioning. In tools like DALL-E 3, Midjourney, and Stable Diffusion, the prompt directly conditions the model. Visual conditioning appears in features like image-to-image and ControlNet. The guidance scale controls how strongly conditioning is applied. Tools discoverable on tasarim.ai use conditioning extensively.
The process of customizing a pre-trained AI model by providing additional training on a specific task, style, or dataset.
A method for efficiently fine-tuning large AI models by adding small, trainable matrices. The original model weights remain unchanged.
A neural network architecture that adds additional control layers to diffusion models to specify structural conditions like pose, edges, and depth maps during image generation.
A fine-tuning method developed by Google that customizes AI models to a specific subject or style using just a few photos.
An evaluation metric that measures how well a generated image aligns with the given text prompt. It quantifies prompt alignment by calculating the cosine similarity between text and image embeddings of the CLIP model.
The preservation of visual consistency between consecutive frames in video and animation generation. It encompasses the techniques used to ensure objects, characters, and backgrounds appear consistent from frame to frame.