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# Glossary of terms used | |
- **Inpainting**: latent text-to-image diffusion model | |
Select part of image and replace it with semantically generated context based on output prompt | |
- **Outpainting**: technique to increase canvas and then use inpainting to fill missing parts | |
- **Upscale**: run resulting image through additional super-size ML model to increase resolution | |
- **Textual inversion**: learn to generate specific concepts (objects, styles, persons) | |
by describing them using new words in the embedding space of a pre-trained model | |
creates embeddings assigned to one or more tokens from sample images | |
- **Diffusers**: used to synthesize results by applying series of applications of denoising autoencoders | |
- **Latent Diffusers**: its basically using diffusers in latent (abstract space) before generating pixel space | |
simply more efficient than running diffusers in pixel space | |
- **Conditioning** or **Encoding**: text or image to semantic map | |
- **Transformers**: generic ML model that add semantic understanding to trained area (text or image or audio or whatever) | |
- **Checkpoint**": when training a model, save it as checkpoint every n epochs so training can be continued from there | |
checkpoint models can be further trained or used as-is | |
- **Finetune model**: adds specific retraining using sample images to existing model | |
different than full retraining as it starts with existing checkpoint | |
- **Hypernetwork**: finetune model and save as extension model instead of modifying original | |
this is basically an adaptive head - it takes information from late in the model but injects information from the prompt 'skipping' the rest of the model | |
similar to fine tuning the last 2 layers of a model but it gets much more signal from the prompt | |
- **Dreambooth**: essentially model fine tuning, which changes the weights of the main model | |
differs from typical fine tuning in that in tries to keep from forgetting/overwriting adjacent concepts during the tuning | |
- **Sampler**: which algorithm or lightweight ML model to use to add noise in each step before diffusion | |
different samplers are better at specific steps ranges and styles | |