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There are three methods in which user-accessible fine-tuning can be applied to a Stable Diffusion model checkpoint: An "embedding" can be trained from a collection of user-provided images, and allows the model to generate visually similar images whenever the name of the embedding is used within a generation prompt. [45]
Stable Diffusion, for example, imposes conditioning in the form of cross-attention mechanism, where the query is an intermediate representation of the image in the U-Net, and both key and value are the conditioning vectors. The conditioning can be selectively applied to only parts of an image, and new kinds of conditionings can be finetuned ...
The LDM is an improvement on standard DM by performing diffusion modeling in a latent space, and by allowing self-attention and cross-attention conditioning. LDMs are widely used in practical diffusion models. For instance, Stable Diffusion versions 1.1 to 2.1 were based on the LDM architecture. [4]
One of the original and now most common means of application checkpointing was a "save state" feature in interactive applications, in which the user of the application could save the state of all variables and other data and either continue working or exit the application and restart the application and restore the saved state at a later time.
A sample path of a diffusion process models the trajectory of a particle embedded in a flowing fluid and subjected to random displacements due to collisions with other particles, which is called Brownian motion.
The Bass diffusion model is derived by assuming that the hazard rate for the uptake of a product or service may be defined as: = () = + [()] where () is the probability density function and () = is the survival function, with () being the cumulative distribution function.
The following steps comprise the finite volume method for one-dimensional steady state diffusion - STEP 1 Grid Generation. Divide the domain into equal parts of small domain. Place nodal points at the center of each small domain. Dividing small domains and assigning nodal points (Figure 1) Create control volumes using these nodal points.
Stable articles are not final, but rather indicate a consensus that a particular version of an article meets a standard as judged by those marking it as stable. Making stable versions is part of the maturation of an article through its lifecycle, preceded by others such as creation, stub, expansion, through to attention, verification, peer ...