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This can be useful for applications such as image super-resolution, inpainting, and stylization. Overall, the use of latent diffusion models for high-resolution image synthesis is a promising area of research that has the potential to revolutionize the field of computer graphics and image processing.

Complementary strengths - Each model type has its strengths and weaknesses. Large language models excel at understanding natural language prompts, while diffusion models specialize in generating high-quality images. By combining these complementary strengths, we can harness the benefits of both.

Stable Diffusion is a latent, text-to-image diffusion model that was released in 2022. Latent diffusion models (LDMs) operate by repeatedly reducing noise in a latent representation space and then converting that representation into a complete image.

Researchers at Stanford Introduce ControlNet: A Neural Network Structure to Control Pre-Trained Large Diffusion Models to Support Additional Input Conditions. ControlNet is one example of a model that finetunes quickly to new visual modalities, which can't be captured by text alone.

ControlNet Stable Diffusion offers a number of benefits over other AI image generation models. First, it allows users to control the output image with unprecedented precision. This is because ControlNet uses a variety of techniques to learn the relationship between the input information and the desired output image.

1:53 24:10 How to use ControlNet in your AI Art - Stable Diffusion Tutorial 2023 YouTube Start of suggested clip End of suggested clip And then multi-control net. You can set it to two three four or however many you want I'm going toMoreAnd then multi-control net. You can set it to two three four or however many you want I'm going to use two today. And then again apply settings. And reload UI to make sure you have several model tabs.

Diffusion models are deep generative models that work by adding noise (Gaussian noise) to the available training data (also known as the forward diffusion process) and then reversing the process (known as denoising or the reverse diffusion process) to recover the data. The model gradually learns to remove the noise.

ControlNet is a neural network that controls a pretrained image Diffusion model (e.g. Stable Diffusion). Its function is to allow input of a conditioning image, which can then be used to manipulate the image generation.

Diffusion models are deep generative models that work by adding noise (Gaussian noise) to the available training data (also known as the forward diffusion process) and then reversing the process (known as denoising or the reverse diffusion process) to recover the data. The model gradually learns to remove the noise.

ControlNet works by introducing an additional input condition known as a depth mask to the Stable Diffusion model. This input condition specifies the areas of an input image to keep or discard.

Unlike other methods, latent diffusion works in the latent space defined by the encoder, rather than in pixel space. Next, Gaussian noise is added to the image in the upper middle part of the figure, as part of the diffusion process that goes from z to zT (in case T steps of noise addition are applied).

Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution video generation, a particularly resource-intensive task.