Stable Diffusion is a deep learning, text-to-image model released in 2022. It is primarily used to generate detailed images conditioned on text descriptions, though it can also be applied to other tasks such as inpainting, outpainting, and generating image-to-image translations guided by a text...
How Does the Text Come into Play in Diffusion and Stable Diffusion? To make the process of image generation guided, we must first convert textual data into vector representations. This is typically done with a GPT-style language model. The embeddings it produces are added to the visual input ...
Dall-E.OpenAI'sDall-Efamily is a portmanteau of surrealistic painter Salvador Dali and the robotic character Wall-E from the Pixar film of the same name. Dall-E combinesvariational autoencodersand transformers but not diffusion models. Dall-E 2, however, uses a diffusion model to improve real...
A checkpoint is a snapshot during the training that captures the state of a model at a specific stage in the training process. In other words, checkpoints are a type of AI models. There are other types of Stable Diffusion models like LoRAs, LoCONs, LoHAs, LECOs and so on, but we wi...
Meaning of Stable Diffusion, a deep learning AI model. Released in 2022, it generates images from text prompts and enhances existing visuals and art creation.
Stable Diffusion Prompt: an infographic illustrating the process of a diffusion model generating a cat image from random noise The Drawbacks While these models sound exciting, they aren’t perfect. The main challenge is that they are computationally heavy. The generation process is slow and can tak...
What is LoRA? LoRA stands for Low-Rank Adaptation. It allows you to use low-rank adaptation technology to quickly fine-tune diffusion models. To put it in simple terms, the LoRA training model makes it easier to train Stable Diffusion on different concepts, such as characters or a specific...
What is diffusion large language model LLM, and why it matters. In the context of Inception Labs releasing Mercury Coder.
Args: model_output (`torch.Tensor`): The direct output from learned diffusion model. timestep (`float`): The current discrete timestep in the diffusion chain. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. generator (`torch.Generator`, *optional*...
algorithm rewards neural network configurations with a lower loss function since they're more similar, while a higher loss function is penalized. This training process lets the autoencoder capture the underlying structure of and latent variables in the training data and model it into the ...