Stable Diffusion from Scratch in PyTorch | Conditional Latent Diffusion Models

This paragraph introduces the topic of the video, which is the continuation of the journey towards stable diffusion. The speaker explains that the focus will be on conditioning a latent diffusion model (LDM) using different types of data. The video will cover class conditioning on the MNIST dataset, image conditioning with spatial conditioning on the CelebA dataset, super-resolution, inpainting tasks, and text conditioning using cross-attention mechanisms. The speaker also provides a brief recap of the previous video, where an unconditional LDM was implemented using an autoencoder and a diffusion model with specific architectural choices. The importance of understanding the model's capability for both unconditional and conditional image generation is emphasized.

The paragraph delves into the specifics of class conditioning for the MNIST dataset, where the goal is to condition the LDM to generate images of digits based on provided class labels. The process involves transforming class labels into embeddings and modifying the UNet architecture to incorporate this new information. The video explains two approaches for class conditioning: using a null class for unconditional generation and using one-hot vectors for class representation. The speaker outlines the code changes required for implementing class conditioning, including adjustments to the data set class, the UNet class, and the training loop, with a focus on maintaining the model's ability to generate images unconditionally as well.

This paragraph discusses image conditioning, specifically spatial conditioning, which is applicable to tasks such as segmentation mask conditioning, super-resolution, and inpainting. The speaker describes the process of concatenating conditioning information to the noisy latent image and feeding it into the model. The architecture of the model remains the same, with minor input changes based on the task. The video also explains how to handle different types of conditioning, such as mask conditioning, by using a 1x1 convolution to process the masks before concatenation. The results of implementing spatial conditioning are showcased, demonstrating the model's ability to generate conditioned images.

The paragraph explores the application of spatial conditioning for super-resolution and inpainting tasks. For super-resolution, the model is trained to generate a latent code based on a degraded input image, which, when decoded, results in a higher-resolution image. Inpainting is addressed by training the model to reconstruct pixels within a masked region, either based on the image context or a text prompt. The speaker explains the theoretical approach to using any diffusion model for inpainting and the practical steps involved in training an inpainting model with access to the original image pixels for fine-tuning. The results of these tasks are presented, illustrating the model's performance in generating higher-quality images and inpainting with better harmony between regions.

This paragraph introduces the concept of text conditioning using cross-attention, which is the core mechanism for integrating textual context into the image generation process. The speaker provides a brief overview of self-attention before transitioning to cross-attention, where the queries are projections of the feature map cells, and the keys and values are projections of context items, such as text embeddings. The video explains how cross-attention can be used to identify the relevance of text tokens to a feature map cell and incorporate this context into the cell's representation. The potential applications of cross-attention for various types of conditioning, including image conditioning, are also discussed.

The paragraph details the implementation of cross-attention for text conditioning in the diffusion model. The speaker explains the changes required in the configuration file and the dataset class to accommodate text conditioning, including fetching captions and selecting a caption for each image. The unit class is modified to accept text embeddings and incorporate cross-attention blocks in the down, mid, and up blocks of the UNet. The forward method of the unit is updated to include cross-attention, and the training loop is adjusted to handle text conditioning, including the use of a pre-trained text encoder and the incorporation of empty text for unconditional generation instances.

This paragraph focuses on the training process of the model with both text and image conditioning. The speaker describes the steps involved in loading the tokenizer and text model, getting representations for empty text, and integrating text conditioning into the training loop. The training loop is modified to handle the fetching of captions, converting them to text encoder representations, and implementing a conditioning dropping mechanism. The results of training the model with text and image conditioning are presented, demonstrating the model's ability to generate images guided by text prompts, although the speaker notes that the results could be improved with further training.

The paragraph discusses the transition from a conditional latent diffusion model to stable diffusion, which is essentially a latent conditional model with a specific text encoder, CLIP. The speaker explains the training process of CLIP, which involves contrasting language-image pre-training to associate text and image representations in a joint space. The video compares the effectiveness of different text encoders for image generation and suggests that CLIP's text encoder may have an advantage due to its training methodology. The speaker also references a paper that compares various text encoders and their ability to associate text with visual appearances.

This paragraph outlines the final steps to implement the CLIP encoder in the code to achieve stable diffusion. The speaker details the minimal changes required in the utility method and configuration file to switch the text encoder to CLIP. The video concludes by emphasizing the simplicity of the implementation changes and provides a brief overview of the entire process covered in the video, from different types of conditioning to the transition to stable diffusion. The speaker also encourages viewers to subscribe and like the video for more content on this topic.