Stable Diffusion from Scratch in PyTorch | Conditional Latent Diffusion Models

This paragraph introduces the topic of the video, which is the continuation of the exploration of stable diffusion and the implementation of a latent diffusion model (LDM) with conditioning. The speaker discusses the plan to cover class conditioning using the MNIST dataset, spatial conditioning with segmentation masks, super-resolution, inpainting, and text conditioning using cross-attention mechanisms. The explanation also includes a brief recap of the previous video's content, which involved building an unconditional LDM with an auto-encoder and diffusion model components, such as down-sampling blocks, mid-blocks, up-sampling blocks, and the incorporation of time step information.

The speaker delves into the specifics of class conditioning for the LDM, explaining the process of transforming class labels into embeddings using an embedding layer. The approach to modifying the UNet architecture to incorporate class embeddings is discussed, alongside the method of maintaining the model's ability to generate images unconditionally by occasionally dropping the conditioning information or using a null class. The paragraph also covers alternative strategies like one-hot encoding and the use of a zero vector for unconditional cases, with a preference for the latter due to its simplicity and intuitiveness.

This section provides a detailed walkthrough of the code implementation for class conditioning in an LDM. The speaker revisits the existing codebase for unconditional LDM, discusses the use of configuration files, and outlines changes to the data set class to include class labels. Modifications to the UNet model are explained, including the addition of an embedding layer for class representations and adjustments to the forward method to incorporate class embeddings. The training script is also reviewed, highlighting changes in the epoch loop to accommodate class conditioning.

The focus shifts to image conditioning, specifically spatial conditioning, which is applicable to tasks like segmentation mask conditioning, super-resolution, and inpainting. The approach involves concatenating the conditioning information to the noisy latent image and training the diffusion model on this new input. The speaker describes the process of handling masks, including resizing and converting them to a compatible format for concatenation with the latent image. The paragraph also touches on the architecture's adaptability to different tasks with minor input adjustments.

This paragraph discusses advanced spatial conditioning techniques for special tasks such as semantic synthesis, super-resolution, and inpainting. The speaker explains the process of using convolutional layers to process masks and the importance of maintaining the same spatial resolution for the latent image and conditioning information. The implementation details for mask conditioning using the CB mask dataset are provided, including the configuration for input and output channels and the use of a 1x1 convolution layer.

The speaker explores the application of spatial conditioning in super-resolution and inpainting tasks. For super-resolution, the model is trained to generate a latent code based on a degraded input image, enabling the generation of higher-resolution images. Inpainting is approached by combining the original image with the generated image using masks to ensure that only the masked regions are reconstructed by the model. The paragraph also discusses the challenges of maintaining harmony between generated and original pixels, especially at higher resolutions, and mentions potential improvements to address these issues.

The paragraph introduces the concept of text conditioning for LDMs, which is achieved through cross-attention mechanisms. The speaker provides an overview of self-attention and how it can be adapted for cross-attention by using text embeddings as context items. The process of obtaining text embeddings using a tokenizer and a pre-trained encoder like BERT is explained. The paragraph sets the stage for understanding how text conditioning can guide the generation process by influencing the feature map representations of the model.

This section details the implementation of cross-attention for text conditioning in the diffusion model. The speaker describes the necessary changes in the configuration file and the dataset class to accommodate text conditioning. The unit class is modified to include cross-attention blocks that take text embeddings as context items. The forward method of the model is updated to include cross-attention layers, and the training loop is adjusted to handle text conditioning by loading tokenizers and text models, and by managing the conditioning dropping probability.

The speaker presents the results of using text and image conditioning, highlighting the model's ability to generate images that honor features mentioned in the text. The limitations of the current model training and the potential for improvement with further training are acknowledged. The paragraph concludes with a discussion on the transition from the current implementation to stable diffusion, emphasizing the use of the CLIP text encoder for its ability to associate text with visual appearances effectively.

The final paragraph outlines the minimal changes required to transition the current implementation to stable diffusion. The speaker explains that replacing the text encoder with the CLIP text encoder and adjusting the configuration for the new encoder's dimension is all that is needed. The simplicity of this transition is emphasized, highlighting the modularity and adaptability of the codebase. The speaker also encourages viewers to subscribe and like the video for more content on this topic.