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stable-diffusion-webui
GitHub Repository
DeveloperAUTOMATIC1111
Written inPython
Stars117k
Watchers 961
Created2022-08-22
Last updated2024-01-05
LicenseGNU Affero General Public License v3.0
RepositoryAUTOMATIC1111/stable-diffusion-webui
Auto Wiki
Generated at2024-01-05
Generated fromCommit cf2772
Version0.0.4

The stable-diffusion-webui repository provides a full-featured web interface for interacting with Stable Diffusion image generation models. It allows using SD models like text-to-image generation, image editing, upscaling, and more through an intuitive browser-based UI.

The key functionality works by leveraging the Python Gradio library to build the web UI components, with a FastAPI backend to handle model loading and image generation. When a user provides a text prompt or uploads an image, this input is sent to the Python backend, which runs the Stable Diffusion model to generate images. These images are then returned and displayed in the UI.

Additional tools like textual inversion, vector quantization, and hypernetwork integration allow customizing model behavior. The UI provides controls over sampling methods, guidance scale, CFG scale, seed, subseed and more to steer outputs.

At the core, the webui() function in webui.py launches the Gradio UI and FastAPI endpoints. The initialize() function handles loading models and extensions. The Python backend defined across /modules exposes endpoints that the JS frontend consumes to initiate generation tasks.

On the frontend, key logic lives in /javascript files like ui.js for task management and progressbar.js for progress. HTML components in /html define reusable UI building blocks.

The modular extension system allows adding new generation modes, processing steps, and customizing functionality by integrating scripts in /extensions-builtin.

Overall this provides a full-stack web application for leveraging Stable Diffusion models through an intuitive browser interface with many options for steering outputs.

User Interface

References: javascript, html

The core frontend code powering interactivity in the Stable Diffusion web user interface is contained within the javascript directory. This directory implements the key interactive components and logic for the UI through JavaScript.

Some important subdirectories and files include:

  • …/ui.js contains functions for core tasks like submitting generation requests, switching between tabs, and handling UI updates. The submit() function allows queuing generation tasks.

  • …/localization.js implements internationalization by dynamically localizing text on page load and content changes. It uses a mutation observer to traverse the DOM and look up translations for text nodes.

  • …/imageviewer.js defines the modal lightbox functionality for previewing images. It constructs the modal DOM elements and handles navigation between images via functions like modalNextImage(). Keyboard shortcuts are also supported.

  • …/hints.js provides helpful tooltips for UI elements by mapping element identifiers to tooltip text in the titles object. The updateTooltip() function checks for elements missing tooltips and adds them if a match is found.

  • …/contextMenus.js allows adding custom right-click context menus for different page elements via functions like appendContextMenuOption(). Options are stored in a Map keyed by the target element selector.

  • …/localStorage.js provides a simple interface for interacting with the browser localStorage through functions like localSet(), localGet() and localRemove().

The key abstraction implemented is using JavaScript and the browser APIs to dynamically construct and manipulate the DOM, handle events, make requests, and provide interactivity - while abstracting away browser differences through utilities. Callbacks integrated with the Gradio app framework allow synchronizing the UI with backend processes. This provides a flexible and customizable frontend for the Stable Diffusion user interface.

UI State and Task Management

References: javascript/ui.js

The JavaScript file …/ui.js handles managing the state of the user interface and generation tasks. The submit() function plays a key role in submitting text-to-image generation tasks. It shows/hides submit buttons, generates a random ID, makes a request to track progress, and constructs the arguments passed to Python using the create_submit_args() utility.

The requestProgress() function tracks generation progress by ID and updates the UI such as image galleries once complete. It utilizes the task ID to check status and completion.

The opts global object manages the model settings JSON. The onOptionsChanged() function updates parts of the UI like the checkpoint hash when the settings change. This ensures the UI stays in sync with the backend model configuration.

Event handlers like onEdit() call functions with a delay after user input to synchronize UI updates with the Python server. This prevents race conditions from out of sync state.

Functions related to specific app modes abstract away switching tabs and interfaces for generation. This includes txt2img, img2img and others, centralizing logic for different generation workflows.

Progress Updates

References: javascript/progressbar.js

The …/progressbar.js file contains the logic for displaying a progress bar and updating it during image generation tasks. It implements the core functionality of monitoring task progress via periodic requests to the backend and updating the UI accordingly.

The requestProgress() function is responsible for making AJAX requests to the "/internal/progress" endpoint to retrieve task status updates. It uses JavaScript's setInterval() to periodically call this endpoint at a fixed interval.

The responses are parsed by requestProgress() and used to update the progress bar DOM elements. The task progress percentage is calculated and applied to the bar width. Functions like formatTime() format the elapsed time for display.

A live preview image is conditionally shown by loading images from the response into the <div> gallery element. This provides feedback to the user on the generation process.

When a task completes or errors, the removeProgressBar() function cleanly removes all progress UI elements. It also calls the onProgress() callback to notify of completion.

Retries are implemented to handle failures - requestProgress() will indefinitely retry requests until the task finishes or timeouts. This provides reliability in monitoring long-running tasks.

Image Drag and Drop

References: javascript/dragdrop.js

The …/dragdrop.js file allows adding images to Gradio prompts and preview panes via dragging and dropping image files or pasting from the clipboard. It provides the core functionality for integrating these features into the user interface.

The dragdrop.js file handles drag and drop events on relevant elements like prompts and images. The isValidImageList() function validates dragged/dropped files, while dropReplaceImage() replaces the image source when an image is dropped. eventHasFiles() checks for files in drag events, and dragDropTargetIsPrompt() identifies prompt elements as drop targets.

On drag over events, the code checks if the target is a valid prompt or image element using these functions. On drop, it differentiates between replacing images in prompts versus regular image elements using the same checks. For paste events, it retrieves the pasted images and replaces the first empty image element.

Event handlers are attached to elements using functions defined in the file. For prompts, it directly sets the file input value to trigger updating. Network requests finish before replacing image sources to ensure updated data. The first visible empty image element is targeted for pasted images.

Image Previews

References: javascript/imageviewer.js

The …/imageviewer.js file implements functionality for previewing images in a modal lightbox. When a gallery image is clicked, the showModal function is called to open the modal popup and load the image. It displays the modal div and loads the source image into the modalImage element.

The modal allows navigating between preview images using functions like modalNextImage and modalPrevImage. Keyboard shortcuts for navigation and closing are also supported through the modalKeyHandler function. Images can be saved using functions such as saveImage.

The setupImageForLightbox function dynamically adds click handlers to gallery images, triggering the modal functionality when an image is clicked. This attaches the necessary event handlers without page reloads.

Keeping the modal image in sync is handled by the updateOnBackgroundChange function. It checks if the currently displayed modalImage has changed, such as after updating the background image, and reloads the image if needed.

UI Hints

References: javascript/hints.js

The …/hints.js file provides tooltips for UI elements in the Stable Diffusion web interface. It implements this functionality through the use of the titles object, which maps element identifiers like text, value, and class to tooltip text summaries.

The updateTooltip function checks UI elements for these identifiers and adds the appropriate tooltip text if a matching mapping is found in titles. It uses the tooltipCheckNodes set and processTooltipCheckNodes function to debounce checks and updates, processing tooltip changes only after the UI has finished updating. This avoids unnecessary processing while the UI is changing.

On initial load and UI updates, the onUiLoaded and onUiUpdate callbacks call processTooltipCheckNodes. This looks through the tooltipCheckNodes for any elements without tooltips yet and adds them by calling updateTooltip. processTooltipCheckNodes is debounced with a timer to delay processing until UI updates are fully complete.

The titles object provides the business logic by mapping element identifiers to tooltip text. This avoids hardcoding tooltips and allows them to be configured through this single object. The updateTooltip function implements actually setting the tooltip on elements by looking for matches in titles. Debouncing the tooltip updates through tooltipCheckNodes and processTooltipCheckNodes prevents unnecessary processing while the UI is changing dynamically.

Extensions Management

References: javascript/extensions.js

This section covers the JavaScript logic for managing extensions in the Stable Diffusion web interface. The file …/extensions.js contains the core functionality for this.

The extensions_apply() function handles applying changes to which extensions are enabled or updated based on checkbox selections. It iterates through the extension checkboxes using querySelectorAll() and collects the names of extensions to disable or update into lists based on their checked status. These lists are returned along with a flag for disabling all extensions.

The extensions_check() function collects the currently disabled extensions into a list. It sets all extension status displays to "Loading" by calling requestProgress() with a callback to populate the installed extensions HTML. This function returns an ID and the disabled extensions list.

The toggle_all_extensions() and toggle_extension() functions manage the relationship between the "select all" checkbox and individual extension checkboxes. toggle_all_extensions() toggles all checkboxes when the select all checkbox changes, while toggle_extension() syncs the select all checkbox based on individual checkboxes.

Parameter Synchronization

References: javascript/generationParams.js

This section of the code synchronizes generation parameters with the currently selected image. When an image is selected in one of the galleries in the txt2img or img2img tabs, the generation parameters displayed in the UI are updated to match those associated with the new image.

The …/generationParams.js file handles this parameter synchronization. It initializes key variables using the onAfterUiUpdate function, which is called when the UI updates. This function initializes the txt2img_gallery and img2img_gallery variables to represent the galleries in each tab, and the modal variable to represent the lightbox modal.

The attachGalleryListeners function then attaches click and keydown listeners to each gallery. A click listener calls the _generation_info_button's click() method, while keydown listeners call the same on left/right arrow keys to synchronize parameters when navigating the gallery.

The modalObserver MutationObserver watches for changes to the lightbox modal style, such as when it is closed. When this happens and one of the tabs is selected, it also calls the generation info button's click() method to synchronize parameters.

This allows the generation parameters to stay in sync with the currently selected image by programmatically clicking the info button on relevant user interactions like selecting a new image, navigating the gallery, or closing the lightbox. It provides a seamless experience where the displayed parameters always match the currently viewed image.

Context Menus

References: javascript/contextMenus.js

The …/contextMenus.js file implements right-click context menus that allow users to interact with elements on the Stable Diffusion web UI page. Context menus provide additional options for elements beyond the standard click interactions.

The contextMenuInit() function initializes the necessary variables to track context menu options for different page elements. It returns the main functions for adding, removing, and handling context menu options. The appendContextMenuOption() function allows dynamically adding new options to an element's context menu by specifying the target element selector, option name, and callback function. This option object is stored in the menuSpecs Map with the element selector as the key.

The removeContextMenuOption() function finds and removes an existing option from the menuSpecs Map by its unique ID. When a right-click contextmenu event fires on the page, the addContextMenuEventListener() handler looks up the options defined for that element's selector in the menuSpecs Map. It generates a DOM element for the menu, and displays it on the page. Clicking a menu option calls its associated callback function.

This allows context menus to be customized for different elements throughout the UI. For example, the sample code at the bottom demonstrates adding options to the generation preview image that allow generating images forever or changing parameters directly from the context menu. These additional interaction options enhance the user experience beyond standard clicks.

Backend Functionality

References: modules, extensions-builtin

The core backend functionality in the stable-diffusion-webui codebase is handled through several key modules and classes. Routes are defined in the Api class located in …/api.py. This class initializes a FastAPI application and adds routes for common generation tasks like text-to-image, image-to-image, model interrogation, and configuration management. It focuses on request parsing, validation, and interfacing with the generation pipeline.

The main models are implemented in the …/models directory. Diffusion models are contained in …/diffusion, with the core UniPC sampling algorithm defined in …/uni_pc.py. This file contains the important UniPC class, which implements the UniPC sampling process. Sampling algorithms are generally contained in …/sd_samplers_timesteps_impl.py, with functions like ddim() and plms() defining popular discrete timestep sampling methods.

Core services such as model initialization and loading are handled in …/initialize.py. This file contains functions for initializing various components, including setting up the main Stable Diffusion model, samplers, and extensions. Configuration loading and validation is implemented in …/initialize_util.py.

Model Initialization

References: modules/initialize.py

The …/initialize.py file handles initializing the core components needed for the web UI. It contains functions for importing dependencies, checking versions, and setting up the main models, samplers, and extensions.

The imports() function brings in key Python packages like PyTorch, TorchVision and Gradio. It records timings using the startup_timer to profile initialization. The check_versions() function validates that package versions are compatible.

The critical initialize() function calls other modules to setup the main models. It uses sd_models to initialize the Stable Diffusion model and loads it asynchronously later. codeformer_model and gfpgan_model initialize the CodeFormer and GFPGAN models. Samplers are initialized with sd_samplers.set_samplers(). Extensions are also initialized here.

The initialize() function relies on configuration options in shared_cmd_options to determine settings. It coordinates loading components across modules to set up the core functionality.

The initialize_rest() function completes initialization by loading additional scripts, upscalers, textual inversion templates, and extra networks. It handles reloading modules if needed. Timings are recorded throughout using startup_timer to profile the process.

Image Processing

References: modules/processing.py

The …/processing.py file contains the core image processing logic. The StableDiffusionProcessingTxt2Img class handles text-to-image generation by implementing a two-pass sampling process. It first runs an initial pass at a lower resolution, then upscales the result and runs a second pass to produce a higher resolution final image.

The sample() method generates conditions for both the initial and high-resolution passes. It starts by running the initial pass. Then the sample_hr_pass() function handles upscaling the initial result and running the second high-res pass. The calculate_target_resolution() and calculate_hr_conds() functions determine the resolution for the second pass and generate the conditioning, respectively.

The main processing loop in process_images() sets everything up by initializing the class, running sample() for the initial pass, decoding the samples, and applying any post-processing before returning the results. This provides an end-to-end workflow for handling the entire text-to-image generation pipeline.

Extension Management

References: modules/extensions.py

The …/extensions.py file handles managing extensions for the web UI. It provides classes and functions to load, configure, and interact with extensions.

The ExtensionMetadata class represents metadata for a single extension, parsed from the metadata.ini file in each extension directory. It has methods like get_script_requirements() to parse requirements from the file.

The Extension class is the main representation of a loaded extension. It contains fields like name, path, status and stores the metadata. The constructor takes the ExtensionMetadata object. It has methods like read_info_from_repo() to integrate with Git repositories if present.

The list_extensions() function scans the builtin and custom extension directories, loading ExtensionMetadata and Extension objects for each. It checks for duplicate names and requirement violations, storing the loaded Extension objects in the global extensions list.

The active() function filters the extensions list based on the configuration to determine which extensions should be active.

Request Handling

References: modules/api

The Api class in …/api.py handles all API requests and responses. It initializes a FastAPI application and adds routes for common generation tasks.

The Api class parses and validates incoming requests. It validates that sampler names are supported with the validate_sampler_name() method. The setUpscalers() method parses upscaler configurations from requests. For text-to-image generation requests, the text2imgapi() route handles parsing the text input and starting the generation pipeline. Image encoding and decoding is done with encode_pil_to_base64() and decode_base64_to_image().

Request and response payloads are defined using Pydantic models generated dynamically by the PydanticModelGenerator class in …/models.py. Enum classes like SamplerItem define allowed options.

The Api class centralizes request handling logic. It focuses on parsing input, validating parameters, and interfacing with generation tasks. Standardized models and routes provide a clean interface between the frontend and backend.

Diffusion Models

References: modules/models

The …/models directory contains implementations of various diffusion probabilistic models and utilities for tasks like training, sampling, and evaluation. The key functionality is implemented in classes and functions within the …/diffusion subdirectory.

The …/uni_pc subdirectory contains implementations of unconditional and conditional diffusion models using the unified predictor-corrector (UniPC) sampling method. The core UniPC class in …/uni_pc.py encapsulates the UniPC sampling algorithm. It takes a diffusion model wrapped by model_wrapper(), a NoiseScheduleVP object defining the noise schedule, and other options. The sample() method iteratively updates the diffusion process via multistep sampling.

The NoiseScheduleVP class in …/uni_pc.py handles different noise schedules for both discrete-time and continuous-time diffusion processes. Different schedules can be passed to UniPC via this class to handle different diffusion settings.

The model_wrapper() function in …/uni_pc.py handles converting between the noise prediction and data prediction representations required for UniPC sampling. It supports various model types and conditioning schemes.

The …/ddpm_edit.py file contains implementations of diffusion models based on Denoising Diffusion Probabilistic Models (DDPM). The LatentDiffusion class extends DDPM to operate on latent codes from an encoder, allowing for flexible conditioning of the diffusion process.

Textual Inversion

References: modules/textual_inversion

The core functionality of associating text embeddings with images via training is handled by the PersonalizedBase class defined in …/dataset.py. This class subclasses PyTorch's Dataset to load images from a directory, resize and normalize them. It extracts text tags from the image filenames and stores everything in DatasetEntry objects. These entries are stored in the dataset attribute. It also tracks image groups by size in the groups attribute.

The PersonalizedBase handles several important aspects of the textual inversion process:

  • Encoding images: It encodes each image as a latent vector.

  • Extracting text tags: It extracts text tags associated with each image from the filename using string processing. These text embeddings are also stored with each DatasetEntry.

  • Grouping by size: It tracks images in the groups attribute sorted by height and width.

  • Storing in DatasetEntry: Each encoded image and associated text is stored as a DatasetEntry object, which are finally collected in the dataset attribute.

The GroupedBatchSampler defined in the same file ensures batches have similarly sized images by sampling from the groups preferentially. The PersonalizedDataLoader subclasses PyTorch's DataLoader to use this custom GroupedBatchSampler.

The training logic itself is implemented in …/textual_inversion.py. This file defines the main train_embedding() function, which handles initializing the model, loss function, optimizer, and training loop over many iterations to optimize the model weights. It uses the PersonalizedBase dataset and PersonalizedDataLoader for feeding samples during optimization.

Vector Quantization

References: modules/codeformer

Vector quantization is performed by the VectorQuantizer class defined in …/vqgan_arch.py. The class takes in the latent representation z produced by the Encoder. It finds the closest entries in a codebook of embeddings by calculating distances between each element of z and all embeddings in the codebook. The closest codebook entry indices are encoded as a one-hot vector for each element of z, producing a discrete code.

The codebook is represented as a trainable parameter of the VectorQuantizer. To train the codebook, the VectorQuantizer calculates a commitment loss between z and the quantized code. This loss encourages z to be close to the codebook embedding of the assigned index. The quantized code produced by the VectorQuantizer is then passed to the Generator to reconstruct the output.

The GumbelQuantizer defined in the same file performs a similar quantization, but uses gumbel-softmax to produce soft assignments to the codebook rather than hard one-hot encodings. It calculates a KL divergence loss to train the soft assignments.

Configuration Management

References: modules/initialize_util.py

This section handles configuration loading and validation for the Stable Diffusion web UI. The …/initialize_util.py file contains several utility functions for initializing the application configuration.

The restore_config_state_file() function loads a previous configuration state file if specified, allowing extension configurations to be restored on restart. It uses the Python pickle module to serialize and deserialize the configuration state object.

Configuration options are validated by various functions. The validate_tls_options() function checks that TLS key and certificate files exist if TLS is enabled. The application configuration is also validated by the initialization code to ensure required options are present and in the correct format.

Callbacks are registered to run when configuration options change. This allows reloading parts of the application like extensions when the configuration is updated.

Hypernetwork Integration

References: modules/hypernetworks

Hypernetwork integration conditions the attention mechanism in Stable Diffusion models by applying hypernetworks to context inputs like text prompts or images. The …/hypernetworks directory contains code for implementing and using hypernetworks.

The core classes are HypernetworkModule and Hypernetwork. HypernetworkModule defines the structure of an individual hypernetwork layer, encapsulating linear transformations, normalization, and activations. Hypernetwork manages a collection of these modules to form the full network, handling loading, saving, and applying the entire hypernetwork.

The train_hypernetwork() function in hypernetwork.py implements the training loop. It loads data using PersonalizedBase, sets up an optimizer like AdamW, and runs training with gradient accumulation over batches. Checkpointing saves models periodically.

At inference, apply_hypernetworks() shows how to integrate a trained hypernetwork into SD. It runs the context through each HypernetworkModule layer to transform the context before passing it to the attention computation. Specifically, attention_CrossAttention_forward() demonstrates applying this transformed context within the CrossAttention module.