In recent years, advancements in artificial intelligence and machine learning have led to remarkable progress in the field of image manipulation and transformation. One particularly intriguing application of these technologies is the ability to alter specific attributes of an image while preserving its overall structure and context. Among these transformative tasks, gender swapping stands out as a captivating endeavor, allowing for the modification of an individual's perceived gender within an image.
The technique of stable diffusion has emerged as a powerful tool in this domain, offering a nuanced approach to image manipulation that prioritizes realism and coherence. By leveraging stable diffusion, practitioners can effectively modify facial features, hair styles, clothing, and other visual cues to achieve a convincing gender transformation while maintaining the integrity of the original image.
This task presents a compelling challenge: to utilize stable diffusion techniques to swap the gender of an input image, transitioning the perceived identity of the individual from one gender to another. Through careful adjustment of key visual elements, such as facial contours, hair characteristics, and clothing styles, participants are tasked with creating a modified image that authentically portrays the individual as their gender-swapped counterpart.
In addition to technical proficiency, this task also calls for creativity, sensitivity, and ethical consideration. Participants must navigate the complexities of gender representation with respect and mindfulness, ensuring that the resulting images uphold the dignity and integrity of the individuals depicted.
Ultimately, the goal of this task is to showcase the potential of stable diffusion in the realm of gender transformation, demonstrating its capacity to produce compelling and realistic results while engaging with broader questions surrounding identity, representation, and artistic expression. Through experimentation, innovation, and attention to detail, participants are invited to explore the possibilities of gender swapping through the lens of stable diffusion, contributing to the ongoing dialogue surrounding image manipulation and its societal implications.
Stable Diffusion, a latent text-to-image diffusion model released in 2022, employs latent diffusion models (LDMs). LDMs iteratively reduce noise in a latent representation space and convert it into complete images. The text-to-image generation process involves an Image Encoder, Text Encoder, Diffusion Model, and Image Decoder. The Image Encoder and Text Encoder transform images and text into latent representations, the Diffusion Model generates new images guided by text, and the Image Decoder reconstructs images from the latent space. Stable Diffusion excels in generating detailed images from text and supports tasks like inpainting and image-to-image translations. Its weights, model card, and code are publicly available.
The model used in this project is called "Realistic Vision". Realistic Vision is an all-rounded model for generating photograph-style images. In addition to realistic people, it is also good for products and scenes. Please visit this link to see details.
FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. It is designed to be easy to use, while also being fast and efficient. Some key features and advantages of FastAPI include:
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Fast and High Performance: FastAPI is built on top of Starlette and Pydantic, utilizing asynchronous programming to achieve high performance. It is one of the fastest web frameworks available for Python.
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Automatic API Documentation: FastAPI automatically generates interactive API documentation (using Swagger UI and ReDoc) based on the Python type hints, making it easy for developers to understand, test, and consume the API.
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Type Hints and Data Validation: FastAPI uses Python type hints for request and response data, enabling automatic data validation. This helps catch errors early in the development process and improves the overall reliability of the API.
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Dependency Injection System: FastAPI provides a built-in dependency injection system, making it easy to manage and inject dependencies into route functions.
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Security: It comes with built-in security features, such as OAuth and JWT token support, which simplifies the implementation of secure authentication and authorization in APIs.
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WebSocket Support: FastAPI supports WebSocket communication, allowing real-time bidirectional communication between clients and the server.
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Synchronous and Asynchronous Code: FastAPI supports both synchronous and asynchronous code, making it flexible for different use cases and allowing developers to leverage the benefits of asynchronous programming when needed.
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Easy Integration with Other Libraries: FastAPI seamlessly integrates with other popular Python libraries and frameworks, such as SQLAlchemy, Tortoise-ORM, and others.
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Automatic Generation of API Client Code: Using the generated OpenAPI documentation, FastAPI can automatically generate API client code in multiple programming languages, reducing the effort required to consume the API.
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Active Development and Community Support: FastAPI is actively developed and has a growing community. The framework is well-documented, and its community actively contributes to its improvement.
Overall, FastAPI is a modern and powerful web framework that prioritizes developer productivity, type safety, and high performance, making it an excellent choice for building APIs with Python.
Uvicorn is an ASGI (Asynchronous Server Gateway Interface) server that is specifically designed to run ASGI applications, such as those built with the FastAPI web framework. ASGI is a specification for asynchronous web servers and applications in Python, providing a standard interface between web servers and Python web applications or frameworks.
Here are some advantages of using Uvicorn:
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ASGI Support: Uvicorn supports the ASGI specification, which is designed to handle asynchronous programming and enables the development of highly concurrent web applications.
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Fast and Efficient: Uvicorn is known for its high performance and efficiency, making it well-suited for handling concurrent connections and delivering fast responses.
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Compatibility with FastAPI: Uvicorn is the recommended server for running FastAPI applications. When paired with FastAPI, it allows developers to take full advantage of asynchronous programming and achieve optimal performance.
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Ease of Use: Uvicorn is easy to install and use. It can be started with a single command, making it accessible for developers at all levels.
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WebSocket Support: Uvicorn supports WebSocket communication, allowing real-time bidirectional communication between clients and the server. This is particularly useful for applications that require real-time updates.
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Graceful Shutdown: Uvicorn supports graceful shutdowns, allowing existing requests to finish processing before the server stops. This helps maintain the stability and reliability of the application.
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Configuration Options: Uvicorn provides various configuration options, allowing developers to customize the server settings based on the requirements of their applications.
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TLS/SSL Support: Uvicorn supports TLS/SSL encryption, providing a secure way to transmit data over the network.
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Active Development and Community Support: Uvicorn is actively maintained and has a supportive community. Regular updates and contributions from the community ensure that the server stays up-to-date and improves over time.
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Integration with Other ASGI Frameworks: While commonly used with FastAPI, Uvicorn is not limited to a specific framework. It can be used with other ASGI frameworks and applications, providing flexibility and compatibility.
In summary, Uvicorn is a versatile and performant ASGI server that excels in handling asynchronous web applications. Its compatibility with FastAPI and support for WebSocket communication make it a popular choice for developers building modern, real-time web applications with Python.
For this project, Uvicorn is using 3 workers. This means there will 3 subprocesses and the users can send requests in parallel. With this feature, the server can accept more than one request at the same time. You can increase the worker number regarding to your VRAM.
Instructions on setting up your project locally. To get a local copy up and running follow these simple steps.
To install the required packages, in a terminal, type:
pip install -r requirements.txtTo download the model, in a terminal, type:
wget https://huggingface.co/spaces/nuwandaa/adcreative-demo-api/resolve/main/weights/realisticVisionV60B1_v20Novae.safetensors\?download\=true --directory-prefix weights --content-dispositionTo run the project, in a terminal, type:
uvicorn app:app --proxy-headers --host 0.0.0.0 --port 8000 --workers 3Then, visit http://localhost:8000/docs to see the endpoints.
Instructions on setting up your project locally using Docker. To get a local copy up and running follow these simple steps.
To build the Docker image, in a terminal, type:
docker build -t gender_swap -f Dockerfile .To run the container, in a terminal, type:
docker run -it -d --gpus all --name gender_swap -p 80:80 gender_swapThen, visit http://localhost/docs to see the endpoints.