FaceChain
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Facechain
Overview :
FaceChain is a deep learning toolkit supported by ModelScope, capable of generating your digital twin with at least one portrait photo and creating personal portraits in different settings (supporting multiple styles). Users can train digital twin models and generate images through FaceChain's Python scripts, the familiar Gradio interface, or sd webui. The main advantages of FaceChain include its ability to generate personalized portraits, support for multiple styles, and an easy-to-use interface.
Target Users :
["Designers: Utilize FaceChain to quickly generate personalized design elements.","Photographers: Create digital twins for personalized displays of photographic works.","Developers: Integrate FaceChain via API to develop personalized applications.","Content Creators: Generate unique visual content to enhance social media appeal."]
Total Visits: 474.6M
Top Region: US(19.34%)
Website Views : 90.3K
Use Cases
Use FaceChain to generate personal portraits for personal brand promotion.
Create virtual try-on experiences for users on e-commerce websites.
Generate unique artistic portraits on social media.
Features
Generate personal digital twin: Create a user's digital twin with at least one portrait photo.
Support for multiple styles: Users can generate personal portraits in various styles.
Python script support: Provides Python scripts to train and generate digital twin models.
Gradio interface: Simplifies the training and generation process through the Gradio interface.
sd webui support: Allows users to directly experience FaceChain on sd webui.
Virtual try-on module: New feature, enhancing user experience.
Super-resolution support: Offers various resolution choices to enhance image detail.
How to Use
Step 1: Visit FaceChain's GitHub page and clone the project locally.
Step 2: Set up the environment according to the installation instructions, including Python version, PyTorch version, and CUDA version.
Step 3: Install necessary dependencies, such as Gradio, controlnet Aux, etc.
Step 4: Run app.py to start the application service and upload at least one photo with a clear face to begin training.
Step 5: After training is complete, use the generated model to create the digital twin.
Step 6: Click 'Start Inference' under the 'Image Experience' tab to generate personal digital images.
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