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Generative-AI Hackathon -- Character.XYZ :

  • The AI model generates avatar faces using a custom stable diffusion model
  • Deploy the model to Hugging Face
  • Test the output using Postman

Expected :

  • Code implementation :

    • def generate_avatar_face(image_path):
      • Parameters:
        - image_path (str): The path to the input image.
      • Returns:
        - avatar_face (PIL.Image): The generated avatar face.
  • Test :

    • Using Postman:
      •  Base URL     : https://example.com/api/
        
      •  Request      : POST /avatar-face
        
      •  Content-Type : png/jpeg
        

Actual :

  • Code Implementation :

    • def generate_avatar_face(prompt, negative_prompt, num_samples, num_inference_steps, guidance_scale, strength, image_url):
      • Parameters:
        • image_path (str) : The path to the input image . [Mandatory]
        • prompt (str) : Description of image . [Optional]
        • negative_prompt(str) : The description which shouldn't be in image . [Optional]
        • num_samples (num) : Number of output images to be produced . [Optional]
        • num_inference_steps(num): Number of steps to process the image . [Optional]
        • guidance_scale(num) : Describes the freedom of model to follow the prompt . [Optional]
        • strength (num) : Describes the noise percentage to be added to original image . [Optional]
      • Returns:
        • avatar_face (.png[Image]): The generated avatar face.
  • Deployment & Testing :

    • Using Gradio :
      • Gradio takes care of
          -  1.Deployment on Hugging-Face
          -  2.API Testing
          -  3.Front-End Web UI
          -  4.So, No need of postman-testing
        
  • Extra-Content :

     -    Stable-Diffusion-webUI using [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui.git)
     -    Very powerful and most efficient stable-diffusion platform
     -    Control-Nets, Multi-Controlnets, Openpose editor and many many extra features can be operated on sd with this
     -    Requries a large of free space (70GB), 8GB VRAM (std.) on NVIDIA GPUs.
    

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