https://github.com/rajaguhan437/custom_stable_diffusion

A Custom stable diffusion model using DALLE-pytorch and hugging face library
https://github.com/rajaguhan437/custom_stable_diffusion

dreambooth generative-ai gradio-interface python3 stable-diffusion stable-diffusion-webui webui

Last synced: about 2 months ago
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A Custom stable diffusion model using DALLE-pytorch and hugging face library

• Host: GitHub
• URL: https://github.com/rajaguhan437/custom_stable_diffusion
• Owner: Rajaguhan437
• Created: 2023-03-13T03:37:30.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2023-06-12T00:48:10.000Z (almost 3 years ago)
• Last Synced: 2025-03-06T00:18:38.760Z (about 1 year ago)
• Topics: dreambooth, generative-ai, gradio-interface, python3, stable-diffusion, stable-diffusion-webui, webui
• Language: Jupyter Notebook
• Homepage:
• Size: 117 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 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.