Do you want to build your own AI Text-to-Image Generator in less than 15 minutes? Join me as I'll walk you through the process of building one using Stable Diffusion within Visual Studio Code!

Prerequisites

Before you start, ensure you have the following:

• Python 3.9 or higher.
• Hugging Face Account.

Step 1: Set Up the Development Environment

In your project directory, create a file named requirements.txt and add the following dependencies to the file:

certifi==2022.9.14  \ncharset-normalizer==2.1.1  \ncolorama==0.4.5  \ncustomtkinter==4.6.1  \ndarkdetect==0.7.1  \ndiffusers==0.3.0  \nfilelock==3.8.0  \nhuggingface-hub==0.9.1  \nidna==3.4  \nimportlib-metadata==4.12.0  \nnumpy==1.23.3  \npackaging==21.3  \nPillow==9.2.0  \npyparsing==3.0.9  \nPyYAML==6.0  \nregex==2022.9.13  \nrequests==2.28.1  \ntk==0.1.0  \ntokenizers==0.12.1  \ntorch==1.12.1+cu113  \ntorchaudio==0.12.1+cu113  \ntorchvision==0.13.1+cu113  \ntqdm==4.64.1  \ntransformers==4.22.1  \ntyping_extensions==4.3.0  \nurllib3==1.26.12  \nzipp==3.8.1

To install the listed dependencies in the requirements.txt file, run the following command in your terminal:

pip install -r requirements.txt

Step 2: Configure Authentication

In your project directory, create a file named authtoken.py and add the following code to the file:

auth_token = \"ACCESS TOKEN FROM HUGGING FACE\"

To obtain access token from Hugging Face, follow these steps:

• Log in to your Hugging Face account.

•  Go to your profile settings and select Access Tokens

• Click on Create new token.
• Choose the token type as Read.
• Enter Token name and click Create token.

•  Copy the generated token and replace ACCESS TOKEN FROM HUGGINGFACE in authtoken.py file with your token.

Step 3: Develop the Application

In your project directory, create a file named application.py and add the following code to the file:

# Import the Tkinter library for GUI\nimport tkinter as tk\n\n# Import the custom Tkinter library for enhanced widgets\nimport customtkinter as ctk  \n\n# Import PyTorch for handling tensors and model\nimport torch  \n\n# Import the Stable Diffusion Pipeline from diffusers library\nfrom diffusers import StableDiffusionPipeline \n\n# Import PIL for image handling\nfrom PIL import Image, ImageTk \n\n# Import the authentication token from a file\nfrom authtoken import auth_token  \n\n\n# Initialize the main Tkinter application window\napp = tk.Tk()\n\n# Set the size of the window\napp.geometry(\"532x632\") \n\n# Set the title of the window\napp.title(\"Text-to-Image Generator\")  \n\n# Set the appearance mode of customtkinter to dark\nctk.set_appearance_mode(\"dark\")  \n\n# Create an entry widget for the prompt text input\nprompt = ctk.CTkEntry(height=40, width=512, text_font=(\"Arial\", 20), text_color=\"black\", fg_color=\"white\")\n\n# Place the entry widget at coordinates (10, 10)\nprompt.place(x=10, y=10)  \n\n# Create a label widget for displaying the generated image\nlmain = ctk.CTkLabel(height=512, width=512)\n\n# Place the label widget at coordinates (10, 110)\nlmain.place(x=10, y=110)  \n\n# Define the model ID for Stable Diffusion\nmodelid = \"CompVis/stable-diffusion-v1-4\"  \n\n# Define the device to run the model on\ndevice = \"cpu\" \n\n# Load the Stable Diffusion model pipeline\npipe = StableDiffusionPipeline.from_pretrained(modelid, revision=\"fp16\", torch_dtype=torch.float32, use_auth_token=auth_token)\n\n# Move the pipeline to the specified device (CPU)\npipe.to(device)  \n\n# Define the function to generate the image from the prompt\ndef generate():\n\n    # Disable gradient calculation for efficiency\n    with torch.no_grad():  \n        \n        # Generate the image with guidance scale\n        image = pipe(prompt.get(), guidance_scale=8.5)[\"sample\"][0]  \n\n    # Convert the image to a PhotoImage for Tkinter\n    img = ImageTk.PhotoImage(image)  \n\n    # Keep a reference to the image to prevent garbage collection\n    lmain.image = img \n\n    # Update the label widget with the new image\n    lmain.configure(image=img)  \n\n# Create a button widget to trigger the image generation\ntrigger = ctk.CTkButton(height=40, width=120, text_font=(\"Arial\", 20), text_color=\"white\", fg_color=\"black\", command=generate)\n\n# Set the text on the button to \"Generate\"\ntrigger.configure(text=\"Generate\")  \n\n# Place the button at coordinates (206, 60)\ntrigger.place(x=206, y=60)  \n\n# Start the Tkinter main loop\napp.mainloop()

To run the application, execute the following command in your terminal:

python application.py

This will launch the GUI where you can enter a text prompt and generate corresponding images by clicking the Generate button.

Congratulations! You have successfully built an AI Text-to-Image Generator using Stable Diffusion in Visual Studio Code. Feel free to explore and enhance the application further by adding new features and improving the user interface. Happy coding!

Do you want to build your own AI Text-to-Image Generator in less than 15 minutes? Join me as I'll walk you through the process of building one using Stable Diffusion within Visual Studio Code!

Prerequisites

Before you start, ensure you have the following:

• Python 3.9 or higher.
• Hugging Face Account.

Step 1: Set Up the Development Environment

In your project directory, create a file named requirements.txt and add the following dependencies to the file:

certifi==2022.9.14  \ncharset-normalizer==2.1.1  \ncolorama==0.4.5  \ncustomtkinter==4.6.1  \ndarkdetect==0.7.1  \ndiffusers==0.3.0  \nfilelock==3.8.0  \nhuggingface-hub==0.9.1  \nidna==3.4  \nimportlib-metadata==4.12.0  \nnumpy==1.23.3  \npackaging==21.3  \nPillow==9.2.0  \npyparsing==3.0.9  \nPyYAML==6.0  \nregex==2022.9.13  \nrequests==2.28.1  \ntk==0.1.0  \ntokenizers==0.12.1  \ntorch==1.12.1+cu113  \ntorchaudio==0.12.1+cu113  \ntorchvision==0.13.1+cu113  \ntqdm==4.64.1  \ntransformers==4.22.1  \ntyping_extensions==4.3.0  \nurllib3==1.26.12  \nzipp==3.8.1

To install the listed dependencies in the requirements.txt file, run the following command in your terminal:

pip install -r requirements.txt

Step 2: Configure Authentication

In your project directory, create a file named authtoken.py and add the following code to the file:

auth_token = \"ACCESS TOKEN FROM HUGGING FACE\"

To obtain access token from Hugging Face, follow these steps:

• Log in to your Hugging Face account.

•  Go to your profile settings and select Access Tokens

• Click on Create new token.
• Choose the token type as Read.
• Enter Token name and click Create token.

•  Copy the generated token and replace ACCESS TOKEN FROM HUGGINGFACE in authtoken.py file with your token.

Step 3: Develop the Application

In your project directory, create a file named application.py and add the following code to the file:

# Import the Tkinter library for GUI\nimport tkinter as tk\n\n# Import the custom Tkinter library for enhanced widgets\nimport customtkinter as ctk  \n\n# Import PyTorch for handling tensors and model\nimport torch  \n\n# Import the Stable Diffusion Pipeline from diffusers library\nfrom diffusers import StableDiffusionPipeline \n\n# Import PIL for image handling\nfrom PIL import Image, ImageTk \n\n# Import the authentication token from a file\nfrom authtoken import auth_token  \n\n\n# Initialize the main Tkinter application window\napp = tk.Tk()\n\n# Set the size of the window\napp.geometry(\"532x632\") \n\n# Set the title of the window\napp.title(\"Text-to-Image Generator\")  \n\n# Set the appearance mode of customtkinter to dark\nctk.set_appearance_mode(\"dark\")  \n\n# Create an entry widget for the prompt text input\nprompt = ctk.CTkEntry(height=40, width=512, text_font=(\"Arial\", 20), text_color=\"black\", fg_color=\"white\")\n\n# Place the entry widget at coordinates (10, 10)\nprompt.place(x=10, y=10)  \n\n# Create a label widget for displaying the generated image\nlmain = ctk.CTkLabel(height=512, width=512)\n\n# Place the label widget at coordinates (10, 110)\nlmain.place(x=10, y=110)  \n\n# Define the model ID for Stable Diffusion\nmodelid = \"CompVis/stable-diffusion-v1-4\"  \n\n# Define the device to run the model on\ndevice = \"cpu\" \n\n# Load the Stable Diffusion model pipeline\npipe = StableDiffusionPipeline.from_pretrained(modelid, revision=\"fp16\", torch_dtype=torch.float32, use_auth_token=auth_token)\n\n# Move the pipeline to the specified device (CPU)\npipe.to(device)  \n\n# Define the function to generate the image from the prompt\ndef generate():\n\n    # Disable gradient calculation for efficiency\n    with torch.no_grad():  \n        \n        # Generate the image with guidance scale\n        image = pipe(prompt.get(), guidance_scale=8.5)[\"sample\"][0]  \n\n    # Convert the image to a PhotoImage for Tkinter\n    img = ImageTk.PhotoImage(image)  \n\n    # Keep a reference to the image to prevent garbage collection\n    lmain.image = img \n\n    # Update the label widget with the new image\n    lmain.configure(image=img)  \n\n# Create a button widget to trigger the image generation\ntrigger = ctk.CTkButton(height=40, width=120, text_font=(\"Arial\", 20), text_color=\"white\", fg_color=\"black\", command=generate)\n\n# Set the text on the button to \"Generate\"\ntrigger.configure(text=\"Generate\")  \n\n# Place the button at coordinates (206, 60)\ntrigger.place(x=206, y=60)  \n\n# Start the Tkinter main loop\napp.mainloop()

To run the application, execute the following command in your terminal:

python application.py

This will launch the GUI where you can enter a text prompt and generate corresponding images by clicking the Generate button.

Congratulations! You have successfully built an AI Text-to-Image Generator using Stable Diffusion in Visual Studio Code. Feel free to explore and enhance the application further by adding new features and improving the user interface. Happy coding!