Text-to-Image-Generation-using-stable-diffusion-2

In this we generate Images with a prompt(Text) Here’s a detailed explanation of the functions used in your project

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Explanation of Functions

1. StableDiffusionPipeline

The StableDiffusionPipeline is the main function from the diffusers library used to generate images from text prompts.

Purpose: It integrates the components of a Stable Diffusion model (text encoder, UNet, scheduler, and VAE decoder) into a single pipeline for streamlined text-to-image generation.

Key Features:

• Converts natural language prompts into visual outputs.
• Supports precision optimizations like FP16 for GPU acceleration.

Important Parameters:

• from_pretrained():
  • model_id: The identifier for the pre-trained Stable Diffusion model (e.g., stabilityai/stable-diffusion-2).
  • torch_dtype: Data type (e.g., torch.float16) to reduce memory usage.
  • revision: Model weights version, such as "fp16" for half-precision.
  • use_auth_token: Token for accessing Hugging Face models.
• .to():
  • Specifies the device (cuda or cpu) where the pipeline will run.

Usage:

image_gen_model = StableDiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2", torch_dtype=torch.float16,
    revision="fp16", use_auth_token='YOUR_HF_TOKEN'
).to("cuda")

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2. generate_image(prompt, model)

This function wraps the pipeline to generate an image based on a user-provided text prompt.

Workflow:

1. Calls the StableDiffusionPipeline with the text prompt.
2. Configures:
   • num_inference_steps: Number of diffusion steps, controlling quality and generation time.
   • guidance_scale: Controls adherence to the prompt.
   • generator: Ensures reproducibility with a fixed seed.
3. The output image is resized to a specified dimension (default: 400x400 pixels).

Example:

image = generate_image("A futuristic city at night", image_gen_model)

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3. CFG Class

The CFG class stores configuration parameters to make the code modular and customizable.

Attributes:

• Device: GPU (cuda) or CPU.
• Seed: Ensures reproducibility of results.
• Generator: A PyTorch random generator seeded for consistency.
• Image Generation:
  • Steps (image_gen_steps): Number of diffusion iterations.
  • Model ID (image_gen_model_id): Specifies the Stable Diffusion version.
  • Size (image_gen_size): Image dimensions.
  • Guidance Scale (image_gen_guidance_scale): Prompt adherence.

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Text-to-Image Generation using Stable Diffusion v2

Overview

This project demonstrates the use of Stable Diffusion v2, a powerful text-to-image generation model. By providing textual descriptions, users can create visually compelling images that reflect their inputs.

Features

• Generate high-quality images from text prompts.
• Configurable settings for image resolution, quality, and creativity.
• GPU-accelerated performance with FP16 precision.
• Modular and reproducible design for consistent results.

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How It Works

1. Stable Diffusion Pipeline

The project uses StableDiffusionPipeline from the diffusers library:

• Text prompts are encoded and processed through a pre-trained model.
• Images are generated iteratively using diffusion techniques.
• Post-processed images are returned for visualization or storage.

2. Key Functions

StableDiffusionPipeline

Loads and initializes the pre-trained Stable Diffusion v2 model.

generate_image(prompt, model)

Generates an image from a given text prompt:

• Input: Text prompt and Stable Diffusion model.
• Output: Resized PIL image.

CFG Class

Holds project settings for customization:

• Device (CPU/GPU).
• Model ID.
• Image generation steps, size, and guidance scale.

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Usage

1. Load the Model: Initialize the pipeline:

   from diffusers import StableDiffusionPipeline
   
   image_gen_model = StableDiffusionPipeline.from_pretrained(
       "stabilityai/stable-diffusion-2", torch_dtype=torch.float16,
       revision="fp16", use_auth_token="YOUR_HF_TOKEN"
   ).to("cuda")

2. Generate Images: Use the generate_image function:

   image = generate_image("A forest with glowing mushrooms", image_gen_model)

3. Display or Save: Visualize the image:

   import matplotlib.pyplot as plt
   
   plt.imshow(image)
   plt.axis("off")
   plt.show()

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Configuration

Modify the CFG class to change default settings:

• Seed: Ensure consistent results.
• Inference Steps: Adjust image quality.
• Guidance Scale: Control prompt adherence.

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Example Output

Prompt	Generated Image
"spiderman and batman fighting each other"
"working woman taking family responsibilities"
"Marvel universe with batman"

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License

This project is licensed under the MIT License.

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