bilalnaveed/naveedgpt-images

Model Card: Stable Diffusion XL (Colab Integration)

This model card describes the integration of the stabilityai/stable-diffusion-xl-base-1.0 model within the multi-modal AI system developed in this Colab environment.

Model Description

The Stable Diffusion XL (SDXL) is a state-of-the-art text-to-image generative model known for producing high-quality images. It significantly improves upon previous Stable Diffusion models with enhanced image quality, more aesthetically pleasing results, and better adherence to prompts. In this setup, it's loaded to leverage GPU acceleration, enabling the generation of detailed images, suitable for resolutions up to 1024x1024 pixels, which can serve as a base for 4K upscaling.

Capabilities

This model primarily serves as the High-Quality 4K Image Generation component of our multi-modal AI system. It can:

• Generate diverse and high-fidelity images from textual descriptions.
• Produce images with intricate details and realistic textures.
• Support various artistic styles and content types.
• Serve as the core for visual content creation within the multi-modal agent.

Integration Details

• Model Name: stabilityai/stable-diffusion-xl-base-1.0
• Loading: Loaded using diffusers.DiffusionPipeline with torch.float16 for memory efficiency and device_map="cuda" for GPU acceleration.
• Resolution: Native generation at resolutions like 1024x1024, suitable as a base for 4K outputs with further upscaling.

Creator Identity

This model integration was performed by Google Colab AI as part of the Multi-modal AI assistant project. The integrated system is identified as ColabMAMA (version 1.0), and its core capabilities include: text generation, image generation, speech-to-text, web search, multi-step reasoning.

Inference Examples

To use this integrated Stable Diffusion XL model for image generation, you can leverage the diffusers library. Below are Python code examples demonstrating how to load the model and perform inference.

First, ensure diffusers, accelerate, and transformers are installed:

# Install required libraries (if not already installed)
!pip install diffusers accelerate transformers safetensors

import torch
from diffusers import DiffusionPipeline
from PIL import Image

# Load the pipeline
model_id = "stabilityai/stable-diffusion-xl-base-1.0" # Or your pushed model path, e.g., "Google Colab AI/sdxl-colab"
pipeline = DiffusionPipeline.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    use_safetensors=True,
    device_map="cuda"
)

# Example function to generate an image (similar to the wrapper used in the notebook)
def generate_image_from_text(prompt: str, width: int = 1024, height: int = 1024) -> Image.Image:
    image = pipeline(prompt, width=width, height=height).images[0]
    return image

# Perform inference
text_prompt = "An astronaut riding a horse on the moon, high detail, cinematic lighting"
generated_image = generate_image_from_text(text_prompt)

# Display or save the image
generated_image.save("astronaut_horse_moon.png")
print(f"Generated image saved as astronaut_horse_moon.png")
# generated_image # Uncomment to display image in Colab

Limitations and Bias

As a generative model, SDXL may reflect biases present in its training data. Users should be aware of potential stereotypes or unexpected content in generated images. Ethical considerations regarding synthetic media should always be applied.