ldm super resolution 4x openimages

Introduction

This document outlines the Latent Diffusion Models (LDM) for super-resolution, focusing on efficient high-resolution image synthesis using diffusion models. These models leverage pretrained autoencoders to optimize the balance between computational efficiency and image fidelity.

Architecture

Latent Diffusion Models (LDMs) operate by applying diffusion models within the latent space of powerful pretrained autoencoders. This approach reduces computational requirements while maintaining high visual fidelity. The architecture incorporates cross-attention layers, enabling flexibility in generating images conditioned on inputs like text or bounding boxes. This method excels in tasks such as image inpainting, semantic scene synthesis, and super-resolution.

Training

The training process for LDMs involves using latent representations rather than pixel-based approaches, significantly cutting down on computational resources. This allows the models to achieve state-of-the-art performance in various image synthesis tasks while avoiding the extensive GPU time typically required by pixel-based diffusion models.

Guide: Running Locally

To run the LDM Super-Resolution pipeline locally, follow these steps:

1. Install the Required Package:

   !pip install git+https://github.com/huggingface/diffusers.git
   

2. Import Libraries:

   import requests
   from PIL import Image
   from io import BytesIO
   from diffusers import LDMSuperResolutionPipeline
   import torch
   

3. Set Up the Environment:

   device = "cuda" if torch.cuda.is_available() else "cpu"
   model_id = "CompVis/ldm-super-resolution-4x-openimages"
   

4. Load the Model and Scheduler:

   pipeline = LDMSuperResolutionPipeline.from_pretrained(model_id)
   pipeline = pipeline.to(device)
   

5. Download and Prepare an Image:

   url = "https://user-images.githubusercontent.com/38061659/199705896-b48e17b8-b231-47cd-a270-4ffa5a93fa3e.png"
   response = requests.get(url)
   low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
   low_res_img = low_res_img.resize((128, 128))
   

6. Run the Pipeline for Inference:

   upscaled_image = pipeline(low_res_img, num_inference_steps=100, eta=1).images[0]
   

7. Save the Output Image:

   upscaled_image.save("ldm_generated_image.png")
   

For faster processing, it's recommended to use cloud GPUs available through platforms like AWS, Google Cloud, or Azure.

License

The LDM Super-Resolution model is licensed under the Apache-2.0 license. This allows for broad use and distribution, provided that the original license terms are adhered to.