ddim celeba hq

Introduction

Denoising Diffusion Implicit Models (DDIM) are a class of models designed to accelerate the sampling process of Denoising Diffusion Probabilistic Models (DDPMs). DDIMs maintain the same training procedure as DDPMs but allow for faster sampling by using non-Markovian diffusion processes.

Architecture

DDIMs operate by constructing non-Markovian diffusion processes that retain the training objectives of DDPMs but enable quicker reverse processes. This results in high-quality image generation at a speed up to 50 times faster than traditional DDPMs. The model allows for a trade-off between computational efficiency and sample quality and supports semantically meaningful image interpolation in latent space.

Training

DDIMs share the same training process as DDPMs, focusing on reversing a diffusion process to generate samples. The training involves optimizing the likelihood of data under the model's generative process, which is defined by the reverse of a diffusion process.

Guide: Running Locally

1. Install Requirements:
   Ensure you have the necessary Python packages by installing diffusers:

   !pip install diffusers
   

2. Load Model:
   Use the DiffusionPipeline to load the DDIM model.

   from diffusers import DiffusionPipeline
   model_id = "fusing/ddim-celeba-hq"
   ddpm = DiffusionPipeline.from_pretrained(model_id)
   

3. Run Inference:
   Generate an image by sampling random noise and denoising it.

   image = ddpm(eta=0.0, num_inference_steps=50)
   

4. Process and Save Image:
   Convert the generated image to a PIL format and save it.

   import PIL.Image
   import numpy as np
   
   image_processed = image.cpu().permute(0, 2, 3, 1)
   image_processed = (image_processed + 1.0) * 127.5
   image_processed = image_processed.numpy().astype(np.uint8)
   image_pil = PIL.Image.fromarray(image_processed[0])
   image_pil.save("test.png")
   

Cloud GPUs: For enhanced performance, consider using cloud-based GPUs such as those offered by AWS, Google Cloud, or Azure.

License

The DDIM implementation and related code are available under licenses specified on the respective model and code repositories, typically aligning with open-source licenses to facilitate research and development.