google/ddpm-cifar10-32
Unconditional Image Generation
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Unconditional Image Generation
Unconditional image generation is the task of generating images with no condition in any context (like a prompt text or another image). Once trained, the model will create images that resemble its training data distribution.
Inputs
Seed
42
Number of images to generate:
4
Unconditional Image Generation Model
Output
About Unconditional Image Generation
About the Task
Unconditional image generation is the task of generating new images without any specific input. The main goal of this is to create novel, original images that are not based on existing images. This can be used for a variety of applications, such as creating new artistic images, improving image recognition algorithms, or generating photorealistic images for virtual reality environments.
Unconditional image generation models usually start with a seed that generates a random noise vector. The model will then use this vector to create an output image similar to the images used for training the model.
An example of unconditional image generation would be generating the image of a face on a model trained with the CelebA dataset or generating a butterfly on a model trained with the Smithsonian Butterflies dataset.
Generative adversarial networks and Diffusion are common architectures for this task.
Use Cases
Unconditional image generation can be used for a variety of applications.
Artistic Expression
Unconditional image generation can be used to create novel, original artwork that is not based on any existing images. This can be used to explore new creative possibilities and produce unique, imaginative images.
Data Augmentation
Unconditional image generation models can be used to generate new images to improve the performance of image recognition algorithms. This makes algorithms more robust and able to handle a broader range of images.
Virtual Reality
Unconditional image generation models can be used to create photorealistic images that can be used in virtual reality environments. This makes the VR experience more immersive and realistic.
Medical Imaging
Unconditional image generation models can generate new medical images, such as CT or MRI scans, that can be used to train and evaluate medical imaging algorithms. This can improve the accuracy and reliability of these algorithms.
Industrial Design
Unconditional image generation models can generate new designs for products, such as clothing or furniture, that are not based on any existing designs. This way, designers can explore new creative possibilities and produce unique, innovative designs.
Model Hosting and Inference
This section should have useful information about Model Hosting and Inference
Useful Resources
- Model Database Diffusion Models Course
- Getting Started with Diffusers
- Unconditional Image Generation Training
Training your own model in just a few seconds
In this area, you can insert useful information about training the model
This page was made possible thanks to the efforts of Someet Sahoo and Juan Carlos Piñeros.
Unconditional Image Generation demo
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Models for Unconditional Image Generation
Browse Models (816)
Note High-quality image generation model trained on the CIFAR-10 dataset. It synthesizes images of the ten classes presented in the dataset using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics.
Note High-quality image generation model trained on the 256x256 CelebA-HQ dataset. It synthesizes images of faces using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics.
Datasets for Unconditional Image Generation
Browse Datasets (23)
Note The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images per class.
Spaces using Unconditional Image Generation
Note An application that can generate realistic faces.
Metrics for Unconditional Image Generation
- Inception score (IS)
- The inception score (IS) evaluates the quality of generated images. It measures the diversity of the generated images (the model predictions are evenly distributed across all possible labels) and their 'distinction' or 'sharpness' (the model confidently predicts a single label for each image).
- Frećhet Inception Distance (FID)
- The Fréchet Inception Distance (FID) evaluates the quality of images created by a generative model by calculating the distance between feature vectors for real and generated images.