Clip-Guided Diffusion Model for Image Generation
Generates pokemon sprites from prompt
Real-ESRGAN super-resolution model from ruDALL-E
face alignment using stylegan-encoding
Image Manipulatinon with Diffusion Autoencoders
Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder
Global Tracking Transformers
Colorization using a Generative Color Prior for Natural Images
Language-Free Training of a Text-to-Image Generator with CLIP
Composable Diffusion
Decoding Micromotion in Low-dimensional Latent Spaces from StyleGAN
VQ-Diffusion for Text-to-Image Synthesis
text-to-image generation
Panoptic Scene Graph Generation
text-to-image with latent diffusion
Unsupervised Night Image Enhancement
Inpainting using Denoising Diffusion Probabilistic Models
stable-diffusion with negative prompts, more scheduler
Pose-Invariant Hairstyle Transfer
End-to-End Document Image Enhancement Transformer
This model is cold. You'll get a fast response if the model is warm and already running, and a slower response if the model is cold and starting up.
This model runs on A100 (80GB). View more.
