clarencechen
clarencechen
### Model/Pipeline/Scheduler description Novel-view synthesis through diffusion models has demonstrated remarkable potential for generating diverse and high-quality images. Yet, the independent process of image generation in these prevailing methods leads...
### Model/Pipeline/Scheduler description The generative priors of pre-trained latent diffusion models have demonstrated great potential to enhance the perceptual quality of image super-resolution (SR) results. Unfortunately, the existing diffusion prior-based...
### Model/Pipeline/Scheduler description Achieving faithful image-to-noise inversion with Denoising Diffusion models remains a challenge, particularly for more recent models trained to generate images with a small number of denoising steps....
### Model/Pipeline/Scheduler description Currently, most existing camera motion control methods for video generation with denoising diffusion models rely on training a temporal camera module, and necessitate substantial computation resources due...
### Model/Pipeline/Scheduler description Applying pretrained Text-to-Video (T2V) Diffusion models to Image-to-video (I2V) generation tasks using SDEdit often results in low source image fidelity in open domains. This method achieves high...
### Model/Pipeline/Scheduler description Existing methods for facial identity transfer for diffusion denoising image generation models face challenges in achieving high fidelity and detailed identity (ID) consistency, primarily due to insufficient...
When coming across your work, I thought of including tests on texts generated through Locally Typical Sampling, a sampling method that selects tokens with conditional logits close to the information...
### Model/Pipeline/Scheduler description The authors propose a novel inference technique based on a pretrained diffusion model for text-conditional video generation. Their approach, called FIFO-Diffusion, is conceptually capable of generating infinitely...