State of open video generation models in Diffusers

Consistency Models

Diffusion models have significantly advanced the fields of image, audio, and video generation, but they depend on an iterative sampling process that causes slow generation.

Exploring Quantization Backends in Diffusers

Video generation models as world simulators

We explore large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios.

Train your ControlNet with diffusers

Stable Diffusion with 🧨 Diffusers

Transition Matching Distillation for Fast Video Generation

arXiv:2601. 09881v2 Announce Type: replace-cross Abstract: Large video diffusion and flow models have achieved remarkable success in high-quality video generation, but their use in real-time interactive applications remains limited due to their inefficient multi-step sampling process.

Bringing Nunchaku 4-bit Diffusion Inference to Diffusers

ControlNet in 🧨 Diffusers

CineMobile: On-Device Image-to-Video Diffusion for Cinematic Camera Motion Generation

arXiv:2607. 03803v1 Announce Type: cross Abstract: The growing demand for image-to-video creation on mobile devices has increasingly focused on cinematic motion effects like bullet time, dolly zoom, slow motion, etc.

Introducing Modular Diffusers - Composable Building Blocks for Diffusion Pipelines

Making Time Editable in Video Diffusion Transformers

arXiv:2606. 10183v1 Announce Type: cross Abstract: Modern Diffusion Transformers for video generation provide limited control over the progression of time and the editing of temporal dynamics.

Introducing Waypoint-1: Real-time interactive video diffusion from Overworld