Diffusers: The State of Open Video Models

Since OpenAI's Sora, the video generation model market has been growing rapidly, with closed models like Google's Veo 2 and Runway's Gen 3 Alpha competing fiercely against open source models like CogVideoX, Mochi-1, and Hunyuan Video.

Current video generation models have the following key limitations:

• High resource requirements: Large-scale datasets and hardware costs make open source model development difficult.
• Lack of generalization ability: They depend on specific prompting methods or are vulnerable to data outside the training data range.
• Latency: High computational and memory requirements limit usage in local environments.

Unlike image generation, video generation has much higher technical difficulty because there are far more factors to consider, such as Motion Dynamics, Spatio-Temporal Consistency, and adherence to input conditions.

The Diffusers team plans to strengthen inference optimization and fine-tuning support to promote the spread of these models.