---
title: AnimateLCM-SVD-Comfy
canonical_url: "https://www.modelscope.cn/models/shareAI/AnimateLCM-SVD-Comfy"
md_url: "https://www.modelscope.cn/models/shareAI/AnimateLCM-SVD-Comfy.md"
repository: shareAI/AnimateLCM-SVD-Comfy
last_updated: 2024-06-14
license: other
parameters: 2.3B
tensor_type:
  - F32
  - F16
library_name:
  - safetensors
downloads: 37
stars: 0
---

# AnimateLCM-SVD-Comfy

> AnimateLCM-SVD-Comfy - shareAI 在 ModelScope 开源的模型。AnimateLCM-SVD-Comfy版本

shareAI/AnimateLCM-SVD-Comfy 是 ModelScope 魔搭社区上的 2.3B 参数机器学习模型，采用 other 许可。

- **Repository**: shareAI/AnimateLCM-SVD-Comfy
- **License**: other
- **Parameters**: 2.3B
- **Downloads**: 37
- **Stars**: 0
- **Last updated**: 2024-06-14

Source: https://www.modelscope.cn/models/shareAI/AnimateLCM-SVD-Comfy

---

Converted weights from diffusers to original format compatible with ComfyUI SVD
Original weights from: https://huggingface.co/wangfuyun/AnimateLCM-SVD-xt

转载地址：https://huggingface.co/Kijai/AnimateLCM-SVD-Comfy/tree/main ，由@Kijai大佬转制权重。

Introduction
Consistency Distilled Stable Video Diffusion Image2Video-XT (SVD-xt) following the strategy proposed in AnimateLCM-paper. AnimateLCM-SVD-xt can generate good quality image-conditioned videos with 25 frames in 2~8 steps with 576x1024 resolutions.

Computation comparsion
AnimateLCM-SVD-xt can generally produces demos with good quality in 4 steps without requiring the classifier-free guidance, and therefore can save 25 x 2 / 4 = 12.5 times compuation resources compared with normal SVD models.