This GGUF file is a direct conversion of Wan-AI/Wan2.2-T2V-A14B Since this is a quantized model, all original licensing terms and usage restrictions remain in effect. Usage The model can be used with the ComfyUI custom node ComfyUI-GGUF by city96 Place model files in ComfyUI/models/unet see the GitHub readme for further installation instructions.
AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data by Fu-Yun Wang et al. For more details, please refer to our [paper] | [code] | [proj-page] | [civitai].
Parameters454M
Context—
Weights4.7 GB
License—
AccessOpen weights
Monthly Downloads43k
Runs On
What it takes to serve AnimateLCM (454M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.9 GB | 1.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.5 GB | 0.5 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.2 GB | 0.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.
Model Card
AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data by Fu-Yun Wang et al. For more details, please refer to our [paper] | [code] | [proj-page] | [civitai].
Excerpt from the card by Fu-Yun Wang.
Identity and Version
- Repository
- wangfuyun/AnimateLCM
- Publisher
- Fu-Yun Wang
- Task
- Text to video
- Modality
- Video
- Library
- diffusers
- Parameters
- 454M parameters
- Languages
- Not stated by the source
- Revision
- 3d4d00fc113225e1040f4d3bec504b6ec750c10c
- First published
- 2024-02-03
- Last updated
- 2024-10-16
Files and Weights
7 files, 4.7 GB in total. The weights are 4 files totalling 4.7 GB in ckpt, safetensors.
Weights4 files · 4.7 GB
Configuration1 file · 376 B
Documentation1 file · 2.0 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| AnimateLCM_sd15_t2v.ckpt | Weights | 1.8 GB | b46c3de62e56 |
| AnimateLCM_sd15_t2v_lora.safetensors | Weights | 134.6 MB | 8f90d840e075 |
| diffusion_pytorch_model.fp16.safetensors | Weights | 907.7 MB | 1997997a9824 |
| diffusion_pytorch_model.safetensors | Weights | 1.8 GB | fb08053b37ee |
| config.json | Configuration | 376 B | — |
| README.md | Documentation | 2.0 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 4.7 GB
Download from Fu-Yun Wang
Released by Fu-Yun Wang through its official repository on Hugging Face.
Built From
- Described by arXiv:2402.00769
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 4.7 GB |
| 16-bit | 0.9 GB |
| 8-bit | 0.5 GB |
| 4-bit | 0.2 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About AnimateLCM
How much GPU memory does AnimateLCM need?
About 1.1 GB at 16-bit and 0.3 GB at 4-bit: the weights (454M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run AnimateLCM on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
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