---
base_model:
- Wan-AI/Wan2.1-T2V-1.3B
- Wan-AI/Wan2.1-T2V-1.3B-Diffusers
datasets: finetrainers/crush-smol
library_name: diffusers
license: other
license_link: https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B/blob/main/LICENSE.txt
widget:
- text: >-
    PIKA_CRUSH A large metal cylinder is seen pressing down on a pile of
    colorful jelly beans, flattening them as if they were under a hydraulic
    press.
  output:
    url: final-3000-0-2-PIKA_CRUSH-A-large-metal-.mp4
- text: >-
    PIKA_CRUSH A green cube is being compressed by a hydraulic press, which
    flattens the object as if it were under a hydraulic press. The press is
    shown in action, with the cube being squeezed into a smaller shape
  output:
    url: final-3000-1-2-PIKA_CRUSH-A-green-cube-i.mp4
- text: >-
    PIKA_CRUSH A large metal cylinder is seen pressing down on a pile of Oreo
    cookies, flattening them as if they were under a hydraulic press.
  output:
    url: final-3000-1-2-PIKA_CRUSH-A-large-metal-.mp4
tags:
- text-to-video
- diffusers-training
- diffusers
- template:sd-lora
- wan
---

<Gallery />

This is a LoRA fine-tune of the [Wan-AI/Wan2.1-T2V-1.3B-Diffusers](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers) model on the
[finetrainers/crush-smol](https://huggingface.co/datasets/finetrainers/crush-smol) dataset.

Code: https://github.com/a-r-r-o-w/finetrainers

> [!IMPORTANT]
> This is an experimental checkpoint and its poor generalization is well-known.

Inference code:

```python
import torch
from diffusers import WanPipeline
from diffusers.utils import export_to_video

pipe = WanPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers", torch_dtype=torch.bfloat16
).to("cuda")
pipe.load_lora_weights("finetrainers/Wan2.1-T2V-1.3B-crush-smol-v0", adapter_name="wan-lora")
pipe.set_adapters(["wan-lora"], [0.75])

video = pipe("<my-awesome-prompt>").frames[0]
export_to_video(video, "output.mp4", fps=24)
```

Training logs are available on WandB [here](https://wandb.ai/aryanvs/finetrainers-wan).