README.md · finetrainers/Wan2.1-T2V-1.3B-crush-smol-v0 at main

metadata

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

Prompt: 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.

Prompt: 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

Prompt: 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.

This is a LoRA fine-tune of the Wan-AI/Wan2.1-T2V-1.3B-Diffusers model on the finetrainers/crush-smol dataset.

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

This is an experimental checkpoint and its poor generalization is well-known.

Inference code:

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.