finetrainers/crush-smol
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How to use finetrainers/Wan2.1-T2V-1.3B-crush-smol-v0 with Diffusers:
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("finetrainers/Wan2.1-T2V-1.3B-crush-smol-v0", dtype=torch.bfloat16, device_map="cuda")
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."
image = pipe(prompt).images[0]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.
Base model
Wan-AI/Wan2.1-T2V-1.3B