finetrainers/crush-smol
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How to use finetrainers/LTX-Video-0.9.0-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/LTX-Video-0.9.0-crush-smol-v0", dtype=torch.bfloat16, device_map="cuda")
prompt = "PIKA_CRUSH A red toy car is being crushed by a large hydraulic press, which is flattening objects as if they were under a hydraulic press."
image = pipe(prompt).images[0]This is a LoRA fine-tune of the Lightricks/LTX-Video 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 LTXPipeline
from diffusers.utils import export_to_video
pipe = LTXPipeline.from_pretrained(
"Lightricks/LTX-Video", torch_dtype=torch.bfloat16
).to("cuda")
pipe.load_lora_weights("my-awesome-name/my-awesome-lora", adapter_name="ltxv-lora")
pipe.set_adapters(["ltxv-lora"], [0.9])
video = pipe("<my-awesome-prompt>").frames[0]
export_to_video(video, "output.mp4", fps=8)
Training logs are available on WandB here.
Base model
Lightricks/LTX-Video