Instructions to use sjmathy/scare with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sjmathy/scare with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("sjmathy/scare", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Vidar Priority27 RoboTwin Training Bundle
This bundle is self-contained for cached-latent LoRA training.
Contents:
code/vidar: Vidar/Wan training code.data/robotwin_priority27_varcond_cache: portableindex.csv, cached latents, cached text embeddings.models/vidar/vidar.pt: Vidar initialization.models/Wan2.2-TI2V-5B: base Wan checkpoint. Included: True.- Source videos included for debugging/provenance: False.
Training uses --use_latent_cache, so source videos are not needed for the training dataloader.
Run:
cd /path/to/vidar_priority27_train_bundle_20260426
bash train_priority27_cached_lora.sh /path/to/output
If using Slurm, adapt scripts/sbatch_train_robotwin_priority27_single121_varcond_*.sh
to your cluster's partition/GPU flags and point paths inside this bundle.
- Downloads last month
- -
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support