Instructions to use Erland/tiny-wan2.1-t2v-debug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Erland/tiny-wan2.1-t2v-debug with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Erland/tiny-wan2.1-t2v-debug", 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
metadata
license: apache-2.0
library_name: diffusers
pipeline_tag: text-to-video
tags:
- diffusers
- wan
- tiny-random
- debug
Tiny Wan2.1 T2V Debug Pipeline
This is a randomly initialized, tiny Diffusers WanPipeline fixture. It is intended for fast
load-path and inference-control debugging only. It is not trained and should not be used for
generation quality evaluation.
from diffusers import WanPipeline
pipe = WanPipeline.from_pretrained("Erland/tiny-wan2.1-t2v-debug")
pipe.set_progress_bar_config(disable=True)
frames = pipe(
prompt="debug prompt",
height=64,
width=64,
num_frames=5,
num_inference_steps=1,
guidance_scale=1.0,
max_sequence_length=8,
).frames[0]