Instructions to use Erland/tiny-fastwan2.1-t2v-dmd-debug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Erland/tiny-fastwan2.1-t2v-dmd-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-fastwan2.1-t2v-dmd-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
File size: 1,005 Bytes
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license: apache-2.0
library_name: diffusers
pipeline_tag: text-to-video
tags:
- diffusers
- wan
- tiny-random
- debug
---
# Tiny FastWan2.1 T2V DMD Debug Pipeline
This is a randomly initialized, tiny `WanDMDPipeline` fixture for `FastVideo/FastWan2.1-T2V-1.3B-Diffusers`.
FastVideo FastWan2.1 DMD-style text-to-video layout represented as a Diffusers-format artifact with `_class_name` patched to `WanDMDPipeline` for FastVideo `VideoGenerator` load-path debugging.
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.
```python
import os
from fastvideo import VideoGenerator
os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "TORCH_SDPA"
generator = VideoGenerator.from_pretrained(
"Erland/tiny-fastwan2.1-t2v-dmd-debug",
num_gpus=1,
)
try:
generator.generate_video(
"debug prompt",
output_path="my_videos/",
save_video=True,
)
finally:
generator.shutdown()
```
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