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
| 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() | |
| ``` | |