Instructions to use Erland/tiny-wan2.1-vsa-t2v-14b-720p-debug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Erland/tiny-wan2.1-vsa-t2v-14b-720p-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-vsa-t2v-14b-720p-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
Add tiny Wan debug pipeline
Browse files
README.md
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# Tiny Wan2.1 VSA T2V 14B 720P Debug Pipeline
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This is a randomly initialized, tiny `WanPipeline` fixture for `FastVideo/Wan2.1-VSA-T2V-14B-720P-Diffusers`.
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FastVideo Wan2.1 VSA 14B 720P text-to-video layout represented as a tiny Diffusers `WanPipeline` artifact for FastVideo `VideoGenerator` and `VIDEO_SPARSE_ATTN` load-path debugging.
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It is intended for fast load-path and inference-control debugging only. It is not trained and should
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not be used for generation quality evaluation.
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# Tiny Wan2.1 VSA T2V 14B 720P Debug Pipeline
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This is a randomly initialized, tiny `WanPipeline` fixture for `FastVideo/Wan2.1-VSA-T2V-14B-720P-Diffusers`.
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FastVideo Wan2.1 VSA 14B 720P text-to-video layout represented as a tiny Diffusers `WanPipeline` artifact for FastVideo `VideoGenerator` and `VIDEO_SPARSE_ATTN` load-path debugging. The saved transformer weights include FastVideo VSA `to_gate_compress` tensors, which are not emitted by Diffusers.
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It is intended for fast load-path and inference-control debugging only. It is not trained and should
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not be used for generation quality evaluation.
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transformer/diffusion_pytorch_model.safetensors
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