Instructions to use APRIL-AIGC/T3-Video with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use APRIL-AIGC/T3-Video with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("APRIL-AIGC/T3-Video", 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 library_name and clean up model card
Browse filesThis PR improves the model card by:
- Adding `library_name: diffusers` to the metadata, which enables the "How to use" widget on the model page with a `diffusers` code snippet.
- Removing a redundant YAML frontmatter block from the markdown content, as all metadata is already (or now will be) correctly placed in the top-level YAML block.
README.md
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---
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license: apache-2.0
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datasets:
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- APRIL-AIGC/UltraVideo
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base_model:
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- Wan-AI/Wan2.1-T2V-1.3B
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pipeline_tag: text-to-video
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tags:
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- video
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- t2v
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- i2v
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---
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# T3-Video
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---
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base_model:
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- Wan-AI/Wan2.1-T2V-1.3B
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datasets:
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- APRIL-AIGC/UltraVideo
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license: apache-2.0
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pipeline_tag: text-to-video
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tags:
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- video
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- t2v
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- i2v
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library_name: diffusers
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# T3-Video
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