Instructions to use Teluv/Wan_Video_Attack_Sprite_Animation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Teluv/Wan_Video_Attack_Sprite_Animation with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Wan-AI/Wan2.2-TI2V-5B,Wan-AI/Wan2.2-I2V-A14B", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Teluv/Wan_Video_Attack_Sprite_Animation") prompt = "A man with short gray hair plays a red electric guitar." input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png") image = pipe(image=input_image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Update README.md
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README.md
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# Model description (English)
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๐ Model Card:
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๐จ Notice
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This model is currently under R&D and experimental training.
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The file may be unstable, and frequent updates with improved quality will be provided soon.
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Base Model: Wan2.2-TI5V
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Training Steps: 3000
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๋ชจ๋ธ ์ค๋ช
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๐ ๋ชจ๋ธ ์นด๋:
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๐จ ์๋ฆผ
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์ด ๋ชจ๋ธ์ ํ์ฌ R&D ๊ณผ์ ์์ ์คํ์ ์ผ๋ก ํ์ต๋ ์ํ์
๋๋ค.
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ํ์ผ์ด ๋ถ์์ ํ ์ ์์ผ๋ฉฐ, ๋น ๋ฅธ ์์ผ ๋ด ํ๋ฆฌํฐ ํฅ์ ์
๋ฐ์ดํธ๊ฐ ์ ๊ณต๋ ์์ ์
๋๋ค.
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๋ชจ๋ธ ์ด๋ฆ: Wan2.2-TI5V-Sprite-Animation-Attack-Lora
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๋ฒ ์ด์ค ๋ชจ๋ธ: Wan2.2-TI5V
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ํ์ต ์คํ
์: 3000
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# Model description (English)
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๐ Model Card: Wan_Video_Attack_Sprite_Animation
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๐จ Notice
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This model is currently under R&D and experimental training.
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The file may be unstable, and frequent updates with improved quality will be provided soon.
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Base Model: Wan2.2
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Training Steps: 3000
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๋ชจ๋ธ ์ค๋ช
(ํ๊ตญ์ด)
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๐ ๋ชจ๋ธ ์นด๋: Wan_Video_Attack_Sprite_Animation
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๐จ ์๋ฆผ
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์ด ๋ชจ๋ธ์ ํ์ฌ R&D ๊ณผ์ ์์ ์คํ์ ์ผ๋ก ํ์ต๋ ์ํ์
๋๋ค.
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ํ์ผ์ด ๋ถ์์ ํ ์ ์์ผ๋ฉฐ, ๋น ๋ฅธ ์์ผ ๋ด ํ๋ฆฌํฐ ํฅ์ ์
๋ฐ์ดํธ๊ฐ ์ ๊ณต๋ ์์ ์
๋๋ค.
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๋ฒ ์ด์ค ๋ชจ๋ธ: Wan2.2-TI5V
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ํ์ต ์คํ
์: 3000
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