Instructions to use MLbackup/T2V_Video_Loras with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MLbackup/T2V_Video_Loras with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MLbackup/T2V_Video_Loras", 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
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README.md
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pipeline_tag: text-to-video
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tags:
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- art
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---
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These are Hunyuan and WAN T2V Loras.
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There may be ONE or more I2V loras, but they are few and far between as we start learning new concepts.
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pipeline_tag: text-to-video
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tags:
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- art
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library_name: diffusers
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---
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These are Hunyuan and WAN T2V Loras.
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There may be ONE or more I2V loras, but they are few and far between as we start learning new concepts.
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