Video-to-Video
Diffusers
English
Chinese
video-editing
streaming
quantization
int8
text-stream-stripping
Instructions to use kylinfires/Joy-face_change_optimized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kylinfires/Joy-face_change_optimized with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kylinfires/Joy-face_change_optimized", 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
Model card metadata
Browse files
README.md
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# Joy-face_change_optimized
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[JoyAI-Video-Edit](https://github.com/jd-opensource/JoyAI-Video-Edit) 的优化分支(Apache-2.0)。
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---
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license: apache-2.0
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language: [en, zh]
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library_name: diffusers
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pipeline_tag: video-to-video
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base_model: jdopensource/JoyAI-Video-Edit
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tags: [video-editing, streaming, quantization, int8, text-stream-stripping]
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---
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# Joy-face_change_optimized
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[JoyAI-Video-Edit](https://github.com/jd-opensource/JoyAI-Video-Edit) 的优化分支(Apache-2.0)。
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