Instructions to use Skywork/Matrix-Game-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Skywork/Matrix-Game-2.0 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("Skywork/Matrix-Game-2.0", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
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README.md
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```
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# clone the repository:
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git clone
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cd Matrix-Game-2.0
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# install dependencies:
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pip install -r requirements.txt
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# inference
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```
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## ⭐ Acknowledgements
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```
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# clone the repository:
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git clone https://github.com/SkyworkAI/Matrix-Game-2.0
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cd Matrix-Game-2.0
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# install dependencies:
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pip install -r requirements.txt
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python setup.py develop
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# inference
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python inference.py \
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--config_path configs/inference_yaml/{your-config}.yaml \
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--checkpoint_path {path-to-the-checkpoint} \
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--img_path {path-to-the-input-image} \
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--output_folder outputs \
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--num_output_frames 150 \
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--seed 42 \
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--pretrained_model_path {path-to-the-vae-folder}
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# inference streaming
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python inference_streaming.py \
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--config_path configs/inference_yaml/{your-config}.yaml \
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--checkpoint_path {path-to-the-checkpoint} \
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--output_folder outputs \
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--seed 42 \
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--pretrained_model_path {path-to-the-vae-folder}
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```
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## ⭐ Acknowledgements
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