Instructions to use BinaryLight1011/Cinemotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BinaryLight1011/Cinemotion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("THUDM/CogVideoX-2b", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("BinaryLight1011/Cinemotion") prompt = "A man with short gray hair plays a red electric guitar." output = pipe(prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Config 49 frames adicionado
Browse files- generation_config.json +17 -0
generation_config.json
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{
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"base_model": "THUDM/CogVideoX-2b",
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"lora_model": "jeffvedd/Cinemotion",
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"recommended_settings": {
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"num_frames": 49,
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"fps": 8,
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"num_inference_steps": 30,
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"guidance_scale": 6.0,
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"lora_scale": 0.8,
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"torch_dtype": "bfloat16"
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},
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"optimizations": [
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"enable_model_cpu_offload",
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"vae.enable_tiling",
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"vae.enable_slicing"
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]
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}
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