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
File size: 367 Bytes
c8e8505 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"base_model": "THUDM/CogVideoX-2b",
"lora_model": "jeffvedd/Cinemotion",
"recommended_settings": {
"num_frames": 49,
"fps": 8,
"num_inference_steps": 30,
"guidance_scale": 6.0,
"lora_scale": 0.8,
"torch_dtype": "bfloat16"
},
"optimizations": [
"enable_model_cpu_offload",
"vae.enable_tiling",
"vae.enable_slicing"
]
} |