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
| { | |
| "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" | |
| ] | |
| } |