Instructions to use fal/LongCat-Video-FlashPack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fal/LongCat-Video-FlashPack with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fal/LongCat-Video-FlashPack", 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
| { | |
| "_class_name": "LongCatVideoPipeline", | |
| "_diffusers_version": "0.35.2", | |
| "dit": [ | |
| "longcat_video.modules.longcat_video_dit", | |
| "LongCatVideoTransformer3DModel" | |
| ], | |
| "scheduler": [ | |
| "longcat_video.modules.scheduling_flow_match_euler_discrete", | |
| "FlowMatchEulerDiscreteScheduler" | |
| ], | |
| "text_encoder": [ | |
| "longcat_video.modules.text_encoder", | |
| "UMT5EncoderModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "T5TokenizerFast" | |
| ], | |
| "vae": [ | |
| "longcat_video.modules.autoencoder_kl_wan", | |
| "AutoencoderKLWan" | |
| ] | |
| } | |