import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("pengxiang/trackdiffusion_ytvis", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]TrackDiffusion Model Card
TrackDiffusion is a diffusion model that takes in tracklets as conditions, and generates a video from it.
Model Details
Model Description
TrackDiffusion is a novel video generation framework that enables fine-grained control over complex dynamics in video synthesis by conditioning the generation process on object trajectories. This approach allows for precise manipulation of object trajectories and interactions, addressing the challenges of managing appearance, disappearance, scale changes, and ensuring consistency across frames.
Uses
Direct Use
We provide the weights for the entire unet, so you can replace it in diffusers pipeline, for example:
pretrained_model_path = "stabilityai/stable-video-diffusion-img2vid"
unet = UNetSpatioTemporalConditionModel.from_pretrained("/path/to/unet", torch_dtype=torch.float16,)
pipe = StableVideoDiffusionPipeline.from_pretrained(
pretrained_model_path,
unet=unet,
torch_dtype=torch.float16,
variant="fp16",
low_cpu_mem_usage=True)
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