Animatelcm

🚀 AnimateLCM：四步實現快速視頻生成

AnimateLCM是一個能夠高效進行視頻生成的模型，無需個性化視頻數據就能實現個性化風格的視頻生成，為視頻創作帶來了新的可能。

🚀 快速開始

使用Diffusers庫調用AnimateLCM

import torch
from diffusers import AnimateDiffPipeline, LCMScheduler, MotionAdapter
from diffusers.utils import export_to_gif

adapter = MotionAdapter.from_pretrained("wangfuyun/AnimateLCM", torch_dtype=torch.float16)
pipe = AnimateDiffPipeline.from_pretrained("emilianJR/epiCRealism", motion_adapter=adapter, torch_dtype=torch.float16)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, beta_schedule="linear")

pipe.load_lora_weights("wangfuyun/AnimateLCM", weight_name="AnimateLCM_sd15_t2v_lora.safetensors", adapter_name="lcm-lora")
pipe.set_adapters(["lcm-lora"], [0.8])

pipe.enable_vae_slicing()
pipe.enable_model_cpu_offload()

output = pipe(
    prompt="A space rocket with trails of smoke behind it launching into space from the desert, 4k, high resolution",
    negative_prompt="bad quality, worse quality, low resolution",
    num_frames=16,
    guidance_scale=2.0,
    num_inference_steps=6,
    generator=torch.Generator("cpu").manual_seed(0),
)
frames = output.frames[0]
export_to_gif(frames, "animatelcm.gif")

✨ 主要特性

• 文本到視頻生成：支持通過文本描述快速生成視頻。
• 圖像到視頻生成：也支持從圖像生成視頻，可參考 AnimateLCM-SVD-xt 和 AnimateLCM-I2V。

📚 詳細文檔

論文引用

AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data ，作者：Fu - Yun Wang 等。

更多信息

如需更多詳細信息，請參考以下鏈接：

• [論文]
• [代碼]
• [項目頁面]
• [Civitai]

示例視頻