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--- |
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pipeline_tag: text-to-video |
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--- |
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# AnimateLCM for Fast Video Generation in 4 steps. |
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[AnimateLCM: Accelerating the Animation of Personalized Diffusion Models and Adapters with Decoupled Consistency Learning](https://arxiv.org/abs/2402.00769) by Fu-Yun Wang et al. |
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For more details, please refer to our [[paper](https://arxiv.org/abs/2402.00769)] | [[code](https://github.com/G-U-N/AnimateLCM)] | [[proj-page](https://animatelcm.github.io/)] | [[civitai](https://civitai.com/models/290375/animatelcm-fast-video-generation)]. |
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<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/63e9e92f20c109718713f5eb/KCwSoZCdxkkmtDg1LuXsP.mp4"></video> |
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## Using AnimateLCM with Diffusers |
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```python |
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import torch |
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from diffusers import AnimateDiffPipeline, LCMScheduler, MotionAdapter |
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from diffusers.utils import export_to_gif |
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adapter = MotionAdapter.from_pretrained("wangfuyun/AnimateLCM", torch_dtype=torch.float16) |
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pipe = AnimateDiffPipeline.from_pretrained("emilianJR/epiCRealism", motion_adapter=adapter, torch_dtype=torch.float16) |
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pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, beta_schedule="linear") |
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pipe.load_lora_weights("wangfuyun/AnimateLCM", weight_name="sd15_lora_beta.safetensors", adapter_name="lcm-lora") |
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pipe.set_adapters(["lcm-lora"], [0.8]) |
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pipe.enable_vae_slicing() |
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pipe.enable_model_cpu_offload() |
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output = pipe( |
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prompt="A space rocket with trails of smoke behind it launching into space from the desert, 4k, high resolution", |
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negative_prompt="bad quality, worse quality, low resolution", |
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num_frames=16, |
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guidance_scale=2.0, |
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num_inference_steps=6, |
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generator=torch.Generator("cpu").manual_seed(0), |
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) |
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frames = output.frames[0] |
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export_to_gif(frames, "animatelcm.gif") |
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``` |
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