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--- |
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license: creativeml-openrail-m |
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tags: |
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- text-to-video |
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- stable-diffusion |
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- animatediff |
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library_name: diffusers |
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inference: false |
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--- |
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# AnimateDiff-Lightning |
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<video src='https://huggingface.co/ByteDance/AnimateDiff-Lightning/resolve/main/animatediff_lightning_samples_t2v.mp4' width="100%" autoplay muted loop style='margin:0'></video> |
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<video src='https://huggingface.co/ByteDance/AnimateDiff-Lightning/resolve/main/animatediff_lightning_samples_v2v.mp4' width="100%" autoplay muted loop style='margin:0'></video> |
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AnimateDiff-Lightning is a lightning-fast text-to-video generation model. It can generate videos more than ten times faster than the original AnimateDiff. For more information, please refer to our research paper: [AnimateDiff-Lightning: Cross-Model Diffusion Distillation](https://huggingface.co/ByteDance/AnimateDiff-Lightning/resolve/main/animatediff_lightning_report.pdf). We release the model as part of the research. |
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Our models are distilled from [AnimateDiff SD1.5 v2](https://huggingface.co/guoyww/animatediff). This repository contains checkpoints for 1-step, 2-step, 4-step, and 8-step distilled models. The generation quality of our 2-step, 4-step, and 8-step model is great. Our 1-step model is only provided for research purposes. |
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## Recommendation |
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AnimateDiff-Lightning produces the best results when used with stylized base models. We recommend using the following base models: |
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Realistic |
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- [epiCRealism](https://civitai.com/models/25694) |
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- [Realistic Vision](https://civitai.com/models/4201) |
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- [DreamShaper](https://civitai.com/models/4384) |
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- [AbsoluteReality](https://civitai.com/models/81458) |
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- [MajicMix Realistic](https://civitai.com/models/43331) |
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Anime & Cartoon |
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- [ToonYou](https://civitai.com/models/30240) |
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- [IMP](https://civitai.com/models/56680) |
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- [Mistoon Anime](https://civitai.com/models/24149) |
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- [DynaVision](https://civitai.com/models/75549) |
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- [RCNZ Cartoon 3d](https://civitai.com/models/66347) |
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- [MajicMix Reverie](https://civitai.com/models/65055) |
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Additionally, feel free to explore different settings. We find using 3 inference steps on the 2-step model produces great results. We find certain base models produces better results with CFG. We also recommend using [Motion LoRAs](https://huggingface.co/guoyww/animatediff/tree/main) as they produce stronger motion. We use Motion LoRAs with strength 0.7~0.8 to avoid watermark. |
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## Diffusers Usage |
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```python |
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import torch |
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from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler |
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from diffusers.utils import export_to_gif |
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from huggingface_hub import hf_hub_download |
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from safetensors.torch import load_file |
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device = "cuda" |
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dtype = torch.float16 |
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step = 4 # Options: [1,2,4,8] |
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repo = "AnimateDiff-Lightning" |
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ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors" |
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base = "SG161222/Realistic_Vision_V5.1_noVAE" # Choose to your favorite base model. |
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adapter = MotionAdapter().to(device, dtype) |
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adapter.load_state_dict(load_file(hf_hub_download(repo ,ckpt), device=device)) |
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pipe = AnimateDiffPipeline.from_pretrained(base, motion_adapter=adapter, torch_dtype=dtype).to(device) |
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pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear") |
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output = pipe(prompt="A girl smiling", guidance_scale=1.0, num_inference_steps=step) |
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export_to_gif(output.frames[0], "animation.gif") |
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``` |
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## ComfyUI Usage |
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1. Download [animatediff_lightning_workflow.json](https://huggingface.co/ByteDance/AnimateDiff-Lightning/raw/main/comfyui/animatediff_lightning_workflow.json) and import it in ComfyUI. |
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1. Install nodes. You can install them manually or use [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager). |
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* [ComfyUI-AnimateDiff-Evolved](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved) |
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* [ComfyUI-VideoHelperSuite](https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite) |
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1. Download your favorite base model checkpoint and put them under `/models/checkpoints/` |
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1. Download AnimateDiff-Lightning checkpoint `animatediff_lightning_Nstep_comfyui.safetensors` and put them under `/custom_nodes/ComfyUI-AnimateDiff-Evolved/models/` |
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## Video-to-Video Generation |
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AnimateDiff-Lightning is great for video-to-video generation. We provide the simplist comfyui workflow using ControlNet. |
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1. Download [animatediff_lightning_v2v_openpose_workflow.json](https://huggingface.co/ByteDance/AnimateDiff-Lightning/raw/main/comfyui/animatediff_lightning_v2v_openpose_workflow.json) and import it in ComfyUI. |
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1. Install nodes. You can install them manually or use [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager). |
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* [ComfyUI-AnimateDiff-Evolved](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved) |
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* [ComfyUI-VideoHelperSuite](https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite) |
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* [ComfyUI-Advanced-ControlNet](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet) |
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* [comfyui_controlnet_aux](https://github.com/Fannovel16/comfyui_controlnet_aux) |
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1. Download your favorite base model checkpoint and put them under `/models/checkpoints/` |
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1. Download AnimateDiff-Lightning checkpoint `animatediff_lightning_Nstep_comfyui.safetensors` and put them under `/custom_nodes/ComfyUI-AnimateDiff-Evolved/models/` |
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1. Download [ControlNet OpenPose](https://huggingface.co/lllyasviel/ControlNet-v1-1/tree/main) `control_v11p_sd15_openpose.pth` checkpoint to `/models/controlnet/` |
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1. Upload your video and run the pipeline. |
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Additional notes: |
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1. Video shouldn't be too long or too high resolution. We used 576x1024 8 second 30fps videos for testing. |
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1. Set the frame rate to match your input video. This allows audio to match with the output video. |
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1. DWPose will download checkpoint itself on its first run. |
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1. DWPose may get stuck in UI, but the pipeline is actually still running in the background. Check ComfyUI log and your output folder. |
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1. This simple pipeline will scene change due to limited context window. More sophisticated pipeline, such as IP-Adapter for identity preservation, matting, or i2v recurring generation can be explored. |
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