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import gradio as gr |
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import torch |
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import os |
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import spaces |
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import uuid |
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from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler |
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from diffusers.utils import export_to_video |
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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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from PIL import Image |
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base = "frankjoshua/toonyou_beta6" |
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repo = "ByteDance/AnimateDiff-Lightning" |
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checkpoints = { |
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"1-Step" : ["animatediff_lightning_1step_diffusers.safetensors", 1], |
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"2-Step" : ["animatediff_lightning_2step_diffusers.safetensors", 2], |
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"4-Step" : ["animatediff_lightning_4step_diffusers.safetensors", 4], |
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"8-Step" : ["animatediff_lightning_8step_diffusers.safetensors", 8], |
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} |
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loaded = None |
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if torch.cuda.is_available(): |
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device = "cuda" |
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dtype = torch.float16 |
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adapter = MotionAdapter().to(device, dtype) |
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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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else: |
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raise NotImplementedError("No GPU detected!") |
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@spaces.GPU(enable_queue=True) |
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def generate_image(prompt, ckpt): |
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global loaded |
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print(prompt, ckpt) |
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checkpoint = checkpoints[ckpt][0] |
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num_inference_steps = checkpoints[ckpt][1] |
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if loaded != num_inference_steps: |
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pipe.unet.load_state_dict(load_file(hf_hub_download(repo, checkpoint), device=device), strict=False) |
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loaded = num_inference_steps |
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output = pipe(prompt=prompt, guidance_scale=1.0, num_inference_steps=num_inference_steps) |
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name = str(uuid.uuid4()).replace("-", "") |
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path = f"/tmp/{name}.mp4" |
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export_to_video(output.frames[0], path, fps=10) |
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return path |
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with gr.Blocks(css="style.css") as demo: |
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gr.HTML("<h1><center>AnimateDiff-Lightning ⚡</center></h1>") |
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gr.HTML("<p><center>Lightning-fast text-to-video generation</center></p><p><center><a href='https://huggingface.co/ByteDance/AnimateDiff-Lightning'>https://huggingface.co/ByteDance/AnimateDiff-Lightning</a></center></p>") |
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with gr.Group(): |
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with gr.Row(): |
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prompt = gr.Textbox(label='Enter your prompt (English)', scale=8) |
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ckpt = gr.Dropdown(label='Select inference steps',choices=['1-Step', '2-Step', '4-Step', '8-Step'], value='4-Step', interactive=True) |
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submit = gr.Button(scale=1, variant='primary') |
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video = gr.Video(label='AnimateDiff-Lightning Generated Image') |
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prompt.submit( |
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fn=generate_image, |
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inputs=[prompt, ckpt], |
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outputs=video, |
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) |
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submit.click( |
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fn=generate_image, |
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inputs=[prompt, ckpt], |
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outputs=video, |
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) |
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demo.queue().launch() |
