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AnimateDiff-Lightning
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import gradio as gr
import torch
import os
import spaces
import uuid

from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler
from diffusers.utils import export_to_video
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from PIL import Image

# Constants
base = "frankjoshua/toonyou_beta6"
repo = "ByteDance/AnimateDiff-Lightning"
checkpoints = {
    "1-Step" : ["animatediff_lightning_1step_diffusers.safetensors", 1],
    "2-Step" : ["animatediff_lightning_2step_diffusers.safetensors", 2],
    "4-Step" : ["animatediff_lightning_4step_diffusers.safetensors", 4],
    "8-Step" : ["animatediff_lightning_8step_diffusers.safetensors", 8],
}
loaded = None

# Ensure model and scheduler are initialized in GPU-enabled function
if torch.cuda.is_available():
    device = "cuda"
    dtype = torch.float16
    adapter = MotionAdapter().to(device, dtype)
    pipe = AnimateDiffPipeline.from_pretrained(base, motion_adapter=adapter, torch_dtype=dtype).to(device)
    pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear")
else:
    raise NotImplementedError("No GPU detected!")

# Function 
@spaces.GPU(enable_queue=True)
def generate_image(prompt, ckpt):
    global loaded
    print(prompt, ckpt)

    checkpoint = checkpoints[ckpt][0]
    num_inference_steps = checkpoints[ckpt][1]

    if loaded != num_inference_steps:
        pipe.unet.load_state_dict(load_file(hf_hub_download(repo, checkpoint), device=device), strict=False)
        loaded = num_inference_steps

    output = pipe(prompt=prompt, guidance_scale=1.0, num_inference_steps=num_inference_steps)
    
    name = str(uuid.uuid4()).replace("-", "")
    path = f"/tmp/{name}.mp4"

    export_to_video(output.frames[0], path, fps=10)

    return path



# Gradio Interface

with gr.Blocks(css="style.css") as demo:
    gr.HTML("<h1><center>AnimateDiff-Lightning ⚡</center></h1>")
    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>")
    with gr.Group():
        with gr.Row():
            prompt = gr.Textbox(label='Enter your prompt (English)', scale=8)
            ckpt = gr.Dropdown(label='Select inference steps',choices=['1-Step', '2-Step', '4-Step', '8-Step'], value='4-Step', interactive=True)
            submit = gr.Button(scale=1, variant='primary')
    video = gr.Video(label='AnimateDiff-Lightning Generated Image')

    prompt.submit(
        fn=generate_image,
        inputs=[prompt, ckpt],
        outputs=video,
    )
    submit.click(
        fn=generate_image,
        inputs=[prompt, ckpt],
        outputs=video,
    )

demo.queue().launch()