app.py

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
bases = {
    "Cartoon": "frankjoshua/toonyou_beta6",
    "Realistic": "emilianJR/epiCRealism", 
    "3d": "Lykon/DreamShaper",
    "Anime": "Yntec/mistoonAnime2"
}
step_loaded = None
base_loaded = "Realistic"
motion_loaded = None

device = "cpu"
dtype = torch.float32
pipe = AnimateDiffPipeline.from_pretrained(bases[base_loaded], torch_dtype=dtype).to(device)
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear")

def generate_image(prompt, base="Realistic", motion="", step=8, duration=5, progress=gr.Progress()):
    global step_loaded
    global base_loaded
    global motion_loaded
    print(prompt, base, step)

    if step_loaded != step:
        repo = "ByteDance/AnimateDiff-Lightning"
        ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
        pipe.unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device), strict=False)
        step_loaded = step

    if base_loaded != base:
        pipe.unet.load_state_dict(torch.load(hf_hub_download(bases[base], "unet/diffusion_pytorch_model.bin"), map_location=device), strict=False)
        base_loaded = base

    if motion_loaded != motion:
        pipe.unload_lora_weights()
        if motion != "":
            pipe.load_lora_weights(motion, adapter_name="motion")
            pipe.set_adapters(["motion"], [0.7])
        motion_loaded = motion

    progress((0, step))
    def progress_callback(i, t, z):
        progress((i+1, step))

    output = pipe(prompt=prompt, guidance_scale=1.2, num_inference_steps=step, callback=progress_callback, callback_steps=1)

    name = str(uuid.uuid4()).replace("-", "")
    path = f"/tmp/{name}.mp4"
    export_to_video(output.frames[0], path, fps=10, duration=duration)
    return path

# Gradio Interface
with gr.Blocks() as demo:
    gr.HTML("<h1><center>AnimateDiff on CPU</center></h1>")
    with gr.Group():
        with gr.Row():
            prompt = gr.Textbox(label='Prompt')
        with gr.Row():
            select_base = gr.Dropdown(
                label='Base model',
                choices=["Cartoon", "Realistic", "3d", "Anime"],
                value=base_loaded,
                interactive=True
            )
            select_motion = gr.Dropdown(
                label='Motion',
                choices=[
                    ("Default", ""),
                    ("Zoom in", "guoyww/animatediff-motion-lora-zoom-in"),
                    ("Zoom out", "guoyww/animatediff-motion-lora-zoom-out"),
                    ("Tilt up", "guoyww/animatediff-motion-lora-tilt-up"),
                    ("Tilt down", "guoyww/animatediff-motion-lora-tilt-down"),
                    ("Pan left", "guoyww/animatediff-motion-lora-pan-left"),
                    ("Pan right", "guoyww/animatediff-motion-lora-pan-right"),
                    ("Roll left", "guoyww/animatediff-motion-lora-rolling-anticlockwise"),
                    ("Roll right", "guoyww/animatediff-motion-lora-rolling-clockwise"),
                ],
                value="",
                interactive=True
            )
            select_step = gr.Dropdown(
                label='Inference steps',
                choices=[('1-Step', 1), ('2-Step', 2), ('4-Step', 4), ('8-Step', 8)],
                value=4,
                interactive=True
            )
            slider_duration = gr.Slider(
                label='Video Duration (seconds)',
                minimum=1,
                maximum=10,
                value=5,
                step=1,
                interactive=True
            )
            submit = gr.Button(scale=1, variant='primary')
    video = gr.Video(
        label='Generated Video',
        autoplay=True,
        height=512,
        width=512,
        elem_id="video_output"
    )

    submit.click(
        fn=generate_image,
        inputs=[prompt, select_base, select_motion, select_step, slider_duration],
        outputs=[video]
    )

demo.queue().launch()