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

# Ensure model and scheduler are initialized in GPU-enabled function
if not torch.cuda.is_available():
    raise NotImplementedError("No GPU detected!")

device = "cuda"
dtype = torch.float16
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")

# Safety checkers
from transformers import CLIPFeatureExtractor

feature_extractor = CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32")

# Function 
@spaces.GPU(duration=60,queue=False)
def generate_image(prompt, base="Realistic", motion="", step=8, progress=gr.Progress()):
    global step_loaded
    global base_loaded
    global motion_loaded
    print(prompt, base, step)

    step = int(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)
    return path

# Gradio Interface
css = """
    body {font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; background-color: #f4f4f9; color: #333;}
    h1 {color: #333; text-align: center; margin-bottom: 20px;}
    .gradio-container {max-width: 800px; margin: auto; padding: 20px; background: #fff; box-shadow: 0px 0px 20px rgba(0,0,0,0.1); border-radius: 10px;}
    .gr-input {margin-bottom: 15px;}
    .gr-button {width: 100%; background-color: #4CAF50; color: white; border: none; padding: 10px 20px; text-align: center; text-decoration: none; display: inline-block; font-size: 16px; border-radius: 5px; cursor: pointer; transition: background-color 0.3s;}
    .gr-button:hover {background-color: #45a049;}
    .gr-video {margin-top: 20px;}
    .gr-examples {margin-top: 30px;}
    .gr-examples .gr-example {display: inline-block; width: 100%; text-align: center; padding: 10px; background: #eaeaea; border-radius: 5px; margin-bottom: 10px;}

    .container {display: flex; flex-wrap: wrap;}
    .inputs, .output {padding: 20px;}
    .inputs {flex: 1; min-width: 300px;}
    .output {flex: 1; min-width: 300px;}

    @media (max-width: 768px) {
        .container {flex-direction: column-reverse;}
    }
    .svelte-1ybb3u7, .svelte-1clup3e {display: none !important;}
"""

with gr.Blocks(css=css) as demo:
    gr.HTML("<h1>Instant⚡ Text to Video</h1>")
    with gr.Row(elem_id="container"):
        with gr.Column(elem_id="inputs"):
            prompt = gr.Textbox(label='Prompt', placeholder="Enter text to generate video...", elem_id="gr-input")
            select_base = gr.Dropdown(
                label='Base model',
                choices=["Cartoon", "Realistic", "3d", "Anime"],
                value=base_loaded,
                interactive=True,
                elem_id="gr-input"
            )
            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="guoyww/animatediff-motion-lora-zoom-in",
                interactive=True,
                elem_id="gr-input"
            )
            select_step = gr.Dropdown(
                label='Inference steps',
                choices=[('1-Step', 1), ('2-Step', 2), ('4-Step', 4), ('8-Step', 8)],
                value=4,
                interactive=True,
                elem_id="gr-input"
            )
            submit = gr.Button("Generate Video", variant='primary', elem_id="gr-button")
        with gr.Column(elem_id="output"):
            video = gr.Video(label='AnimateDiff-Lightning', autoplay=True, height=512, width=512, elem_id="gr-video")

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

    gr.Examples(
        examples=[
            ["Focus: Eiffel Tower (Animate: Clouds moving)"],
            ["Focus: Trees In forest (Animate: Lion running)"],
            ["Focus: Astronaut in Space"],
            ["Focus: Group of Birds in sky (Animate:  Birds Moving) (Shot From distance)"],
            ["Focus:  Statue of liberty (Shot from Drone) (Animate: Drone coming toward statue)"],
            ["Focus: Panda in Forest (Animate: Drinking Tea)"],
            ["Focus: Kids Playing (Season: Winter)"],
            ["Focus: Cars in Street (Season: Rain, Daytime) (Shot from Distance) (Movement: Cars running)"]
        ],
        fn=generate_image,
        inputs=[prompt],
        outputs=video,
        cache_examples=True,
        elem_id="gr-examples"
    )

demo.queue().launch(show_error=True)