app.py

import gradio as gr
import os
import spaces
import sys
from copy import deepcopy
sys.path.append('./VADER-VideoCrafter/scripts/main')
sys.path.append('./VADER-VideoCrafter/scripts')
sys.path.append('./VADER-VideoCrafter')


from train_t2v_lora import main_fn, setup_model

examples = [
    ["Fairy and Magical Flowers: A fairy tends to enchanted, glowing flowers.", 'huggingface-hps-aesthetic', 
     8, 901, 384, 512, 12.0, 25, 1.0, 24, 10],
    ["A cat playing an electric guitar in a loft with industrial-style decor and soft, multicolored lights.", 
     'huggingface-hps-aesthetic', 8, 208, 384, 512, 12.0, 25, 1.0, 24, 10],
    ["A raccoon playing a guitar under a blossoming cherry tree.", 
     'huggingface-hps-aesthetic', 8, 180, 384, 512, 12.0, 25, 1.0, 24, 10],
    ["A raccoon playing an electric bass in a garage band setting.", 
     'huggingface-hps-aesthetic', 8, 400, 384, 512, 12.0, 25, 1.0, 24, 10],
    ["A talking bird with shimmering feathers and a melodious voice finds a legendary treasure, guiding through enchanted forests, ancient ruins, and mystical challenges.",
     "huggingface-pickscore", 16, 200, 384, 512, 12.0, 25, 1.0, 24, 10],
    ["A snow princess stands on the balcony of her ice castle, her hair adorned with delicate snowflakes, overlooking her serene realm.",
     "huggingface-pickscore", 16, 400, 384, 512, 12.0, 25, 1.0, 24, 10],
    ["A mermaid with flowing hair and a shimmering tail discovers a hidden underwater kingdom adorned with coral palaces, glowing pearls, and schools of colorful fish, encountering both wonders and dangers along the way.",
     "huggingface-pickscore", 16, 800, 384, 512, 12.0, 25, 1.0, 24, 10],
]

model = setup_model()

@spaces.GPU(duration=180)
def gradio_main_fn(prompt, lora_model, lora_rank, seed, height, width, unconditional_guidance_scale, ddim_steps, ddim_eta,
                   frames, savefps):
    global model
    if model is None:
        return "Model is not loaded. Please load the model first."
    video_path = main_fn(prompt=prompt,
                    lora_model=lora_model,
                    lora_rank=int(lora_rank),
                    seed=int(seed),
                    height=int(height), 
                    width=int(width),
                    unconditional_guidance_scale=float(unconditional_guidance_scale),
                    ddim_steps=int(ddim_steps),
                    ddim_eta=float(ddim_eta), 
                    frames=int(frames),
                    savefps=int(savefps),
                    model=deepcopy(model))

    return video_path

def reset_fn():
    return ("A mermaid with flowing hair and a shimmering tail discovers a hidden underwater kingdom adorned with coral palaces, glowing pearls, and schools of colorful fish, encountering both wonders and dangers along the way.", 
            200, 384, 512, 12.0, 25, 1.0, 24, 16, 10, "huggingface-pickscore")

def update_lora_rank(lora_model):
    if lora_model == "huggingface-pickscore":
        return gr.update(value=16)
    elif lora_model == "huggingface-hps-aesthetic":
        return gr.update(value=8)
    else: # "Base Model"
        return gr.update(value=8)

def update_dropdown(lora_rank):
    if lora_rank == 16:
        return gr.update(value="huggingface-pickscore")
    elif lora_rank == 8:
        return gr.update(value="huggingface-hps-aesthetic")
    else: # 0
        return gr.update(value="Base Model")

custom_css = """
    #centered {
        display: flex;
        justify-content: center;
        width: 60%;
        margin: 0 auto;
    }
    .column-centered {
        display: flex;
        flex-direction: column;
        align-items: center;
        width: 60%;
    }
    #image-upload {
        flex-grow: 1;
    }
    #params .tabs {
        display: flex;
        flex-direction: column;
        flex-grow: 1;
    }
    #params .tabitem[style="display: block;"] {
        flex-grow: 1;
        display: flex !important;
    }
    #params .gap {
        flex-grow: 1;
    }
    #params .form {
        flex-grow: 1 !important;
    }
    #params .form > :last-child{
        flex-grow: 1;
    }
"""

with gr.Blocks(css=custom_css) as demo:
    with gr.Row():
        with gr.Column():
            gr.HTML(
                """
                <h1 style='text-align: center; font-size: 3.2em; margin-bottom: 0.5em; font-family: Arial, sans-serif; margin: 20px;'>
                    Video Diffusion Alignment via Reward Gradient
                </h1>
                """
            )
            gr.HTML(
                """
                <style>
                    body {
                        font-family: Arial, sans-serif;
                        text-align: center;
                        margin: 50px;
                    }
                    a {
                        text-decoration: none !important;
                        color: black !important;
                    }
                </style>
                <body>
                <div style="font-size: 1.4em; margin-bottom: 0.5em; ">
                    <a href="https://mihirp1998.github.io">Mihir Prabhudesai</a><sup>*</sup>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
                    <a href="https://russellmendonca.github.io/">Russell Mendonca</a><sup>*</sup>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
                    <a href="mailto: [email protected]">Zheyang Qin</a><sup>*</sup>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
                    <a href="https://www.cs.cmu.edu/~katef/">Katerina Fragkiadaki</a><sup></sup>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
                    <a href="https://www.cs.cmu.edu/~dpathak/">Deepak Pathak</a><sup></sup>


                </div>
                <div style="font-size: 1.3em; font-style: italic;">
                    Carnegie Mellon University
                </div>
                </body>
                """
            )
            gr.HTML(
                """
                <head>
                <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0-beta3/css/all.min.css">

                <style>
                .button-container {
                    display: flex;
                    justify-content: center;
                    gap: 10px;
                    margin-top: 10px;
                }

                .button-container a {
                    display: inline-flex;
                    align-items: center;
                    padding: 10px 20px;
                    border-radius: 30px;
                    border: 1px solid #ccc;
                    text-decoration: none;
                    color: #333 !important;
                    font-size: 16px;
                    text-decoration: none !important;
                }

                .button-container a i {
                    margin-right: 8px;
                }
                </style>
                </head>

                <div class="button-container">
                <a href="https://arxiv.org/abs/2407.08737" class="btn btn-outline-primary">
                    <i class="fa-solid fa-file-pdf"></i> Paper
                </a>
                <a href="https://vader-vid.github.io/" class="btn btn-outline-danger">
                    <i class="fa-solid fa-video"></i> Website
                <a href="https://github.com/mihirp1998/VADER" class="btn btn-outline-secondary">
                    <i class="fa-brands fa-github"></i> Code
                </a>
                </div>
                """
            )

    with gr.Row(elem_id="centered"):
        with gr.Column(elem_id="params"):
            lora_model = gr.Dropdown(
                label="VADER Model",
                choices=["huggingface-pickscore", "huggingface-hps-aesthetic"],
                value="huggingface-pickscore"
            )
            lora_rank = gr.Slider(minimum=8, maximum=16, label="LoRA Rank", step = 8, value=16)
            prompt = gr.Textbox(placeholder="Enter prompt text here", lines=4, label="Text Prompt",
                                value="A mermaid with flowing hair and a shimmering tail discovers a hidden underwater kingdom adorned with coral palaces, glowing pearls, and schools of colorful fish, encountering both wonders and dangers along the way.")
            run_btn = gr.Button("Run Inference")

        with gr.Column():
            output_video = gr.Video(elem_id="image-upload")
            
    with gr.Row(elem_id="centered"):
        with gr.Column():      
           

            seed = gr.Slider(minimum=0, maximum=65536, label="Seed", step = 1, value=200)

            with gr.Row():
                height = gr.Slider(minimum=0, maximum=512, label="Height", step = 16, value=384)
                width = gr.Slider(minimum=0, maximum=512, label="Width", step = 16, value=512)

            with gr.Row():
                frames = gr.Slider(minimum=0, maximum=50, label="Frames", step = 1, value=24)
                savefps = gr.Slider(minimum=0, maximum=30, label="Save FPS", step = 1, value=10)
            
            
            with gr.Row():
                DDIM_Steps = gr.Slider(minimum=0, maximum=50, label="DDIM Steps", step = 1, value=25)
                unconditional_guidance_scale = gr.Slider(minimum=0, maximum=50, label="Guidance Scale", step = 0.1, value=12.0)
                DDIM_Eta = gr.Slider(minimum=0, maximum=1, label="DDIM Eta", step = 0.01, value=1.0)

            # reset button
            reset_btn = gr.Button("Reset")
            
            reset_btn.click(fn=reset_fn, outputs=[prompt, seed, height, width, unconditional_guidance_scale, DDIM_Steps, DDIM_Eta, frames, lora_rank, savefps, lora_model])
                

            run_btn.click(fn=gradio_main_fn, 
                        inputs=[prompt, lora_model, lora_rank,
                                seed, height, width, unconditional_guidance_scale, 
                                DDIM_Steps, DDIM_Eta, frames, savefps],
                        outputs=output_video
                        )
            
            lora_model.change(fn=update_lora_rank, inputs=lora_model, outputs=lora_rank)
            lora_rank.change(fn=update_dropdown, inputs=lora_rank, outputs=lora_model)

            gr.Examples(examples=examples,
                    inputs=[prompt, lora_model, lora_rank, seed, 
                            height, width, unconditional_guidance_scale, 
                            DDIM_Steps, DDIM_Eta, frames, savefps],
                    outputs=output_video,
                    fn=gradio_main_fn,
                    run_on_click=False,
                    cache_examples="lazy",
                    )

demo.launch(share=True)