app_bk.py

import gradio as gr
import os
import spaces
import sys
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 = [
    ["A fairy tends to enchanted, glowing flowers.", 'huggingface-hps-aesthetic', 8, 400, 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, 206, 384, 512, 12.0, 25, 1.0, 24, 10],
    ["A raccoon playing a guitar under a blossoming cherry tree.", 'huggingface-hps-aesthetic', 8, 204, 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, 205, 384, 512, 12.0, 25, 1.0, 24, 10],
    ["A talking bird with shimmering feathers and a melodious voice leads an adventure to find a legendary treasure, guiding through enchanted forests, ancient ruins, and mystical challenges.",
     "huggingface-pickscore", 16, 204, 384, 512, 12.0, 25, 1.0, 24, 10]
]

model = None # Placeholder for model

@spaces.GPU(duration=70)
def gradio_main_fn(prompt, 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,
                    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=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")

@spaces.GPU(duration=120)
def setup_model_progress(lora_model, lora_rank):
    global model

    # Disable buttons and show loading indicator
    yield (gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=False), "Loading model...")

    model = setup_model(lora_model, lora_rank)  # Ensure you pass the necessary parameters to the setup_model function
    
    # Enable buttons after loading and update indicator
    yield (gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True), "Model loaded successfully")

@spaces.GPU(duration=180)
def generate_example(prompt, lora_model, lora_rank, seed, height, width, unconditional_guidance_scale, ddim_steps, ddim_eta,
                   frames, savefps):
    global model
    model = setup_model(lora_model, lora_rank)
    video_path = main_fn(prompt=prompt,
                        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=model)
    return video_path

custom_css = """
    #centered {
        display: flex;
        justify-content: center;
    }
    .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://zenai.biz">Website</a><sup>*</sup>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
                    <a href="https://us.zenai.biz">Arena</a><sup>*</sup>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
                    <a href="https://github.com/Bluenot3">GitHub</a><sup>*</sup>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
                </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://zenai.biz" class="btn btn-outline-primary">
                    <i class="fa-solid fa-file-pdf"></i> Website
                </a>
                <a href="https://us.zenai.biz" class="btn btn-outline-danger">
                    <i class="fa-solid fa-video"></i> Arena
                </a>
                <a href="https://github.com/Bluenot3" class="btn btn-outline-secondary">
                    <i class="fa-brands fa-github"></i> GitHub
                </a>
                </div>
                """
            )

    with gr.Row(elem_id="centered"):
        with gr.Column(scale=0.3, elem_id="params"):
            lora_model = gr.Dropdown(
                label="VADER Model",
                choices=["huggingface-pickscore", "huggingface-hps-aesthetic", "Base Model"],
                value="huggingface-pickscore"
            )
            lora_rank = gr.Slider(minimum=8, maximum=16, label="LoRA Rank", step = 8, value=16)
            load_btn = gr.Button("Load Model")
            # Add a label to show the loading indicator
            loading_indicator = gr.Label(value="", label="Loading Indicator")

        with gr.Column(scale=0.3):
            output_video = gr.Video(elem_id="image-upload")
            
    with gr.Row(elem_id="centered"):
        with gr.Column(scale=0.6):      
            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.")

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

            run_btn = gr.Button("Run Inference")

            
            with gr.Row():
                height = gr.Slider(minimum=0, maximum=1024, label="Height", step = 16, value=384)
                width = gr.Slider(minimum=0, maximum=1024, 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=60, label="Save FPS", step = 1, value=10)
            
            
            with gr.Row():
                DDIM_Steps = gr.Slider(minimum=0, maximum=100, 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])
                


            load_btn.click(fn=setup_model_progress, inputs=[lora_model, lora_rank], outputs=[load_btn, run_btn, reset_btn, loading_indicator])
            run_btn.click(fn=gradio_main_fn, 
                        inputs=[prompt, 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=generate_example,
                    run_on_click=False,
                    cache_examples="lazy",
                    )

demo.launch(share=True)