app.py

import os
import torch
import gradio as gr
from PIL import Image, ImageOps

from huggingface_hub import snapshot_download
from pyramid_dit import PyramidDiTForVideoGeneration
from diffusers.utils import export_to_video

import spaces 
import uuid

# Constants
MODEL_PATH = "pyramid-flow-model"
MODEL_REPO = "rain1011/pyramid-flow-sd3"
MODEL_VARIANT = "diffusion_transformer_384p"
MODEL_DTYPE = "bf16"

def center_crop(image, target_width, target_height):
    width, height = image.size
    aspect_ratio_target = target_width / target_height
    aspect_ratio_image = width / height

    if aspect_ratio_image > aspect_ratio_target:
        # Crop the width (left and right)
        new_width = int(height * aspect_ratio_target)
        left = (width - new_width) // 2
        right = left + new_width
        top, bottom = 0, height
    else:
        # Crop the height (top and bottom)
        new_height = int(width / aspect_ratio_target)
        top = (height - new_height) // 2
        bottom = top + new_height
        left, right = 0, width

    image = image.crop((left, top, right, bottom))
    return image

# Download and load the model
def load_model():
    if not os.path.exists(MODEL_PATH):
        snapshot_download(MODEL_REPO, local_dir=MODEL_PATH, local_dir_use_symlinks=False, repo_type='model')
    
    model = PyramidDiTForVideoGeneration(
        MODEL_PATH,
        MODEL_DTYPE,
        model_variant=MODEL_VARIANT,
    )
    
    model.vae.to("cuda")
    model.dit.to("cuda")
    model.text_encoder.to("cuda")
    model.vae.enable_tiling()
    
    return model

# Global model variable
model = load_model()

# Text-to-video generation function
@spaces.GPU(duration=120)
def generate_video(prompt, image=None, duration=5, guidance_scale=9, video_guidance_scale=5, progress=gr.Progress(track_tqdm=True)):
    multiplier = 3
    temp = int(duration * multiplier) + 1  # Convert seconds to temp value (assuming 24 FPS)
    torch_dtype = torch.bfloat16 if MODEL_DTYPE == "bf16" else torch.float32
    if(image):
        cropped_image = center_crop(image, 640, 384)
        resized_image = cropped_image.resize((640, 384))
        with torch.no_grad(), torch.cuda.amp.autocast(enabled=True, dtype=torch_dtype):
            frames = model.generate_i2v(
                prompt=prompt,
                input_image=resized_image,
                num_inference_steps=[10, 10, 10],
                temp=temp,
                video_guidance_scale=video_guidance_scale,
                output_type="pil",
                save_memory=True,
            )
    else:
        with torch.no_grad(), torch.cuda.amp.autocast(enabled=True, dtype=torch_dtype):
            frames = model.generate(
                prompt=prompt,
                num_inference_steps=[20, 20, 20],
                video_num_inference_steps=[10, 10, 10],
                height=384,
                width=640,
                temp=temp,
                guidance_scale=guidance_scale,
                video_guidance_scale=video_guidance_scale,
                output_type="pil",
                save_memory=True,
            )
    output_path = f"{str(uuid.uuid4())}_output_video.mp4"
    export_to_video(frames, output_path, fps=24)
    return output_path

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Pyramid Flow 384p demo")
    gr.Markdown("Pyramid Flow is a training-efficient **Autoregressive Video Generation** model based on **Flow Matching**. It is trained only on open-source datasets within 20.7k A100 GPU hours")
    gr.Markdown("[[Paper](https://arxiv.org/pdf/2410.05954)], [[Model](https://huggingface.co/rain1011/pyramid-flow-sd3)], [[Code](https://github.com/jy0205/Pyramid-Flow)] [[Project Page]](https://pyramid-flow.github.io)")
    
    with gr.Row():
        with gr.Column():
            with gr.Accordion("Image to Video (optional)", open=False):
                i2v_image = gr.Image(type="pil", label="Input Image")
            t2v_prompt = gr.Textbox(label="Prompt")
            with gr.Accordion("Advanced settings", open=False):
                t2v_duration = gr.Slider(minimum=1, maximum=10, value=5, step=1, label="Duration (seconds)")
                t2v_guidance_scale = gr.Slider(minimum=1, maximum=15, value=7, step=0.1, label="Guidance Scale")
                t2v_video_guidance_scale = gr.Slider(minimum=1, maximum=15, value=5, step=0.1, label="Video Guidance Scale")
            t2v_generate_btn = gr.Button("Generate Video")
        with gr.Column():
            t2v_output = gr.Video(label="Generated Video")
    gr.Examples(
        examples=[
            "A futuristic explorer, 30 years old, travels across distant galaxies in a sleek silver space suit, gliding through a glowing nebula. The scene is illuminated by vibrant starbursts and cosmic dust, captured with a futuristic drone in ultra-high-definition, showcasing vibrant purples and blues",
            "In a serene winter landscape, a futuristic metropolis hums with life. The camera glides along an icy street as citizens, wrapped in advanced thermal suits, enjoy the wintry scene. Holographic advertisements flicker above snow-covered buildings, while sleek flying vehicles zip overhead. In the background, delicate crystalline structures refract light through the snowflakes."
        ],
        fn=generate_video,
        inputs=t2v_prompt,
        outputs=t2v_output,
        cache_examples="lazy"
    )
    t2v_generate_btn.click(
        generate_video,
        inputs=[t2v_prompt, i2v_image, t2v_duration, t2v_guidance_scale, t2v_video_guidance_scale],
        outputs=t2v_output
    )

demo.launch()