app_gradio.py

import os
import cv2
import torch
import gradio as gr
import torchvision
import warnings
import numpy as np
from PIL import Image, ImageSequence
from moviepy.editor import VideoFileClip
import imageio
from diffusers import (
    TextToVideoSDPipeline,
    AutoencoderKL,
    DDPMScheduler,
    DDIMScheduler,
    UNet3DConditionModel,
)
from transformers import CLIPTokenizer, CLIPTextModel
from diffusers.utils import export_to_video
from typing import List
from text2vid_modded import TextToVideoSDPipelineModded
from invert_utils import ddim_inversion as dd_inversion
from gifs_filter import filter
import subprocess
import spaces


def load_frames(image: Image, mode='RGBA'):
    return np.array([np.array(frame.convert(mode)) for frame in ImageSequence.Iterator(image)])


def run_setup():
    try:
        # Step 1: Install Git LFS
        subprocess.run(["git", "lfs", "install"], check=True)

        # Step 2: Clone the repository
        repo_url = "https://huggingface.co/Hmrishav/t2v_sketch-lora"
        subprocess.run(["git", "clone", repo_url], check=True)

        # Step 3: Move the checkpoint file
        source = "t2v_sketch-lora/checkpoint-2500"
        destination = "./checkpoint-2500/"
        os.rename(source, destination)

        print("Setup completed successfully!")
    except subprocess.CalledProcessError as e:
        print(f"Error during setup: {e}")
    except FileNotFoundError as e:
        print(f"File operation error: {e}")
    except Exception as e:
        print(f"Unexpected error: {e}")

# Automatically run setup during app initialization
run_setup()


def save_gif(frames, path):
    imageio.mimsave(
        path,
        [frame.astype(np.uint8) for frame in frames],
        format="GIF",
        duration=1 / 10,
        loop=0  # 0 means infinite loop
    )

def load_image(imgname, target_size=None):
    pil_img = Image.open(imgname).convert('RGB')
    if target_size:
        if isinstance(target_size, int):
            target_size = (target_size, target_size)
        pil_img = pil_img.resize(target_size, Image.Resampling.LANCZOS)
    return torchvision.transforms.ToTensor()(pil_img).unsqueeze(0)

def prepare_latents(pipe, x_aug):
    with torch.cuda.amp.autocast():
        batch_size, num_frames, channels, height, width = x_aug.shape
        x_aug = x_aug.reshape(batch_size * num_frames, channels, height, width)
        latents = pipe.vae.encode(x_aug).latent_dist.sample()
        latents = latents.view(batch_size, num_frames, -1, latents.shape[2], latents.shape[3])
        latents = latents.permute(0, 2, 1, 3, 4)
    return pipe.vae.config.scaling_factor * latents


@torch.no_grad()
def invert(pipe, inv, load_name, device="cuda", dtype=torch.bfloat16):
    input_img = [load_image(load_name, 256).to(device, dtype=dtype).unsqueeze(1)] * 5
    input_img = torch.cat(input_img, dim=1)
    latents = prepare_latents(pipe, input_img).to(torch.bfloat16)
    inv.set_timesteps(25)
    id_latents = dd_inversion(pipe, inv, video_latent=latents, num_inv_steps=25, prompt="")[-1].to(dtype)
    return torch.mean(id_latents, dim=2, keepdim=True)

def load_primary_models(pretrained_model_path):
    return (
        DDPMScheduler.from_config(pretrained_model_path, subfolder="scheduler"),
        CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer"),
        CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder"),
        AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae"),
        UNet3DConditionModel.from_pretrained(pretrained_model_path, subfolder="unet"),
    )

def initialize_pipeline(model: str, device: str = "cuda"):
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        scheduler, tokenizer, text_encoder, vae, unet = load_primary_models(model)
    pipe = TextToVideoSDPipeline.from_pretrained(
        pretrained_model_name_or_path="damo-vilab/text-to-video-ms-1.7b",
        scheduler=scheduler,
        tokenizer=tokenizer,
        text_encoder=text_encoder.to(device=device, dtype=torch.bfloat16),
        vae=vae.to(device=device, dtype=torch.bfloat16),
        unet=unet.to(device=device, dtype=torch.bfloat16),
    )
    pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
    return pipe, pipe.scheduler

# Initialize the models
LORA_CHECKPOINT = "checkpoint-2500"
os.environ["TORCH_CUDNN_V8_API_ENABLED"] = "1"
device = 'cuda' if torch.cuda.is_available() else 'cpu'
dtype = torch.bfloat16

pipe_inversion, inv = initialize_pipeline(LORA_CHECKPOINT, device)
pipe = TextToVideoSDPipelineModded.from_pretrained(
    pretrained_model_name_or_path="damo-vilab/text-to-video-ms-1.7b",
    scheduler=pipe_inversion.scheduler,
    tokenizer=pipe_inversion.tokenizer,
    text_encoder=pipe_inversion.text_encoder,
    vae=pipe_inversion.vae,
    unet=pipe_inversion.unet,
).to(device)

@spaces.GPU(duration=100)
@torch.no_grad()
def process_video(num_frames, num_seeds, generator, exp_dir, load_name, caption, lambda_):
    pipe_inversion.to(device)
    id_latents = invert(pipe_inversion, inv, load_name).to(device, dtype=dtype)
    latents = id_latents.repeat(num_seeds, 1, 1, 1, 1)
    generator = [torch.Generator(device="cuda").manual_seed(i) for i in range(num_seeds)]
    video_frames = pipe(
        prompt=caption,
        negative_prompt="",
        num_frames=num_frames,
        num_inference_steps=25,
        inv_latents=latents,
        guidance_scale=9,
        generator=generator,
        lambda_=lambda_,
    ).frames

    gifs = []
    for seed in range(num_seeds):
        vid_name = f"{exp_dir}/mp4_logs/vid_{os.path.basename(load_name)[:-4]}-rand{seed}.mp4"
        gif_name = f"{exp_dir}/gif_logs/vid_{os.path.basename(load_name)[:-4]}-rand{seed}.gif"
        
        os.makedirs(os.path.dirname(vid_name), exist_ok=True)
        os.makedirs(os.path.dirname(gif_name), exist_ok=True)
        
        video_path = export_to_video(video_frames[seed], output_video_path=vid_name)
        VideoFileClip(vid_name).write_gif(gif_name)
        
        with Image.open(gif_name) as im:
            frames = load_frames(im)

        frames_collect = np.empty((0, 1024, 1024), int)
        for frame in frames:
            frame = cv2.resize(frame, (1024, 1024))[:, :, :3]
            frame = cv2.cvtColor(255 - frame, cv2.COLOR_RGB2GRAY)
            _, frame = cv2.threshold(255 - frame, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
            frames_collect = np.append(frames_collect, [frame], axis=0)

        save_gif(frames_collect, gif_name)
        gifs.append(gif_name)

    return gifs

def generate_output(image, prompt: str, num_seeds: int = 3, lambda_value: float = 0.5) -> List[str]:
    """Main function to generate output GIFs"""
    exp_dir = "static/app_tmp"
    os.makedirs(exp_dir, exist_ok=True)
    
    # Save the input image temporarily
    temp_image_path = os.path.join(exp_dir, "temp_input.png")
    image.save(temp_image_path)
    
    # Generate the GIFs
    generated_gifs = process_video(
        num_frames=10,
        num_seeds=num_seeds,
        generator=None,
        exp_dir=exp_dir,
        load_name=temp_image_path,
        caption=prompt,
        lambda_=1 - lambda_value
    )
    
    # Apply filtering (assuming filter function is imported)
    filtered_gifs = filter(generated_gifs, temp_image_path)
    
    return filtered_gifs

css=""" """

def create_gradio_interface():
    with gr.Blocks(css=css) as demo:
        with gr.Column():
            gr.Markdown(
            """
                        
            <div align="center" id = "user-content-toc">
            <img align="left" width="70" height="70" src="https://github.com/user-attachments/assets/c61cec76-3c4b-42eb-8c65-f07e0166b7d8" alt="">
            
            # [FlipSketch: Flipping assets Drawings to Text-Guided Sketch Animations](https://hmrishavbandy.github.io/flipsketch-web/)
            ## [Hmrishav Bandyopadhyay](https://hmrishavbandy.github.io/) . [Yi-Zhe Song](https://personalpages.surrey.ac.uk/y.song/)
            </div>

            """
            )
        
        with gr.Row():
            with gr.Column():        
                
                input_sketch = gr.Image(
                    type="pil",
                    label="Selected Sketch",
                    scale=1,
                    interactive=True,
                    height=300  # Fixed height for consistency
                )
               
                motion_prompt = gr.Textbox(
                    label="Prompt",
                    placeholder="Describe the motion...",
                    lines=3
                )

                gr.Examples(
                    examples=[
                        ['./static/examples/sketch1.png', 'The camel walks slowly'],
                        ['./static/examples/sketch2.png', 'The wine in the wine glass sways from side to side'],
                        ['./static/examples/sketch3.png', 'The squirrel is eating a nut'],
                        ['./static/examples/sketch4.png', 'The surfer surfs on the waves'],
                        ['./static/examples/sketch5.png', 'A galloping horse'],
                        ['./static/examples/sketch6.png', 'The cat walks forward'],
                        ['./static/examples/sketch7.png', 'The eagle flies in the sky'],
                        ['./static/examples/sketch8.png', 'The flower is blooming slowly'],
                        ['./static/examples/sketch9.png', 'The reindeer looks around'],
                        ['./static/examples/sketch10.png', 'The cloud floats in the sky'],
                        ['./static/examples/sketch11.png', 'The jazz saxophonist performs on stage with a rhythmic sway, his upper body sways subtly to the rhythm of the music.'],
                        ['./static/examples/sketch12.png', 'The biker rides on the road']
                   ],
                   inputs=[input_sketch, motion_prompt],
                   examples_per_page=1
               )
                
                with gr.Row():
                    num_seeds = gr.Slider(
                        minimum=1, 
                        maximum=10, 
                        value=5, 
                        step=1, 
                        label="Seeds"
                    )
                    lambda_ = gr.Slider(
                        minimum=0, 
                        maximum=1, 
                        value=0.5, 
                        step=0.1, 
                        label="Motion Strength"
                    )
                generate_btn = gr.Button(
                    "Generate Animation",
                    variant="primary",
                    elem_classes="generate-btn",
                    interactive=True,
                )

            with gr.Column():              
                
                output_gallery = gr.Gallery(
                    label="Results",
                    elem_classes="output-gallery",
                    columns=3,
                    rows=2,
                    height="auto",
                    allow_preview=False,  # Disable preview expansion
                    show_share_button=False,
                    object_fit="cover",
                    preview=False
                )
            
           
        
        # Event handlers
        example_generate_btn.click(
            fn=generate_output,
            inputs=[
                input_sketch,
                motion_prompt,
                num_seeds,
                lambda_
            ],
            outputs=output_gallery
        )
    
    return demo

# Launch the app
if __name__ == "__main__":
    demo = create_gradio_interface()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        show_api=False,
        ssr_mode=False
    )