app.py

import spaces
import os

import torch

import subprocess
package_to_uninstall = "flash-attn"
command = ["python", "-m", "pip", "uninstall", "-y", package_to_uninstall]
result = subprocess.run(command, check=True, capture_output=True, text=True)

import gradio as gr
import imageio
import time
import random
import gc
from PIL import Image

# Import necessary components from the cloned repository
from skyreels_v2_infer.modules import download_model
from skyreels_v2_infer.pipelines import Image2VideoPipeline, resizecrop

# --- Global Configuration & Model Loading ---
#MODEL_ID = "Skywork/SkyReels-V2-I2V-14B-720P"
MODEL_ID = "Skywork/SkyReels-V2-I2V-1.3B-540P"
HEIGHT = 540
WIDTH = 540
OUTPUT_DIR = "video_out"

# Create output directory
os.makedirs(OUTPUT_DIR, exist_ok=True)

print("Downloading and loading model... This may take a while.")
# Download model files to the cache
cached_model_path = download_model(MODEL_ID)

# Load the pipeline. This is done once when the Space starts.
# We enable offload by default to be compatible with GPUs like A10G-Large (24GB VRAM)
pipe = Image2VideoPipeline(
    model_path=cached_model_path,
    dit_path=cached_model_path,
    use_usp=False,
    offload=True  # Enable CPU offload to save VRAM
)
print("Model loaded successfully.")

# --- Inference Function ---
@spaces.GPU(required=True)
def generate_video(input_image, prompt, guidance_scale, inference_steps, num_frames, fps, seed):
    """
    Main function to generate video from an image and a prompt.
    """
    if input_image is None:
        raise gr.Error("You must upload an initial image.")
    if not prompt:
        raise gr.Error("Prompt cannot be empty.")

    # Use provided seed or generate a random one
    if seed == -1:
        seed = random.randint(0, 2**32 - 1)
    
    generator = torch.Generator(device="cuda").manual_seed(seed)
    
    # Prepare the input image (resize and crop)
    image = Image.fromarray(input_image).convert("RGB")
    processed_image = resizecrop(image, HEIGHT, WIDTH)

    # Define a default negative prompt
    negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, worst quality, low quality, JPEG compression residue, ugly, deformed."

    # Set up generation parameters
    kwargs = {
        "image": processed_image,
        "prompt": prompt,
        "negative_prompt": negative_prompt,
        "num_frames": num_frames,
        "num_inference_steps": inference_steps,
        "guidance_scale": guidance_scale,
        "shift": 8.0, # Default value from original script
        "generator": generator,
        "height": HEIGHT,
        "width": WIDTH,
    }
    
    print(f"Generating video with seed: {seed}")
    start_time = time.time()
    
    # Run inference
    with torch.cuda.amp.autocast(dtype=pipe.transformer.dtype), torch.no_grad():
        video_frames = pipe(**kwargs)[0]

    end_time = time.time()
    print(f"Inference took {end_time - start_time:.2f} seconds.")

    # Save the output video
    # Sanitize prompt for filename
    safe_prompt = "".join(c for c in prompt if c.isalnum() or c in " _-").strip()[:50]
    output_filename = f"{safe_prompt}_{seed}.mp4"
    output_path = os.path.join(OUTPUT_DIR, output_filename)
    
    imageio.mimwrite(output_path, video_frames, fps=fps, quality=8, output_params=["-loglevel", "error"])
    
    print(f"Video saved to {output_path}")
    
    # Clean up memory
    gc.collect()
    torch.cuda.empty_cache()
    
    return output_path

# --- Gradio UI ---
with gr.Blocks(css="footer {display: none !important}") as demo:
    gr.Markdown(
        """
        # SkyReels-V2 Image-to-Video Generator
        ### Model: Skywork/SkyReels-V2-I2V-14B-720P
        This Space demonstrates the SkyReels V2 model for generating video from a single starting image and a text prompt.
        **Note:** This is a very large model. Generation can take several minutes, even on powerful GPUs.
        """
    )
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(type="numpy", label="Initial Image")
            prompt = gr.Textbox(label="Prompt", placeholder="e.g., A cinematic shot of a car driving on a rainy street at night.")
            
            with gr.Accordion("Advanced Settings", open=False):
                guidance_scale = gr.Slider(minimum=1.0, maximum=15.0, value=6.0, step=0.5, label="Guidance Scale")
                inference_steps = gr.Slider(minimum=10, maximum=100, value=30, step=1, label="Inference Steps")
                num_frames = gr.Slider(minimum=25, maximum=145, value=97, step=8, label="Number of Frames")
                fps = gr.Slider(minimum=8, maximum=30, value=24, step=1, label="Frames Per Second (FPS)")
                seed = gr.Number(value=-1, label="Seed (-1 for random)")
        
        with gr.Column():
            output_video = gr.Video(label="Generated Video")
            run_button = gr.Button("Generate Video", variant="primary")

    gr.Examples(
        examples=[
            ["./examples/car.png", "A cinematic shot of a car driving on a rainy street at night, neon lights reflecting on the wet pavement.", 7.0, 30, 97, 24, 12345],
            ["./examples/castle.png", "An epic fantasy castle in the mountains, dragons flying in the sky, cinematic lighting.", 6.0, 40, 97, 12, 54321],
        ],
        inputs=[input_image, prompt, guidance_scale, inference_steps, num_frames, fps, seed],
        outputs=output_video,
        fn=generate_video,
        cache_examples=False, # Set to True if you have GPU and want to pre-process examples
    )

    # Add example images to your space in a folder named 'examples' for this to work
    # Or simply remove the gr.Examples block.

    run_button.click(fn=generate_video, inputs=[input_image, prompt, guidance_scale, inference_steps, num_frames, fps, seed], outputs=output_video)

if __name__ == "__main__":
    demo.launch()