app.py

import gradio as gr
import torch
from huggingface_hub import snapshot_download

from xora.models.autoencoders.causal_video_autoencoder import CausalVideoAutoencoder
from xora.models.transformers.transformer3d import Transformer3DModel
from xora.models.transformers.symmetric_patchifier import SymmetricPatchifier
from xora.schedulers.rf import RectifiedFlowScheduler
from xora.pipelines.pipeline_xora_video import XoraVideoPipeline
from transformers import T5EncoderModel, T5Tokenizer
from xora.utils.conditioning_method import ConditioningMethod
from pathlib import Path
import safetensors.torch
import json
import numpy as np
import cv2
from PIL import Image
import tempfile
import os

# Load Hugging Face token if needed
hf_token = os.getenv("HF_TOKEN")

# Set model download directory within Hugging Face Spaces
model_path = "asset"
if not os.path.exists(model_path):
    snapshot_download("Lightricks/LTX-Video", local_dir=model_path, repo_type='model', token=hf_token)("Lightricks/LTX-Video", local_dir=model_path, repo_type='model', token=hf_token)

# Global variables to load components
vae_dir = Path(model_path) / 'vae'
unet_dir = Path(model_path) / 'unet'
scheduler_dir = Path(model_path) / 'scheduler'

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


def load_vae(vae_dir):
    vae_ckpt_path = vae_dir / "vae_diffusion_pytorch_model.safetensors"
    vae_config_path = vae_dir / "config.json"
    with open(vae_config_path, 'r') as f:
        vae_config = json.load(f)
    vae = CausalVideoAutoencoder.from_config(vae_config)
    vae_state_dict = safetensors.torch.load_file(vae_ckpt_path)
    vae.load_state_dict(vae_state_dict)
    return vae.cuda().to(torch.bfloat16)


def load_unet(unet_dir):
    unet_ckpt_path = unet_dir / "unet_diffusion_pytorch_model.safetensors"
    unet_config_path = unet_dir / "config.json"
    transformer_config = Transformer3DModel.load_config(unet_config_path)
    transformer = Transformer3DModel.from_config(transformer_config)
    unet_state_dict = safetensors.torch.load_file(unet_ckpt_path)
    transformer.load_state_dict(unet_state_dict, strict=True)
    return transformer.to(device)


def load_scheduler(scheduler_dir):
    scheduler_config_path = scheduler_dir / "scheduler_config.json"
    scheduler_config = RectifiedFlowScheduler.load_config(scheduler_config_path)
    return RectifiedFlowScheduler.from_config(scheduler_config)


# Helper function for image processing
def center_crop_and_resize(frame, target_height, target_width):
    h, w, _ = frame.shape
    aspect_ratio_target = target_width / target_height
    aspect_ratio_frame = w / h
    if aspect_ratio_frame > aspect_ratio_target:
        new_width = int(h * aspect_ratio_target)
        x_start = (w - new_width) // 2
        frame_cropped = frame[:, x_start:x_start + new_width]
    else:
        new_height = int(w / aspect_ratio_target)
        y_start = (h - new_height) // 2
        frame_cropped = frame[y_start:y_start + new_height, :]
    frame_resized = cv2.resize(frame_cropped, (target_width, target_height))
    return frame_resized


def load_image_to_tensor_with_resize(image_path, target_height=512, target_width=768):
    image = Image.open(image_path).convert("RGB")
    image_np = np.array(image)
    frame_resized = center_crop_and_resize(image_np, target_height, target_width)
    frame_tensor = torch.tensor(frame_resized).permute(2, 0, 1).float()
    frame_tensor = (frame_tensor / 127.5) - 1.0
    return frame_tensor.unsqueeze(0).unsqueeze(2)


# Preset options for resolution and frame configuration
preset_options = [
    {"label": "1216x704, 41 frames", "width": 1216, "height": 704, "num_frames": 41},
    {"label": "1088x704, 49 frames", "width": 1088, "height": 704, "num_frames": 49},
    {"label": "1056x640, 57 frames", "width": 1056, "height": 640, "num_frames": 57},
    {"label": "992x608, 65 frames", "width": 992, "height": 608, "num_frames": 65},
    {"label": "896x608, 73 frames", "width": 896, "height": 608, "num_frames": 73},
    {"label": "896x544, 81 frames", "width": 896, "height": 544, "num_frames": 81},
    {"label": "832x544, 89 frames", "width": 832, "height": 544, "num_frames": 89},
    {"label": "800x512, 97 frames", "width": 800, "height": 512, "num_frames": 97},
    {"label": "768x512, 97 frames", "width": 768, "height": 512, "num_frames": 97},
    {"label": "800x480, 105 frames", "width": 800, "height": 480, "num_frames": 105},
    {"label": "736x480, 113 frames", "width": 736, "height": 480, "num_frames": 113},
    {"label": "704x480, 121 frames", "width": 704, "height": 480, "num_frames": 121},
    {"label": "704x448, 129 frames", "width": 704, "height": 448, "num_frames": 129},
    {"label": "672x448, 137 frames", "width": 672, "height": 448, "num_frames": 137},
    {"label": "640x416, 153 frames", "width": 640, "height": 416, "num_frames": 153},
    {"label": "672x384, 161 frames", "width": 672, "height": 384, "num_frames": 161},
    {"label": "640x384, 169 frames", "width": 640, "height": 384, "num_frames": 169},
    {"label": "608x384, 177 frames", "width": 608, "height": 384, "num_frames": 177},
    {"label": "576x384, 185 frames", "width": 576, "height": 384, "num_frames": 185},
    {"label": "608x352, 193 frames", "width": 608, "height": 352, "num_frames": 193},
    {"label": "576x352, 201 frames", "width": 576, "height": 352, "num_frames": 201},
    {"label": "544x352, 209 frames", "width": 544, "height": 352, "num_frames": 209},
    {"label": "512x352, 225 frames", "width": 512, "height": 352, "num_frames": 225},
    {"label": "512x352, 233 frames", "width": 512, "height": 352, "num_frames": 233},
    {"label": "544x320, 241 frames", "width": 544, "height": 320, "num_frames": 241},
    {"label": "512x320, 249 frames", "width": 512, "height": 320, "num_frames": 249},
    {"label": "512x320, 257 frames", "width": 512, "height": 320, "num_frames": 257},
    {"label": "Custom", "height": None, "width": None, "num_frames": None}
]


# Function to toggle visibility of sliders based on preset selection
def preset_changed(preset):
    if preset != "Custom":
        selected = next(item for item in preset_options if item["label"] == preset)
        return (
            selected["height"],
            selected["width"],
            selected["num_frames"],
            gr.update(visible=False),
            gr.update(visible=False),
            gr.update(visible=False)
        )
    else:
        return None, None, None, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)


# Load models
vae = load_vae(vae_dir)
unet = load_unet(unet_dir)
scheduler = load_scheduler(scheduler_dir)
patchifier = SymmetricPatchifier(patch_size=1)
text_encoder = T5EncoderModel.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="text_encoder").to(device)
tokenizer = T5Tokenizer.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="tokenizer")

pipeline = XoraVideoPipeline(
    transformer=unet,
    patchifier=patchifier,
    text_encoder=text_encoder,
    tokenizer=tokenizer,
    scheduler=scheduler,
    vae=vae,
).to(device)


# Modified function to include validation with gr.Error
#@spaces.GPU(duration=120)
def generate_video(image_path=None, prompt="", negative_prompt="",
                   seed=171198, num_inference_steps=40, num_images_per_prompt=1,
                   guidance_scale=3, height=512, width=768, num_frames=121, frame_rate=25, progress=gr.Progress()):
    # Check prompt length and raise an error if it's too short
    if len(prompt.strip()) < 50:
        raise gr.Error("Prompt must be at least 50 characters long. Please provide more details for the best results.", duration=5)

    if image_path:
        media_items = load_image_to_tensor_with_resize(image_path, height, width).to(device)
    else:
        raise ValueError("Image path must be provided.")

    sample = {
        "prompt": prompt,
        'prompt_attention_mask': None,
        'negative_prompt': negative_prompt,
        'negative_prompt_attention_mask': None,
        'media_items': media_items,
    }

    generator = torch.Generator(device="cpu").manual_seed(seed)

    def gradio_progress_callback(self, step, timestep, kwargs):
        progress((step + 1) / num_inference_steps)

    images = pipeline(
        num_inference_steps=num_inference_steps,
        num_images_per_prompt=num_images_per_prompt,
        guidance_scale=guidance_scale,
        generator=generator,
        output_type="pt",
        height=height,
        width=width,
        num_frames=num_frames,
        frame_rate=frame_rate,
        **sample,
        is_video=True,
        vae_per_channel_normalize=True,
        conditioning_method=ConditioningMethod.FIRST_FRAME,
        mixed_precision=True,
        callback_on_step_end=gradio_progress_callback
    ).images

    output_path = tempfile.mktemp(suffix=".mp4")
    video_np = images.squeeze(0).permute(1, 2, 3, 0).cpu().float().numpy()
    video_np = (video_np * 255).astype(np.uint8)
    height, width = video_np.shape[1:3]
    out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), frame_rate, (width, height))
    for frame in video_np[..., ::-1]:
        out.write(frame)
    out.release()

    return output_path


# Define the Gradio interface with presets
with gr.Blocks() as iface:
    gr.Markdown("# Video Generation with Xora")

    with gr.Row():
        with gr.Column():
            image_input = gr.Image(type="filepath", label="Image Input")
            prompt = gr.Textbox(label="Prompt", value="A man riding a motorcycle down a winding road, surrounded by lush, green scenery and distant mountains. The sky is clear with a few wispy clouds, and the sunlight glistens on the motorcycle as it speeds along. The rider is dressed in a black leather jacket and helmet, leaning slightly forward as the wind rustles through nearby trees. The wheels kick up dust, creating a slight trail behind the motorcycle, adding a sense of speed and excitement to the scene.")
            negative_prompt = gr.Textbox(label="Negative Prompt", value="worst quality, inconsistent motion...")

            # Preset dropdown for resolution and frame settings
            preset_dropdown = gr.Dropdown(
                choices=[p["label"] for p in preset_options],
                value="704x1216, 41 frames",
                label="Resolution Preset"
            )

            # Advanced options section
            with gr.Accordion("Advanced Options", open=False):
                seed = gr.Slider(label="Seed", minimum=0, maximum=1000000, step=1, value=171198)
                inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=100, step=1, value=40)
                images_per_prompt = gr.Slider(label="Images per Prompt", minimum=1, maximum=10, step=1, value=1)
                guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=20.0, step=0.1, value=3.0)

                # Sliders to appear at the end of the advanced settings
                height_slider = gr.Slider(label="Height", minimum=256, maximum=1024, step=64, value=704, visible=False)
                width_slider = gr.Slider(label="Width", minimum=256, maximum=1024, step=64, value=1216, visible=False)
                num_frames_slider = gr.Slider(label="Number of Frames", minimum=1, maximum=200, step=1, value=41,
                                              visible=False)

                frame_rate = gr.Slider(label="Frame Rate", minimum=1, maximum=60, step=1, value=25, visible=False)

            generate_button = gr.Button("Generate Video")

        with gr.Column():
            output_video = gr.Video(label="Generated Video")

    # Link dropdown change to update sliders visibility and values
    preset_dropdown.change(
        fn=preset_changed,
        inputs=[preset_dropdown],
        outputs=[height_slider, width_slider, num_frames_slider, height_slider, width_slider, frame_rate]
    )

    generate_button.click(
        fn=generate_video,
        inputs=[image_input, prompt, negative_prompt, seed, inference_steps, images_per_prompt, guidance_scale,
                height_slider, width_slider, num_frames_slider, frame_rate],
        outputs=output_video
    )

iface.launch(share=True)