gradio_appp.py

import gradio as gr

from gradio_toggle import Toggle
import argparse
import json
import os
import random
from datetime import datetime
from pathlib import Path
from diffusers.utils import logging

import imageio
import numpy as np
import safetensors.torch
import torch
import torch.nn.functional as F
from PIL import Image
from transformers import T5EncoderModel, T5Tokenizer
import tempfile
from ltx_video.models.autoencoders.causal_video_autoencoder import (
    CausalVideoAutoencoder,
)
from ltx_video.models.transformers.symmetric_patchifier import SymmetricPatchifier
from ltx_video.models.transformers.transformer3d import Transformer3DModel
from ltx_video.pipelines.pipeline_ltx_video import LTXVideoPipeline
from ltx_video.schedulers.rf import RectifiedFlowScheduler
from ltx_video.utils.conditioning_method import ConditioningMethod
from torchao.quantization import quantize_, int8_weight_only

MAX_HEIGHT = 720
MAX_WIDTH = 1280
MAX_NUM_FRAMES = 257


def load_vae(vae_dir, int8=False):
    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)
    # Ensure everything runs on the CPU
    vae = vae.to('cpu')
    if int8:
        print("vae - quantization = true")
        quantize_(vae, int8_weight_only())
    return vae


def load_unet(unet_dir, int8=False):
    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)
    # Ensure everything runs on the CPU
    transformer = transformer.to('cpu')
    if int8:
        print("unet - quantization = true")
        quantize_(transformer, int8_weight_only())
    return transformer


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)


def load_image_to_tensor_with_resize_and_crop(image_path, target_height=512, target_width=768):
    image = Image.open(image_path).convert("RGB")
    input_width, input_height = image.size
    aspect_ratio_target = target_width / target_height
    aspect_ratio_frame = input_width / input_height
    if aspect_ratio_frame > aspect_ratio_target:
        new_width = int(input_height * aspect_ratio_target)
        new_height = input_height
        x_start = (input_width - new_width) // 2
        y_start = 0
    else:
        new_width = input_width
        new_height = int(input_width / aspect_ratio_target)
        x_start = 0
        y_start = (input_height - new_height) // 2

    image = image.crop((x_start, y_start, x_start + new_width, y_start + new_height))
    image = image.resize((target_width, target_height))
    frame_tensor = torch.tensor(np.array(image)).permute(2, 0, 1).float()
    frame_tensor = (frame_tensor / 127.5) - 1.0
    # Create 5D tensor: (batch_size=1, channels=3, num_frames=1, height, width)
    return frame_tensor.unsqueeze(0).unsqueeze(2)


def calculate_padding(

    source_height: int, source_width: int, target_height: int, target_width: int

) -> tuple[int, int, int, int]:

    # Calculate total padding needed
    pad_height = target_height - source_height
    pad_width = target_width - source_width

    # Calculate padding for each side
    pad_top = pad_height // 2
    pad_bottom = pad_height - pad_top  # Handles odd padding
    pad_left = pad_width // 2
    pad_right = pad_width - pad_left  # Handles odd padding

    # Return padded tensor
    # Padding format is (left, right, top, bottom)
    padding = (pad_left, pad_right, pad_top, pad_bottom)
    return padding


def convert_prompt_to_filename(text: str, max_len: int = 20) -> str:
    # Remove non-letters and convert to lowercase
    clean_text = "".join(
        char.lower() for char in text if char.isalpha() or char.isspace()
    )

    # Split into words
    words = clean_text.split()

    # Build result string keeping track of length
    result = []
    current_length = 0

    for word in words:
        # Add word length plus 1 for underscore (except for first word)
        new_length = current_length + len(word)

        if new_length <= max_len:
            result.append(word)
            current_length += len(word)
        else:
            break

    return "-".join(result)


# Generate output video name
def get_unique_filename(

    base: str,

    ext: str,

    prompt: str,

    seed: int,

    resolution: tuple[int, int, int],

    dir: Path,

    endswith=None,

    index_range=1000,

) -> Path:
    base_filename = f"{base}_{convert_prompt_to_filename(prompt, max_len=30)}_{seed}_{resolution[0]}x{resolution[1]}x{resolution[2]}"
    for i in range(index_range):
        filename = dir / f"{base_filename}_{i}{endswith if endswith else ''}{ext}"
        if not os.path.exists(filename):
            return filename
    raise FileExistsError(
        f"Could not find a unique filename after {index_range} attempts."
    )


def seed_everething(seed: int):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)


def main(

        img2vid_image="",

        prompt="",

        txt2vid_analytics_toggle=False,

        negative_prompt="",

        frame_rate=25,

        seed=0,

        num_inference_steps=30,

        guidance_scale=3,

        height=512,

        width=768,

        num_frames=121,

        progress=gr.Progress(),

    ):
    
    logger = logging.get_logger(__name__)
    
    args = {
                "ckpt_dir": "Lightricks/LTX-Video",
                "num_inference_steps": num_inference_steps,
                "guidance_scale": guidance_scale,
                "height": height,
                "width": width,
                "num_frames": num_frames,
                "frame_rate": frame_rate,
                "prompt": prompt,
                "negative_prompt": negative_prompt,
                "seed": 0,
                "output_path": os.path.join(tempfile.gettempdir(), "gradio"),
                "num_images_per_prompt": 1,
                "input_image_path": img2vid_image,
                "input_video_path": "",
                "bfloat16": True,
                "disable_load_needed_only": False
            }
    logger.warning(f"Running generation with arguments: {args}")

    seed_everething(args['seed'])

    output_dir = (
        Path(args['output_path'])
        if args['output_path']
        else Path(f"outputs/{datetime.today().strftime('%Y-%m-%d')}")
    )
    output_dir.mkdir(parents=True, exist_ok=True)

    # Load image
    if args['input_image_path']:
        media_items_prepad = load_image_to_tensor_with_resize_and_crop(
            args['input_image_path'], args['height'], args['width']
        )
    else:
        media_items_prepad = None

    height = args['height'] if args['height'] else media_items_prepad.shape[-2]
    width = args['width'] if args['width'] else media_items_prepad.shape[-1]
    num_frames = args['num_frames']

    if height > MAX_HEIGHT or width > MAX_WIDTH or num_frames > MAX_NUM_FRAMES:
        logger.warning(
            f"Input resolution or number of frames {height}x{width}x{num_frames} is too big, it is suggested to use the resolution below {MAX_HEIGHT}x{MAX_WIDTH}x{MAX_NUM_FRAMES}."
        )

    # Adjust dimensions to be divisible by 32 and num_frames to be (N * 8 + 1)
    height_padded = ((height - 1) // 32 + 1) * 32
    width_padded = ((width - 1) // 32 + 1) * 32
    num_frames_padded = ((num_frames - 2) // 8 + 1) * 8 + 1

    padding = calculate_padding(height, width, height_padded, width_padded)

    logger.warning(
        f"Padded dimensions: {height_padded}x{width_padded}x{num_frames_padded}"
    )

    if media_items_prepad is not None:
        media_items = F.pad(
            media_items_prepad, padding, mode="constant", value=-1
        )  # -1 is the value for padding since the image is normalized to -1, 1
    else:
        media_items = None

    # Load models
    vae = load_vae(Path(args['ckpt_dir']) / "vae", txt2vid_analytics_toggle)
    unet = load_unet(Path(args['ckpt_dir']) / "unet", txt2vid_analytics_toggle)
    scheduler = load_scheduler(Path(args['ckpt_dir']) / "scheduler")
    patchifier = SymmetricPatchifier(patch_size=1)
    text_encoder = T5EncoderModel.from_pretrained(
        "PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="text_encoder"
    ).to('cpu')  # Force to CPU

    tokenizer = T5Tokenizer.from_pretrained(
        "PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="tokenizer"
    )

    # Use submodels for the pipeline
    submodel_dict = {
        "transformer": unet,
        "patchifier": patchifier,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "scheduler": scheduler,
        "vae": vae,
    }

    pipeline = LTXVideoPipeline(**submodel_dict)
    pipeline = pipeline.to('cpu')  # Ensure pipeline runs on CPU

    # Prepare input for the pipeline
    sample = {
        "prompt": args['prompt'],
        "prompt_attention_mask": None,
        "negative_prompt": args['negative_prompt'],
        "negative_prompt_attention_mask": None,
        "media_items": media_items,
    }

    generator = torch.Generator(device="cpu").manual_seed(args['seed'])  # Force CPU

    images = pipeline(
        num_inference_steps=args['num_inference_steps'],
        num_images_per_prompt=args['num_images_per_prompt'],
        guidance_scale=args['guidance_scale'],
        generator=generator,
        output_type="pt",
        callback_on_step_end=None,
        height=height_padded,
        width=width_padded,
        num_frames=num_frames_padded,
        frame_rate=args['frame_rate'],
        **sample,
        is_video=True,
        vae_per_channel_normalize=True,
        conditioning_method=(
            ConditioningMethod.FIRST_FRAME
            if media_items is not None
            else ConditioningMethod.UNCONDITIONAL
        ),
        mixed_precision=not args['bfloat16'],
        load_needed_only=not args['disable_load_needed_only']
    ).images

    # Further processing and saving logic can go here...