app.py

import math
import os
from glob import glob
from pathlib import Path
from typing import Optional

import cv2
import numpy as np
import torch
from einops import rearrange, repeat
from fire import Fire
from omegaconf import OmegaConf
from PIL import Image
from torchvision.transforms import ToTensor

from scripts.util.detection.nsfw_and_watermark_dectection import \
    DeepFloydDataFiltering
from sgm.inference.helpers import embed_watermark
from sgm.util import default, instantiate_from_config

hf_hub_download(repo_id="stabilityai/stable-video-diffusion-img2vid-xt", filename="svd_xt.safetensors", local_dir="checkpoints") 

version = "svd_xt"
device = "cuda"
def load_model(
    config: str,
    device: str,
    num_frames: int,
    num_steps: int,
):
    config = OmegaConf.load(config)
    if device == "cuda":
        config.model.params.conditioner_config.params.emb_models[
            0
        ].params.open_clip_embedding_config.params.init_device = device

    config.model.params.sampler_config.params.num_steps = num_steps
    config.model.params.sampler_config.params.guider_config.params.num_frames = (
        num_frames
    )
    if device == "cuda":
        with torch.device(device):
            model = instantiate_from_config(config.model).to(device).eval()
    else:
        model = instantiate_from_config(config.model).to(device).eval()

    filter = DeepFloydDataFiltering(verbose=False, device=device)
    return model, filter

if version == "svd_xt":
    num_frames = 25
    num_steps = 30
    model_config = "scripts/sampling/configs/svd_xt.yaml"
else:
    raise ValueError(f"Version {version} does not exist.")

model, filter = load_model(
    model_config,
    device,
    num_frames,
    num_steps,
)

def sample(
    input_path: str = "assets/test_image.png",  # Can either be image file or folder with image files
    version: str = "svd_xt",
    fps_id: int = 6,
    motion_bucket_id: int = 127,
    cond_aug: float = 0.02,
    seed: int = 23,
    decoding_t: int = 7,  # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
    device: str = "cuda",
    output_folder: str = "outputs",
):
    """
    Simple script to generate a single sample conditioned on an image `input_path` or multiple images, one for each
    image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t`.
    """
    torch.manual_seed(seed)
    
    path = Path(input_path)
    all_img_paths = []
    if path.is_file():
        if any([input_path.endswith(x) for x in ["jpg", "jpeg", "png"]]):
            all_img_paths = [input_path]
        else:
            raise ValueError("Path is not valid image file.")
    elif path.is_dir():
        all_img_paths = sorted(
            [
                f
                for f in path.iterdir()
                if f.is_file() and f.suffix.lower() in [".jpg", ".jpeg", ".png"]
            ]
        )
        if len(all_img_paths) == 0:
            raise ValueError("Folder does not contain any images.")
    else:
        raise ValueError

    for input_img_path in all_img_paths:
        with Image.open(input_img_path) as image:
            if image.mode == "RGBA":
                image = image.convert("RGB")
            w, h = image.size

            if h % 64 != 0 or w % 64 != 0:
                width, height = map(lambda x: x - x % 64, (w, h))
                image = image.resize((width, height))
                print(
                    f"WARNING: Your image is of size {h}x{w} which is not divisible by 64. We are resizing to {height}x{width}!"
                )

            image = ToTensor()(image)
            image = image * 2.0 - 1.0

        image = image.unsqueeze(0).to(device)
        H, W = image.shape[2:]
        assert image.shape[1] == 3
        F = 8
        C = 4
        shape = (num_frames, C, H // F, W // F)
        if (H, W) != (576, 1024):
            print(
                "WARNING: The conditioning frame you provided is not 576x1024. This leads to suboptimal performance as model was only trained on 576x1024. Consider increasing `cond_aug`."
            )
        if motion_bucket_id > 255:
            print(
                "WARNING: High motion bucket! This may lead to suboptimal performance."
            )

        if fps_id < 5:
            print("WARNING: Small fps value! This may lead to suboptimal performance.")

        if fps_id > 30:
            print("WARNING: Large fps value! This may lead to suboptimal performance.")

        value_dict = {}
        value_dict["motion_bucket_id"] = motion_bucket_id
        value_dict["fps_id"] = fps_id
        value_dict["cond_aug"] = cond_aug
        value_dict["cond_frames_without_noise"] = image
        value_dict["cond_frames"] = image + cond_aug * torch.randn_like(image)
        value_dict["cond_aug"] = cond_aug

        with torch.no_grad():
            with torch.autocast(device):
                batch, batch_uc = get_batch(
                    get_unique_embedder_keys_from_conditioner(model.conditioner),
                    value_dict,
                    [1, num_frames],
                    T=num_frames,
                    device=device,
                )
                c, uc = model.conditioner.get_unconditional_conditioning(
                    batch,
                    batch_uc=batch_uc,
                    force_uc_zero_embeddings=[
                        "cond_frames",
                        "cond_frames_without_noise",
                    ],
                )

                for k in ["crossattn", "concat"]:
                    uc[k] = repeat(uc[k], "b ... -> b t ...", t=num_frames)
                    uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=num_frames)
                    c[k] = repeat(c[k], "b ... -> b t ...", t=num_frames)
                    c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=num_frames)

                randn = torch.randn(shape, device=device)

                additional_model_inputs = {}
                additional_model_inputs["image_only_indicator"] = torch.zeros(
                    2, num_frames
                ).to(device)
                additional_model_inputs["num_video_frames"] = batch["num_video_frames"]

                def denoiser(input, sigma, c):
                    return model.denoiser(
                        model.model, input, sigma, c, **additional_model_inputs
                    )

                samples_z = model.sampler(denoiser, randn, cond=c, uc=uc)
                model.en_and_decode_n_samples_a_time = decoding_t
                samples_x = model.decode_first_stage(samples_z)
                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)

                os.makedirs(output_folder, exist_ok=True)
                base_count = len(glob(os.path.join(output_folder, "*.mp4")))
                video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
                writer = cv2.VideoWriter(
                    video_path,
                    cv2.VideoWriter_fourcc(*"mp4v"),
                    fps_id + 1,
                    (samples.shape[-1], samples.shape[-2]),
                )

                samples = embed_watermark(samples)
                samples = filter(samples)
                vid = (
                    (rearrange(samples, "t c h w -> t h w c") * 255)
                    .cpu()
                    .numpy()
                    .astype(np.uint8)
                )
                for frame in vid:
                    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
                    writer.write(frame)
                writer.release()
        
        return video_path

def get_unique_embedder_keys_from_conditioner(conditioner):
    return list(set([x.input_key for x in conditioner.embedders]))


def get_batch(keys, value_dict, N, T, device):
    batch = {}
    batch_uc = {}

    for key in keys:
        if key == "fps_id":
            batch[key] = (
                torch.tensor([value_dict["fps_id"]])
                .to(device)
                .repeat(int(math.prod(N)))
            )
        elif key == "motion_bucket_id":
            batch[key] = (
                torch.tensor([value_dict["motion_bucket_id"]])
                .to(device)
                .repeat(int(math.prod(N)))
            )
        elif key == "cond_aug":
            batch[key] = repeat(
                torch.tensor([value_dict["cond_aug"]]).to(device),
                "1 -> b",
                b=math.prod(N),
            )
        elif key == "cond_frames":
            batch[key] = repeat(value_dict["cond_frames"], "1 ... -> b ...", b=N[0])
        elif key == "cond_frames_without_noise":
            batch[key] = repeat(
                value_dict["cond_frames_without_noise"], "1 ... -> b ...", b=N[0]
            )
        else:
            batch[key] = value_dict[key]

    if T is not None:
        batch["num_video_frames"] = T

    for key in batch.keys():
        if key not in batch_uc and isinstance(batch[key], torch.Tensor):
            batch_uc[key] = torch.clone(batch[key])
    return batch, batch_uc

import gradio as gr
import uuid
def resize_image(image_path, output_size=(1024, 576)):
    image = Image.open(image_path)
    # Calculate aspect ratios
    target_aspect = output_size[0] / output_size[1]  # Aspect ratio of the desired size
    image_aspect = image.width / image.height  # Aspect ratio of the original image

    # Resize then crop if the original image is larger
    if image_aspect > target_aspect:
        # Resize the image to match the target height, maintaining aspect ratio
        new_height = output_size[1]
        new_width = int(new_height * image_aspect)
        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
        # Calculate coordinates for cropping
        left = (new_width - output_size[0]) / 2
        top = 0
        right = (new_width + output_size[0]) / 2
        bottom = output_size[1]
    else:
        # Resize the image to match the target width, maintaining aspect ratio
        new_width = output_size[0]
        new_height = int(new_width / image_aspect)
        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
        # Calculate coordinates for cropping
        left = 0
        top = (new_height - output_size[1]) / 2
        right = output_size[0]
        bottom = (new_height + output_size[1]) / 2

    # Crop the image
    cropped_image = resized_image.crop((left, top, right, bottom))

    return cropped_image

with gr.Blocks() as demo:
  gr.Markdown('''# Stable Video Diffusion - Image2Video - XT
Generate 25 frames of video from a single image using SDV-XT. 
  ''')
  with gr.Column():
    image = gr.Image(label="Upload your image (it will be center cropped to 1024x576)", type="filepath")
    generate_btn = gr.Button("Generate")
  with gr.Column():
    video = gr.Video()
  image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
  generate_btn.click(fn=sample, inputs=image, outputs=video, api_name="video")

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