Image to Video Generation with Stable Video Diffusion

This Jupyter notebook can be launched after a local installation only.

Stable Video Diffusion (SVD) Image-to-Video is a diffusion model that takes in a still image as a conditioning frame, and generates a video from it. In this tutorial we consider how to convert and run Stable Video Diffusion using OpenVINO. We will use stable-video-diffusion-img2video-xt model as example. Additionally, to speedup video generation process we apply AnimateLCM LoRA weights and run optimization with NNCF.

Table of contents:

• Prerequisites

• Download PyTorch Model

• Convert Model to OpenVINO Intermediate Representation

  • Image Encoder

  • U-net

  • VAE Encoder and Decoder

• Prepare Inference Pipeline

• Run Video Generation

  • Select Inference Device

• Quantization

  • Prepare calibration dataset

  • Run Hybrid Model Quantization

  • Run Weight Compression

  • Compare model file sizes

  • Compare inference time of the FP16 and INT8 pipelines

• Interactive Demo

Prerequisites

%pip install -q "torch>=2.1" "diffusers>=0.25" "peft==0.6.2" "transformers" "openvino>=2024.1.0" Pillow opencv-python tqdm  "gradio>=4.19" safetensors --extra-index-url https://download.pytorch.org/whl/cpu
%pip install -q datasets "nncf>=2.10.0"

Note: you may need to restart the kernel to use updated packages.
Note: you may need to restart the kernel to use updated packages.

Download PyTorch Model

The code below load Stable Video Diffusion XT model using Diffusers library and apply Consistency Distilled AnimateLCM weights.

import torch
from pathlib import Path
from diffusers import StableVideoDiffusionPipeline
from diffusers.utils import load_image, export_to_video
from diffusers.models.attention_processor import AttnProcessor
from safetensors import safe_open
import gc
import requests

lcm_scheduler_url = "https://huggingface.co/spaces/wangfuyun/AnimateLCM-SVD/raw/main/lcm_scheduler.py"

r = requests.get(lcm_scheduler_url)

with open("lcm_scheduler.py", "w") as f:
    f.write(r.text)

from lcm_scheduler import AnimateLCMSVDStochasticIterativeScheduler
from huggingface_hub import hf_hub_download

MODEL_DIR = Path("model")

IMAGE_ENCODER_PATH = MODEL_DIR / "image_encoder.xml"
VAE_ENCODER_PATH = MODEL_DIR / "vae_encoder.xml"
VAE_DECODER_PATH = MODEL_DIR / "vae_decoder.xml"
UNET_PATH = MODEL_DIR / "unet.xml"


load_pt_pipeline = not (VAE_ENCODER_PATH.exists() and VAE_DECODER_PATH.exists() and UNET_PATH.exists() and IMAGE_ENCODER_PATH.exists())

unet, vae, image_encoder = None, None, None
if load_pt_pipeline:
    noise_scheduler = AnimateLCMSVDStochasticIterativeScheduler(
        num_train_timesteps=40,
        sigma_min=0.002,
        sigma_max=700.0,
        sigma_data=1.0,
        s_noise=1.0,
        rho=7,
        clip_denoised=False,
    )
    pipe = StableVideoDiffusionPipeline.from_pretrained(
        "stabilityai/stable-video-diffusion-img2vid-xt",
        variant="fp16",
        scheduler=noise_scheduler,
    )
    pipe.unet.set_attn_processor(AttnProcessor())
    hf_hub_download(
        repo_id="wangfuyun/AnimateLCM-SVD-xt",
        filename="AnimateLCM-SVD-xt.safetensors",
        local_dir="./checkpoints",
    )
    state_dict = {}
    LCM_LORA_PATH = Path(
        "checkpoints/AnimateLCM-SVD-xt.safetensors",
    )
    with safe_open(LCM_LORA_PATH, framework="pt", device="cpu") as f:
        for key in f.keys():
            state_dict[key] = f.get_tensor(key)
    missing, unexpected = pipe.unet.load_state_dict(state_dict, strict=True)

    pipe.scheduler.save_pretrained(MODEL_DIR / "scheduler")
    pipe.feature_extractor.save_pretrained(MODEL_DIR / "feature_extractor")
    unet = pipe.unet
    unet.eval()
    vae = pipe.vae
    vae.eval()
    image_encoder = pipe.image_encoder
    image_encoder.eval()
    del pipe
    gc.collect()

# Load the conditioning image
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png?download=true")
image = image.resize((512, 256))

Convert Model to OpenVINO Intermediate Representation

OpenVINO supports PyTorch models via conversion into Intermediate Representation (IR) format. We need to provide a model object, input data for model tracing to ov.convert_model function to obtain OpenVINO ov.Model object instance. Model can be saved on disk for next deployment using ov.save_model function.

Stable Video Diffusion consists of 3 parts:

• Image Encoder for extraction embeddings from the input image.

• U-Net for step-by-step denoising video clip.

• VAE for encoding input image into latent space and decoding generated video.

Let’s convert each part.

Image Encoder

import openvino as ov


def cleanup_torchscript_cache():
    """
    Helper for removing cached model representation
    """
    torch._C._jit_clear_class_registry()
    torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
    torch.jit._state._clear_class_state()


if not IMAGE_ENCODER_PATH.exists():
    with torch.no_grad():
        ov_model = ov.convert_model(
            image_encoder,
            example_input=torch.zeros((1, 3, 224, 224)),
            input=[-1, 3, 224, 224],
        )
    ov.save_model(ov_model, IMAGE_ENCODER_PATH)
    del ov_model
    cleanup_torchscript_cache()
    print(f"Image Encoder successfully converted to IR and saved to {IMAGE_ENCODER_PATH}")
del image_encoder
gc.collect();

U-net

if not UNET_PATH.exists():
    unet_inputs = {
        "sample": torch.ones([2, 2, 8, 32, 32]),
        "timestep": torch.tensor(1.256),
        "encoder_hidden_states": torch.zeros([2, 1, 1024]),
        "added_time_ids": torch.ones([2, 3]),
    }
    with torch.no_grad():
        ov_model = ov.convert_model(unet, example_input=unet_inputs)
    ov.save_model(ov_model, UNET_PATH)
    del ov_model
    cleanup_torchscript_cache()
    print(f"UNet successfully converted to IR and saved to {UNET_PATH}")

del unet
gc.collect();

VAE Encoder and Decoder

As discussed above VAE model used for encoding initial image and decoding generated video. Encoding and Decoding happen on different pipeline stages, so for convenient usage we separate VAE on 2 parts: Encoder and Decoder.

class VAEEncoderWrapper(torch.nn.Module):
    def __init__(self, vae):
        super().__init__()
        self.vae = vae

    def forward(self, image):
        return self.vae.encode(x=image)["latent_dist"].sample()


class VAEDecoderWrapper(torch.nn.Module):
    def __init__(self, vae):
        super().__init__()
        self.vae = vae

    def forward(self, latents, num_frames: int):
        return self.vae.decode(latents, num_frames=num_frames)


if not VAE_ENCODER_PATH.exists():
    vae_encoder = VAEEncoderWrapper(vae)
    with torch.no_grad():
        ov_model = ov.convert_model(vae_encoder, example_input=torch.zeros((1, 3, 576, 1024)))
    ov.save_model(ov_model, VAE_ENCODER_PATH)
    cleanup_torchscript_cache()
    print(f"VAE Encoder successfully converted to IR and saved to {VAE_ENCODER_PATH}")
    del vae_encoder
    gc.collect()

if not VAE_DECODER_PATH.exists():
    vae_decoder = VAEDecoderWrapper(vae)
    with torch.no_grad():
        ov_model = ov.convert_model(vae_decoder, example_input=(torch.zeros((8, 4, 72, 128)), torch.tensor(8)))
    ov.save_model(ov_model, VAE_DECODER_PATH)
    cleanup_torchscript_cache()
    print(f"VAE Decoder successfully converted to IR and saved to {VAE_ENCODER_PATH}")
    del vae_decoder
    gc.collect()

del vae
gc.collect();

Prepare Inference Pipeline

The code bellow implements OVStableVideoDiffusionPipeline class for running video generation using OpenVINO. The pipeline accepts input image and returns the sequence of generated frames The diagram below represents a simplified pipeline workflow.

svd

The pipeline is very similar to Stable Diffusion Image to Image Generation pipeline with the only difference that Image Encoder is used instead of Text Encoder. Model takes input image and random seed as initial prompt. Then image encoded into embeddings space using Image Encoder and into latent space using VAE Encoder and passed as input to U-Net model. Next, the U-Net iteratively denoises the random latent video representations while being conditioned on the image embeddings. The output of the U-Net, being the noise residual, is used to compute a denoised latent image representation via a scheduler algorithm for next iteration in generation cycle. This process repeats the given number of times and, finally, VAE decoder converts denoised latents into sequence of video frames.

from diffusers.pipelines.pipeline_utils import DiffusionPipeline
import PIL.Image
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils.torch_utils import randn_tensor
from typing import Callable, Dict, List, Optional, Union
from diffusers.pipelines.stable_video_diffusion import (
    StableVideoDiffusionPipelineOutput,
)


def _append_dims(x, target_dims):
    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
    dims_to_append = target_dims - x.ndim
    if dims_to_append < 0:
        raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
    return x[(...,) + (None,) * dims_to_append]


def tensor2vid(video: torch.Tensor, processor, output_type="np"):
    # Based on:
    # https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78

    batch_size, channels, num_frames, height, width = video.shape
    outputs = []
    for batch_idx in range(batch_size):
        batch_vid = video[batch_idx].permute(1, 0, 2, 3)
        batch_output = processor.postprocess(batch_vid, output_type)

        outputs.append(batch_output)

    return outputs


class OVStableVideoDiffusionPipeline(DiffusionPipeline):
    r"""
    Pipeline to generate video from an input image using Stable Video Diffusion.

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
    implemented for all pipelines (downloading, saving, running on a particular device, etc.).

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
        image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):
            Frozen CLIP image-encoder ([laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K)).
        unet ([`UNetSpatioTemporalConditionModel`]):
            A `UNetSpatioTemporalConditionModel` to denoise the encoded image latents.
        scheduler ([`EulerDiscreteScheduler`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images.
    """

    def __init__(
        self,
        vae_encoder,
        image_encoder,
        unet,
        vae_decoder,
        scheduler,
        feature_extractor,
    ):
        super().__init__()
        self.vae_encoder = vae_encoder
        self.vae_decoder = vae_decoder
        self.image_encoder = image_encoder
        self.register_to_config(unet=unet)
        self.scheduler = scheduler
        self.feature_extractor = feature_extractor
        self.vae_scale_factor = 2 ** (4 - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)

    def _encode_image(self, image, device, num_videos_per_prompt, do_classifier_free_guidance):
        dtype = torch.float32

        if not isinstance(image, torch.Tensor):
            image = self.image_processor.pil_to_numpy(image)
            image = self.image_processor.numpy_to_pt(image)

            # We normalize the image before resizing to match with the original implementation.
            # Then we unnormalize it after resizing.
            image = image * 2.0 - 1.0
            image = _resize_with_antialiasing(image, (224, 224))
            image = (image + 1.0) / 2.0

            # Normalize the image with for CLIP input
            image = self.feature_extractor(
                images=image,
                do_normalize=True,
                do_center_crop=False,
                do_resize=False,
                do_rescale=False,
                return_tensors="pt",
            ).pixel_values

        image = image.to(device=device, dtype=dtype)
        image_embeddings = torch.from_numpy(self.image_encoder(image)[0])
        image_embeddings = image_embeddings.unsqueeze(1)

        # duplicate image embeddings for each generation per prompt, using mps friendly method
        bs_embed, seq_len, _ = image_embeddings.shape
        image_embeddings = image_embeddings.repeat(1, num_videos_per_prompt, 1)
        image_embeddings = image_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)

        if do_classifier_free_guidance:
            negative_image_embeddings = torch.zeros_like(image_embeddings)

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            image_embeddings = torch.cat([negative_image_embeddings, image_embeddings])
        return image_embeddings

    def _encode_vae_image(
        self,
        image: torch.Tensor,
        device,
        num_videos_per_prompt,
        do_classifier_free_guidance,
    ):
        image_latents = torch.from_numpy(self.vae_encoder(image)[0])

        if do_classifier_free_guidance:
            negative_image_latents = torch.zeros_like(image_latents)

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            image_latents = torch.cat([negative_image_latents, image_latents])

        # duplicate image_latents for each generation per prompt, using mps friendly method
        image_latents = image_latents.repeat(num_videos_per_prompt, 1, 1, 1)

        return image_latents

    def _get_add_time_ids(
        self,
        fps,
        motion_bucket_id,
        noise_aug_strength,
        dtype,
        batch_size,
        num_videos_per_prompt,
        do_classifier_free_guidance,
    ):
        add_time_ids = [fps, motion_bucket_id, noise_aug_strength]

        passed_add_embed_dim = 256 * len(add_time_ids)
        expected_add_embed_dim = 3 * 256

        if expected_add_embed_dim != passed_add_embed_dim:
            raise ValueError(
                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
            )

        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
        add_time_ids = add_time_ids.repeat(batch_size * num_videos_per_prompt, 1)

        if do_classifier_free_guidance:
            add_time_ids = torch.cat([add_time_ids, add_time_ids])

        return add_time_ids

    def decode_latents(self, latents, num_frames, decode_chunk_size=14):
        # [batch, frames, channels, height, width] -> [batch*frames, channels, height, width]
        latents = latents.flatten(0, 1)

        latents = 1 / 0.18215 * latents

        # decode decode_chunk_size frames at a time to avoid OOM
        frames = []
        for i in range(0, latents.shape[0], decode_chunk_size):
            frame = torch.from_numpy(self.vae_decoder([latents[i : i + decode_chunk_size], num_frames])[0])
            frames.append(frame)
        frames = torch.cat(frames, dim=0)

        # [batch*frames, channels, height, width] -> [batch, channels, frames, height, width]
        frames = frames.reshape(-1, num_frames, *frames.shape[1:]).permute(0, 2, 1, 3, 4)

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        frames = frames.float()
        return frames

    def check_inputs(self, image, height, width):
        if not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image) and not isinstance(image, list):
            raise ValueError("`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is" f" {type(image)}")

        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

    def prepare_latents(
        self,
        batch_size,
        num_frames,
        num_channels_latents,
        height,
        width,
        dtype,
        device,
        generator,
        latents=None,
    ):
        shape = (
            batch_size,
            num_frames,
            num_channels_latents // 2,
            height // self.vae_scale_factor,
            width // self.vae_scale_factor,
        )
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

        if latents is None:
            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
        else:
            latents = latents.to(device)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma
        return latents

    @torch.no_grad()
    def __call__(
        self,
        image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor],
        height: int = 320,
        width: int = 512,
        num_frames: Optional[int] = 8,
        num_inference_steps: int = 4,
        min_guidance_scale: float = 1.0,
        max_guidance_scale: float = 1.2,
        fps: int = 7,
        motion_bucket_id: int = 80,
        noise_aug_strength: int = 0.01,
        decode_chunk_size: Optional[int] = None,
        num_videos_per_prompt: Optional[int] = 1,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
        return_dict: bool = True,
    ):
        r"""
        The call function to the pipeline for generation.

        Args:discussed
            image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`):
                Image or images to guide image generation. If you provide a tensor, it needs to be compatible with
                [`CLIPImageProcessor`](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json).
            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
                The width in pixels of the generated image.
            num_frames (`int`, *optional*):
                The number of video frames to generate. Defaults to 14 for `stable-video-diffusion-img2vid` and to 25 for `stable-video-diffusion-img2vid-xt`
            num_inference_steps (`int`, *optional*, defaults to 25):


                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference. This parameter is modulated by `strength`.
            min_guidance_scale (`float`, *optional*, defaults to 1.0):
                The minimum guidance scale. Used for the classifier free guidance with first frame.
            max_guidance_scale (`float`, *optional*, defaults to 3.0):
                The maximum guidance scale. Used for the classifier free guidance with last frame.
            fps (`int`, *optional*, defaults to 7):
                Frames per second. The rate at which the generated images shall be exported to a video after generation.
                Note that Stable Diffusion Video's UNet was micro-conditioned on fps-1 during training.
            motion_bucket_id (`int`, *optional*, defaults to 127):
                The motion bucket ID. Used as conditioning for the generation. The higher the number the more motion will be in the video.
            noise_aug_strength (`int`, *optional*, defaults to 0.02):
                The amount of noise added to the init image, the higher it is the less the video will look like the init image. Increase it for more motion.
            decode_chunk_size (`int`, *optional*):
                The number of frames to decode at a time. The higher the chunk size, the higher the temporal consistency
                between frames, but also the higher the memory consumption. By default, the decoder will decode all frames at once
                for maximal quality. Reduce `decode_chunk_size` to reduce memory usage.
            num_videos_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
                generation deterministic.
            latents (`torch.FloatTensor`, *optional*):
                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor is generated by sampling using the supplied random `generator`.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
            callback_on_step_end (`Callable`, *optional*):
                A function that calls at the end of each denoising steps during the inference. The function is called
                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
                `callback_on_step_end_tensor_inputs`.
            callback_on_step_end_tensor_inputs (`List`, *optional*):
                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                `._callback_tensor_inputs` attribute of your pipeline class.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.

        Returns:
            [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] is returned,
                otherwise a `tuple` is returned where the first element is a list of list with the generated frames.

        Examples:

        ```py
        from diffusers import StableVideoDiffusionPipeline
        from diffusers.utils import load_image, export_to_video

        pipe = StableVideoDiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16")
        pipe.to("cuda")

        image = load_image("https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200")
        image = image.resize((1024, 576))

        frames = pipe(image, num_frames=25, decode_chunk_size=8).frames[0]
        export_to_video(frames, "generated.mp4", fps=7)
        ```
        """
        # 0. Default height and width to unet
        height = height or 96 * self.vae_scale_factor
        width = width or 96 * self.vae_scale_factor

        num_frames = num_frames if num_frames is not None else 25
        decode_chunk_size = decode_chunk_size if decode_chunk_size is not None else num_frames

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(image, height, width)

        # 2. Define call parameters
        if isinstance(image, PIL.Image.Image):
            batch_size = 1
        elif isinstance(image, list):
            batch_size = len(image)
        else:
            batch_size = image.shape[0]
        device = torch.device("cpu")

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = max_guidance_scale > 1.0

        # 3. Encode input image
        image_embeddings = self._encode_image(image, device, num_videos_per_prompt, do_classifier_free_guidance)

        # NOTE: Stable Diffusion Video was conditioned on fps - 1, which
        # is why it is reduced here.
        # See: https://github.com/Stability-AI/generative-models/blob/ed0997173f98eaf8f4edf7ba5fe8f15c6b877fd3/scripts/sampling/simple_video_sample.py#L188
        fps = fps - 1

        # 4. Encode input image using VAE
        image = self.image_processor.preprocess(image, height=height, width=width)
        noise = randn_tensor(image.shape, generator=generator, device=image.device, dtype=image.dtype)
        image = image + noise_aug_strength * noise

        image_latents = self._encode_vae_image(image, device, num_videos_per_prompt, do_classifier_free_guidance)
        image_latents = image_latents.to(image_embeddings.dtype)

        # Repeat the image latents for each frame so we can concatenate them with the noise
        # image_latents [batch, channels, height, width] ->[batch, num_frames, channels, height, width]
        image_latents = image_latents.unsqueeze(1).repeat(1, num_frames, 1, 1, 1)

        # 5. Get Added Time IDs
        added_time_ids = self._get_add_time_ids(
            fps,
            motion_bucket_id,
            noise_aug_strength,
            image_embeddings.dtype,
            batch_size,
            num_videos_per_prompt,
            do_classifier_free_guidance,
        )
        added_time_ids = added_time_ids

        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps
        # 5. Prepare latent variables
        num_channels_latents = 8
        latents = self.prepare_latents(
            batch_size * num_videos_per_prompt,
            num_frames,
            num_channels_latents,
            height,
            width,
            image_embeddings.dtype,
            device,
            generator,
            latents,
        )

        # 7. Prepare guidance scale
        guidance_scale = torch.linspace(min_guidance_scale, max_guidance_scale, num_frames).unsqueeze(0)
        guidance_scale = guidance_scale.to(device, latents.dtype)
        guidance_scale = guidance_scale.repeat(batch_size * num_videos_per_prompt, 1)
        guidance_scale = _append_dims(guidance_scale, latents.ndim)

        # 8. Denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        num_timesteps = len(timesteps)
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                # Concatenate image_latents over channels dimention
                latent_model_input = torch.cat([latent_model_input, image_latents], dim=2)
                # predict the noise residual
                noise_pred = torch.from_numpy(
                    self.unet(
                        [
                            latent_model_input,
                            t,
                            image_embeddings,
                            added_time_ids,
                        ]
                    )[0]
                )
                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents).prev_sample

                if callback_on_step_end is not None:
                    callback_kwargs = {}
                    for k in callback_on_step_end_tensor_inputs:
                        callback_kwargs[k] = locals()[k]
                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)

                    latents = callback_outputs.pop("latents", latents)

                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()

        if not output_type == "latent":
            frames = self.decode_latents(latents, num_frames, decode_chunk_size)
            frames = tensor2vid(frames, self.image_processor, output_type=output_type)
        else:
            frames = latents

        if not return_dict:
            return frames

        return StableVideoDiffusionPipelineOutput(frames=frames)


# resizing utils
def _resize_with_antialiasing(input, size, interpolation="bicubic", align_corners=True):
    h, w = input.shape[-2:]
    factors = (h / size[0], w / size[1])

    # First, we have to determine sigma
    # Taken from skimage: https://github.com/scikit-image/scikit-image/blob/v0.19.2/skimage/transform/_warps.py#L171
    sigmas = (
        max((factors[0] - 1.0) / 2.0, 0.001),
        max((factors[1] - 1.0) / 2.0, 0.001),
    )
    # Now kernel size. Good results are for 3 sigma, but that is kind of slow. Pillow uses 1 sigma
    # https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Resample.c#L206
    # But they do it in the 2 passes, which gives better results. Let's try 2 sigmas for now
    ks = int(max(2.0 * 2 * sigmas[0], 3)), int(max(2.0 * 2 * sigmas[1], 3))

    # Make sure it is odd
    if (ks[0] % 2) == 0:
        ks = ks[0] + 1, ks[1]

    if (ks[1] % 2) == 0:

        ks = ks[0], ks[1] + 1

    input = _gaussian_blur2d(input, ks, sigmas)

    output = torch.nn.functional.interpolate(input, size=size, mode=interpolation, align_corners=align_corners)
    return output


def _compute_padding(kernel_size):
    """Compute padding tuple."""
    # 4 or 6 ints:  (padding_left, padding_right,padding_top,padding_bottom)
    # https://pytorch.org/docs/stable/nn.html#torch.nn.functional.pad
    if len(kernel_size) < 2:
        raise AssertionError(kernel_size)
    computed = [k - 1 for k in kernel_size]

    # for even kernels we need to do asymmetric padding :(
    out_padding = 2 * len(kernel_size) * [0]

    for i in range(len(kernel_size)):
        computed_tmp = computed[-(i + 1)]

        pad_front = computed_tmp // 2
        pad_rear = computed_tmp - pad_front

        out_padding[2 * i + 0] = pad_front
        out_padding[2 * i + 1] = pad_rear

    return out_padding


def _filter2d(input, kernel):
    # prepare kernel
    b, c, h, w = input.shape
    tmp_kernel = kernel[:, None, ...].to(device=input.device, dtype=input.dtype)

    tmp_kernel = tmp_kernel.expand(-1, c, -1, -1)

    height, width = tmp_kernel.shape[-2:]

    padding_shape: list[int] = _compute_padding([height, width])
    input = torch.nn.functional.pad(input, padding_shape, mode="reflect")

    # kernel and input tensor reshape to align element-wise or batch-wise params
    tmp_kernel = tmp_kernel.reshape(-1, 1, height, width)
    input = input.view(-1, tmp_kernel.size(0), input.size(-2), input.size(-1))

    # convolve the tensor with the kernel.
    output = torch.nn.functional.conv2d(input, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1)

    out = output.view(b, c, h, w)
    return out


def _gaussian(window_size: int, sigma):
    if isinstance(sigma, float):
        sigma = torch.tensor([[sigma]])

    batch_size = sigma.shape[0]

    x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1)

    if window_size % 2 == 0:

        x = x + 0.5

    gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0)))

    return gauss / gauss.sum(-1, keepdim=True)


def _gaussian_blur2d(input, kernel_size, sigma):
    if isinstance(sigma, tuple):
        sigma = torch.tensor([sigma], dtype=input.dtype)
    else:
        sigma = sigma.to(dtype=input.dtype)

    ky, kx = int(kernel_size[0]), int(kernel_size[1])
    bs = sigma.shape[0]
    kernel_x = _gaussian(kx, sigma[:, 1].view(bs, 1))
    kernel_y = _gaussian(ky, sigma[:, 0].view(bs, 1))
    out_x = _filter2d(input, kernel_x[..., None, :])
    out = _filter2d(out_x, kernel_y[..., None])

    return out

Run Video Generation

Select Inference Device

import ipywidgets as widgets

core = ov.Core()

device = widgets.Dropdown(
    options=core.available_devices + ["AUTO"],
    value="AUTO",
    description="Device:",
    disabled=False,
)

device

Dropdown(description='Device:', index=4, options=('CPU', 'GPU.0', 'GPU.1', 'GPU.2', 'AUTO'), value='AUTO')

from transformers import CLIPImageProcessor


vae_encoder = core.compile_model(VAE_ENCODER_PATH, device.value)
image_encoder = core.compile_model(IMAGE_ENCODER_PATH, device.value)
unet = core.compile_model(UNET_PATH, device.value)
vae_decoder = core.compile_model(VAE_DECODER_PATH, device.value)
scheduler = AnimateLCMSVDStochasticIterativeScheduler.from_pretrained(MODEL_DIR / "scheduler")
feature_extractor = CLIPImageProcessor.from_pretrained(MODEL_DIR / "feature_extractor")

Now, let’s see model in action. > Please, note, video generation is memory and time consuming process. For reducing memory consumption, we decreased input video resolution to 576x320 and number of generated frames that may affect quality of generated video. You can change these settings manually providing height, width and num_frames parameters into pipeline.

ov_pipe = OVStableVideoDiffusionPipeline(vae_encoder, image_encoder, unet, vae_decoder, scheduler, feature_extractor)

frames = ov_pipe(
    image,
    num_inference_steps=4,
    motion_bucket_id=60,
    num_frames=8,
    height=320,
    width=512,
    generator=torch.manual_seed(12342),
).frames[0]

0%|          | 0/4 [00:00<?, ?it/s]

denoise currently
tensor(128.5637)
denoise currently
tensor(13.6784)
denoise currently
tensor(0.4969)
denoise currently
tensor(0.)

out_path = Path("generated.mp4")

export_to_video(frames, str(out_path), fps=7)
frames[0].save(
    "generated.gif",
    save_all=True,
    append_images=frames[1:],
    optimize=False,
    duration=120,
    loop=0,
)

from IPython.display import HTML

HTML('<img src="generated.gif">')

Quantization

NNCF enables post-training quantization by adding quantization layers into model graph and then using a subset of the training dataset to initialize the parameters of these additional quantization layers. Quantized operations are executed in INT8 instead of FP32/FP16 making model inference faster.

According to OVStableVideoDiffusionPipeline structure, the diffusion model takes up significant portion of the overall pipeline execution time. Now we will show you how to optimize the UNet part using NNCF to reduce computation cost and speed up the pipeline. Quantizing the rest of the pipeline does not significantly improve inference performance but can lead to a substantial degradation of accuracy. That’s why we use only weight compression for the vae encoder and vae decoder to reduce the memory footprint.

For the UNet model we apply quantization in hybrid mode which means that we quantize: (1) weights of MatMul and Embedding layers and (2) activations of other layers. The steps are the following:

1. Create a calibration dataset for quantization.

2. Collect operations with weights.

3. Run nncf.compress_model() to compress only the model weights.

4. Run nncf.quantize() on the compressed model with weighted operations ignored by providing ignored_scope parameter.

5. Save the INT8 model using openvino.save_model() function.

Please select below whether you would like to run quantization to improve model inference speed.

NOTE: Quantization is time and memory consuming operation. Running quantization code below may take some time.

to_quantize = widgets.Checkbox(
    value=True,
    description="Quantization",
    disabled=False,
)

to_quantize

Checkbox(value=True, description='Quantization')

# Fetch `skip_kernel_extension` module
import requests

r = requests.get(
    url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
)
open("skip_kernel_extension.py", "w").write(r.text)

ov_int8_pipeline = None
OV_INT8_UNET_PATH = MODEL_DIR / "unet_int8.xml"
OV_INT8_VAE_ENCODER_PATH = MODEL_DIR / "vae_encoder_int8.xml"
OV_INT8_VAE_DECODER_PATH = MODEL_DIR / "vae_decoder_int8.xml"

%load_ext skip_kernel_extension

Prepare calibration dataset

We use a portion of fusing/instructpix2pix-1000-samples dataset from Hugging Face as calibration data. To collect intermediate model inputs for UNet optimization we should customize CompiledModel.

%%skip not $to_quantize.value

from typing import Any

import datasets
import numpy as np
from tqdm.notebook import tqdm
from IPython.utils import io


class CompiledModelDecorator(ov.CompiledModel):
    def __init__(self, compiled_model: ov.CompiledModel, data_cache: List[Any] = None, keep_prob: float = 0.5):
        super().__init__(compiled_model)
        self.data_cache = data_cache if data_cache is not None else []
        self.keep_prob = keep_prob

    def __call__(self, *args, **kwargs):
        if np.random.rand() <= self.keep_prob:
            self.data_cache.append(*args)
        return super().__call__(*args, **kwargs)


def collect_calibration_data(ov_pipe, calibration_dataset_size: int, num_inference_steps: int = 50) -> List[Dict]:
    original_unet = ov_pipe.unet
    calibration_data = []
    ov_pipe.unet = CompiledModelDecorator(original_unet, calibration_data, keep_prob=1)

    dataset = datasets.load_dataset("fusing/instructpix2pix-1000-samples", split="train", streaming=False).shuffle(seed=42)
    # Run inference for data collection
    pbar = tqdm(total=calibration_dataset_size)
    for batch in dataset:
        image = batch["input_image"]

        with io.capture_output() as captured:
            ov_pipe(
                image,
                num_inference_steps=4,
                motion_bucket_id=60,
                num_frames=8,
                height=256,
                width=256,
                generator=torch.manual_seed(12342),
            )
        pbar.update(len(calibration_data) - pbar.n)
        if len(calibration_data) >= calibration_dataset_size:
            break

    ov_pipe.unet = original_unet
    return calibration_data[:calibration_dataset_size]

%%skip not $to_quantize.value

if not OV_INT8_UNET_PATH.exists():
    subset_size = 200
    calibration_data = collect_calibration_data(ov_pipe, calibration_dataset_size=subset_size)

Run Hybrid Model Quantization

%%skip not $to_quantize.value

from collections import deque

def get_operation_const_op(operation, const_port_id: int):
    node = operation.input_value(const_port_id).get_node()
    queue = deque([node])
    constant_node = None
    allowed_propagation_types_list = ["Convert", "FakeQuantize", "Reshape"]

    while len(queue) != 0:
        curr_node = queue.popleft()
        if curr_node.get_type_name() == "Constant":
            constant_node = curr_node
            break
        if len(curr_node.inputs()) == 0:
            break
        if curr_node.get_type_name() in allowed_propagation_types_list:
            queue.append(curr_node.input_value(0).get_node())

    return constant_node


def is_embedding(node) -> bool:
    allowed_types_list = ["f16", "f32", "f64"]
    const_port_id = 0
    input_tensor = node.input_value(const_port_id)
    if input_tensor.get_element_type().get_type_name() in allowed_types_list:
        const_node = get_operation_const_op(node, const_port_id)
        if const_node is not None:
            return True

    return False


def collect_ops_with_weights(model):
    ops_with_weights = []
    for op in model.get_ops():
        if op.get_type_name() == "MatMul":
            constant_node_0 = get_operation_const_op(op, const_port_id=0)
            constant_node_1 = get_operation_const_op(op, const_port_id=1)
            if constant_node_0 or constant_node_1:
                ops_with_weights.append(op.get_friendly_name())
        if op.get_type_name() == "Gather" and is_embedding(op):
            ops_with_weights.append(op.get_friendly_name())

    return ops_with_weights

%%skip not $to_quantize.value

import nncf
import logging
from nncf.quantization.advanced_parameters import AdvancedSmoothQuantParameters

nncf.set_log_level(logging.ERROR)

if not OV_INT8_UNET_PATH.exists():
    diffusion_model = core.read_model(UNET_PATH)
    unet_ignored_scope = collect_ops_with_weights(diffusion_model)
    compressed_diffusion_model = nncf.compress_weights(diffusion_model, ignored_scope=nncf.IgnoredScope(types=['Convolution']))
    quantized_diffusion_model = nncf.quantize(
        model=diffusion_model,
        calibration_dataset=nncf.Dataset(calibration_data),
        subset_size=subset_size,
        model_type=nncf.ModelType.TRANSFORMER,
        # We additionally ignore the first convolution to improve the quality of generations
        ignored_scope=nncf.IgnoredScope(names=unet_ignored_scope + ["__module.conv_in/aten::_convolution/Convolution"]),
        advanced_parameters=nncf.AdvancedQuantizationParameters(smooth_quant_alphas=AdvancedSmoothQuantParameters(matmul=-1))
    )
    ov.save_model(quantized_diffusion_model, OV_INT8_UNET_PATH)

Run Weight Compression

Quantizing of the vae encoder and vae decoder does not significantly improve inference performance but can lead to a substantial degradation of accuracy. Only weight compression will be applied for footprint reduction.

%%skip not $to_quantize.value

nncf.set_log_level(logging.INFO)

if not OV_INT8_VAE_ENCODER_PATH.exists():
    text_encoder_model = core.read_model(VAE_ENCODER_PATH)
    compressed_text_encoder_model = nncf.compress_weights(text_encoder_model, mode=nncf.CompressWeightsMode.INT4_SYM, group_size=64)
    ov.save_model(compressed_text_encoder_model, OV_INT8_VAE_ENCODER_PATH)

if not OV_INT8_VAE_DECODER_PATH.exists():
    decoder_model = core.read_model(VAE_DECODER_PATH)
    compressed_decoder_model = nncf.compress_weights(decoder_model, mode=nncf.CompressWeightsMode.INT4_SYM, group_size=64)
    ov.save_model(compressed_decoder_model, OV_INT8_VAE_DECODER_PATH)

INFO:nncf:Statistics of the bitwidth distribution:
┍━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┑
│   Num bits (N) │ % all parameters (layers)   │ % ratio-defining parameters (layers)   │
┝━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┥
│              8 │ 98% (29 / 32)               │ 0% (0 / 3)                             │
├────────────────┼─────────────────────────────┼────────────────────────────────────────┤
│              4 │ 2% (3 / 32)                 │ 100% (3 / 3)                           │
┕━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┙

Output()

INFO:nncf:Statistics of the bitwidth distribution:
┍━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┑
│   Num bits (N) │ % all parameters (layers)   │ % ratio-defining parameters (layers)   │
┝━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┥
│              8 │ 99% (65 / 68)               │ 0% (0 / 3)                             │
├────────────────┼─────────────────────────────┼────────────────────────────────────────┤
│              4 │ 1% (3 / 68)                 │ 100% (3 / 3)                           │
┕━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┙

Output()

Let’s compare the video generated by the original and optimized pipelines.

%%skip not $to_quantize.value

ov_int8_vae_encoder = core.compile_model(OV_INT8_VAE_ENCODER_PATH, device.value)
ov_int8_unet = core.compile_model(OV_INT8_UNET_PATH, device.value)
ov_int8_decoder = core.compile_model(OV_INT8_VAE_DECODER_PATH, device.value)

ov_int8_pipeline = OVStableVideoDiffusionPipeline(
    ov_int8_vae_encoder, image_encoder, ov_int8_unet, ov_int8_decoder, scheduler, feature_extractor
)

int8_frames = ov_int8_pipeline(
    image,
    num_inference_steps=4,
    motion_bucket_id=60,
    num_frames=8,
    height=320,
    width=512,
    generator=torch.manual_seed(12342),
).frames[0]

0%|          | 0/4 [00:00<?, ?it/s]

/home/ltalamanova/env_ci/lib/python3.8/site-packages/diffusers/configuration_utils.py:139: FutureWarning: Accessing config attribute unet directly via 'OVStableVideoDiffusionPipeline' object attribute is deprecated. Please access 'unet' over 'OVStableVideoDiffusionPipeline's config object instead, e.g. 'scheduler.config.unet'.
  deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)

denoise currently
tensor(128.5637)
denoise currently
tensor(13.6784)
denoise currently
tensor(0.4969)
denoise currently
tensor(0.)

int8_out_path = Path("generated_int8.mp4")

export_to_video(frames, str(out_path), fps=7)
int8_frames[0].save(
    "generated_int8.gif",
    save_all=True,
    append_images=int8_frames[1:],
    optimize=False,
    duration=120,
    loop=0,
)
HTML('<img src="generated_int8.gif">')

Compare model file sizes

%%skip not $to_quantize.value

fp16_model_paths = [VAE_ENCODER_PATH, UNET_PATH, VAE_DECODER_PATH]
int8_model_paths = [OV_INT8_VAE_ENCODER_PATH, OV_INT8_UNET_PATH, OV_INT8_VAE_DECODER_PATH]

for fp16_path, int8_path in zip(fp16_model_paths, int8_model_paths):
    fp16_ir_model_size = fp16_path.with_suffix(".bin").stat().st_size
    int8_model_size = int8_path.with_suffix(".bin").stat().st_size
    print(f"{fp16_path.stem} compression rate: {fp16_ir_model_size / int8_model_size:.3f}")

vae_encoder compression rate: 2.018
unet compression rate: 1.996
vae_decoder compression rate: 2.007

Compare inference time of the FP16 and INT8 pipelines

To measure the inference performance of the FP16 and INT8 pipelines, we use median inference time on calibration subset.

NOTE: For the most accurate performance estimation, it is recommended to run benchmark_app in a terminal/command prompt after closing other applications.

%%skip not $to_quantize.value

import time

def calculate_inference_time(pipeline, validation_data):
    inference_time = []
    for prompt in validation_data:
        start = time.perf_counter()
        with io.capture_output() as captured:
            _ = pipeline(
                image,
                num_inference_steps=4,
                motion_bucket_id=60,
                num_frames=8,
                height=320,
                width=512,
                generator=torch.manual_seed(12342),
            )
        end = time.perf_counter()
        delta = end - start
        inference_time.append(delta)
    return np.median(inference_time)

%%skip not $to_quantize.value

validation_size = 3
validation_dataset = datasets.load_dataset("fusing/instructpix2pix-1000-samples", split="train", streaming=True).shuffle(seed=42).take(validation_size)
validation_data = [data["input_image"] for data in validation_dataset]

fp_latency = calculate_inference_time(ov_pipe, validation_data)
int8_latency = calculate_inference_time(ov_int8_pipeline, validation_data)
print(f"Performance speed-up: {fp_latency / int8_latency:.3f}")

Performance speed-up: 1.243

Interactive Demo

Please select below whether you would like to use the quantized model to launch the interactive demo.

quantized_model_present = ov_int8_pipeline is not None

use_quantized_model = widgets.Checkbox(
    value=quantized_model_present,
    description="Use quantized model",
    disabled=not quantized_model_present,
)

use_quantized_model

Checkbox(value=True, description='Use quantized model')

import gradio as gr
import random

max_64_bit_int = 2**63 - 1
pipeline = ov_int8_pipeline if use_quantized_model.value else ov_pipe

example_images_urls = [
    "https://huggingface.co/spaces/wangfuyun/AnimateLCM-SVD/resolve/main/test_imgs/ship-7833921_1280.jpg?download=true",
    "https://huggingface.co/spaces/wangfuyun/AnimateLCM-SVD/resolve/main/test_imgs/ai-generated-8476858_1280.png?download=true",
    "https://huggingface.co/spaces/wangfuyun/AnimateLCM-SVD/resolve/main/test_imgs/ai-generated-8481641_1280.jpg?download=true",
    "https://huggingface.co/spaces/wangfuyun/AnimateLCM-SVD/resolve/main/test_imgs/dog-7396912_1280.jpg?download=true",
    "https://huggingface.co/spaces/wangfuyun/AnimateLCM-SVD/resolve/main/test_imgs/cupcakes-380178_1280.jpg?download=true",
]

example_images_dir = Path("example_images")
example_images_dir.mkdir(exist_ok=True)
example_imgs = []

for image_id, url in enumerate(example_images_urls):
    img = load_image(url)
    image_path = example_images_dir / f"{image_id}.png"
    img.save(image_path)
    example_imgs.append([image_path])


def sample(
    image: PIL.Image,
    seed: Optional[int] = 42,
    randomize_seed: bool = True,
    motion_bucket_id: int = 127,
    fps_id: int = 6,
    num_inference_steps: int = 15,
    num_frames: int = 4,
    max_guidance_scale=1.0,
    min_guidance_scale=1.0,
    decoding_t: int = 8,  # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
    output_folder: str = "outputs",
    progress=gr.Progress(track_tqdm=True),
):
    if image.mode == "RGBA":
        image = image.convert("RGB")

    if randomize_seed:
        seed = random.randint(0, max_64_bit_int)
    generator = torch.manual_seed(seed)

    output_folder = Path(output_folder)
    output_folder.mkdir(exist_ok=True)
    base_count = len(list(output_folder.glob("*.mp4")))
    video_path = output_folder / f"{base_count:06d}.mp4"

    frames = pipeline(
        image,
        decode_chunk_size=decoding_t,
        generator=generator,
        motion_bucket_id=motion_bucket_id,
        noise_aug_strength=0.1,
        num_frames=num_frames,
        num_inference_steps=num_inference_steps,
        max_guidance_scale=max_guidance_scale,
        min_guidance_scale=min_guidance_scale,
    ).frames[0]
    export_to_video(frames, str(video_path), fps=fps_id)

    return video_path, seed


def resize_image(image, output_size=(512, 320)):
    # 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), PIL.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), PIL.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: Image to Video Generation with OpenVINO.
  """
    )
    with gr.Row():
        with gr.Column():
            image_in = gr.Image(label="Upload your image", type="pil")
            generate_btn = gr.Button("Generate")
        video = gr.Video()
    with gr.Accordion("Advanced options", open=False):
        seed = gr.Slider(
            label="Seed",
            value=42,
            randomize=True,
            minimum=0,
            maximum=max_64_bit_int,
            step=1,
        )
        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
        motion_bucket_id = gr.Slider(
            label="Motion bucket id",
            info="Controls how much motion to add/remove from the image",
            value=127,
            minimum=1,
            maximum=255,
        )
        fps_id = gr.Slider(
            label="Frames per second",
            info="The length of your video in seconds will be num_frames / fps",
            value=6,
            minimum=5,
            maximum=30,
            step=1,
        )
        num_frames = gr.Slider(label="Number of Frames", value=8, minimum=2, maximum=25, step=1)
        num_steps = gr.Slider(label="Number of generation steps", value=4, minimum=1, maximum=8, step=1)
        max_guidance_scale = gr.Slider(
            label="Max guidance scale",
            info="classifier-free guidance strength",
            value=1.2,
            minimum=1,
            maximum=2,
        )
        min_guidance_scale = gr.Slider(
            label="Min guidance scale",
            info="classifier-free guidance strength",
            value=1,
            minimum=1,
            maximum=1.5,
        )
    examples = gr.Examples(
        examples=example_imgs,
        inputs=[image_in],
        outputs=[video, seed],
    )

    image_in.upload(fn=resize_image, inputs=image_in, outputs=image_in)
    generate_btn.click(
        fn=sample,
        inputs=[
            image_in,
            seed,
            randomize_seed,
            motion_bucket_id,
            fps_id,
            num_steps,
            num_frames,
            max_guidance_scale,
            min_guidance_scale,
        ],
        outputs=[video, seed],
        api_name="video",
    )


try:
    demo.queue().launch(debug=False)
except Exception:
    demo.queue().launch(debug=False, share=True)
# if you are launching remotely, specify server_name and server_port
# demo.launch(server_name='your server name', server_port='server port in int')
# Read more in the docs: https://gradio.app/docs/