app.py

import spaces
import gradio as gr
import torch
import torchvision as tv
import random, os
from diffusers import StableVideoDiffusionPipeline 
from PIL import Image
from glob import glob
from typing import Optional

from tdd_svd_scheduler import TDDSVDStochasticIterativeScheduler
from utils import load_lora_weights, save_video

# LOCAL = True
LOCAL = False

if LOCAL:
    svd_path = '/share2/duanyuxuan/diff_playground/diffusers_models/stable-video-diffusion-img2vid-xt-1-1'
    lora_file_path = '/share2/duanyuxuan/diff_playground/SVD-TDD/svd-xt-1-1_tdd_lora_weights.safetensors'
else:
    svd_path = 'stabilityai/stable-video-diffusion-img2vid-xt-1-1'
    lora_repo_path = 'RED-AIGC/TDD'
    lora_weight_name = 'svd-xt-1-1_tdd_lora_weights.safetensors'

if torch.cuda.is_available():
    noise_scheduler = TDDSVDStochasticIterativeScheduler(num_train_timesteps = 250, sigma_min = 0.002, sigma_max = 700.0, sigma_data = 1.0, 
                                                        s_noise = 1.0, rho = 7, clip_denoised = False)
    
    pipeline = StableVideoDiffusionPipeline.from_pretrained(svd_path, scheduler = noise_scheduler, torch_dtype = torch.float16, variant = "fp16").to('cuda')
    if LOCAL:
        load_lora_weights(pipeline.unet, lora_file_path)
    else:
        load_lora_weights(pipeline.unet, lora_repo_path, weight_name = lora_weight_name)

max_64_bit_int = 2**63 - 1

@spaces.GPU
def sample(
    image: Image,
    seed: Optional[int] = 1,
    randomize_seed: bool = False,
    num_inference_steps: int = 4,
    eta: float = 0.3,
    min_guidance_scale: float = 1.0,
    max_guidance_scale: float = 1.0,

    fps: int = 7,
    width: int = 512,
    height: int = 512,
    num_frames: int = 25,
    motion_bucket_id: int = 127,
    output_folder: str = "outputs_gradio",
):
    pipeline.scheduler.set_eta(eta)

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

    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")

    with torch.autocast("cuda"):
        frames = pipeline(
            image, height = height, width = width,
            num_inference_steps = num_inference_steps,
            min_guidance_scale = min_guidance_scale,
            max_guidance_scale = max_guidance_scale,
            num_frames = num_frames, fps = fps, motion_bucket_id = motion_bucket_id,
            decode_chunk_size = 8,
            noise_aug_strength = 0.02,
            generator = generator,
        ).frames[0]
    save_video(frames, video_path, fps = fps, quality = 5.0)
    torch.manual_seed(seed)

    return video_path, seed


def preprocess_image(image, height = 512, width = 512):
    image = image.convert('RGB')
    if image.size[0] != image.size[1]:
        image = tv.transforms.functional.pil_to_tensor(image)
        image = tv.transforms.functional.center_crop(image, min(image.shape[-2:]))
        image = tv.transforms.functional.to_pil_image(image)
    image = image.resize((width, height))
    return image

css = """
h1 {
    text-align: center;
    display:block;
}
.gradio-container {
  max-width: 70.5rem !important;
}
"""

with gr.Blocks(css = css) as demo:
    gr.Markdown(
        """
        # Stable Video Diffusion distilled by ✨Target-Driven Distillation✨

        Target-Driven Distillation (TDD) is a state-of-the-art consistency distillation model that largely accelerates the inference processes of diffusion models. Using its delicate strategies of *target timestep selection* and *decoupled guidance*, models distilled by TDD can generated highly detailed images with only a few steps.

        Besides, TDD is also available for distilling video generation models. This space presents TDD-distilled [SVD-xt 1.1](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt-1-1).

        [**Project Page**](https://redaigc.github.io/TDD/) **|** [**Paper**](https://arxiv.org/abs/2409.01347) **|** [**Code**](https://github.com/RedAIGC/Target-Driven-Distillation) **|** [**Model**](https://huggingface.co/RED-AIGC/TDD) **|** [🤗 **TDD-SDXL Demo**](https://huggingface.co/spaces/RED-AIGC/TDD) **|** [🤗 **TDD-SVD Demo**](https://huggingface.co/spaces/RED-AIGC/SVD-TDD)

        The codes of this space are built on [AnimateLCM-SVD](https://huggingface.co/spaces/wangfuyun/AnimateLCM-SVD) and we acknowledge their contribution.
        """
    )
    with gr.Row():
        with gr.Column():
            image = gr.Image(label="Upload your image", type="pil")
            generate_btn = gr.Button("Generate")
        video = gr.Video()
    with gr.Accordion("Options", open = True):
        seed = gr.Slider(
            label="Seed",
            value=1,
            randomize=False,
            minimum=0,
            maximum=max_64_bit_int,
            step=1,
        )
        randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
        min_guidance_scale = gr.Slider(
            label="Min guidance scale",
            info="min strength of classifier-free guidance",
            value=1.0,
            minimum=1.0,
            maximum=1.5,
        )
        max_guidance_scale = gr.Slider(
            label="Max guidance scale",
            info="max strength of classifier-free guidance, it should not be less than Min guidance scale",
            value=1.0,
            minimum=1.0,
            maximum=3.0,
        )
        num_inference_steps = gr.Slider(
            label="Num inference steps",
            info="steps for inference",
            value=4,
            minimum=4,
            maximum=8,
            step=1,
        )
        eta = gr.Slider(
            label = "Eta",
            info = "the value of gamma in gamma-sampling",
            value = 0.3,
            minimum = 0.0,
            maximum = 1.0,
            step = 0.1,
        )

    image.upload(fn = preprocess_image, inputs = image, outputs = image, queue = False)
    generate_btn.click(
        fn = sample,
        inputs = [
            image,
            seed,
            randomize_seed,
            num_inference_steps,
            eta,
            min_guidance_scale,
            max_guidance_scale,
        ],
        outputs = [video, seed],
        api_name = "video",
    )
    # safetensors_dropdown.change(fn=model_select, inputs=safetensors_dropdown)

    # gr.Examples(
    #     examples=[
    #         ["examples/ipadapter_cat.jpg"],
    #     ],
    #     inputs=[image],
    #     outputs=[video, seed],
    #     fn=sample,
    #     cache_examples=True,
    # )

if __name__ == "__main__":
    if LOCAL:
        demo.queue().launch(share=True, server_name='0.0.0.0')
    else:
        demo.queue(api_open=False).launch(show_api=False)