app.py

import spaces
import os
import json
import torch
import random
import requests
from PIL import Image
import numpy as np

import gradio as gr
from datetime import datetime

import torchvision.transforms as T

from diffusers import DDIMScheduler
from diffusers.utils.import_utils import is_xformers_available
from consisti2v.pipelines.pipeline_conditional_animation import ConditionalAnimationPipeline
from consisti2v.utils.util import save_videos_grid
from omegaconf import OmegaConf


sample_idx     = 0
scheduler_dict = {
    "DDIM": DDIMScheduler,
}

css = """
.toolbutton {
    margin-buttom: 0em 0em 0em 0em;
    max-width: 2.5em;
    min-width: 2.5em !important;
    height: 2.5em;
}
"""


basedir        = os.getcwd()
savedir        = os.path.join(basedir, "samples/Gradio", datetime.now().strftime("%Y-%m-%dT%H-%M-%S"))
savedir_sample = os.path.join(savedir, "sample")
os.makedirs(savedir, exist_ok=True)

EXAMPLES = [ # prompt, first frame, width, height, center crop, seed
    ["timelapse at the snow land with aurora in the sky.", "example/example_01.png"],
    ["fireworks.", "example/example_02.png"],
    ["clown fish swimming through the coral reef.", "example/example_03.png"],
    ["melting ice cream dripping down the cone.", "example/example_04.png"],
]

EXAMPLES_HIDDEN = {
    "timelapse at the snow land with aurora in the sky.": ["example/example_01.png", 256, 256, True, 21800],
    "fireworks.": ["example/example_02.png", 256, 256, True, 21800],
    "clown fish swimming through the coral reef.": ["example/example_03.png", 256, 256, True, 75692375],
    "melting ice cream dripping down the cone.": ["example/example_04.png", 256, 256, True, 21800]
}

def update_and_resize_image(input_image_path, height_slider, width_slider, center_crop):
    if input_image_path.startswith("http://") or input_image_path.startswith("https://"):
        pil_image = Image.open(requests.get(input_image_path, stream=True).raw).convert('RGB')
    else:
        pil_image = Image.open(input_image_path).convert('RGB')
    original_width, original_height = pil_image.size
    
    if center_crop:
        crop_aspect_ratio = width_slider / height_slider
        aspect_ratio = original_width / original_height
        if aspect_ratio > crop_aspect_ratio:
            new_width = int(crop_aspect_ratio * original_height)
            left = (original_width - new_width) / 2
            top = 0
            right = left + new_width
            bottom = original_height
            pil_image = pil_image.crop((left, top, right, bottom))
        elif aspect_ratio < crop_aspect_ratio:
            new_height = int(original_width / crop_aspect_ratio)
            top = (original_height - new_height) / 2
            left = 0
            right = original_width
            bottom = top + new_height
            pil_image = pil_image.crop((left, top, right, bottom))
    
    pil_image = pil_image.resize((width_slider, height_slider))
    return gr.Image(value=np.array(pil_image))


def get_examples(prompt_textbox, input_image):
    input_image_path = EXAMPLES_HIDDEN[prompt_textbox][0]
    width_slider = EXAMPLES_HIDDEN[prompt_textbox][1]
    height_slider = EXAMPLES_HIDDEN[prompt_textbox][2]
    center_crop = EXAMPLES_HIDDEN[prompt_textbox][3]
    seed_textbox = EXAMPLES_HIDDEN[prompt_textbox][4]
    input_image = update_and_resize_image(input_image_path, height_slider, width_slider, center_crop)
    return prompt_textbox, input_image, input_image_path, width_slider, height_slider, center_crop, seed_textbox

# config models
pipeline = ConditionalAnimationPipeline.from_pretrained("TIGER-Lab/ConsistI2V", torch_dtype=torch.float16)
pipeline.to("cuda")


def update_textbox_and_save_image(input_image, height_slider, width_slider, center_crop):
    pil_image = Image.fromarray(input_image.astype(np.uint8)).convert("RGB")
    img_path = os.path.join(savedir, "input_image.png")
    pil_image.save(img_path)

    original_width, original_height = pil_image.size
    if center_crop:
        crop_aspect_ratio = width_slider / height_slider
        aspect_ratio = original_width / original_height
        if aspect_ratio > crop_aspect_ratio:
            new_width = int(crop_aspect_ratio * original_height)
            left = (original_width - new_width) / 2
            top = 0
            right = left + new_width
            bottom = original_height
            pil_image = pil_image.crop((left, top, right, bottom))
        elif aspect_ratio < crop_aspect_ratio:
            new_height = int(original_width / crop_aspect_ratio)
            top = (original_height - new_height) / 2
            left = 0
            right = original_width
            bottom = top + new_height
            pil_image = pil_image.crop((left, top, right, bottom))

    pil_image = pil_image.resize((width_slider, height_slider))
    return gr.Textbox(value=img_path), gr.Image(value=np.array(pil_image))


@spaces.GPU(duration=60)
def animate(
    prompt_textbox, 
    negative_prompt_textbox,
    input_image_path,
    sampler_dropdown, 
    sample_step_slider, 
    width_slider, 
    height_slider, 
    txt_cfg_scale_slider,
    img_cfg_scale_slider,
    center_crop,
    frame_stride,
    use_frameinit,
    frame_init_noise_level,
    seed_textbox
):
    width_slider = int(width_slider)
    height_slider = int(height_slider)
    frame_stride = int(frame_stride)
    sample_step_slider = int(sample_step_slider)
    txt_cfg_scale_slider = float(txt_cfg_scale_slider)
    img_cfg_scale_slider = float(img_cfg_scale_slider)
    frame_init_noise_level = int(frame_init_noise_level)

    if pipeline is None:
        raise gr.Error(f"Please select a pretrained pipeline path.")
    if input_image_path == "":
        raise gr.Error(f"Please upload an input image.")
    if (not center_crop) and (width_slider % 8 != 0 or height_slider % 8 != 0):
        raise gr.Error(f"`height` and `width` have to be divisible by 8 but are {height_slider} and {width_slider}.")
    if center_crop and (width_slider % 8 != 0 or height_slider % 8 != 0):
        raise gr.Error(f"`height` and `width` (after cropping) have to be divisible by 8 but are {height_slider} and {width_slider}.")

    if is_xformers_available() and int(torch.__version__.split(".")[0]) < 2: pipeline.unet.enable_xformers_memory_efficient_attention()

    if seed_textbox != -1 and seed_textbox != "": torch.manual_seed(int(seed_textbox))
    else: torch.seed()
    seed = torch.initial_seed()

    if input_image_path.startswith("http://") or input_image_path.startswith("https://"):
        first_frame = Image.open(requests.get(input_image_path, stream=True).raw).convert('RGB')
    else:
        first_frame = Image.open(input_image_path).convert('RGB')
    
    original_width, original_height = first_frame.size

    if not center_crop:
        img_transform = T.Compose([
            T.ToTensor(),
            T.Resize((height_slider, width_slider), antialias=None),
            T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
        ])
    else:
        aspect_ratio = original_width / original_height
        crop_aspect_ratio = width_slider / height_slider
        if aspect_ratio > crop_aspect_ratio:
            center_crop_width = int(crop_aspect_ratio * original_height)
            center_crop_height = original_height
        elif aspect_ratio < crop_aspect_ratio:
            center_crop_width = original_width
            center_crop_height = int(original_width / crop_aspect_ratio)
        else:
            center_crop_width = original_width
            center_crop_height = original_height
        img_transform = T.Compose([
            T.ToTensor(),
            T.CenterCrop((center_crop_height, center_crop_width)),
            T.Resize((height_slider, width_slider), antialias=None),
            T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
        ])
    
    first_frame = img_transform(first_frame).unsqueeze(0)
    first_frame = first_frame.to("cuda")

    if use_frameinit:
        pipeline.init_filter(
            width         = width_slider,
            height        = height_slider,
            video_length  = 16,
            filter_params = OmegaConf.create({'method': 'gaussian', 'd_s': 0.25, 'd_t': 0.25,})
        )

    sample = pipeline(
        prompt_textbox,
        negative_prompt       = negative_prompt_textbox,
        first_frames          = first_frame,
        num_inference_steps   = sample_step_slider,
        guidance_scale_txt    = txt_cfg_scale_slider,
        guidance_scale_img    = img_cfg_scale_slider,
        width                 = width_slider,
        height                = height_slider,
        video_length          = 16,
        noise_sampling_method = "pyoco_mixed",
        noise_alpha           = 1.0,
        frame_stride          = frame_stride,
        use_frameinit         = use_frameinit,
        frameinit_noise_level = frame_init_noise_level,
        camera_motion         = None,
    ).videos

    global sample_idx
    sample_idx += 1
    save_sample_path = os.path.join(savedir_sample, f"{sample_idx}.mp4")
    save_videos_grid(sample, save_sample_path, format="mp4")

    sample_config = {
        "prompt": prompt_textbox,
        "n_prompt": negative_prompt_textbox,
        "first_frame_path": input_image_path,
        "sampler": sampler_dropdown,
        "num_inference_steps": sample_step_slider,
        "guidance_scale_text": txt_cfg_scale_slider,
        "guidance_scale_image": img_cfg_scale_slider,
        "width": width_slider,
        "height": height_slider,
        "video_length": 8,
        "seed": seed
    }
    json_str = json.dumps(sample_config, indent=4)
    with open(os.path.join(savedir, "logs.json"), "a") as f:
        f.write(json_str)
        f.write("\n\n")
        
    return gr.Video(value=save_sample_path)

def ui():
    with gr.Blocks(css=css) as demo:
        gr.Markdown(
            """
            # ConsistI2V Text+Image to Video Generation
            Input image will be used as the first frame of the video. Text prompts will be used to control the output video content.
            """
        )

        with gr.Column(variant="panel"):
            gr.Markdown(
                """
                - Input image can be specified using the "Input Image URL" text box or uploaded by clicking or dragging the image to the "Input Image" box. The uploaded image will be temporarily stored in the "samples/Gradio" folder under the project root folder.
                - Input image can be resized and/or center cropped to a given resolution by adjusting the "Width" and "Height" sliders. It is recommended to use the same resolution as the training resolution (256x256).
                - After setting the input image path or changed the width/height of the input image, press the "Preview" button to visualize the resized input image.
                """
            )
            
            with gr.Row():
                prompt_textbox = gr.Textbox(label="Prompt", lines=2)
                negative_prompt_textbox = gr.Textbox(label="Negative prompt", lines=2)
                
            with gr.Row(equal_height=False):
                with gr.Column():
                    with gr.Row():
                        sampler_dropdown   = gr.Dropdown(label="Sampling method", choices=list(scheduler_dict.keys()), value=list(scheduler_dict.keys())[0])
                        sample_step_slider = gr.Slider(label="Sampling steps", value=50, minimum=10, maximum=250, step=1)
                    
                    with gr.Row():
                        center_crop   = gr.Checkbox(label="Center Crop the Image", value=True)
                        width_slider  = gr.Slider(label="Width",  value=256, minimum=0, maximum=512, step=64)
                        height_slider = gr.Slider(label="Height", value=256, minimum=0, maximum=512, step=64)
                    with gr.Row():
                        txt_cfg_scale_slider = gr.Slider(label="Text CFG Scale",   value=7.5, minimum=1.0,   maximum=20.0, step=0.5)
                        img_cfg_scale_slider = gr.Slider(label="Image CFG Scale",  value=1.0, minimum=1.0,   maximum=20.0, step=0.5)
                        frame_stride         = gr.Slider(label="Frame Stride",     value=3,   minimum=1,     maximum=5,    step=1)
                    
                    with gr.Row():
                        use_frameinit = gr.Checkbox(label="Enable FrameInit", value=True)
                        frameinit_noise_level = gr.Slider(label="FrameInit Noise Level", value=850, minimum=1, maximum=999, step=1)

                        
                        seed_textbox = gr.Textbox(label="Seed", value=-1)
                        seed_button  = gr.Button(value="\U0001F3B2", elem_classes="toolbutton")
                        seed_button.click(fn=lambda: gr.Textbox(value=random.randint(1, 1e8)), inputs=[], outputs=[seed_textbox])
                    
                    
            
                    generate_button = gr.Button(value="Generate", variant='primary')
                
                with gr.Column():
                    with gr.Row():
                        input_image_path = gr.Textbox(label="Input Image URL", lines=1, scale=10, info="Press Enter or the Preview button to confirm the input image.")
                        preview_button = gr.Button(value="Preview")
                    
                    with gr.Row():
                        input_image = gr.Image(label="Input Image", interactive=True)
                        input_image.upload(fn=update_textbox_and_save_image, inputs=[input_image, height_slider, width_slider, center_crop], outputs=[input_image_path, input_image])
                        result_video = gr.Video(label="Generated Animation", interactive=False, autoplay=True)
                
            with gr.Row():
                batch_examples = gr.Examples(
                    examples=EXAMPLES,
                    fn=get_examples,
                    cache_examples=True,
                    examples_per_page=4,
                    inputs=[prompt_textbox, input_image],
                    outputs=[prompt_textbox, input_image, input_image_path, width_slider, height_slider, center_crop, seed_textbox],
                )
            
            preview_button.click(fn=update_and_resize_image, inputs=[input_image_path, height_slider, width_slider, center_crop], outputs=[input_image])
            input_image_path.submit(fn=update_and_resize_image, inputs=[input_image_path, height_slider, width_slider, center_crop], outputs=[input_image])

            generate_button.click(
                fn=animate,
                inputs=[
                    prompt_textbox,
                    negative_prompt_textbox,
                    input_image_path,
                    sampler_dropdown,
                    sample_step_slider,
                    width_slider,
                    height_slider,
                    txt_cfg_scale_slider,
                    img_cfg_scale_slider,
                    center_crop,
                    frame_stride,
                    use_frameinit,
                    frameinit_noise_level,
                    seed_textbox,
                ],
                outputs=[result_video]
            )
        
            
    return demo


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