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;
}
"""

class AnimateController:
    def __init__(self):
        
        # config dirs
        self.basedir        = os.getcwd()
        self.savedir        = os.path.join(self.basedir, "samples/Gradio", datetime.now().strftime("%Y-%m-%dT%H-%M-%S"))
        self.savedir_sample = os.path.join(self.savedir, "sample")
        os.makedirs(self.savedir, exist_ok=True)

        self.image_resolution = (256, 256)
        # config models
        self.pipeline = ConditionalAnimationPipeline.from_pretrained("TIGER-Lab/ConsistI2V", torch_dtype=torch.float16,)
        self.pipeline.to("cuda")

    def update_textbox_and_save_image(self, input_image, height_slider, width_slider, center_crop):
        pil_image = Image.fromarray(input_image.astype(np.uint8)).convert("RGB")
        img_path = os.path.join(self.savedir, "input_image.png")
        pil_image.save(img_path)
        self.image_resolution = pil_image.size
        pil_image = pil_image.resize((width_slider, height_slider))
        if center_crop:
            width, height = width_slider, height_slider
            aspect_ratio = width / height
            if aspect_ratio > 16 / 10:
                pil_image = pil_image.crop((int((width - height * 16 / 10) / 2), 0, int((width + height * 16 / 10) / 2), height))
            elif aspect_ratio < 16 / 10:
                pil_image = pil_image.crop((0, int((height - width * 10 / 16) / 2), width, int((height + width * 10 / 16) / 2)))
        return gr.Textbox.update(value=img_path), gr.Image.update(value=np.array(pil_image))

    @spaces.GPU
    def animate(
        self,
        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
    ):
        if self.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: self.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:
            self.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 = self.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(self.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(self.savedir, "logs.json"), "a") as f:
            f.write(json_str)
            f.write("\n\n")
            
        return gr.Video.update(value=save_sample_path)
        

controller = AnimateController()


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 Path/URL" text box (this can be either a local image path or an image URL) 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().style(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.update(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 Path/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=controller.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)

            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')
                controller.image_resolution = pil_image.size
                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.update(value=np.array(pil_image))
            
            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=controller.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)