scripts/gradio/i2v_test_application.py

import os
import time
from omegaconf import OmegaConf
import torch
from scripts.evaluation.funcs import load_model_checkpoint, save_videos, batch_ddim_sampling, get_latent_z
from utils.utils import instantiate_from_config
from huggingface_hub import hf_hub_download
from einops import repeat
import torchvision.transforms as transforms
from pytorch_lightning import seed_everything
from einops import rearrange
from cldm.model import load_state_dict
import cv2

import spaces 

def extract_frames(video_path):
    # 動画ファイルを読み込む
    cap = cv2.VideoCapture(video_path)
    
    frame_list = []
    frame_num = 0
    
    while True:
        # フレームを読み込む
        ret, frame = cap.read()
        if not ret:
            break
        
        # フレームをリストに追加
        frame_list.append(frame)
        frame_num += 1

    print("load video length:", len(frame_list))
    # 動画ファイルを閉じる
    cap.release()
    
    return frame_list

class Image2Video():
    def __init__(self,result_dir='./tmp/',gpu_num=1,resolution='256_256') -> None:
        self.resolution = (int(resolution.split('_')[0]), int(resolution.split('_')[1])) #hw
        self.download_model()
        print("after download model")
        self.result_dir = result_dir
        if not os.path.exists(self.result_dir):
            os.mkdir(self.result_dir)
        
        #ToonCrafterModel
        ckpt_path='checkpoints/tooncrafter_'+resolution.split('_')[1]+'_interp_v1/model.ckpt'
        config_file='configs/inference_'+resolution.split('_')[1]+'_v1.0.yaml'
        config = OmegaConf.load(config_file)
        model_config = config.pop("model", OmegaConf.create())
        model_config['params']['unet_config']['params']['use_checkpoint']=False  

        #ControlModel
        cn_ckpt_path = "control_models/sketch_encoder.ckpt"
        cn_config_file = 'configs/cldm_v21.yaml'
        cn_config = OmegaConf.load(cn_config_file)
        cn_model_config = cn_config.pop("control_stage_config", OmegaConf.create())

        print("before init") 
        model_list = []

        for gpu_id in range(gpu_num):
            model = instantiate_from_config(model_config)
            cn_model = instantiate_from_config(cn_model_config)

            # model = model.cuda(gpu_id)
            assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!"
            model = load_model_checkpoint(model, ckpt_path)
            model.eval()

            cn_model.load_state_dict(load_state_dict(cn_ckpt_path, location='cpu'))  
            cn_model.eval()

            model.control_model = cn_model
            
            model_list.append(model)
        self.model_list = model_list
        self.save_fps = 8
        print("init done.")

    @spaces.GPU(duration=100)
    def get_image(self, image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123, image2=None, frame_guides=None,control_scale=0.6):
        print("enter fn")
        control_frames = extract_frames(frame_guides)
        print("extract frames")
        seed_everything(seed)
        transform = transforms.Compose([
            transforms.Resize(min(self.resolution)),
            transforms.CenterCrop(self.resolution),
            ])
        print("before empty cache")
        torch.cuda.empty_cache()
        print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())))
        start = time.time()
        gpu_id=0
        if steps > 60:
            steps = 60 
        model = self.model_list[gpu_id]
        model = model.cuda()
        batch_size=1
        channels = model.model.diffusion_model.out_channels
        frames = model.temporal_length
        h, w = self.resolution[0] // 8, self.resolution[1] // 8
        noise_shape = [batch_size, channels, frames, h, w]

        # text cond
        with torch.no_grad(), torch.cuda.amp.autocast():
            text_emb = model.get_learned_conditioning([prompt])
            print("before control")
            #control cond
            if frame_guides is not None:
                cn_videos = []
                for frame in control_frames:
                    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
                    frame = cv2.bitwise_not(frame)
                    cn_tensor = torch.from_numpy(frame).unsqueeze(2).permute(2, 0, 1).float().to(model.device)
                    
                    #cn_tensor = (cn_tensor / 255. - 0.5) * 2
                    cn_tensor = ( cn_tensor/255.0 )
                    cn_tensor_resized = transform(cn_tensor) #3,h,w

                    cn_video = cn_tensor_resized.unsqueeze(0).unsqueeze(2) # bc1hw
                    cn_videos.append(cn_video)
                
                cn_videos = torch.cat(cn_videos, dim=2)
                model_list = []
                for model in self.model_list:
                    model.control_scale = control_scale
                    model_list.append(model)
                self.model_list = model_list
                
            else:
                cn_videos = None

            print("image cond")

            # img cond
            img_tensor = torch.from_numpy(image).permute(2, 0, 1).float().to(model.device)
            img_tensor = (img_tensor / 255. - 0.5) * 2

            image_tensor_resized = transform(img_tensor) #3,h,w
            videos = image_tensor_resized.unsqueeze(0).unsqueeze(2) # bc1hw
            print("get latent z")
            # z = get_latent_z(model, videos) #bc,1,hw
            videos = repeat(videos, 'b c t h w -> b c (repeat t) h w', repeat=frames//2)

            img_tensor2 = torch.from_numpy(image2).permute(2, 0, 1).float().to(model.device)
            img_tensor2 = (img_tensor2 / 255. - 0.5) * 2
            image_tensor_resized2 = transform(img_tensor2) #3,h,w
            videos2 = image_tensor_resized2.unsqueeze(0).unsqueeze(2) # bchw
            videos2 = repeat(videos2, 'b c t h w -> b c (repeat t) h w', repeat=frames//2)
            
            
            videos = torch.cat([videos, videos2], dim=2)
            z, hs = self.get_latent_z_with_hidden_states(model, videos)

            img_tensor_repeat = torch.zeros_like(z)

            img_tensor_repeat[:,:,:1,:,:] = z[:,:,:1,:,:]
            img_tensor_repeat[:,:,-1:,:,:] = z[:,:,-1:,:,:]

            print("image embedder")
            cond_images = model.embedder(img_tensor.unsqueeze(0)) ## blc
            img_emb = model.image_proj_model(cond_images)

            imtext_cond = torch.cat([text_emb, img_emb], dim=1)

            fs = torch.tensor([fs], dtype=torch.long, device=model.device)
            cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat], "control_cond": cn_videos}

            print("before sample loop")
            ## inference
            batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale, hs=hs)

            ## remove the last frame
            if image2 is None:
                batch_samples = batch_samples[:,:,:,:-1,...]
            ## b,samples,c,t,h,w
            prompt_str = prompt.replace("/", "_slash_") if "/" in prompt else prompt
            prompt_str = prompt_str.replace(" ", "_") if " " in prompt else prompt_str
            prompt_str=prompt_str[:40]
            if len(prompt_str) == 0:
                prompt_str = 'empty_prompt'

        save_videos(batch_samples, self.result_dir, filenames=[prompt_str], fps=self.save_fps)
        print(f"Saved in {prompt_str}. Time used: {(time.time() - start):.2f} seconds")
        model = model.cpu()
        result_dir = os.path.join(self.result_dir, f"{prompt_str}.mp4")
        print("result saved to:", result_dir)
        return result_dir

        # import torchvision
        # batch_tensors = batch_samples
        # n_samples = batch_tensors.shape[1]
        # for idx, vid_tensor in enumerate(batch_tensors):
        #     video = vid_tensor.detach().cpu()
        #     video = torch.clamp(video.float(), -1., 1.)
        #     video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w
        #     frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(n_samples)) for framesheet in video] #[3, 1*h, n*w]
        #     grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, n*h, w]
        #     grid = (grid + 1.0) / 2.0
        #     grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1)
        #     # savepath = os.path.join(savedir, f"{filenames[idx]}.mp4")
        #     # torchvision.io.write_video(savepath, grid, fps=fps, video_codec='h264', options={'crf': '10'})
        #     return grid

    def download_model(self):
        REPO_ID = 'Doubiiu/ToonCrafter'
        filename_list = ['model.ckpt']
        if not os.path.exists('./checkpoints/tooncrafter_'+str(self.resolution[1])+'_interp_v1/'):
            os.makedirs('./checkpoints/tooncrafter_'+str(self.resolution[1])+'_interp_v1/')
        for filename in filename_list:
            local_file = os.path.join('./checkpoints/tooncrafter_'+str(self.resolution[1])+'_interp_v1/', filename)
            if not os.path.exists(local_file):
                hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/tooncrafter_'+str(self.resolution[1])+'_interp_v1/', local_dir_use_symlinks=False)
    
    def get_latent_z_with_hidden_states(self, model, videos):
        b, c, t, h, w = videos.shape
        x = rearrange(videos, 'b c t h w -> (b t) c h w')
        encoder_posterior, hidden_states = model.first_stage_model.encode(x, return_hidden_states=True)

        hidden_states_first_last = []
        ### use only the first and last hidden states
        for hid in hidden_states:
            hid = rearrange(hid, '(b t) c h w -> b c t h w', t=t)
            hid_new = torch.cat([hid[:, :, 0:1], hid[:, :, -1:]], dim=2)
            hidden_states_first_last.append(hid_new)

        z = model.get_first_stage_encoding(encoder_posterior).detach()
        z = rearrange(z, '(b t) c h w -> b c t h w', b=b, t=t)
        return z, hidden_states_first_last
if __name__ == '__main__':
    i2v = Image2Video()
    video_path = i2v.get_image('prompts/art.png','man fishing in a boat at sunset')
    print('done', video_path)