gradio_app.py

import os, argparse
import sys
import gradio as gr
# from scripts.gradio.i2v_test_application import Image2Video
sys.path.insert(1, os.path.join(sys.path[0], 'lvdm'))
import spaces
from lvdm.models.samplers.ddim import DDIMSampler

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 torch
print("cuda available:", torch.cuda.is_available())


from huggingface_hub import snapshot_download
import os



def download_model():
    REPO_ID = 'fbnnb/TC_sketch'
    filename_list = ['tc_sketch.pt']
    tar_dir = './checkpoints/tooncrafter_1024_interp_sketch/'
    if not os.path.exists(tar_dir):
        os.makedirs(tar_dir)
    for filename in filename_list:
        local_file = os.path.join(tar_dir, filename)
        if not os.path.exists(local_file):
            hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir=tar_dir, local_dir_use_symlinks=False)
    print("downloaded")
    

def get_latent_z_with_hidden_states(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



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


resolution = '576_1024'
resolution = (576, 1024)
download_model()
print("after download model")
result_dir = "./results/"
if not os.path.exists(result_dir):
    os.mkdir(result_dir)

#ToonCrafterModel
ckpt_path='checkpoints/tooncrafter_1024_interp_sketch/tc_sketch.pt'
config_file='configs/inference_1024_v1.0.yaml'
config = OmegaConf.load(config_file)
model_config = config.pop("model", OmegaConf.create())
model_config['params']['unet_config']['params']['use_checkpoint']=False  

model = instantiate_from_config(model_config).cuda()
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)

save_fps = 8
print("resolution:", resolution)
print("init done.")



def transpose_if_needed(tensor):
    h = tensor.shape[-2]
    w = tensor.shape[-1]
    if h > w:
        tensor = tensor.permute(0, 2, 1)
    return tensor

def untranspose(tensor):
    ndim = tensor.ndim
    return tensor.transpose(ndim-1, ndim-2)

# [i2v_input_image, i2v_input_text, i2v_input_image, i2v_input_image2,  i2v_steps, i2v_eta, i2v_motion, i2v_seed],
@spaces.GPU(duration=200)
def get_image(image1, prompt, image2, dim_steps=50, ddim_eta=1., fs=None, seed=123, \
                        unconditional_guidance_scale=1.0, cfg_img=None,  text_input=False, multiple_cond_cfg=False, \
                        loop=False, interp=False, timestep_spacing='uniform', guidance_rescale=0.0, noise_shape=[72, 108], n_samples=1, **kwargs):

    with torch.no_grad():
        seed_everything(seed)
        video_size = (576, 1024)
        transform = transforms.Compose([
            transforms.Resize(min(video_size)),
            transforms.CenterCrop(video_size),
            # transforms.ToTensor(),
            # transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
        ])
    
        image1 = torch.from_numpy(image1).permute(2, 0, 1).float().cuda()
        input_h, input_w = image1.shape[1:]
        image1 = (image1 / 255. - 0.5) * 2
    
        image2 = torch.from_numpy(image2).permute(2, 0, 1).float().cuda()
        input_h, input_w = image2.shape[1:]
        image2 = (image2 / 255. - 0.5) * 2
    
        
        # image1 = Image.open(file_list[2*idx]).convert('RGB')
        image_tensor1 = transform(image1).unsqueeze(1) # [c,1,h,w]
        # image2 = Image.open(file_list[2*idx+1]).convert('RGB')
        image_tensor2 = transform(image2).unsqueeze(1) # [c,1,h,w]
        frame_tensor1 = repeat(image_tensor1, 'c t h w -> c (repeat t) h w', repeat=8)
        frame_tensor2 = repeat(image_tensor2, 'c t h w -> c (repeat t) h w', repeat=8)
        videos = torch.cat([frame_tensor1, frame_tensor2], dim=1).unsqueeze(0)
        # frame_tensor = torch.cat([frame_tensor1, frame_tensor1], dim=1)
        # _, filename = os.path.split(file_list[idx*2])
    
        global model
        model.cuda()
        
        ddim_sampler = DDIMSampler(model) if not multiple_cond_cfg else DDIMSampler_multicond(model)
        batch_size = 1
        fs = torch.tensor([fs], dtype=torch.long, device=model.device)
    
        if not text_input:
            prompts = [""]*batch_size
    
        img = videos[:,:,0] #bchw
        img_emb = model.embedder(img) ## blc
        img_emb = model.image_proj_model(img_emb)
    
        cond_emb = model.get_learned_conditioning(prompts)
        cond = {"c_crossattn": [torch.cat([cond_emb,img_emb], dim=1)]}
        if model.model.conditioning_key == 'hybrid':
            z, hs = get_latent_z_with_hidden_states(model, videos) # b c t h w
            if loop or interp:
                img_cat_cond = torch.zeros_like(z)
                img_cat_cond[:,:,0,:,:] = z[:,:,0,:,:]
                img_cat_cond[:,:,-1,:,:] = z[:,:,-1,:,:]
            else:
                img_cat_cond = z[:,:,:1,:,:]
                img_cat_cond = repeat(img_cat_cond, 'b c t h w -> b c (repeat t) h w', repeat=z.shape[2])
            cond["c_concat"] = [img_cat_cond] # b c 1 h w
        
        if unconditional_guidance_scale != 1.0:
            if model.uncond_type == "empty_seq":
                prompts = batch_size * [""]
                uc_emb = model.get_learned_conditioning(prompts)
            elif model.uncond_type == "zero_embed":
                uc_emb = torch.zeros_like(cond_emb)
            uc_img_emb = model.embedder(torch.zeros_like(img)) ## b l c
            uc_img_emb = model.image_proj_model(uc_img_emb)
            uc = {"c_crossattn": [torch.cat([uc_emb,uc_img_emb],dim=1)]}
            if model.model.conditioning_key == 'hybrid':
                uc["c_concat"] = [img_cat_cond]
        else:
            uc = None
    #
        # for i, h in enumerate(hs):
            # print("h:", h.shape)
            # hs[i] = hs[i][:,:,0,:,:].unsqueeze(2)
        additional_decode_kwargs = {'ref_context': hs}
        # additional_decode_kwargs = {'ref_context': None}
    
        ## we need one more unconditioning image=yes, text=""
        if multiple_cond_cfg and cfg_img != 1.0:
            uc_2 = {"c_crossattn": [torch.cat([uc_emb,img_emb],dim=1)]}
            if model.model.conditioning_key == 'hybrid':
                uc_2["c_concat"] = [img_cat_cond]
            kwargs.update({"unconditional_conditioning_img_nonetext": uc_2})
        else:
            kwargs.update({"unconditional_conditioning_img_nonetext": None})
    
        z0 = None
        cond_mask = None
    
        batch_variants = []
        for _ in range(n_samples):
    
            if z0 is not None:
                cond_z0 = z0.clone()
                kwargs.update({"clean_cond": True})
            else:
                cond_z0 = None
            if ddim_sampler is not None:
    
                samples, _ = ddim_sampler.sample(S=ddim_steps,
                                                conditioning=cond,
                                                batch_size=batch_size,
                                                shape=noise_shape,
                                                verbose=False,
                                                unconditional_guidance_scale=unconditional_guidance_scale,
                                                unconditional_conditioning=uc,
                                                eta=ddim_eta,
                                                cfg_img=cfg_img, 
                                                mask=cond_mask,
                                                x0=cond_z0,
                                                fs=fs,
                                                timestep_spacing=timestep_spacing,
                                                guidance_rescale=guidance_rescale,
                                                **kwargs
                                                )
    
            ## reconstruct from latent to pixel space
            batch_images = model.decode_first_stage(samples, **additional_decode_kwargs)
    
            index = list(range(samples.shape[2]))
            del index[1]
            del index[-2]
            samples = samples[:,:,index,:,:]
            ## reconstruct from latent to pixel space
            batch_images_middle = model.decode_first_stage(samples, **additional_decode_kwargs)
            batch_images[:,:,batch_images.shape[2]//2-1:batch_images.shape[2]//2+1] = batch_images_middle[:,:,batch_images.shape[2]//2-2:batch_images.shape[2]//2]
    
    
    
            batch_variants.append(batch_images)
        ## variants, batch, c, t, h, w
        batch_variants = torch.stack(batch_variants)
        # return batch_variants.permute(1, 0, 2, 3, 4, 5)
    
        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'
    
        result_dir = "./tmp/"
        save_videos(batch_image, result_dir, filenames=[prompt_str], fps=8)
        print(f"Saved in {prompt_str}. Time used: {(time.time() - start):.2f} seconds")
        model = model.cpu()
        saved_result_dir = os.path.join(result_dir, f"{prompt_str}.mp4")
        print("result saved to:", saved_result_dir)
        return saved_result_dir

    

    


def dynamicrafter_demo(result_dir='./tmp/', res=1024):
    if res == 1024:
        resolution = '576_1024'
        css = """#input_img {max-width: 1024px !important} #output_vid {max-width: 1024px; max-height:576px}"""
    elif res == 512:
        resolution = '320_512'
        css = """#input_img {max-width: 512px !important} #output_vid {max-width: 512px; max-height: 320px} #input_img2 {max-width: 512px !important} #output_vid {max-width: 512px; max-height: 320px}"""
    elif res == 256:
        resolution = '256_256'
        css = """#input_img {max-width: 256px !important} #output_vid {max-width: 256px; max-height: 256px}"""
    else:
        raise NotImplementedError(f"Unsupported resolution: {res}")
    # image2video = Image2Video(result_dir, resolution=resolution)
    with gr.Blocks(analytics_enabled=False, css=css) as dynamicrafter_iface:



        with gr.Tab(label='ToonCrafter_576x1024'):
            with gr.Column():
                with gr.Row():
                    with gr.Column():
                        with gr.Row():
                            i2v_input_image = gr.Image(label="Input Image1",elem_id="input_img")
                            # frame_guides = gr.Video(label="Input Guidance",elem_id="input_guidance", autoplay=True,show_share_button=True)
                        with gr.Row():
                            i2v_input_text = gr.Text(label='Prompts')
                        with gr.Row():
                            i2v_seed = gr.Slider(label='Random Seed', minimum=0, maximum=50000, step=1, value=123)
                            i2v_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='ETA', value=1.0, elem_id="i2v_eta")
                            i2v_cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.5, elem_id="i2v_cfg_scale")
                        with gr.Row():
                            i2v_steps = gr.Slider(minimum=1, maximum=60, step=1, elem_id="i2v_steps", label="Sampling steps", value=50)
                            i2v_motion = gr.Slider(minimum=5, maximum=30, step=1, elem_id="i2v_motion", label="FPS", value=10)
                            control_scale = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, elem_id="i2v_ctrl_scale", label="control_scale", value=0.6)
                        i2v_end_btn = gr.Button("Generate")
                    with gr.Column():
                        with gr.Row():
                            i2v_input_image2 = gr.Image(label="Input Image 2",elem_id="input_img2")
                        with gr.Row():
                            i2v_output_video = gr.Video(label="Generated Video",elem_id="output_vid",autoplay=True,show_share_button=True)


                # s(model, prompts, image1, image2, noise_shape, n_samples=1, ddim_steps=50, ddim_eta=1., \
                #         unconditional_guidance_scale=1.0, cfg_img=None, fs=None, text_input=False, multiple_cond_cfg=False, \
                #            loop=False, interp=False, timestep_spacing='uniform', guidance_rescale=0.0, **kwargs):
                
                # gr.Examples(examples=i2v_examples_interp_1024,
                #             inputs=[i2v_input_image, i2v_input_text, i2v_input_image, i2v_input_image2, [72, 108], 1, i2v_steps, i2v_eta, 1.0, None, i2v_motion, i2v_seed],
                #             outputs=[i2v_output_video],
                #             fn = get_image,
                #             cache_examples=False,
                # )
            img_size = [72, 108]
            
            i2v_end_btn.click(inputs=[i2v_input_image, i2v_input_text, i2v_input_image2, i2v_steps, i2v_eta, i2v_motion, i2v_seed],
                            outputs=[i2v_output_video],
                            fn = get_image
            )


    return dynamicrafter_iface


def get_parser():
    parser = argparse.ArgumentParser()
    return parser
    

if __name__ == "__main__":
    parser = get_parser()
    args = parser.parse_args()

    result_dir = os.path.join('./', 'results')
    dynamicrafter_iface = dynamicrafter_demo(result_dir)
    dynamicrafter_iface.queue(max_size=12)
    print("launching...")
    dynamicrafter_iface.launch(max_threads=1, share=True)
    
    # dynamicrafter_iface.launch(server_name='0.0.0.0', server_port=12345)
    # dynamicrafter_iface.launch()
    # print("launched...")