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 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 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_1024' filename_list = ['tc1024_4k.ckpt'] 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/tc1024_4k.ckpt' # ckpt_path="/group/40005/gzhiwang/tc1024_4k.ckpt" 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) assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!" # model = load_model_checkpoint(model, ckpt_path) state = torch.load(ckpt_path, map_location='cpu') if 'state_dict' in state: state = state['state_dict'] if 'module' in state: state = state['module'] missing, unexpected = model.load_state_dict(state, strict=False) print("missing:", missing) print("unexpected:", unexpected) 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) @spaces.GPU(duration=200) def get_image(image, sketch, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123, 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(resolution)), transforms.CenterCrop(resolution), ]) transform = transforms.Compose([ transforms.Resize(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 global model # model = model_list[gpu_id] model = model.cuda() batch_size=1 channels = model.model.diffusion_model.out_channels frames = model.temporal_length h, w = resolution[0] // 8, resolution[1] // 8 noise_shape = [batch_size, channels, frames, h, w] # text cond transposed = False with torch.no_grad(), torch.cuda.amp.autocast(dtype=torch.float16): 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 = transpose_if_needed(cn_tensor) # 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) # if cn_videos.shape[2] > frames: # idxs = [] # for i in range(frames): # index = int((i + 0.5) * cn_videos.shape[2] / frames) # idxs.append(min(index, cn_videos.shape[2] - 1)) # cn_videos = cn_videos[:, :, idxs, :, :] # print("cn_videos.shape after slicing", cn_videos.shape) # model_list = [] # for model in model_list: # model.control_scale = control_scale # model_list.append(model) # else: cn_videos = None print("image cond") # img cond img_tensor = torch.from_numpy(image).permute(2, 0, 1).float().to(model.device) input_h, input_w = img_tensor.shape[1:] img_tensor = (img_tensor / 255. - 0.5) * 2 img_tensor = transpose_if_needed(img_tensor) 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) if sketch is not None: img_tensor2 = torch.from_numpy(sketch).permute(2, 0, 1).float().to(model.device) img_tensor2 = (img_tensor2 / 255. - 0.5) * 2 img_tensor2 = transpose_if_needed(img_tensor2) 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) else: videos = torch.cat([videos, videos], dim=2) z, hs = 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) # print("cn videos:",cn_videos.shape, "img emb:", img_emb.shape) 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' global result_dir global save_fps if input_h > input_w: batch_samples = untranspose(batch_samples) save_videos(batch_samples, result_dir, filenames=[prompt_str], fps=save_fps) 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 # @spaces.GPU # i2v_examples_interp_1024 = [ # ['prompts/1024_interp/frame_000000.jpg', 'prompts/1024_interp/frame_000041.jpg', 'a cat is eating', 50, 7.5, 1.0, 10, 123] # ] i2v_examples_interp_1024 = [ ['prompts/1024_interp/74906_1462_frame1.png', 'prompts/1024_interp/74906_1462_frame3.png', 'an anime scene', 50, 7.5, 1.0, 10, 123] ] 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_sketch = gr.Image(label="Input Image2",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) gr.Examples(examples=i2v_examples_interp_1024, inputs=[i2v_input_image, i2v_input_sketch, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed, control_scale], outputs=[i2v_output_video], fn = get_image, cache_examples=False, ) i2v_end_btn.click(inputs=[i2v_input_image, i2v_input_sketch, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed, control_scale], 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...")