TC_1024 / gradio_app.py
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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):
# 動画フゑむルをθͺ­γΏθΎΌγ‚€
cap = cv2.VideoCapture(video_path)
frame_list = []
frame_num = 0
while True:
# フレームをθͺ­γΏθΎΌγ‚€
ret, frame = cap.read()
if not ret:
break
# フレームをγƒͺγ‚Ήγƒˆγ«θΏ½εŠ 
frame_list.append(frame)
frame_num += 1
print("load video length:", len(frame_list))
# ε‹•η”»γƒ•γ‚‘γ‚€γƒ«γ‚’ι–‰γ˜γ‚‹
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...")