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import gradio as gr
import os
import torch
import argparse
import spaces
import torchvision
from transformers import pipeline
from pipelines.pipeline_videogen import VideoGenPipeline
from diffusers.schedulers import DDIMScheduler
from diffusers.models import AutoencoderKL
from diffusers.models import AutoencoderKLTemporalDecoder
from transformers import CLIPTokenizer, CLIPTextModel
from omegaconf import OmegaConf
import os, sys
sys.path.append(os.path.split(sys.path[0])[0])
from models import get_models
import imageio
from PIL import Image
import numpy as np
from datasets import video_transforms
from torchvision import transforms
from einops import rearrange, repeat
from utils import dct_low_pass_filter, exchanged_mixed_dct_freq
from copy import deepcopy
import requests
from datetime import datetime
import random
# ๋ฒ์ญ ํ์ดํ๋ผ์ธ ์์ฑ
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
# ๋ฒ์ญ ํจ์
def translate_prompt(korean_prompt):
translation = translator(korean_prompt, max_length=512)
return translation[0]['translation_text']
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default="./configs/sample.yaml")
args = parser.parse_args()
args = OmegaConf.load(args.config)
torch.set_grad_enabled(False)
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16
unet = get_models(args).to(device, dtype=dtype)
if args.enable_vae_temporal_decoder:
if args.use_dct:
vae_for_base_content = AutoencoderKLTemporalDecoder.from_pretrained(args.pretrained_model_path, subfolder="vae_temporal_decoder", torch_dtype=torch.float64).to(device)
else:
vae_for_base_content = AutoencoderKLTemporalDecoder.from_pretrained(args.pretrained_model_path, subfolder="vae_temporal_decoder", torch_dtype=torch.float16).to(device)
vae = deepcopy(vae_for_base_content).to(dtype=dtype)
else:
vae_for_base_content = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae",).to(device, dtype=torch.float64)
vae = deepcopy(vae_for_base_content).to(dtype=dtype)
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_path, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_path, subfolder="text_encoder", torch_dtype=dtype).to(device)
unet.eval()
vae.eval()
text_encoder.eval()
basedir = os.getcwd()
savedir = os.path.join(basedir, "samples/Gradio", datetime.now().strftime("%Y-%m-%dT%H-%M-%S"))
savedir_sample = os.path.join(savedir, "sample")
os.makedirs(savedir, exist_ok=True)
def update_and_resize_image(input_image_path, height_slider, width_slider):
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')
original_width, original_height = pil_image.size
if original_height == height_slider and original_width == width_slider:
return gr.Image(value=np.array(pil_image))
ratio1 = height_slider / original_height
ratio2 = width_slider / original_width
if ratio1 > ratio2:
new_width = int(original_width * ratio1)
new_height = int(original_height * ratio1)
else:
new_width = int(original_width * ratio2)
new_height = int(original_height * ratio2)
pil_image = pil_image.resize((new_width, new_height), Image.LANCZOS)
left = (new_width - width_slider) / 2
top = (new_height - height_slider) / 2
right = left + width_slider
bottom = top + height_slider
pil_image = pil_image.crop((left, top, right, bottom))
return gr.Image(value=np.array(pil_image))
def update_textbox_and_save_image(input_image, height_slider, width_slider):
pil_image = Image.fromarray(input_image.astype(np.uint8)).convert("RGB")
original_width, original_height = pil_image.size
ratio1 = height_slider / original_height
ratio2 = width_slider / original_width
if ratio1 > ratio2:
new_width = int(original_width * ratio1)
new_height = int(original_height * ratio1)
else:
new_width = int(original_width * ratio2)
new_height = int(original_height * ratio2)
pil_image = pil_image.resize((new_width, new_height), Image.LANCZOS)
left = (new_width - width_slider) / 2
top = (new_height - height_slider) / 2
right = left + width_slider
bottom = top + height_slider
pil_image = pil_image.crop((left, top, right, bottom))
img_path = os.path.join(savedir, "input_image.png")
pil_image.save(img_path)
return gr.Textbox(value=img_path), gr.Image(value=np.array(pil_image))
def prepare_image(image, vae, transform_video, device, dtype=torch.float16):
image = torch.as_tensor(np.array(image, dtype=np.uint8, copy=True)).unsqueeze(0).permute(0, 3, 1, 2)
image = transform_video(image)
image = vae.encode(image.to(dtype=dtype, device=device)).latent_dist.sample().mul_(vae.config.scaling_factor)
image = image.unsqueeze(2)
return image
@spaces.GPU
def gen_video(input_image, korean_prompt, negative_prompt, diffusion_step, height, width, scfg_scale, use_dctinit, dct_coefficients, noise_level, motion_bucket_id, seed):
english_prompt = translate_prompt(korean_prompt)
torch.manual_seed(seed)
scheduler = DDIMScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule)
videogen_pipeline = VideoGenPipeline(vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
scheduler=scheduler,
unet=unet).to(device)
transform_video = transforms.Compose([
video_transforms.ToTensorVideo(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
])
if args.use_dct:
base_content = prepare_image(input_image, vae_for_base_content, transform_video, device, dtype=torch.float64).to(device)
else:
base_content = prepare_image(input_image, vae_for_base_content, transform_video, device, dtype=torch.float16).to(device)
if use_dctinit:
base_content_repeat = repeat(base_content, 'b c f h w -> b c (f r) h w', r=15).contiguous()
freq_filter = dct_low_pass_filter(dct_coefficients=base_content, percentage=dct_coefficients)
noise = torch.randn(1, 4, 15, 40, 64).to(device)
diffuse_timesteps = torch.full((1,), int(noise_level))
diffuse_timesteps = diffuse_timesteps.long()
base_content_noise = scheduler.add_noise(
original_samples=base_content_repeat.to(device),
noise=noise,
timesteps=diffuse_timesteps.to(device))
latents = exchanged_mixed_dct_freq(noise=noise,
base_content=base_content_noise,
LPF_3d=freq_filter).to(dtype=torch.float16)
base_content = base_content.to(dtype=torch.float16)
videos = videogen_pipeline(english_prompt,
negative_prompt=negative_prompt,
latents=latents if use_dctinit else None,
base_content=base_content,
video_length=15,
height=height,
width=width,
num_inference_steps=diffusion_step,
guidance_scale=scfg_scale,
motion_bucket_id=100-motion_bucket_id,
enable_vae_temporal_decoder=args.enable_vae_temporal_decoder).video
save_path = args.save_img_path + 'temp' + '.mp4'
imageio.mimwrite(save_path, videos[0], fps=8, quality=7)
return save_path
if not os.path.exists(args.save_img_path):
os.makedirs(args.save_img_path)
css = """
footer {
visibility: hidden;
}
"""
with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as demo:
with gr.Column(variant="panel"):
with gr.Row():
prompt_textbox = gr.Textbox(label="Korean Prompt", lines=1)
negative_prompt_textbox = gr.Textbox(label="Negative prompt", lines=1)
with gr.Row(equal_height=False):
with gr.Column():
with gr.Row():
input_image = gr.Image(label="Input Image", interactive=True)
result_video = gr.Video(label="Generated Animation", interactive=False, autoplay=True)
generate_button = gr.Button(value="Generate", variant='primary')
with gr.Accordion("Advanced options", open=False):
with gr.Column():
with gr.Row():
input_image_path = gr.Textbox(label="Input Image 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():
sample_step_slider = gr.Slider(label="Sampling steps", value=50, minimum=10, maximum=250, step=1)
with gr.Row():
seed_textbox = gr.Slider(label="Seed", value=100, minimum=1, maximum=int(1e8), step=1, interactive=True)
with gr.Row():
height = gr.Slider(label="Height", value=320, minimum=0, maximum=512, step=16, interactive=False)
width = gr.Slider(label="Width", value=512, minimum=0, maximum=512, step=16, interactive=False)
with gr.Row():
txt_cfg_scale = gr.Slider(label="CFG Scale", value=7.5, minimum=1.0, maximum=20.0, step=0.1, interactive=True)
motion_bucket_id = gr.Slider(label="Motion Intensity", value=10, minimum=1, maximum=20, step=1, interactive=True)
with gr.Row():
use_dctinit = gr.Checkbox(label="Enable DCTInit", value=True)
dct_coefficients = gr.Slider(label="DCT Coefficients", value=0.23, minimum=0, maximum=1, step=0.01, interactive=True)
noise_level = gr.Slider(label="Noise Level", value=985, minimum=1, maximum=999, step=1, interactive=True)
input_image.upload(fn=update_textbox_and_save_image, inputs=[input_image, height, width], outputs=[input_image_path, input_image])
preview_button.click(fn=update_and_resize_image, inputs=[input_image_path, height, width], outputs=[input_image])
input_image_path.submit(fn=update_and_resize_image, inputs=[input_image_path, height, width], outputs=[input_image])
generate_button.click(
fn=gen_video,
inputs=[
input_image,
prompt_textbox,
negative_prompt_textbox,
sample_step_slider,
height,
width,
txt_cfg_scale,
use_dctinit,
dct_coefficients,
noise_level,
motion_bucket_id,
seed_textbox,
],
outputs=[result_video]
)
demo.launch(debug=False, share=True)
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